147 20 14MB
English Pages 573 [557] Year 2022
Claudio Ortolani
Flow Cytometry Today Everything You Need to Know about Flow Cytometry
Flow Cytometry Today
Claudio Ortolani
Flow Cytometry Today Everything You Need to Know about Flow Cytometry
With the contribution of: Stefano Papa Liam Whitby Barbara Canonico Bruno Brando Sabrina Buoro Mario D’Atri Genny Del Zotto Antonello Tecchio Matteo Vidali
Claudio Ortolani University of Urbino Urbino, Italy With Contribution by: Stefano Papa, Barbara Canonico, Department of Biomolecular Sciences, University of Urbino, Urbino, Italy Bruno Brando (retired), Transfusion Center and Hematology Laboratory, Western Milan Area Hospital Consortium, Legnano General Hospital, Legnano, Italy Mario D'Atri, Sharp Solutions, Udine, Italy Antonello Tecchio (retired), National Technical Instrument Specialist at Becton Dickinson, Padua, Italy
Liam Whitby, UK NEQAS for Leucocyte Immunophenotyping, Sheffield Teaching Hospitals, Sheffield, UK Sabrina Buoro, Regional Reference Center for the Quality of Laboratory Medicine Services in the Lombardy Region, Niguarda General Hospital, Milan, Italy Genny Del Zotto, Dipartimento Integrato dei Servizi e Laboratori, IRCCS Istituto Giannina Gaslini, Genoa, Italy Matteo Vidali, Laboratorio Analisi, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
ISBN 978-3-031-10835-8 ISBN 978-3-031-10836-5 https://doi.org/10.1007/978-3-031-10836-5
(eBook)
English translation from the originally Italian language edition: CITOMETRIA A FLUSSO by Claudio Ortolani; Milano, 2019; Copyright: Edi. Ermes Srl Translation from the Italian language edition: “CITOMETRIA A FLUSSO” by Claudio Ortolani, © Edi. Ermes 2019. Published by Edi. Ermes Srl, Viale Enrico Forlanini, 65, 20134 Milano, Italy. All Rights Reserved. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2019, 2022, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
To the memory of Howard Maurice Shapiro “Il miglior fabbro.”
Foreword
What is most needed today in the field of flow cytometry? An up-to-date reference book containing accurate in-depth information that covers every possible aspect of the field. Flow Cytometry Today is exactly that. Flow cytometry has become a significantly more complex technology whose instruments tend more and more to be magical ‘black boxes” to most users. I suspect that the COVID pandemic may well have created one big positive for the flow cytometry community—time to focus on a new book! When I read this book, I realized that we have been missing this level of knowledge since Howard Shapiro’s last volume appeared nearly 20 years ago. Howard’s untimely passing robbed us of another Practical Flow Cytometry, but Dr. Ortolani and his colleagues have now presented us with a mammoth volume packed to the hilt with valuable information and dedicated to Dr. Shapiro. I have no doubt that Howard is looking down on this volume with satisfaction. Flow Cytometry Today is incredibly detailed. I thought I knew most stuff in this field until I read this book. The detail is quite extraordinary and the material absolutely timely. The sections on fluorescent probes are particularly valuable, as from a practical perspective, today’s flow cytometry is essentially unlimited when it comes to approaches using different fluorescent dyes. Since the early volumes of Dick Haugland’s Handbook of Fluorescent Probes about 20 years ago, the application field has become so complex that until this volume appeared I wondered if there would ever be a way to manage the nomenclature, chemical groups, functions, and applications. Just unraveling the multitude of names assigned to the same chemical meant hours of literature review, but now that information is right here in print. Understanding the basics of flow cytometry requires knowing how many components work, and Dr. Ortolani has woven the many aspects of the physics, optics, electronics, and fluidics into a detailed and very readable narrative. Because of Dr. Ortolani’s years of experience in using many different systems, he has morphed the complexities of hardware nicely into the practical needs of both the clinical and the research flow cytometry laboratory. What I found impressive was the frequent vii
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insertion of a critical notation in the clinical domain that required particular attention. While research flow users may not appreciate the importance of those insertions, knowing that there are aspects unique to existing clinical applications is valuable. For those who never did understand the nature of the various file formats, there is probably more detail that most will need, but for those interested this is the most detailed review of file formats I have seen. Similarly, the sections on data structure and data transformations provide a wonderfully readable discussion of the advantages and disadvantages of different format representations. The same can be said for calibration in flow cytometry. It is clear that the author is replicating years of in-lab practical experience topped off with theoretical technical detail. While publications might provide the details of approaches, it often takes a book chapter to clearly outline all the options and possibilities and what they mean in the context of real-life laboratory use. The evidence of clear laboratory experience in everyday use of many different instruments cannot be replicated in a single paper, but we do have all this in this volume. This book is going to be a valuable laboratory reference for years to come. Purdue University, West Lafayette, IN, USA
J. Paul Robinson
Preface
It is exceedingly difficult to synthesize, even preliminarily, flow cytometry’s peculiarities and features. This is because of its exquisitely transversal nature, which requires those who embark on this enterprise to navigate very different seas and be capable of resolving unexpected pitfalls. Therefore, it is impossible to have this work completed, which I hope will be useful in a publishing landscape that has traditionally focused on the technique’s applications rather than its intrinsic characteristics, without inadvertently introducing an occasional error. The author assumes moral responsibility for any such error but refuses any responsibility for the consequences of ill-advised use of the information provided. The responsibility for exploiting this information belongs exclusively to those who will translate such information into practice. However, the author will be grateful and in debt to all the colleagues who will spot and communicate such errors, contributing to improved future editions. Urbino, Italy
Claudio Ortolani
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Contents
1
General Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Flow Cytometers General Layout . . . . . . . . . . . . . . . . . . . . . . 1.2 Flow Cytometers Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Parameters and Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 3 4 5 6 7
2
Signals: Scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Forward Scatter (FSC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Side Scatter (SSC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Raman Scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Depolarized Scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11 14 15 16 18 19
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Signals: Fluorescence, Phosphorescence, Impedance, Extinction . . . 3.1 Fluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Depolarized Fluorescence . . . . . . . . . . . . . . . . . . . . 3.1.2 Autofluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Phosphorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Impedance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Axial Extinction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23 23 25 25 28 28 30 31
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Fluidics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Overview on Fluids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Laminar Flow and Turbulent Flow . . . . . . . . . . . . . . 4.1.2 Hydrodynamic Focusing . . . . . . . . . . . . . . . . . . . . . 4.2 Cytometer Fluidics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Sheath and Core . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Flow System Components . . . . . . . . . . . . . . . . . . . . 4.2.3 Flow Rate Control . . . . . . . . . . . . . . . . . . . . . . . . .
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4.2.4 Sample Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Event-Light Interaction: The Interrogation Point . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43 47 50
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Light Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Arc Lamps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Lasers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Gas Lasers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Solid-State Lasers (SSLs) . . . . . . . . . . . . . . . . . . . 5.2.3 Liquid State Lasers (Dye Lasers) . . . . . . . . . . . . . . 5.3 Light Emitting Diodes (LEDs) . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . .
53 54 55 58 62 72 74 74
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Optical Benches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 From the Light Source(s) to the Interrogation Point(s) . . . . . . . 6.2 From the Interrogation Point(s) to the Detector(s) . . . . . . . . . . 6.3 Optical Bench Components . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Absorption Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Interference Filters . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Neutral Density Filters . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Polarizing Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Beam Splitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Wavelength Division Multiplexing (WDM) . . . . . . . 6.3.7 Prisms, Gratings, Coarse WDM (CWDM) . . . . . . . . 6.4 Optical Bench Layouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Transmission Benches . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Reflection Benches . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Multilaser Benches . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Special Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79 79 81 82 83 83 86 87 87 88 88 88 89 90 90 94 95
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Detectors and Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Photodetectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Photodiodes (PDs) . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Avalanche Photodiodes (APDs) . . . . . . . . . . . . . . . . 7.1.3 Photomultipliers (PMTs) . . . . . . . . . . . . . . . . . . . . . 7.1.4 Multi-anode Photomultipliers . . . . . . . . . . . . . . . . . 7.1.5 Silicon Photomultipliers (SiPMs) . . . . . . . . . . . . . . . 7.1.6 Charged-Coupled Devices (CCDs) . . . . . . . . . . . . . . 7.1.7 Trans-impedance Amplifiers (TIAs) . . . . . . . . . . . . . 7.2 Circuitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Analog Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Digital Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Hybrid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97 97 97 98 99 101 101 102 102 102 104 113 117 117
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Signal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 The Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Instrumental Background (BCAL) . . . . . . . . . . . . . . . 8.1.2 Experimental Background (Bsos) . . . . . . . . . . . . . . . 8.2 The Pulse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Pulse Analysis in Analog Systems . . . . . . . . . . . . . . 8.2.2 Pulse Analysis in Digital Systems . . . . . . . . . . . . . . 8.2.3 Practical Applications of Pulse Analysis . . . . . . . . . . 8.3 Dynamic Range of the Signal . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Effective Resolution . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Picket Fence Phenomenon . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
121 121 122 122 122 125 126 129 131 132 133 134
9
The Cytometric File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 FCS Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Header Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Text Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Data Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.4 Analysis Segment . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.5 Optional Segments . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Standard Keywords . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Non-standard Keywords . . . . . . . . . . . . . . . . . . . . . 9.3.3 Relationships Between Keywords and Compensation Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
137 138 139 140 140 141 142 142 143 144 147
10
Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Logarithmic Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Log-Like Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Polynomial Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
151 151 152 154 155
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Data Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Histograms of Lin Amplified Data . . . . . . . . . . . . . . 11.1.2 Histograms of Log Amplified/Transformed Data . . . . 11.1.3 Histograms of Log-Like Transformed Data . . . . . . . . 11.2 Cytograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Representation by Dots (Dot Plot) . . . . . . . . . . . . . . 11.2.2 Representation by Contours (Contour Plot) . . . . . . . . 11.2.3 Representation by False Colors or Gray Tones . . . . . 11.2.4 Pseudo-Three-Dimensional Representation . . . . . . . . 11.2.5 Three-Dimensional Representation . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
157 157 158 159 162 162 163 165 167 168 169 170
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Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Immunofluorescence Measurements . . . . . . . . . . . . . . . . . . . . 12.1.1 The Vexed Question of the Negative Control . . . . . . 12.1.2 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.3 Cytograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.4 Weak Positivity in Immunofluorescence . . . . . . . . . . 12.2 DNA Content Measurements . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 DNA Content versus BrdU Incorporation . . . . . . . . . 12.3 Concept of Gate and Concept of Region . . . . . . . . . . . . . . . . . 12.3.1 Combined (Boolean) Use of Regions and Gates . . . . 12.4 Advanced Tools and Future Perspectives . . . . . . . . . . . . . . . . 12.4.1 Pre-processing Programs . . . . . . . . . . . . . . . . . . . . . 12.4.2 Data Processing Programs . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
171 171 171 173 176 177 182 185 185 187 195 197 198 200
13
Standards, Setup, Calibration, and Control Techniques . . . . . . . . . 13.1 Standards in Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . 13.1.1 Natural Standards . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1.2 Artificial Standards . . . . . . . . . . . . . . . . . . . . . . . . . 13.1.3 Standard Use in Quality Procedures . . . . . . . . . . . . . 13.2 Optical Bench Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Photodetectors’ Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.1 SDen (Electronic Noise Standard Deviation) . . . . . . . 13.3.2 PMT Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.3 APD Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 Calibration in ERF . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 Calibration in MESF . . . . . . . . . . . . . . . . . . . . . . . . 13.4.3 Calibration in ABC . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.4 Calibration in FLU . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.5 Calibration in Nanometers . . . . . . . . . . . . . . . . . . . . 13.5 Instrument Performance and Its Control . . . . . . . . . . . . . . . . . 13.5.1 Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.2 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.3 Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.4 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.5 Limits of Blank (LOB), Detection (LOD), and Quantification (LOQ) . . . . . . . . . . . . . . . . . . . . . . . 13.5.6 Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.7 Specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
205 206 206 207 217 218 220 220 221 223 223 225 225 225 226 227 229 229 229 231 232
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Fluorochromes: Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 14.1 Spectral Behavior of Fluorescent Molecules . . . . . . . . . . . . . . 249 14.2 Relationships with the Environment . . . . . . . . . . . . . . . . . . . . 253
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14.2.1 Spectral Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Other Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Accessory Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
253 254 256 257
Fluorochromes Suitable for Antibody Conjugation . . . . . . . . . . . . . 15.1 Large Protein Molecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.1 Phycobiliproteins . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.2 Peridinin–Chlorophyll–Protein (PerCP) . . . . . . . . . . 15.1.3 AmCyan and AmCyan 100 . . . . . . . . . . . . . . . . . . . 15.2 Small Organic Molecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 Pyrene Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.2 Pyrydil-Oxazole Derivatives . . . . . . . . . . . . . . . . . . 15.2.3 Coumarin Derivatives . . . . . . . . . . . . . . . . . . . . . . . 15.2.4 Xanthene Derivatives . . . . . . . . . . . . . . . . . . . . . . . 15.2.5 Cyanines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.6 Proprietary Molecules . . . . . . . . . . . . . . . . . . . . . . . 15.3 π-Conjugated Organic Polymers (Brilliant Violet and Others) . . 15.3.1 Organic Polymers-Based Tandems . . . . . . . . . . . . . . 15.3.2 Narrow-Band Emissive Chromoforic Polymer Dots (Pdots) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Nanocrystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.1 Quantum Dots (Qdots) . . . . . . . . . . . . . . . . . . . . . . 15.4.2 Upconverting Nanoparticles (UCNPs) . . . . . . . . . . . 15.5 Tandem Fluorochromes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.1 UV-Excited Tandem . . . . . . . . . . . . . . . . . . . . . . . . 15.5.2 Violet-Excited Tandem . . . . . . . . . . . . . . . . . . . . . . 15.5.3 Blue-Excited Tandems . . . . . . . . . . . . . . . . . . . . . . 15.5.4 Green-Yellow-Excited Tandems . . . . . . . . . . . . . . . 15.5.5 Red-Excited Tandems . . . . . . . . . . . . . . . . . . . . . . . 15.6 New Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6.1 Nanoparticle-Encapsulated Fluorophores . . . . . . . . . 15.6.2 Backbone-Linked Fluorophores (Kiravia, NovaFluor) . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
259 259 260 265 267 268 268 269 270 271 278 283 288 289
Fluorochromes That Bind Nucleic Acids . . . . . . . . . . . . . . . . . . . . . 16.1 Triaryl Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Stylbene Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Phenylindole Derivatives (DAPI, DIPI) . . . . . . . . . . . . . . . . . 16.4 Bisbenzimidazole Derivatives . . . . . . . . . . . . . . . . . . . . . . . . 16.4.1 HO33258 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.2 HO33342 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.3 HO34580 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.4 Dyecycle Violet (DCV) . . . . . . . . . . . . . . . . . . . . . .
325 329 330 330 333 334 335 338 338
290 291 291 293 293 297 300 301 306 307 308 308 309 311
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Contents
16.5
Phenanthridine Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5.1 Propidium Iodide (PI) . . . . . . . . . . . . . . . . . . . . . . . 16.5.2 Ethidium Bromide . . . . . . . . . . . . . . . . . . . . . . . . . 16.5.3 Benzophenanthridine Alkaloids . . . . . . . . . . . . . . . . 16.6 Cyanines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.6.1 TOTO Series Molecules . . . . . . . . . . . . . . . . . . . . . 16.6.2 TO-PRO Series Molecules . . . . . . . . . . . . . . . . . . . 16.6.3 SYTOX® Series Molecules . . . . . . . . . . . . . . . . . . . 16.6.4 SYTO® Series Molecules . . . . . . . . . . . . . . . . . . . . 16.6.5 SYBR Series Molecules . . . . . . . . . . . . . . . . . . . . . 16.7 Aromatic Molecules, Heterocyclic . . . . . . . . . . . . . . . . . . . . . 16.7.1 Acridines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.7.2 Oxazines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.7.3 Thiazole Orange (TO) and Its Analogs (DETC) . . . . 16.7.4 7-Amino-Actinomycin D . . . . . . . . . . . . . . . . . . . . . 16.7.5 Pyronin Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.7.6 LDS-751 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.7.7 Berberine (BRB) . . . . . . . . . . . . . . . . . . . . . . . . . . 16.8 Aromatic Molecules, Non-heterocyclic . . . . . . . . . . . . . . . . . . 16.8.1 Tricyclic Antibiotics . . . . . . . . . . . . . . . . . . . . . . . . 16.8.2 Anthraquinones . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
339 339 344 344 344 345 347 349 350 351 352 353 355 355 357 358 359 361 361 361 362 364
Fluorochromes for the Study of the Cell Features . . . . . . . . . . . . . . 17.1 Protein Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Nucleic Acid Content and Chromatin Organization . . . . . . . . . 17.3 Cell Viability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3.1 DNA Impermeant Probes . . . . . . . . . . . . . . . . . . . . 17.3.2 Amine Reactive Dyes . . . . . . . . . . . . . . . . . . . . . . . 17.3.3 Fluorescein Derivatives . . . . . . . . . . . . . . . . . . . . . . 17.3.4 Calcofluor White . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3.5 Trypan Blue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 Membrane Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4.1 Carbocyanines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4.2 Oxonol Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . 17.4.3 Xanthene Derivatives . . . . . . . . . . . . . . . . . . . . . . . 17.5 Mitochondrial Membrane Potential . . . . . . . . . . . . . . . . . . . . . 17.5.1 JC-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6 Mitochondrial Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6.1 Nonyl Acridine Orange (NAO) . . . . . . . . . . . . . . . . 17.6.2 Mitotracker Molecules . . . . . . . . . . . . . . . . . . . . . . 17.6.3 Mitofluor Molecules . . . . . . . . . . . . . . . . . . . . . . . . 17.7 Intracellular pH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.7.1 DCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.7.2 Bcecf-Acetoxymethyl Ester . . . . . . . . . . . . . . . . . . .
381 382 384 384 385 385 387 389 389 390 391 394 394 395 395 397 397 397 399 399 400 400
Contents
17.7.3 SNARF-1 Acetoxymethyl Ester . . . . . . . . . . . . . . . . Lysosomal Mass and Lysosomal pH . . . . . . . . . . . . . . . . . . . . 17.8.1 Lysotracker® and Lysohunt Molecules . . . . . . . . . . . 17.8.2 Lysosensor™ Molecules . . . . . . . . . . . . . . . . . . . . . 17.8.3 Acridine Orange (AO) . . . . . . . . . . . . . . . . . . . . . . 17.9 Free Radicals (Oxidative Burst) . . . . . . . . . . . . . . . . . . . . . . . 17.9.1 Dihydroethidium (DHE) . . . . . . . . . . . . . . . . . . . . . 17.9.2 Dichlorofluorescin Diacetate (DCF-DA) . . . . . . . . . . 17.9.3 Dihydrorhodamine 123 (DHR123) . . . . . . . . . . . . . . 17.9.4 Tetrazolium Derivatives . . . . . . . . . . . . . . . . . . . . . 17.10 Calcium Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.10.1 INDO-1 Acetoxymethyl Ester . . . . . . . . . . . . . . . . . 17.10.2 Xanthene Derivatives (Fluo Molecules and Others) . . 17.10.3 Fura Molecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.10.4 BTC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.11 Sodium Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.11.1 SBFI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.11.2 Sodium Green . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.12 Potassium Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.12.1 PBFI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.12.2 BCECF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.13 Chloride Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.14 Magnesium Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.15 Glutathione Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.16 Heavy Metals Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.17 Cell Proliferation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.17.1 Fluorescein Esters (CFSE) . . . . . . . . . . . . . . . . . . . . 17.17.2 Lipophilic Carbocyanines . . . . . . . . . . . . . . . . . . . . 17.17.3 CellVue® Series Molecules . . . . . . . . . . . . . . . . . . . 17.18 Multidrug Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.18.1 Rhodamine 123 (RH123) . . . . . . . . . . . . . . . . . . . . 17.18.2 Calcein Acetoxymethyl Ester . . . . . . . . . . . . . . . . . . 17.19 Membrane Fluidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.20 Lipid Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.20.1 NILE RED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.20.2 Bodipy and Its Derivatives . . . . . . . . . . . . . . . . . . . 17.21 Lipid Peroxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.21.1 Cis-Parinaric Acid . . . . . . . . . . . . . . . . . . . . . . . . . 17.21.2 C11-BODIPY581/591 . . . . . . . . . . . . . . . . . . . . . . . 17.22 Endoplasmic Reticulum (ER) Labeling . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.8
18
xvii
401 402 402 403 404 404 406 406 406 406 407 408 409 410 412 413 413 413 414 414 415 415 415 416 416 417 418 418 420 421 422 422 423 424 424 425 426 426 426 427 427
Fluorescent Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 18.1 Fluorescent Proteins and Flow Cytometry . . . . . . . . . . . . . . . . 447 18.1.1 Green Fluorescent Proteins (GFPs) . . . . . . . . . . . . . 448
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Contents
18.1.2 18.1.3 18.1.4 18.1.5 18.1.6 18.1.7 References . . . .
Blue Fluorescent Proteins (BFPs) . . . . . . . . . . . . . . . Cyan Fluorescent Proteins (CFPs) . . . . . . . . . . . . . . Yellow Fluorescent Proteins (YFPs) . . . . . . . . . . . . . Orange Fluorescent Proteins (OFPs) . . . . . . . . . . . . . Red Fluorescent Proteins (RFP) . . . . . . . . . . . . . . . . Infrared Fluorescent Proteins (iRFPs) . . . . . . . . . . . . ........................................
449 450 450 450 451 452 453
19
Spillover and Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1 Spillover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.1 Intra-Laser Spillover . . . . . . . . . . . . . . . . . . . . . . . . 19.1.2 Inter-Laser Spillover . . . . . . . . . . . . . . . . . . . . . . . . 19.1.3 Spillover Matrix and Compensation Matrix . . . . . . . 19.2 Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.1 Paradoxical Effects . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.2 Negative Values and their Management . . . . . . . . . . 19.2.3 Compensation by Hardware . . . . . . . . . . . . . . . . . . 19.2.4 Compensation by Software . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
457 457 457 462 463 464 466 469 470 471 472
20
Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.1 Escapees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2 Debris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3 Other Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3.1 Due to Anticoagulants . . . . . . . . . . . . . . . . . . . . . . . 20.3.2 Due to Fluorochromes . . . . . . . . . . . . . . . . . . . . . . . 20.3.3 Due to Serum Factors . . . . . . . . . . . . . . . . . . . . . . . 20.3.4 Anecdotal Reports . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
475 475 477 477 477 478 480 481 482
21
Cell Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1 Fluid Switching Sorters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.1 Pros and Cons of Fluid Switching Sorters . . . . . . . . . 21.2 Electrostatic Sorters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Sorting Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Sorting Modalities . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.3 High-Pressure Systems . . . . . . . . . . . . . . . . . . . . . . 21.3 Pneumatic Sorters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
485 485 486 486 488 490 494 494 495
22
Non-Conventional Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . 22.1 Imaging Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.1 CCD-Based Imaging Flow Cytometry . . . . . . . . . . . 22.1.2 PMT-Based Imaging Flow Cytometry . . . . . . . . . . . 22.2 Spectral Flow Cytometry (SFC) . . . . . . . . . . . . . . . . . . . . . . . 22.2.1 Optical Bench in Spectral Flow Cytometry . . . . . . . .
497 497 498 500 501 502
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22.2.2 Spectral Unmixing in Spectral Flow Cytometry . . . . 22.2.3 Format Issues in Spectral Flow Cytometry . . . . . . . . 22.2.4 PROs and CONs in Spectral Flow Cytometry . . . . . . 22.3 Mass Cytometry (MC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3.1 Format Issues in Mass Cytometry . . . . . . . . . . . . . . 22.3.2 PROs and CONs in Mass Cytometry . . . . . . . . . . . . 22.4 Lifetime Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.5 Raman Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.6 Microfluidic Devices (Labs on Chips) . . . . . . . . . . . . . . . . . . . 22.6.1 Hydraulic Issues in Microfluidic Devices . . . . . . . . . 22.6.2 Sorting Procedures in Microfluidic Devices . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
502 503 503 504 505 506 507 509 510 511 512 513
Statistics: A Cytometric Point of View . . . . . . . . . . . . . . . . . . . . . . . 23.1 Concept of Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.1.1 Log-Normal Distribution . . . . . . . . . . . . . . . . . . . . . 23.1.2 Normal (Gaussian) Distribution . . . . . . . . . . . . . . . . 23.1.3 Poisson Distribution . . . . . . . . . . . . . . . . . . . . . . . . 23.2 Location Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.1 Mean (Am) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.2 Geometric Mean (gM) . . . . . . . . . . . . . . . . . . . . . . 23.2.3 Truncated Mean . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.4 Mode (v0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.5 Median (Me) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3 Spread Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3.1 Variance (Var) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3.2 Standard Deviation (SD) . . . . . . . . . . . . . . . . . . . . . 23.3.3 Median Absolute Deviation (MAD) . . . . . . . . . . . . . 23.3.4 Robust Standard Deviation (rSD) . . . . . . . . . . . . . . . 23.3.5 Coefficient of Variation (CV) . . . . . . . . . . . . . . . . . 23.3.6 Robust Coefficient of Variation (rCV) . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
523 523 523 524 524 525 526 526 526 527 527 527 528 528 528 529 529 530 530
Correction to: Fluorochromes That Bind Nucleic Acids . . . . . . . . . . . . .
C1
23
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
Abbreviations
7-AAD A ABC ABCC1 ABCG ACS ADC AF ALDH aM AMCA AML AO AOD AOM APC APCB APD APF ARP ASCII AT ATP AU AVOs B B BAAA BALF BAPTA
7-amino-actinomycin D area antibody binding capacity see MRP1 ATP-binding cassette transporter G Archival Cytometry Standard analog-to-digital converter Alexa Fluor aldehyde dehydrogenase arithmetic mean amino-methyl-coumarin-acetate acute myeloid leukemia acridine orange acousto-optic deflector acousto-optic modulator allophycocyanin allophycocyanin B avalanche photodiode actual probability function average residual percentage American Standard Code for Information Interchange adenine-thymine adenosine triphosphate adenine-uracil acidic vesicular organelles background brightness, or brilliance BODIPY aminoacetaldehyde bronchoalveolar lavage fluid bis-amino phenoxy-ethane tetraacetic acid xxi
xxii
BB Bcal BCECF-AM B-CLL BFP BODIPY BOE BP B-PE BR BRB BrdU BRV BSA Bsos Btot BUdR BUV BV BYG CA3 CAD CAS CB CCD CFDA CFDA-SE CFP CFSE CFW CGD CHE CHL CHR cif CMEDA CMFDA CNS CO Con-A COPAS COT CR CRBC
Abbreviations
brilliant blue instrumental background carboxyfluorescein acetoxymethyl ester carboxyethyl B-cell chronic lymphocytic leukemia blue fluorescent protein dipyrrometheneboron difluoride binary optical element band pass B-phycoerythrin blue and red (lasers) berberine bromo-deoxyuridine blue, red, and violet (lasers) bovine serum albumin experimental background total background bromo-deoxyuridine brilliant ultraviolet brilliant violet brilliant yellow-green chromomycin-A3 computer-assisted design Chemical Abstract Service cascade blue charge-coupled device carboxyfluorescein diacetate succinimidyl carboxyfluorescein diacetate ester cyan fluorescent protein succinimidyl carboxyfluorescein ester calcofluor white chronic granulomatous disease chelerythrine chelilutine chelirubine compensated image file chloromethyleosin-diacetate chloromethylfluorescein-diacetate central nervous system Coriphosphine O concanavalin A Complex Object Parametric Analyzer and Sorter cyclooctatetraene response coefficient chicken nucleated red blood cells
Abbreviations
CRC CTC CTC CTN CV CWDM CY CY2 CY3 CY3.5 CY5 CY5.5 CY7 CY7.5 daf DAPI DAQ DC DCF DCF-DA DCFH DCV DDF DEP DEQTC DETC DHE DHR123 DIFC DIPI DM DNA DPH DPSSL DPX DSP dsRNA DT DTAF DTT DUV E E EB
xxiii
cyclic redundancy check circulating tumor cells cyano-ditolyl tetrazolium chloride calf thymocyte nuclei coefficient of variation coarse wavelength division multiplexing cascade yellow cyanine 2 cyanine 3 cyanine 3.5 cyanine 5 cyanine 5.5 cyanine 7 cyanine 7.5 data analysis file diamidino phenylindole data acquisition board direct current dichlorofluorescein dichlorofluorescin diacetate dichlorofluorescin DyeCycle violet drop driving frequency dielectrophoresis diethylquinolylthiacyanine iodide diethylquinolylthiacyanine iodide dihydroethidium dihydrorhodamine 123 diffuse in vivo flow cytometry dihydro-imidazol-phenyl-indole dichroic mirror deoxyribonucleic acid diphenyl-hexatriene diode-pumped solid-state laser Dapoxil digital signal processor double stranded RNA detection threshold dichlorotriazinylaminofluorescein dithiothreitol deep ultraviolet energy extinction coefficient ethidium bromide
xxiv
ECD ECFP EDTA EdU EGFP em EMA EMP en ENOB EP EQA ER ER ERF ex EYFP FACS FAD FALS FCM FCS FCS FDA FET FIRE FISH FITC FLECKD FLIM FLU fMLP FMO FP FPGA FRET FSC G GC GFP GJIC gM gMFI
Abbreviations
energy-coupled dye enhanced cyan fluorescent protein ethylenediaminetetraacetic acid ethynyl-deoxyuridine enhanced green fluorescent protein emission ethidium monoazide endothelial microparticles electric noise effective number of bit event packet external quality assessment effective resolution endoplasmic reticulum equivalent number of reference fluorophore extinction enhanced yellow fluorescent protein fluorescence activated cell sorting flavin adenine dinucleotide forward-angle light scatter flow cytometry fetal calf serum flow cytometry standard fluorescein diacetate field-effect transistor fluorescence imaging using radiofrequency-tagged emission fluorescent in situ hybridization techniques fluorescein isothiocyanate fluorescence-lifetime excitation cytometry by kinetic dithering fluorescence lifetime imaging microscopy fluorescence intensity unit formyl-methionyl-leucyl-phenylalanine fluorescence minus one fluorescent protein field-programmable gate array fluorescence resonance energy transfer, but also Förster resonance energy transfer forward scatter gate guanine-cytosine green fluorescent protein gap junction-mediated intercellular communication geometric mean MFI based on geometric mean
Abbreviations
GQs H HI HPCV HSC HSFCM ICP ICS IFC IHC IMC IMD IQC IR iRFP ISAC ISC ISHAGE IUPAC JCGM/WG kHz KO LAIP laser LD700 LED LL LLOQ lmd LOB LOD LOQ LP LR LSB LSM LST LY MA Mab MAD MBB MC
xxv
G-quadruplexes height hexidium iodide half peak coefficient of variation hematopoietic stem cell high sensitivity flow cytometers inductively coupled plasma image-enabled cell sorter imaging flow cytometry immunohistochemistry Imaging mass cytometry integrated mass data internal quality control infrared infrared fluorescent proteins International Society for Advancement in Cytometry, formerly International Society for Analytical Cytology intersystem crossing International Society of Hematotherapy and Graft Engineering International Union of Pure and Applied Chemistry Joint Committee for Guides in Metrology/Working Group thousands of cycles per second Krome orange leukemia-associated immunophenotype light amplification by stimulated emission of radiation rhodamine 700 light-emitting diode lower left lower limit of quantification list mode data limit of blank limit of detection limit of quantitation long pass lower right least significant bit least-squares methods lymphoid screening tube Lucifer yellow macarpine monoclonal antibody median absolute deviation monobromobimane mass cytometry
xxvi
MCR MDR MDR1 MDV Me MEMS MESF MFG MFI MG MHz MO MPO MPPC MQAE MRD MRP1 MTG MTH NA NADPH NAFLD NAO NBA NBT ND NetCDF NGF NIR NR NUV OCOS OD OFPs OL OPA OPSL OPT PAFP PB PBFI PC PCA PD
Abbreviations
multivariate curve resolution multidrug resistance multidrug resistance transporter mitochondria-derived vesicle median micro-electrical-mechanical systems molecules of equivalent soluble fluorochrome Mitofluor Green mean fluorescence intensity methyl green millions of cycles per second mercury orange myeloperoxidase multi-pixel photon counter methoxyquinolyl acetoethyl ester minimal residual disease multidrug resistance-associated protein Mitotracker® Green mithramycin numerical aperture nicotinamide adenine dinucleotide phosphate nonalcoholic fatty liver disease nonyl acridine orange nitrobenzyl alcohol tetrazolium nitroblue neutral density network common data form next-generation flow cytometry near-infrared Nile red near-ultraviolet one cell-one shot optical density orange fluorescent protein olivomycin ortho-phthalaldehyde optically pumped semiconductor laser ortho-phthalaldehyde photoactivable protein Pacific blue potassium-binding benzofuran isophthalate phycocyanin C principal component analysis photodiode
Abbreviations
PDE PE PEBBLE PEG PerCP PG P-gp1 PI PID PMA PMT PO PPP PS PSA PS-ODN PyMPO Q Qdots QF QFC Qr QY R R800 RALS RAM RB 200 RBC RBV rCV RFI RFP RH123 rif RMF RNA ROT R-PE rSD S0 S1 S2 SA
xxvii
photodetection efficiency phycoerythrin probe encapsulated by biologically localized embedding polyethylene glycol peridinin-chlorophyll-protein complex Pacific green P-glycoprotein 1, see MDR1 propidium iodide primary immunodeficiency phorbol myristate acetate photomultiplier Pacific orange primary performance parameter phosphatidylserine probabilistic spectrum analysis phosphorothioate oligodeoxynucleotides methoxyphenyl-oxazol-pyridinium bromide brightness quantum dots quadratic form quantitative fluorescence calibration relative brightness (optoelectronic efficiency) quantum yield region rhodamine 800 right-angle light scatter random access memory lissamine rhodamine red blood cells red, blue, and violet robust coefficient of variation relative fluorescence intensity red fluorescent protein rhodamine 123 raw image file relative median fluorescence ribonucleic acid electrorotation R-phycoerythrin robust standard deviation singlet state zero, ground state singlet state 1 singlet state 2 sanguinarine
xxviii
SBFI SD SDen SERS SFC SFIT SI SIHON SiPM SIps SL SNARF SNE SNR SOD SOM SORP SP SP SPAD SPADE SPQ SQI SRB SRB SS SSC SSL SSM ssRNA STED T T1 T2 T668 T669 TCSPC TEM TI TIA TMA-DPH tMFI TMR
Abbreviations
sodium-binding benzofuran isophthalate standard deviation standard deviation of electrical noise surface-enhanced Raman scattering spectral flow cytometry Simple FITting stain index (resolution index) Dutch Foundation for Immunophenotyping of Haematological Malignancies silicon photomultiplier panel specific separating index sanguilutine seminaphtha-rhodafluor stochastic neighbor embedding signal-to-noise ratio superoxide dismutase self-organizing map special order research products short pass side population single-photon avalanche photodiode spanning tree progression analysis of density-normalized events sulfopropyl quinolinium spread quantification index sanguirubine sulforhodamine B spillover spreading side scatter solid-state laser spillover spreading matrix single stranded RNA stimulated emission depletion triplet state triplet state 1 triplet state 2 see TMRM see TMRE time-correlated single-photon counting transverse electromagnetic modalities trajectory inference trans-impedance amplifier trimethylammonium-diphenyl-hexatriene MFI based on trimmed mean tetramethylrhodamine
Abbreviations
TMRE TMRM TO TOF TOF-ICPMS TPE TPEN TR TRBC TRITC TSQ TVAC UCNP UL UMAP UR UV Var VBR W WBC WDM WGA XML YFP ZCV ZP1
xxix
tetramethylrhodamine ethyl ester tetramethylrhodamine methyl ester thiazole orange time of flight time-of-flight inductively coupled plasma mass spectrometry two-photon excitation tetrakis-pyridylmethyl-ethylenediamine Texas red rainbow trout nucleated red blood cells tetramethylrhodamine isothiocyanate methoxy-quinolyl-p-toluenesulfonamide true volumetric absolute counting up-converting nanoparticles upper low uniform manifold approximation and projection upper right ultraviolet variance violet, blue, and red (lasers) width white blood cells wavelength division multiplexer wheat germ agglutinin extensible markup language yellow fluorescent protein zero channel value Zinpyr-1
Chapter 1
General Principles
The term “Flow Cytometry” (FCM) officially started in 1978, during the American Engineering Foundation Conference held in Pensacola, Florida, and is currently used to define a technology previously known as “pulse cytophotometry” (Goehde 2014). However, it is of note that the origins of Flow Cytometry, known as a technique performing the count of cells in suspension, have their roots in the 1930s and can be traced back to some experimental apparatuses designed for blood cell counting (Moldavan 1934; Crosland-Taylor 1953). In-depth treatment of Flow Cytometry history is beyond the scope of this book, and further information on the subject can be obtained from the consultation of a series of articles and monographs to which we refer (Melamed et al. 1990; Keating and Cambrosio 2003; Shapiro 2004, 2010; Macey 2007; Robinson 2009). Briefly described, Flow Cytometry is an analytical technique in which a series of objects in suspension, called from now on “events,” are moved in a fluid to intercept a beam of light radiation. The signals produced by this interaction are collected and digitized and provide quantitative information on some properties of the analyzed events, henceforth referred to as “parameters” (Herzenberg et al. 1976; Van Dilla et al. 1985; van den Engh and Stokdijk 1989; Hiebert 1990; Huller et al. 1990; Lindmo et al. 1990; Salzman et al. 1990; Steen 1990; Shapiro 1993, 1995). This definition is the simplest and also applies to other strictly related technologies, i.e.: 1. “Spectral Flow Cytometry” (SFC), which evaluates not the signal’s peak but the whole signal spanning from visible to near-infrared region (Robinson et al. 2004, 2005a, b, 2007; Robinson 2004; Leavesley et al. 2005; Gregori et al. 2012; Nolan and Condello 2013). 2. “Imaging Flow Cytometry” (IFC), in which an image of each event is detected by a CCD sensor and resolved in a pixel matrix assigned to that event (George et al. 2004; Basiji 2005; Basiji et al. 2007). The technological progress of recent years has also allowed the appearance of another instrumental solution known as “Mass Cytometry” (MC), in which the tracer © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_1
1
2
1
General Principles
is a metal isotope, and the signal is detected by a system akin to a mass spectrometer (Bandura et al. 2009; Ornatsky et al. 2010; Bjornson et al. 2013). Spectral Flow Cytometry (SFC), Imaging Flow Cytometry (IFC), and Mass Cytometry (MC) are briefly discussed together with a few other methods in Chap. 22, dedicated to “NonConventional Flow Cytometry.” Finally, it should be remembered that some cytometers can physically separate the events according to criteria pre-selected by the operator; these instruments are briefly discussed in Chap. 21, dedicated to “Cell Sorters.” Strictly speaking, the term “flow cytometer” can also be applied to the hematology analyzers and the devices for automated urinalysis commonly found in the Clinical Analysis Laboratory. The first difference between a hematology analyzer and a flow cytometer is that the first reports absolute counts, while the second, apart from some exceptions dealt with below (for further information about this topic, see Sect. 4.2.4.2), reports percentage counts. But what mainly distinguishes the two types of instrumentation is the nature of the measured signal. Besides the light scattering exploited in both types, the hematology analyzers’ primary signal varies with the implemented technology, including impedance, axial extinction, conductivity, and scattered light’s polarization. In contrast, the one measured in the flow cytometers is typically a fluorescence signal, which causes the flow cytometer to take also the name of “cytofluorograph” or “cytofluorimeter.” These considerations, however, retain an exclusive educational value, threatened by the rapid evolution of technologies. It is hardly necessary to observe that some hematology analyzers associate the analysis of the fluorescence (Arneth and Menschikowki 2014) with the analysis of the other signals and that some flow cytometers, commercial or not, are or have been able to detect additional signals such as axial extinction (quantitative absorption) (Schonbrun et al. 2014), impedance (Kachel et al. 1977), and (de)polarized fluorescence (Keene and Hodgson 1980). Again, a note of a terminological nature. In the early years of the cytometric era, any flow cytometer was referred to as “FACS,” which stands for “Fluorescenceactivated cell sorter.” This approach combines two different inaccuracies: the first consists in the fact that the word FACS, as such, should not be applied to instruments with only analytical functions, while the second consists in the fact that the term FACS, being a trademark filed by the company Becton Dickinson, should not be extended to instruments produced by other companies. Finally, another cautionary terminological note is needed in light of the appearance of the already cited new technologies: 1. “Spectral Flow Cytometry” (SFC) has also been called “Hyperspectral Flow Cytometry,” “Multispectral Cytometry,” or “Full Spectrum Cytometry.” 2. “Imaging Flow Cytometry” (IFC) has also been called “Imaging Multispectral Flow Cytometry” because of its fluorescence detection approach and must not be confused with “Spectral Cytometry.” 3. Flow Cytometry (FCM) based on fluorochromes’ peak detection is also sometimes called “Conventional,” “Traditional,” or “Polychromatic Flow Cytometry.”
1.1
Flow Cytometers General Layout
3
4. Mass Cytometry (MC) based on isotopes’ atomic weight detection is sometimes also called “TOF (Time of Flight) Cytometry;” the related imaging technique takes the name of “Imaging Mass Cytometry (IMC).”
1.1
Flow Cytometers General Layout
A fluorescence detection-based flow cytometer consists of four functional components, which integrate each other (Fig. 1.1). The first functional component is the fluidic component, also called hydraulic, in which a fluid carries the monodispersed sample to intercept a light radiation source. Traditionally it comprises (1) the flow liquid reservoir, (2) the drain tank, (3) the valve, pumps, resistors, and piping assembly, and in most cases, (4) an analysis cell called a cuvette. The second functional component is the optical component, through which the light signals produced by the interaction between events and light radiation reach the committed sensors. Traditionally it includes (1) the source (or sources) of light, (2) the optical bench with all the devices aimed to select and sort the different wavelengths, and (3) the detectors translating the light signals into electrical signals. According to this schematic representation, the analysis cell constitutes the hinge between the hydraulic and optical components.
Fig. 1.1 Schematic representation of a conventional flow cytometer. The incident ray, the events’ flow, and the axis of fluorescence detection and side scatter signals reside in each spatial plane. The signal evoked at the interrogation point consists of a flow of photons (1), converted first into a flow of electrons (2) and then in a potential difference (3), to be eventually digitized (4), i.e., converted into numbers
4
1
General Principles
The third functional component is the electronic component, which manages the electrical signal generated by detectors. Depending on the implemented circuit model (analog or digital), the electronic component includes various boards whose functions are described in Chap. 7. According to this schematic representation, the detector constitutes the hinge between the optical and electronic components, sometimes considered together and collectively defined as an “optoelectronic component.” The fourth component is the computer, which houses the data files (see Chap. 8) and the software needed to control the cytometer, perform the analyzes, and store the results. Except for the FCS file format, the discussion of Flow Cytometry’s informatic aspects is beyond this book’s scope.
1.2
Flow Cytometers Features
Flow Cytometry is an analytical technique that presents several peculiar characteristics, which make up its strengths and establish its limits. The first characteristic is that, as an indispensable prerequisite, Flow Cytometry only analyzes samples in which events are present in suspension. This condition restricts the analysis to situations where this requirement is naturally met or satisfied by particular preanalytical procedures, such as mechanical or enzymatic disaggregation, and has automatically promoted the tremendous success of Flow Cytometry in the study of hematological diseases, in which the sample is naturally monodisperse, or easily reduced in this state. The second characteristic is the globality of the detection, i.e., the method can only detect the signal’s presence or absence. Except in very particular cases, it cannot determine which event’s component originates the signal. This feature is a significant limitation, but it is now overcome by one of the previously mentioned techniques (Imaging Flow Cytometry, IFC), which digitizes each event’s image thanks to a CCD (charge-coupled device) sensor. The third characteristic is the analysis speed, ranging between a few hundred to almost one hundred thousand events per second. This high speed and the fact that the maximum number of analyzed events is theoretically only limited by the available mass memory size (and operator’s endurance) ensure that the method is particularly suitable for detecting extremely low-frequency populations. This condition often occurs in biological sample analysis and, by definition, in the minimal residual disease (MRD) study. In this context, analyzing a massive number of events constitutes a fundamental prerequisite for the results’ statistical robustness. The fourth characteristic is the ability to measure multiple parameters at once. This possibility is a desirable feature, as it is a prerequisite for multivariate data analysis. The highest number of extrinsic parameters besides scattering measured now at once is 28 for conventional (Liechti and Roederer 2019a, b) and 43 for spectral cytometry (Sahir et al. 2020).
1.3
Parameters and Signals
5
The fifth characteristic is its extreme flexibility, which depends on the host of fluorescent probes available. In some cases, Flow Cytometers can physically separate cells based on criteria selected by the operator. These instruments take the name of “Cell Sorters” and are dealt with in Chap. 21.
1.3
Parameters and Signals
As a “parameter,” we mean a distinct characteristic of the analyzed population, while as a “signal,” we mean a quantity that varies over time. In Flow Cytometry, the parameter was also defined as the signal produced by one of the cytometer detectors. At the same time, the value is the digital (numerical) representation of that parameter’s magnitude. In Flow Cytometry, parameters are traditionally split into intrinsic parameters, which do not require the introduction of probes into the sample, and extrinsic parameters, which require a specific probe for each analyzed parameter. Even if other signals can be theoretically exploited, the first type’s detection depends on scatter signals, while the second type’s detection depends on fluorescence signals. The intrinsic parameters generally correspond to the event’s physical features, including mass, volume, membrane roughness, and, when the event is a eukaryotic cell, autofluorescence, cytoplasm granularity, and nucleus/cytoplasm ratio. The extrinsic parameters make up a particularly large and heterogeneous group, including expression of antigenic structures determined by immunological techniques, protein content (see Sect. 17.1), nucleic acid content (see Chap. 14), chromatin organization (see Sect. 17.2), membrane permeability and cell viability (see Sect. 17.3), membrane potential (see Sect. 17.4), mitochondrial potential (see Sect. 17.5), mitochondrial mass (see Sect. 17.6), intracellular pH (see Sect. 17.7), lysosomal mass and pH (see Sect. 17.8), production of free radicals and oxidative burst (see Sect. 17.9), Calcium content (see Sect. 17.10), Sodium content (see Sect. 17.11), Potassium content (see Sect. 17.12), Chloride content (see Sect. 17.13), Magnesium content (see Sect. 17.14), Glutathione content (see Sect. 17.15), and the content of various heavy metals including Cadmium, Iron, Mercury, Niobium, Lead, Copper, and Zinc (see Sect. 17.16), as well as the determination of proliferation (see Sect. 17.17), multidrug resistance (MDR) (see Sect. 17.18), membrane fluidity (see Sect. 17.19), lipid content (see Sect. 17.20), lipid oxidative status (see Sect. 17.21), and endoplasmic reticulum (ER) labeling (see Sect. 17.22). Evaluating these parameters allows studying complex cellular functions, including apoptosis, autophagy, cell cycle, cellular activation, cytotoxic activity, degranulation, phagocytosis, et cetera. In conventional Flow Cytometry, signals consist of variations in the photons’ flow up to the detector and electrons’ after that. In any case, the signal consists of a component generated by the events and a component independent from them, called background (for further news about the background, see Sect. 13.5.4.1). Each
6
1
General Principles
Fig. 1.2 The behavior of a homoskedastic signal (panel a) compared to that of a heteroskedastic signal (panel b). The homoskedastic signal maintains its variance regardless of intensity, while the heteroskedastic signal modifies it (in this case, increasing it)
signal’s component results in various sub-components, displaying different distributions and variances. It follows that the signal is heteroskedastic and tends to increase its variance with its intensity (Gondhalekar et al. 2018) (Fig. 1.2). This behavior affects several conditions, ranging from choosing the optimal photomultiplier (PMT) power supply to the compensation procedures. Being related to the physical characteristics of the event, scatter signals are the natural expression of intrinsic parameters. Nevertheless, when elicited by a probe such as a monoclonal antibody conjugated with colloidal gold particles, they become the expression of extrinsic parameters. Analogously, fluorescence signals are related to a fluorescent probe and are considered the expression of extrinsic parameters but become the expression of intrinsic parameters in the case of autofluorescence, which is a constitutive and unavoidable attribute of the eukaryotic cells and other microorganisms (Cyanobacteria and unicellular algae). Consequently, the following sections deal with the signals detected in Flow Cytometry independently of their intrinsic or extrinsic nature.
1.4
Time
Time can be assimilated to a parameter, as it expresses, for each event analyzed, the value relative to its positioning within the analytic series. Therefore, the time is recorded in the cytometric file for each event and can be graphically represented versus any other parameter analyzed in the same run. Time is essential in evaluating the behavior of a probe over time as it happens in the follow-up of the cytoplasmatic Calcium mobilization following an activating stimulus (June et al. 1986; Rabinovitch et al. 1986).
References
7
It is essential to realize that transient perturbations can result in the emergence of entirely artifactual populations. The operator should check time’s graphic representation versus the parameter displaying suspicious behavior in response to unexpected results (Watson 1987). Finally, it is essential to remind that properly set analytical gates exclude from the analysis the undesired contribution of possible perturbations (Kusuda and Melamed 1994).
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General Principles
Keating P, Cambrosio A (2003) Medical platforms. Realigning the normal and the pathological in late-twentieth-century medicine. MIT Press, Cambridge, MA Keene JP, Hodgson BW (1980) A fluorescence polarization flow cytometer. Cytometry 1(2): 118–126 Kusuda L, Melamed MR (1994) Display and correction of flow cytometry time-dependent fluorescence changes. Cytometry 17(4):340–342 Leavesley S, Ahmed W, Bayraktar B, Rajwa B, Sturgis J, Robinson JP (2005) Multispectral imaging analysis: spectral deconvolution and applications in biology. Proc SPIE 5699:121–128 Liechti T, Roederer M (2019a) OMIP-058: 30-parameter flow cytometry panel to characterize iNKT, NK, unconventional and conventional T cells. Cytometry A 95(9):946–951. https://doi. org/10.1002/cyto.a.23850 Liechti T, Roederer M (2019b) OMIP-060: 30-parameter flow cytometry panel to assess T cell effector functions and regulatory T cells. Cytometry A 95(11):1129–1134. https://doi.org/10. 1002/cyto.a.23853 Lindmo T, Peters DC, Sweet RG (1990) Flow sorters for biological cells. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 145–169 Macey MG (2007) Principles of flow cytometry. In: Macey MG (ed) Flow cytometry: principle and applications. Humana Press, Totowa, NJ, pp 1–16 Melamed MR, Mullaney PF, Shapiro HM (1990) An historical review of the development of flow cytometers and sorters. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 1–10 Moldavan A (1934) Photo electric technique for the counting of microscopical cells. Science 80(2069):188–189. https://doi.org/10.1126/science.80.2069.188 Nolan JP, Condello D (2013) Spectral flow cytometry. Curr Protoc Cytom 63(1):1–27. https://doi. org/10.1002/0471142956.cy0127s63 Ornatsky O, Bandura D, Baranov V, Nitz M, Winnik MA, Tanner S (2010) Highly multiparametric analysis by mass cytometry. J Immunol Methods 361(1–2):1–20. https://doi.org/10.1016/j.jim. 2010.07.002 Rabinovitch PS, June CH, Grossman A, Ledbetter JA (1986) Heterogeneity among T cells in intracellular free calcium responses after mitogen stimulation with PHA or anti-CD3, simultaneous use of Indo-1 and immunofluorescence with flow cytometry. J Immunol 137(3):952–961 Robinson JP (2004) Multispectral cytometry: the next generation. Biophoton Int (October):36–40 Robinson JP (2009) Cytometry – a definitive history of the early days. In: Sack U, Tarnok A, Rothe G (eds) Cellular diagnostics. Basic principles, methods and clinical applications of flow cytometry. Karger, Basel, pp 1–28 Robinson JP, Rajwa B, Grégori G, Jones J, Patsekin V (2004) Collection hardware for high speed multispectral single particle analysis (abstract). Cytometry 59A(1):12 Robinson JP, Patsekin V, Grégori G, Rajwa B, Jones J (2005a) Multispectral flow cytometry: next generation tools for automated classification. Microsc Microanal. https://doi.org/10.1017/ S1431927605510328 Robinson JP, Rajwa B, Grégori G, Jones J, Patsekin V (2005b) Multispectral cytometry of single bio-particles using a 32-channel detector. In: Vo-Dinh T, Grundfest WS, Benaron DA, Cohn GE (eds) Advanced biomedical and clinical diagnostic systems III. SPIE, Bellingham, WA, pp 359–365 Robinson JP, Rajwa B, Grégori G, Patsekin V (2007) USA patent 7280204 B2. Multispectral detector and analysis system. https://pdfpiw.uspto.gov/.piw?docid¼07280204. Accessed 12 Feb 2022 Sahir F, Mateo JM, Steinhoff M, Siveen KS (2020) Development of a 43 color panel for the characterization of conventional and unconventional T-cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry. Cytometry A. https://doi.org/10.1002/cyto.a.24288
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Salzman GC, Singham SB, Johnston RG, Bohren CF (1990) Light scattering and cytometry. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 81–107 Schonbrun E, Malka R, Di Caprio G, Schaak D, Higgins JM (2014) Quantitative absorption cytometry for measuring red blood cell hemoglobin mass and volume. Cytometry A 85(4): 332–338. https://doi.org/10.1002/cyto.a.22450 Shapiro HM (1993) Trends and developments in flow cytometry instrumentation. Ann N Y Acad Sci 677:155–166 Shapiro HM (1995) Practical flow cytometry, 3rd edn. Wiley, New York Shapiro HM (2004) The evolution of cytometers. Cytometry A 58(1):13–20. https://doi.org/10. 1002/cyto.a.10111 Shapiro HM (2010) A history of flow cytometry and sorting. In: Ligler FS, Kim JS (eds) The microflow cytometer. CRC Press – Taylor & Francis Group, Boca Raton Steen HB (1990) Characteristics of flow cytometers. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 11–25 van den Engh G, Stokdijk W (1989) Parallel processing data acquisition system for multilaser flow cytometry and cell sorting. Cytometry 10(3):282–293 Van Dilla M, Dean P, Laerum O, Melamed M (eds) (1985) Flow cytometry: instrumentation and data analysis. Academic Press, Orlando Watson JV (1987) Time, a quality-control parameter in flow cytometry. Cytometry 8(6):646–649
Chapter 2
Signals: Scattering
As pointed out previously, intrinsic parameters correspond to the physical characteristics of the event. When the event crosses the interrogation point, its physical characteristics evoke a signal known as light scattering or scatter. This term refers to the diffusion of incident light radiation in various spatial directions. The scatter signal’s value is generally proportional to the value of the event’s intrinsic variables. Still, it depends on several other conditions, including (1) the polarization of the incident radiation, (2) the spatial position in which the phenomenon’s observation occurs, and (3) the refractive index established between the event and the medium in which the event is in suspension. In eukaryotic cells, the refractive index is a function of the membrane features of the cells and intracytoplasmic structures (nucleus and organelles) (de Grooth et al. 1987; Salzman et al. 1990). In exceptional cases, such as staining with colloidal Gold-conjugated Mabs, the scatter signals can be related to extrinsic parameters because an increase in side scattering reports the presence of the target (Bohmer and King 1984a, b; Hansen et al. 2012). An instrument is known that has exploited this technical solution in the count of CD4+ lymphocytes (Pointcare NOW System, PointCare, Marlborough, USA) (Bergeron et al. 2012). Other authors have exploited two Mabs simultaneously, the first conjugated with Silver-bound and the second with Gold-bound polystyrene microbeads, since differences in forward and side scatter values could distinguish different cell subsets (Siiman et al. 2000). Others again have used mAb-coated latex beads (Fortin and Hugo 1999). These methodological approaches are currently of purely historical or theoretical interest. Even though familiar to the cytometrist and widely exploited in biomedical instruments, light scattering is an extremely complex phenomenon whose in-depth description goes beyond this chapter’s objectives. Further details on this subject can be found in dedicated publications (van de Hulst 1957; Kerker 1969, 1983; Bohren and Huffman 1983; Born and Wolf 1986; Horvath 2009). From the perspective of traditional optics, light scattering results from reflection, refraction, diffraction, and interference phenomena evoked by an event’s interrogation by an incident light radiation (Asbury et al. 2000). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_2
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Fig. 2.1 Schematic representation of the distribution of light scattering in space. When detected along the incident radiation axis at an angle close to zero, it takes the name of “Forward Scatter,” and it is a function of a series of intrinsic parameters, including the event’s size. In contrast, when detected from a lateral position (around 90°), it takes the name of “Side Scatter,” which is a function of the event structural complexity
From the perspective of quantum mechanics, light scattering occurs since molecules are dipoles set in phase. According to this model, the dipoles affected by electromagnetic radiation behave like antennas, starting to oscillate and giving rise to electromagnetic radiation of the same wavelength as the incident light radiation, which propagates in all directions of space. The light scattered by the particle is the sum of all the radiation scattered by its dipoles and displays the same wavelength of the incident radiation. The total value depends on the total number of dipoles and the phase relationships established between the waves issued (Salzman et al. 1990). For this last reason, the scatter total value depends on the direction the waves propagate, i.e., the detector’s position. This point is of the utmost importance. Several general theories on scattering depend on the size, the energetic state, and the shape of the involved events; the best known are the Rayleigh and Lorenz-Mie theories. Rayleigh’s theory applies to atoms and small molecules that absorb blue and violet and explains why the sky is light blue. Lorenz-Mie’s theory applies to spherical particles whose dimensions span from a quarter to several tens of times the wavelength of the incident radiation, i.e., similar to the events analyzed in Flow Cytometry. It predicts that for spherical particles similar to cells in suspension, and in this case only, the value of the scatter is proportional to the cross-section of the sphere and depends on many other factors such as (1) the refractive index, (2) the wavelength, (3) the incident light phase, and (4) the particle absorption coefficient (Latimer and Pyle 1972). According to Lorenz-Mie’s model, an important scattering feature is its non-homogeneous propagation in space; it follows that its value depends on the detector’s position, i.e., its detection axis (Fig. 2.1). When detected from an angle very close to the incident radiation axis, the scatter takes the name of Forward Scatter
Signals: Scattering
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Fig. 2.2 Representation of FSC and SSC analysis of a peripheral whole blood sample. The cytogram resolves the suspension in its different cell subpopulations depending on the size (FSC) and complexity (SSC) values. RBCs were removed by lysis
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(FSC) (also direct scatter or forward-angle light scatter (FALS)) and mainly varies with the event’s size. Forward scatter values also vary according to the angle from which they are detected, and this explains how different optical benches give hardly identical results; some instruments allow for slightly varying the angle of detection to optimize the signal. Conversely, when detected from a perpendicular or however lateral angle to the axis in question, the scatter takes the name of Side Scatter (SSC) (also lateral scatter, orthogonal scatter, or right-angle light scatter (RALS)). Side Scatter demonstrates a different behavior: in fact, not only is its intensity much lower than the Forward Scatter but it covariates with other parameters, including structural complexity. In this way, by correlating the Forward and Side Scatter signals produced by a heterogeneous cell suspension, it is possible to display their heterogeneity as a function of the values of the two intrinsic parameters, size and granularity (Salzman et al. 1975b) (Fig. 2.2). In Flow Cytometry, a laser beam usually elicits Forward Scatter and Side Scatter. However, particular situations deviated from this model in the past because the light source was an arc lamp, and the instrument’s constructive design did not allow an SSC dedicated detector. In these cases, it was attempted to detect the light scatter through a lens, creating a light cone; then, a complex of diaphragms and mirrors sampled the different cone’s zones, telling the cone’s core from its peripheral areas (Mansberg et al. 1974; Steen and Lindmo 1985; Steen 1986, 1990b). The cone’s core signal approximated the Forward Scatter, while the peripheral area signal approximated the Side Scatter.
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The scatter signals elicited by laser incident radiation are strongly polarized due to the Brewster windows (for further information on this topic, see Sect. 5.2), and this feature can unexpectedly affect the final result (Asbury et al. 2000). The availability of multiple lasers on the same platform makes it possible to collect the scatter signals off each of them differentially; lower wavelengths such as from violet lasers can better resolve small events (McVey et al. 2018). According to some authors, the scatter signal produced by the excitation at 405 nm of a diluted whole blood sample would allow identifying not only platelets, red blood cells, and leukocytes but also lymphocytes, monocytes, and granulocytes (Ost et al. 1998).
2.1
Forward Scatter (FSC)
Forward Scatter (FSC), also known as direct scatter or forward-angle light scatter (FALS), is a signal that, according to the Fraunhofer diffraction theory, is predominantly constituted by the diffractive component (Born and Wolf 1986). The FSC is a signal of relatively high intensity and is generally detected by a photodiode, which in comparison to the photomultiplier (PMT) or the avalanche photodiode (APD), is a transducer with much less efficiency. Lorenz-Mie’s theory, valid for spherical and homogeneous particles, predicts the forward scatter as particularly intense and proportional to the event’s mass and volume (Salzman et al. 1990), and this behavior has been shown experimentally (Mullaney et al. 1969). Still, many other factors can influence the signal, such as (1) the wavelength of the incident radiation, (2) the amplitude of the angle of signal detection, (3) the event absorbance, (4) the refraction index between the event and the suspension medium, and (5) the features of the light collecting optical system. This last factor explains the different results given out by different optical benches (Salzman et al. 1975a; Loken et al. 1976; Sharpless and Melamed 1976; Kerker 1983; van der Pol et al. 2018). The influence of the refraction index on light scattering allows the identification of dead and damaged cells. A lower refraction index due to the membrane’s change generates lower FSC values, which allows the dead cell exclusion from the analysis by positioning proper gates (Loken and Houck 1981; Terstappen et al. 1988b). On the contrary, it is more difficult to interpret the experimental finding that cells subjected to variable osmotic regimes provide scatter signals inversely proportional to their volume (McGann et al. 1988). This behavior is in disagreement not only with the concept of FSC as a non-linear function of the cell volume (Salzman et al. 1990 #485) but also with the model of Latimer and Pyle, which states a direct correlation between FSC and volume in spherical particles (Latimer and Pyle 1972). The proposed explanations were (1) the variation of the membrane conditions, which would lose “roughness” during the volume increase, consequently reducing the FSC (McGann et al. 1988), but above all, (2) the decrease in the refractive index of the nucleus and cytoplasm versus the suspension medium (Sloot et al. 1988), and (3) the nucleus volume variation (Sloot et al. 1988). Beyond the true causes of this
2.2
Side Scatter (SSC)
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paradoxical behavior, this example must induce the utmost caution in this parameter’s interpretation. It is hardly necessary to remember that, with the exceptions discussed in Sect. 13. 4.5, scattering signals from microbeads of known size may not be related to cell dimensions or exploited “as such” as volume or size calibrators.
2.2
Side Scatter (SSC)
The lateral scatter, also called orthogonal scatter, right-angle light scatter (RALS), or Side Scatter (SSC), is a much less intense signal than the Forward Scatter and is generally detected by a photomultiplier (PMT) instead of a photodiode (PD). The event’s internal structural complexity strongly affects the SSC, whose final value depends on the phase relationships between the waves issued by the dipoles (Salzman et al. 1990). According to some authors, the multiple reflections occurring between the organelles’ membranes re-synchronize the phase of the laterally issued waves, thus increasing the signal’s intensity (Salzman et al. 1990). This effect makes it possible to distinguish between polymorphonucleates (neutrophils and eosinophils) and mononucleates (lymphocytes and monocytes) and, to a lesser extent, even between granular and non-granular lymphocytes (Terstappen et al. 1990b), as well as between “classical” and “non-classical” monocytes (Passlick et al. 1989). As for other cells very similar to each other, such as mast cells and basophils, it is noteworthy that the mast cells show high SSC values, similar to or greater than neutrophils (Orfao et al. 1996), while basophils show low SSC values similar to lymphocytes. This paradoxical behavior is probably due to their granules’ particular refractive indices (Terstappen et al. 1990a, b). It has been reported that the FACSCanto cytometer (marketed by Becton Dickinson) can resolve from the background noise microbeads of 0.5 μm (500 nm) based on the FSC signal and 0.16 μm (160 nm) based on the SSC signal (Alkhatatbeh et al. 2018). As for the influence of the incident radiation’s wavelength, the SSC from a violet laser beam is more efficient than a blue laser beam in detecting very small events such as extracellular vesicles (McVey et al. 2018). This technical solution is easily implementable in all the cytometers equipped with a violet laser and allows distinguishing latex microbeads of at least 0.18 μm (180 nm) diameter from the background noise (McVey et al. 2018). It has also been reported that the CytoFlex cytometer (marketed by Beckman Coulter) can resolve from the background noise polystyrene microbeads of 0.07 μm (70 nm) based on the violet SSC signal (Brittain et al. 2019). It is also possible to evaluate the side scatter signals from the 405 and the 488 nm lasers at once. According to a non-peer-reviewed paper available on the Internet, this method would allow the identification of the platelets in a non-lysed peripheral blood sample without the use of platelet-specific monoclonal antibodies (Invitrogen 2017). It is noteworthy that the relationship between the SSC and the investigated event’s dimensions can assume an unexpected behavior under certain conditions.
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Fig. 2.3 Cytogram produced by representing SSC (X-axis) and FSC (Y-axis) of a polystyrene microbeads sample. FSC and SSC’s relationship is not linear because FSC increases progressively, but SSC fluctuates in a narrow range. The phenomenon is even more evident in doublets and triplets (in the black frame)
Lorenz-Mie’s theory predicts a high dependence between particle size and side scatter values for homogeneous spheres; still, it may happen that, during the analysis of microspheres with a high refractive index, the relationship assumes not a linear but a sinusoidal trend. It follows that irregular patterns, known as Lissajous figures or Lissajous loops, can occur. This phenomenon is experimentally verifiable by analyzing polystyrene microbeads commonly used in calibration procedures (Figs. 2.3 and 13.8) (Doornbos et al. 1994; Hoekstra et al. 1994). This behavior is probably not relevant in most of the determinations. Still, it is not advisable to rely on SSC optimization during instrument adjustment procedures because there is no correlation between the obtained CVs and the adjustment quality (Doornbos et al. 1994).
2.3
Raman Scattering
The scattering phenomenon described by the Rayleigh and Lorenz-Mie theories is “elastic” because the absorbing molecule gives back photons with the same energy (or wavelength) as the incident light. Still, there is also an “inelastic” scattering (Kerker 1983), called Raman scattering, as described by Raman and Krishnan in the first half of the past century (Raman and Krishnan 1928).
2.3
Raman Scattering
17
Fig. 2.4 Diagram of Jablonski, showing the fundamental state (S0), one different singlet energy state (S1), and three vibrational levels (v0, v1, and v2) for each state. The figure briefly describes the light scatter phenomenon, understood as the transition (green arrow) of an electron from the fundamental state S0 to a higher energy state S1, followed by its return to the fundamental state S0 and emission of a photon. If the electron comes back to the same previous vibrational level, the scattering is elastic, and the excitation and emission wavelengths are the same (Rayleigh scattering, left panel). If the electron comes back to an energetically higher vibrational level, the scatter is inelastic, and the emission wavelength is longer than excitation (Raman scattering, central panel). If the electron comes back to an energetically lower vibrational level, the scatter is inelastic, and the emission wavelength is shorter than excitation (anti-Stokes Raman scattering, right panel). E energy, S singlet state, v vibrational level, ex excitation, em emission
In Raman scattering, the photon coming back to the ground status can reach a vibrational level different from the starting one, with increased or, more rarely, less energy. It follows that Raman scattering can occur at a longer (Stokes scatter) or, more rarely, shorter (anti-Stokes scatter) wavelength than the excitation (Fig. 2.4). Raman scattering displays a series of interesting features (Nolan and Sebba 2011), i.e.: 1. Any excitation wavelength can elicit it. 2. In a given molecule, its emission wavelength always shows the same spectral shift from that of excitation. 3. It is not subject to saturation or photobleaching. Raman scattering features an intensity equal to 1/1000 of Lorenz-Mye scattering and even much less the anti-Stokes emission. Still, it can be a significant source of disturbance in Flow Cytometry (Yamamoto and Robinson 2021). Moreover, given that the Raman scattering occurs at a fixed frequency shift from the exciting line, it is susceptible to adding a specific background to the measurement of some fluorochromes. In particular, 1. The Raman scattering produced by water irradiated at 488 nm has a wavelength of around 590 nm, susceptible to increasing the background in Phycoerythrin measurement (Steen 1990a).
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2. The Raman scattering produced by water irradiated at 532 nm has a wavelength of around 649 nm, susceptible to increasing the background in the measurement of Allophycocyanin (Bigos 2017). 3. The Raman scattering produced by the water irradiated at 544 nm from a HeliumNeon laser (used to excite PE, PE-TR, and PE-CY5) has a wavelength of around 667 nm, susceptible to increasing the background in the measurement of the PE-CY5 tandem (Hudson et al. 1995). 4. The Raman scattering produced by the water irradiated at 561 nm has a wavelength around 693 nm, susceptible to increasing the background in the measurement of Cyanine 5.5 and PerCP (Bigos 2017). As can be deduced from the above, Raman scattering is generally considered a disturbing element capable of limiting the measurement’s sensitivity. Nonetheless, there is also a radically different approach in which Raman scattering constitutes the selectively measured signal. This approach is the basis of an analytical technique known as “Raman spectroscopy” and its application to Flow Cytometry (Watson et al. 2008; Nolan and Sebba 2011; Nolan et al. 2012) (for further information on this topic, see Sect. 22.5).
2.4
Depolarized Scatter
Due to the presence of Brewster windows (for further information on this topic, see Sect. 5.2), the electromagnetic radiation produced by a laser is highly polarized, i.e., it only oscillates on one of the infinite possible planes along its propagation axis. Consequently, the scatter signal elicited by a laser is also highly polarized; in some instruments, the scatter value depends on the detector’s sensitivity to polarization and the optical bench features (Asbury et al. 2000). Suppose the scatter is elicited by a non-polarized source, as in the case of an arc lamp. In that case, it is still possible to polarize the incident radiation using polarizing filters, according to the model described by Maude and collaborators for conventional optical microscopes (Maude et al. 2009). In particular conditions, the scatter polarization can be lost. This behavior is typically seen in eosinophils, whose granules evoke multiple reflection processes that depolarize the scatter and make them selectively detectable by a detector with a polarizer adjusted to give minimal transmittance of the polarized SSC signal (de Grooth et al. 1987; Terstappen et al. 1988a; Lavigne et al. 1997) (Fig. 2.5). This effect has been patented and exploited in a commercial hematology analyzer (Abbott Diagnostics, California, USA). Malaria infection causes hemozoin deposition in phagocytes, which depolarizes the cell scatter. These phenomena have been exploited in automated diagnosis and therapy monitoring of malaria (Hanscheid et al. 2000; Frita et al. 2011; Rebelo et al. 2015).
References
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Fig. 2.5 Panel a: graphical representation of the distribution of two different FSC signals sampled at different angles to the incident radiation axis. Panel b: graphical representation of the distribution of two different SSC signals, the first polarized and the second depolarized. The image refers to a peripheral blood sample from a subject with eosinophilia performed with the CELL-DYN Sapphire instrument (Abbott). In panel b, we note the presence of a population (green in the picture) whose lateral scatter has lost the original polarization, which corresponds to eosinophils. Courtesy of Giorgio Darin
References Alkhatatbeh MJ, Enjeti AK, Baqar S, Ekinci EI, Liu D, Thorne RF, Lincz LF (2018) Strategies for enumeration of circulating microvesicles on a conventional flow cytometer: counting beads and scatter parameters. J Circ Biomark. https://doi.org/10.1177/1849454418766966 Asbury CL, Uy JL, van Den Engh G (2000) Polarization of scatter and fluorescence signals in flow cytometry. Cytometry 40(2):88–101 Bergeron M, Daneau G, Ding T, Sitoe NE, Westerman LE, Stokx J, Jani IV, Coetzee LM, Scott L, De Weggheleire A, Boel L, Stevens WS, Glencross DK, Peter TF (2012) Performance of the PointCare NOW system for CD4 counting in HIV patients based on five independent evaluations. PLoS One. https://doi.org/10.1371/journal.pone.0041166 Bigos M (2017) 532 nm or 561 nm laser upgrade. Purdue Cytometry Discussion List. https://lists. purdue.edu/pipermail/cytometry/2017-September/052108.html. Accessed 21 Oct 2018 Bohmer RM, King NJ (1984a) Flow cytometric analysis of immunogold cell surface label. Cytometry 5(5):543–546 Bohmer RM, King NJ (1984b) Immuno-gold labeling for flow cytometric analysis. J Immunol Methods 74(1):49–57 Bohren CF, Huffman DR (1983) Absorption and scattering of light by small particles. Wiley, New York Born M, Wolf E (1986) Principles of optics. Pergamon Press, Oxford Brittain GC, Chen YQ, Martinez E, Tang VA, Renner TM, Langlois MA, Gulnik S (2019) A novel semiconductor-based flow cytometer with enhanced light-scatter sensitivity for the analysis of biological nanoparticles. Sci Rep. https://doi.org/10.1038/s41598-019-52366-4 de Grooth BG, Terstappen LW, Puppels GJ, Greve J (1987) Light scattering polarization measurement as a new parameter in flow cytometry. Cytometry 8(6):539–544
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Doornbos RM, Hoekstra AG, Deurloo KE, De Grooth BG, Sloot PM, Greve J (1994) Lissajous-like patterns in scatter plots of calibration beads. Cytometry 16(3):236–242 Fortin M, Hugo P (1999) Surface antigen detection with non fluorescent, antibody-coated microbeads: an alternative method compatible with conventional fluorochrome-based labeling. Cytometry 36(1):27–35 Frita R, Rebelo M, Pamplona A, Vigario AM, Mota MM, Grobusch MP, Hanscheid T (2011) Simple flow cytometric detection of haemozoin containing leukocytes and erythrocytes for research on diagnosis, immunology and drug sensitivity testing. Malar J 10:74. https://doi.org/ 10.1186/1475-2875-10-74 Hanscheid T, Valadas E, Grobusch MP (2000) Automated malaria diagnosis using pigment detection. Parasitol Today (Personal ed) 16(12):549–551 Hansen P, Barry D, Restell A, Sylvia D, Magnin O, Dombkowski D, Preffer F (2012) Physics of a rapid CD4 lymphocyte count with colloidal gold. Cytometry A 81(3):222–231. https://doi.org/ 10.1002/cyto.a.21139 Hoekstra AG, Doornbos RM, Deurloo KE, Noordmans HJ, Grooth BG, Sloot PM (1994) Another face of Lorenz-Mie scattering: monodisperse distributions of spheres produce Lissajous-like patterns. Appl Opt 33(3):494–500 Horvath H (2009) Gustav Mie and the scattering and absorption of light by particles: historic developments and basics. JQSRT 110(11):787–799 Hudson JC, Porcelli RT, Russell TR (1995) Flow cytometric immunofluorescence and DNA analysis using a 1.5 mW Helium-Neon laser (544 nm). Cytometry 21(2):211–217 Invitrogen (2017) Detection of platelets in whole blood using the Attune NxT Flow Cytometer. Invitrogen application note. White Paper. https://www.thermofisher.com/it/en/home/lifescience/cell-analysis/flow-cytometry/flow-cytometry-learning-center/flow-cytometry-resourcelibrary/flow-cytometry-application-notes/detection-platelets-in-whole-blood-using-attune-nxtflow-cytometer.html. Accessed 2 Feb 2019 Kerker M (1969) The scattering of light and other electromagnetic radiations. Academic Press, New York Kerker M (1983) Elastic and inelastic light scattering in flow cytometry. Cytometry 4(1):1–10 Latimer P, Pyle BE (1972) Light scattering at various angles: theoretical predictions of particle volume changes. Biophys J 12(7):764–773 Lavigne S, Bosse M, Boulet LP, Laviolette M (1997) Identification and analysis of eosinophils by flow cytometry using the depolarized side scatter-saponin method. Cytometry 29(3):197–203 Loken MR, Houck DW (1981) Light scattered at two wavelengths can discriminate viable lymphoid populations on a fluorescence activated cell sorter. J Histochem Cytochem 29(5):609–615 Loken MR, Sweet RG, Herzenberg LA (1976) Cell discrimination by multiangle light scattering. J Histochem Cytochem 24(1):284–291 Mansberg HP, Saunders AM, Groner W (1974) The Hemalog D white cell differential system. J Histochem Cytochem 22(7):711–724 Maude RJ, Buapetch W, Silamut K (2009) A simplified, low-cost method for polarized light microscopy. Am J Trop Med Hyg 81(5):782–783. https://doi.org/10.4269/ajtmh.2009.09-0383 McGann LE, Walterson ML, Hogg LM (1988) Light scattering and cell volumes in osmotically stressed and frozen-thawed cells. Cytometry 9(1):33–38 McVey MJ, Spring CM, Kuebler WM (2018) Improved resolution in extracellular vesicle populations using 405 instead of 488 nm side scatter. J Extracell Vesicles. https://doi.org/10. 1080/20013078.2018.1454776 Mullaney PF, Van Dilla MA, Coulter JR, Dean PN (1969) Cell sizing: a light scattering photometer for rapid volume determination. Rev Sci Instrum 40:1029–1032 Nolan JP, Sebba DS (2011) Surface-enhanced Raman scattering (SERS) cytometry. Methods Cell Biol 102:515–532. https://doi.org/10.1016/b978-0-12-374912-3.00020-1 Nolan JP, Duggan E, Liu E, Condello D, Dave I, Stoner SA (2012) Single cell analysis using surface enhanced Raman scattering (SERS) tags. Methods 57(3):272–279. https://doi.org/10. 1016/j.ymeth.2012.03.024
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Orfao A, Escribano L, Villarrubia J, Velasco JL, Cervero C, Ciudad J, Navarro JL, San Miguel JF (1996) Flow cytometric analysis of mast cells from normal and pathological human bone marrow samples: identification and enumeration. Am J Pathol 149(5):1493–1499 Ost V, Neukammer J, Rinneberg H (1998) Flow cytometric differentiation of erythrocytes and leukocytes in diluted whole blood by light scattering. Cytometry 32(3):191–197 Passlick B, Flieger D, Ziegler-Heitbrock HW (1989) Identification and characterization of a novel monocyte subpopulation in human peripheral blood. Blood 74(7):2527–2534 Raman CV, Krishnan KS (1928) A new type of secondary radiation. Nature 121:501–502 Rebelo M, Tempera C, Bispo C, Andrade C, Gardner R, Shapiro HM, Hanscheid T (2015) Light depolarization measurements in malaria: a new job for an old friend. Cytometry A 87(5):437–445. https://doi.org/10.1002/cyto.a.22659 Salzman GC, Crowell JM, Goad CA, Hansen KM, Hiebert RD, LaBauve PM, Martin JC, Ingram ML, Mullaney PF (1975a) A flow-system multiangle light-scattering instrument for cell characterization. Clin Chem 21(9):1297–1304 Salzman GC, Crowell JM, Martin JC, Trujillo TT, Romero A, Mullaney PF, LaBauve PM (1975b) Cell classification by laser light scattering: identification and separation of unstained leucocytes. Acta Cytol 19(4):374–377 Salzman GC, Singham SB, Johnston RG, Bohren CF (1990) Light scattering and cytometry. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 81–107 Sharpless TK, Melamed MR (1976) Estimation of cell size from pulse shape in flow cytofluorometry. J Histochem Cytochem 24(1):257–264 Siiman O, Gordon K, Burshteyn A, Maples JA, Whitesell JK (2000) Immunophenotyping using gold or silver nanoparticle-polystyrene bead conjugates with multiple light scatter. Cytometry 41(4):298–307 Sloot PM, Hoekstra AG, Figdor CG (1988) Osmotic response of lymphocytes measured by means of forward light scattering: theoretical considerations. Cytometry 9(6):636–641 Steen HB (1986) Simultaneous separate detection of low angle and large angle light scattering in an arc lamp-based flow cytometer. Cytometry 7(5):445–449 Steen HB (1990a) Characteristics of flow cytometers. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 11–25 Steen HB (1990b) Light scattering measurement in an arc lamp-based flow cytometer. Cytometry 11(2):223–230 Steen HB, Lindmo T (1985) Differential of light-scattering detection in an arc-lamp-based epi-illumination flow cytometer. Cytometry 6(4):281–285 Terstappen LW, de Grooth BG, Visscher K, van Kouterik FA, Greve J (1988a) Four-parameter white blood cell differential counting based on light scattering measurements. Cytometry 9(1):39–43 Terstappen LW, Shah VO, Conrad MP, Recktenwald D, Loken MR (1988b) Discriminating between damaged and intact cells in fixed flow cytometric samples. Cytometry 9(5):477–484 Terstappen LW, Hollander A, Meiners H, Loken MR (1990a) Quantitative comparison of myeloid antigens on five lineages of mature peripheral blood cells. J Leukoc Biol 48(2):138–148 Terstappen LW, Mickaels RA, Dost R, Loken MR (1990b) Increased light scattering resolution facilitates multidimensional flow cytometric analysis. Cytometry 11(4):506–512 van de Hulst HC (1957) Light scattering by small particles. Wiley, New York van der Pol E, de Rond L, Coumans FAW, Gool EL, Boing AN, Sturk A, Nieuwland R, van Leeuwen TG (2018) Absolute sizing and label-free identification of extracellular vesicles by flow cytometry. Nanomedicine 14(3):801–810. https://doi.org/10.1016/j.nano.2017.12.012 Watson DA, Brown LO, Gaskill DF, Naivar M, Graves SW, Doorn SK, Nolan JP (2008) A flow cytometer for the measurement of Raman spectra. Cytometry A 73(2):119–128 Yamamoto M, Robinson JP (2021) Quantum approach for nanoparticle fluorescence by sub-ns photon detection. Cytometry A 99(2):145–151. https://doi.org/10.1002/cyto.a.24310
Chapter 3
Signals: Fluorescence, Phosphorescence, Impedance, Extinction
3.1
Fluorescence
Fluorescence is a physical property in virtue of which a molecule that absorbs a photon reaches a transitory state of excitation, from which it recedes, emitting, in turn, another photon, usually characterized by lower energy. The in-depth discussion of fluorescence theory goes beyond this chapter’s objectives, which only provides information on the relevance of the phenomenon in Flow Cytometry. Further details on this topic can be obtained from the consultation of a series of articles and monographs to which we refer (Jablonski 1935; Williams and Bridges 1964; Lakowicz 2006). The processes underlying fluorescence are described in the Jablonski diagram (Jablonski 1935) (Fig. 3.1). According to this scheme, the most probable phenomenon that occurs in a molecule that has absorbed a photon is the transition of an electron from an energetic state called fundamental (S0) to a higher energetic state, which, depending on the energy absorbed, can take on a higher (S2) or a lower value (S1). In this passage, the electron moves from a lower to an upper orbital, maintaining the same spin orientation and consequently conserving the singlet state; hence a letter S is attributed to this energetic state. In reality, each energetic state S results from the sum of a series of different energy levels, depending on the molecule’s vibrational, rotational, and translational features and its electronic and nuclear spin orientation. Conversions between different energy levels in the same energy state can occur, which means that a given energy state’s total energy value can vary within certain limits. After reaching the upper energy state, the electron remains there for a very short time, assessable around some nanoseconds, after which it comes back to the fundamental energy state S0, returning the received energy in the form of another photon. The energy returned from coming back to S0 equals received from outside minus
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_3
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Signals: Fluorescence, Phosphorescence, Impedance, Extinction
Fig. 3.1 The figure briefly describes the fluorescence phenomenon, understood as the passage of an electron from the fundamental state S0 to a higher energy state S1 (blue arrow), followed by the return to the fundamental state S0 (red arrow) and emission of a photon. E energy, EX extinction, EM emission, S0, S1, S2 singlet states, T1 and T2 triplet states, v0, v1, v2 vibrational levels
that dissipated in conversions between the S1 different energy levels. In short, the energy emitted is always less than the energy absorbed. The relations by the energy of a photon and the frequency of the electromagnetic radiation are described by E ¼ hν, where h stands for the Planck constant and ν stands for the frequency of electromagnetic radiation. It follows that a fluorescent molecule always emits radiation of a wavelength greater than that absorbed, the only exceptions to this rule being the upconversion occurring in the two-photon excitation and up-converting nanoparticles covered in Sect. 15.4.2. In two-photon excitation, a phenomenon exploited in some confocal microscopy applications, a fluorochrome is hit at once by two photons, which behave like a single photon with an energy equal to the sum of them. This excitation is currently used in confocal microscopy but not in Flow Cytometry. The distance between the excitation and the emission peak wavelength is called Stokes shift, a value dependent on the molecule’s structural characteristics. The greater the energy loss before returning to the fundamental state S0, the more significant the Stokes shift’s extent. The time an excited fluorochrome spends between the excitation of a fluorochrome and the emission of fluorescence, i.e., before coming back to the fundamental state, is known as Fluorescence Lifetime (τ). The Fluorescence Lifetime generally varies from 100 ps to 15 ns, depending on the structural characteristics of the
3.1
Fluorescence
25
molecule (Lakowicz 2006), and two molecules sharing the same emission peak can differ in their lifetimes (further information on this topic can be found in Sect. 22.4). Some currently available instruments can resolve events with fluorescence values below 30 MESF (Molecules of Equivalent Soluble Fluorochrome) for FITC and 10 MESF for PE (Beckman Coulter 2015). This extremely high sensitivity to fluorescence signals is not necessary to analyze eukaryotic cells, displaying by default a certain amount of autofluorescence, but it becomes fundamental in the study of non-autofluorescent exosomes and extracellular vesicles. In these events, the low number of epitopes, due to their minimal size, binds a reduced number of conjugated antibodies, resulting in the expression of very low signal levels.
3.1.1
Depolarized Fluorescence
As already said about the scatter, the electromagnetic radiation produced by a laser is highly polarized, i.e., it oscillates along the propagation axis of only one of all the infinite possible planes. The fluorescence signal emitted by a fluorochrome excited by a laser tends to be polarized too because a laser preferentially excites the molecules parallelly oriented to its polarization plane; it follows that its magnitude also depends on the spatial relationships between the various molecules. Depolarized fluorescence has not been exploited much in Flow Cytometry. In a study on the mitogen-induced activation of Fluorescein diacetate (FDA) treated lymphocytes, a decrease in the fluorescence polarization was correlated with changes in the fluidity of the activated cell membrane (Dimitropoulos et al. 1988). Other studies on fluorescence depolarization in stimulated lymphocytes have also been performed with CFSE (ex495/em519) (Cohen-Kashi et al. 1997), BCECF-AM (ex505/em545) (Gelman-Zhornitsky et al. 1997), TMA-DPH (ex350/em452) (Schaap et al. 1984), and DPH (ex350/em452) (Schaap et al. 1984) (for further information on this topic, see Sect. 17.19).
3.1.2
Autofluorescence
As mentioned above, cellular autofluorescence is an intrinsic parameter, generally due to intracytoplasmic reduced coenzymes. Autofluorescence is particularly intense if elicited with deep UV (280 nm), UV, and violet light (Telford et al. 2019) because of nicotinamide adenine dinucleotide phosphate (NADPH) (Chance and Thorell 1959), which emits at 400–470 nm. Still, it is also present if elicited with blue light due to the flavin adenine dinucleotide (FAD), which, in turn, emits at 520–540 nm (Aubin 1979; Benson et al. 1979). Autofluorescence is much less relevant if elicited with red light (Loken et al. 1987), but it is still present in red
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Signals: Fluorescence, Phosphorescence, Impedance, Extinction
Fig. 3.2 In cytograms in panels a and b, it is possible to highlight the autofluorescence of eosinophils (in blue). Cells were excited with a violet laser (405 nm), and signals were detected in the channels reserved for Krome Orange (panel a) (BP550/40) and Pacific Blue (panel b) (BP450/40)
and infrared regions because of the porphyrins (Koenig and Schneckenburger 1994) and the oxidation products (Semenov et al. 2020). The autofluorescence of unlabeled human leukocytes excited at 488 nm and detected around 520 nm is different for lymphocytes, monocytes, and neutrophils, and it has been estimated to be equivalent to 650, 930, and 1200 MESF of FITC, respectively (Schwartz and Fernandez-Repollet 1993). In certain cell types, autofluorescence is due to lipofuscin, or other substances, such as tobacco tar, in the alveolar macrophages of smokers (Pauly et al. 2005). In the specific case of lipofuscin, a cytometric study has exploited the autofluorescence to measure the lipofuscin granules in homogenates of formalin-fixed central nervous system (CNS) cells from lobster (Sheehy 2002). Another study on fecal microbes isolated in particular mouse strains has shown an unexpectedly high autofluorescence in the red and infra-red regions (Denu et al. 2019). Autofluorescence appears particularly intense in some cells, including eosinophils (Weil and Chused 1981) (Fig. 3.2), mesenchymal stem cells (Anwer et al. 2012), microglia cells (Burns et al. 2021), and alveolar macrophages (Skold et al. 1989), and it may vary within the same cell type, such as buccal mucosa cells, which show greater autofluorescence in smokers (Paszkiewicz et al. 2008). The latter data is in agreement with the fact that autofluorescence has been identified as a response to stressors both in prokaryotic and eukaryotic cells (Surre et al. 2018). Autofluorescence may also be due to preanalytical aspects. For example, (1) fixation with formaldehyde or (2) phenol red in the suspension is likely to worsen the phenomenon (Stewart et al. 2007; Ettinger and Wittmann 2014). Several attempts have been made to reduce the autofluorescence signal, including:
3.1
Fluorescence
27
1. Use of corrective algorithms (Corsetti et al. 1988). 2. Selective excitation of the autofluorescence signal with a dedicated laser, its detection in a dedicated channel (dummy dye channel), and its subsequent subtraction from the probe’s signal (Steinkamp and Stewart 1986); this approach has been recently revived by the release of the AutoSpill algorithm based on robust linear regression (Roca et al. 2021) (for further information on this topic, see Sect. 12.4.1.3). 3. Use of quenching substances, such as dithiothreitol (DTT) (Hulspas and Bauman 1992), Trypan Blue (Mosiman et al. 1997; Srivastava et al. 2011; Shilova et al. 2017), or Crystal Violet, alone (Hallden et al. 1991; Umino et al. 1999) or associated with η-octyl β-D-galactopyranoside (Hodge et al. 2004). All in all, despite the previously reported hints and with the possible exception of the use of AutoSpill, the only way to truly get rid of autofluorescence seems to resort to technologies in which autofluorescence (1) is not detected (Mass Cytometry), or (2) selectively ignored based on the different fluorescence lifetime of the exploited probes (Lifetime Cytometry), or (3) subtracted by spectral unmixing, like in Spectral Flow Cytometry (SFC) (Nitta et al. 2015) (for further information about this topic, see Sect. 22.2.4) or spectrally enhanced Conventional Flow Cytometry (FCM) (for further information about this topic, see Sect. 6.4.4.3). In Imaging Flow Cytometry (IFC), the autofluorescence signal can be subtracted pixel by pixel with algorithms similar to the compensation procedures. Autofluorescence is not necessarily a counterproductive phenomenon. Some micro-organisms, such as Cyanobacteria and Unicellular Algae, contain molecules capable of intense autofluorescence, generally engaged in the respiratory chain. The detection of the various autofluorescence signals produced in these organisms is of utmost importance in their study and classification (Olson et al. 1989; Phinney and Cucci 1989; Frankel et al. 1996; Marie et al. 2001). The autofluorescence signal was also useful in a series of investigations, including (1) the study of eosinophils (Weil and Chused 1981; Thurau et al. 1996), (2) the study of protoplasts (Harkins and Galbraith 1987), (3) the study of murine pulmonary dendritic cells (Vermaelen and Pauwels 2004), (4) the isolation of murine medullary neutrophils (Watt et al. 1980), (5) the study of murine myeloid cells (Mitchell et al. 2010), (6) the study of oxidative damage in red blood cells of uremic subjects (Stoya et al. 2002), (7) the study of macrophages derived from atherosclerotic lesions (Liu-Wu et al. 1997), (8) the identification of the neoplastic mast cells (Escribano et al. 1998), and, jointly with the molecule YOYO-1, (9) the study of cells parasitized by Plasmodium falciparum (Campo et al. 2011). The autofluorescence of neutrophils with toxic granulations is selectively increased by dipicolylamine, and this effect has been exploited in the evaluation of acute bacterial infections (Kim et al. 2009). In erythropoietic protoporphyria, erythrocytes emit a strong red autofluorescence signal when excited by a violet line (Lau and Lam 2008). Autofluorescence contributes to the experimental background (Bsos) together with unbound fluorochrome and compensation-related spreading (Perfetto et al. 2014) (for further information on the background, see Sect. 8.1).
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Last but not least, it should not be forgotten that an often underrated autofluorescence exists not from the sample but from constituent materials of the cytometer, such as quartz, optical glass, and sheath liquid. This autofluorescence is generally negligible in common cytometric analyses, but it can become relevant in photon-counting measurements (Yamamoto 2017).
3.2
Phosphorescence
Phosphorescence is a phenomenon very similar to fluorescence and, as such, represents an aspect of the more general phenomenon known as photoluminescence. The two phenomena differ in their generation mechanism and other characteristics, including the emission range and duration. Besides isolated attempts (Jin et al. 2007), phosphorescence does not find—at least for now—use in Flow Cytometry, but it deserves a brief explanation. Like fluorescence, phosphorescence is due to the restitution of a quantum of energy previously absorbed as a photon. Still, there are substantial differences. In fluorescence, the transition occurs between energetic states characterized by electrons with opposite or “paired” spin (singlet states, Sn). In phosphorescence, the transition occurs instead through states with lower energetic content that house electrons with the same spin (triplet states, Tn). The path between singlet (Sn) and triplet state (Tn) is called Inter System Crossing (ISC). In summary, in the fluorescence, the return to the fundamental state S0 starts from the singlet state S1, while in the phosphorescence, it starts from the relative and less energetic triplet state T1 (Fig. 3.3). The emission resulting from the transition from T1 to S0 takes the name of phosphorescence. It is characterized by high wavelengths—consequent of the low energy level of T1—and by high duration in time, measurable in milliseconds. Some authors have observed that the phosphorescence signal’s tail might be improperly collected by the downstream lasers’ tributary sensors in a multi-laser cytometric system and have proposed a program to correct the alleged problem (Potasek et al. 2013). Still, others have drastically challenged its need in some nonpeer-reviewed observations available on the Internet (Roederer 2012; Shapiro 2012), stating, among other points, that the would-be phosphorescence tails would be entirely corrected by the compensation procedure traditionally implemented.
3.3
Impedance
The impedance is the capacity to oppose the passage of an electric current and can be considered the conductance’s inverse. The impedance is exploited in an apparatus for counting cells and particles known as the Coulter counter.
3.3
Impedance
29
Fig. 3.3 The figure briefly describes the phosphorescence phenomenon, understood as the passage of an electron from the fundamental state S0 to a higher energetic state S1 (blue arrow), followed by the passage to the triplet state T1 through the intersystem crossing (gray arrow) and return to the fundamental state S0 (red arrow) with emission of a photon. E energy, EX extinction, EM emission, ISC intersystem crossing, S0, S1, S2 singlet states, T1 and T2 triplet states, v0, v1, v2 vibrational levels
Consider a capillary immersed in an electrolytic solution in which an electric current is flowing across the capillary’s orifice. If an event passes through the orifice, it displaces an equivalent electrolytic solution’s volume and causes an impedance increase; in case a constant power is applied, this impedance increase causes a voltage pulse, according to Ohm’s law (Pinkel and Stovel 1985; Kachel 1990). It follows (1) that each pulse appearance indicates the passage of an event and (2) that the pulse value is proportional to the event volume. This effect takes the name of the “Coulter effect” from Wallace Henry Coulter, who devised it and exploited it in the first automatic blood cell analyzer (Coulter 1956). The evaluation of the cell size by the Coulter effect exploits a direct current, but a high-frequency (radiofrequency) alternate current can also be used. In this case, the cell membrane behaves as it were partially current-permeable, and the signal is proportional to the density and the size of the nucleus (Ruzicka et al. 2001). A method for assessing the event volume also exists, which is conceptually similar to the former but relies on detecting a fluorescence signal. In this method, the cell passage through the orifice displaces a fluorescent solution rather than a saline solution causing a decrease in the fluorescence signal, whose magnitude is proportional to the event volume (Gray et al. 1983). This principle has been implemented in an experimental cytometer (Gray et al. 1983) but has no use in commercial instruments. As previously mentioned, the volume of an event is a typical intrinsic parameter. Still, it can be seen as an extrinsic parameter if a probe can modulate it according to
30
3 Signals: Fluorescence, Phosphorescence, Impedance, Extinction
the presence of a specific cellular characteristic. This eventuality happens in tests carried out with monoclonal antibodies conjugated to polystyrene microspheres. In these experiments, the bound between monoclonal antibody and target cell results in an increased volume of the cell-microsphere complex, detectable with an impedance-based hematology analyzer (Hudson et al. 1995). Although easily carried out “at home” with an impedance counter, this method has never been exploited commercially and has merely historical interest.
3.4
Axial Extinction
Axial extinction, also known as “light loss,” is the inverse of transmittance measured along an axis parallel to the incident radiation and is a function of an intrinsic parameter, which we could define as “transparency” (Stewart et al. 1989). In Flow Cytometry, the exploitation of this parameter relies on non-fluorescent probes and may happen according to different scenarios. In the first scenario, the cell component specifically binds a probe capable of absorbing light radiation. A hematological analyzer developed in the past was able to tell basophils from the other white blood cells based on their affinity with Alcian Blue, a dye specific for mucopolysaccharides (Saunders and Scott 1974; Gilbert and Ornstein 1975). The second scenario exploits the enzymatic activity as in the method used by Dolbeare to determine the cell peptidases (Dolbeare and Smith 1977), with the difference that it relies not on a fluorogenic substrate but on the production of an opaque intracellular precipitate. This precipitate increases the absorbance so that the positive cells become immediately distinguishable from the other cells. The mechanism involved in the second scenario can rely on two different approaches. The first approach demonstrates a naturally occurring intracellular enzyme by adding the appropriate substrate to the cell suspension under analysis. This approach, also known as “flow cytochemistry,” has had tremendous practical success and has long been the basis of some hematology analyzers performing differential analyses of leukocytes based on their scatter signal and peroxidase content (Fig. 3.4). Although mostly used for peroxidase demonstration (Rosvoll et al. 1979), this system can also reveal other enzymes, and it has been used in the past to demonstrate the monocytes’ esterases (Kaplow et al. 1976). The second approach relies on Mabs conjugated with an enzyme able to catalyze insoluble precipitation and is conceptually similar to immunofluorescence, even though it replaces fluorescence with axial extinction. This approach has been sporadically practiced in the past (Kim et al. 1985, 1992; d’Onofrio et al. 1989) but is now definitively obsolete. For the sake of completeness, an instrument called QAC (quantitative absorption cytometer) should be remembered, which was capable of simultaneously evaluating the red blood cells’ volume and their hemoglobin content based on absorbance
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Fig. 3.4 Graphical representation of the Side Scatter and Myeloperoxidase content distribution evaluated according to the axial extinction after incubation with an enzyme substrate producing an opaque precipitate. The image refers to two samples of peripheral blood, the first from a normal subject (panel a) and the second from a subject with congenital peroxidase deficiency (panel b). It is immediately evident that the cells with high scatter (polymorphonuclear cells) do not demonstrate axial extinction in the pathological subject’s peroxidase channel. The analysis was performed with an H1 hematology analyzer (Technicon)
signals. In this system, whose fluidics present analogies with impedance systems, a suspension of red blood cells in a dye solution absorbing in the red spectrum region circulates through a capillary on which two LEDs are focused, the first emitting at 630 nm and the second emitting at 421 nm. The passage of a red blood cell extrudes the dye from the interrogation point, decreasing the absorbance at 630 nm in a way proportional to its volume; the Hemoglobin concentration is calculated based on the absorbance at 421 nm, close to the so-called Soret band, which is the major band of Hemoglobin absorption spectrum (Schonbrun et al. 2014).
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Stewart JC, Villasmil ML, Frampton MW (2007) Changes in fluorescence intensity of selected leukocyte surface markers following fixation. Cytometry A 71(6):379–385 Stoya G, Klemm A, Baumann E, Vogelsang H, Ott U, Linss W, Stein G (2002) Determination of autofluorescence of red blood cells (RbCs) in uremic patients as a marker of oxidative damage. Clin Nephrol 58(3):198–204 Surre J, Saint-Ruf C, Collin V, Orenga S, Ramjeet M, Matic I (2018) Strong increase in the autofluorescence of cells signals struggle for survival. Sci Rep. https://doi.org/10.1038/ s41598-018-30623-2 Telford W, Georges T, Miller C, Voluer P (2019) Deep ultraviolet lasers for flow cytometry. Cytometry A 95(2):227–233. https://doi.org/10.1002/cyto.a.23640 Thurau AM, Schulz U, Wolf V, Krug N, Schauer U (1996) Identification of eosinophils by flow cytometry. Cytometry 23(2):150–158 Umino T, Skold CM, Pirruccello SJ, Spurzem JR, Rennard SI (1999) Two-colour flow-cytometric analysis of pulmonary alveolar macrophages from smokers. Eur Respir J 13(4):894–899 Vermaelen K, Pauwels R (2004) Accurate and simple discrimination of mouse pulmonary dendritic cell and macrophage populations by flow cytometry: methodology and new insights. Cytometry A 61(2):170–177. https://doi.org/10.1002/cyto.a.20064 Watt SM, Burgess AW, Metcalf D, Battye FL (1980) Isolation of mouse bone marrow neutrophils by light scatter and autofluorescence. J Histochem Cytochem 28(9):934–946 Weil GJ, Chused TM (1981) Eosinophil autofluorescence and its use in isolation and analysis of human eosinophils using flow microfluorometry. Blood 57(6):1099–1104 Williams RT, Bridges JW (1964) Fluorescence of solutions. A review. J Clin Pathol 17(4):371–394 Yamamoto M (2017) Photon detection: current status. In: Robinson JP, Cossarizza A (eds) Single cell analysis. Contemporary research and clinical applications, vol 227–242. Springer, Singapore
Chapter 4
Fluidics
In Flow Cytometry, the fluidics conditions are of extraordinary importance because any perturbation, turbulence, or variation in flow speed can irreparably compromise the analytical quality (Dean 1985). A discussion of fluid dynamics goes beyond this chapter’s objectives, which only provides some information regarding the relevance of the phenomenon in Flow Cytometry. Further information can be obtained from the consultation of a series of articles and monographs to which we refer (Pinkel and Stovel 1985; Kachel et al. 1990; Austin Suthanthiraraj and Graves 2013).
4.1
Overview on Fluids
The main properties of a fluid moving along a conduit are linked together by the law of Poiseuille. This law, also known as the general equation of fluids, says that “the flow rate (i.e., the volume of fluid which crosses a conduit’s section in the unit of time) is directly proportional to the pressure gradient and the square of the conduit surface, but inversely proportional to the length of the conduit and the viscosity of the fluid” according to the formula (adapted to a cylindrical conduit) q ¼ πR4 Δp=8ηl, where q is the flow rate, R is the radius of the conduit, Δp is the pressure gradient, η the fluid viscosity, and l is the conduit length. In a fluid flowing in a conduit, a balance exists between inertial and viscous forces, whose ratio is proportional to a factor known as the Reynolds number. The Reynolds number Re depends on a series of variables, including the radius of the conduit R, the flow speed v, the fluid’s density ρ, and the fluid viscosity η, according to the formula
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_4
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Fluidics
Re ¼ ρvR=η: As mentioned below, the behavior of a fluid that proceeds along a duct varies as a function of the Reynolds number since for values below a certain limit, it tends to be laminar, while for higher values, it tends to be turbulent. It should, therefore, not be forgotten that in the miniaturized fluidics systems, inertial effects become negligible, and viscosity, density, and surface tension take over (Holmes and Gawad 2010); it follows that the Reynolds number collapses, and the flow keeps being laminar in virtually all the situations (for further information on these special conditions, see Sect. 22.6.1).
4.1.1
Laminar Flow and Turbulent Flow
If the conditions in a cylindrical conduit are such that the Reynolds number is less than 2000, then the fluid proceeds ideally divided into concentric sections or layers. This regimen is called laminar flow. The core is the innermost region of a fluid that moves in laminar conditions (Fig. 4.1). Suppose the conditions within the fluidic component are such that the Reynolds number is higher than 2300. In that case, the viscosity may no longer be able to counteract the inertial forces. The balance between the layers breaks, and each fluid particle proceeds in the conduit section chaotically and unpredictably. This condition is known as turbulent flow. Under laminar flow conditions, the various layers do not mix but flow one in the other so that any event carried in an individual section tends to remain there (Austin Suthanthiraraj and Graves 2013). Still, the laminarity of a flow does not prevent the molecules from spreading for diffusion from one section to another (Pinkel and Stovel 1985). This condition can assume practical importance if the sample has been stained with DNA dyes under equilibrium or when the sheath has salinity features drastically different from the fluid in which the sample is dwelling. If the laminar flow proceeds along a sufficiently long conduit, after some time, the resistance between the different layers causes the central section, or core, to proceed at the highest speed. In contrast, the outermost layers’ speed progressively degrades
Fig. 4.1 Schematic representation of a laminar flow proceeding along a cylindrical conduit. The fluid moves forward, ideally divided into separate concentric sections or layers
4.1
Overview on Fluids
39
Fig. 4.2 Schematic representation of a laminar flow proceeding along a cylindrical conduit. The fluid is ideally divided into concentric sections. The concentric sections “velocities” distribution assumes a parabolic distribution, with the fastest section at the center and the peripheral layers at a progressively decreasing rate from the center to the periphery
to that of the layers adjacent to the tube walls, which is the lowest and tends to zero. It follows that the individual layers’ velocity profile takes on a parabolic shape (Fig. 4.2). It is noteworthy that this behavior can suffer from exceptions; in fact, the fluid in the microdevice electro-osmotic-driven hydraulic system displays a flat-head velocity profile (Holmes and Gawad 2010). The laminarity of the flow constitutes an indispensable condition, and it is an essential requirement for hydrodynamic focusing.
4.1.2
Hydrodynamic Focusing
When referring to hydrodynamic focusing in flow cytometry, we mean the confinement of the events under analysis in the innermost section of the laminar flow, often called the “core.” The process consists of injecting the sample in the center of the conduit, in a point just preceding the cuvette or nozzle (Crosland-Taylor 1953) (Fig. 4.3). By the law of mass conservation, if the pressure keeps constant, the speed of the flow increases, the core shrinks, and the events injected into the core upstream of the narrowing section also remain confined in the core downstream of the narrowing (Shuler et al. 1972). Hydrodynamic focusing is fundamental for two reasons. The first is that the core’s shrinkage is assumed to make the events pass one at a time through the interrogation point. The second is that the laser beam delivers the excitation energy to the interrogation spot in Gaussian mode. Suppose events cross the interrogation spot always at the same point. In that case, the evoked signal is a function of the number of fluorochrome molecules, not the position held by the event inside the conduit (Zarrin and Dovichi 1987). The core section should be as narrow as possible for this to happen.
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Fig. 4.3 Schematic representation of the hydrodynamic focusing process. The events are injected at the center of a laminar flow before narrowing the conduit diameter. Because of the fluid’s laminarity, the events tend to maintain the central position assumed initially. Due to the principle of mass conservation, the section that contains them increases its speed and reduces its size, further limiting the spatial variability of the path. It follows that the events pass through the interrogation point one at a time
4.2
Cytometer Fluidics
The fluidics of a flow cytometer is the component in which the fluid circulates, transporting the events through the interrogation spot(s). This component is also known as the hydraulic component. According to the Poiseuille law, the flow rate of a cytometer, made possible by a pressure gradient, is equal to the conduit section area’s flow speed product. flow rate ¼ flow speed area of the conduit section:
4.2.1
Sheath and Core
The fluid circulating inside the hydraulic system is globally called “the sheath,” even though to be exact, it splits between the core, that is, the central section into which the sample is injected, and the actual sheath, consisting of all the other sections wrapping the core. Generally, commercially available sheath liquid consists of a buffered, filtered isotonic saline solution. To these solutions, commercial companies add substances capable of inhibiting the growth of algae and bacteria, such as sodium azide (NaN3), which is also very poisonous to humans (Richardson et al. 1975; Edmonds and Bourne 1982). Commercial sheath fluid also contains surfactants to prevent bubbles
4.2
Cytometer Fluidics
41
and adhesion to the flow cell walls. When in the same laboratory, cytometers and hematology analyzers often use the sheath liquid produced for the latter with satisfactory results. However, it should be considered that some multi-purpose diluents for use in blood analysis by electronic instruments may contain sodium fluoride (Armstrong 1976), susceptible to exerting some cytotoxicity (Wang et al. 2001); it follows that these substitutive solutions should only be adopted for analytical purposes. Finally, filtered seawater has also been successfully used in phycological analyzes (Gasol and Del Giorgio 2000). Given that the sheath fluid does not mix with the core fluid, in principle, the sheath liquid could also consist of simple distilled water since the events in the core pass through the interrogation spot before diffusion phenomena modify the core composition. However, different salinities between sheaths and cores can cause refractive mismatches susceptible to influencing the forward scatter behavior (Cucci and Sieracki 2001). A similar phenomenon has been reported when the sheath is protein-free and the sample is suspended in a high protein concentration fluid (Tanqri et al. 2013). Distilled water can not be used as a sheath liquid if the cytometer is equipped with Coulter-type volume sensors or during sorting procedures in the air. Both cases require a solution capable of conducting and thus containing electrolytes. In this regard, it should be remembered that during sorting procedures, the difference in potential applied at the break-off back propagates to all the sheath fluid and from it to its eventual metal tank, which is not surprisingly equipped with a base of insulating material to avoid dispersion. Consequently, it is strongly advised against touching the metal walls of the sheath tanks during sorting procedures.
4.2.2
Flow System Components
In the flow cytometers with only analytical functions, the components of the fluidics are generally (Fig. 4.4):
Fig. 4.4 Schematic representation of the hydraulic component of a flow cytometer without sorting capabilities (flow analyzer)
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1. 2. 3. 4. 5.
The fluid reservoir or sheath The waste tank or waste The sample tube containing the suspension of the events under analysis A device that ensures a pressure gradient or pump A system of valves, resistors, and pressure regulators which:
Fluidics
(a) Regulates the flow rate, and consequently its speed, intervening on the pressure applied to the sheath (b) Regulates the sample rate, and consequently its speed, intervening on the pressure applied to the sample suspension (sample differential) (c) Makes it possible to exert sudden pressure increases (bursts) or transient suctions (backflush) near the cuvette to remove bubbles or clogs 6. A cuvette, or flow cell, or flow chamber, in which the interaction occurs between the light radiation focused on the interrogation point and the event that crosses the interrogation point The fluidics of flow cytometers with sorting capabilities may present slight modifications of the layout depending on the underlying technologies (for further information about this topic, see Chap. 21). The flow of a stream-in-air cell separator must proceed from top to bottom by force of circumstances. Analyzers are not bound to these limits so that the flow can pass through the reading cell even from the bottom up (Austin Suthanthiraraj and Graves 2013). This system best manages bubbles, which automatically flow upwards due to their floating properties instead of remaining in the nozzle. A level sensor system is also present to check the levels of the sheath and waste tanks, preventing the system from operating in inappropriate conditions. It is necessary to remember that the sheath tank level sensor’s failure prevents the system from leaving the stand-by conditions. In contrast, the failure of the waste tank level sensor can produce a series of adverse events ranging from laboratory flooding to system blockage, depending on the instrument’s model. Finally, the fluidics of all the cytometers use a stand-by option, which drastically reduces the pressure applied to the system to limit sheath in the event of inactivity.
4.2.3
Flow Rate Control
As stated in the introduction, a flow cytometer’s fluidic component is subordinated to a pressure regime, generally obtained by exerting positive pressure on the sheath tank by a pump, a compressor, or a compressed gas cylinder. In the latter case, the gas delivered is generally medical air or Nitrogen. Nitrogen has been recommended in the cytometric analysis of anaerobic bacteria in case they must be kept alive.
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Cytometer Fluidics
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In some models, the pressure differential relies on placing the sheath tank on a higher level than the discharge tank or applying a negative pressure downstream of the interrogation point. In any case, the sheath’s speed in the hydraulic circuit, i.e., the flow rate, is directly dependent on the pressure gradient exerted on the sheath. While in some sorters, it is possible to intervene on the sheath pressure and, consequently, on the flow rate, the flow rate is fixed and preset in the factory in most analyzers. In a standard bench-top analyzer, the typical value of the flow rate is about 1 L/h, reduced to less than one-thousandth in the stand-by conditions (Becton Dickinson 2007). Flow rate control is critical in multilaser systems because the time delay between the different interrogation points must be kept stable (see the following section). Suitably calibrated systems can exploit the flow rate’s stability for executing absolute “single platform” counts even in the absence of an internal standard (Storie et al. 2003).
4.2.4
Sample Injection
During analysis, the sample’s entry occurs through direct injection into the sheath center, or core, by a pressure exerted on the sample. The differential pressure between the sample line and the sheath is known as the “sample differential” and affects the sample velocity. The sample volume injected into the core is minimal, so the flow rate varies negligibly if the sheath’s pressure remains constant. However, following the principle of mass conservation, the increase in pressure exerted in the core by the sample injection causes an increase in the core section. This effect is important from a cytometric point of view since the variability of the individual events’ trajectories through the interrogation point increases with the core section’s dimensions (Zarrin and Dovichi 1987). The result of this phenomenon is generally negligible in immunophenotyping analysis. Still, it is particularly important in analyzing low variability biological parameters, such as the DNA content or aneuploid cells. Howsoever, the events’ wandering in a broadened core does not occur in systems equipped with acoustic focusing (for further information about this topic, see Sect. 4.2.5.4). In practice, core and sheath behave like two different but interrelated systems, where the size of the core depends on the speed of the sheath and the sample rate, according to the equation d ¼ 1:13 1000
pffiffiffiffiffiffiffi q=v,
where d is the diameter of the core in micrometers, q is the sample rate in milliliters per second, and v is the speed in meters per second (Pinkel and Stovel 1985).
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An elegant empirical system for measuring the relationship between core and sheath consists of acquiring as a sample (and consequently injecting in the core) a solution of Potassium dichromate; the ratio between the absorbance of the injected solution and the absorbance of the sheath downstream of the interrogation point is a function of the ratio between flow-rate and sample rate (Blume 1989).
4.2.4.1
Sample Differential Control
The sample must be injected into the sheath fluid to interact with the incident radiation. This injection requires a further pressure gradient between the sample line containing the sample suspension and the sheath fluid (sheath). This gradient, called sample differential, can be assured by various technical solutions, among which: 1. The direct injection of the sample through a micro syringe or other similar devices 2. The use of peristaltic pumps placed along the sample line 3. The differential distribution of the pressures between the tank containing the sheath fluid and the tube containing the sample, i.e., pressurizing the sample more than the sheath fluid (most widely used and tested option) As for peristaltic pumps, a particular constructive solution provides the combined application of two pumps in series, one placed before the reading cell and one placed after it, with sample input placed between the first pump and the cell. This system, implemented on the Accuri Cytometer, would allow, according to the manufacturer, a pressure differential between the two pumps capable of determining the entry of the sample into the reading cell by suction and not via injection (Becton Dickinson 2016). In some instruments, it is possible to adjust the sample injection speed according to a continuum. Still, more often, it is graded according to three options called Low, Medium, and High, which, by way of example, correspond to a sample flow rate respectively of 10, 60, and 120 μL/min in the FACSCanto cytometer (Becton Dickinson 2007) and 10, 30, and 60 μL/min in the Gallios cytometer (Beckman Coulter 2014). The reported values are indicative as they depend on many factors, including the sample’s viscosity, which hinges upon the specific type of solution used in the whole blood lysis (Storie et al. 2003). The sample flow rate (sample volume) is generally constant over time and can be calibrated and monitored by counting samples containing a known number of microbeads (Storie et al. 2003). A motorized microsyringe, driven by a rack or worm screw controlled by a computer, is capable of the sample’s most regular and homogeneous supply (Steen 2002); it injects known and preset volumes and allows the execution of absolute counts. Unfortunately, its characteristics preclude its use in analyzing high sample volumes. In any case, it is good to keep in mind that an increase in the sample differential does not cause an increase in the flow velocity of the core but rather an increase in its diameter.
4.2
Cytometer Fluidics
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The pressure value delivered in the various sections of the hydraulics according to the different operating conditions is difficult to know because the real value often does not appear as such in the control panel but as the voltage supplied to the pressure-controlling devices. Accuri and FACSCanto cytometers are currently marketed by Becton Dickinson (https://www.bd.com/), whereas the Gallios cytometer is currently marketed by Beckman Coulter (https/www.beckmancoulter.com/).
4.2.4.2
Absolute Counts
With few exceptions, flow cytometers do not produce absolute counts but only percentage counts. A way to obtain absolute counts (e.g., the number of CD4+ lymphocytes per microliter) is to multiply the absolute number of lymphocytes provided by a cell counter by the percentage number of CD4+ lymphocytes provided by a cytometer. These counts are called “dual-platform counts.” Nevertheless, absolute counts are possible even without standards but require special technical solutions. The first technical solution relies on two sensors applied to the sampler needle (Fig. 4.5). In this technical solution, the two sensors are distant from each other of a length equal to that which runs between the meniscuses of two different levels defining a predetermined volume in pre-calibrated tubes. During aspiration, the sample’s progressive exhaustion brings the suspension’s meniscus to cross the first sensor, which starts the sample’s acquisition, and finally crosses the second, which
Fig. 4.5 Absolute volumetric count. In a, the instrument aspirates the sample without making the count. In b, the liquid suspension’s meniscus activates the y sensor and triggers the counts’ start. In c, the counts continue as the sensor x is not activated yet. In d, the meniscus activates the sensor x and triggers the end of the counts. Consequently, the counts can be related to that precise sample volume that intervenes between the y and x sensors in that tube
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instead stops it. In this way, the counts are absolute because they are related to a predefined and known volume (Schweppe et al. 1992). This technical solution requires analyzing blood samples treated with lyse-no-wash techniques to avoid perturbing volumes with washing procedures. Still, a series of factors can compromise the accuracy of the volumetric counts. They include pipetting accuracy, events sedimentation, and the adhesion of events to the tube walls. A similar device, called TVAC (True Volumetric Absolute Counting), was implemented on the CyFlow Cube and CyFlow Space instruments marketed by Sysmex (https://www.sysmexeurope.com/). The second technical solution relies on using a microsyringe to deliver accurate volumes of the sample suspension; this solution is implemented in some commercial instruments, among which (1) Aquios (Beckman Coulter 2015), currently marketed by Beckman Coulter, (2) Attune, currently marketed by Thermo Fisher Scientific, (3) MACSQuant® Analyzer (Miltenyi Biotec 2013), currently marketed by Miltenyi Biotec, (4) Omnicyt, currently marketed by Cytognos, and (5) Potomac, currently marketed by Kinetic River (2022). A conceptually similar approach relies on using a peristaltic pump; this solution is implemented in the CytoFlex flow cytometer (Grenot and Luche 2018). Another non-hardware-based solution adopts internal standards consisting of a known and certified number of microbeads. In these cases, the number of events per unit of volume is obtained by multiplying the number of counted events by the ratio between the number of microbeads certified in the standard and the number of microbeads actually counted. These counts are known as “single-platform counts” since they only require one instrument and are the recommended way to perform CD34+ cell evaluation (Keeney et al. 1998). This methodology requires the utmost accuracy in pipetting and can be flawed because the microbeads constituting the internal control can adhere to the tube walls during the analytical run and disappear from the counts. This phenomenon is known as the “vanishing beads phenomenon” (Brando et al. 2001). Currently, many different packages of microbeads for absolute counts are sold by many different manufacturers, including, but not limited to, Bright Count Microspheres™ (IQ Products), CountBright™ Absolute Counting Beads (Thermo Fisher Scientific), FlowCount (Beckman Coulter), Flow Cytometry Absolute Count Standard™ (Bangs Laboratories Inc., Bio-Rad, Polysciences), Perfect-Count™ Microspheres (Cytognos), Precision Count Beads™ (Biolegend), Sphero™ Accucount Fluorescent Particles (Spherotech), Trucount Absolute Counting Tubes IVD (Becton Dickinson), and PKH26 Reference Microbeads (Sigma), this last to be used to obtain absolute counts in proliferation tests with cells stained with PKH26.
4.2
Cytometer Fluidics
4.2.5
Event-Light Interaction: The Interrogation Point
4.2.5.1
In-Cuvette Interaction
47
The “in-cuvette” interaction is a solution adopted in most recent electrostatic sorters, in all the fluidic sorters, and virtually in all the analyzers, except for some discussed in the next section entitled “Special solutions.” The cuvette, or flow cell, or flow chamber, generally consists of an optical quartz conduit characterized by thick walls and a narrowing in the lumen, on which the laser’s (or lasers’) beam focuses (Fig. 4.6). The conduit’s section is usually quadrangular, but other solutions also exist. From a fluidic point of view, the cuvette is akin to a conduit in which a temporary transition occurs from a larger to a smaller diameter. According to the equation of Pinkel and Stovel (Pinkel and Stovel 1985), the parabolic distribution of the speed of the various threads takes place at a certain distance from the beginning of the smaller diameter conduit. It follows that the multiple threads of the laminar flow proceed in a synchronous or almost synchronous way until the distance from the narrowing assumes the value predicted by the equation. The quartz cuvette offers a series of advantages, summarized in a better sensitivity, due to lower background noise and greater efficiency in collecting the light signal. This condition depends on the characteristics of the refractive indices that insist between fluid and quartz, which are more favorable than those operating between fluid and air. Three other factors can further improve the light collection, i.e., (1) the interposition of an optical gel between cuvette and collecting lens surface, which practically gets rid of the interface between quartz and air, (2) the short distance between cuvette and collecting lens, which allows the use of lenses with a high numerical aperture Fig. 4.6 Schematic representation of a flow cell or cuvette. In correspondence to narrowing the conduit section dimensions, the events forced inside the core due to the hydrodynamic focusing interact with the laser beams present in the instrument
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(Wheeless and Kay 1985), and (3) the presence of a plano-concave retro-catadioptric mirror able to collect the signals directed backward and refocusing them again forward toward the optical bench. The last solution has been implemented in the CytoFlex instrument (Brittain et al. 2019). Some cuvettes can collect both scatter signals and impedance signals (Schuette et al. 1984) due to the saline solution’s displacement caused by the event’s passage through a capillary orifice crossed by an electric current according to the Coulter principle. These special cuvettes are probably the only sensors able to provide accurate information on the “true” cell volume.
4.2.5.2
Stream-in-Air Interaction
In the electrostatic sorters, the interaction between the event and light radiation occurs immediately after the nozzle’s exit, thanks to the laser’s direct focus on the liquid stream, which behaves like a cylindrical lens. In some models, the last part of the nozzle can be transparent and behave as a cuvette. For further information on this topic, see Chap. 21.
4.2.5.3
Interrogation on an Open Surface
In a system adopted in the past on some cytometers, the sample suspension was tangentially injected into the open surface of a microscope glass slide covered by a liquid film flowing downwards; the glass slide behaved as the wall of a cuvette and transmitted the signals to the objective of a microscopy epi-illumination system (Lindmo and Steen 1979; Steen 1980, 1983) (Fig. 4.7). This layout reproduced the optical path of fluorescence microscopes, adopted UV-emitting arc lamps, and provided good results with particularly small events. Instruments based on this layout have been marketed by a few manufacturers, including Bio-Rad (https://www.bio-rad.com/) and Bruker (https://www.bruker. com/).
4.2.5.4
Systems Based on Acoustic Focusing
In acoustic focusing systems, events are kept in the conduit’s center not by the laminar condition of the flow but by a gradient of acoustic waves generated at the conduit’s sides by a special transducer (Yasuda et al. 1997). This system allows the events’ confinement in the center of the core regardless of the diameter of the core itself (Goddard et al. 2006; Ward et al. 2009) (Fig. 4.8). This condition is advantageous since it allows the confinement of the events despite a high core speed, and consequently, better precision and the analysis of a high number of events in a shorter time (Piyasena et al. 2012); moreover, the higher speed obtainable can reduce the number of clogs. This technical solution is currently implemented in some
4.2
Cytometer Fluidics
49
Fig. 4.7 Scheme of an optical bench with the stream on an open surface. The sample inside the core is injected onto a surface made up of a microscope slide and interacts with the incident radiation focused through a microscope lens, which also collects the signals. This model, present in some old European cytometers, reproduces the optical path of a fluorescence microscope
Fig. 4.8 In a system without acoustic focusing (panel a), an increase in the sample rate increases the core’s diameter so that the events no longer proceed aligned as before. In a system with acoustic focusing (panel b), the events are kept aligned by the acoustic wave even at higher sample rates
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Fluidics
commercial instruments, including Attune NxT (Thermo Fisher Scientific) and its IVD-licensed version Omnicyt (Cytognos).
4.2.5.5
Sheathless Systems
An alternative system implemented in some instruments consists of a hydraulic apparatus with neither sheath nor core. The flow crossing the interrogation point consists of the sample’s suspension alone, injected “as it is” into a microcapillary or a fluidic microcircuit (Dubeau-Laramee et al. 2014; Xun et al. 2015; Caselli and Bisegna 2017). In this system, whose microcapillary behaves as a cuvette, the signal comes from the whole sample in the microcapillary cross-section and not only from that confined in the core as in the hydrodynamically focused systems. It follows that this system is susceptible to the presence of free fluorochrome in the sample suspension (Duggan 2011). Sheathless flow cytometers can rely on acoustic focusing (Austin Suthanthiraraj and Graves 2013); a sheathless microdevice has been recently devised in which the events are three-dimensionally focused in the center of the conduit by an acoustic wave-based microchip (Wang et al. 2021).
References Armstrong D (1976) USA patent 1976 3962125. Multi-purpose diluent for use in blood analysis by electronic instrumentation of the Coulter type. https://patentimages.storage.googleapis.com/8a/ d8/7c/cdc5690c32f42d/US3962125.pdf. Accessed 12 Feb 2022 Austin Suthanthiraraj PP, Graves SW (2013) Fluidics. Curr Protoc Cytom 65(1):1–2. https://doi. org/10.1002/0471142956.cy0102s65 Beckman Coulter (2014) Gallios flow cytometer – instruction for use. White Paper. https:// scheduleit.mec.cuny.edu/wp-content/uploads/2017/12/gallios_manual.pdf. Accessed 2 Feb 2022 Beckman Coulter (2015) AQUIOS Tetra – Rx only – system guide. White Paper. https://www. beckmancoulter.com/wsrportal/techdocs?docname¼B26364AB.pdf. Accessed 5 Sept 2021 Becton Dickinson (2007) BD FACSCanto II users guide. White Paper. https://pdfslide.net/ download/link/bd-facs-canto-ii-users-guide. Accessed 2 Feb 2022 Becton Dickinson (2016) BD Accuri™ C6 plus system user’s guide. White Paper. https://www. bdbiosciences.com/content/dam/bdb/marketing-documents/BD-Accuri-C6-Plus-Users-Guide. pdf. Accessed 2 Feb 2022 Blume P (1989) A spectrophotometric method for determining the stream sample core diameter of a flow cytometer. Cytometry 10(3):351–353 Brando B, Gohde WJ, Scarpati B, D’Avanzo G (2001) The “vanishing counting bead” phenomenon: effect on absolute CD34+ cell counting in phosphate-buffered saline-diluted leukapheresis samples. Cytometry 43(2):154–160 Brittain GC, Chen YQ, Martinez E, Tang VA, Renner TM, Langlois MA, Gulnik S (2019) A novel semiconductor-based flow cytometer with enhanced light-scatter sensitivity for the analysis of biological nanoparticles. Sci Rep. https://doi.org/10.1038/s41598-019-52366-4 Caselli F, Bisegna P (2017) Simulation and performance analysis of a novel high-accuracy sheathless microfluidic impedance cytometer with coplanar electrode layout. Med Eng Phys 48:81–89. https://doi.org/10.1016/j.medengphy.2017.04.005
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Crosland-Taylor PJ (1953) A device for counting small particles suspended in a fluid through a tube. Nature 171(4340):37–38 Cucci TL, Sieracki ME (2001) Effects of mismatched refractive indices in aquatic flow cytometry. Cytometry 44(3):173–178 Dean PN (1985) Helpful hints in flow cytometry and sorting. Cytometry 6(1):62–64 Dubeau-Laramee G, Riviere C, Jean I, Mermut O, Cohen LY (2014) Microflow1, a sheathless fiberoptic flow cytometry biomedical platform: demonstration onboard the international space station. Cytometry A 85(4):322–331. https://doi.org/10.1002/cyto.a.22427 Duggan R (2011) EMD-Millipore 8HT review. http://ucflow.blogspot.it/2011/05/emd-millipore8ht-review.html. Accessed 26 Jan 2019 Edmonds OP, Bourne MS (1982) Sodium azide poisoning in five laboratory technicians. Br J Ind Med 39(3):308–309 Gasol JM, Del Giorgio PA (2000) Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci Mar 64(2):197–224 Goddard G, Martin JC, Graves SW, Kaduchak G (2006) Ultrasonic particle-concentration for sheathless focusing of particles for analysis in a flow cytometer. Cytometry A 69(2):66–74. https://doi.org/10.1002/cyto.a.20205 Grenot P, Luche H (2018) Beadless absolute counting. White Paper – Beckman Coulter. https:// www.beckman.com/gated-media?mediaId¼%7B59BBD0A5-8262-4A5E-94E5-6D0 D7DF59652%7D. Accessed 29 Oct 2021 Holmes D, Gawad S (2010) The application of microfluidics in biology. In: Hughes MP, Hoettges KF (eds) Microengineering in biotechnology. Methods in molecular biology. Humana Press, Hatfield, Hertfordshire, pp 55–80. https://doi.org/10.1007/978-1-60327-106-6_2 Kachel V, Fellner-Feldegg H, Menke E (1990) Hydrodynamic properties of flow cytometry instruments. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 27–44 Keeney M, Chin-Yee I, Weir K, Popma J, Nayar R, Sutherland DR (1998) Single platform flow cytometric absolute CD34+ cell counts based on the ISHAGE guidelines. Cytometry 34(2): 61–70 Kinetic River. Potomac – customizable modular flow cytometer – tech notes. White Paper. https:// www.kineticriver.com/wp-content/uploads/2021/09/KRC-Potomac-Data-Sheet-1v7.pdf. Accessed 2 Feb 2022 Lindmo T, Steen HB (1979) Characteristics of a simple, high-resolution flow cytometer based on a new flow configuration. Biophys J 28(1):33–44 Miltenyi Biotec (2013) MACSQuant® instrument user manual. White Paper. http://www.med.uvm. edu/docs/default-source/flow-cytometry-documents/ macsquantinstrumentusermanualversion5_130322.pdf?sfvrsn¼c9a0d353_2. Accessed 5 Sept 2021 Pinkel D, Stovel H (1985) Flow chambers and sample handling. In: Van Dilla MA, Dean PN, Laerum OD, Melamed MR (eds) Flow cytometry: instrumentation and data analysis. Academic Press, Orlando, FL, pp 77–128 Piyasena ME, Austin Suthanthiraraj PP, Applegate RW Jr, Goumas AM, Woods TA, Lopez GP, Graves SW (2012) Multinode acoustic focusing for parallel flow cytometry. Anal Chem 84(4): 1831–1839. https://doi.org/10.1021/ac200963n Richardson SG, Giles C, Swan CH (1975) Two cases of sodium azide poisoning by accidental ingestion of Isoton. J Clin Pathol 28(5):350–351 Schuette WH, Shackney SE, Plowman FA, Tipton HW, Smith CA, MacCollum MA (1984) Design of flow chamber with electronic cell volume capability and light detection optics for multilaser flow cytometry. Cytometry 5(6):652–656. https://doi.org/10.1002/cyto.990050616 Schweppe F, Hausmann M, Hexel K, Barths J, Cremer C (1992) An adapter for defined sample volumes makes it possible to count absolute particle numbers in flow cytometry. Anal Cell Pathol 4(4):325–334
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Shuler ML, Aris R, Tsuchiya HM (1972) Hydrodynamic focusing and electronic cell-sizing techniques. Appl Microbiol 24(3):384–388 Steen HB (1980) Further developments of a microscope-based flow cytometer: light scatter detection and excitation intensity compensation. Cytometry 1(1):26–31 Steen HB (1983) A microscope-based flow cytophotometer. Histochem J 15(2):147–160 Steen HB (2002) A sample injection device for flow cytometers. Cytometry 49(2):70–72. https:// doi.org/10.1002/cyto.10147 Storie I, Sawle A, Goodfellow K, Whitby L, Granger V, Reilly JT, Barnett D (2003) Flow rate calibration I: a novel approach for performing absolute cell counts. Cytometry 55B(1):1–7 Tanqri S, Vall H, Kaplan D, Hoffman B, Purvis N, Porwit A, Hunsberger B, Shankey TV (2013) Validation of cell-based fluorescence assays: practice guidelines from the ICSH and ICCS – part III – analytical issues. Cytometry B Clin Cytom 84(5):291–308. https://doi.org/10.1002/cyto.b. 21106 Wang P, Verin AD, Birukova A, Gilbert-McClain LI, Jacobs K, Garcia JG (2001) Mechanisms of sodium fluoride-induced endothelial cell barrier dysfunction: role of MLC phosphorylation. Am J Physiol Lung Cell Mol Physiol 281(6):L1472–L1483. https://doi.org/10.1152/ajplung.2001. 281.6.L1472 Wang C, Ma Y, Chen Z, Wu Y, Song F, Qiu J, Shi M, Wu X (2021) Sheathless microflow cytometer utilizing two bulk standing acoustic waves. Cytometry A. https://doi.org/10.1002/ cyto.a.24362 Ward M, Turner P, DeJohn M, Kaduchak G (2009) Fundamentals of acoustic cytometry. Curr Protoc Cytom 49(1):1–22 Wheeless LL Jr, Kay DB (1985) Optics, light sources, filters and optical systems. In: Van Dilla MA, Dean PN, Laerum OD, Melamed MR (eds) Flow cytometry: instrumentation and data analysis. Academic Press, Orlando, FL, pp 21–76 Xun W, Feng J, Chang H (2015) A microflow cytometer based on a disposable microfluidic chip with side scatter and fluorescence detection capability. IEEE Trans Nanobioscience 14(8): 850–856. https://doi.org/10.1109/tnb.2015.2455073 Yasuda K, Haupt SS, Umemura S, Yagi T, Nishida M, Shibata Y (1997) Using acoustic radiation force as a concentration method for erythrocytes. J Acoust Soc Am 102(1):642–645 Zarrin F, Dovichi NJ (1987) Effect of sample stream radius upon light scatter distributions generated with a Gaussian beam light source in the sheath flow cuvette. Anal Chem 59(6): 846–850
Chapter 5
Light Sources
An in-depth description of the technologies underlying light sources is beyond this book’s scope, which merely considers the minimum necessary to understand their use in Flow Cytometry. Further information can be usefully gathered from the consultation of other works dedicated to the topic (Csele 2004; Breck et al. 2012; Eichorn 2014). The light sources currently implemented in flow cytometers only consist, with isolated exceptions, of solid-state lasers. Arc lamps, which will be discussed for completeness, belong to the distant past, as well as high-power gas lasers and dye lasers, which will also be treated for the same reason. Light-emitting diodes (LEDs), improperly called diode lasers, are a technology that is probably not yet mature. As always, it is possible that what has been said could be called into question by future unpredictable changes. The victory of lasers on lamps comes after a long period of conflict and is based on several factors, including: 1. 2. 3. 4.
The provision of a series of spectral bands not obtainable with other sources Increased reliability The collapse of market prices The superiority of the delivered light due to the intrinsic characteristics of the laser sources
The overwhelming importance of the last factor is immediately evident, considering that, due to its coherence, a beam of laser light can virtually focus on one point. In contrast, the extended sources, like lamps and somehow the LEDs, radiate throughout the space and cannot effectively be concentrated on an area as small as the interrogation point, which, if lasers are employed, can have a diameter of no more than 5 μm. It follows that, while in a laser cytometer, the flow of photons can be virtually concentrated on the event as a whole, in a lamp cytometer, the flow of “useful” photons is a fraction of that emitted from the source, and this unfavorable condition
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_5
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persists despite the adoption of particular measures such as the use of parabolic mirrors and efficient optics.
5.1
Arc Lamps
The arc lamps used in flow cytometers are xenon vapor lamps and mercury vapor lamps. Arc lamps were present in the past in European-designed cytometers. Their adoption was essentially due to their reasonable cost, which was exceedingly lower than the lasers of the cytometer models designed in the USA. Another reason for their adoption was that these cytometers stemmed from fluorescence microscopes replicating epi-illumination systems, so arc lamp adoption was natural and reasonable. A further point in favor of arc lamps consisted in the fact that some of them possess a consistent emission in the UV region, particularly desirable in those years when most of the work carried out with cytometers consisted in the measurement of DNA performed utilizing probes excited in this spectral region (DAPI and Hoechst dyes). However, arc lamps are not an efficient choice for a variety of reasons. Firstly, as already explained, arc lamps are not powerful enough. A 100 W mercury arc lamp’s nominal power is undoubtedly higher than that supplied by the most powerful Argon lasers used in cytometry, which supplied (and delivered) total powers of 5–7 W in multiline mode. However, the arc lamp’s 100 W emission spans along the entire visible spectrum and beyond and radiates in space at 360 . In contrast, the laser emits a coherent and monochromatic beam focused on a spot (interrogation point) with measurable dimensions in micrometers. It follows that the number of photons delivered by the laser at the interrogation point is incomparably greater than that produced by an arc lamp, which also requires complicated optics to optimize the irradiation. Moreover, with the sole exception of the ultraviolet band, well represented above all in mercury lamps (Fig. 5.1), arc lamp spectral characteristics are not always suitable for fluorochromes generally used in flow cytometry (Fig. 5.2). Even though well present in the ultraviolet region, a mercury lamp’s emission spectrum also appears quite deficient at 488 nm, which still constitutes one of the most widely exploited spectral lines. Finally, the spatial instability of the spark established between the electrodes of an arc lamp generates a continuous variation in the origin of the light emission; the phenomenon, called “flickering,” negatively affects the precision of the measurements and contributes to increasing the background. Lastly, the coherence of the light produced by an arc lamp is not the best for measuring the Side Scatter. For a long time, the need to have an ultraviolet (UV) source at a reasonable price justified the adoption of an arc lamp, but the increasing availability of solid-state lasers of reasonable cost, capable of emitting in the ultraviolet (UV) and nearultraviolet regions (NUV), makes this last motivation obsolete.
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Lasers
55
Fig. 5.1 The spectrum of emission of a Mercury arc lamp. Efficient emission peaks in the deep UV, UV, yellow-green, and orange are appreciated. The 488 nm band, on the other hand, is missing. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
5.2
Lasers
The term “laser” derives from the acronym “Light Amplification by Stimulated Emission of Radiation.” A laser’s operation relies on the fact that a continuous energy supply brings a population of atoms (or molecules) to an excited state. The atoms’ return to their fundamental state causes the release of the energy received in the form of light. The continuous external energy supply ensures that part of the atomic population is always in an excited state. This phenomenon is called “population’s inversion” and causes light emission, continuous over time and amplified by other atoms’ emissions. The emission power of the lasers used in Flow Cytometry depends on their construction design, and generally, it does not exceed a few hundred milliwatts in the more powerful models. In the oldest high-power, water-cooled gas laser, the power could go up to some watts in multiline mode; in some models, the operator could modulate the emission power by changing the energy applied to the active medium. Moreover, it was sometimes possible to select the emission mode according to two different options, called “constant current” and “constant light.” In the first one, the laser emitted according to a certain level of power kept constant, while in the second one, the laser constantly delivered the required power, modifying its alimentation through a feedback process. This latter was the most used because, except for measurements based on ratio procedures, the instability in the emission’s
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Light Sources
Fig. 5.2 The spectrum of emission of a Xenon arc lamp. It is possible to appreciate the presence of a constant emission over the entire visible band, the reduced efficiency in the UV region, and the presence of distinct emission peaks in the IR region. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
power increases the measurement error (for further information on this topic, see Sect. 5.2.1.5). From a structural point of view, there are three essential components of a laser, (1) the active medium, i.e., the substance in which the inversion of the population occurs, (2) the energy transfer system that causes the inversion of the population, and (3) the optical cavity or the space in which the light beam waves resonate (Fig. 5.3). In the various types of lasers, the energy transfer system and the type of optical cavity vary depending on the active medium’s nature, but the general principles of operation are the same. Unlike the light emitted by conventional sources, the light emitted by a laser is coherent, monochromatic, polarized, and has a high radiance. The coherence, known as the property of maintaining a specific phase relation during the propagation, depends on the fact that the mirrors’ resonance puts all the light beam waves in phase. The monochromaticity of the emitted lines depends on the fact that the emitted energy is a function of the differences between the orbitals’ energy levels affected by the transitions, and consequently, it is constant as these transitions are constant. The polarization, known as the capacity of the electromagnetic radiation to oscillate along the propagation axis only on one of all the infinite possible planes, is due to the Brewster windows, which only let the waves resound if they display the same degree of polarization.
5.2
Lasers
57
Fig. 5.3 Construction scheme of a laser. The active medium, or lasing medium, i.e., the substance in which the inversion of the population takes place; the system of transfer of energy that causes the inversion of the population; and the optical cavity, or the space between the two mirrors where the light waves resonate
The radiance, known as the power delivered in the unit of time per unit of surface, depends on a series of factors, including the source constructive characteristics, which allow delivery of the virtual totality of emitted photons. Because of the optical cavity’s constructive characteristics and the nature of the emitted electromagnetic radiation, the distribution of the intensity of the stationary wave can occur with different features. These features are called transverse electromagnetic modes, or TEMpl (Transverse Electromagnetic Modalities), where p and l are numbers that spatially define the transverse and longitudinal modes. Consequently, according to the various possible modes, the spot of a laser beam projected on a flat surface perpendicular to it can take different shapes, represented as concentric circles or multiple spots. With some exceptions, the mode generally used in Flow Cytometry is called TEM00, whose representation is a single spot in which the energy emitted is distributed in a Gaussian manner (Shapiro and Telford 2009). The laser light Gaussian distribution is also due to the lenses between the laser output coupler and the interrogation point, consisting of two crossed cylindrical lenses with different focal lengths. Even if a correct hydrodynamic focusing can cope with the uneven light delivery on the interrogation point, it would be preferable that the laser spot be rectangular and have a flat top to make the excitation independent from the event’s position in the conduit. This goal can be achieved by interpolating a binary optical element (BOE) along the laser beam (Zhao and You 2016); “flat head” lasers are currently implemented on a few cytometers, among which the instrument Omnicyt (Cytognos). The laser emission can be continuous or pulsed. Continuous emission is usually exploited in Flow Cytometry, but pulsed emission lasers can be used in fluorescence lifetime-based cytometers. Some time ago, a pulsed supercontinuum optical source was implemented in an experimental instrument that exploited a protocol known as OCOS (one cell-one shot), able to synchronize the pulse frequency with the
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Light Sources
frequency of the analyzed events to deliver a packet of photons on every single event keeping the laser inactive between one event and another (Rongeat et al. 2012). It is possible to classify lasers based on different characteristics, including the active medium. According to this criterion, lasers can be divided into gas lasers, liquid lasers, and solid-state lasers, and all of them have found application in Flow Cytometry.
5.2.1
Gas Lasers
Gas lasers split into neutral atom gas lasers (Helium-Neon), ion gas lasers (HeliumCadmium, Argon, Krypton), and molecular gas lasers (N2, CO2) with the latter group not used in Flow Cytometry. In gas lasers, a current of electrons flowing along a solenoid built inside the plasma tube is the energy pumping source leading to the population’s inversion. The plasma tube, generally made of boron-silicate glass, ends at both extremities with two transparent optical structures, called Brewster windows, which transmit only polarized photons because of their angle with the axis along which the light waves resonate. While leaving the plasma tube through Brewster windows, photons resonate between two mirrors at the optical cavity ends just outside the plasma tube. The first mirror is placed at the front (outer coupler), and the other one at the rear (rear mirror or high reflector). Thanks to this reflection, the light waves are phased and amplified, thus allowing their exit through the front mirror as a coherent light beam. Suppose the excited atom’s structural characteristics allow the simultaneous presence of atomic populations with different excitation states and, therefore, different energies. In this case, the return of atoms to the fundamental state generates the simultaneous emission of multiple spectral lines with different energies and therefore with different frequencies, as in the case of the Krypton laser, which is capable of emitting simultaneously in the UV and at 407, 413, 415, 468, 476, 482, 521, 531, 568, 647, and 676 nm. Depending on the needs, a gas laser can emit in multiline mode (all the light lines together from the front mirror) or single-line mode. A prism, called Littrow prism, is placed before the back mirror in single-line mode. This prism diverges the various lines according to their wavelength, allowing only the single chosen line’s resonance. Technical solutions can also allow the simultaneous emission of selected lines, such as Blue and UV. Gas lasers were the first lasers used in Flow Cytometry but are now obsolete, with the possible exception of low-power Argon lasers and Helium-Neon lasers.
5.2.1.1
Argon Ion Lasers
In Flow Cytometry, Argon ion lasers have been displaced by solid-state lasers. Nevertheless, in this section, the main characteristics are reported for their
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Lasers
59
importance from a historical point of view and because many still operating old instruments (Epics XL, FACSCalibur, FACScan, FC500) are equipped with small, air-cooled Argon ion lasers. High-power Argon ion lasers can emit at 351, 363, 455, 458, 466, 477, 488, 497, 502, 514, 528, and 1092 nm with total powers of a few Watts. The emission of certain lines is present only in some models, and not all lines are emitted at the same power; the main lines in the Argon lasers are the lines at 488 and 514 nm. As a rule, small, air-cooled Argon ion lasers only emit at 488 nm. An experimental instrument has been reported in which a non-linear optic device transformed a 514 nm line into a 257 nm line; coupled to an electro-optical modulator; the deep-UV line allowed a kind of negative sorting by photo-destruction of previously selected cellular targets (Keij et al. 1993). In the beginning, due to the inefficiency of their optical systems, cell sorters required the most powerful laser versions. In turn, these high powers required very high voltages and amperages and complex water-cooling devices. The tube’s limited life, which required refilling new gas at regular intervals, would worsen the management. Besides these premises, argon lasers are stable and robust and allow measurements with a precision (CV) typically around 1%. The ability to emit in the UV (excitation line of many DNA-specific fluorochromes) and at 458 nm (excitation peak of Chromomycin A3, which binds preferentially to the base pair AT) made these lasers very coveted in laboratories working in flow cytogenetics. In the following years, the increasing interest in immunophenotyping and the appearance of analytical cytometers equipped with cuvette and high-efficiency optics made high powers redundant in most cases. With the line at 488 nm, the Argon lasers (and the 488 nm emitting solid-state laser) can excite: 1. FITC and AF488 2. PE, PE-based tandems, and other molecules, including PerCP and PerCP based tandems 3. The NovaBlue molecules of the NovaFluor series (see Sect. 15.6.2) 4. Some DNA fluorochromes, including Propidium iodide, Acridine Orange, 7-amino-Actinomycin 5. Some molecules used in the study of cellular functions, including Carboxyfluorescein, DiOC6(3), JC1, BCECF, Fluo3, Fura Red 6. The green Fluorescent Proteins The 458 nm line of an Argon laser has been used to excite at once the Fluorescent Proteins ECFP, EGFP, and EYFP (Beavis and Kalejta 1999), while the 514 nm line has been exploited to excite some blue-excited molecules such as SNARF (ex488/ em570-670) (Wieder et al. 1993), PE (ex488/em570) and PE-based tandems, but not PerCP (488 nm/670 nm) or PerCP-based tandems. A family of Argon lasers was commercially available (Innova Enterprise, Coherent, https://www.coherent.com/), which could emit simultaneously in the blue and UV at 360 nm. The two lines could be distinguished by using a prism and then re-aligned parallelly. Some no more available models belonging to this family were also able to emit simultaneously in UV, blue and green, while others emitted
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Light Sources
selectively at 458 nm or in UV in non-polarized mode (Durack et al. 1993). Low-power Argon laser had been marketed by Cyonics (bought by JDS Uniphase and currently no longer active) and Omnichrome (Melles Griot).
5.2.1.2
Krypton Ion Lasers
In Flow Cytometry, Krypton ion lasers can now be considered definitively obsolete. Nevertheless, their main characteristics are reported in this section because of their importance from a historical perspective. Originally used to excite APC, Krypton ion lasers can emit in the UV (337 nm) and at 407, 413, 415, 468, 476, 482, 521, 531, 568, 647, 676, 752, and 859 nm with a total power of the order of a few Watts. The emission of certain lines is present only in some models, and not all lines are emitted at the same power; the main lines in the Krypton lasers are the lines at 647 and 676 nm. The demand for a high power supply, the need for an external cooling apparatus, the high initial purchase cost, the high cost of management, the complex adjustment procedures, and above all, the availability of cheap lasers emitting in the violet and in red, quickly led to the disappearance of this laser from Flow Cytometry.
5.2.1.3
Mixed-Gas Ion Lasers (Argon/Krypton)
Some years ago, a laser loaded with a mixture of Argon and Krypton (Innova 70 C Spectrum, Coherent, https://www.coherent.com/) was commercially available; it could be tuned to emit in single-line mode choosing a line between those available (476, 488, 514, 521, 568, 647 and 752 nm) (Durack et al. 1993; Lee et al. 1993), or in multiline mode emitting both 568 and 647 nm lines at once (Lopez and Corcoran 2000).
5.2.1.4
Helium-Neon Atom Lasers
Helium-Neon atom laser is a stable, reliable, relatively inexpensive, and typically air-cooled laser. It can emit several wavelengths, i. e., 528, 544, 594, 612, and 633 nm. A 544 nm emitting version was exploited to excite Cy3 in the FACSCount Cytometer (Becton Dickinson) (Shapiro 1997), an instrument devised for CD4+ lymphocyte evaluation and also exploited in sperm counting (Christensen et al. 2004); moreover, there is also a report on experiences made with a Helium-Neon laser tuned to emit 1.5 mW at 544 nm and used to excite PE, PE-ECD, PE-CY5, POPO-3, and BOBO-3 (Hudson et al. 1995). It is noteworthy that the Raman scattering from 544 nm irradiated water displays a wavelength around 667 nm and can theoretically interfere with the emission of PE-CY5 (Hudson et al. 1995).
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Lasers
61
Despite these experiences, the most available version is the 633 nm-emitting one, sometimes still used as a red-emitting laser in multi-laser systems. A red HeliumNeon atom laser can excite: 1. Allophycocyanin (ex633/em670), Allophycocyanin based tandem, and other molecules including Phycocyanin (PC) (ex621/em642), Cyanine 5 (ex625/ em670), and some molecules of the Alexa group (Alexa Fluor 633 (ex633/ em647) (Telford 2015b), Alexa Fluor 647 (ex650/em665) (Telford 2015b), Alexa Fluor 660 (ex663/em690) (quite unexpectedly) (Telford 2015b), and Alexa Fluor 700 (ex702/em723) (albeit suboptimally) (Telford 2015b) 2. The NovaRed molecules of the NovaFluor series (see Sect. 15.5.6.2) 3. Some DNA fluorochromes, including Oxazine 750 (ex633/em>650), TO-PRO3 (ex642/em661), and DRAQ molecules (ex488-633/em>660) 4. Some molecules used in the study of cellular functions, such as MitoFluor Far Red 680 dye (ex685/em705), MiTO-Probe™ DiIC1(5) (ex638/658em), Mitotracker® Deep Red (ex644/em665), and the molecules of the CellVue® series with the excitation peaks in the red and deep red 5. Some Fluorescent Proteins with infrared emission (iRFP) (Telford et al. 2015) In principle, the Helium-Neon laser can be replaced by a Gallium-Arsenide (GaAs) solid-state laser with emissions between 635 and 640 nm. However, Helium-Neon lasers are devoid of emission “tails” towards lower frequency regions, which in the past have been pointed out in solid-state lasers (Shapiro 2002). Moreover, HeliumNeon lasers’ transmission mode (TEM) is generally unimodal and does not require optical correction. For these reasons, Helium-Neon lasers are still used as a source for 633 nm emission in Flow Cytometry. Helium-Neon lasers are still marketed by many manufacturers (see also the site https://www.photonics.com/Buyers_Guide).
5.2.1.5
Helium-Cadmium Ion Lasers
The Helium-Cadmium (HeCd) ion laser, capable of emitting up to a few tens of mW in the UV around 320 nm and the blue around 440 nm, is an air-cooled laser introduced in the past as an attempt to offer a relatively cheap alternative to large Argon or Krypton ion lasers, the only lasers at that time capable of emitting in UV (Goller and Kubbies 1992). Despite these features, the Helium-Cadmium ion laser did not arouse particular enthusiasm, as its emission was weak and quite far from the optimal absorption peaks of the UV-excitable fluorochromes, which require a 360 nm line instead. Moreover, its emission power displayed a relatively high variation coefficient (Shapiro 1993), which was not optimal for the DNA content’s cytometric determination. On the contrary, its behavior in determining intracellular Calcium concentration using Indo-1 dye was satisfactory because the accuracy of this determination, based on the ratio between two signals, was not affected by intensity fluctuations in the excitation line (Shapiro 1993). The HeCd laser was also successfully used in the
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excitation of the cis-parinaric acid (ex320/em420), exploited in the flow cytometric measurement of lipid peroxidation (Hedley and Chow 1992). Since the 320 and 440 nm lines are relatively close to the absorption peaks of Hoechst 33258 (ex365/em450) and Chromomycin A3 (ex425/em475), respectively, a pair of Helium-Cadmium laser has been satisfactorily used in chromosome analysis, run through an experimental platform built specifically for this purpose (Frey et al. 1994). In commercial advertising material, the Helium-Cadmium laser was accredited as being able to excite the fluorochromes Alexa Fluor® 350 (ex346/ em442), AMCA (ex355/em450), CF™ 350 (ex347/em448), and DyLight® 350 (ex353/em432) (Biotium 2013). A 320 nm HeCd laser was implemented by default in the LSR-I cytometer manufactured by Becton Dickinson (Coder 2000). Melles Griot currently still markets heCd lasers.
5.2.1.6
Helium-Silver and Neon-Copper Metal Vapor Lasers
Helium-Silver (HeAg) and Neon-Copper (NeCu) metal vapor lasers are commercially available, emitting at 224 and 249 nm. No use in Flow Cytometry has been documented so far.
5.2.2
Solid-State Lasers (SSLs)
Solid-state lasers, also known as SSLs, are lasers in which the active medium is a solid matrix. It is possible to further classify solid-state lasers according to the different technologies implemented. Those currently used in Flow Cytometry encompass three different types, namely: 1. Diode lasers 2. Solid-state lasers pumped from an external source consisting (a) Of a diode (diode-pumped solid-state lasers, or DPSSLs) (b) Of another source (optically pumped semiconductor lasers, or OPSLs) 3. Fiber lasers Except for diode lasers with emissions in violet (405 nm) and red (635 or 637 nm) and fiber lasers, still widely experimental in this context, the solid-state lasers currently operating in Flow Cytometry are all pumped lasers (DPSSLs or OPSLs). The introduction of solid-state lasers is one of the most significant technological advances in Flow Cytometry and has helped reduce instrumentation costs and drastically increase the method’s flexibility. Compared to lasers based on other technologies, solid-state lasers are generally small, cheap, reliable, stable, and durable and usually do not require a high power supply or special cooling equipment.
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63
Table 5.1 Main laser lines available in flow cytometry (wavelength in nm) Source Ar
DUV /
UV 351* 364*
NUV /
Violet /
Ar/Kr Diode
/ /
/ /
/ /
/ 405
DPSS
280
320 355
375 390 395
405 415 423
HeAg HeCd HeNe Kr
224 / / /
/ 320/ / 337
/ / / /
NeCu
249
/
/
/ / / 407 413 415 /
Blue 458* 477* 488 488 /
Green 514*
Yellow /
Orange /
Red /
IR 1092*
514 /
568 /
/ /
859 /
445 457 460 473 488
505 514 532
542 553 561
580 594 607 612
/ 440 / 482
/ / 528 521
/ / 544 568
/ /4 594 /
647 635 637 628 633 637 640 642 647 / / 633 647
/
/
/
/
/
852 915 1064
/ / /
/
*: line available in special models; DUV: deep-ultraviolet; UV: ultraviolet; NUV: near-ultraviolet; IR: infrared
At present, solid-state lasers are implemented or available, capable of emitting in the following regions: ultraviolet (UV) (320 and 355 nm), near UV (near-UV, or NUV) (375, 390, and 395 nm), violet (405, 415, 423 nm), blue (445, 457, 460, 473, and 488 nm), green (505, 514, and 532 nm), green-yellow (542, 552, and 561 nm), orange (580, 594, 607, and 612 nm), red (628, 633, 637, 640, 642, and 647 nm) and IR (852, 915, and 1064 nm) (Table 5.1). A solid-state deep-ultraviolet laser (280 nm) is currently being evaluated (Telford et al. 2019). Solid-state lasers used in Flow Cytometry are marketed by a series of manufacturers, including Coherent, Hübner Photonics, and Spectra-Physics. In diode lasers, the active medium consists of a junction between two components of a semiconductor, to each of which a procedure called “doping” has added extraneous atoms. The application of a potential difference to the two components makes the diode emit light with a wavelength that is a function of the added atoms’ nature. In DPSSLs, the active medium is generally a crystal, also called “garnet,” whose atoms receive energy from a source consisting of a diode. The frequency of the radiation emitted by the crystal can be subsequently doubled or tripled using particular optics called non-linear optics. A typical example is the green emission DPSS laser, where a Gallium and Aluminum arsenide diode excites a NdYAG crystal (Yttrium and Neodymium doped Aluminum), which emits at a wavelength of 1064 nm; subsequently, the doubling and tripling of the original wavelength
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allows to obtain emissions in the green (532) and the UV (355 nm). In OPSLs, the active medium is generally a diode whose atoms receive energy from a source made up of another diode. Some solid-state lasers have recently been made available that can simultaneously provide multiple colinear beams of different frequencies. These models are capable of emitting up to four different wavelengths at once, consisting of a line chosen from a panel including 532, 553, and 561 nm lines, plus three other lines chosen from a panel including 405, 445, 457, 473, 488, 525, 633, 638, 647 and 660 nm lines (Photonics 2019) (Cobolt Skyra™, Hübner Photonics). In fiber lasers, the active medium is an optical fiber doped with rare earth elements. Fiber lasers have long been used sporadically in Flow Cytometry, where they have provided emission in yellow and orange (Kapoor et al. 2007; Telford et al. 2009b, 2015). This technology appears promising because it allows the building of tunable sources, and even though the choice of the emission wavelength is in a relatively restricted range, nevertheless tunable lasers can already deliver different lines in a range of a few dozen of nanometers in green-yellow, with powers varying between 50 and 150 mW (Akulov et al. 2007).
5.2.2.1
Ultraviolet (UV) Emitting SSLs
It is possible to classify modern solid-state lasers with a UV emission in lasers emitting at 320 nm and around 355 nm. Moreover, “deep UV” lasers exist, which emit below 300 nm and are still experimental (Telford et al. 2019).
Deep UV The term “deep UV” (DUV) lasers refers to a family of solid-state lasers with emissions below 300 nm. There are also gas lasers capable of emitting in this range: Helium-Silver vapor lasers emitting at 224 nm and Neon-Copper vapor lasers emitting at 249 nm. DUV lasers are still experimental for several reasons, including the poor efficiency at these wavelengths of conventional cytometers optics and the current absence of fluorescent molecules exploitable in this spectrum region. However, some experiments with a 280 nm source could manage, even though not optimally, Quantum Dots and Brilliant Ultraviolet group (BUV) molecules (Telford et al. 2019); moreover, some non (yet) commercially available members of the Pdot family can be excited by a 266 nm line (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2).
320 nm The lasers emitting at around 320 nm are DPSS lasers (Telford et al. 2017). These lasers can excite the Indo dye and, even though with progressively decreasing
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efficiency, the fluorochromes of the Brilliant Ultraviolet (BUV) and Brilliant Violet series (Telford et al. 2017); moreover, for transitive properties, they should also be able to excite the fluorochromes excited by a Helium-Cadmium laser, among which Alexa Fluor® 350 (ex346/em442), AMCA (ex355/em450), CF™ 350 (ex347/ em448), DyLight® 350 (ex353/em432), cis-parinaric acid (ex320/em420) (Hedley and Chow 1992), and Quantum Dots. Moreover, they stimulate all the molecules usually excited by a 355 nm line and re-excite those belonging to the Brilliant Violet series (BV). 320 nm lasers generate autofluorescence in the emission range of BUV 395, reducing its stain index (Telford et al. 2017) (for further information on this topic, see Sect. 13.5.4.1). A solid-state 320 nm laser is currently being offered as an optional source for the ID7000™ Spectral Cell Analyzer marketed by Sony (Sony Biotechnology Inc. 2021).
355 nm The lasers emitting around 355 nm are DPSS lasers of the Nd:YVO4 type at triplicated frequency. Although much cheaper than UV gas lasers, they still have high prices that limit their widespread use and have dimensions that, although reduced compared to gas lasers, limit their adoption in benchtop analyzers. The lasers emitting around 355 nm have typical powers of about 20–50 mW and allow a series of measurements, including the determination of the intracellular Calcium with Indo-1 (ex365/em405-485) (Rabinovitch and June 1990), the determination of the “side population” (SP) with the Hoechst fluorochromes 33342 (ex365/em450) (Goodell et al. 1996) and 33258 (Tang et al. 2014), and the determination of the DNA content with DAPI (ex340/em450) or with fluorochromes of the Hoechst series (Bernheim and Miglierina 1989). As for the immuno-phenotype, UV lasers, used in the past to exciting the AMCA molecule (aminomethyl-cumaryl acetate, ex355/em450) (Delia et al. 1991), are now able to manage a series of fluorochromes among which some molecules of the Alexa series, the Quantum Dots, and the molecules of the Brilliant Ultraviolet and StarBright Ultraviolet Dyes series.
5.2.2.2
Near-Ultraviolet Emitting SSLs
The solid-state lasers with near-UV emission (NUV lasers) emit in a range from 370 to 390 nm with a typical emission at 375 nm and provide typical powers between 50 and 100 mW. They replace UV lasers in many applications because they cost less than UV lasers while still stimulating most of their traditional probes. However, UV and NUV lasers are not entirely interchangeable for cytometric determinations. Before choosing the right one, it is necessary to consider many details, including the fact that the NUV lasers excite (1) Marina Blue (ex360/em460) (Telford 2004), (2) the molecules of the Hoechst series, DAPI (ex340/em450), and (3) the molecules of the Brilliant Ultraviolet (and presumably StarBright Ultraviolet Dyes) series, but are not suitable for the management of Indo-1, the Ca-bound form
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of which cannot be excited at this wavelength (Telford 2015a). It is noteworthy that some molecules excited by a NUV laser may provide a signal good enough for analytical purposes but may require UV excitation in sorting procedures. Another point to consider is that NUV lasers can re-stimulate the Brilliant Violet (BV) series’s fluorochromes, generating an inter-laser spillover greater than UV lasers in the same conditions.
5.2.2.3
Violet Emitting SSLs
The solid-state lasers with the violet emit at 405 nm, close to the 413 and 422 nm Krypton lasers’ line. Thanks to the development of a wide range of violet-driven fluorochromes, the violet laser has not only become the third most frequently used laser in multi-laser platforms but is competing for the role of the primary light source. Still, the blue laser remains the most used source in studying cellular functions due to the xanthene structure common to many molecules used in this type of analysis. The violet laser can excite an extensive series of fluorochromes used in the conjugation of monoclonal antibodies, including: 1. All the fluorochromes of the Brilliant Violet and StarBright Ultraviolet Dyes series 2. All the Qdots 3. A series of small organic molecules including Pacific Blue (ex410/em455), Pacific Green (ex410/em500), Pacific Orange (ex410/em551), Krome Orange (ex398/em528), Horizon VH450 (ex405/em450), and Horizon V500 (ex415/ em500) 4. The Cyan Fluorescent Proteins eCFP (ex439/em476) and Cerulean (ex407-413458/em BP 470/20, BP 485/22), as well as the other molecules belonging to this group, even though in a sub-optimal way The spectral proximity between violet (405 nm) and UV (360 nm) makes the violet lasers suitable to excite a series of previously UV-driven molecules used in the evaluation of DNA content and the so-called “side population.” In this regard, it has been pointed out that violet lasers are able to excite the fluorochrome DyeCycle Violet (ex365/em440) (Telford 2010), and, even though suboptimally, the molecules Hoechst 33258 (ex365/em450, BP 455/30) and Hoechst 33342 (ex365/em450, BP455/30) as well (Telford and Frolova 2004). The violet laser has some advantages compared to the blue laser in some particular situations. It has been shown that: 1. The scatter evoked by the violet laser is more efficient in the separation of very small events from the background noise, as in the case of extracellular vesicle analysis (McVey et al. 2018). 2. When excited by a violet line, the aggregated form of JC-1 displays an emission spectrum characterized by a lower coefficient of variation and a minor overlap to
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the monomer’s signal, resulting in less need for compensation (Perelman et al. 2012). 5.2.2.4
Blue Emitting SSLs
It is possible to classify the solid-state lasers with blue emission into deep blue (Deep Blue) and blue-green lasers (Blue-Green).
Deep Blue Deep blue lasers include diode lasers with 440–450 nm output and DPSS lasers with 445, 457, and 460 nm output. Deep Blue lasers can excite the antibiotic Chromomycin (ex425/em475), whose absorption peak is close to 458 nm, as well as Cyan (CFPs) and Green Fluorescent Proteins (GFPs). The stray light of a deep blue laser with emission at 445 nm is likely to disturb the PMT with BP450/50 filter. In this case, a notch filter centered at 445 nm can fix the problem (Mazel 2015). Fifty milliwatts of a 458 nm line from an Argon laser have been used to excite at once the Fluorescent Proteins ECFP, EGFP, and EYFP (Beavis and Kalejta 1999), and there is no reason to think that this is not feasible with a deep-blue emitting laser of the same power.
Blue-Green The blue-green lasers emit at 473 and 488 nm with a power ranging from 20 to 200 mW; the solid-state lasers with 488 nm emission are interchangeable with low-power Argon lasers as they are exciting the same fluorochromes, i.e.: 1. Fluorochromes for antibody conjugation, including FITC (ex488/em520), Alexa Fluor 488 (ex495/em519), PE (ex488-520/em570), PerCP (ex488/em670), PEand PerCP-based tandems, Brilliant Blue, and the molecules belonging to the StarBright Blue Dyes series. 2. Fluorochromes specific for nucleic acids, including Propidium iodide (ex488/ em590), Ethidium bromide (ex488/em580), LDS-751 (ex543/em700), 7-AAD (ex543/em647), DRAQ5 (ex488-633/em>660), DRAQ7 (ex488-633/em>660), and dsDNA complexed benzophenanthridine alkaloids (ex488/em620) (Slaninova et al. 2007; Rajecky et al. 2013). 3. The xanthene-based fluorochromes used in the study of cellular functions, including CSFE and BCECF. 4. Fluorescent Proteins belonging to the GFP and YFP groups, such as eGFP (ex488/em 509), Emerald GFP (ex487/em 509), Venus (ex515/em 527), and mCitrine (ex516/em 529).
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5.2.2.5
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Green and Yellow-Green Emitting SSLs
Solid-state lasers emitting in green and green-yellow are usually DPSS lasers; satisfactory experiments have also been performed with fiber lasers (Telford, 2009 #25566) not yet available on commercial instruments. The solid-state lasers belonging to this group can emit at 532, 552, and 561 nm, with typical powers ranging from tens to two hundred milliwatts depending on models and wavelengths. All the three wavelengths produced by this class of lasers are unable to excite FITC (ex488/ em520), PerCP (ex488/em670), and PerCP based tandem but effectively excite PE (ex488-520/em570) and its associated tandems, Cyanine 3 (ex550/em570), and most of the molecules commonly known as Fluorescent Proteins. Since PE (ex488-520/ em570) absorbs more effectively in green than in blue, its excitation by a green line produces a brighter emission, an increase in the stain index, and a decrease in the emissions variation coefficient in compliance with the Poisson law applied to photoelectron statistics. Although very similar, the three available lines in the green and green-yellow range have some specific differences that should be considered for the optimal setup of polychromatic analyses.
532 nm The 532 laser is a DPSS laser, where an Aluminium Gallium Arsenide diode excites a NdYAG crystal (Yttrium and Neodymium doped Aluminium garnet) emitting at a wavelength of 1064 nm, then halved to 532 nm. The 532 nm laser excites more or less effectively the Fluorescent Proteins DsRed (ex558/em583), HcRed (ex592/ em625), mTomato (ex554/em581), mRFP (ex584/em607), and mOrange (ex548/ em562). Moreover, it excites PE (ex488-520/em570) and its tandems, TRITC (ex550/em570), Cyanine 3 (ex550/em570) and its tandems, Alexa Fluor 532 (ex532/em554), Alexa Fluor 546 (ex546/em573), Alexa Fluor 555 (ex555/ em565), and Alexa Fluor 568 (ex578/em603) (Telford et al. 2005, 2012). The 532 laser line can disturb the PMT dedicated to FITC; in this case, the optimal filter to be placed in front of this PMT is a BP510/21 or a BP515/20 instead of the standard one (Perfetto and Roederer 2007; Telford et al. 2012). The 532 nm laser excites the YFP better than a 488 nm laser. A dual laser system equipped with both a 488 nm and a 532 nm line can excite together EGFP (Enhanced Green Fluorescent Protein, ex458-488/em BP510/20, BP530/30) and EYFP (Enhanced Yellow Fluorescent Protein) (ex458-488-514/em BP530/30, BP546/20, BP550/30) better than a 488 nm-only system, allowing for better compensation management (Telford et al. 2012). The 532 nm line was defined as the best excitation line to excite the SYTOX® Orange molecule (ex536/em575), which is the first choice probe for the analysis of DNA fragments with high sensitivity flow cytometers (HSFCM) (Marrone et al. 2005).
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According to a non-peer-reviewed observation available on the Internet, the Raman scattering produced by the water irradiated at 532 nm has a wavelength of 649 nm, susceptible to increasing the background of the Allophycocyanin measurement; however, there is no evidence of the real practical relevance of this phenomenon (Bigos 2017).
552 nm The 552 nm laser is generally a diode laser; fiber lasers emitting at this wavelength exist but are still experimental. According to some authors, the 552 nm line is particularly advantageous because, while being perfectly able to excite the greenyellow driven Fluorescent Proteins DsRed (ex558/em583), mTomato (ex554/ em581), mRFP (ex584/em607), and mOrange (ex548/em562), it is still far enough from both FITC (ex488/em520) emission and APC (ex633/em670) excitation range, thus minimizing the need for corrective action on the optical bench (Telford et al. 2009a).
561 nm The 561 nm laser excites the Fluorescent Proteins DsRed (ex558/em583), mTomato (ex554/em581), mRFP (ex584/em607), and mOrange (ex548/em562), and is particularly indicated for the management of mStrawberry (ex574/em596) and mCherry (ex587/em610) (Telford et al. 2012). The 561 nm laser efficiently excites the Alexa Fluor 568 molecule (ex578/em603) and the Tetramethyl Rhodamine (TMR) (ex554/em576), but not yet Texas Red (ex589/em615), Alexa Fluor 594 (ex590/em617), Allophycocyanin (ex633/ em670), and the Allophycocyanin based tandems (Telford et al. 2005). The 561 nm laser also excites the molecules of the BYG (see Sect. 15.5.4) and NovaYellow molecules of the NovaFluor series (see Sect. 15.5.6.2). The 561 nm laser line can disturb the PE dedicated PMT; in this case, the optimal filter to be placed in front of this PMT is a BP590/20 or a BP593/40 instead of the standard one (Perfetto and Roederer 2007; Telford et al. 2012). A dual laser system exciting FITC with a 488 nm line and PE with a 561 nm line allows acquiring bivariate analyses without the need to compensate between the two detectors; with the same approach, it is possible to analyze EGFP and PE (Telford et al. 2005). According to a non-peer-reviewed observation available on the Internet, the Raman scatter produced by the water irradiated at 561 nm has a wavelength of 693 nm, susceptible to increasing the background of the measurement of Cyanine 5.5 and PerCP; however, there is no evidence of the real practical relevance of this phenomenon (Bigos 2017).
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5.2.2.6
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Orange Emitting SSLs
Solid-state lasers with emission in orange encompass fiber lasers emitting at 580 nm with powers higher than 500 mW and DPSS lasers emitting at 588 nm and 594 nm with powers in the tens of mW (Kapoor et al. 2008); their application allows the simultaneous excitation of Alexa Fluor 590 (ex590/em617), Texas-Red (ex589/ em615) and equivalents, APC (ex633/em670) and APC based tandems. However, the excitation of APC is suboptimal and requires the adoption of powers appropriate to the purpose. Solid-state lasers with emission in orange compete with 561 nm lasers in the management of red fluorescent protein (RFP) with more red-shifted excitation peaks, such as mStrawberry (ex574/em596), mCherry (ex587/em610), mPlum (ex590/ em649), mKate (ex588/em635), and Katushka (ex588/em635).
5.2.2.7
Red Emitting SSLs
It is possible to classify the red solid-state lasers into “short-red” lasers emitting at 620 and 628 nm and “full-red” lasers emitting at 635, 640, 641, 647, and 660 nm, with power generally ranging from 25 to 50 mW. They are all diode lasers; red-emitting fiber lasers exist but are not yet available on commercial instruments (Telford et al. 2015).
Short Red The laser with emission at 620 and 628 nm, also called “short red laser,” find their primary use as an alternative to 561 nm yellow lasers in the excitation of some Fluorescent Proteins of the RFP group, including eqFP670 (ex605/em670) and E2 Crimson (ex611/em646). These lasers can handle all the fluorochromes excited by a “traditional” red line at 633/635 nm and are beginning to replace the Gallium Arsenide diode lasers (GaAs).
Full Red Full red lasers encompass lasers with 635, 650, and 660 nm emissions. Gallium Arsenide diode lasers (GaAs) with 635 nm emission were the first diode lasers used in Flow Cytometry as secondary lasers instead of the more expensive Helium-Neon lasers with emission at 633 nm. However, although the emission line is almost the same, the GaAs diode lasers with emission at 635 nm do not necessarily have the same Helium-Neon laser features. Unlike Helium-Neon lasers, GaAs diode lasers can produce emission “tails” with a frequency lower than expected, disturbing the
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APC-dedicated PMT (Shapiro 2002). Inserting a suitable band-pass or long-pass filter immediately after the light beam exit point can fix the problem.
5.2.2.8
Infrared (IR) Emitting SSLs
Solid-state lasers with IR emission can emit at 685, 705, 713, 720, 730, 752, 785, 808, 830, 852, 915, 980, and 1064 nm, with powers around 40–100 mW. At present, their use is rather sporadic, but their interest is increasing, both in Flow Cytometry and imaging techniques. This favor is due to a series of factors, i.e.; 1. Their relatively low cost 2. Their good reliability 3. The availability of photodetectors with good efficiency in this spectral range (Lawrence et al. 2008) 4. The lack of autofluorescence at these wavelengths 5. The better signal diffusion in the tissues, useful in imaging procedures 6. The availability of new IR-excited probes, including: (a) Molecules for conjugation with Mabs or streptavidin, as: • CY7.5 (ex788/em808), Alexa Fluor 700 (ex702/em723), Alexa Fluor 750 (ex749/em775), and Alexa Fluor 790 (ex782/em805) • DyLight 650 (ex652/em672), DyLight 755 (ex754/776em), DyLight 800 (777ex/em794), and DyLight830 (ex730/em830) (marketed by Thermo Fisher) • IRDye680 (ex680/em694), IRDye700 (ex680/em697), IRDye750 (ex766/ em776), and IRDye800CW (ex778/em794) (marketed by Li-Cor, Lincoln, NE, USA) • VivoTag 680 (ex665/em688), VivoTag 750 (ex750/em775), and VivoTag 800 (ex785/em810) (marketed by Perkin-Elmer) (Swirski et al. 2007) • CF™750 (ex755/em555), CF™770 (ex770/em797), and CF™790 (ex784/ em806) (marketed by Biotium) (b) Probes for cell function like indocyanine green (ex780/em812), the NIR heptamethine dye IR-780 (ex766/em782) (Shi et al. 2016), IRDye800CW (ex778/em794) (Foster et al. 2008), cyanine dye IR783 (ex766/em782) (James et al. 2013; Shi et al. 2016), and Rhodamine 800 (ex685/em705) (c) Probes for DNA staining like Rhodamine 800 (ex685/em705) and TO-PRO-5 (ex747/em770) Moreover, some non (yet) commercially available members of the Pdot family have been designed to be excited by a 700, 750, 800, 900, 980, and 1064 nm laser line (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2). There is a series of anecdotal reports in the literature and on the Internet that document:
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1. The use of a 685 nm line to excite the following Fluorescent Proteins: iRFP682 (ex663/em682), iRFP 702 (ex673/em702), iRFP 713 (ex690/em713), and iRFP 720 (ex702/em720) (Telford et al. 2015). 2. The use of a 685 nm line to excite the following molecules: AF700 (ex702/ em723), AF750 (ex749/em775), and AF790 (ex782/em805) (Telford 2015b). 3. The use of a 713 nm line to excite the following Fluorescent Proteins: iRFP 702 (ex673/em702), iRFP 713 (ex690/em713), and iRFP 720 (ex702/em720) (Telford et al. 2015). 4. The use of a 730 nm line to excite the following molecules: AF700 (ex702/ em723), AF750 (ex749/em775), and AF790 (ex782/em805) (Telford 2015b). 5. The use of a 785 nm line to excite simultaneously, on a spectral platform, the following molecules: AF750 (ex749/em775), CY7.5 (ex788/em808), and DyLight830 (ex730/em830) at once (Nolan et al. 2013). 6. The use of a 785 nm line to excite the following molecules: the PTIR 283 (ex785/ em814) lipophilic membrane dye and IRDye 800 (ex774/em789) (Stewart et al. 2005). 7. The use of a 980 nm line to excite up-converting nanoparticles (Bartosik et al. 2020); for further information on this topic, see Sect. 15.5.2. 5.2.2.9
Supercontinuum White Light Emitting SSLs
The supercontinuum lasers with white light emission are fiber lasers capable of continuously emitting along the entire visible spectrum according to a variable range from IR to NUV. The white light supercontinuum lasers have a series of highly desirable features, including: 1. The possibility of producing radiation of any wavelength within its operating range (generally 400–1400 nm) 2. The possibility to select the desired wavelength by simply inserting a band-pass filter at the output of the light beam 3. The possibility of producing at once radiations of different wavelengths Although some prior experience exists (Kapoor et al. 2007; Telford et al. 2009b; Rongeat et al. 2012), the high cost and the low power of these sources have hampered until now their adoption in Flow Cytometry (Telford 2011).
5.2.3
Liquid State Lasers (Dye Lasers)
Liquid state lasers, more generally known as color lasers, or “dye-lasers,” are lasers in which the excited population is a solution of a fluorescent dye contained in a particular apparatus called “head,” and in which the energy delivered for its excitation consists of light radiation supplied by another laser, called “pump,” whose beam focuses on the solution. The spectral characteristics vary depending on the
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Fig. 5.4 Schematic representation of a dye-laser or color laser. In this example, the active medium is a fluorochrome solution (in the specific case, Rhodamine 6G) recirculating in a special compartment called “head,” which also acts as an optical cavity. The energy transfer system that causes the population’s inversion is called a “pump” and consists of a laser (in this case, a high-power Argon laser that emits a 514 nm line destined to excite the Rhodamine 6G)
fluorochrome in the head, whose excitation must match the wavelength of the radiation delivered by the pump (Fig. 5.4). A fluidics system recirculates the fluorochrome in the head to avoid photo-bleaching, and the emitted light radiation resonates between two mirrors as in a gas laser. A unique feature of dye lasers is their tunability, i.e., the capability of tweaking the wavelength of their emission. Unlike atoms, which, if excited, emit by lines according to their quantum characteristics, fluorochromes emit in a spectral range of some amplitude because the excited molecules can occupy a series of different energy states very close to each other. On returning to their fundamental state, these molecules give back photons with similar but still different energies, i.e., emit light with close but still different wavelengths. An appropriate band-pass before the front mirror (outer coupler) allows the exit of a selected wavelength within the fluorochrome’s emission range. In a technical solution exploited in the past, an optical system made of mirrors and special prisms separated a multiline Argon laser emission in a 488 nm line to excite blue-driven fluorochromes and in a 514 nm line to pump a Rhodamine 6G dye laser to excite APC and Texas Red together (Weichel et al. 1985; Woronicz and Rice 1989). The dye-lasers had been devices designed to obtain emissions in the spectrum regions in which the lasers then available did not emit and had gained favor in the late 1980s when Texas Red required an orange excitation, not otherwise possible except with a Krypton laser. Specifically, the laser dye designed for the Texas Red management resulted in a head loaded with Rhodamine 6G (emission peak 548 nm) and a pump consisting of a high-power Argon laser tuned to emit at 514 nm (Fig. 5.4).
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Dye lasers can exploit several fluorochromes other than Rhodamine 6G, as Rhodamine 110 (emission peak 545 nm), DCM (emission peak 660 nm), Pyridine 2 (emission peak 720 nm), and Styryl 9 M (emission peak 830 nm); of course, each different fluorochrome requires its spectrally matched excitation source. The new solid-state lasers’ appearance has made—at least for the moment—dye lasers disappear from Flow Cytometry. Nevertheless, dye lasers may regain new favor soon since they can emit IR lines other than those provided by solid-state lasers.
5.3
Light Emitting Diodes (LEDs)
A recently available light source is the Light Emitting Diode or LED. At present, LEDs are capable of emitting in UV (340 nm), blue (430 and 475 nm), green (511 and 525 nm), green-yellow (555 nm), orange (575 and 590 nm), red (630 and 650 nm), and the infrared (IR) (735 nm). At this moment, none of these sources is available on a cytometer, except for isolated experimental instruments with UV-emitting LEDs (Jin et al. 2006). LEDs are also known as “LED lasers.” However, this designation is improper because, albeit conceptually quite similar to diode lasers, LEDs cannot be considered lasers for many reasons, not least because of the absence of the amplification caused by radiation-stimulated emission. Moreover, LEDs also differ in many other details, among which the production of a light that is not strictly monochromatic but results from the coexistence of multiple wavelengths very close to each other; it follows that the LED’s emission tail can overlap the fluorescent probe emission and needs subtracting (Schulze and Latimer 2014). Another reason for the poor success of LEDs in Flow Cytometry is that they behave like an extended conventional source (Coherent 2019). Consequently, similarly to arc lamps, the power delivered to the interrogation point is only a fraction of the total power and is very low. The use of LEDs in cytometers requires the adoption of supplementary optics to optimize the signal’s collection, whose cost reduces the technology’s competitiveness (Schulze and Latimer 2014).
References Akulov VA, Afanasiev DM, Babin SA, Churkin DV, Kablukov SI, Rybakov MA, Vlasov AA (2007) Frequency tuning and doubling in Yb-doped fiber lasers. Laser Phys 17(2):124–129 Bartosik PB, Fitzgerald JE, El Khatib M, Yaseen MA, Vinogradov SA, Niedre M (2020) Prospects for the use of upconverting nanoparticles as a contrast agent for enumeration of circulating cells in vivo. Int J Nanomedicine 15:1709–1719. https://doi.org/10.2147/ijn.s243157 Beavis AJ, Kalejta RF (1999) Simultaneous analysis of the cyan, yellow and green fluorescent proteins by flow cytometry using single-laser excitation at 458 nm. Cytometry 37(1):68–73
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Chapter 6
Optical Benches
The optical bench of a flow cytometer separates the various light signals evoked in the interrogation points and presents them to the respective sensors. Strictly speaking, the cuvette, light sources, and photodetectors are also components of the optical bench; nevertheless, they are treated in other dedicated sections. As already pointed out, the European cytometers’ optical benches were conceptually similar to those of fluorescence microscopes and exploited their light sources, optics, and epi-illumination layouts; those models were equipped with arc lamps and focused the light on the interrogation point throughout a series of condensers. The adoption of laser sources allowed the building of instruments with layouts developed in three spatial planes, of which the first (x) was reserved for the incident radiation path and the detection of the forward scatter (FSC), the second (z) reserved for the sample path, and the third (y) reserved for the detection of fluorescence and side scatter (SSC) signals. Thanks to its advantages and reduced laser costs, this layout prevailed over time and is virtually present in all the commercially available cytometers. In this model, the laser beam is led to the focusing lenses either directly or through a series of prisms or mirrors or by the aid of optical fibers (Mariella et al. 1996); finally, the focusing lenses concentrate the light source onto the interrogation point which, depending on the constructive characteristics of the cytometer, can be placed in a liquid jet in-air (stream) or a cuvette. The interrogation point is the spot at which the hydraulic component and the optical component of a cytometer meet (for further information on this topic, see Sect. 4.2.5).
6.1
From the Light Source(s) to the Interrogation Point(s)
The need to have in some SORP (Special Order Research Products) machines different lasers incompatible with the cytometer’s size has induced the production of devices known as “laser merge modules.” These devices transport the beams © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_6
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Fig. 6.1 Schematic representation of an elliptical lens resulting from the assembly of two crossed cylindrical lenses that produce an elliptical spot
collinearly from lasers dwelling out of the instrument and reconstitute their original parallel relationships before the interaction with the reading cuvette to re-establish the distinct interrogation points originally planned (Telford 2011). It is essential to remember that the transport of particularly short wavelengths such as UV light requires optical fibers made of special materials because high energy radiation can damage their material over time. Unlike first sorters, in virtually all commercial instruments, the light sources are inside the instrument, and each laser beam concentrates on its spot through mirrors and prisms. Mirrors are the best solution since they virtually do not reduce the power delivered to the final target, while the prisms mounted on the older models were likely to do so, even though the high power generally available in these instruments made the phenomenon practically negligible. Measured at its exit point, the beam of a laser has a diameter ranging from 0.5 to 2 mm, and in a flow cytometer, it must further focus on the so-called interrogation point, which represents the place where the interaction between event and incident radiation occurs. The geometry and dimensions of the interrogation point(s), or interrogation spot (s), depend on the type of lenses interposed along the laser beam’s path and are chosen according to each instrumentation task. The lenses used in the first instrumentations consisted of spherical lenses, capable of concentrating the laser beam in a circular spot with a few tens of micrometers in diameter (Fig. 6.1). The lens assembly currently in use, on the other hand, is generally constituted by two crossed cylindrical or plano-convex lenses, each of which has a focus coincident in the center of the spot (Fig. 6.1) (it is also possible to cross a spherical lens with a cylindrical lens). This device, improperly defined as an “elliptical lens,” generates an oval spot of variable dimensions, with the smaller diameter ranging from 5 to 20 μm and the larger one from 80 to 200 μm depending on the model. An elliptical spot can
6.2
From the Interrogation Point(s) to the Detector(s)
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also result from a particular device consisting of prisms instead of lenses (Chupp 1985; Blasenheim 2004). The spot dimensions are crucial, as they condition the event’s lighting time, affecting the measurement’s sensitivity. Moreover, they define the range of the event’s dimensions beyond which the pulse’s height (H) is no longer directly proportional to the total fluorescence (for further information on this topic, see Sect. 8.2). Suppose it is necessary to produce particularly small spots, such as those used in the slit-scanning procedures (for further information on this topic, see Sect. 8.2.1). In that case, the only solution is to resort to lenses with a shorter focal length or interpolating a lens along the optical path, called a beam expander, capable of diverging the laser beam before it enters the focusing lenses. The beam expander is divided into “galileian” beam expanders, consisting of the assembly of a planeconcave lens and a biconvex lens, and “keplerian” beam expanders, consisting of the assembly of two biconvex lenses. As mentioned previously (see Sect. 5.2), the energy emitted by a laser working in TEM00 mode distributes to the interrogation point in a Gaussian manner. This distribution is not optimal because the spot’s peripheral areas do not receive enough energy, but obtaining more “flat” distributions (top hat) requires a series of complex interventions, including the insertion of additional optics or manipulating the TEM mode. Worthy of note is a recent technical solution, called BOE (binary optical element), used in a microflow cytometer, based on the principle of optical diffraction and able to produce almost flat rectangular spots (rectangular quasi-flat-top spot) (Zhao and You 2016).
6.2
From the Interrogation Point(s) to the Detector(s)
Two lenses placed along two different axes collect the signals produced in the interrogation point by the interaction between the event and light radiation (Fig. 2.1). The first lens, intended for the forward scatter signal, is in front of the laser emission and looks at the interrogation point, placed between the laser and the lens along an axis coincident with the excitation beam. The second lens, intended for the side scatter and fluorescence signals, looks at the interrogation point from a perpendicular or lateral position concerning the excitation beam. This layout contributes to the already mentioned orthogonal configuration, where the excitation beam, the fluid stream carrying the events, and the collection of fluorescence signals lie in one of the three planes of the space. Usually, a narrow metal blade, called the obscuration bar, protects the center of the lenses. This bar is usually placed along the lens’s horizontal diameter, but its position is adjustable by the operator and can influence the signal/noise ratio. Its function is to intercept the diffracted light, shielding the center of the solid angle; as such, it is indispensable to the forward scatter collecting lens, where it prevents the
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impinging of the laser beam into the sensor, but it is less critical for the other lens and may be missing. Optimizing the collection of photons emitted at the interrogation point is possible with high numerical aperture (NA) lenses, where numerical aperture (NA) means the maximum angle useful for the system to receive light. The formula defines the numerical aperture as NA ¼ n θ, where n means the index of refraction between the lens’s surface and the external environment, and θ is the lens’s angular aperture. The numerical aperture is inversely proportional to the lens’s focal distance, which benefits the instruments with only analytical function. Unlike cell sorters, where excessive proximity between the interrogation point and the lens can lead to aerosol deposition on the lens’s surface, the lens surface dwells almost in contact with the reading cell in most analytical instruments. In some platforms, a high refractive optical gel fills the gap between the two interfaces and contributes to increasing the lens’s numerical aperture, which can reach values of 1.2 NA, improving the signal collection’s efficiency and the overall system’s sensitivity (Arnold and Lannigan 2011). The light collected by the lens enters the optical bench through a hole called a pinhole, which acts as a field diaphragm, or field-stop. Not affecting the numerical aperture, the field diaphragm does not reduce the light collection’s efficiency but limits the observation to the area represented by the interrogation point, reducing the stray light entrance (Fox and Coulter 1980). Each laser in use corresponds to a different pinhole in a conventional system. The forward scatter signal reaches its sensor directly or through a series of prisms or mirrors. Along the path, band-pass filters can eliminate any parasitic signals. Neutral-density filters can also be placed on the light path to attenuate too strong signals, which can saturate the sensor, generally a diode. Instruments exist that detect the forward scatter using avalanche diodes or photomultipliers; these more sophisticated technological solutions allow the study of exceedingly small events (extracellular vesicles, virions, bacteria, et cetera), where the signal/noise ratio is a crucial variable. It is also possible to vary the signal collection’s solid angle amplitude in some instruments.
6.3
Optical Bench Components
Except for the optical bench of spectral cytometers (for further information on this topic, see Sect. 22.2.1), the optical bench of flow cytometers results from the assembly of particular devices, which according to the characteristics of their action, can consist of absorption filters, interference filters, neutral density filters, polarizing filters, and semi-transparent mirrors, or beam splitters.
6.3
Optical Bench Components
83
These devices’ function is to construct optical paths that distinguish the light signals according to their wavelength and distribute them spatially, assigning them to committed photodetectors. In the most complex instruments, the operator can change filters and mirrors to adapt their instrument’s configuration to the spectral characteristics of the fluorochromes used; this flexibility is generally missing in simpler models. Evaluating a filter’s efficiency, i.e., the capability to block certain wavelengths, is entrusted to a system of measurement units expressed in OD (Optical Density). According to the formula OD ¼ log 10 ðT Þ OD is the negative of the base-10 logarithm of the T-transmission, placed as a variable from 0 to 1. The filters implemented in the flow cytometers’ optical bench usually display an OD value equal to or greater than 6. It is possible to correct an unsatisfactory behavior of a filter with a low OD value using a couple of filters with the same characteristics. It is also possible to achieve the spatial separation of different wavelengths through prisms or particular diffraction grating devices. The gratings, consisting of transparent surfaces with rectilinear and parallel grooves engraved at a reciprocal distance close to visible light wavelengths, separate the various spectral components because different diffraction angles characterize the wavelengths. These technical solutions have been adopted in some spectral cytometers but not currently in conventional ones. Another technical solution exists, i.e., the Wavelength Division Multiplexing (WDM) (for further information on this topic, see Sect. 6.3.6).
6.3.1
Absorption Filters
The absorption filters are optical devices made of glassy or plastic material containing molecules of a compound that absorbs light radiations of a given wavelength, transforming them into heat. These filters are cheap but tend to deteriorate over time due to the compound’s progressive decay capable of absorbing light (photo-bleaching). It is also possible that the compound in question, having absorbed the light radiation, emits, in turn, a fluorescent signal. Due to these practical issues, absorption filters are currently no longer used in Flow Cytometry.
6.3.2
Interference Filters
Interference filters, also known as multi-layer filters or dichroic filters, assume different colors if viewed from different angles and consist of several transparent layers glued together. They work because the between-layer interfaces have
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Fig. 6.2 Schematic representation of a long-pass filter or LP. According to the spectral behavior graph, the represented filter only transmits frequencies above a certain threshold, estimated at around 540 nm, i.e., between green and yellow. T transmittance, λ wavelength
refractive indices progressively different from each other. Depending on the refractive index between the layers, some interfaces allow the transmission of certain wavelengths but refract the others progressively till their complete reflection. According to their function, which consists of precisely transmitting some colors and reflecting others, the interference filters take the name of dichroic mirrors (DM). Interference filters are delicate and sensitive to changes in humidity, temperature, and physical “shocks”; because of all these factors, the bonding between the various layers can alter over time, making the filter unusable. Depending on their spectral behavior, these filters belong to one of these four categories: 1. Long-pass filters, or LP, which transmit wavelengths above a certain threshold but reflect the lower ones; for example, a long-pass filter transmits red and orange but reflects yellow, green, blue, and violet (Fig. 6.2). 2. Short-pass filters, or SP, which transmit wavelengths below a certain threshold but reflect the higher ones; for example, a short-pass filter transmits violet, blue and green, but reflects yellow, red, and orange (Fig. 6.3). 3. Band-pass filters, or BP, which transmit only wavelengths within a given range but reflect both the lower and higher ones; for example, a band-pass filter transmits green and yellow, but not violet and blue, nor orange and red (Fig. 6.4). 4. “Notch” filters, which selectively block transmission in a narrow, determined range; they are useful for preventing the parasitic light coming into the sensors from the lasers of which they are not tributaries (Fig. 6.5).
6.3
Optical Bench Components
85
Fig. 6.3 Schematic representation of a short-pass filter or SP. According to the spectral behavior graph, the represented filter only transmits frequencies above a certain threshold, estimated at around 540 nm, i.e., between yellow and green. T transmittance, λ wavelength
Fig. 6.4 Schematic representation of a band-pass filter or BP. According to the spectral behavior graph, the represented filter only transmits wavelengths included in a certain range, estimated between 480 and 570 nm, i.e., deep blue and yellow. T transmittance, λ wavelength
Interference filters are important in constructing an optical bench because they eliminate unwanted spectral components and spatially distribute the signals based on their wavelength. This behavior occurs when the interference filter dwells along the light radiation path with a particular angle to the incident ray. The angle between the dichroic
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Fig. 6.5 Schematic representation of a notch filter. According to the spectral behavior graph, the represented filter transmits all the wavelengths except those in a very narrow range centered in the green region. T transmittance, λ wavelength
mirror and the incident ray determines the angle between the incident ray and the reflected ray. It follows that the selection of filters with proper spectral characteristics and orientation in the optical path produce the precise spatial distribution of the various wavelengths, allowing them to selectively reach their sensors located at various points of the optical bench (Fig. 6.6). The interference filters carry abbreviations that define their optical properties: for example, the abbreviation SP (short pass) 500 indicates that the filter in question transmits wavelengths below 500 nm but reflects those above; the abbreviation LP (long pass) 650 indicates that the filter transmits wavelengths above 650 nm but reflects those below; the abbreviation BP (band-pass) 570/30 means that the filter transmits only the wavelengths located in a range (band) between 555 and 585 nm. Interference filters only work in one direction, and in some examples, they carry a small arrow ! on the edge that must be facing the light source. In other models, the specific conformation of the filter’s socket ensures its correct insertion. In any case, the filter choice must balance the best photons’ detection with the best spillover prevention.
6.3.3
Neutral Density Filters
Neutral density filters, or ND filters, attenuate the transmission of light radiations regardless of their wavelengths. These filters often reside before the detectors committed for the physical parameters (FSC and SSC). Neutral density filters only work in one direction, and in some cases, they carry a small arrow ! on the edge that
6.3
Optical Bench Components
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Fig. 6.6 Schematic representation of a dichroic mirror (DM) and its action when placed at a suitable angle to the incident radiation. The dichroic mirror, in this case, a mirror with long pass characteristics, placed at 45 to the incident radiation, transmits wavelengths above a certain threshold, estimated around 540 nm, i.e., between green and yellow, and reflects the lower wavelengths, distributing them spatially at right angles to those transmitted. T transmittance, λ wavelength
must face the light source. The neutral density filters differ depending on the percentage of radiation they allow to pass, varying from 50 to 0.01% of the whole signal.
6.3.4
Polarizing Filters
The polarizing filters let the light radiations pass not based on their wavelength but on the spatial plane on which they oscillate. The polarizing filters can sense the signal’s depolarization in a system where the excitation consists of polarized light, and the evoked signals are also polarized. In a system in which unpolarized radiation elicits the scatter, as in the case of an arc lamp, it is still possible to polarize the incident radiation through the use of polarizing filters, as described by Maude and collaborators in the case of a conventional optical microscope (Maude et al. 2009).
6.3.5
Beam Splitters
The beam splitter is a mirror that reflects the incident light but lets a fraction pass without affecting its spectral composition. A typical beam splitter is often placed in
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the transmission benches between the side scatter detector, to which it conveys 10% of the signal, and the subsequent fluorescence detectors, to which it reflects the remaining 90%.
6.3.6
Wavelength Division Multiplexing (WDM)
The Wavelength Division Multiplexing (WDM) is a technology borrowed from telecommunications and based on optical fiber technology principles that allows the mixing of different frequencies thanks to a device called a multiplexer, their transportation through the same medium (usually an optical fiber), and their separation on arrival thanks to a second device called a demultiplexer. In Flow Cytometry, WDM can separately transport and distribute wavelength trains of different frequencies without the need for dichroic mirrors (Brittain et al. 2019). WDM is used in the optical bench of conventional (CytoFlex, Beckman Coulter) and spectral (Aurora and Northern Lights, Cytek) cytometers (see also below).
6.3.7
Prisms, Gratings, Coarse WDM (CWDM)
Dispersion optics diffract the light components spatially with angles depending on their wavelength. Dispersion optics consist mainly of prisms and diffraction gratings, but a WDM-based device can also behave as a dispersion optic, and coarse WDM (CWDM) demultiplexer arrays are implemented on the Cytek Aurora and Northern Lights spectral cytometers (Cytek Biosciences 2019). Dispersion optics are usually implemented on spectral or Raman cytometers, where they distribute the whole range of the events’ signals to the detectors. Prisms and diffraction gratings can differ for the linearity of the dispersion, i.e., the proportionality between the wavelength range and the angle of diffusion; besides the structural differences, diffraction gratings give linear spreading, while prisms result in non-linear light dispersion. In old stream-in-air cell sorters, prisms were used to lead the laser beam from the outer coupler to the interrogation point. In these cases, prisms were used as mirrors and not as dispersion optics.
6.4
Optical Bench Layouts
Although structured according to very different construction schemes, the various optical benches can be traced back to two types, i.e., “transmission” type and “reflection” type. We can further distinguish transmission benches in optical benches with short-pass and optical benches with long-pass dichroic mirrors (Fig. 6.7).
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Fig. 6.7 Schematic comparison between an optical transmission bench based on dichroic short pass mirrors (SP) (panel 1) and an optical transmission bench based on dichroic long pass mirrors (LP) (panel 2). In the first case, the short-pass filter allows the longer, less energetic wavelengths to reach their sensors before the shorter, more energetic ones. It follows that the longer wavelengths go across fewer filters than the shorter, with a minor signal loss due to transmission. In the second case, the opposite happens
Finally, we can consider the spectral optical bench, which presents a very different structure (for further information on this topic, see Sect. 22.2.1). The presence of multiple laser sources represents an additional level of complexity. As a rule, the beams emitted by different lasers are spatially separated from each other; sometimes, each beam manages its specific optical bench for the fluorescence signals that the beam evokes. Other technical solutions rely on collinear (coaxial) lasers or shared sensors.
6.4.1
Transmission Benches
In transmission benches , the collected light signals resulting from different wavelengths sequentially pass through a series of dichroic mirrors, gradually losing the frequencies that the mirrors’ spectral characteristics make diverge until the last wavelength reaches the final photomultiplier of its competence. This model requires that the signal travels across a certain number of mirrors proportional to the sensors’ number. Since crossing a dichroic mirror involves a loss of energy (Brittain et al. 2019), transmission benches, especially if of great complexity, are susceptible to attenuating the signal received by the last sensors. This drawback is made worse in the benches equipped with long-pass dichroic mirrors; in this case, the first sorted wavelengths are shorter, while the less energetic longer ones cross the higher number of mirrors (Fig. 6.7) progressively. The transmission benches assume a linear and branched configuration, similar to the diagram shown in Fig. 6.8.
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Fig. 6.8 Example of transmission optical bench scheme; in this case, the last transducer (PMT 8) receives its signal after it has passed through all the optical filters along its path
6.4.2
Reflection Benches
In reflection benches, the dichroic mirrors, characterized by different spectral features, are placed so that the various wavelengths proceed by reflection and not by transmission, bouncing from mirror to mirror until the right mirrors allow their transmission to the committed sensors. It follows that each different wavelength only passes through a single mirror, i.e., the one placed in front of the photomultiplier to which it belongs. Compared to transmission systems, this system can decrease signal loss and theoretically ensure a better sensitivity, especially when based on a system of dichroic short-pass mirrors, which deliver to the respective sensors the less energetic wavelengths first, continuing gradually with the progressively more energetic ones. Some reflecting optical benches tend to assume a polygonal configuration (trigon, octagon, or decagon), but also reflection benches based on a linear scheme exist (“Boulevard” scheme). In both cases, the light signals run through the optical bench reflected by a series of dichroic mirrors until each different wavelength meets the dichroic mirror, which allows it to pass through into the committed photomultiplier (Fig. 6.9). The polygonal configuration is typical of Becton Dickinson instruments, while the “boulevard” scheme is implemented in the Navios and Gallios machines manufactured by Beckman Coulter.
6.4.3
Multilaser Benches
Most of the instruments currently on the market are equipped with more than one laser, mainly to excite the maximum number of fluorochromes. The configurations with which the different lasers are mounted are two, namely (1) parallel lasers (also known as spaced lasers) or (2) collinear lasers.
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Fig. 6.9 Two examples of a reflection bench model, respectively implemented in Canto/Fortessa (Becton Dickinson) (panel a) and Navios/Gallios (Beckman Coulter) instruments (panel b)
The parallel laser configuration requires that the rays emitted by the lasers proceed parallel to each other or intercept the stream at different points, generating more than one interrogation point. The collinear laser configuration allows only one interrogation point, regardless of the number of lasers present. It is theoretically possible to focus more lasers on the same interrogation point or align the different emission lines in the same beam.
6.4.3.1
Spaced Lasers and Separate Pathways/Detectors
In conventional multilaser platforms, the laser beams are usually parallel and spaced from each other, and each beam focuses on a different point of the path held by the event transported by the fluid. In a typical multilaser platform, each event encounters different and successive interrogation points with a typical interval of a few tens of microseconds (μs) between each of them (Macey 2007). Fluorochromes with similar emissions but different excitations are excited at different times and points, and their signals, separated both in space and time, are distinguished from each other, collected separately, and routed to different sensors. It follows that each signal usually has its own path managed by a series of filters and mirrors, its own photomultiplier, and its own dedicated circuit; this path often takes the name of “optical channel” or simply “channel.” In the cytometers of this type, the same event produces different signals at different times, and there must be a device storing the time delay that the subsequent signals accumulate compared to the first. Subsequently, this device synchronizes the signals attributing all of them to the same event. This time delay must remain the same for the whole duration of the analytic run for this to happen successfully (Fig. 6.10).
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Fig. 6.10 Schemes of a multilaser optical bench. In panel a, the beams of the two lasers proceed separately and parallel, with each beam focussed on a separate interrogation point, while in panel b, the beams of the two lasers proceed collinearly and focus on the same interrogation point. The distance X (panel a) represents the time delay
6.4.3.2
Spaced Lasers and Shared Pathways/Detectors
As far as we know, this configuration is not implemented in commercial instruments, but we discuss it here because it represents the link with subsequent solutions based on pulsed lasers. Like models with parallel lasers and separate detectors, this model also allows the event’s passage through different interrogation points, evoking different signals at different times and points. However, unlike the separate detector model, the two distinct signals are led to the same sensor. The task of distinguishing them from one other is entrusted to a specific device, which considers the delay between the two signals and correctly assigns them to their evoking laser based on their timing. In an experimental prototype designed in the late 1990s, the task of distinguishing the two signals was entrusted to a “chopper” consisting of a disk placed before the PMT characterized by the presence of two peripheral and concentric crowns of holes displaced from each other. Given that the chopper was rotating with a speed synchronized to the sample, the system could distinguish the signal’s source according to the chopper’s position (Hoffman et al. 1993).
6.4.3.3
Time Delay
Since the same event produces different signals at different times in the cytometers with parallel lasers, a circuit board must memorize the delay between the signals (time delay) to bring all of them back together to the same event. This task requires
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that the time delay is kept the same throughout the analytical run, which depends on the hydraulics ability to keep a stable flow rate. A non-standard custom key exists in the FCS format (LASERnDELAY), which records the time delay, where n is the number of each laser according to the order in which the events pass through their beam spot. In multilaser platforms, maintaining a constant time delay is critical, and the more lasers are present, the more difficult this becomes. Any disturbance in the flow has repercussions on the pulses’ generation, particularly on those elicited by the last laser, which is the first to suffer. One way to mitigate the problems associated with possible time delay fluctuations in digital cytometers is to increase the Window’s Extension (for further information on this topic, see Sect. 8.2.2.1). Still, as this method likely leads to additional problems, it must be considered cautiously. In any case, it does not exempt the user from carrying out the hydraulics review procedures capable of eliminating the problem. In systems where positive pressure regulates the flow rate, even the variations in the volumes of free air, such as those that physiologically occur on the sheath and waste tanks during the analytical activity, can perturb the time delay; therefore, the measurement. This effect is well known, and several such models of cell sorter include specialized, technical solutions to get rid of the problem. The existence of time delay makes the inter-laser compensation in analog instrumentation critical. Since the compensation is performed by hardware between analog pulses, the signals to be compensated for must be isochrone, i.e., present at the same time. On-line compensation between signals produced by spatially separated lasers requires a circuit synchronizing all of them together. This circuit contributes to increasing electrical noise and decreases the signal/noise ratio (Baumgarth and Roederer 2000). This circuit is missing in digital cytometers, where the compensation takes place offline in a computational way.
6.4.3.4
Continuous Collinear Lasers
A dual-laser cytometer equipped with two continuous collinear lasers (usually blue and red, Fig. 6.11) allows the analysis of the same number of parameters as the single-laser version because it features the same number of sensors but allows a wider choice of fluorochrome panel, including red- and blue-driven molecules instead of blue-driven only. FC500 Cytometer, manufactured some years ago by Beckman Coulter, had been commercialized in two different versions, the first with a single blue laser and the second with two continuous and collinear blue and red lasers (Rothe 2009; Beckman Coulter 2016).
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Fig. 6.11 Schematic optical bench with collinear lasers and a shared detector. Panel a shows a model in which the collinear beams are continuous, while panel b shows a model in which the collinear beams are pulsed and synchronized. In panel a, the detector cannot distinguish between the fluorochromes excited by one or the other laser, while in panel b, the distinction is possible based on the different times of excitation
6.4.3.5
Pulsed Collinear Lasers
In this configuration, two pulsed emission collinear red and blue lasers are synchronized with each other to deliver mutually exclusive photon packages with a different energy to the interrogation point (Fig. 6.11). The signals evoked in every single event by this dual excitation in blue and red are detected by the same detector but selectively distinguished according to the moment of their detection, with a final result similar to that expected in a conventional system equipped with parallel lasers with continuous emission. The advantage of using pulsed collinear lasers lies in simplifying the optical bench and eliminating the circuits responsible for controlling the delay between the different interrogation points (time delay). This technical solution has been implemented in the multilaser models of the Guava instrument manufactured by Luminex (Duggan 2011) and in the Accuri cytometer manufactured by Becton Dickinson (Becton Dickinson 2012).
6.4.4
Special Solutions
6.4.4.1
Pie-Shaped Design
In the “Pie-shaped” optical bench, the photomultipliers are positioned all around the reading cuvette to optimize the light collection and make the instrument’s
References
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adjustment easier. This technical solution has been implemented in the Accuri cytometer manufactured by Becton Dickinson (Becton Dickinson 2012).
6.4.4.2
Benches for (De)Polarized Signals
Technical solutions for detecting depolarized scatter are not usually offered on commercial flow cytometers. Nevertheless, it is relatively easy to set up a cytometer to detect this signal, provided that the optical bench is susceptible to being modified (Rolland et al. 1985). It is sufficient to provide the sensor for the depolarized signal with a polarizing filter oriented in such a way as to stop the polarized light. The appearance of a response from that sensor reveals the events whose signals have lost the original polarization evoked by the polarized incident radiation.
6.4.4.3
Spectrally Enhanced Optical Benches
Each detector is devoted to a given fluorochrome in a conventional flow cytometer. However, nothing forbids collecting the signal of a given fluorochrome through all the detectors available on the instrument. If the number of detectors is sufficiently high, the probes analyzed will provide information on their emission along the entire spectrum and not only in their peak region, even though according to wider spectral intervals than those available in “spectral born” flow cytometers. In cases like this, the information obtained can allow (1) the spectral-based subtraction of autofluorescence signals and (2) a spillover correction based on spectral unmixing algorithms (Becton Dickinson 2021). This technological solution is made commercially available on some cytometers (FACSymphony A5 SE) manufactured by Becton Dickinson. Of note, what does it change in a spectrally enhanced optical bench is not the structure of the bench but the filter set bound to collect the entire spectrum of the emitted light; the new management of the signals is computationally carried out.
References Arnold LW, Lannigan J (2011) Practical issues in high-speed cell sorting. Curr Protoc Cytom 51(1): 1–24 Baumgarth N, Roederer M (2000) A practical approach to multicolor flow cytometry for immunophenotyping. J Immunol Methods 243(1–2):77–92 Beckman Coulter (2016) Cytomics FC500 – instructions for use. White Paper. https://www. manualslib.com/download/1284267/Beckman-Coulter-Cytomics-Fc-500.html. Accessed 3 Feb 2022 Becton Dickinson (2012) Making flow cytometry personal. White Paper – Technical Bulletin. https://www.bdbiosciences.com/content/dam/bdb/marketing-documents/Accuri-TB-MakingFlow-Cyto-Personal.pdf. Accessed 7 Sept 2021
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Becton Dickinson (2021) FACSymphony™ A5 SE flow cytometer user’s guide. White Paper. https://www.bdbiosciences.com/content/dam/bdb/marketing-documents/23-23473-00_ FACSymphony_A5_SE_UG.pdf. Accessed 22 Jan 2022 Blasenheim BJ (2004) USA patent 2004/0061853 A1. Prism-based flow cytometry excitation optics. https://patentimages.storage.googleapis.com/a0/e6/f5/d9799534e657cb/US20040061 853A1.pdf. Accessed 11 Feb 2022 Brittain GC, Chen YQ, Martinez E, Tang VA, Renner TM, Langlois MA, Gulnik S (2019) A novel semiconductor-based flow cytometer with enhanced light-scatter sensitivity for the analysis of biological nanoparticles. Sci Rep. https://doi.org/10.1038/s41598-019-52366-4 Chupp V (1985) USA patent 1985. Prismatic beam expander for light beam shaping in a flow cytometry apparatus Cytek Biosciences (2019) Spectral analysis meets flow cytometry. White Paper. https://www. accela.eu/files/products/233/spectral_analysis_meets_flow_cytometry.pdf. Accessed 24 Sept 2021 Duggan R (2011) EMD-millipore 8HT review. http://ucflow.blogspot.it/2011/05/emd-millipore8ht-review.html. Accessed 26 Jan 2019 Fox MH, Coulter JR (1980) Enhanced light collection in a flow cytometer. Cytometry 1(1):21–25. https://doi.org/10.1002/cyto.990010106 Hoffman RA, Stokdijk W, Davis K (1993) Detecting two immunofluorescence colors with one PMT (abstract). Cytometry 14(Suppl 6):18 Macey MG (2007) Principles of flow cytometry. In: Macey MG (ed) Flow cytometry: principle and applications. Humana Press, Totowa, NJ, pp 1–16 Mariella RP Jr, van den Engh G, Masquelier D, Eveleth G (1996) Flow-stream waveguide for collection of perpendicular light scatter in flow cytometry. Cytometry 24(1):27–31 Maude RJ, Buapetch W, Silamut K (2009) A simplified, low-cost method for polarized light microscopy. Am J Trop Med Hyg 81(5):782–783. https://doi.org/10.4269/ajtmh.2009.09-0383 Rolland JM, Dimitropoulos K, Bishop A, Hocking GR, Nairn RC (1985) Fluorescence polarization assay by flow cytometry. J Immunol Methods 76(1):1–10 Rothe G (2009) Technical background and methodological principles of flow cytometry. In: Sack U, Tarnok A, Rothe G (eds) Cellular diagnostics. Basic principles, methods and clinical applications of flow cytometry. Karger, Basel, pp 53–88 Telford WG (2011) Lasers in flow cytometry. In: Darzynkiewicz Z, Holden E, Orfao A, Telford W, Wlodkowic D (eds) Recent advances in cytometry, part A, vol 102, 5th edn. Academic Press, pp 375–409. https://doi.org/10.1016/b978-0-12-374912-3.00015-8 Zhao J, You Z (2016) A microflow cytometer with a rectangular quasi-flat-top laser spot. Sensors (Basel, Switzerland). https://doi.org/10.3390/s16091474
Chapter 7
Detectors and Electronics
7.1
Photodetectors
In Flow Cytometry, the photodetectors, also known as transducers, convert the light signal into an electrical signal, thus acting as a junction between the optical bench and the electronic or circuit component. It is of the utmost importance to realize that—except for detectors operating in Geiger mode, i.e., counting every single photon—the conversion between signals is an intrinsically analog phenomenon, regardless of the type of circuit—analog or digital—that will intervene downstream of the sensor. The photodetectors used in Flow Cytometry are photodiodes (PDs), avalanche photodiodes (APDs), photomultipliers (PMTs), and Silicon photomultipliers (SiPMs), which, albeit sharing the same function, have different characteristics and different operating modes. Multi-anode photomultipliers and charge-coupled devices (CCDs) are also used presently in non-conventional cytometry (spectral flow cytometry (SFC), imaging flow cytometry (IFC), and cytometry applied to marine biology studies (Kachel and Wietzorrek 2000)); Silicon photomultipliers are implemented in the NovoCyte Quanteon Flow Cytometer, currently marketed by Agilent (Acea Biosciences Inc. 2018).
7.1.1
Photodiodes (PDs)
The photodiodes (PDs) are solid-state signal photodetectors in which the photoelectric effect transforms the incident light into an electric signal proportional to the strength of the received light signal. Photodiodes have a fixed gain, typically much lower than photomultipliers (PMT). Compared to PMTs, photodiodes feature a simpler structure which does not require an external power supply. In Flow Cytometry, Photodiodes are restricted © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_7
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to the FSC signal detection because FSC is typically more intense than SSC and fluorescence signals. In some cases, the FSC signal can also be managed by photomultipliers, especially if a particular sensitivity is required, as in microbiological or micro-vesicle dedicated studies.
7.1.2
Avalanche Photodiodes (APDs)
The avalanche photodiodes (APDs) are a particular type of photodiode made up of several layers of semiconductors that amplify the photoelectric effect thanks to a fixed potential difference applied to the device. Unlike PMTs, APDs do not receive a modulable power supply, but a high reverse bias fixed voltage; their output signal does not depend on the power supply value, as in PMTs, but can be amplified according to a gain scale. APDs are beginning to replace PMTs in many applications, including Flow Cytometry. Compared to PMTs, APDs have a series of functional advantages, counterbalanced by other less desirable characteristics (Table 7.1). Whereas the PMTs feature a low quantum efficiency, defined as the ratio between emitted electrons and received photons, and a high internal gain, APDs have a higher quantum efficiency and a lower internal gain (Lawrence et al. 2008b). The higher quantum efficiency of APDs allows at least theoretically a better signal-to-noise ratio, but unfortunately, these sensors also have high levels of dark current that limit their resolution in the low range of signal (Lawrence et al. 2008a); moreover, at high gains, the amplification of the noise can impair the signal-to-noise ratio (Bhowmick et al. 2020). Not all APDs have the same sensitivity at different wavelengths but vary their efficiency depending on their physical composition. The Silicon APDs respond well between 300 and 1100 nm, those made of Germanium between 800 and 1600 nm,
Table 7.1 Differential features of the detectors Differential features of the detectors most used in Flow Cytometry PMT Quantum efficiency Low Internal gain High Dark current Low Noise at high gain Low Response Fast Sensitivity in IR Poora Tunable power supply Yes Linearity In a part of the scale Set-up By voltration
APD High Low High High Slow Good No On the whole scale By gaintration
a The availability of new gallium arsenide phosphide (GaAsP) PMTs has considerably reduced the issues connected with a lesser sensitivity in the IR region
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and those made of Arsenide, Indium, and Gallium show an excellent response with low noise, between 900 and 1700 nm (Excelitas 2011). The APDs have proved better than traditional PMTs in collecting a fluorescence signal emitted in the deep red or infrared range, demonstrating a quantum efficiency of 80% up to 800 nm and beyond (Lawrence et al. 2008a, b). The response value of the signals produced by the APDs is lower than that produced by PMTs, but can be (and usually is) modified thanks to circuits with amplifying functions applied to their outputs (Lawrence et al. 2008a); the response is so linear that it is possible to modify the gain without modifying the values previously attributed to a spillover matrix (Beckman Coulter 2019).
7.1.3
Photomultipliers (PMTs)
The photomultipliers (PMTs) are photodetectors characterized by a fast response, high gain, and low noise. From a constructive perspective, the PMTs consist of a vacuum glass tube containing an initial electrode, called the photocathode, a final electrode, called the anode, and a series of intermediate electrodes placed between cathode and anode, known as dynodes (Fig. 7.1). Between cathode and anode, there is a potential difference, which is distributed progressively between the various dynodes so that the first dynode possesses a potential higher than the cathode, each dynode possesses a potential higher than the previous dynode, and the last dynode has a potential immediately lower than that of the anode (Hamamatsu 2007). In a typical PMT widely used in Flow Cytometry, the maximum potential difference between cathode and anode can be around or immediately above 1000 V.
Fig. 7.1 PMT scheme. The difference in potential between photocathode and anode forces photoelectrons produced by the photocathode to pass from one dynode to the next, multiplying the number of photoelectrons produced at each step
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The photocathode constitutes the structure of the PMT, which receives light radiation. The photons enter the PMT through a particular window and hit the photocathode, which by external photoelectric effect produces in the vacuum a certain number of electrons, in this context defined as photoelectrons. Whenever hit, each dynode releases more electrons than those received because of the progressively increasing potential difference (Hamamatsu 2007), and this mechanism generates a global cascade multiplication effect capable of amplifying the signal with a gain that can reach up to 107 times and is a function of the total potential difference applied between the cathode and the anode (Lawrence et al. 2008b). Not all photomultipliers have the same sensitivity at different wavelengths, but their efficiency varies depending on the photocathode composition (Hiebert 1990). In particular, the PMTs with bi-alkaline photocathode respond well in the range between 400 and 550 nm, and the PMTs with multi-alkaline photocathode respond well up to 750 nm, but only the PMTs with Gallium Arsenide photocathode behave satisfactorily in the infrared. During some experiments with Fluorescent Proteins with infrared emission (iRFP), a PMT with a multi-alkaline photocathode (Hamamatsu 3896 multi-alkali PMT) has proved efficient and linear up to 800 nm. In a cytometer, the signal produced by the PMTs theoretically varies within a range from 0 to 10 V. In reality, due to the dynodes’ tendency to spontaneously emit a certain number of electrons, a PMT produces a minimum flow of electrons even in resting conditions. This flow, which is the current emitted by a PMT under tension in absolute darkness, depends on a series of factors, including the variability of the supply voltage, the photocathode thermionic emission, the field effect, and the environmental radiation. This electric current, known as “dark current,” constitutes an unavoidable component of the signal, but some circuits, known as “baseline restorers,” can contribute to its limitation (Flyckt and Marmonier 2002). Finally, a proportional conversion between photons occurs only for a range of the alimentation values applied to the PMT; it follows that linearity requires alimentation values neither too low nor too high. Evaluating samples made up of negative and positive standard subsets allows optimizing the signal/noise ratio (for further information, see Sect. 13.3.2) and choosing the best alimentation values. It is noteworthy that increasing the PMT voltage produces the same effect as increasing the amplifier’s gain, that is, an increase of the final gain; nonetheless, the final result is not necessarily the same because amplifiers usually present their specific linearity features and higher noise values than PMTs. Consequently, within the limits of the considerations previously expressed, in the case of weak signals, increasing the voltage to the PMT is better than increasing the amplifier gain (Snow 2004). Recently, micro PMTs based on micro-electrical-mechanical systems (MEMS) have appeared on the market, characterized by a particularly low dark current and a particularly narrow pulse susceptible to further increasing the dynamic range (Yamamoto 2017).
7.1
Photodetectors
7.1.4
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Multi-anode Photomultipliers
The multi-anode PMTs, also known as multichannel PMTs, are conceivable as a set of side-by-side-mounted PMTs, each of which has the task of transducing the signals in the spectral range section of its competence (Catchpole and Johnson 1972). Multianode PMTs are exploited in spectral cytometers, where a prism or a diffraction grating resolves the various signals along with the whole spectral range from UV to IR. Generally, a multi-anode PMT comprises up to 32 functionally equivalent channels. In some multi-anode PMT models, it is possible to control each channel’s gain (Futamura et al. 2015).
7.1.5
Silicon Photomultipliers (SiPMs)
The Silicon photomultiplier (SiPM)s results from assembling a very high number of microsensors called “single-photon avalanche diodes” (SPADs) (Mascotto 2011; Piatek 2018). The SiPM is a solid-state detector with the advantage of a small footprint, great structural simplicity, and a consequent reduction in costs. Like avalanche photodiodes the SiPMs do not need a variable power supply; similarly to the APDs, the photoelectron generation process must be controlled by gaintration (Bhowmick et al. 2021). Compared to traditional PMTs, SiPMs demonstrate similar characteristics of photodetection efficiency (PDE), gain, and excess noise but are immune from the presence of magnetic fields (E&B field immunity) (Piatek 2018). Again, in SiPMs, the intensity of the dark current is greater than PMTs and is proportional to the number of photosensitive units present in the array (Yamamoto 2017). However, since its extent depends on the temperature, it is possible to reduce it thanks to a Peltier effect-based refrigerating device (Yamamoto 2017; Piatek 2018). Like the APDs, the SiPMS demonstrate excellent efficiency in the deep red and infrared regions; it follows that, according to the photoelectron statistics, the spectral spreading of compensated populations stained with a deep-red emitting probe is reduced in instruments relying on this type of detector (Bhowmick et al. 2021). SiPMs also compete with APDs for higher gain and lower excess noise; compared to PMTs, they have some disadvantages since their excess noise increases with the gain, whereas the excess noise of PMTs decreases with it (Piatek 2018). As in the other detectors exploited in Flow Cytometry, the output of a SiPM is an analog phenomenon and consists of a flow of electrons (electric current) proportional to the number of incident photons, which can be transformed into a potential difference. Nonetheless, the detector can behave digitally and produce direct photon counting (Geiger mode) (Piatek 2018) in some particular conditions, such as a supply voltage over break-down voltage.
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SiPMs have been considered in a project concerning a possible cell sorter based on fluorescence lifetime (Rocca et al. 2016).
7.1.6
Charged-Coupled Devices (CCDs)
Charged-Coupled Devices (CCDs) convert an image into a pixel matrix, making it available to its analog representation and subsequent digital processing. CCDs are not present in Flow Cytometry, except for Imaging Flow Cytometry (IFC), in which the image of each analyzed event is an additional parameter assigned to the event from which it was generated (Basiji et al. 2007), and some models of spectral cytometer (Nolan and Condello 2013). Compared to multi-anode PMTs, also adopted in spectral cytometers, CCDs ensure higher spectral resolution, greater quantum efficiency, and better response in the red and infrared regions. Unfortunately, they display a lower gain and a slower speed, limiting the number of events that can be analyzed in the unit of time and ruling out their use in systems where a high speed of analysis is required, such as cell sorters.
7.1.7
Trans-impedance Amplifiers (TIAs)
The trans-impedance amplifier (TIA), albeit a separate circuitry, can conceptually be considered a functional part of the photodetector. Thanks to a circuit based on resistors, the trans-impedance amplifier transforms the output current into a potential difference. Classically, a PMT produces current values not exceeding 100 μA, which with a 100 KΩ resistor generate potential differences not exceeding 10 V. The transimpedance amplifier can also work as a pre-amplifier stage for the circuits designed to amplify the signal further (Hiebert 1990).
7.2
Circuitry
The circuitry of a flow cytometer is the circuit set responsible for managing the electrical signals and their digitization. As a result, the acquisition program stores for each event the numerical values of height, area, and duration of each pulse related to each signal evoked by each parameter under analysis, as well as the time of analysis and a progressive number relative to the position occupied by the single event within the analytic run. Two main circuitry models exist that differ substantially and radically influence signal handling. The first is the analog model, implemented on instruments designed until the 1990s, while the second is the digital model, implemented on the
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Circuitry
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Fig. 7.2 Schematic representation of the main circuit architecture models implemented in flow cytometers. (1) Analog model, (2) hybrid model, (3) digital model. The stages in which the signal is analog are blue, while the stages in which it is digital are pink (PMT photomultiplier, Pre AMP pre-amplifier, ADC analog-to-digital converter, DAQ data acquisition board, FPGA fieldprogrammable gate array, DSP digital signal processor, PC personal computer)
instruments manufactured from this period. An ingenious hybrid architectural model had been implemented in some commercial machines produced in the past (Fig. 7.2); this model is treated separately at the end of the chapter. The distinction between the analog and the digital circuitry is essential because the circuitry type determines how the cytometer performs pulse measurement, signal amplification, spillover compensation, and the ratio between parameters. Nowadays, all the currently manufactured cytometers rely on digital architecture. Nevertheless, many operating instruments are still analog circuit machines that often coexist in the same laboratory with digital instruments. It follows that it is necessary to know the peculiarities of both circuits, which can be summarized as follows: in the analog model, the signal is digitized as late as possible, i.e., immediately before storing in the cytometric file, while in the digital model the signal is digitized as soon as possible, i.e., immediately after the trans-impedance amplifier. For accuracy’s sake, the preamp card includes the trans-impedance amplifier, the baseline restorer, and a low pass filter. In any case, the flow of digitized data is eventually transferred to a computer, which houses the application responsible for storing them in a cytometric file. This transfer is usually mediated by controller cards connected with a GPI IO (general purpose input/output) cable.
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Analog Model
In the circuit architecture of an analog flow cytometer, dedicated circuits perform sequentially and, for each parameter, a series of tasks, i.e.: (Fig. 7.3): 1. The photodetectors transform the light signal into an analog electrical signal (electric current). 2. The trans-impedance amplifier transforms the electric current in potential difference and performs the linear preamplification of the electrical signal. 3. The baseline restorer cleans the signal from any spurious components generated by the PMT. 4. The comparator, also known as the discriminator, selects the signals to transmit to the downstream electronics based on the overcoming of a variable threshold chosen by the operator. 5. Other electronic boards perform the integration between different signals, variously aimed at the calculation of the ratio between parameters or the correction of the spillover. 6. The amplifiers amplify the signals either in a linear or a logarithmic way. 7. The DC restorer cleans the signal from any spurious components generated by the amplifier. 8. Peak detectors and integrators measure the components of the pulse (height H, area A, and width W ). 9. The analog-to-digital converters (ADCs) digitize the values related to each pulse component.
Fig. 7.3 Schematic representation of the circuitry implemented in an analog cytometer (Modified from Snow C. K. Flow cytometer electronics, Cytometry 2004; 57A: 63). The arrows in black represent the signal path, while those in red represent the signal’s processing logic’s connections
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A distinct set of circuits, including all those above, must be available for each parameter measured on the event; this set also takes the name of “electronic channel” or “channel,” a term often extended to include the optics upstream. A clear and extensive discussion of analog architecture can be found in the excellent article by Snow, to which reference is made (Snow 2004).
7.2.1.1
Baseline Restorers
The dark current’s presence affects the electric signal with a series of disturbances at the trans-impedance amplifier’s output. Some other electronic circuits, called baseline restorers, act as filters, attenuating the disturbances and lowering the signal produced in the absence of a measurable event to a few millivolts. Inappropriate signals may also come from other sources, such as free fluorochrome present in the solution where the events are monodisperse. This drawback can occur in DNA analysis, in which the DNA-bound probe equilibrates with that present in the suspension buffer and immunophenotypic analyses of samples stained with no-wash techniques. An overly aggressive action of the baseline restorer can produce an artifact consisting of interpolating negative values (Wood 1998) (Fig. 7.4).
7.2.1.2
Comparators and Threshold
The comparator, or discriminator, is a “front end” circuit designed to compare an input signal’s value with a predetermined value, also known as a threshold, set by the operator. If the input signal’s value exceeds the threshold, that signal triggers the system, i.e., overcomes the threshold and is processed by the circuits. In practice, an input signal’s fate depends on the comparator’s response, which decides whether the
Fig. 7.4 Schematic representation of the signal before (panel a) and after (panel b) the baseline restorer intervention. If the filter’s action is too aggressive, some very low values can become negative (red in the picture) after subtracting the background noise
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signal is generated by an event’s presence or rather by fluctuations of the baseline. The comparator also performs another fundamental task, i.e., the generation of signals necessary for synchronizing the different pulse processing phases. In analog instrumentation, the threshold can be set not only on every parameter evoked by the first laser but also on the other parameters without distinction, provided circuits hold the signals off the first laser until the arrival of the other signals. A threshold can also consist of a combination of various parameters, provided that the signals are isochronous, i.e., synchronized by appropriate circuits. It is noteworthy that, in this context, the definition of primary laser refers to the first laser whose spot is crossed by the analyzed events. For example, in the FACSCalibur cytometer by Becton Dickinson, the first laser is the red one, and all the signals generated by the event crossing its spot are delayed by about 20 μs to synchronize with those from the same event crossing the blue laser (Lannigan 2003). This synchronizing device allows the online compensation of the inter-laser spillover in analog machines. The concept of the threshold is crucial because the threshold defines irreversibly what the instrument considers worthy of being acquired; a too low threshold allows the acquisition of entirely artifactual events, consisting in the expression of the various components of the electrical noise, while a too high threshold can exclude the analytically relevant events without any chance of recovery. For example, setting CD45 as a threshold in analyzing CD45 negative leukemic blasts results in the selective loss of the diagnostically relevant population.
7.2.1.3
Accessory Circuits
Other analog circuits of great practical utility include (1) the circuits that recalculate the ratio between two input signals (ratio performing circuits) and (2) those allowing addition and subtraction operations (compensation performing circuits). They work on the voltages coming out of the photodetectors. Ratio performing circuits allow evaluating the metachromatic probes, which change their spectral behavior according to the features of the parameter under analysis. To this group of probes belong, for example, Indo-1 (ex365/em405-485), which moves its emission from blue/green to violet with the increase of intracellular Calcium concentration (Tsien 1989), or SNARF-1 (ex488/em570-670), which changes its emission from orange to red with the increase of the intracellular pH (Wieder et al. 1993). By operating the ratio between the emission of these molecules in the two different spectral regions (blue and violet for the Indo-1, or red and orange for the SNARF) and comparing the obtained values with those from a reference curve, it is possible to go back to the intracellular Calcium concentration or the intracellular pH of the analyzed cells, respectively. Another use of the ratio between parameters calculates the density of surface antigen sites by dividing the fluorescence intensity by the cell surface area, derived in turn from the cell volume measurement (Steinkamp and Kraemer 1974).
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The circuits capable of performing addition or subtraction between pairs of different signals are generally differential amplifiers; they are used in “online” compensation between different fluorescences (for further information on this topic, see Sect. 19.2.4). The circuits that perform addition or subtraction allow the user to calculate the ratio between two signals not as the ratio of the two amplified signals in a linear way but as the logarithm of their difference, as it is known that logFL1logFL2 ¼ FL1/FL2 (Musgrove and Hedley 1990). This solution allows calculating the ratio between two signals even in situations characterized by a wide dynamic range of output signals (Rabinovitch et al. 1986; Roederer et al. 1995).
7.2.1.4
Amplifiers
The signal voltage measured at the output of the trans-impedance amplifier is very low and must be amplified. This amplification can occur linearly, with a linear gain chosen by the operator or a logarithmic gain (Fig. 7.5). The choice between linear and logarithmic amplification depends on the dynamic range of the measured signals. A pre-amplifier section, essentially consisting of the trans-impedance amplifier, is placed before the amplifiers, either logarithmic or linear; it follows that both linear and logarithmic amplifiers are a two-stage structure whose first stage is, in any case, characterized by a linear response.
Linear Amplifiers When the parameter varies from a minimum to a maximum, not exceeding a certain number of times the minimum, it is possible to place all the analyzed events on the
Fig. 7.5 An ideal representation of the response of a linear amplifier (panel a) and a logarithmic amplifier (panel b). In the first case, the ratio between the input signal and output signals is determined by the relation Vout ¼ kVin, where k is the gain factor, while in the second case, the ratio between the input and output signals is determined by the relation Vout ¼ k (lnVin/lnVref), where Vout is the output voltage, Vin is the input voltage, k is the scaling factor, and Vref is the reference voltage
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histogram scale, provided that the right gain is adopted. This possibility classically occurs when studying cycling cells’ behavior, whose DNA content theoretically varies from x to double (except for the occurring polyploid events), and explains why cellular kinetics studies exploit linear amplification. The response of a linear amplifier is described by the relation Vout ¼ kVin, where Vout is the output voltage, Vin is the input voltage, and k is the gain factor. The gain factor is under the operator’s control, who, depending on the instrument type, can vary it continuously or choose from multiple prefixed values (solution implemented in the instruments of more ancient design, like FACStar and FACStar Plus. Linear amplifiers usually behave in a “linear” way, but deviations from ideal behavior are possible (Bagwell et al. 1989). It is worth noting that histograms with the same coefficient of variation tend to modify their shape with a linear amplification, progressively widening it as they move toward the end of the scale (Koper and Blanken 1981).
Logarithmic Amplifiers If need be to study a variable parameter from a minimum to a maximum exceeding tens of thousands of times the minimum, the attempt to place all the analyzed events on the same scale is frustrated by the fact that a gain high enough to “see” the lower value events piles up the events too large to the right of the scale, while, on the other hand, a gain low enough to “see” the higher value events piles up the events too small to the left of the scale. This eventuality classically occurs when studying a membrane antigen’s expression, whose signal can vary according to different magnitude orders. One trick to keep all events in scale is to amplify the electrical signals in a logarithmic way. The response of a logarithmic amplifier is described by the relation y ¼ a ½ ln ðx þ bÞ þ c, where y is the output, x is the input, a is the gain, b is a number that represents the lower limit of the dynamic range, and c is the offset (Koper and Blanken 1981). Two features define the behavior of a specific logarithmic amplifier, i.e., its dynamic range, known as the ratio between the minimum and the maximum manageable voltage, and its scaling factor (also called slope), known as the number of Volts resolved for each decade of the scale (Gandler and Shapiro 1990). The use of logarithmic amplification generates a series of substantial effects. Since the distribution of a vast number of biological parameters tends to be log-normal (Heath 1967; Sweet et al. 1981; Gandler and Shapiro 1990), it follows that, after logarithmic amplification, the histograms related to an immunophenotypic analysis are likely to assume a bell shape (Watson and Walport 1985). This phenomenon facilitates the visual recognition of the parameter distribution, but above all allows comparing the series with parametric statistical methods. However, it must be
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kept in mind that, despite these theoretical premises, the actual distribution of some log amplified parameters is often asymmetric (skewed). On a linear scale, the differences between equal increments are equal. In a logarithmic scale, the ratios between equal increments are equal, not the differences, which means that histograms with the same coefficient of variation maintain their shape on the whole scale (Koper and Blanken 1981). Logarithmic amplification involves some problems because they are hardly really “logarithmic” on the whole scale (Parks et al. 1988; Schmid et al. 1988; Gandler and Shapiro 1990). As a result, the attempt to linearly convert the logarithmically acquired data is frustrated by serious inaccuracies, despite the existence of algorithms and methods explicitly designed to solve this problem (Muirhead et al. 1983). The non-ideal behavior of logarithmic amplifiers introduces ineradicable inaccuracies in the fluorescence compensation procedures carried out computationally on data converted from a logarithmic to a linear mode (for further information on this topic, see Sect. 19.2.4.2).
7.2.1.5
DC Restorers
At the amplifier output, the signal may contain an additional noise generated by the amplifier, quite similar to what happens in the audio amplifiers, known as DC bias or DC offset, but appropriate electronic filters known as DC restorers correct this disturbance. As with baseline restorers, DC restorers can also generate artifacts (Wood 1998).
7.2.1.6
Peak Detectors and Integrators
The sensors’ analog signal, or “pulse,” can be visualized with an oscilloscope. Extrapolating the value of a pulse produced by a single event against its duration over time, we obtain a bell-shaped curve characterized by height (H ), an area under the curve (A), and width (W ) (Fig. 7.6; for further information on this topic, see Sect. 8.2). The measurement of the values of height (H ), area (A), and width (W) of a pulse is entrusted to a series of circuits that reside together in an integrated board; the circuits in charge of measuring H are called peak detectors, while those responsible for measuring A are called integrators.
Measurement of H (Peak Detectors) The peak detectors are integrated circuits (peak & hold) that receive the signal from the amplifier and hold it until a release message is produced by the comparator, after which they discharge themselves by transmitting the charge to the analog-to-digital converter (ADC).
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Fig. 7.6 Schematic representation of an analog pulse, resolved in its Height (H ), Area (A), and Width (W ) components
Measurement of A (Integrators) The integration process appears to be somewhat more complex; it divides into passive and active based on the circuits’ structural characteristics that perform the task. The passive integration process, implemented in the most ancient analog instrumentations, did not require an integrator and could be done directly by the peak detector because the slowness of the amplifiers then available would generate an increase in the potential values close to the value of the integrated peak (Snow 2004) so that the final output of the detector was proportional to the area of the pulse. A separated peak detector, a tributary of a faster circuit such as the pre-amplifier, provided the height value. The active integration process, implemented in the most recent analog instruments, requires a dedicated circuit called an integrator. The integrator is an integrated circuit that receives the charge in a time interval depending on the pulse trend and is defined by switches consisting of field-effect transistors (FET) controlled by Schmitt’s triggers (Hiebert 1990). The signal sampling starts immediately when the pulse begins, but thanks to a circuit called the analog delay line, the signal reaches the integrator only after releasing the comparator’s reset message. The interposition of the analog delay line is needed since the comparator must see the increase of the pulse before releasing the reset message; without this delay, the time needed for the comparator’s change of state would leave the integrator without the first part of the signal (Hiebert 1990; Shapiro 2000).
Measurement of W A dedicated circuit produces a potential difference directly proportional to the time spent by the pulse over the threshold, i.e., to its width (W ) (Shapiro 2000); this value
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is also maintained in an integrated circuit and then delivered to the analog-to-digital converter (ADC). Given that A is a function of both H and W, it follows that the ratio between H and A can produce an approximate value of W in the absence of direct measurement, according to the equation W ¼ H=A: This shortcut is sometimes exploited in digital architectures.
Pulse Processing Synchronization Pulse processing is a series of sequential and cyclic operations repeated for each event that triggers the system. Simply put, when the pulse begins, the comparator, activated by the triggering event, generates a reset message, which marks the end of the previous cycle and the beginning of the new one. The reset message on one side gets the integrated circuit to release the charge acquired during the previous event’s analysis, allowing the transmission to the respective ADC (end of the previous cycle). However, on the other side, it generates a hold signal that prepares the integrated circuit to receive the new signal, isolating them from the system to prevent premature sampling. After a period defined by a synchronization signal, a series of “sample and hold” signals allows the integrated circuit to charge and hold the charge until the following reset message, marking the end of the cycle started with the previous reset message. The time elapsing from the activation of the comparator circuit at the end of the sampling period constitutes the minimum time required for processing a pulse and has a duration that varies according to the type of instrument but generally is around a few microseconds. During this period, called the refractory period, the system cannot process other signals, so events are aborted if they are too close to each other; this phenomenon is called “coincidence.” One of the biggest recurring problems in pulse processing is the fact that the signals related to the same parameter (H, A, and W ) must be processed at the same time; this condition is even more critical because it holds for all the other parameters of the same event, some of which can be explored by probes excited by different lasers at different times. This situation requires synchronization systems, which may consist of digital signals that propagate through the circuitry together with the signals to be processed and tell the various sections what to do and when to do it, or of the presence of several parallel layers of “sample and hold” circuits which hold the signals all together till their definitive release (Snow 2004).
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Analog-to-Digital Converters (ADCs)
At the output from the section made up of peak detectors and integrators, the signal is still analog, as it varies from a minimum to a maximum through a theoretically infinite number of intermediate levels. The signal must be digitized to be recorded and processed, i.e., transformed into a number. In other words, the theoretically unlimited range of voltages must distribute in a finite series of intervals, also called discrete channels. This task is performed by an electronic circuit known as an analog-to-digital converter, or ADC. An ADC is defined based on its number of bits, i.e., the number of intervals or channels which can produce, and its clock number, i.e., the frequency with which it samples the signal.
Bit Number The number of intervals or channels that an ADC can produce is a function of its number of bits, i.e., the number of binary digits managed by its circuitry. For example, an 8-bit ADC, present in numerous analog cytometers, can generate 256 intervals, and this is because the minimum binary number that can write with the eight digits (or bits) available to the circuitry is 00000000, equal to 0 in decimal notation, while the maximum binary number is 11111111, equal to 255 in decimal notation, for a total of 256 intervals. In 2008 the highest resolution available for a commercial ADC exploited in Flow Cytometry was 24 bit, equal to 1,677,216 channels (Ostruszka 2008); these technical characteristics have been currently further improved. A series of corollaries comes from these considerations, namely: 1. The total number of available channels is always a power of two. 2. The number of the last channel is even but can also be odd if zero is assigned to the first channel. 3. The greater the number of bits available for the circuit, the greater the number of channels or intervals in which the ADC resolves the signal range. 4. The greater the number of channels or intervals, the greater the theoretical resolution of the instrumentation, i.e., the smaller the differences in intensity the instrumentation can distinguish; these differences may be expressed in LSB (least significant bit), as the difference in voltage between channels (for further information on this topic see Table 8.1 and Sect. 8.3.1). 5. The random assignment of a pulse to one of two adjacent channels is always possible; this unavoidable feature of the ADC contributes to impairing the measurement’s accuracy and increasing its variation coefficient. Because of the background noise always present in the electronic circuits, the number of the “useful” intervals generated by an ADC, called ENOB (effective number of bit), is always smaller than the theoretical one (Xiong and Tanik 2015).
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Clock Number The number of signal samplings made by an ADC in the time unit is a function of its clock number and is expressed in millions of cycles per second (MHz). In observance of Nyquist-Shannon’s theorem, the sampling frequency must be at least twice that of the most frequent signal, under the penalty of failure to distinguish between different signals close to each other (Nyquist 2002). Since the circuitry must determine most accurately and for each event, the maximum value to be digitized, the pulse must be sampled with a frequency much higher than that used to distinguish between different pulses. In Flow Cytometry, the theoretically desirable sampling rate of an ADC is at or above 40 MHz (Snow 2004). Even faster sampling (160 MHz) is exploited in electronics dedicated to the analysis of signals evoked by the BD CellViewTM module implemented in image-enabled cell sorting (ICS) instruments devised by Becton Dickinson (Schraivogel et al. 2022) (for further information on the topic, see Sect. 22.1.1). In some digital models, the ADC samples the signals at a frequency twice that typically expected; this feature would increase the digitized signal’s resolution beyond the theoretical limit provided by the number of bits available to the ADC (Duggan 2014).
7.2.2
Digital Model
The digital model was conceived at the end of the last century (Shapiro et al. 1998), but its realization had to await the commercial availability of electronic circuits with the required performances. Strictly speaking, the currently available digital instruments should not be considered “fully” digital because the first step of the process, i.e., the transformation of the light signal into the electric signal, is still analog. Nevertheless, the term “digital” is maintained in the text because it is of common use and can distinguish the new instruments from the old analog cytometers. In the digital model, the signals are immediately digitized at the transducer’s output, thanks to a series of circuits dwelling in a data acquisition board (DAQ), and eventually stored as “raw” signals, i.e., not subjected to further processing. As a consequence, in digital instrumentation, there are no comparators, no circuits to correct the spillover (compensation), no circuits for other accessory functions such as the ratio between parameters, no peak detectors, no integrators, and no amplifiers; furthermore, the ADCs are present not as a final stage, but as a front-end circuitry inside the data acquisition board (DAQ) responsible for signal management (Fig. 7.7).
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Fig. 7.7 Scheme of a DAQ acquisition card, the heart of the Diva digital circuit present in some instruments currently produced by Becton Dickinson. The example refers to an instrument with sorting capabilities, and for simplicity’s sake, to a six-color configuration. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
7.2.2.1
Data Acquisition Boards (DAQs)
The first digital model on the market was the Diva™ system, made available by Becton Dickinson in 2002 (Verwer 2002). This model is still one of the most widespread and tested models; its brief description is the object of this section, the conclusions of which, while remaining conceptually valid when translated to other systems (Zilmer et al. 1995), do not automatically apply to models other than that reported. The heart of the Diva™ digital system consists of a series of boards known as Data Acquisition Boards (DAQs), which work in parallel to manage the signals coming from the different detectors. DAQs are complex circuitries mainly consisting of: 1. A series of analog-to-digital converters (ADC) capable of sampling at 14 bits at a frequency of 10 MHz 2. A series of random access memories (RAM) aimed at temporarily storing the signals 3. A complex of programmable logic gates called FPGA (Field-Programmable Gate Array) 4. A series of digital signal processors called DSP (Digital Signal Processor), designed to execute recurrent instructions, integrate signals, and dialog with FPGAs
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Analog-to-Digital Converters (ADCs) From a conceptual point of view, the ADCs present in the DAQs of the Diva system are similar to their counterparts implemented in the analog circuitries, even though they exhibit extremely higher performances. What changes drastically is the context in which they perform their function. In fact, in an analog system, the pulse is born as analog with virtually unlimited dynamics, and the ADC digitalizes it in the last stage of its processing. On the contrary, the pulse is born as digital in a digital system because the ADC digitalizes it in the first stage, sampling it when ordered by the system clock. A similar consideration is about the clock number. Analog circuits “know” when to start the signals’ measurement since they are alerted by the comparator, which in turn is triggered by the event. On the contrary, the digital circuitry does not have any information about it since it does not have any comparator circuit. Consequently, it is at the mercy of a continuous stream of input signals, and the distinction between one event and the next is entrusted to the ADC’s ability to distinguish one pulse from another by sampling the signal. In the digital model, the ADC’s serial sampling fulfills the twofold task of defining each pulse’s characteristics and distinguishing each pulse from the others. In the absence of the comparator, all the decisions about the threshold management are entrusted to the DAQs, in which a circuitry (FPGA) made for this specific purpose logically combines all the triggering signals, generating an appropriate window’s gate and overturning it on the electronics that manage the related channels. Thanks to this circuitry, the digital machines’ threshold can be placed on any parameter or parameters’ combination.
Field-Programmable Gate Arrays (FPGAs) The FPGAs process each signal digitized by the ADCs and build a pulse for each event’s parameter. The values of the pulse are assigned in the following manner: 1. The value of the Height (H ) is the highest value detected during the sampling of that given pulse. 2. The value of Area (A) is the sum of all the values detected sampling that given pulse. 3. The value of Width (W ) is derived from the number of samplings taken on that given pulse (area slices multiplied by a thousand) or as a ratio between Height and Area. It is hardly necessary to note that the height of the pulse generated by a single event can not be modified, while the area is a function of the time that the circuitry assigns to the measurement of that pulse, i.e., the window’s gate (for further information on this topic see below and Sect. 8.2.2.1).
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The FPGAs can provide other information about the pulse, i.e., information about its waveform, including kurtosis, skewness, and other features susceptible to be transformed according to Fourier (Zilmer et al. 1995). The FPGAs are programmable and perform various activities, including modifying the window’s gate by adding extra acquisition time (window’s extension). The window’s extension is helpful in a series of procedures such as area scaling, the distinction between different pulses, and the correction of errors due to time delay issues. Another function of FPGAs is to filter the signal, increasing the signal/noise ratio above 80 dB and bringing the resolution beyond what is expected based on the features of the ADCs implemented in the board (Xiong and Tanik 2015).
Digital Signal Processors (DSPs) DSPs receive and integrate information from the FPGAs and communicate it to the sorting control boards and the computer connected to the instrument, which is the interface between the instrument and the operator. DSPs control the sorting procedures and apply a series of operator decisions, including the compensation values, the coordinates of the sorting windows, and the drop charging control.
7.2.2.2
General Considerations on the Digital Model
The digital model allows many advantages, including a background decrease due to a less complicated circuitry and a higher reading speed due to the comparator’s disappearance, with no need to wait to restore baseline conditions before recognizing the arrival of a new event. In digital instruments, besides some gain tweaking, the only instrumental variable under the operator’s control is the PMT voltage or the APD gain, depending on the detectors in use; in most cases, given the APDs’ high linearity and dynamic range, the analyzed events are on the scale without the need to act on the detectors’ gain. Finally, it is of the utmost importance to realize that in a digital system, the values relating to each event’s parameter are digitized immediately at their exit from the photodetectors and are memorized as generated, i.e., in linear scale without any manipulation. Any further processing occurs by software and not by hardware; consequently, it is possible to carry out, with the cytometer switched off, all the operations that in an analog cytometer must be decided on the instrument before the acquisition, i.e., the correction of the spillover, the setting of the ratio between different parameters, and the choice between linear and logarithmic amplification.
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Hybrid Model
The hybrid approach , devised in the early 90s of the past century, represented an ingenious attempt, technically known as “sub-ranging,” to overcome the problem of logarithmic amplification via hardware. In this approach, (1) no logarithmic amplifiers exist, (2) the analog signals are linearly amplified and digitalized “as such,” and (3) the computer performs the logarithmic transformation of the data. Since, at the time of its ideation, it was impossible to find ADCs with a bit number suitable to properly resolve the dynamic range, the solution to the problem was obtained by dividing the latter into two adjacent segments, each managed by a separate 15 bit ADC (Auer et al. 1993, 1994). In short, the first segment’s signals were sent “as such” to the first ADC, and those from the second one were sent to the second ADC after being linearly amplified 32 times. In the case of low-value signals, the system exploited the amplified values, while in the case of high-value signals, the system exploited the non-amplified ones, moving them 5-bit on the scale to compensate for the amplification previously applied to the low-level signals, thus emulating a total dynamic range of 20 bits. This technical solution was implemented in the bench analyzer Coulter Epics XL and in other instruments produced by Beckman Coulter.
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Rabinovitch PS, June CH, Grossman A, Ledbetter JA (1986) Heterogeneity among T cells in intracellular free calcium responses after mitogen stimulation with PHA or anti-CD3, simultaneous use of Indo-1 and immunofluorescence with flow cytometry. J Immunol 137(3):952–961 Rocca FM, Nedbal J, Tyndall D, Krstajić N, Li DD, Ameer-Beg SM, Henderson RK (2016) Realtime fluorescence lifetime actuation for cell sorting using a CMOS SPAD silicon photomultiplier. Opt Lett 41(4):673–676. https://doi.org/10.1364/ol.41.000673 Roederer M, Bigos M, Nozaki T, Stovel RT, Parks DR, Herzenberg LA (1995) Heterogeneous calcium flux in peripheral T cell subsets revealed by five-color flow cytometry using log-ratio circuitry. Cytometry 21(2):187–196 Schmid I, Schmid P, Giorgi JV (1988) Conversion of logarithmic channel numbers into relative linear fluorescence intensity. Cytometry 9(6):533–538 Schraivogel D, Kuhn TM, Rauscher B, Rodríguez-Martínez M, Paulsen M, Owsley K, Middlebrook A, Tischer C, Ramasz B, Ordoñez-Rueda D, Dees M, Cuylen-Haering S, Diebold E, Steinmetz LM (2022) High-speed fluorescence image-enabled cell sorting. Supplementary materials. Science 375(6578):315–320. https://doi.org/10.1126/science.abj3013 Shapiro H (2000) How flow cytometers work – and don’t work. In: Diamond RA, DeMaggio S (eds) In living color. Protocols in flow cytometry and cell sorting. Springer, Berlin, pp 39–56 Shapiro HM, Perlmutter NG, Stein PG (1998) A flow cytometer designed for fluorescence calibration. Cytometry 33(2):280–287 Snow CK (2004) Flow cytometer electronics. Cytometry 57A(2):63–69 Steinkamp JA, Kraemer PM (1974) Flow microfluorimetric studies of lectin binding to mammalian cells. II. Estimation of the surface density of receptor sites by multiparameter analysis. J Cell Physiol 84(2):197–204 Sweet R, Parks D, Nozaki T, Herzenberg L (1981) A 3 1/2 decade logarithmic amplifier for cell fluorescence data (abstract). Cytometry 2(2):130 Tsien R (1989) Fluorescent indicators of ion concentrations. In: Taylor DL, Wang Y-L (eds) Methods in cell biology. Academic Press, New York, pp 127–156 Verwer B (2002) BD FACSDiVa options. White Paper Becton Dickinson. http://www. bdbiosciences.com/ds/is/others/23-6579.pdf. Accessed 8 Jan 2021 Watson JV, Walport MJ (1985) How does flow cytometry express gaussian distributed biological information? J Immunol Methods 77(2):321–330 Wieder ED, Hang H, Fox MH (1993) Measurement of intracellular pH using flow cytometry with Carboxy-SNARF-1. Cytometry 14(8):916–921 Wood JC (1998) Fundamental flow cytometer properties governing sensitivity and resolution. Cytometry 33(2):260–266 Xiong F, Tanik MM (2015) Deciphering flow cytometry electronics SoutheastCon 2015. https:// doi.org/10.1109/SECON.2015.7133019 Yamamoto M (2017) Photon detection: current status. In: Robinson JP, Cossarizza A (eds) Single cell analysis. Contemporary research and clinical applications. Springer, Singapore, pp 227–242 Zilmer NA, Godavarti M, Rodriguez JJ, Yopp TA, Lambert GM, Galbraith DW (1995) Flow cytometric analysis using digital signal processing. Cytometry 20(2):102–117
Chapter 8
Signal Analysis
Like many other measurement systems, the signal measured in a flow cytometer results from the combination of many different components, including the signals related to the parameter and the signals related to the background. These components do not have the same distribution, and their contribution to the signal variance is proportional to their share in the total signal. It follows that the signal is heteroskedastic, i.e., it presents a variance that changes with the intensity of the signal itself (Novo et al. 2013; Futamura et al. 2015; Gondhalekar et al. 2018). This behavior is of great practical importance, as it affects: 1. The compensation procedures, which are based on linear regression and contribute to the spreading of compensated populations 2. The accuracy of the results provided by linear algebra-based spectral unmixing procedures 3. The photodetectors’ adjustment procedures
8.1
The Background
Generally speaking, the background signal is any signal other than the signal generated by the parameter under analysis, and in Flow Cytometry, it can be identified with the negative component of the test. In Flow Cytometry, the background is a heteroskedastic phenomenon resulting from the sum of different components (Bagwell et al. 2016); while some of them have a Gaussian distribution, others are Poisson compliant. Furthermore, some of them increase their variance with the intensity of the signal, while others do not. Once the compensation is achieved, the different ratios between these components can lead to unexpected results in the distribution of the negative population (Roederer 2016). This effect is worsened because the background distribution is not necessarily that of the signal of interest. In populations characterized by low © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_8
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fluorescence emission, the background distribution can become prominent, moving the population appearance away from the expected one. From a cytometric point of view, the total background, called Btot, results from the sum of two components: the instrumental background (Bcal) and the experimental background (Bsos). Btot ¼ Bcal þ Bsos
8.1.1
Instrumental Background (BCAL)
The instrumental background Bcal consists of a series of factors attributable to the instrument, i.e., (1) the electrical noise of the circuitry, which is a gain-dependent component whose variance increases with the square of measurement intensity, (2) the Poisson-related error in photo-electron counting, whose variance increases linearly with the measurement intensity, (3) the photo-electrons released by the detectors because of thermionic or field effect, whose variance is constant (Bagwell et al. 2016). From a practical point of view, the instrumental background Bcal is equivalent to the standard deviation of the electronic noise, also known as SDen (Perfetto et al. 2014).
8.1.2
Experimental Background (Bsos)
The experimental background Bsos consists of a series of factors attributable to the experimental conditions, i.e., (1) the non-signal-related counting errors due to stray light, sheath impurities, and free fluorochrome in the suspension, (2) the Raman scattering, (3) the autofluorescence, and (4) the spreading phenomenon due to spillover compensation (Fig. 8.1) (Perfetto et al. 2014). From a practical point of view, the experimental background (Bsos) is equivalent to the 90th percentile of the signal produced by every single conjugated antibody for each primary detector (Roederer 2001; Perfetto et al. 2012).
8.2
The Pulse
Unlike non-vector quantities, identified by a scalar value (intensity), vector quantities are identified by intensity, verse, and direction and describe phenomena characterized by quantitative variations over time. It follows that the electrical impulse, defined here as “pulse,” can also be conceived as a vector quantity, and precisely as
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The Pulse
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Fig. 8.1 Bivariate analysis of two positive clusters mutually exclusive for FLx and FLy. The figure shows the analysis results before (panels a and b) and after (panels c and d) the compensation procedures. In panel a, the uncompensated FLx neg Fly pos cluster (blue in the picture) is placed improperly along the cytogram’s bisector. After compensation, the FLx neg FLy pos cluster assumes the expected position (panel c) but increases its variation coefficient (for further information on this topic, see Sect. 19.2.1.1). This increased variance expands its projection on the FLx axis, drastically increasing the experimental background on FLx axis from X (panel b) to X0 (panel d)
the temporary application of an electromotive force to an electrical circuit, or, simplifying, as a rapid and brief variation of the intensity of electric current or electrical voltage. Accordingly, the term pulse identifies the variation in the potential difference at the trans-impedance amplifier’s output after detecting an event. The graphic representation of a pulse is a curve, which can be resolved into three main components called Height (H ), Area (A), and Width (W ). In some instruments, the Height is also referred to as “Peak” (Fig. 8.2).
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Fig. 8.2 Schematic representation of an analog pulse, resolved in its Height (H ), Area (A), and Width (W ) components
Fig. 8.3 Suppose the event is smaller than the beam waist (panel a). In that case, it dwells entirely in the beam at a given time, all its fluorescent molecules are excited at once, and its peak Height (H ) is the direct function of its total fluorescence. If, on the other hand, the event is greater than the beam waist, it won’t dwell entirely in the beam at any time, and its fluorescent molecules are never excited at once. It follows that, in this case, the peak Area, and not the peak Height (H ), is the function of its total fluorescence
Both H and A are a function of the analyzed event’s signal intensity, and in a flow cytometer, their value depends on the power supplied to the photomultipliers or on the gain applied to the avalanche photodiodes output. It is noteworthy that the relationship between H and A depends on the relationship between the event’s size and the size of the interrogation point (Fig. 8.3) because: 1. If the event is smaller than the interrogation point, it is entirely excited by the radiation, and both H and A of the pulse are a direct function of the signal intensity (A ¼ H ). 2. If the event is larger than the interrogation point, the radiation excites only a part of it; it follows that H is no longer proportional to the total signal intensity, which under these conditions is proportional to A instead (A > H ).
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According to these premises, A is selected to represent the signal when the accuracy is fundamental, such as in DNA measurement in cell kinetic studies and evaluating aneuploid peaks. W defines the interval between the appearance and disappearance of the pulse and is independent of the PMT’s voltage. It depends on the speed of the flow and the threshold for signal detection and is a function of the transit time, which depends on the event’s size that generates the pulse. It follows that at the same flow rates, larger events produce a pulse with a greater W. It should be noted that impulse analysis, an analog phenomenon, hardly evaluates any additional characteristics. On the contrary, the analysis of a previously digitized impulse makes it possible to extract additional information relating to its shape, which allows distinguishing events that are not otherwise discriminable by analyzing only the characteristics of H, A, and W (Godavarti et al. 1996).
8.2.1
Pulse Analysis in Analog Systems
In analog instruments, as described in Sect. 7.2.1.6, a peak detector measures H, and an integrator measures A. W is measured either by calculating the ratio between A and H or thanks to a system clock that measures the interval between the signal’s appearance and disappearance. In these conditions, the signal is analog and remains analog until the last moment before being stored; as said before, the circuitry measures H, A, and W but can not provide information on the signal waveform. A partial exception to this rule consists of the “slit-scan” flow cytometers, designed to solve the problem of doublets in automated cytology (search of multiple pulse peaks) (Wheeless et al. 1977), to increase the information of flow karyotyping (search for centromere position based on the peak position inside the pulse) (Bartholdi et al. 1990), and to evaluate the sperm head morphology (Benaron et al. 1982). At the time, the technical problems encountered in the construction of “slit-scan” cytometers were (1) the creation of interrogation points (“slit”) small enough to scan the event for all its length and (2) the creation of circuitry suitable to manage the particular signal requirements (Wheeless 1990). During the pulse processing, the analog circuitry cannot do anything else; this refractory period is a dead time, during which no other event can be taken into consideration. Consequently, in an analog cytometer, the maximum number of events that can be analyzed per unit of time depends on the cytometer circuitry’s processing speed.
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Pulse Analysis in Digital Systems
Signal processing and pulse analysis occur in a dedicated card in digital systems, known as a Data Acquisition Board (DAQ). In the DAQ, the signal proceeds sequentially through a series of circuits mainly consisting of an analog-to-digital converter (ADC), a complex of programmable logic gates called FPGA (FieldProgrammable Gate Array), and a digital signal processor called DSP (Digital Signal Processor) (see note number 10). The ADC continuously samples the signal coming from the preamplifier board (for further information on this topic, see Sect. 7.2) according to the frequency allowed by its internal clock, and at each sampling assigns a digital value included in the range defined by the number of the bits at its disposal. All the measurements are conveyed to the FPGA, which assigns the highest sampled value to H, and reconstructs the value of A, summing all the values measured during sampling (Fig. 8.4). Besides determining the values attributed to components A, H, and W, a digital circuit “knows” the distribution of the different values of H sampled along with the entire pulse as it was itself assigning them in correspondence to each sampling performed by the ADC, and is, therefore, able to produce supplementary information concerning the shape of the signal (Zilmer et al. 1995).
Fig. 8.4 Schematic representation of the digital pulse creation. The pulse is sampled at the ADC frequency implemented on the instrument (vertical bars). At each sampling, the pulse value is digitalized by the ADC (horizontal bars, nine values in this example). The FPGA builds the pulse based on the values provided by the ADC, assigning as Area the sum of the measured values and as Height, the highest value digitized during sampling
8.2
The Pulse
8.2.2.1
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Window’s Gate and Window’s Extension
While the analog circuitry is alerted to start the measurement from a message coming from the comparator circuit, the digital circuitry has no information about it and decides that a pulse begins and ends when the value of the pulse height exceeds first in one direction and then in the other the value of a given threshold. In the Diva architecture, the system keeps recording until the signal decreases by 75% compared to the initial threshold value (Becton Dickinson 2010). The time interval during which the pulse is measured is called Window’s Gate (Fig. 8.5). Given that the FPGA boards are programmable, in some instruments, including digital cytometers built by Becton Dickinson, the duration of the Window’s Gate can be modified by the operator who can extend it, when deemed necessary, adding before and after the Window’s Gate an extra time whose cumulative measure is called Window’s Extension (Becton Dickinson 2010). A change in the duration of the Window’s Gate modifies the number of samplings taken on the pulse and, consequently, the final value attributed to Area (A), which results from the sum of the results produced by the samplings carried out, while the value of Height (H ) remains unchanged. The Window’s Gate must not be confused with the Analysis Window, definable as the fluorescence range that is theoretically measurable by an instrument in correspondence to a particular instrumental set-up (for further information, see paragraph 11.5.3). A procedure similar to Window’s extension, called System Dynamic Integrating Window, can be implemented in other digital models, as the instruments belonging to the CytoFlex group (Beckman Coulter) (Duggan 2014).
Fig. 8.5 The interval during which a digital system measures the pulse is called Window’s Gate and corresponds to when the pulse exceeds a predetermined value called the threshold. The operator can increase the Window’s Gate through the FPGA in digital instruments, adding additional times to Window’s Gate, called Window’s Extension. Among other effects, an increase in the Window’s Gate increases A’s values without modifying H’s values
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Signal Analysis
Area Scaling
The procedure known as Area Scaling is available only for some instruments and consists of modifying the value of A until it is on the scale with the value of H, which is unchangeable since it corresponds to the highest value sampled by the ADC (Perfetto et al. 2006; Hazen et al. 2018). The operator applies a coefficient, called Area Scaling Factor, to the FSC-A values to make its position on the horizontal axis correspond to the values of FSC-H on the vertical axis. Consequently, with the procedure correctly performed, a population represented by FSC-H and FSC-A resides along a diagonal drawn between the lower-left corner and the upper-right corner of the cytogram FSC-A vs. FSC-H (Fig. 8.6). The Area Scaling optimization is a prerequisite to recognizing doublets through physical parameter analysis and is particularly important in sorting procedures, where the failure to recognize doublets compromises the final quality of the separation. The Area Scaling procedure is also essential because it ensures that the measurement of FSC-A does not occur in the presence of signal saturation, a condition that cannot be ascertained only by monitoring H. The Area Scaling procedure should also be performed for the Side Scatter and all the fluorescence signals.
Fig. 8.6 Example of correct Scaling Area procedure. The correlation between the A and H components of the pulse relative to the FSC parameter resides along the cytogram’s diagonal
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The simultaneous evaluation of A, H, and W allows for identifying the events made up of aggregates and drawing an analysis and acquisition gate to exclude the undesired elements. This result can be achieved through various approaches, which can be implemented depending on the occasion.
8.2.3.1
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This approach relies on the same principles of the Scaling Area procedure. In a correctly set up system, events reside along the FSC-A vs. FSC-H cytogram’s diagonal; it follows that events outside this cluster and with FSC-A values significantly higher than FSC-H have a high probability of being doublets or aggregates (Fig. 8.7). This approach can also be followed by evaluating the A and H of the side scatter (SSC). Unlike the FSC-A and FSC-H parameters, the FSC-H and FSC-W parameters are not correlated and independent of the Scaling Area. Their combinate analysis allows the highlighting of doublets that display increased FSC-W values. By the same token, it is possible to evaluate the side scatter (SSC) instead of the forward scatter (FSC). Caution must be taken in the analysis of heterogeneous populations because the possible outliers are not necessarily doublets but single cells instead, exhibiting different physical characteristics from the other cells in the sample, as demonstrated
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Fig. 8.7 After a correctly performed Area Scaling procedure, the comparison between the A and H components of the pulse relative to the FSC parameter (panel a) allows highlighting the presence of doublets and aggregates (shown in red in the picture), identifiable as events that present A values exceeding H values. It is noteworthy that these events also present increased values for W (panel b)
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by a study in which this method allowed to discriminating between T resting lymphocytes and T cycling lymphocytes (Bohmer et al. 2011).
8.2.3.2
FL-A vs. FL-W
This conceptually similar approach allows determining doublets in cellular populations labeled with a fluorochrome for DNA. It consists of the contemporary representation of A and W relative to the fluorescence signal produced by a fluorochrome stoichiometrically bound to DNA. This method is of practical importance to distinguish G2/M phase cells from doublets/aggregates constituted by two cells in phase G0/G1. In fact, in both cases, the DNA content is double, but the width of the pulse produced by the doublets (W ) is greater than that of a single cell in G2/M; consequently, in an FL-A vs. FL-W cytogram, the doublets are immediately recognizable by their specific position and can be eliminated from the acquisition gate (Wersto et al. 2001) (Fig. 8.8).
8.2.3.3
FL-H vs. FL-W
The pulse analysis concerning the ratio between the H and W can provide information on the intracellular distribution of protein structures made fluorescent by a specific marker. It has been exploited in a series of studies encompassing (1) the evaluation of the nuclear enlargement in etoposide-treated cells (Kang et al. 2010), (2) the intracellular localization of aggregated tracking proteins (Ramdzan et al.
Fig. 8.8 Demonstration of the presence of doublets or aggregates by visualizing the relations between the A and W components of a pulse correlated to a fluorochrome’s fluorescence signal that binds DNA stoichiometrically. The doublets (in red in the picture) mimic values of FL2-A (propidium iodide) similar to those of cells in the G2/M (panels a and b) phase but show increased FL2-W values (panel b)
8.3
Dynamic Range of the Signal
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2012), (3) the quantitation of intracellular trafficking (Chia and Gleeson 2013; Chia et al. 2014; Toh et al. 2015), (4) the study on the translocation of proteins from the cytoplasm to the nucleus, or from the plasma membrane to Golgi body (Ramdzan et al. 2013; Chia et al. 2014), and (v) the quantification of endolysosomal escape of immunotoxins (Wensley et al. 2019).
8.3
Dynamic Range of the Signal
Knowing some concepts on dynamic range, dynamics of a signal, and resolution is important to understand the notion of effective resolution and identify the cause of the artifact known as the “picket fence” (Fig. 8.9). By dynamic range, we mean the ratio between the maximum and minimum values of a variable quantity. In an electronic system, and therefore in flow cytometers, the dynamic range, also known as signal-to-noise ratio (SNR), is the ratio between the maximum signal intensity sustained by that system and the background noise (Inoue and Spring 1997). By dynamic of a signal, we mean its range of variations, i.e., the number of values that H can assume within the dynamic range, i.e., the number of intervals in which H can be distributed. The dynamic range of an analog pulse is unlimited, but after the pulse’s digitization, it assumes a finite value depending on the bit number available to the ADC. By resolution, we mean the ability to detect small variations of a physical quantity under analysis. The term also defines the numerical value that quantitatively expresses this capacity. At the transducer’s output, the signal is a potential difference; it follows that in Flow Cytometry, the resolution is the voltage difference between two adjacent channels. The greater the range, the smaller the difference in voltage between adjacent channels, defined as the least significant bit (LSB). Therefore, it is immediately evident that a cytometric measurement’s resolution depends on the number of intervals, improperly called “bit density,” available for the measurement. As already pointed out, this number is proportional to the digitization process’s efficiency, i.e., the number of bits available to the ADC. Those unfamiliar with the matter might question why digital cytometers need to work with resolutions higher than 1 million channels when analog cytometers with 256 channels can accomplish their tasks quite well. The answer is that in analog circuitry, the signal is always analog before digitization and is dynamically unlimited as such. In contrast, in digital circuitry, the signal has, by definition, a finite dynamic range. In the case of its logarithmic transformation, this dynamic range may not be wide enough to occupy all the intervals available in the first decade of the scale.
Signal Analysis
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Am Cyan-A Fig. 8.9 The figure shows an example of the so-called “picket fence” phenomenon (red in the picture), consisting of the appearance of isolated peaks in the first decades of the logarithmic scale. The picket fence is shown along the whole scale (panel a) and the first decade (panel b)
8.3.1
Effective Resolution
Each bin of a logarithmic scale contains a number of linear units depending on the position of that bin on the scale; the more the bin is moved to the right, the more linear units it includes (Table 8.1). In representing a log-transformed signal, a way to ascertain the resolution available on the scale is to resolve the scale with a new unit, defined as Effective Resolution (ER), equivalent to the number of linear units available to a given bin (interval) on the scale. As an example, in a five-decade scale produced by a cytometer equipped with an 18 bit ADC, the whole scale comprises 262,144 ER
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Table 8.1 Correlation between ADC bit number, decades’ number, and number of effective resolution (ER) units available in the first decade LSB Bit ADC Bin TOT
610 μV 14 16,384
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153 μV 38.1 μV 9.54 μV 2.38 μV 16 18 20 22 65,536 262,144 1,048,576 4,194,304 Effective resolution (ER) units available in the first decade 164 655 2621 10,486 41,943 16 66 262 1049 4194 2 7 26 104 419 / 1 3 11 42 / / / 1 4
596 nV 24 16,777,216 167,772 16,777 1678 168 17
ADC analog-to-digital converter, ER effective resolution, LSB least significant bit
units, of which roughly 259,235 (262,14426,214) in the fifth decade, 23,593 (26,2142621) in the fourth decade, 2359 (2621262) in the third decade, 236 (26226) in the second decade, and 23 (263) in the first decade. Moreover, the total number of “useful” ER units available for resolving the signal should be reduced by one order of magnitude to counterbalance background noise, measurement errors, and other variables. There is a relationship between the number of bits available to the ADC, the number of decades of the logarithmic scale, and the number of theoretical intervals, i.e., bins, i.e., ER units, available in the first decade (Table 8.1). Theoretically speaking, only a 24-bin ADC can avoid the “picket fence” phenomenon on a seven-decade scale. The number of “useful” bins available should nevertheless be scaled down by one order of magnitude because of the background and measurement errors.
8.3.2
Picket Fence Phenomenon
In digital cytometers, the number of effective ER units available in the first decade of the log scale can be lower than the number of linear units theoretically assigned to that decade. As a result, some linear units of the log scale can not associate with a value; consequently, they appear “empty,” producing a graphic effect called “picket fence” (Fig. 8.9). Consultation of Table 8.1 clarifies that, with a five-decade logarithmic scale, the picket fence phenomenon occurs with ADCs having a number of bits lower than 18, to be raised to 20 because of the background and the measurement errors. In analog cytometers, the phenomenon does not occur because the log-transformed but still analog signal features a theoretically unlimited dynamic; in this circuit architecture, the digitization happens at the end, and the signal is therefore always capable of filling all the linear units provided by the log scale for its
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graphic representation. The picket fence phenomenon does not have great practical importance because it affects segments of the dynamic range that are hardly relevant in data analysis. However, interventions aimed at a cosmetic correction exist, generally consisting of the interpolation of noise in the first decade to “fill” the free spaces (jittering).
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Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay P, Roederer M (2006) Quality assurance for polychromatic flow cytometry. Nat Protoc 1(3):1522–1530 Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay PK, Roederer M (2012) Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat Protoc 7(12):2067–2079. https://doi.org/10.1038/nprot.2012.126 Perfetto SP, Chattopadhyay PK, Wood J, Nguyen R, Ambrozak D, Hill JP, Roederer M (2014) Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels. Cytometry A 85(12):1037–1048. https://doi.org/10.1002/cyto.a.22579 Ramdzan YM, Polling S, Chia CP, Ng IH, Ormsby AR, Croft NP, Purcell AW, Bogoyevitch MA, Ng DC, Gleeson PA, Hatters DM (2012) Tracking protein aggregation and mislocalization in cells with flow cytometry. Nat Methods. https://doi.org/10.1038/nmeth.1930 Ramdzan YM, Wood R, Hatters DM (2013) Pulse shape analysis (PulSA) to track protein translocalization in cells by flow cytometry: applications for polyglutamine aggregation. Methods Mol Biol 1017:85–93. https://doi.org/10.1007/978-1-62703-438-8_6 Roederer M (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45(3):194–205 Roederer M (2016) Distributions of autofluorescence after compensation: be panglossian, fret not. Cytometry A 89(4):398–402. https://doi.org/10.1002/cyto.a.22820 Toh WH, Houghton FJ, Chia PZ, Ramdzan YM, Hatters DM, Gleeson PA (2015) Application of flow cytometry to analyze intracellular location and trafficking of cargo in cell populations. Methods Mol Biol 1270:227–238. https://doi.org/10.1007/978-1-4939-2309-0_17 Wensley HJ, Johnston DA, Smith WS, Holmes SE, Flavell SU, Flavell DJ (2019) A flow cytometric method to quantify the endosomal escape of a protein toxin to the cytosol of target cells. Pharm Res 37(1):16. https://doi.org/10.1007/s11095-019-2725-1 Wersto RP, Chrest FJ, Leary JF, Morris C, Stetler-Stevenson MA, Gabrielson E (2001) Doublet discrimination in DNA cell-cycle analysis. Cytometry 46(5):296–306 Wheeless LL Jr (1990) Slit-scanning. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley, New York, pp 109–125 Wheeless LL Jr, Kay DB, Cambier MA, Cambier JL, Patten SF Jr (1977) Imaging systems for correlation of false alarms in flow. J Histochem Cytochem 25(7):864–869 Zilmer NA, Godavarti M, Rodriguez JJ, Yopp TA, Lambert GM, Galbraith DW (1995) Flow cytometric analysis using digital signal processing. Cytometry 20(2):102–117
Chapter 9
The Cytometric File
The final result of a cytometric analysis consists of a file called the “data file.” This file contains data belonging to two different groups. The first group consists of the so-called metadata, i.e., pieces of information concerning the analytical conditions (patient identification, analysis date, type of instrument, instrumental setting values, et cetera). In contrast, the second group consists of the real data, that is, the quantitative values assigned by the instrument to each of the three components (height (H), area (A), width (W)) of the pulses produced by each parameter related to each event, plus time. The data organization’s modalities in the file take the name of “standard” or “format;” the sharing of the format by files produced by different instruments ensures the portability of files between platforms and the use of specific applications designed by third parties. The most common cytometric format is the so-called FCS format, discussed extensively in the following sections. FCS does not constitute the only existing cytometric format, as there are also other formats. They include: 1. The Gating-ML standard, an XML-based file format devised to allow exchanging of some cytometric data, such as gating, compensation, and transformation data (Spidlen et al. 2015). 2. The ACS (Archival Cytometry Standard) format, which is an archive of files compressed in Zip format whose contents are linked to each other based on the conditions reported in an XML-based table also stored in the archive (Spidlen et al. 2008, 2011). 3. The NetCDF (Network Common Data Form) format, a set of applications for the processing of scientific data which is independent of a given format (Leif et al. 2009). 4. The formats currently produced by the programs used in the Imaging Flow Cytometers from Luminex (Amnis Corporation 2013), i.e.,
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(a) The rif format (raw image file), produced by the Inspire program and containing the raw data (pixel intensity data and uncorrected image data) and the instrument set-up data). (b) The daf format (data analysis file), produced by the Ideas program and containing the calculated feature values, the graphs, and the statistics. (c) The cif (compensated image file) format, produced by the Ideas program and containing the compensated data. 5. The formats currently used to store the data produced by some commercial cytometers like sraw (for files produced by Sony spectral cytometers), mqd (for files produced by the instrument Miltenyi MacsQuant), and c6, cfl, and ci (for files produced by the instrument BD Accuri™). 6. The LMD format, which stands for “list mode data,” used to store the data produced by some instruments from Beckman Coulter. 7. The FAL format, which contains the spectral data exported in ASCII by spectral or Raman cytometers. Finally, it is noteworthy that the parameters’ values can also be exported as values separated by commas, i.e., in the universally known .csv format; this solution is often adopted in Spectral and Raman Flow Cytometry to save data generated by CCD. The different formats produced by the available cytometers can be opened and read by commercial programs written for the purpose by dedicated Software Houses.
9.1
FCS Format
As previously reported, the FCS (flow cytometry standard) format consists of a series of conventions concerning the modalities with which an acquisition program must write a cytometric file (data file) containing the acquisition results. The FCS format files feature the extension “fcs,” but this extension is not necessary for an FCS file to be recognized as such and opened by its specific applications. The files produced by many hematology analyzers are also often totally or partially compliant with the FCS standard and can be opened by third-party analytical programs designed for cytometric files. Over time, the FCS format has evolved into a series of versions progressively adapted to the new technological advances; as a rule of thumb, analog instruments produce FCS files in the FCS 2.0 version, while digital ones produce FCS files from version 3.0 onwards. Regardless of the version, all FCS files contain one or more sections, called data sets; each data set contains a variable number of parts, called segments. The number of data sets and the number and function of segments can vary according to the FCS format version. In all the versions, a segment (called Text segment from version FCS 2.0 onwards) contains a series of keywords housing metadata, and another segment (called Data segment in all the versions) contains the quantitative data produced by the analysis.
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Segments
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The first version of this format was born in 1984 with the name FCS 1.0 (Murphy and Chused 1984). Of this standard, subsequently adopted and modified by the ISAC (International Society for Analytical Cytology), successive versions have appeared over time, called respectively FCS 2.0 (Dean et al. 1990), FCS 3.0 (Seamer et al. 1997), FCS 3.1 (Spidlen et al. 2010; Bray et al. 2012), and FCS 3.2 (Spidlen et al. 2020). Perhaps the most significant difference between versions is that until version FCS 2.0, the data were stored after being log-transformed and compensated, while from version 3.0 on, the data are stored “raw,” i.e., linearly amplified and not compensated (Becton Dickinson 2007). As for the FCS 3.0 version, the new features include (1) the possibility of introducing a 16-bit cyclic redundancy check (CRC) in the last two bits of the data set to identify the presence of random errors, and (2) the new maximum file size which can exceed 100 Megabytes. FCS 3.0 stores the compensation matrix in the $COMP keyword and the preferred data transformation method in the $PnE keyword, which becomes required from this version. The FCS 3.1 version includes some changes consisting (1) in the support to the Unicode format for the keywords, (2) in the deprecation of ASCII data type and the keys for the gating parameters, (3) in the addition of the keyword $PnD, which contains information on the user’s preferred display scaling, and (4) in the introduction of the $SPILLOVER keyword containing the spillover matrix. Finally, FCS 3.2 still introduces minor changes, including creating new optional keywords to better define parameters, detectors, and fluorochromes; moreover, FCS 3.2 removes the keys deprecated in the previous versions and allows the concurrent use of different data types for all the data. The FCS standard version currently in use is FCS 3.0 with its various versions; however, while FCS 1.0 version is definitively obsolete, analog cytometers are still operative in many laboratories producing FCS 2.0 files. The FCS format is backward compatible, and the acquisition programs can read all the FCS files in the previous versions, but exceptions can occur. A final terminological observation: the term “data set” or “data-set” is also often used to define the data segment content, i.e., the set of quantitative data produced by the analysis of events.
9.2
Segments
Even if an FCS data file is a continuous stream of bits, it can be functionally split into separate sections called “segments” (Fig. 9.1), whose type and number depend on the standard version. In the FCS 1 version, the mandatory segments were TEXT, DATA, and ANALYSIS, while in the following versions, the required segments were HEADER, TEXT, and DATA, to which the ANALYSIS segment and other optional segments can be added. From the version FCS 2.0 on, a cytometric file (data file) can contain
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Fig. 9.1 Schematic representation of the structure of a file in FCS 3.0 format. The file is divided into four main segments called “header,” “text,” “data,” and “analysis”
more than one data set; according to the standard, all the data sets must derive one from another, i.e., related to the same analytical run.
9.2.1
Header Segment
The HEADER segment shows the format’s version and some information regarding some files’ features, including the offset (position) in which the other segments start and end (Fig. 9.2). The HEADER segment was not present in the FCS1 version, which started directly with the TEXT segment. The length of the HEADER segment could take on any value; nevertheless, some applications open the files only if the length of their HEADER segment corresponds to precisely 256 bytes.
9.2.2
Text Segment
The TEXT segment contains metadata, i.e., information regarding the sample, the analytical run conditions, the recommended preferences for representing the data, and the number of data sets. The TEXT segment records the various pieces of information as key-value pairs, where the keyword, or key, is the label of the field containing the value (Fig. 9.3). The FCS 2.0 version allows only one TEXT segment, while the FCS 3.0 format admits an additional TEXT segment, which can only contain optional keys.
9.2
Segments
141
Fig. 9.2 Features of a cytometric file in format FCS 3.0. The header, text, data, and analysis segments are immediately evident in the menu bar, highlighted by the red frame. In the space below are the header segment’s specifications, consisting of information regarding the format’s version, the addresses of the text, data, and analysis segments, the number of data sets, and the name of the key containing compensation data (the information was extracted from the FCS file thanks to the FCSWizard software, Courtesy of Mario D’Atri)
9.2.3
Data Segment
The DATA segment contains the digitized values related to the pulse components produced by the signals evoked by the analyzed events’ parameters, plus information about the progressive chronology of their analysis. In ListMode modality, the only data storage modality allowed by FCS 3.2 version, the digitized values can be imagined as written in a spreadsheet whose columns are related to the explored parameters and whose rows are related to every single event (Fig. 9.4). As already mentioned, a cytometric file can contain more than one data set, each of which encompasses its different segments; it follows that two DATA segments can be present in a data file. This situation occurs in the data files produced by some instruments; in this case, one DATA segment contains uncompensated and linear data (“raw” data), while the other contains compensated and, if needed, logarithmically transformed data. It may sometimes be desirable to have the digitized data alone, without the rest of the information stored in the file, to process them with third-party algorithms. For this purpose, some dedicated applications can export digitized data as files in Excel or other spreadsheet formats (“csv” or comma-separated values format).
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Fig. 9.3 Features of a cytometric file in format FCS 3.0. In the space inside the red frame below the menu, it is possible to read part of the list of the keywords relative to the first of the data sets present in the file, selected thanks to the Datasets/Change DataSet/DataSet 1 command. This list contains required standard keywords (prefix $, dark red), optional standard keywords (prefix $, green), and non-standard keywords (prefix @ in this example) (information extracted from the FCS file thanks to the FCSWizard software, Courtesy of Mario D’Atri)
9.2.4
Analysis Segment
The ANALYSIS segment should contain data processing results, but conventionally it is not utilized and is generally left empty.
9.2.5
Optional Segments
The optional segment, very rarely implemented, are OTHER and CRC (cyclic redundancy check). The OTHER segment could add data in any format, but it is usually left empty. Paradoxically, the need to record additional data, such as the analytical layout characteristics, is sometimes satisfied by entering the data in another segment in violation of the format or adding external files in XML (extensible markup language) format. This last feature occurs in data files produced by the Diva software, which can be exported either as FCS files alone or as “experiments,” including the FCS files and an XML file containing the additional data (for example, gates’ coordinates).
9.3
Keywords
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Fig. 9.4 Representation of a data segment of a cytometric file in FCS format. Columns refer to the parameters, rows refer to the analyzed events (in chronological order), and cells contain the digitized value related to the parameter of each analyzed event. It is possible to export the data for analysis with other programs (information extracted from the FCS file thanks to the FCSWizard software, Courtesy of Mario D’Atri)
The CRC segment was created to contain a 16-bit value (checksum) used to verify the absence of errors in the file. This value, used as a divider for the data, has been chosen to generate a remainder of 0. After any transmission or manipulation, the checksum algorithm re-checks the data, and any result other than 0 proves the presence of data corruption.
9.3
Keywords
Any single piece of information concerning the analytical and pre-analytical variables (metadata) is associated in a biunique way with a specific keyword in the TEXT segment. Given that all the information of the same nature—for example, the patient’s name—are “announced” by the same keyword, it is possible to identify the same piece of information in every cytometric file, regardless of the cytometer generating that file. The keywords are divided into standard keywords, starting with $, and non-standard keywords, which are not subject to any obligation other than not starting with $. The type and number of standard keys depend on the particular
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version of the FCS standard. Non-standard keywords are format-independent and have variable meanings depending on the programmer responsible for writing the acquisition software. Knowing the meaning of the keywords is also crucial for operators not directly involved in Flow Cytometry’s IT aspects. The availability of applications capable of editing the keywords and their values makes it necessary to understand the information’s nature. A rudimentary knowledge of their peculiarities also allows the operators to troubleshoot the problems connected to data portability and definitively eliminate the sensitive data. Likewise to the segments, the keywords have changed over time with new versions of the standard. In comparison with FCS 1.0 version, the most significant changes in FCS 2.0 version fundamentally concern the keywords that regulate the writing modality of the DATA segment (substitution of $ASC with $DATATYPE) and the keys that regulate the data representation modalities (establishment of $PnE) (Dean et al. 1990). The addition of the key $BYTEORD is particularly relevant, introducing portability between various platforms, as it allows the reading of files created in the context of different operating systems (Dean et al. 1990).
9.3.1
Standard Keywords
Standard keywords are of two types, i.e., required (R) and optional keywords (O).
9.3.1.1
Required Keywords
The required keywords take this name because they must always be present in an FCS format compliant file. In the FCS 3.0 format, the keys are the following: 1. $BEGINANALYSIS, which shows the position in which the ANALYSIS segment begins. 2. $BEGINDATA, which shows the position where the DATA segment begins. 3. $BEGINSTEXT, which shows the position where an additional TEXT segment begins. 4. $BYTEORD, which shows how the computer creator of the file used bytes to write numbers greater than 255. 5. $DATATYPE, which shows the typology with which the values contained in the DATA segment are written. 6. $ENDANALYSIS, which shows the position in which the ANALYSIS segment ends. 7. $ENDDATA, which shows the position where the DATA segment ends. 8. $ENDSTEXT, which shows the position in which the TEXT segment ends. 9. $MODE, which shows the way the data was acquired and subsequently stored (L list mode, C unrelated parameters, U parameters related to pairs).
9.3
Keywords
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10. $NEXTDATA, which shows the position of a data segment possibly following the first one. 11. $PAR, which shows the number of parameters used. 12. $PnB, which shows the number of bits or characters with which the values of a given parameter n are written in the DATA segment. 13. $PnE, which shows the amplification used during the acquisition of a given parameter n. 14. $PnR, which reports the maximum value that a given parameter n can assume, generally equal to the number of intervals produced by the digitization process. 15. $TOT, which shows the number of events stored in the data set. FCS 3.1 version keeps all these keywords with the addition of $PnN, which contains the abbreviated name for the parameter n.
9.3.1.2
Optional Keywords
Optional keywords may be missing without violating the FCS standard. The most important of them in the version FCS 3.0 are the following: 1. $ABRT, which shows the number of events lost due to coincidences. 2. $BTIM, which shows the time marked by the computer clock at the time of the start of data acquisition. 3. $CELLS, which contains a description of the measured events. 4. $COM, which contains a possible comment. 5. $COMP, which shows the matrix used for fluorescence compensation; this key is present in the FCS3.0 version. 6. $CYT, which shows the type of cytometer. 7. $CYTSN, which shows the serial number of the cytometer. 8. $DATE, which shows the date of acquisition. 9. $ETIM, which shows the time marked by the computer clock at the time of the data acquisition end. 10. $EXP, which shows the name of the researcher who started the experiment. 11. $FIL, which shows the name of the file containing the data set. 12. $GATE, which shows the number of parameters on which the gate was traced. 13. $GATING, which shows the combination of regions used to trace the gate. 14. $INST, which shows the name of the institution in which the data were acquired. 15. $LOST, which reports the number of lost events because, during the acquisition, the computer CPU was performing other tasks and could not cope with them. 16. $OP, which shows the name of the operator. 17. $PnF, which shows the characteristics of the optical filter intended for a specific parameter n. 18. $PnG, which shows the gain of the amplifier used for the acquisition of a given parameter n; if $PnG is not specified, and $PnE suggests a linear amplification ($PnE/0,0 /), then the value of $PnG should be set to 1 (Spidlen 2008).
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19. $PnL, which shows the wavelength of the line used to excite the probe associated with a given parameter n. 20. $PnN, which shows the abbreviated name attributed to a specific parameter n. 21. $PnO, which shows the power of the line used for the excitation of a given parameter n. 22. $PnS, which shows the name given as a label to the scale of a histogram for a given parameter n. 23. $PnT, which shows the type of sensor used to detect a given parameter n. 24. $PnV, which reports the voltage supplied to the sensor used to detect a given parameter n. 25. $PROJ, which shows the name of the experimental project. 26. $SMNO, which shows the sample identification. 27. $SRC, which contains information on the origin of the sample (patient’s name, cell type, presumptive diagnosis, et cetera). 28. $SYS, which reports the type of computer and operating system used in the acquisition. 29. $TIMESTEP, which shows the hour increase used for the “time” parameter. 30. $TR, which reports the parameters used as triggers, and their threshold. FCS 3.1 version keeps all these keywords with the addition of some other, among which: 1. $LAST_MODIFIED, which reports the day and time of the last change made to the data set. 2. $LAST_MODIFIER, which shows the name of the person who made the last change to the data set. 3. $ORIGINALITY, which contains information regarding a possible modification of the data set made after the acquisition. 4. $PLATEID, which shows the identifier of an eventual well plate. 5. $PLATENAME, which shows the name of an eventual well plate. 6. $PnCALIBRATION, which reports the conversion of the values of a given parameter n into defined units (for example, MESF). 7. $PnD, which shows the suggested scale for displaying a given parameter n. 8. $VOL, which shows the volume of the sample aspirated during data acquisition. 9. $WELLID, which shows the identification of the plate well relative to the acquisition. Moreover, the FCS 3.1 version replaces the optional keyword $COMP, which houses a compensation matrix, with the optional keyword $SPILLOVER, which houses a spillover matrix. It is hardly necessary to remember that the compensation matrix is the inverse of the spillover matrix (Spidlen et al. 2010) (for further information on this topic, see Sect. 19.1.3).
9.3
Keywords
9.3.2
147
Non-standard Keywords
Non-standard keywords, also known as custom keywords, are not provided for by the FCS standard; their presence and function depend only on the programmer’s imagination responsible for the software that generates the file. The FCS format allows their presence, with the only obligation to start with a character other than $, reserved for standard keys. The non-standard keys are various; some have no initial distinctive character (EXPERIMENT NAME, WINDOW EXTENSION, SPILL, et cetera), whereas others may have identification marks such as the @ character or the #character; still, others use combinations of characters like flowCore_ $, BD $, and GTI $. Some of them satisfy needs not met by the current standard versions and can be found in fcs files produced by instruments from different manufacturers. Important and recurrent non-standard keywords are: 1. The key PnDISPLAY, which reports the suggested scale for displaying a given parameter n and can allow the choice between the logarithmic scale and the linear scale; this key has the syntax PnDISPLAY/string, where n is the parameter, and the string can be either “LIN” or “LOG.” 2. The key LASERnDELAY, which contains information regarding the time delay; in this key, n is the progressive number attributed to each laser according to the sequence with which an event passes through the various interrogation points. 3. The keywords SPILL and SPILLOVER, which house the compensation values as elements of a compensation matrix (Qian et al. 2012).
9.3.3
Relationships Between Keywords and Compensation Procedures
The keywords reporting information about compensation procedures depend on the format versions. These keywords are the keyword $DFCmn, operating in the format FCS 1.0 (Murphy and Chused 1984), the keyword $DFCiTOj, operating in the format FCS 2.0 (Dean et al. 1990), the keyword $COMP, operating in the format FCS 3.0 (Seamer et al. 1997), the keyword $SPILLOVER, operating in the format FCS 3.1 and FCS 3.2 (Spidlen et al. 2010, 2020), and the non-standard keywords SPILL, SPILLOVER, and INFINISPILL. Other custom keywords with the same functions can exist. All these keywords are optional; it follows that compensation information can be missing, and this is the rule for data sets from analog instruments, which do not need it since the compensation is set beforehand by hardware. Besides the already mentioned SPILL and SPILLOVER, the most important compensation-related keywords are $COMP and $SPILLOVER. $COMP keyword is an optional standard keyword in the FCS 3.0 format, whose syntax is $COMP/n, f1, f2, f3, .../(Seamer et al. 1997; Seamer 1997). Its value
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consists of a series of numbers divided by commas, which represent a square matrix with n dimensions (n n), where n is the number of parameters to be compensated, and f1, f2, and f3, ... are floating-point numbers representing the elements of the compensation matrix (Qian et al. 2012). It is noteworthy that there is no information on the parameters’ names. This limitation can create difficulties in evaluating complex compensation matrices such as those needed in polychromatic analyses. $SPILLOVER keyword is an optional standard key in the FCS 3.1 and FCS 3.2 format, conceived to overcome the limitations of the $COMP keyword, whose syntax is $SPILLOVER/n, string1, string2, ..., stringn, S11, S12, ..., S1n, S21, S22, ... Snn/(Spidlen et al. 2009, 2010). The value of the key $SPILLOVER consists (1) of a positive integer number n, which represents the number of parameters in the matrix, (2) of at least two strings, which represent the name of at least two out the parameters specified in the keyword $PnN, and (3) of at least four floating-point numbers, each definable as Sij and representing the spillover matrix components, where “j” is the percentage of the signal that must be subtracted from the “i” signal (Spidlen et al. 2009). Of note, the $SPILLOVER keyword shows the spillover matrix’s values, while the $COMP keyword shows the compensation matrix instead (for further information on this topic, see Sect. 19.1.3).
References Amnis Corporation (2013) IDEAS – image data exploration and analysis software user's manual – version 6. White Paper. https://www.pedsresearch.org/uploads/pages/img/IDEAS_User_Man ual_6.pdf. Accessed 3 Feb 2022 Becton Dickinson (2007) BD FACSDiva software 6.0 reference manual. White Paper. https://www. bu.edu/flow-cytometry/files/2010/10/BDFACSDivaSoftwareReferenceManual.pdf. Accessed 8 Jan 2021 Bray C, Spidlen J, Brinkman RR (2012) FCS 3.1 implementation guidance. Cytometry A 81(6): 523–526. https://doi.org/10.1002/cyto.a.22018 Dean PN, Bagwell B, Lindmo T, Murphy RF, Salzman GC (1990) Data file standard for flow cytometry. Cytometry 11(3):323–332 Leif RC, Spidlen J, Brinkman RR (2009) Cytometry standards continuum – art. no. 68590Q. SPIE BIOS Proceedings. https://doi.org/10.1117/12762514 Murphy RF, Chused TM (1984) A proposal for a flow cytometric data file standard. Cytometry 5(5):553–555 Qian Y, Liu Y, Campbell J, Thomson E, Kong YM, Scheuermann RH (2012) FCSTrans: an open source software system for FCS file conversion and data transformation. Cytometry A 81(5): 353–356. https://doi.org/10.1002/cyto.a.22037 Seamer L (1997) Data file standard for flow cytometry, FCS 3.0. Curr Protoc Cytom Chapter 10:10– 12 Seamer LC, Bagwell CB, Barden L, Redelman D, Salzman GC, Wood JC, Murphy RF (1997) Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 28(2):118–122 Spidlen J (2008) $PnG keyword in FCS3 spec. Purdue Cytometry Discussion List. https://lists. purdue.edu/pipermail/cytometry/2008-November/036241.html. Accessed 26 Nov 2018 Spidlen J, Leif RC, Moore W, Roederer M, Brinkman RR (2008) Gating-ML: XML-based gating descriptions in flow cytometry. Cytometry A 73A(12):1151–1157
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Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Hyun B, Hubbard M, Lange S, Lefebvre G, Leif R, Novo D, Ostruszka L, Treister A, Wood J, Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR (2009) Data file standard for flow cytometry version FCS 3.1 normative reference. White Paper – ISAC Data Standards Task Force. http://isac-net.org/ Resources-for-Cytometrists/Data-Standards/Data-File-Standards/Flow-Cytometry-Data-FileFormat-Standards.aspx Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Gorombey P, Hyun B, Hubbard M, Lange S, Lefebvre R, Leif R, Novo D, Ostruszka L, Treister A, Wood J, Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR (2010) Data file standard for flow cytometry, version FCS 3.1. Cytometry A 77A(1):97–100 Spidlen J, Shooshtari P, Kollmann TR, Brinkman RR (2011) Flow cytometry data standards. BMC Res Notes. https://doi.org/10.1186/1756-0500-4-50 Spidlen J, Moore W, Brinkman RR (2015) ISAC’s Gating-ML 2.0 data exchange standard for gating description. Cytometry A 87(7):683–687. https://doi.org/10.1002/cyto.a.22690 Spidlen J, Moore W, Parks D, Goldberg M, Blenman K, Cavenaugh JS, Brinkman R (2020) Data file standard for flow cytometry, version FCS 3.2. Cytometry A 99(1):100–102. https://doi.org/ 10.1002/cyto.a.24225
Chapter 10
Data Transformation
The analysis programs perform a series of routine operations on the data consisting of their transformation, representation, and analysis, which can be carried out at various levels of complexity according to the needs. These three phases are practically consensual but will be treated separately for didactic purposes. As for the transformation, it should be remembered that the analog instruments— when required—perform the transformation of data via hardware, as in the case of the notorious logarithmic transformation. No further transformations are usually applied to already log-transformed data. It follows that data transformation is computationally applied only to linearly amplified and not manipulated data sets, such as those produced by digital cytometers (theoretically, “raw” data sets can also be produced by analog cytometers working in a linear amplification regime without the intervention of any accessory circuit). Besides the logarithmic transformation, many transformations have been made available over time, most conceived to overcome the issues related to the former. These algorithms rely on different mathematical models, including (1) the addition of a constant to the data set before its log transformation (log (x + c)) (Durbin and Rocke 2003) or (2) the inverse hyperbolic sine transformation (Bagwell 2005; Parks et al. 2006; Battye 2006; Herzenberg et al. 2006; Trotter 2007; Bagwell et al. 2016).
10.1
Logarithmic Transformation
Logarithmic transformation produces the same results carried out by the logarithmic amplifiers but without the unavoidable inaccuracies of these devices (for further information on this topic, see Sect. 7.2.1.4). Unfortunately, the logarithmic transformation is unfit, for its nature, to manage the negative values produced by the compensation procedures or directly from the hardware. As elsewhere stated (see
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Sect. 11.1.2), these values, either logarithmically transformed or amplified, tend to occupy the first available channels, perturbing the negative distribution.
10.2
Log-Like Transformations
The functions belonging to this group are variously based on the inverse hyperbolic sine transformation and share a fundamental concept: they modulate the transformation of data according to data value so that for values greater than a given threshold, also known as the inflection point, the algorithm transforms the data in a logarithmic manner, while for smaller values, the algorithm transforms the data in a linear manner (Bagwell 2005) (Fig. 10.1). The most interesting log-like transformations applied to Flow Cytometry data sets are glog, Hyperlog, Logicle, and Vlog algorithms. Hyperlog algorithm (Parks et al. 2006) is also available in FlowJo and Diva software, marketed by Becton Dickinson,
Fig. 10.1 Example of different methods used for signal representation. In panel b, the signal is logarithmically transformed on the whole scale. This transformation causes the crowding of events in the first channels and the picket fence phenomenon due to an ADC not performing enough to distribute the signal in all the intervals available in the first decade. In panel a, the signal is transformed through the Hyperlog algorithm. This transformation keeps a logarithmic transformation on the right side of the inflection point (in red in the picture). Still, it adopts a linear transformation to the left, allowing the correct management of negative values, the disappearance of the picket fence, and the easy visual determination of the negative control distribution
10.2
Log-Like Transformations
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Fig. 10.2 Representation of the bivariate analysis of CD4 APC vs. CD8 AF700 in a compensated system. The data is transformed in a logarithmic (panel a) or hyperlogarithmic (panel b) way. Only the hyperlog transformation allows ascertaining the various clusters’ distribution visually
while Logicle (Bagwell 2005) and Vlog (Bagwell et al. 2016) algorithms are available in the WinList software, marketed by Verity Software House. A Logicle Scale is also available in Kaluza, a software marketed by Beckman Coulter. Hyperlog and Logicle produce superimposable results, of which the most relevant is the representation of the negative distribution. Given that the determination of the location measures of the negative component is of the utmost importance in the compensation procedures, this type of transformation is necessary to correctly analyze the results obtained through polychromatic techniques (Figs. 10.2 and 10.3). The choice of the transition point between lin and log transformation can be under the operator’s control, and special attention must be paid to the remaining space at the left of the negative, which must be as small as possible. This precaution avoids the compression of the decades at the right of the negative, which could hamper the visual inspection of the data distribution; in this regard, the visual comparison between different histograms only makes sense when they share the same transition point. Another interesting algorithm is VLog, which behaves like the popular hyperbolic sine type of transform under certain conditions and appears especially able to stabilize the basic components of measurement variability. VLog appears particularly interesting at present when the availability of extremely high-performance ADCs allows the representation of data in scales with a variable number of decades because it consents to compare instruments equipped with ADCs with different bit numbers.
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Fig. 10.3 The figure shows the behavior of a 7-peak rainbow standard in the PE-CY7 channel after subtraction of the PE spillover. The data are transformed in a logarithmic (panel a) or hyperlogarithmic way (panel b). Only the hyperlog transformation allows ascertaining the various clusters’ distribution visually
10.3
Polynomial Transformation
Another sometimes used transformation is the so-called polynomial transformation, which expands the lower range at the expense of the higher range. This transformation usually applies to physical parameters in data sets produced by analog machines, which traditionally store those values linearly amplified. In peripheral blood analysis, this transformation aims to better separate lymphocytes and monocytes and even between granular and non-granular lymphocytes (Terstappen et al. 1990) (Fig. 10.4). Polynomial transformation is available in some programs, including Infinicyt (Omnicyt) and Paint-A-Gate (Leukobyte).
References
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Fig. 10.4 Example of a polynomial transformation of the physical parameters expressed by the main cell subsets in a normal subject’s peripheral blood. The traditional image connected to the linear transformation (panel a) is transformed (panel b), improving the resolution between clusters of events with low side scatter values, such as lymphocytes and monocytes. Cytograms were created by the software Paint-A-Gate (Leukobyte)
References Bagwell CB (2005) Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64A(1):34–42 Bagwell CB, Hill BL, Herbert DJ, Bray CM, Hunsberger BC (2016) Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry. Cytometry A 89(12): 1097–1105. https://doi.org/10.1002/cyto.a.23017 Battye FL (2006) A mathematically simple alternative to the logarithmic transform for flow cytometric fluorescence data displays (abstract). Cytometry 69A(5):413–414 Durbin B, Rocke DM (2003) Estimation of transformation parameters for microarray data. Bioinformatics 19(11):1360–1367 Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR (2006) Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 7(7):681–685 Parks DR, Roederer M, Moore WA (2006) A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A 69(6): 541–551 Terstappen LW, Mickaels RA, Dost R, Loken MR (1990) Increased light scattering resolution facilitates multidimensional flow cytometric analysis. Cytometry 11(4):506–512 Trotter J (2007) Alternatives to log-scale data display. Curr Protoc Cytom 42:10–16
Chapter 11
Data Representation
The graphical representation of the data aims at providing the observer with information on the distribution of the parameters of interest within the population under analysis. Data representation can be mono-parametric, in which case it generates histograms, or bi-parametric, in which case it generates a two-dimensional histogram, also known as cytogram. Data representation of multiparametric analysis provides a series of cytograms linked together, representing all the possible combinations between the analyzed parameters. The number of cytograms produced by a multiparametric analysis is n2n, where n is the number of parameters explored. However, half of the cytograms are redundant, as the FLx vs. FLy does not provide further information than its counterpart, Fly vs. Flx. Come as it may, the number of cytograms to be analyzed constitutes one of the bottlenecks of multiparametric cytometry and opens the way to a series of algorithms capable of automatically performing most of the analytical work.
11.1
Histogram
The histogram is the traditional way of graphic representation of mono-parametric data. It results from the association between the intensity of a given parameter’s expression, placed on the abscissa, and the number of events, placed on the ordinate, expressing the parameter at that intensity. As for every histogram, a histogram produced by a cytometer is the graphical representation of that parameter’s actual probability function (APF) (Novo and Wood 2008), i.e., its distribution within the explored population. The distribution can be described by a series of measurements, classifiable as location and spread measurements (for further information on this topic, see Sects. 23.2 and 23.3); the visual inspection of a histogram allows an immediate qualitative assessment of the main characteristics of the distribution that it describes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_11
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The following equations describe the relationship between linear units and channel numbers a ¼ 10ðb=xÞ and b ¼ x log ðaÞ, where a is the value of the linear unit, b is the channel number, and x is the scale factor, obtained by dividing the number of available channels by the number of available logarithmic decades, which is equivalent to the number of channels available for a decade (Schmid et al. 1988).
11.1.1
Histograms of Lin Amplified Data
The representation of linearly acquired data generates a histogram where the horizontal axis intervals, called linear channels, define the smallest interval into which the scale can be divided. All linear channels identify a constant value range (Fig. 11.1). As anticipated elsewhere, the unit relative to the linear scale is the channel number, i.e., one of the possible intervals to which the ADC can assign a value. In turn, the channel is the smallest interval in which the histogram’s linear scale can be divided, and the total number of available channels is defined by the number of bits of the ADC. Sometimes, the channel number is also referred to as “relative brightness.” With the technology’s progress, the number of channels has dramatically grown from 256, a typical value allowed by analog circuitry with 8-bit ADCs, into 106 and beyond, a typical value allowed by digital circuitry with 24-bit ADC. Consequently, obtaining an adequate graphic representation has required the data to be displayed at a lower resolution than the native one, i.e., on fewer channels than theoretically possible (Novo and Wood 2008). This procedure is done by binning, according to which a data pre-processing operation assigns to each new interval, or “bin,” a representative value of the data in multiple adjacent channels, such as their central value (Hunsberger et al. 2003). The new number of intervals to represent the data depends on variables, including the sample size (Scott 1979). It is hardly necessary to observe that, similarly to what is foreseen by the Nyquist-Shannon theorem for signal sampling, an excessive reduction in resolution can hinder the recognition of adjacent peaks. The histogram relating to a linearly amplified population has the same coefficient of variation regardless of the gain but modifies its shape as a function of the position engaged on the scale. It appears progressively wider, moving from the left to the
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Histogram
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Fig. 11.1 Example of scales used for representing the signal. In analog systems, the type of scale (lin or log) depends on the type of amplification selected during acquisition, while in digital systems depends on the transformation algorithm applied to data. Panel a demonstrates a logarithmic scale, resolved in decades, each containing a variable number of intervals said linear units (for “linear unit,” see also Sect. 11.1.2). Panel b demonstrates a linear scale resolved in a given number of equal intervals called linear channels
right, while the histogram relating to a logarithmic amplified population has the same appearance regardless of the point on the logarithmic scale on which it resides. This phenomenon is because the number of channels in a linearly amplified histogram is multiplied by the same factor, increasing the ratio between the last and the first channel. On the contrary, in logarithmic amplification, the ratio between the first and last channel is always the same regardless of the scale’s position. Consequently, the appearance does not change.
11.1.2
Histograms of Log Amplified/Transformed Data
The representation of a signal acquired or amplified in a logarithmic way generates a histogram whose horizontal axis is resolved in a logarithmic scale (Fig. 11.1).
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The unit relative to the logarithmic scale is the linear unit, also known as the “linear value.” Calling units in logarithmic scale “linear units” can create confusion with units originating in linear scale; this unfortunate choice of terms was made in the 80s of the past century and has never been amended. The linear unit is one of the progressive numerical units included in the logarithmic decades. As an example, if 100 is, as usual, the initial scale value, then linear units span 1–10 in the first decade, 11–100 in the second, 101–1000 in the third, 1001–10,000 in the fourth, et cetera. It follows that the number of linear units in a bin depends on the position the bin occupies on the scale. This situation explains the low use of indirect staining procedures in flow cytometry immunophenotyping: the gain of the specific signal dependent on the greater number of fluorophores does not significantly modify the position of the histogram relating to positive events against a probable substantial increase in the background signal. The number of linear units and the number of decades depends on the instrument’s resolution. In analog instruments where logarithmic amplification is performed via hardware and the number of bits available to the ADCs ranges from 8 to 10, the number of decades is generally between three and a half and four and is pre-set by the acquisition program. In digital instruments, the number of bits available for the ADC can be higher than 16 million (24-bit ADC); consequently, they can represent the data with logarithmic scales up to nine decades. These solutions allow data to be on the scale without intervening with the detectors’ power supply or gain. However, the number of available decades exceeds the signal dynamics, leaving some empty decades in the left part of the scale. Furthermore, analyses performed on different instruments and represented with a different number of decades are difficult to compare, and some methods have been devised to overcome this problem. These methods include (1) the elimination of the decades to the left of the first decade containing useful values, (2) data-zooming (zooming on a specific segment of the histogram), and (3) application of the VLog function, which fits the number of represented decades to the dynamics of the signal (Bagwell et al. 2016). In some data analysis programs, it is possible to change the number of decades on which the signal is distributed (Fig. 11.2), a goal that can also be achieved by manipulating the variables of the standard key $PnE. It is noteworthy that this function does not change the resolution, which, in this case, only depends on the number of bits available to the ADC. Moreover, even if the number of decades can artificially increase, it cannot grow indefinitely under the penalty of losing resolution in the lowest decades. Suppose 100 is the minimum bin number needed to obtain a satisfactory resolution in the first decade, which most frequently ranges from 100 to 101 (the choice of this number for the first decade depends on the fact that the necessary bit density is greater in the real world than expected theoretically because of the measurement errors and background noise). The only way to achieve this result is to ensure a theoretical bit density of 17 bits for the four-decade dynamic range, 20 bits for the five-decade dynamic range, 24 bits for the six-decade dynamic range, and so on (see Table 8.1).
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Histogram
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Fig. 11.2 Result of the modification of the number of decades. The option increases the number of decades available to the scale (four in panel a, five in panel b, and six in panel c) but does not change the resolution
The use of logarithmic transformation, both hardware, and software, generates a series of noticeable effects since it successfully manages the broad dynamic range of immunophenotypic analyses and tends to correct the heteroskedasticity of the signal. Moreover, since the distribution of most biological parameters tends to be log-normal (Heath 1967; Sweet et al. 1981; Gandler and Shapiro 1990; Limpert et al. 2001; Diwakar 2017), the histograms resulting from immunophenotype studies tend to assume a Gaussian (normal) distribution after logarithmic amplification (Watson and Walport 1985). This condition enables the visual recognition of the parameter distribution and makes it possible to compare the various distributions using parametric statistical methods. However, it must be kept in mind that, despite these theoretical premises, the distributions of some parameters amplified in this way are often remarkably asymmetrical (skewed). Unfortunately, the logarithmic function has a serious limitation since it cannot manage zero or negative numbers. This feature generates a series of artifacts, which occur when the signal destined for logarithmic amplification is close to zero or negative because of a series of conditions, including the spillover correction procedures and an excessively aggressive action exerted by the baseline restorers (Wood 1998) (for further information on this topic, see Sect. 7.2.1.1). Logarithmic amplifiers in analog cytometers cannot process signals near or below zero, nor algorithms in digital cytometers can log-transform these values, which in both cases pile up in the first channels of the histogram. This condition alters the graphical representation of the parameter distribution and, in some cases, generates histograms with the negative component characterized by a false bimodality, susceptible to being interpreted by inexperienced operators as a weak positivity (Herzenberg et al. 2006) (Fig. 11.3).
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Fig. 11.3 Graphic representation of data acquired/transformed in a logarithmic way relative to a negative population of events. The first channels in region P3 show a high number of counts related to the events with values near or below zero. In the specific case, the events in question constitute more than half (57.6%) of the negative events under analysis. The representation of the logarithmically transformed values produces a histogram with a completely artifactual bimodal distribution, which is not representative of the actual distribution
11.1.3
Histograms of Log-Like Transformed Data
The representation of a log-like transformed data generates a histogram whose horizontal axis is resolved in two different spaces, whose border is a so-called inflection point. The scale is logarithmic to the right of the inflection point, while to the left, it is linear. The scale on the left admits the presence of negative values and resolves the negative component of the test according to a distribution centered on the zero value (see Fig. 10.1). This solution allows the correct management of negative values, unveils the effective distribution of the test’s negative component, and prevents the “picket-fence” effect.
11.2
Cytograms
Cytograms, also known as two-dimensional histograms, are the graphical representation of two parameters’ distribution. Cytograms are similar to a Cartesian plane, in which (1) each axis is related to a parameter and (2) each point is related to an event. Each axis reports the scale (lin, log, or hyperlog/logicle) provided by the algorithm used to transform the data; each point corresponds to an event whose position in the plane depends on its values, defined by its orthogonal projection on the scale of the related parameter. It is noteworthy that a data set, no matter how high the number of parameters, can always be expressed with a series of cytograms that combine the various parameters until all possible combinations are displayed. The selection of a cluster in any cytograms allows its identification in all the other combinations, enabling a practical approach to a visual multivariate analysis of the entire data set. Moreover, the
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Cytograms
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different clusters can be assimilated to sets and subjected to Boolean algebraic operations (for further information on this topic, see Sect. 12.3.1); this approach is of the utmost utility in evaluating the minimal residual disease. Depending on the chosen graphical mode of data representation, the cytogram can take the appearance of points (dot plot), contours (contour plot), shades of gray, false colors, et cetera.
11.2.1
Representation by Dots (Dot Plot)
In dot plot representation, each event corresponds to a dot (Fig. 11.4). Consequently, a heterogeneous population resolves into a series of aggregates of points, or clouds, which can be defined more correctly as clusters. Each cluster is the expression of the aggregation of individual events that are in some way united by a certain degree of similarity or of proximity to a location measurement attributed to the cluster itself; in the context of cellular analysis, each cluster generally coincides with a cell population characterized by its own specific biological identity. Knowing every dot’s position and the distances between the various dots is preliminary to a computational artificial intelligence approach, allowing the automatic analysis of data using data mining techniques, including supervised and unsupervised clustering techniques. The graphical representation of data by dots is intuitive and immediate.
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Fig. 11.4 Dot plot graphic representation. Each dot corresponds to an event depicted on the Cartesian plane in a position that depends on its values and can be inferred by projecting the dot on the relative parameters’ axes
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Data Representation
Fig. 11.5 An example of overplotting. The increased number of the represented events progressively occupies almost all the free space available in the cytogram. In the example in question, the number of events represented progressively varies from 10,000 (panel a) to 100,000 (panel b) and 500,000 (panel c)
Fig. 11.6 Effect of the variation of the number of pixels (density) associated with single dots, decreasing from left to right. The total number of represented events does not change, but the decrease in the pixels attributed to each event causes a graphic thinning effect that can facilitate the analysis of samples affected by overplotting. On the other hand, an excessive decrease in the number of pixels can hamper the appreciation of low-numbered clusters
The practical limitation of dot-based cytograms is that the representation of an excessive number of events crowds the events with similar values into the same space until complete saturation (Fig. 11.5). Even though every single event recorded in the data set maintains its individuality, the dots confluence produces a homogeneous blackening of the spaces, which can prevent the visual assessment of the actual parameters’ characteristics and distribution. This phenomenon is called overplotting and can be counteracted by decreasing the number of dots displayed together or the number of pixels associated with each dot. Decreasing the number of pixels increases the graphic representation’s resolution, making visible clusters otherwise superimposed (Fig. 11.6). It is customary to represent a certain number of dots simultaneously (i.e., 5000–10,000) during sample acquisition, which refreshes to monitor acquisition regularity and prevent overplotting.
11.2
Cytograms
11.2.2
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Representation by Contours (Contour Plot)
The contour plot represents not single events but levels, i.e., the points in which the events counts reach the same pre-determined value. Given that a line connects these points, the contour plot represents each cluster with a series of concentric lines similar to the iso-elevation curves of a topographic map (Fig. 11.7). The pre-determined value of the first level and the relationships between levels are often under the operator’s control, who can choose the data representation methods according to the number of events, avoiding underestimating the presence of low-numbered clusters. From a computational point of view, the most frequently used methods are the linear density method (linear density), the logarithmic density method (log density), and the equal probability method (probability) (Moore and Kautz 1986; Becton Dickinson 1998, 2007) (Fig. 11.8). Moreover, the representation of a contour plot can make use of a series of artifices, including a greater or lesser degree of rectification of the contours, called “smoothing,” the attribution to concentric zones of different intensity or tonalities of the same color, as well as the isolated or combined representation of contour lines and color tones. Another essential tool is the visualization of the outliers, i.e., events that are not numerous enough to cause the relative isometric line’s appearance under the preselected conditions (Fig. 11.9). Consequently, contour representation should always be performed together with outliers’ visualization, under the penalty of missing small populations of events. The linear density method calculates the different levels of the contours as percentages of the maximum number of events, i.e., percentages of the peak height. Under the operator’s control, the “percentage of linear density” variable calculates a
Fig. 11.7 Graphic representation of contours with (panel a) and without (panel b) the presence of outliers. Each contour corresponds to a plane region where the counts of events reach the same pre-determined value
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Fig. 11.8 A comparative example of three different algorithms for tracing contour applied to the same file. The algorithms used are linear density (line A), logarithmic density (line B), and probability (line C). The starting contour’s basic data percentage is specified at the top above each competence column. Linear density and probability methods provide similar results and tend to over-represent the levels below the selected variable
Fig. 11.9 Graphic representation of contours with (panel a) and without (panel b) the presence of outliers. The non-representation of the outliers is likely to prevent the recognition of low-numbered clusters (arrows)
starting contour line (peripheral or external) representing a percentage of data equal to half the value set for the variable itself. From this peripheral contour line, centripetal contours branch off equally spaced along the vertical axis z, each
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Cytograms
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containing a percentage increase in the maximum number of events equal to the variable’s value chosen. This number is equal between the different levels, which keep being drawn until event exhaustion. This technique is also known as “geographical” or “topological survey” and tends to under-represent the levels below the selected variable.
11.2.2.1
Logarithmic Density
The logarithmic density method calculates the different levels of the contours as percentages of the maximum number of events depicted, i.e., percentages of the peak height. Under the operator’s control, the “percentage of log density” variable calculates a starting (central or internal) contour line representing a percentage of data equal to the variable’s value chosen. From this central contour, centrifugal contours depart, representing 50% of the events represented by the previous contour, which keep being drawn until they reach the threshold value of 1. This technique tends to over-represent the levels below the selected variable.
11.2.2.2
Probability
The probability method , or equal probability, calculates the contours’ different levels as percentages of the total number of events depicted. Under the operator’s control, the “percentage of probability” variable calculates a starting contour line (peripheral or external), representing a percentage of data equal to half the value of the variable’s value chosen. From this central contour, centripetal contours start, each related to a percentage of events equal to the value set for the variable. The new levels keep being drawn automatically until event exhaustion. This method gives very similar results to the linear density method.
11.2.3
Representation by False Colors or Gray Tones
The false-color cytogram is a conceptual variant of the contour plot, in which the various levels are defined not by lines but by the borders between areas of different colors. The colors attributed to the interposed areas between the various levels are chosen in such a way as to convey the feeling of low numbers (cold colors, more intense shades) or high numbers (warm colors, less intense shades) (Fig. 11.10). The result is comparable to that produced by the graphic representation of scintigraphy, which distinguishes high uptake areas from those at low uptake thanks to the color adopted for their representation. The gray tones cytogram is akin to the false-color cytogram, with the only difference being that instead of the different colors, different shades of gray are adopted (Fig. 11.10).
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Fig. 11.10 False-color (panel a) or gray tone graphic representation (panel b)
Fig. 11.11 Pseudo-threedimensional graphic representation. The vertical axis is related to the number of events
11.2.4
Pseudo-Three-Dimensional Representation
The pseudo-three-dimensional cytogram is similar to a two-dimensional cytogram in which the values associated with the third dimension are not related to a third parameter but represent the number of events (Fig. 11.11). The result of the analysis is akin to a mountain landscape, in which the shape of the mountains depends on the distribution of the analyzed parameters (X, Y), while the height Z is a function of the number of events that constitute the peak.
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Cytograms
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Fig. 11.12 Three-dimensional graphical representation of some parameters selected from a peripheral blood sample analysis. Panel a shows a representation of the parameters CD45 (X-axis), FSC (Y-axis), and SSC (Z-axis), while panel b shows a representation of the parameters CD8 (X-axis), CD4 (Y-axis), and SSC (Z-axis). Monocytes are represented in red and display intermediate scatter values and low expression of CD4
11.2.5
Three-Dimensional Representation
The “true” three-dimensional cytogram, not to be confused with the pseudo-threedimensional cytogram, is the contemporary representation of the three parameters’ distribution and consists of the two-dimensional perspective representation of a three-dimensional space. In the “true” three-dimensional cytogram (cubic cytogram), every single point corresponds to an event whose position is defined by a three-dimensional vector whose values are proportional to those of the represented parameters (Fig. 11.12). The “true” three-dimensional cytogram represents the logical progression of a two-dimensional cytogram. However, this progression does not proceed beyond this level, given that the spatial dimensions available to the graphic representation do not exceed three. Moreover, a three-dimensional cytogram is often of no immediate interpretation and never displays anything that cannot be shown—sometimes in a much clearer way—by creating a series of bivariate cytograms combining the various parameters. As a rule, the operator can choose the various axes’ spatial positions and rotate the cubic cytogram in all dimensions.
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References Bagwell CB, Hill BL, Herbert DJ, Bray CM, Hunsberger BC (2016) Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry. Cytometry A 89(12): 1097–1105. https://doi.org/10.1002/cyto.a.23017 Becton Dickinson (1998) CellQuest software reference manual. White Paper. https:// timothyspringer.org/files/tas/files/cellquest-softwarereference.pdf. Accessed 22 Nov 2021 Becton Dickinson (2007) BD FACSDiva software 6.0 reference manual. White Paper. https://www. bu.edu/flow-cytometry/files/2010/10/BDFACSDivaSoftwareReferenceManual.pdf. Accessed 8 Jan 2021 Diwakar R (2017) An evaluation of normal versus log-normal distribution in data description and empirical analysis. Pract Assess Res Eval 22(13) http://pareonline.net/getvn.asp?v¼22&n¼13. Accessed 18 June 2021 Gandler W, Shapiro H (1990) Logarithmic amplifiers. Cytometry 11(3):447–450 Heath DF (1967) Normal or log-normal: appropriate distributions. Nature 213(5081):1159–1160 Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR (2006) Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 7(7):681–685 Hunsberger B, Bagwell CB, Herbert D, Bray C, Langweiler M (2003) Effects of resolution reduction on data analysis. Cytometry 53A(2):103–111 Limpert E, Stahel WA, Abbt M (2001) Log-normal distributions across the sciences: keys and clues. Bioscience 51(5):341–352 Moore WA, Kautz RA (1986) Data analysis in flow cytometry. In: Weir DM, Herzenberg LA, Blackwell CM, Herzenberg LA (eds) Handbook of experimental immunology, 4th edn. Blackwell Scientific Publications, Edinburgh, pp 30.1–30.11 Novo D, Wood J (2008) Flow cytometry histograms: transformations, resolution, and display. Cytometry A 73(8):685–692 Schmid I, Schmid P, Giorgi JV (1988) Conversion of logarithmic channel numbers into relative linear fluorescence intensity. Cytometry 9(6):533–538 Scott DW (1979) On optimal and data-based histograms. Biometrika 66(3):605–610 Sweet R, Parks D, Nozaki T, Herzenberg L (1981) A 3 1/2 decade logarithmic amplifier for cell fluorescence data (abstract). Cytometry 2(2):130 Watson JV, Walport MJ (1985) How does flow cytometry express Gaussian distributed biological information? J Immunol Methods 77(2):321–330 Wood JC (1998) Fundamental flow cytometer properties governing sensitivity and resolution. Cytometry 33(2):260–266
Chapter 12
Data Analysis
At the dawn of flow cytometry, the problems related to data analysis basically concerned the accuracy of the percentages of positivity for a given immunophenotypic marker and the interpretation and comparison of histograms generated by single-parameter DNA analysis. Further information on this topic can be acquired by consultation of dedicated monographs, among which the classic text by Watson, which takes into consideration the problems related to immunofluorescence measurements, the analysis of DNA histograms, the study of cell kinetics, and the evaluation of dynamic cellular processes (Watson 1992).
12.1
Immunofluorescence Measurements
Immunofluorescence measurements are sometimes problematic because eukaryotic cells exhibit autofluorescence, which contributes to most of the negative components of the measurement and interferes with weak positive signals, not to speak of the fact that the dynamic range of possible signals is very high, in virtually all cases requiring a log or log-like transformation of data.
12.1.1
The Vexed Question of the Negative Control
In Conventional Flow Cytometry, negative control has long been a debated issue, and even now, there is no complete agreement on the subject. The negative controls considered over time were (1) the isotype control, (2) the isoclonic control, (3) the so-called fluorescence minus one control, also known as FMO control, and (4) the unstained control, i.e., a control consisting of the unmarked sample. Finally, some words should be spent for the negative controls in Spectral Cytometry. Unlike Conventional Cytometry, in Spectral Cytometry, the controls © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_12
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should consist (1) of single-stained standards needed to know the spectral behavior of the probes along all the channels available on the instrument and (2) of non-stained samples needed to know the spectral behavior of the autofluorescence. These controls are called Spectral Reference Control and are used to establish reference vectors in Spectral Unmixing (Ferrer-Font et al. 2020).
12.1.1.1
Isotype Control
The isotype control consists of antibodies of unknown or irrelevant specificity (for example, against key limpet hemocyanin). Compared to the Mabs used in the labeling, these antibodies (1) must be obtained in the same species, (2) must be conjugated with the same fluorochrome, and (3) must belong to the same immunoglobulin subclass (isotype). Isotype controls have been considered redundant in the analysis of discretely distributed markers, i.e., without counts between the negative and the positive populations (Sreenan et al. 1997); furthermore, they have been criticized because they are unlikely to behave like the Mabs used in the labeling. It has been observed that the true isotype control is constituted by the negative component of a test, as long as the cells in the negative and positive populations belong to the same cell type (Hulspas et al. 2009); as an example, in labeling carried out with an anti-CD3 Mab, the negative CD3 lymphocytes represent an internal control (Hulspas et al. 2009), as they were incubated with the very same Mab. The reasons why isotype controls can behave differently than the Mabs exploited in the staining include (1) a different concentration, (2) a different fluorochrome to protein (F/P) ratio, (3) a different tendency to bind the receptors for the immunoglobulin Fc fragment, (4) a Mab different molecular structure (e.g., different glycosylation) bound to establish weak force-mediated bonds, and (5) a structurally different fluorochrome, also bound to establish non-specific bonds (this behavior may occasionally crop up with phycobilins) (Keeney et al. 1998; Hulspas et al. 2009; van der Strate et al. 2017). Even if some authors report that, at least in cytokine evaluation, it provides inconsistent results (Prussin and Metcalfe 1995; Schultz et al. 2002), isotype control is still considered in intracytoplasmic staining procedures (Keeney et al. 1998; Koester and Bolton 2000). Other authors also defend its utility in analyzing cells whose physical characteristics drastically change compared to the control population, as in the case of stimulated cells (O’Gorman and Thomas 1999; Hulspas et al. 2009). A last drawback of the isotype control is that it does not provide information about its spillover in the other channels (Maecker and Trotter 2006), but this observation is quite specious because this control has not been devised for this purpose, excellently absolved by the FMO control.
12.1
Immunofluorescence Measurements
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Isoclonic Control
The isoclonic control consists of the conjugated specific Mab used to stain the sample, mixed with an excess of the same non-conjugated Mab (Keeney et al. 1998). The rationale for this approach is that the excess of non-conjugated Mab would saturate all the possible immunoglobulin Fc receptors, allowing the selective detection of non-specific fluorochrome-mediated bonds. This control, rarely reported in the literature, has not been recommended in the quantitative evaluation of positive events; the use of Fc receptor blocking factors has been suggested instead (Hulspas et al. 2009).
12.1.1.3
Fluorescence-Minus-One (FMO) Control
The fluorescence-minus-one (FMO) control consists of a mixture of all the conjugated specific Mab used in the staining except that relative to the antigen to be controlled (Baumgarth and Roederer 2000). This approach is much needed in polychromatic cytometry since it allows to determine the influence of the other fluorochromes in the channel under analysis. Still, it does not provide information on the behavior of the conjugated Mab intended for that channel. This control is quite cumbersome and expensive and could be reserved for situations in which the determination of the negative boundary is critical (Maecker and Trotter 2006).
12.1.1.4
Unstained Control
The unstained control consists of the unmarked sample providing information about autofluorescence (Keeney et al. 1998). As said before, it has been recommended to analyze discretely distributed markers, i.e., without counts between the negative and the positive populations (Sreenan et al. 1997).
12.1.2
Histograms
In a heterogeneous cell suspension containing both positively and negatively stained events, the definition of the positive events traditionally involves the completion of the following procedures: 1. Preparation of the negative control 2. Analysis of the negative control and registration of a marker immediately to the right of the resulting histogram 3. Reproduction of the marker in the same position of the histogram produced by the analysis of the stained sample (test)
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Fig. 12.1 Monoparametric immunofluorescence measurements represented on histograms according to the 2% of background method. The red marker in the negative control histogram (panel a) allows defining the 2% of all events as positive. The red marker placed in the same position as the test histogram (panel b) allows 26% of all events to be defined as positive. It follows that the positive events are 24% (262%) of the total events. As explained later in the text, this represents a paradigmatic case of particular simplicity
4. Attribution of positivity to all the test events in the channels to the marker’s right (Fig. 12.1) In this method, the positioning of the marker on the abscissa immediately to the right of the negative control assumes a critical value, and some authors have decided to standardize it by deciding that its positioning is correct when it returns a 2% of positive events (Overton 1988) (Fig. 12.1). This percentage can also be different, depending on the conventions adopted. The analysis software usually provides information about a series of statistical data, including: 1. Absolute and percentage values of the events in the analysis gate and the various hierarchical gates performed during the analysis 2. Mean, Mode, Median, Geometric Mean, coefficient of variation, and standard deviation of the population in every Gate
12.1.2.1
MFI (Mean Fluorescence Intensity)
The fluorescence intensity of a population of events can be expressed as mean fluorescence intensity (MFI). MFI corresponds to the value related to the Mean or Median of the population under analysis; in a system calibrated in FLU, MFI is a function of the detector’s
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photoelectrons (Perfetto et al. 2014) (for further information on this topic, see Sect. 13.4.4). MFI can only apply to unimodal distributions. The choice of the location measurement can also encompass the arithmetic Mean, geometric Mean, Median, and Mode (peak channel), but there is a consensus on choosing the Median because it is less sensitive to the presence of extreme values. Depending on the chosen index, it can be useful to express the MFI as MedianFI (Median), or MeanFI (arithmetic Mean), or gMFI (geometric Mean), or tMFI (trimmed Mean), or ModeFI (Mode) (for more information on this topic, see Chap. 23). Another seldom used type of MFI is the integrated MFI (iMFI), i.e., the product of the value of the MFI for the number of cells to which the MFI refers. In some experimental models, this type of MFI would correlate with the biological variables under analysis better than the “traditional” MFI (Darrah et al. 2007). Finally, we must mention the isolated appearance in the literature of the term MPCF (mean peak of channel fluorescence), which is used in titration procedures and appears synonymous with peak channel, or MFI measured on mode (ModeFI) (Collino et al. 2007). It should not be necessary to remember that the chosen location measurement must be kept the same within the same experiment.
12.1.2.2
RFI (Relative Fluorescence Intensity)
The Ratio between Fluorescence Intensities, also known as relative fluorescence intensity (RFI) or relative median fluorescence (RMF), is the ratio between two MFI values. In a discrete percentage of cases, this ratio applies to data sets produced by immunophenotypic analyzes to compare the fluorescence of the analyzed population (test) with the fluorescence of another population or negative control. In performing RFI, it is important not to forget that a ratio between the MFI of a positive population and that of a control population is flawed, as it compares two different things, namely the fluorescence of a population that has legitimately linked the fluorochrome (test) and the fluorescence of a population that has not (control). Consequently, the control solely consists of the background signal, whose distribution may depend on variables that have nothing to do with those that rule the test specific fluorescence distribution. Because of this observation, some authors have suggested introducing a positive dim internal control in all the analytical runs to compare the various positive populations (Roederer 2010). Moreover, as already mentioned (Wells and Loken 2008), the ratio between fluorescence intensities only makes sense if the following requisites are fulfilled: 1. The populations are unimodal. 2. The ratio applies in the context of the same analytical run or under strictly controlled conditions.
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3. In the case of immunophenotyping, the staining must be carried out with the same antibody directly conjugated to the same fluorochrome (and ideally belonging to the same batch). 4. No unexpected factors should affect the event fluorescence intensity, as occurs instead in the hypothetical case of a stimulated population, which increases in size while the non-responsive counterpart does not. Instead of the classic MFI, the MFI per surface unit should be considered in this last case, resulting from the ratio between the average fluorescence intensity and the average FSC value (Novo et al. 1999). From a computational point of view, it is useful to consider some preliminary points. If data are linearly amplified, the ratio takes place between the two populations’ MFI values expressed in channel numbers. If data are log-transformed, a plain ratio also works, provided it takes place between the two peaks’ linear units and not between the two peaks’ channel numbers. It is also possible to obtain an RFI between A and B by subtracting the MFI of B from the MFI of A, both being expressed in channel numbers according to the formula: RFI ¼ f10½aðMean FI A=bÞg f10½aðMean FI B=bÞg, where RFI is the ratio between different fluorescence intensities, a is the number of available decades, and b is the number of available channels (Miwa et al. 1998). It has also been observed that, since in this equation, the value of the control’s MFI is negligible compared to the test, we might as well consider the test’s MFI “as such” without subtracting the MFI of the control (Shapiro 2001). Sometimes, to keep the results in scale, it may be necessary to use a constant to modify one of the two variable values. In linear amplification, the constant multiplies every single value, while in logarithmic amplification, the constant adds to each value, according to the principle that the logarithm of a product is equivalent to the sum of the operators’ logarithms.
12.1.3
Cytograms
By analogy, in the case of bi-parametric analyses, the application of the markers divides the space of the cytogram into quadrants, defined as (1) upper left (or UL), (2) upper right (UR), (3) lower left (or LL), and (4) lower right (LR) (Fig. 12.2). In analogy with the concept of marker described in the previous section, the inscription of the event in one of the four quadrants would define its positivity or negativity for the considered parameters. As it will become evident later on (see Sect. 19.2.1.1), the division of the cytogram into four quadrants drawn around the negative is a hyper-simplification
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Fig. 12.2 Example of bivariate immunofluorescence measurements represented by cytograms. The preparation of the negative control (left panel) allows to divide the area of the cytogram into four quadrangular fields, defined as LL (lower left), LR (lower right), UL (upper left), and UR (upper right), associated (1) to the negativity for the two parameters (LL, /), (2) to the positivity for the parameter associated with the horizontal axis with negativity for the one associated to the vertical axis (LR, +/), (3) to the positivity for the two parameters (UR, +/+), and (4) to the negativity for the parameter associated with the horizontal axis with positivity for the associated one to the vertical axis (UL, /+). In the example shown in the panel on the right, almost all the events analyzed are double-positive. As later pointed out, this represents a paradigmatic case of particular simplicity
since the boundaries between the various sectors of the cytogram must be placed taking into account the spillover spreading of compensated clusters. Overcoming this problem is made possible by some analysis programs, which allow “tilting” the boundaries between quadrants to encompass the otherwise misplaced events (“hinged” gating) (Fig. 12.3).
12.1.4
Weak Positivity in Immunofluorescence
Positive and negative concepts are challenging when analyzing eukaryotic cell fluorescence signals. Due to reduced coenzymes and other naturally fluorescent molecules, the autofluorescence makes these events weakly positive, even if negative for the parameter under analysis. It follows that the distribution of a weak positive population can partially overlap with the distribution of the negative component, which, by definition, should correspond to the distribution of the negative control. In this case, it is no longer possible to consider positive only the events at the marker’s right, and it is necessary to proceed with a different approach.
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Fig. 12.3 Example of bivariate immunofluorescence measurements represented by cytograms. The boundaries between the quadrants have been modified (hinged) to cope with the spillover spreading of the compensated cluster
From an empirical point of view, two paradigmatic cases have been distinguished, which in this section are considered separately despite being different sides of the same coin. In the first case, called the weak positive sample with a negative component, the test distribution is bimodal, with the negative component superimposed to the control and with the positive component partially overlapping the negative. In this case, the problem is correctly evaluating the events in the channels shared by the control’s negative component and the weak positive test. In the second case, called the weak positive sample without a negative component, the test distribution is apparently unimodal and partially overlaps the control’s negative component. In this case, the problem is twofold, i.e., to verify the effective unimodality of the test and correctly evaluate the positive events. As for the unimodality evaluation, the problem is more difficult than generally thought since the traditional statistical instruments are not always up to the task (Johnsson et al. 2017).
12.1.4.1
Weak Positivity with a Negative Component
As anticipated, it is sometimes possible that the positive component of the histogram produced by the test partially overlaps the histogram of the control (Fig. 12.4). In this case, the positive component quantization cannot be entrusted to the fixed percentage method, as this would underestimate the counts in an inadmissible way but must rely on other systems. If the distribution is Gaussian and if the spread measurements of the two series are similar, then it is possible to hypothesize that, by empirically placing the marker on
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Fig. 12.4 Example of weak positive with a negative component. Compared to the negative control (panel a), the test (panel b) returns a bimodal histogram, in which the first mode coincides with the negative control mode, but the second one, which represents the distribution of the positive component of the test, overlaps the first mode (panel c). The overlap between negative control and test (panel c) clearly shows how the marker placed to the right of the negative control in panel a (vertical red bar) is inadequate for calculating the positive test component, which would be seriously underestimated
the inflection point of the two overlapping histograms, the counts improperly contributed by negative to positive equals those contributed by positive to negative, therefore compensating each other. In most cases, these assumptions are not valid, and it is necessary to resort to a series of different methods, including: 1. The channel-by-channel subtraction method, which subtracts the counts in negative channels from the corresponding test channels; in the case of negative results, the value is set to zero (Overton 1988). 2. The cumulative subtraction method, which also subtracts the counts in negative channels from those in the corresponding test channels; the difference with the previous method consists of the fact that, in case of negative results, it integrates the differences with the positive counts of the lower channels, with mutual compensation (Overton 1988). 3. The maximum positive difference method, which identifies through an algorithm the channel corresponding to the maximum difference between the two histograms and subtracts from the test the percentage of control placed on its left (Overton 1988). 4. The cumulative frequency subtraction plus ratio analysis of means, followed by Kolmogorov–Smirnov analysis and Student’s t-test for validation (Watson 2001). 5. A series of more complex methods based on histogram deconvolution (Cox et al. 1988; Sladek and Jacobberger 1993; Lampariello 1994, 2009). When possible, the weakly positive overlapping population can be accurately quantified if resolved by an additional parameter expressed at a sufficient intensity to allow its definitive extrapolation in the other direction of a two-dimensional cytogram (Fig. 12.5).
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Fig. 12.5 Example of a test weakly positive for FL1 superimposed on the control (panel a), evaluated for another independent parameter (FL2) (panel b). The weakly positive population “migrates” into the cytogram’s Cartesian space as a function of the second parameter expression and becomes unmistakably quantifiable
Fig. 12.6 Example of weak positive without a negative component. Compared to the negative control (panel a), the test (panel b) appears unimodal and partially overlaps the distribution of the negative control (panel c). The overlap between negative control and test (panel c) clearly shows how, in this case, an uncritical application of the marker (vertical red bar) cannot provide adequate information but artificially resolves the test into a negative and a positive component. In this specific case, the test (panels b and c) shows the kappa light chain distribution in a population of B neoplastic lymphocytes taken from a subject suffering from B-CLL
12.1.4.2
Weak Positivity Without an Apparent Negative Component
Another frequently encountered situation is when a histogram produced by a test has only one unimodal positive component that partially overlaps the control histogram (Fig. 12.6). In evaluating a weak positive unimodal cell population, the fixed percentage method is not suitable, as this approach automatically splits the unimodal continuous
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population into two adjacent “emi-populations,” each with an opposite biological meaning. This approach’s limits are immediately evident when considering a real case with a clonal population of B lymphocytes restricted for the kappa light chains expressed at low intensity. In this case, the homogeneous population would split into two subsets, the first negative for kappa chains to the left of the marker and the second positive for the same antigen to the right of the same marker (Fig. 12.6). In an attempt to overcome this paradox, some years ago, it was established that, in the case of neoplastic populations, the overcoming of a positivity threshold set at 30% for mature cells and 20% for blastic cells be sufficient to judge as positive the entire population under investigation (Bain et al. 2002). Unfortunately, this solution is not satisfactory because it systematically interprets as negative, a positive population characterized by an expression lower than that arbitrarily required to overcome the threshold. The solution to this problem requires that the hypothesis of unimodality is reasonably proved (Johnsson et al. 2017) and relies on different approaches. The oldest approach is based on comparing histograms through the KolmogorovSmirnov test (Young 1977). This test has been used in many instances, among which the demonstration of the so-called “clonal excess” due to the presence of a monoclonal B-cell population (Ault 1979), the evaluation of the expression of the ZAP-70 protein in the lymphocytes of chronic leukemia of B lymphocytes (B-CLL) (Van Bockstaele et al. 2006) and the study of minimal residual disease (MRD) (Rawstron et al. 2001; Uhrmacher et al. 2010; Chovancova et al. 2015). The KolmogorovSmirnov test is exceedingly sensitive and ultimately leaves the operator responsible for identifying the value above which the two curves are different (Lampariello 2000; Brescia and Sarti 2008). Other approaches encompass (1) the use of modeling algorithms (“curve fitting”) able to reconstruct the actual distribution of the test, ruling out the presence of an occult minor negative component (Lampariello 1994), (2) the analysis of the ratio of the cumulative distributions associated with the test and negative control histograms (Lampariello 2009), and (3) other methods for histogram comparison based on chi-square (Cox et al. 1988; Roederer et al. 2001), Student’s test (Bagwell et al. 1979), and quadratic form (QF) statistics (Bernas et al. 2008). Many of these approaches presume the test positive and negative components share the same distribution. However, this homogeneity is not granted since the two components represent two different phenomena, i.e., (1) the fluorescence due to the probe’s legitimate binding and (2) the background encompassing autofluorescence and electronic noise (Roederer 2016). Finally, some authors have proposed that a cell population be considered positive when its median value is more than two standard deviations from the median of the negative component (Basso et al. 2001). This assumption has been rejected in a nonpeer-reviewed communication available on the Internet (Roederer 2014).
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DNA Content Measurements
The quantitative measurement of the DNA content, performed in Flow Cytometry with stoichiometrically binding probes (for further information on this topic, see Chap. 16), constitutes a valuable tool for exploring the kinetics of quiescent, stimulated, or perturbed cellular populations (Krishan 1975), and is of inestimable value in a series of fields including Experimental Oncology and Pharmacology. From a cytometric point of view, the signal’s limited dynamic range requires a linear amplification, while the high accuracy required by this type of analysis prompts considering the pulse Area (A) and not its Height (H). The combined study of A, H, and W contributes to correctly selecting the events to be analyzed and eliminating the doublets (for further information on this topic, see Sect. 8.2.3). Based on the quantitative measurement of the DNA content, the cell cycle splits into different phases (Fig. 12.7), which can be described in summary form as follows (Hartwell et al. 1974): 1. A G1/G0 phase (G from “gap”), populated by elements with diploid DNA content. 2. An S phase (S from synthesis), populated by elements with a DNA content intermediate between that of cells in the G1/G0 phase and G2/M phase. 3. A G2/M phase (G from “gap,” M from mitosis) comprising cells with tetraploid DNA content. The difference between G0 and G1 phases consists of the fact that the G0 phase comprises quiescent cells that do not cross the cell cycle, while the G1 phase comprises cycling cells just before entering the S phase, and therefore containing Fig. 12.7 Distribution of DNA content in an exponentially cycling population. The parameter in abscissa refers to the fluorescent emission of propidium iodide
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significant quantities of RNA needed for the production of the tools aimed to their DNA synthesis (Darzynkiewicz et al. 1977). As for the G2/M phase, it can be observed that the G2 phase comprises cells with tetraploid DNA content not yet entered into mitosis, while the M phase includes the cells in mitosis, or rather the elements involved in the cytodieresis, but not yet separated into two distinct cells; on the sole basis of the DNA content, these two compartments are indistinguishable from each other. Furthermore, although the cell cycle can ideally split into discrete phases according to the DNA content distribution, the accurate calculation of the elements belonging to the various phases is difficult since the phases pass one into another seamlessly and overlap due to the measurement uncertainty. This problem worsens when the DNA distribution is evaluated in variously perturbed populations. The solution requires a series of analytical approaches based on different principles, developed according to the analyzed population’s kinetic conditions (Dean 1987). These approaches include both graphical methods, adopting a “geometric” approach to the problem and tracing gates based on certain criteria, and computational methods, resorting to mathematical models iteratively applied until the maximum possible likelihood of the result produced is reached. Among these are included: 1. The graphic method called “peak reflect method,” which postulates (1) that the population G1 is included in an area consisting of twice the left half of the peak G1, (2) that the population G2/M is included in an area consisting of twice the right half of the G2/M peak, and (3) that the population in phase S is the total of the population explored minus the populations G1 and G2/M calculated in the predetermined way (Barlogie et al. 1976) (Fig. 12.8). 2. The graphic method called “rectangle graphic method,” in which the phase S is assimilated to a quadrilateral built on the abscissas, whose left side coincides with the peak channel of the population G1, the right side coincides with the peak channel of the population G2/M, and the upper side is extrapolated along the curve of phase S (Baisch et al. 1975) (Fig. 12.9). 3. The “broadened polynomials” computational method, in which the G1 and G2/M phases are assimilated to a Gaussian curve, and a second-order polynomial equation represents the phase S (Dean and Jett 1974). 4. The SFIT (from Simple FITting) computational method, which first represents the S phase not overlapping the G1 and G2/M phases with a second-degree polynomial equation, and then adapts it by extending it toward the center of the G1 and G2/M (Dean 1980). 5. The “sum of Gaussian curves” computational method, solving the S phase in a series of different Gaussians (Fried 1976). The various methods are not equivalent to each other, and none of them is entirely satisfactory. It is accepted that: 1. The graphic methods, or geometric methods, are satisfactory only in the analysis of exponentially growing populations.
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2. The analysis of perturbed populations requires the use of computational methods based on the division of the S phase into a series of different compartments, mainly Gaussian. 3. The errors evaluating the G1 and G2/M phase can reach 100% regardless of the method if large populations occur in the early or late S phase (Dean 1987).
12.3
Concept of Gate and Concept of Region
12.2.1
185
DNA Content versus BrdU Incorporation
A breakthrough in the analysis of the cell cycle was the introduction of techniques based on the evaluation of DNA combined with the incorporation of pyrimidine analogs, consisting mainly of bromo-deoxyuridine (BrdU, or BUdR) (Morstyn et al. 1983) (Fig. 12.10) or ethynyl-deoxyuridine (EdU) (Sun et al. 2012). Mabs exist that can recognize these analogs, and it is feasible to explore cycling cells for their DNA content and their DNA synthesis because these Mabs recognize the cells that have taken up the analog from the medium culture. Consequently, it is possible to accurately know the S phase actual entity (Lacombe et al. 1988) since only the analog uptaking elements belong to this phase.
12.3
Concept of Gate and Concept of Region
Before addressing this topic, it may be useful to point out that there is no universal consensus on the distinction between Gate and Region and that in some contexts, only the concept of the Gate is used. Nevertheless, in this chapter, the distinction is maintained, both to make the tools available for the interpretation of unfamiliar situations and because the distinction between Gate and Region is exploited in the ISHAGE protocol, which has had and still has great clinical relevance (for further information about this topic, see Sect. 12.3.1.1). The Region, customarily defined with the capital letter R and a progressive number, is the result of the procedure by which, based on specific requirements, the operator identifies a subset of events, usually drawing a boundary around them
Fig. 12.10 Bivariate analysis of DNA content, defined by PI staining, and DNA synthesis performed by an anti-BUdR Mab conjugated with FITC. The positive elements for BUdR (in red in panels a and b) represent the true elements in the S phase
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Fig. 12.11 Examples related to the concepts of Region (R) and Gate (G). In the cytogram, based on the physical parameters (panel a), two Regions were drawn; namely, Region 1 on the lymphocytes (in blue in the picture) and Region 2 on the monocytes (in red in the picture), acting as Gate 1 and Gate 2, respectively. Panel b shows the distribution of CD4 and CD8 in Gate 2 (G2 ¼ R2), panel c shows the same distribution in Gate 1 (G1 ¼ R1), and panel d shows the same distribution in the combination of the two regions (G3 ¼ R1 or R2)
(Fig. 12.11). The term Region suggests the selective value of the operation, which only considers a fraction of the landscape represented in the cytogram. The Gate, usually defined with the capital letter G and a progressive number, results from the Boolean combination of previously traced regions; if only one Region has been drawn, then Region and Gate coincide, i.e., R ¼ G. Since both Regions and Gates ultimately define a subset of the data set, it follows that it is conceptually possible to combine them together. These operations produce the final subset of the data set on which the analytical algorithm insists. It is immediately evident that Gate and Threshold share the same concept. However, it is essential to remember that the gating is provisional, carried out by
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software, and is part of the post-analytical phase. In contrast, the threshold is irrevocable, carried out by hardware, and is part of the analytical phase. The combination of Gates and Regions can be performed (1) by hierarchical nest gating, in which one Region is drawn into another), (2) according to the principles of Boolean algebra (see below), or (3) in a combined way, as it happens in many cases. Finally, here are a few words about the so-called backgating: backgating is the procedure that verifies the behavior of some parameters, generally physical, expressed by a population selected for parameters different from the previous ones. An example of backgating consists of checking the physical parameters of viable cells identified for the expression of CD34 and CD45 in the HSC evaluation according to the ISHAGE (International Society of Hematotherapy and Graft Engineering) protocol (Sutherland et al. 1996). In the following example (Fig. 12.11), a simple sequential gating procedure is shown, in which each cytogram includes and shows the events selected (gated) by the combination of Regions previously drawn.
12.3.1
Combined (Boolean) Use of Regions and Gates
According to the set theory, each subset of events defined by a Region or a Gate (or a combination of them) can be considered a set of elements featuring a known value. It follows that it is possible to perform operations between different sets according to the logic of Boolean algebra, based on the concept of “true” and “false.” To immediately reconnect these concepts to the immunophenotyping procedures, an example of analysis is shown to identify and enumerate viable hematopoietic stem cells (HSCs), which can be regarded as an event set (1) “true” for CD45 expression, (2) “false” for 7-AAD uptake, (3) “true” for CD34 expression, and (4) “true” for pre-selected intrinsic parameter criteria evaluated by backgating (Figs. 12.13, 12.14, 12.15, 12.16 and 12.17). Moreover, each set element displays a known quantitative value for each considered parameter. The boolean algebraic operations (boolean gating) rely on a series of operators, among which the most used in the analysis of flow cytometric data are the operators “AND,” “OR,” and “NOT.” Applying these operators to different cell subsets gives out a new cell subset whose components are compliant with the logic underlying the chosen operators. The role of the operators in the combined use of Regions and Gates can be better explained by the following example (Fig. 12.12).
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Fig. 12.12 Graphic representation of the most used boolean operator. Panel 1: the expression “A AND B” defines a new set (red in the picture) only containing the elements shared by A and B; panel 2: the expression “A XOR B” defines a new set (red in the picture) containing the elements belonging to the sets A and B except for the elements common to the sets A and B; panel 3: the expression “A OR B” defines a new set (red in the picture) containing all the elements belonging to the sets A and B; panel 4: the expression “A NOT B” defines a new set (red in the picture) containing the elements belonging to A except the elements belonging to B
“AND” Operator When applied to two or more sets, the operator “AND” defines a new set only containing the elements shared by the sets considered in operation. For example, the combination of “CD45 expression” AND “7-AAD uptake” defines the CD45+ cells, which are also dead, and this approach can be useful in subtracting the dead cells from the analysis. “OR” Operator When applied to two or more sets, the operator “OR” defines a new set containing the elements originally belonging to all the sets considered in operation. For example, the combination of “CD3 expression” OR “CD19 (continued)
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expression” defines a set made up of all the T and B lymphocytes with the exclusion of NK cells. “NOT” Operator When applied to two or more sets, the operator “NOT” defines a new set containing the elements of the first set except those of the other sets considered by the operator independently of each other. For example, the combination of “CD19 expression” NOT “lambda light chain expression” defines a set of all the B lymphocytes exclusively expressing the light kappa chain. The “NOT” operator is mostly used to exclude undesired events from the analysis. The “NOT” operator is activated in certain analysis programs by inverting an AND operator (i.e., Invert Gate option). “XOR” Operator When applied to two or more sets, the operator “XOR” defines a new set containing all the elements belonging to the sets except the elements shared by the sets. The combination of Gates and Regions is a formidable analytical method able to accurately identify minority populations by overcoming the problems related to the negative control and solving complex samples by increasing the dimensionality of the analysis. In principle, logical gate sequences can be entrusted to dedicated algorithms, thus opening the door to the automatic analysis of cell subsets.
12.3.1.1
In the Determination of Hematopoietic Stem Cells (HSCs)
A typical example of the combined use of Regions and Gates can be found in the ISHAGE protocol, used in the enumeration of hematopoietic stem cells (HSCs), whose aim is to accurately count the CD34+ cells (Sutherland et al. 1996). This protocol combines a series of Regions and Gates selected for specific requirements (i.e., physical parameters, expression of CD34, expression of CD45, viability) and provides a standardized, accurate, and repeatable evaluation of the hematopoietic precursors in the sample. The ISHAGE protocol proceeds by progressive gating instructions, as in the following example, concerning the exploitation of the BD Trucount beads in a single platform. HSC count (Becton Dickinson 2009).
ISHAGE Protocol STEP 1 (Fig. 12.13). Display the ungated 7-AAD vs. Side Scatter cytogram. Draw Region R1 to include the 7-AAD negative viable cells. Note that the BD Trucount
190 1000
800
Side Scatter
Fig. 12.13 This figure shows Region R1, which encompasses the 7-AAD negative, henceforth viable cells; outside the Region, Trucount standard related cluster can be seen (arrow), whose fluorescence is also detected in the 7-AAD channel
12 Data Analysis
Trucount
600
400
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R1: viable cells
R1 0 100
101
102
103
7-AAD
beads (arrow) fluoresce in the red Region of the spectrum and fall outside the gate R1. STEP 2 (Fig. 12.14). Display the ungated CD45 vs. SSC cytogram. 1. Draw Region R2 to include the CD45+ cells (panel a). Note that the BD Trucount beads (arrow) also fluoresce in the green Region of the spectrum and fall inside the Gate. Note that Gate 1 (G1 ¼ R1 AND R2) includes all the viable leukocytes in the sample; 2. Draw Region 3 to include the lymphocytes (green in the picture, panel b). Note that gate G2 (G2 ¼ G1 AND R3) includes all the viable lymphocytes. STEP 3 (Fig. 12.15). Display the ungated CD34 vs. SSC cytogram and draw Region R4 to include CD34+ cells (violet in the picture) (panel a); then display R4 gated CD45 vs. SSC (panel b), and FSC vs. SSC (panel c) cytograms, and check the boundaries of the CD34+ cluster. This procedure is also known as “backgating” and is used to verify the homogeneity of the physical parameters of a cell population previously only defined by fluorescent markers. STEP 4 (Fig. 12.16). Display the FSC vs. SSC cytogram gated on G2 (G2 ¼ R1 and R3), i.e., viable lymphocytes. Draw Region R7 to include all the lymphocytes and Region R8 to include the debris (panel a). In the same cytogram (panel b), perform the back-scattering of the CD34+ events defined by the R4 Region and verify (1) that they fall within the larger Region R7 drawn around the lymphocytes and (2) the absence of dim positive events, which could consist of platelets or dead cells aspecifically binding the anti-CD34 Mab. STEP 8 (Fig. 12.17). Display the ungated CD45 vs. CD34 cytogram, and draw Region R9 to define the microbeads (black in the picture). As a final result, displaying the ungated plot, it results that (see also Table 12.1):
12.3
Concept of Gate and Concept of Region
1000
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1000
R2
R2: CD45+
B
R2
A
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Side Scatter
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600
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0
0
R3:LYMPH 100
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CD45 FITC
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CD45 FITC
Fig. 12.14 This figure shows Region R2, which encompasses the CD45 positive cells (panel a), and Region R3 (panel b), which individuates the lymphocyte based on side scatter and CD45 expression (green in the picture)
1000 B
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A
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R4: CD34+
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R5:CD34+(R4) boundaries in CD45 vs. SSC dimensions
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CD34 PE
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R6:CD34+(R4) boundaries in FSC vs. SSC dimensions
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0
C
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Side Scatter
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Side Scatter
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B
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CD45 FITC
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0 0
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Forward Scatter
Fig. 12.15 This figure shows (1) Region R(4), which encompasses the CD34 positive cells (pink in the picture) (panel a), and (2) the boundaries of the CD34+ cells in the CD45 vs. SSC (panel b) and FSC vs. SSC (panel c) cytograms. These last steps are required to define better CD34+ cells based on their typical CD45 expression, and appropriate low/intermediate side scatter
1. The total viable CD45+ cells are identified by the boolean Gate G1, resulting from the combination of the Regions R1 (7-AAD negative, viable cells) AND R2 (CD45+ cells). 2. The viable lymphocytes (cyan in the picture) are identified by the boolean Gate G2, resulting from the combination of Regions R3 (lymphocytes) AND R1 (7-AAD negative, viable cells). 3. The viable HSCs (pink in the picture) are identified by the boolean Gate G5 ¼ G3 AND G4, a combination that selects the cells that share the following features: (1) viability (R1), (2) CD45 expression (R2), (3) CD34 expression (R4), (4) expected distribution of CD45 (R5), and (5) expected distribution of FSC (R6).
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Fig. 12.16 This figure shows Region R7 (panel a), which includes the viable lymphocytes (cyan in the picture) and defines the expected boundaries of their physical parameters, and Region R8, which includes the debris if any. Moreover, it displays the physical parameter distribution of the viable CD34+ cells (panel b)
103
R9: microbeads R9 102
CD34 PE
Fig. 12.17 This figure shows the results of a stem cell enumeration procedure according to the ISHAGE protocol, i.e., (1) the viable HSCs (violet in the picture), (2) the viable lymphocytes (cyan in the picture), (3) the WBCs other than lymphocytes (blue in the picture), (4) the dead cells (brown in the picture), and (5) the standard microbeads (black in the picture) gated by the Region R9
101
100
100
101
102
103
CD45 FITC
4. The dead cells are identified by the boolean Gate G6 ¼ ((R2 NOT R1) NOT (R8 OR R9)). Of note, the CD34+ cells have been counted in an objective, accurate, and reproducible manner without the need for negative control. The software provides
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Concept of Gate and Concept of Region
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Table 12.1 Definitions and meaning of logical gating in a stem cell enumeration procedure according to the ISHAGE protocol G /
R R1
Cytogram in which G or R has been drawn CD45 vs. SSC, ungated
/
R2
CD45 vs. SSC, ungated
/
R3
CD45 vs. SSC, ungated
/ /
R4 R5
CD34 vs. SSC, ungated CD45 vs. SSC, gated on R4 (viable HSCs)
/
R6
FSC vs. SSC, gated on R4 (viable HSCs)
/
R7
/
R8
/ G1
R9 R1 AND R2
FSC vs. SSC, gated on G2 (G2 ¼ R1 and R3, i.e., viable lymphocytes) FSC vs. SSC, gated on G2 (G2 ¼ R1 and R3, i.e., viable lymphocytes) CD45 vs. CD34, ungated /
G2 G3
G1 AND R3 G1 AND R4
/ /
G4
R5 AND R6
/
G5
G3 AND G4
/
G6
((R2 NOT R1) NOT (R8 OR R9))
/
Selected population, and underlying rationale Viable cells 7-AAD negative (NB: Region 1, drawn on the ungated global population, does not contain the beads which fluoresce in the 7-AAD channel) CD45 positive events (in this example, Region R(1)2 contains microbeads that fluoresce in the FITC channel) Lymphocytes; Region R3 allows checking the boundaries of the lymphocyte cluster in the SSC vs. CD45 dimensions CD34 positive cells Region R5 allows checking the boundaries of the HSC cluster in the SSC vs. CD45 dimensions Region R6 allows checking the boundaries of the HSC cluster in the FSC vs. SSC dimensions Region R5 allows checking the boundaries of the viable lymphocytes cluster in the FSC vs. SSC dimensions Debris
Microbeads Viable leukocytes (CD45 positive, 7-AAD negative events) Viable lymphocytes Viable HSCs (R4 ¼ CD34 positive cells) gated on G1 (viable CD45+ cells) HSC cluster checked in (1) SSC vs. CD45 (R5) and (2) FSC vs. SSC (R6) HSCs, selected on (1) viability (R1), (2) CD45 expression (R2), (3) CD34 expression (R4), and expected distribution of (4) CD45 (R5) and (5) FSC (R6) Dead cells
the relative values of all the populations and transforms them into absolute values according to the counts of the internal standard made of a known number of added microbeads (for further information on this topic, see Sect. 13.1.2.3).
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12.3.1.2
12 Data Analysis
In the Determination of the Minimal Residual Disease (MRD)
Frequent and intuitive use of the Boolean approach is used in evaluating the minimal residual disease (MRD). The unsupervised combination of Gates corresponding to the different antigenic expressions of the population of interest leads automatically to the identification of events belonging to it (Fig. 12.18), with a maximum sensitivity theoretically ranging between 105 and 106. Apart from the technical and statistical problems associated with rare event analysis, it is hardly necessary to remember that “the more complex the analytical process employed, the greater the likelihood that flow cytometry will be able to identify and characterize an abnormal population in a heterogeneous sample” (Borowitz et al. 1997).
Fig. 12.18 Boolean approach to the combined use of Regions and Gates to determine the minimal residual disease (MRD). In panels x, a, b, and c (upper row), the picture shows the phenotype of a case of T-ALL at the onset. Note the large blast population (panel x) and its leukemia-associated phenotypes (LAIP) CD8 dim+ CD4/ (Region 1, panel a), mCD3- CD7+ (Region 2, panel b), and CD5+ CD10+ (Region 3, panel c). In panel y (lower row), the picture shows the pattern of CD45 and SSC of the same case in complete remission. Nonetheless, despite the normal appearance of the sample, the Boolean combination of the three Regions drawn around the LAIPs at the onset (G1 ¼ R1 AND R2 AND R3) reveals the hidden presence of a small mCD3-, CD4/, CD5+, CD7+, CD8 dim+, and CD10+ leukemic subpopulation (red in the picture, panels d, e, and f). Total analyzed events 2 106, abnormal events 116, frequency of abnormal events 0.006%
12.4
Advanced Tools and Future Perspectives
12.3.1.3
195
In the Augmentation of the Dimensionality in Cell Subset Analysis
Combining Gates and Regions can increase the dimensionality of the cytometric analysis. Marking non-overlapping populations with the same combination of fluorochromes and subsequently restricting the analysis to differential gates traced on each of them allows exploring at once a number of antigens greater than the number of fluorochromes used in the labeling. A staining protocol based on these concepts has been devised in compliance with the Euroflow consortium indications (FloresMontero et al. 2019), and a tube compliant with these premises and pre-charged with dried Mabs (LST tube, lymphoid screening tube) has also been marketed for diagnostic purposes by some manufacturers, including Becton Dickinson, and Cytognos. Figure 12.19 explains how the LST tube works, depicting the result of a multiparametric analysis of a sample of peripheral blood from a patient affected by reactive lymphocytosis. The staining procedure exploits the following protocol: (1) FITC: CD8 and sIg lambda, (2) PE: CD56 and Ig kappa, (3) PerCP-CY5.5: CD5, (4) PE-CY7: CD19 and TCR gamma/delta, (5) APC: CD3, (6) APC-H7: CD38, (7) Pacific Blue: CD20 and CD4, (8) Pacific Orange: CD45, consequently encompassing 8 fluorochromes and 12 different monoclonal antibodies that share the same fluorochrome in 4 cases. Other examples can be found in the literature, the most complex of which features a total of 16 fluorochromes and 28 different monoclonal antibodies, allowing to trace at once 40 distinct peripheral blood lymphocyte subsets (Jachimowicz et al. 2020; Huys et al. 2021).
12.4
Advanced Tools and Future Perspectives
The evolution of Conventional Flow Cytometry and the emergence of technologies such as Spectral and Mass Cytometry have radically expanded the analytical power of Flow Cytometry, which is currently called Next-Generation Flow Cytometry (NGF) when operating under high standardization and multiparametricity conditions. Data analysis issues are currently of the utmost importance in Next-Generation Flow Cytometry. Their relevance depends on the fact that (1) the analysis, the elaboration, and above all, the comparison of the data sets requires the most homogeneous and “clean” conditions of data production, and (2) the high throughput of data makes it impossible to rely on the traditional analytical approach, based on the inspection of all the combinations of relevant markers followed by manual sequential gating. Together with other techniques specifically designed for the data produced by gene expression analysis, these algorithms contribute to the so-called
Fig. 12.19 Because of the mutually exclusive antigen expression, T cells (blue in the picture) and B cells (red) can be separately defined even if different Mabs share the same fluorochrome
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Advanced Tools and Future Perspectives
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“bioinformatics,” i.e., an interdisciplinary specialty specifically born to manage complex biological data. These techniques encompass a set of algorithms that, depending on the phase in which they intervene, can be divided into tools dedicated to the solution of preanalytical problems (data pre-processing) and tools dedicated to the execution of the actual data analysis. Although commercial diffusion makes their interface easier over time, managing these analytical techniques is often hampered by the need to know the R language and be familiar with programming tools. Some of these algorithms are embedded in commercial data analysis programs, while others are available on Internet, in platforms aimed at the progress of Bioinformatics like Bioconductor (Gentleman et al. 2004) (https://www.bioconductor.org/ ) or Cytobank (Kotecha et al. 2010; O’Neill et al. 2013) (https://www.cytobank.org/ ). More information on this topic can be found in a series of excellent dedicated reviews (Montante and Brinkman 2019; Saelens et al. 2019; Wang and Brinkman 2019; Liu et al. 2020; Rybakowska et al. 2020; Hu et al. 2021).
12.4.1
Pre-processing Programs
The pre-processing programs, devised to create accurate, homogeneous, and errorfree data sets, allow data optimization and simultaneous analysis and comparison of a more or less numerous series of different datasets, as happens in the study of populations. They are applied to raw data sets and perform the automatic execution of a series of functions, including (1) quality assessment, (2) data normalization, (3) data compensation, and (4) data transformation.
12.4.1.1
Quality Assessment
Quality assessment programs have been mainly devised to check the presence of flow troubles able to compromise the quality of the data by monitoring the signal’s consistency against time. Examples of this type of software are the flowAI (Monaco et al. 2016), flowClean (Fletez-Brant et al. 2016), and flowCut (Meskas et al. 2020) programs, which can check and correct data in an automatic and unattended way.
12.4.1.2
Data Normalization
Data normalization programs have been devised to remove all the inter-sample variations that do not depend on the variability of the parameter under analysis. Examples of this type of software are the fdaNorm (Hahne et al. 2010; Finak et al. 2014) and gaussNorm (Hahne et al. 2010) programs, which align the density peaks shared by the samples. Of note, normalization is preliminary to automatic gating procedures.
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12 Data Analysis
Data Compensation
In Conventional Flow Cytometry, data compensation is usually achieved through the algorithms implemented in the acquisitions programs; the resulting spillover or compensation matrix, depending on the case, is subsequently registered in the FCS file in the relative keywords described in Sect. 9.3.3. Virtually all the data compensation (and many spectral unmixing) programs are based on linear regression algorithms. Still, a new algorithm has been recently devised which relies on robust linear regression instead (Roca et al. 2021, 62530). This algorithm, called AutoSpill, has been described as able to display some advantages over the traditional compensation programs, among which (1) better calculation of spillover coefficients, (2) no need for a clearly defined bimodal distribution of the signal in the population to be compensated, (3) lesser spillover spreading of compensated populations, and (4) ability to subtract the autofluorescence signal (Roca et al. 2021, 62530). Autospill is an open-source program that can be downloaded from https://autospill.vib.be and is also available in FlowJo. The intersample management of compensation and other data (for example, gating data) is made possible by the flowUtils package implemented in the BioConductor flowCore framework (Spidlen et al. 2021) in compliance with the Gating-ML standard (Spidlen et al. 2008).
12.4.1.4
Data Transformation
Data transformation is usually performed by a series of different algorithms, including biexponential, linlog, generalized BoxCox, and generalized hyperbolic sine functions, often implemented in the acquisition programs (see also Chap. 10). All these algorithms can be found in the BioConductor flowCore framework (Finak et al. 2010).
12.4.2
Data Processing Programs
Data analysis programs are finalized for the data visualization and are the ideal automated prosecution of manual gating. Depending on the computational approach, they can be divided into (1) methods based on dimensionality reduction and (2) methods based on clustering, either supervised or unsupervised. The programs belonging to the former group try to identify and visualize the various objects working mostly on the parameters that best highlight their diversity, ignoring those less meaningful. In contrast, those belonging to the latter try to identify and visualize the various objects based on their mutual proximity,
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Advanced Tools and Future Perspectives
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ascertained with a series of different algorithms. Some programs exist that exploit both clustering and dimensionality reduction algorithms (Shekhar et al. 2014).
12.4.2.1
Dimensionality Reduction Based Programs
The dimensionality reduction programs can be classified into linear and non-linear algorithms. The linear algorithms rely on Principal Component Analysis (Lever et al. 2017), which maximizes the distances from dissimilar parameters; non-linear ones group together similar events, exploiting their reciprocal closeness. The most successful programs based on dimensionality reduction are the methods based on the so-called Stochastic Neighbor Embedding (SNE), which include several algorithms, among which t-SNE (Van der Maaten and Hinton 2008), somewhat impaired by its high computational needs, and a few less demanding programs like viSNE (Amir el et al. 2013) and others. Another interesting algorithm is UMAP (Uniform Manifold Approximation and Projection), credited to be particularly robust and fast (McInnes et al. 2020).
12.4.2.2
Clustering-Based Programs
Clustering-based programs define the closeness between the events, aggregating them together in discrete subpopulations according to the results provided by the chosen algorithms. Even though several algorithms exist, they basically work in the same way but can be divided into “supervised” or “not supervised,” depending on whether the operator must or must not define the number of clusters to be found in advance. While it is clear that a supervised approach allows the quantization of already known clusters, only the unsupervised approach allows detecting the presence of unexpected populations. Another group of clustering based-programs encompasses the trajectory inference (TI) methods, also known as pseudotemporal ordering methods, which analyze the relationships between related events placed differently along an evolutionary line, like cell cycle, differentiation, or activation. One of the most successful supervised clustering-based programs is flowDensity (Malek et al. 2015); the most used unsupervised clustering algorithms include Phenograph (Levine et al. 2015), SPADE (Qiu et al. 2011), and FlowSOM (Van Gassen et al. 2015), which exploits a two-step computational approach consisting in a self-organizing map (SOM) based on an artificial neural network and a consensus hierarchical clustering. As for TI methods, their choice depends on a series of variables, including the model of progression to which they are to be applied, i.e., linear, bifurcated, or branched (Cannoodt et al. 2016); frequently used TI methods are (1) Wanderlust (Bendall et al. 2014), which is also one of the first methods made available, (2) SCUBA (single-cell clustering using bifurcation analysis) (Marco et al. 2014), and (3) Wishbone (Setty et al. 2016).
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Keeney M, Gratama JW, Chin-Yee IH, Sutherland DR (1998) Isotype controls in the analysis of lymphocytes and CD34+ stem and progenitor cells by flow cytometry – time to let go! Cytometry 34(6):280–283 Koester SK, Bolton WE (2000) Intracellular markers. J Immunol Methods 243(1–2):99–106 Kotecha N, Krutzik PO, Irish JM (2010) Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom 53:10–17 Krishan A (1975) Rapid flow cytofluorometric analysis of mammalian cell cycle by propidium iodide staining. J Cell Biol 66(1):188–193 Lacombe F, Belloc F, Bernard P, Boisseau MR (1988) Evaluation of four methods of DNA distribution data analysis based on bromodeoxyuridine/DNA bivariate data. Cytometry 9(3): 245–253. https://doi.org/10.1002/cyto.990090310 Lampariello F (1994) Evaluation of the number of positive cells from flow cytometric immunoassays by mathematical modeling of cellular autofluorescence. Cytometry 15(4):294–301 Lampariello F (2000) On the use of the Kolmogorov-Smirnov statistical test for immunofluorescence histogram comparison. Cytometry 39(3):179–188 Lampariello F (2009) Ratio analysis of cumulatives for labeled cell quantification from immunofluorescence histograms derived from cells expressing low antigen levels. Cytometry A 75(8): 665–674 Lever J, Krzywinski M, Altman N (2017) Principal component analysis. Nat Methods 14(7): 641–642 Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe’er D, Nolan GP (2015) Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162(1):184–197. https://doi.org/10.1016/j.cell.2015.05.047 Liu P, Liu S, Fang Y, Xue X, Zou J, Tseng G, Konnikova L (2020) Recent advances in computerassisted algorithms for cell subtype identification of cytometry data. Front Cell Dev Biol. https:// doi.org/10.3389/fcell.2020.00234 Maecker HT, Trotter J (2006) Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry A 69(9):1037–1042 Malek M, Taghiyar MJ, Chong L, Finak G, Gottardo R, Brinkman RR (2015) flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31(4):606–607. https://doi.org/10.1093/bioinformatics/btu677 Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC (2014) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A 111(52):E5643–E5650. https://doi.org/10.1073/pnas.1408993111 McInnes L, Healy J, Melville J (2020) UMAP: uniform manifold approximation and projection for dimension reduction. J Open Source Software. https://doi.org/10.21105/joss.00861 Meskas J, Wang S, Brinkman R (2020) flowCut — an R package for precise and accurate automated removal of outlier events and flagging of files based on time versus fluorescence analysis. bioRxiv: the preprint server for biology. https://doi.org/10.1101/2020.04.23.058545 Miwa H, Mizutani M, Mahmud N, Yamaguchi M, Takahashi T, Shikami M, Shiku H, Tanaka I, Nakase K, Nasu K, Dohy H, Ueda T, Kamada N, Kita K (1998) Biphasic expression of CD4 in acute myelocytic leukemia (AML) cells: AML of monocyte origin and hematopoietic precursor cell origin. Leukemia 12(1):44–51 Monaco G, Chen H, Poidinger M, Chen J, de Magalhães JP, Larbi A (2016) flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics 32(16): 2473–2480. https://doi.org/10.1093/bioinformatics/btw191 Montante S, Brinkman RR (2019) Flow cytometry data analysis: recent tools and algorithms. Int J Lab Hematol 41(Suppl 1):56–62. https://doi.org/10.1111/ijlh.13016 Morstyn G, Hsu SM, Kinsella T, Gratzner H, Russo A, Mitchell JB (1983) Bromodeoxyuridine in tumors and chromosomes detected with a monoclonal antibody. J Clin Invest 72(5):1844–1850. https://doi.org/10.1172/jci111145
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Sladek TL, Jacobberger JW (1993) Flow cytometric titration of retroviral expression vectors: comparison of methods for analysis of immunofluorescence histograms derived from cells expressing low antigen levels. Cytometry 14(1):23–31 Spidlen J, Leif RC, Moore W, Roederer M, Brinkman RR (2008) Gating-ML: XML-based gating descriptions in flow cytometry. Cytometry A 73A(12):1151–1157 Spidlen J, Gopalakrishnan N, Hahne F, Ellis B, Gentleman R, Dalphin M, LeMeur N, Purcell B, Jiang W (2021) Utilities for flow cytometry. White Paper – Bioconductor. https://www. bioconductor.org/packages/devel/bioc/manuals/flowUtils/man/flowUtils.pdf. Accessed 23 Oct 2021 Sreenan JJ, Tbakhi A, Edinger MG, Tubbs RR (1997) The use of isotypic control antibodies in the analysis of CD3+ and CD3+, CD4+ lymphocyte subsets by flow cytometry. Are they really necessary? Arch Pathol Lab Med 121(2):118–121 Sun Y, Lin G, Zhang R, Zhang K, Xie J, Wang L, Li J (2012) Multicolor flow cytometry analysis of the proliferations of T-lymphocyte subsets in vitro by EdU incorporation. Cytometry A 81(10): 901–909. https://doi.org/10.1002/cyto.a.22113 Sutherland DR, Anderson L, Keeney M, Nayar R, Chin-Yee I (1996) The ISHAGE guidelines for CD34+ cell determination by flow cytometry. J Hematother 5(3):213–226 Uhrmacher S, Erdfelder F, Kreuzer KA (2010) Flow cytometry and polymerase chain reactionbased analyses of minimal residual disease in chronic lymphocytic leukemia. Adv Hematol. https://doi.org/10.1155/2010/272517 Van Bockstaele F, Janssens A, Piette A, Callewaert F, Pede V, Offner F, Verhasselt B, Philippe J (2006) Kolmogorov-Smirnov statistical test for analysis of ZAP-70 expression in B-CLL, compared with quantitative PCR and IgV(H) mutation status. Cytometry B Clin Cytom 70(4): 302–308 Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605 van der Strate B, Longdin R, Geerlings M, Bachmayer N, Cavallin M, Litwin V, Patel M, PasseCoutrin W, Schoelch C, Companjen A, Fjording MS (2017) Best practices in performing flow cytometry in a regulated environment: feedback from experience within the European Bioanalysis Forum. Bioanalysis 9(16):1253–1264 Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, Saeys Y (2015) FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87(7):636–645. https://doi.org/10.1002/cyto.a.22625 Wang S, Brinkman RR (2019) Data-driven flow cytometry analysis. Methods Mol Biol 1989:245– 265. https://doi.org/10.1007/978-1-4939-9454-0_16 Watson JV (1992) Flow cytometry data analysis. Basic concepts and statistics. Cambridge University Press, Cambridge Watson JV (2001) Proof without prejudice revisited: immunofluorescence histogram analysis using cumulative frequency subtraction plus ratio analysis of means. Cytometry 43(1):55–68 Wells AD, Loken MR (2008) Flow cytometric mean fluorescence intensity: the biophysics behind the number. Leuk Res 32(6):845–846 Young IT (1977) Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources. J Histochem Cytochem 25(7):935–941
Chapter 13
Standards, Setup, Calibration, and Control Techniques
It is important to anticipate some terminological clarification to understand better the topics covered in this section since the term “calibration” assumes different meanings depending on the geo-linguistic context. The International Dictionary of Metrology states that calibration is “that operation which, under specific conditions, is in the first place able to establish a relationship between the quantitative values produced by the standards and the quantitative values indicated for them, and secondly, to use this information to establish a report aimed at producing a measurement based on the quantitative values indicated” (Working Group 2 of the Joint Committee for Guides in Metrology (JCGM/WG 2) 2008). In the English-speaking world, the term “calibration” includes (1) the above definition and (2) the operations needed to define the metrological features of an instrument, usually performed by the manufacturer and beyond the possibility of the operator. In Europe, the term “calibration” is often mistakenly used to define the operations performed on an instrument to optimize the measurement. These operations should more properly be called “adjustment” or even “setting” or “setup.” In the context of fluorescence quantification, the term “calibration” also identifies “an empirical system aimed at calibrating the fluorescence intensity in a way that preserves the stoichiometric ratio present between the concentration of the fluorochrome in solution and equivalent molar amount of fluorochrome present on the object of the measurement, whether it consists of cells, microspheres or other” (NCCLS 2004a). This system is called QFC, an acronym for Quantitative Fluorescence Calibration.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_13
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Standards in Flow Cytometry
A standard is a sample with a known value. In Flow Cytometry, this value can be approximatively declared (low, high, very high) or also certified with accuracy (e.g., 123,456 MESF). The certified value can refer to different parameters, such as the diameter of spherical microbeads, the fluorescence intensity, the number of binding sites, or the number of microbeads making up the standard itself. Depending on the needs, a standard can be exploited in (1) calibration procedures, where it is called “the calibrator,” (2) verification procedures, where it is called “the standard” or simply “the control,” and (3) instrument setup. The standards are also exploited in Quality Management procedures, where they are called internal controls or external controls according to a series of different conditions, not least whether their value is known or not to the operator. Finally, the standards can be natural or artificial.
13.1.1
Natural Standards
The most frequently used natural standards in Flow Cytometry are generally employed in the quantitative evaluation of DNA, and consist of aneuploid cell lines with known DNA index (Tannenbaum et al. 1978), unstimulated peripheral human lymphocytes (Tannenbaum et al. 1978; Dressler 1990), chicken nucleated red blood cells, also called CRBCs (Noguchi and Browne 1978; Tannenbaum et al. 1978; Vindelov et al. 1983; Dressler 1990), rainbow trout nucleated red blood cells, also called TRBCs (Vindelov et al. 1983; Iversen and Laerum 1987; Vindelov and Christensen 1990; Dressler 1990), and calf thymocyte nuclei (CTNs) (Becton Dickinson 2015). Before use, these standards require staining with a stoichiometric DNA probe (usually Propidium iodide) and are exploited in several situations, including: 1. The evaluation of the aneuploidy index (Iversen and Laerum 1987) since the DNA content of these standards is known and constant; in particular, the non-stimulated human lymphocytes behave naturally as an internal diploid standard, while the nucleated RBC are used because their DNA content corresponds to a known fraction of that of human diploid cells, and precisely 35% for CRBC, and 80% for TRBC (Vindelov et al. 1983). 2. The evaluation of the imprecision of the measurement, performed by studying the coefficient of variation of a series of consecutive determinations, based on the fact that the natural variability of the DNA content in nucleated red cells or resting lymphocytes is lower than 2% (Shapiro 1983; Thomas et al. 2001). 3. Instrumental adjustment procedures, also made possible by the low natural variability of this standard type.
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Unlike other natural standards, calf thymocytes are in all cell cycle phases. They include doublets and aggregates to be used to assess the linearity of the instrumentation and the reliability of the pulse analysis procedures (Becton Dickinson 2015). Another type of natural standard consists of lyophilized or stabilized cells displaying a known phenotype. They are exploited in Internal Quality Control (IQC) or External Quality Assessment (EQA) activities cyclically held in clinical diagnostic laboratories (for further information on these topics, see Sects. 13.1.3.1 and 13.1.3.2). A particular type of natural standard, which behaves as an internal type III standard (see below) and is immediately available in human immunophenotypic analysis, consists of cellular populations inside the sample which express antigens with a known and constant intensity. The antigens in question are CD4 (Gratama et al. 1998) and CD45 (Bikoue et al. 2002) on fresh lymphocytes and CD38 on steady-state monocytes (Coetzee et al. 2009). Finally, a very particular natural standard is worth mentioning: the bacterium Escherichia coli, which possesses a DNA composed of AT and GC bases in equal parts and consequently provides useful information on the base pair preference of the DNA binding fluorochromes (Shapiro 2016). Chicken Red Blood Cells (CRBCs), calf thymocyte nuclei (CTNs), and other natural standards are made commercially available by BioSure (https://www. biosure.co.uk/).
13.1.2
Artificial Standards
Artificial standards generally consist of microscopical beads made of polystyrene; depending on the diameter, they can be distinguished in nanobeads (20–500 nm) and microbeads (0.5–5 μm and beyond) (Grützkau and Radbruch 2010). These microbeads can be fluorescent, either binding the fluorescent molecules by covalent bonds or entrapping them in their polymeric matrix (hard dyed microbeads) (Arshady 1993). The fluorochromes which make the beads fluorescent can be all-purposes specially synthesized, usually not disclosed molecules, or well-known molecules like DAPI, FITC, APC, et cetera. The former is used in type IIa and IIIa standards, whereas the latter is used in type IIb and IIIb standards; it follows that some standards can be excited by all the main lines available in a cytometer and give a signal along the whole spectrum, whereas others are more selective and can only be used in specific configurations. The fluorescent intensity can be unstated or declared according to various measurement units, including the Molecules of Equivalent Soluble Fluorochrome (MESF), which define the fluorescence intensity produced in the same analytical conditions by an equivalent number of molecules of the same fluorochrome in solution (Vogt et al. 1989; Schwartz et al. 2002, 2004) (for further information on this topic, see Sect. 13.4.2).
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Table 13.1 Classification of commercially available artificial standards Binding sites No
Multiple subsets No
Yes (very bright intensity)
No
No
Yes (intensity lower than type I standards, broadspectrum) Yes (intensity lower than type I standards, single fluorochrome) No
No
No
No
No
Reference, compensation
Calibrite3,7
Yes
No
Compensation
Yes (arbitrary intensity, no MESF calibration) Yes (MESF calibrated intensity)
No
Yes
Instrumental performance
CompBeads3,6, Compensation Beads9, OneComp8, UltraComp8, VersaComp2 Rainbow1,3,5–7
No
Yes
No
Yes
Yes
Instrumental performance, calibration in MESF# Calibration in ABC
Type 0
Fluorescence No
Ia, Ib
IIa
IIb
IIc
IIIa
IIIb
IIIc
Intended use Evaluation of noise level* Set-up, evaluation of precision Reference
Commercial example Certified Blank1 Fluoresbrite1,5
Fluorosphere2
Rainbow,3,5–8 (#limited to MESF-calibrated beads), Quantum™ MESF1 Quantum Simply Cellular1,5 Qifi Kit4
Manufacturers/distributors include but are not limited to 1Bangs, 2Beckman Coulter, 3Becton Dickinson, 4Dako, 5Polysciences, 6Spherotech, 7Takara, 8Thermo Fisher Scientific, and 9 BioLegend MESF-calibrated IIIb standards are available for the following molecules: AF4881,5, AF6471,5, APC1,5,6, FITC1,5,6, GFP6,7, mCherry7, Pacific Blue1, PE5,6, and PE-CY51,5,6
Some standards are not fluorescent but can bind other molecules, such as conjugated antibodies of various species, Fluorescent Proteins (mostly GFP), or fluorescent amine molecules similar to those commonly used to determine cell viability. Artificial (or natural) standards are usually exploited in evaluating fluorescence signals. Standards for intrinsic parameters also exist, even though their use can be quite problematic since their scatter values depend on the refraction indices, which vary according to the experimental conditions. Nevertheless, at least two different standard types are commercially available for this purpose (for further information on this topic, see Sect. 13.4.5). Artificial standards are classified into different types based on their size and other features (Table 13.1) (Schwartz and Fernandez-Repollet 1993).
13.1
Standards in Flow Cytometry
13.1.2.1
209
Type 0 Artificial Standards
Type 0 standards include non-fluorescent microbeads whose autofluorescence signal is lower than non-stained human lymphocytes (Schwartz and Fernandez-Repollet 1993). Type 0 standards are mainly used in instrumental performance monitoring (Figs. 13.1 and 13.2) and, combined with other type of standards, can be exploited in a series of different situations. As a matter of fact:
0
25
50
Count
75
100
125
1. Combining a type 0 and a type IIc capture standard allows reproducible and reliable compensation procedures (see below in this chapter). 2. Combining a type 0 and a type IIIb standard allows identifying the lowest level of fluorescence detectable by a given cytometer, which corresponds precisely to the fluorescence level measured for a non-fluorescent standard, such as a type 0 standard; this level is called “Detection Threshold” (DT) and is one of the four Primary Performance Parameters (Fig. 13.2) (Schwartz et al. 1996) (for further information on this topic, see Sect. 13.1.2.5). 3. Combining a type 0 and a type IIIb standard makes it possible to standardize the PMTs setting as well (for further information on this topic, see Sect. 13.3). 4. Combining a type 0 and a type IIIc capture standard makes it possible to perform calibration in FLU (Fluorescence Intensity Unit), an indispensable premise for the evaluation of the background values (B) and optoelectronic efficiency (Qr) of
-106
0
102
103
104
FL1 FITC-A
Fig. 13.1 Typical histogram produced by fluorescence analysis of a type 0 standard
105
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Fig. 13.2 Histogram produced by the simultaneous analysis of a mix made up of a type 0 (light blue in the picture) and a type III standard (gray in the picture) (MESF molecules of equivalent soluble fluorochrome, DT detection threshold, ZCV zero channel value)
every single channel of a given instrument (Perfetto et al. 2014) (for further information on this topic, see Sect. 13.4.4). Interestingly, it has been stated that the robust standard deviation (rSD) of a type 0 standard is the expression of the instrumental background (Bcal) (Perfetto et al. 2014). Standards of this type, also known as “certified blank” (Schwartz et al. 1998), are made commercially available by several commercial Companies, including Bangs, BioTrend, KyvoBio, and Polysciences.
13.1.2.2
Type I Artificial Standards
Type I standards (Fig. 13.3) include fluorescent microbeads with bright emission intensity, known spectral features, and known dimensions; depending on the diameter, they split into Ia (2 μm) and Ib (5–10 μm) types (Schwartz et al. 1998). These standards find their utilization in the optical bench adjustment procedures, where it is necessary to obtain the maximum fluorescence intensity value with the minimum
Standards in Flow Cytometry
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Count
0
100
200
300
400
500
600
13.1
-106
0
102
103
104
105
FL1 FITC-A
Fig. 13.3 Histogram produced by fluorescence analysis of a type I standard
CV (for further information on this topic, see Sect. 13.2). Moreover, they can be used in precision assessment. Standards of this type are made commercially available by several companies, including Bangs (Fluoresbrite), Beckman Coulter (Flow Check), Biotrend (Fluoresbrite), Bioz (Fluoresbrite), Clinisciences (Fluoresbrite), Cosmobio (Fluoresbrite), Fisher Scientific (Flow Check), Histoline (Fluoresbrite), Hexabiogen (Fluoresbrite), KyvoBio (Fluoresbrite), Polysciences (Fluoresbrite), Sysmex (Calibration beads), and Tebu-bio (Fluoresbrite).
13.1.2.3
Type II Artificial Standards
Type II standards display an emission intensity lower than type I standards and find their main utilization as reference standards to set the analysis window. They are split into type IIa, IIb, and IIc standards.
Type IIa Type IIa standards consist of fluorescent microbeads conjugated with a broad spectrum fluorochrome. These standards find their main utilization as a reference
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but can be useful in the optical bench adjustment procedures and the follow-up of instrumental performances through control charts maintenance. An example of this standard type is Fluorosphere, made commercially available by Beckman Coulter. A particular type II artificial standard is a broad spectrum emission standard, which can be found as dried matter in the bottom of special tubes or added as a suspension in fixed amounts, depending on the manufacturer. Each dose, dried in the tube or added to it, contains a known and certificated number of fluorescent microbeads, which can perform as an internal control in the single platform absolute evaluation of a particular cell type, like the CD34 positive hematopoietic stem cell (HSCs) (for further information on this topic, see Sect. 4.2.4.2). This type of standard is marketed by many manufacturers, including Becton Dickinson (Trucount) and Beckman Coulter (Flow-Count™ Fluorospheres) (Brocklebank and Sparrow 2001).
Type IIb Type IIb standards consist of fluorescent microbeads conjugated with a specific fluorochrome. These standards find their main utilization as a reference but can be useful in compensation procedures and the follow-up of instrumental performances through control charts. An example of this standard type is Calibrite, made commercially available by Becton Dickinson.
Type IIc Type IIc standards are non-fluorescent microbeads, also known as “capture standards” or “capture beads,” characterized by a number of binding sites specific to a given species’ immunoglobulins. Depending on the manufacturers, they are separately marketed as specific for mouse, rat, hamster, rabbit, donkey, and recombinant human antibodies. Type IIc standards, when stained with the same Mabs used to tag the samples and analyzed in conjunction with a type 0 standard, find their utilization in compensation procedures, where they behave as standards binding the fluorochromes exploited in the staining procedures. This point is particularly important because the commercially available standards can have different spectral characteristics from those used in staining procedures (Vogt et al. 1994; Schwartz et al. 1998), not to mention the tandem fluorochromes, which display different spectral behavior from batch to batch and even in the same batch over time (for further information on this topic, see Sect. 15.5). From a theoretical point of view, the results obtained with microbeads or cells should be the same; nevertheless, there are isolated cases in which it is advisable to compensate using cells instead of capture beads. These cases concern the AmCyan fluorophore (DiGiuseppe and Cardinali 2011) and some proprietary polymeric molecules of the Brilliant Violet or Super Bright series and their tandems (Richter 2018). Moreover, it has been reported that some proprietary buffers used in the management of polymer dyes can affect the spectral behavior of some latex beads; great attention
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Standards in Flow Cytometry
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should be paid in these selected cases, comparing the spectral behavior of beads and cells stained with the same fluorochrome (Ferrer-Font et al. 2020). Type IIc standards specifically devised for fluorescence compensation are made commercially available with different names by different producers/distributors, including Beckman Coulter (VersaComp), Becton Dickinson (CompBeads), BioLegend (Compensation Beads), and Thermo Fisher Scientific (OneComp and UltraComp).
13.1.2.4
Type III Artificial Standards
0
100
200
300
Count
400
500
600
Type III standards (Fig. 13.4) consist of a mixture of multiple fluorescent microbead subsets (generally from four to eight) with an approximate diameter of around 30 micrometers (μm). They find their main utilization in calibration; follow-up of instrumental performances is also feasible, both through maintenance control charts and by calculating Primary Performance Parameters (for further information on this topic, see Sect. 13.1.2.5). Depending on their different characteristics, they split into:
-106
0
102
103
104
105
FL1 FITC-A
Fig. 13.4 The behavior of a type IIIb standard in conditions of sufficient resolution of a low-intensity signal. Note how the coefficient of variation of the histograms produced by the analysis of the various standards tends to increase with decreasing fluorescence intensity, and this is because of the Poisson law applied to photoelectron statistics (fewer fluorescent molecules bound to the microsphere ! fewer counted photo-electrons ! increase in spread measurement ! enlargement of the base of the histogram representing the phenomenon)
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Fig. 13.5 The intercept of the calibration curve with the ordinate axis configures the minimum level of fluorescence theoretically detectable by the system. This value is called the zero channel value (ZCV) and corresponds to analysis window’s left boundary. The fluorescence value measured on the type 0 standard (blue in the picture) takes the detection threshold (DT) name and establishes the minimum significant signal measurable with that cytometer. DT should not be confused with the blank limit (LOB, see below), as LOB presupposes the measurement of unmarked cells, whose behavior is not necessarily that of a type 0 standard, an artificial standard
1. Type IIIa standards, consisting of multiple fluorescent microbead subsets, each having a different but not specified level of fluorescence intensity. 2. Type IIIb standards, consisting of multiple fluorescent microbead subsets, each having a different and specified level of fluorescence intensity, expressed in MESF (Mean Equivalent Soluble Fluorochrome) (Fig. 13.5). 3. Type IIIc standards, consisting of multiple non-fluorescent microbead subsets, each having a different and specified number of antibody binding sites, expressed in ABC (Antibody Binding Capacity). Type IIIa Type IIIa standards are a mixture of multiple microbead subsets which display increasing fluorescence intensity. Still, the fluorescence intensity is expressed in arbitrary units; it follows that calibration is impossible. However,
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Standards in Flow Cytometry
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Fig. 13.6 The figure shows an example of the empirical information provided by observing a type IIIb standard behavior in a determinate range of the scale (red bar). In panel a, the behavior suggests a problem in the resolution of low-intensity signals; in panel b, the behavior suggests a problem in the linearity of the measurement. Given the diversity of different optical benches, the histogram interpretation should always occur by comparing data already produced by the same standard in the same instrument in optimal conditions
1. The visual inspection of the behavior of the peaks in the first decades of the histogram allows a rapid evaluation of the resolution, i.e., the ability to resolve events characterized by low fluorescence intensities (Fig. 13.6). 2. The visual inspection of the behavior of the peaks in the intermediate decades can offer summary indications on the measurement’s linearity (Fig. 13.6). 3. The measured value of the last peak can provide information useful for the instrumental follow-up (Wang and Hoffman 2017). A multicolor type IIIb standard does not always resolve all the populations in every channel, depending on the instrument. The histogram interpretation must compare with data previously produced in known optimal conditions by the same standard in the same channel.
Type IIIb Type IIIb standards are a mixture of multiple microbead subsets which display increasing fluorescence intensity certified in MESF units. It follows that type IIIb standards can be used as calibrators (Schwartz et al. 1996) (Fig. 13.5) and make quantitative analysis feasible. For calibration purposes, type IIIb standards are also marketed conjugated with the Fluorescent Proteins mCherry and EGFP (Enhanced Green Fluorescence Protein), calibrated in MESF (Becton Dickinson 2002) (see also
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https://www.takarabio.com/products/gene-function/fluorescent-proteins/flowcytometer-calibration-beads). Calibration curves provide the value of some parameters useful to monitor the instrument’s performance; these parameters are known as Primary Performance Parameters (PPPs) (Schwartz et al. 1996) and are derived by the straight-line equation (for further information on this topic, see Sect. 13.1.2.5. The combined use of a Type 0 and a Type IIIb standard makes it possible to standardize the PMT settings (for further information on this topic, see Sect. 13.3), and, with the further aid of a Type IIIc capture standard, to perform calibration in FLU (FLuorescence Intensity Unit) (Perfetto et al. 2014) (for further information on this topic, see Sect. 13.4.4). Standards belonging to group IIIb are marketed by various manufacturers and distributors with various names, including Rainbow (Becton Dickinson) or CytoCal™ (Beckman Coulter).
Type IIIc Standards in Daily Practice Type IIIc standards are a mixture of multiple non-fluorescent microbead subsets, which display an increasing number of binding sites calibrated in ABC (antibody binding capacity) and specific to a given species’ immunoglobulins. Accordingly, they are separately marketed as specific for mouse, rat, and human antibodies. Type IIIc standards can be used as calibrators (Schwartz et al. 1996) and make quantitative analysis feasible. The use of a type IIIc capture standard, combined with a Type 0 and a Type IIIb standard, allows for calibration in FLU (FLuorescence intensity Unit) (Perfetto et al. 2014) (for further information on this topic, see Sect. 13.4.4). A particular use of type IIIc calibrators is the quantitative evaluation of events marked with indirect staining techniques (Poncelet and Carayon 1985) (for further information on this topic, see Sect. 13.4.3.1). Standards belonging to group III are marketed by various manufacturers and distributors, including Bangs, Beckman Coulter, Becton Dickinson, Dako, Polysciences, Sigma, and Spherotech.
13.1.2.5
Primary Performance Parameters (PPP)
Primary Performance Parameters were especially relevant in the era of analog instrumentation but yet remain a useful and inexpensive tool for an easy follow-up of the instrumental behavior; they include (1) the average percentage of deviations, or Average Residual Percentage (ARP), (2) the fluorescence value relative to the Zero Channel Value (ZCV), (3) the Detection Threshold (DT) and (4) the Response Coefficient (CR) (Fig. 13.5).
13.1
Standards in Flow Cytometry
217
The Average Residual Percentage (ARP) is the average of the absolute deviation percentages between the declared and experimentally determined values; it provides an estimate of the system’s linearity. The Zero Channel Value (ZCV) results from the calibration curve’s intersection with the ordinates’ axis and corresponds to the value b of the straight line equation (Fig. 13.5). The value of ZCV corresponds to the lowest fluorescence value theoretically detectable and represents the beginning of the Analysis Window, defined as the fluorescence range theoretically measurable by the system for a particular instrumental setup (Schwartz and Fernandez-Repollet 1993). ZCV provides an estimate of the system’s sensitivity. The Detection Threshold (DT) is the fluorescence value provided by analyzing a fluorescence-free standard, i.e., a type 0 standard (Fig. 13.5). The DT must not be confused with the limit of blank (LOB, see below), produced by the measurement of unlabeled cells whose behavior is not necessarily the same as a type 0 standard. DT also provides an estimate of the system’s sensitivity. The Coefficient of Response (CR) is a measure of the dynamic range of the fluorescence channel and a function of the calibration curve slope (Schwartz et al. 1996), which, according to the IUPAC definition, identifies the sensitivity in the “slope of the calibration curve” (IUPAC 2014); it follows that the CR is also a further indicator of the instrument’s sensitivity. If the signal is amplified or transformed logarithmically, the CR should correspond to the number of channels assigned by the regression line at each decade of the logarithmic scale; this number must be equal to the number of theoretical channels produced by the analog-to-digital converter (ADC) divided by the number of available decades, with an approximation of less than 10%. The CR’s importance was crucial in old analog instruments but now is significantly diminished in light of the availability of high-performance digital electronic circuits, making this parameter meaningless in practice.
13.1.3
Standard Use in Quality Procedures
Besides adjustment checks, quality procedures in a clinical setting require monitoring the analytical results using two main tools, i.e., Internal Quality Controls (IQCs) and External Quality Assessments (EQAs).
13.1.3.1
Internal Quality Controls (IQCs)
Internal Quality Controls (IQCs) rely on the systematic measurement of a biological standard of known value. The repeated measurements allow building control charts to ensure the analytical process consistency over time. Each analyte under analysis in a Laboratory should have its control; this target can be easily reached in a Chemical Chemistry Laboratory, but it is virtually impossible in a Clinical Cytometry setting because controls are available only for the most frequently analyzed antigens and
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few other critical markers, like TdT or CD34. Where available, these controls usually consist of lyophilized or stabilized cells; they should be managed like other cells to check all the analytical process phases. Of note, control charts can also be built with the systematic measurement of artificial standards.
13.1.3.2
External Quality Assessments (EQAs)
External Quality Assessments (EQAs) measure a biological standard of unknown value. The measurement results are then compared with the results from all the laboratories enrolled in the activity or an accepted and certified result. EQAs are difficult to organize for two reasons, i.e., for the difficulty of keeping the sample suitable over time and the substantial unavailability of a sufficient quantity of sample, especially for pathological material. A new possibility is setting up an external “in silico” quality assessment and distributing files in FCS format instead of samples to be analyzed. This activity would be restricted to evaluating the postanalytical phase alone but could enroll a virtually unlimited number of participants. A few EQA exercises are commercially available for the clinical Flow Cytometry laboratory and allow monitoring a series of analytical procedures, including the evaluation of the main lymphocyte subpopulations, the quantification of CD34+ cells, the detection and quantification of PNH clones, and the immunophenotypic diagnosis of oncohematology clinical cases. The government agencies of many Countries expect clinical cytometry laboratories to participate in EQAs, access to which is made available by the agencies themselves or by a few commercial distributors, among which Instand (https://www.instand-ev.de/) and UK-NEQAS (https://ukneqas.org.uk).
13.2
Optical Bench Setup
The optical bench setup is also known as “adjustment” or “optimization.” It is sometimes called “alignment,” but this is a mistake because “alignment” is a Quality System procedure consisting of harmonizing different instruments assigned to the same analysis (Vidali et al. 2020). The optical bench setup optimizes the spatial relationships between the light source, the interrogation point, and the detectors. Its purpose is to achieve the best possible signal collection conditions, i.e., collecting the largest possible number of photons produced by the event’s interaction with the incident radiation. The procedure consists of tweaking and tuning the optical system’s components (path and focus of laser’s beam(s), obscuration bar position, the opening of field-stop diaphragms, et cetera); the procedure is repeated until both the coefficient of variation and the intensity of the signal produced by an appropriate standard appear no more improvable (Fig. 13.7) (Jett et al. 2001, 2009).
13.2
Optical Bench Setup
219
Fig. 13.7 Schematic representation of the fluorescence signals produced by analyzing a type I standard before (panel a) and after the optical bench adjustment (panel b). After adjustment, both count precision and signal intensity drastically increase
Unlike old stream-in-air cell sorters, where the adjustment was a procedure to be performed often, if not every day, in modern bench-top analyzers, it is performed by the assistance personnel at the time of installation and is to be checked only in the case of deviations from the expected behavior. It is interesting to note that the optical bench adjustment procedures must be monitored with the help of fluorescence signals because it has been shown that there is no relationship between the quality of the adjustment and the coefficient of variation of scattering signals (Doornbos et al. 1994). After adjustment, instead of producing a symmetrical cluster of restricted dimensions, the standard can produce not a spot but an irregular shape figure, which at higher magnification results in a figure of Lissajous (Fig. 13.8) (for further information on this topic, see Sect. 2.2). The standards used in the optical bench adjustment procedures belong to type I or II. Besides the optical bench adjustment, the optimization of a cytometer may include the check of the time delay (for further information on this topic, see Sect. 6.4.3.3), the check of the noise control (for further information on this topic, see Sect. 13.5.3), and the setup of the photodetectors (for further information on this topic, see Sect. 13.3) (Perfetto et al. 2006, 2012).
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Fig. 13.8 Bivariate representation of FSC and SSC scatters of a microbeads population used in instrumental adjustment procedures. After adjustment, instead of producing a symmetrical cluster of restricted dimensions, the standard produces an irregular shape (panel a), which at higher magnification (panel b) results in a figure of Lissajous (for further information on this topic, see Sect. 2.2)
13.3
Photodetectors’ Setup
This section’s considerations are related to the immunophenotypic analysis of human cells, but the underlying principles are theoretically valid in all situations. The procedure’s primary purpose is to balance background reduction and the best signal collection. This operation’s methods are conceptually similar for both PMTs and APDs but may vary according to their different technical characteristics. Practically, it is based on tweaking the voltage applied to the PMTs, or the gain applied to the APDs’ outputs (“voltration” and “gaintration,” according to two gruesome neologisms).
13.3.1
SDen (Electronic Noise Standard Deviation)
The variance of the electric signal, also electronic noise, is the main source of the instrumental background Bcal (for further information on this topic, see Sect. 8.1). The variance of the electric signal differs between instruments and even in the same instrument depending on the setup values (voltration or gaintration status). The dependence of the electric noise on the setup values is because, in a flow cytometer, the electrical signal is heteroskedastic, i.e., it results from a series of different components, each displaying its variance, which behaves differently (Bagwell et al. 2016). It follows that the different ratios between the components
13.3
Photodetectors’ Setup
221
can unexpectedly affect the global electric signal variance. The Standard Deviation of the electronic noise (SDen) is a very useful parameter because: 1. Allows the objective and reproducible evaluation of the Bcal background in a given instrument 2. Contributes to the evaluation of the sensitivity in a given instrument 3. Allows the comparison between different instruments 4. Allows the choice of the best conditions for the measurement, intended as the best trade-off between noise and signal intensity, i.e., the best S/N ratio, which can also be defined as the ratio between the median of the positive and the median of the negative population (Maecker and Trotter 2006) The determination of the SDen is a rather complex procedure, which, if needed, should be performed for each fluorescence channel. This procedure relies on the following steps (Bhowmick et al. 2020): 1. Analysis of a Rainbow type standard at different voltration or gaintration levels 2. Recording of peak 2 rCV (rCV2) and peak 2 MFI (MFI2) for each voltration or gaintration level 3. Drawing a plot of the relationships between 1/MFI2 and rCV2 for each voltration or gaintration level 4. Calculating the slope of the linear regression line in the plot mentioned above 5. Assuming the slope of this line as the variance of the electronic noise 6. Obtaining the SDen performing the square root of this variance
13.3.2
PMT Setup
Historically, the setup of photomultipliers was empirically performed by applying to the PMT a voltage (voltration) high enough to place the negative control in the first decade approximately. This method is no longer recommendable with the advent of multicolor analysis and related compensation procedures (Maecker and Trotter 2006). Currently, there are several methods for setting up photomultipliers; in any case, it is necessary to consider three particularly relevant points. The first point consists of the fact that, according to the influence of the Poisson law on photoelectron statistics, the spread measurement of a signal behaves inversely proportional to its intensity. The second point is that the electronic noise standard deviation (SDen) increases with the power applied to PMTs. The third point is that the measurement should occur in the range in which the detector response is linear. From a practical point of view, there are several ways to determine the correct voltage to apply to photomultipliers. This section reports the following methods. The first method (Maecker and Trotter 2006) consists in: 1. Measuring the coefficients of variation (CV) and the standard deviation (SD) of the signal produced by different analyses of the same low fluorescence standard carried out at increasing voltage settings
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Fig. 13.9 Graph representing the relationships between the power supply to the photomultiplier (green curve in the picture), the coefficient of variation (blue curve), and the standard deviation (red curve) related to the analysis of a dimly fluorescent standard. The vertical bar identifies the power supply’s voltage, ensuring the best possible compromise between the two monitored variables
2. Choosing as operational the voltage from which the CV no longer decreases, but the SD does not increase yet (Fig. 13.9) The second method (Becton Dickinson 2008) consists in: 1. Establishing the median fluorescence intensity (MedianFI) of electrical noise, i.e., the MedianFI produced by a low fluorescence standard analyzed at the lowest possible voltage setting 2. Applying the voltage setting at which the same standard produces a value of MedianFI ten times greater; this PMT voltage is known as PMTV (Becton Dickinson 2007). The third method (Meinelt et al. 2012) consists in: 1. Measuring the SDen produced by different analyses of the same low fluorescence standard carried out at different voltage settings 2. Recording the SDen produced at the lowest possible voltage setting (the voltage at which it is still possible to appreciate the standard on the scale) 3. Adopting as operational the level at which the negative cell population’s analysis produces an SD equal to or no greater than 2.5 times the SDen Finally, a fourth method (Perfetto et al. 2012) consists in: 1. Performing multiple analyses of a sample made up of type 0 and a type IIIb standard, carrying out the analyses at different voltage settings spanning from minimum to maximum at 50 V intervals. 2. Choosing as operational the voltage at which (1) the best separation occurs between the negative population (standard type 0) and the first (weakest) positive population of the type IIIb standard, and (2) there is a consistency of separation between the first and second positive populations of the type IIIb standard.
13.4
Calibration
223
For digital instruments manufactured by Becton Dickinson, a package is commercially available (CS&T), which encompasses a standard and a program devised to optimize the PMT setup according to the principle referred to in point 1 (Becton Dickinson 2008). This program can define critical parameters as PMT voltages and measurement performance parameters (background and detector efficiency) and allows tracking them over time. This software requires pre-calibrated standards and is available for Becton Dickinson digital instruments (Meinelt et al. 2012).
13.3.3
APD Setup
A method exists for the digital CytoFlex instruments manufactured by Beckman Coulter to optimize the APDs set up according to the principle in point 1 of the past section (Bhowmick et al. 2020). Since APDs receive a fixed voltage, the serial measurements occur by incremental gains (gaintration) applied to the output signal and not by gradual voltage increases to the detectors (voltration). This method consists in: 1. Measuring the coefficients of variation (CV) produced by repeated analyzes of the same low fluorescence standard carried out at incremental gain values 2. Choosing as operational the gain from which the CV no longer decreases, keeping in mind that the CV rises again at the highest gain values according to this type of detector’s intrinsic characteristics
13.4
Calibration
Calibration is the operation that first establishes a relationship between the quantitative values produced by the standard measurement with the quantitative values attributed to those standards and then uses the information obtained to establish the quantitative value to be attributed to a sample of unknown value (Working Group 2 of the Joint Committee for Guides in Metrology (JCGM/WG 2) 2008). Calibration procedures are performed by analyzing standards, called “calibrators,” which belong to group IIIb of standards since they consist of a mixture of multiple microbead populations (generally from four to eight) expressing known and increasing values of the interest parameters. In Flow Cytometry, the fluorescence signals calibration procedures depend on the calibrators’ availability, which in practice exists only for AF4881,5, AF6471,5, APC1,5,6, FITC1,5,6, GFP6,7, mCherry7, Pacific Blue1, PE5,6, and PE-CY51,5,6 (1Bangs, 2Beckman Coulter, 3Becton Dickinson, 4Dako, 5Polysciences, 6 Spherotech, 7Takara, 8Thermo Fisher Scientific).
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Fig. 13.10 Schematic representation of the calibration curve for the fluorescein channel (FITC), with the fluorescence intensity values in the ordinate (MESF), and the instrument’s response in the abscissa
As for the calibration of physical parameters (light scatter), calibrators exist with known values expressed in nanometers (for further information on this topic, see Sect. 13.4.5). The calibrators constitute the independent variable in calibration procedures, and the instrument’s response is the dependent variable. From a graphic point of view, the independent variable is usually placed on the abscissa axis and the dependent on the ordinate axis. In the case of Flow Cytometry, it is tradition to invert this combination, reporting the instrument’s response to the abscissa and the values of the calibrators in the ordinate (Fig. 13.10); this choice is to maintain a graphic and conceptual analogy with the histograms produced by the instrument, which precisely report the values on the abscissa axis. However, this is an “aesthetic” choice that does not influence the results, providing that the regression line is calculated correctly. Consequently, a calibration curve correlates the log scale linear unit relative to each calibrator with the fluorescence intensity value indicated for that calibrator. This correlation only holds until changes occur in the instrumental setting. The inaccurate evaluation of fluorescence intensity is the bane of cytometric techniques in general and immunophenotyping in particular. Furthermore, the meager availability of fluorescent calibrators, the inaccurate behavior of logarithmic amplifiers (where present), the pre-analytical variables involved in staining procedures, and the differences between instruments (even of the same type) all contribute to worsening this issue. These factors affect the comparison between analyses carried out in different laboratories or even in the same laboratory over time.
13.4
Calibration
13.4.1
225
Calibration in ERF
In this calibration system, the parameter is the fluorescence, and the unit of the fluorescence intensity is the ERF (Equivalent number of Reference Fluorophore), i.e., the fluorescence intensity produced under the same analytical conditions by an equivalent number of fluorescent molecules in solution characterized by the same excitation and emission characteristics (Wang and Hoffman 2017). ERF calibrators can serve as secondary standards for MESF calibrators’ definition; an important role in this area has been proposed for the Quantum Dot molecules (Buranda et al. 2011).
13.4.2
Calibration in MESF
In this calibration system, the parameter is the fluorescence, and the unit of the fluorescence intensity is the MESF (Molecules of Equivalent Soluble Fluorochrome), i.e., the fluorescence intensity produced under the same analytical conditions by an equivalent number of molecules of the same fluorochrome in solution (Vogt et al. 1989; Schwartz et al. 2002, 2004). As such, the MESF calibration system represents a special case of the ERF calibration system, in which the reference fluorochromes are not just molecules with the same spectral behavior but precisely the same molecule used in the analyses. Many standards expressed in MESF have been calibrated in MESF thanks to a secondary standard calibrated in ERF.
13.4.3
Calibration in ABC
In this calibration system, the parameter is the binding activity, the unit of the binding activity is the ABC (Antibody Binding Capacity), and the calibrator consists of different subsets of microbeads, each of them conjugated with an increasing and known number of binding sites, i.e., sites which bind antibodies of the same species in which the Mab used in the staining has been raised. ABC calibration systems are not completely free of problems. Firstly, the calibrators only must be used with Mabs elicited in species recognized by their binding sites. Secondly, the binding kinetics must be well known to avoid incurring a system under- or over-saturation. Finally, even if in excess, the antibodies do not always behave as a monovalent probe (Junghans 1999).
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With Conjugated Antibodies
If stained with the same conjugated Mab exploited in the analysis, a calibrator calibrated in ABC allows the correlation between its fluorescence intensity and the number of binding sites. In a condition of antibody excess, each conjugated Mab molecule should behave as a monovalent probe, i.e., only engaging one out of its two variable regions. Consequently, the fluorescence intensity of a cell should be proportional to the number of binding sites, allowing expression intensity in ABC instead of MESF; it follows that it should be theoretically possible to set up a calibration system directly expressed in antigenic sites (Davis et al. 1998). One standard used in this situation is known as Quantum™ Simply Cellular® and is marketed by Bangs and Polysciences.
13.4.3.2
With Unconjugated Antibodies
A special calibration system in ABC exists to measure fluorescence intensity in labeling techniques with mouse Mabs not directly conjugated (Poncelet and Carayon 1985). This system is based (1) on adding a type IIIc standard calibrated in ABC to the sample to be stained and (2) on the subsequent staining of the resulting mixture with the unconjugated monoclonal antibody. After incubation and washing, all the events under analysis, i.e., cells and calibrators, display a number of binding sites unknown for the cells but known for the calibrators. Further staining of the system with a secondary conjugated anti-mouse antibody allows building a calibration curve from which it is possible to measure the antigen’s expression in ABC. One standard used in this situation is commercially known as QIFIKit® and is marketed by Agilent, Dako.
13.4.4
Calibration in FLU
In this calibration system, the parameter is the fluorescence, and the fluorescence intensity unit is the FLU (FLuorescence Intensity Unit), i.e., a non-arbitrary unit independent of the instrument and defining the actual number of photons delivered to the detector (Perfetto et al. 2014). This system also calculates the so-called “Panel Specific Separating Index” (SIps), which is a metric aimed at (1) evaluating the performance of individual detectors, (2) comparing their performance between different instruments, and (3) establishing detector efficiency (Q) and instrumental background (Bcal) in each fluorescence channel of every instrument under evaluation.
13.4
Calibration
227
The FLU calibration is a rather complex procedure, which is based on a series of different measurements made with different standards (Perfetto et al. 2014), and consists of: 1. Analyzing a six-level type IIIb standard (Cyto-Cal™ in the original paper), and selecting the first three low-intensity fluorescence subsets, defined on purpose as Dim-1, Dim-2, and Dim-3, and the most bright one, defined as “Bright” 2. Recording the channel number and the rCV of the four peaks 3. Analyzing a five-level-subset type IIIc standard (Quantum™ Simply Cellular® in the original paper) stained with a Mab conjugated with the fluorochrome measured by the channel under evaluation 4. Correlating the channel number provided by the type IIIb standard analysis with the known intensity of fluorescence produced by the IIIc standard analysis 5. Performing the calibration in FLU, based on the results referred to in point 4, of the three subsets Dim-1, Dim-2, Dim-3, and “Bright” 6. Obtaining the SD2 variance of Dim-1, Dim-2, and Dim-3, deducible from their robust CV calculated using the formula: SD2 ¼ FLU2 rCV2 dim rCV2 bright ; 1. Drawing a regression line for each “Dim” standard between FLU and SD2 values 2. Calculating the value of Q, i.e., the inverse of the slope 3. Calculating Bcal, i.e., the product of Q for the value of the intercept of the line on the ordinate axis) 4. Calculating SIPS according to the formula: SIPS ¼ MFIðmAbÞ
pffiffiffiffi Q=ðBCAL þ BSOS Þ,
where the MFI(mAb) is expressed in FLU units. The derived unit SIPS allows objectively comparing the performance of instrumental channels belonging to different instruments. Correct execution of this method dictates that the dim standards’ rCV values depend on photoelectron statistics and not on standard intrinsic variations; it follows that the comparison between different instruments must rely on the same standard batch.
13.4.5
Calibration in Nanometers
In this calibration system, the parameter is the event size, referred to as the diameter of a spherical event, and the unit of measurement is the nanometer. The size is a derivate parameter because the instrument measures the side scatter, which depends on the event’s size and the refractive index between the event and the solution (medium) in which the event is suspended. This index, in turn, depends on a
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series of other variables, including the standard’s features, the event’s features, the medium’s features, and the instrument’s constructive design. Despite these fundamental difficulties, at least two systems for calibration in nanometers are commercially available. The first system, known as “Rosetta calibration,” relies on a commercial standard consisting of multiple subsets of non-fluorescent polystyrene microbeads, each with a known refractive index and known and indicated dimensions ranging from 100 to 1000 nm (de Rond et al. 2018; van der Pol et al. 2018). Being made up of multiple microbead populations, “Rosetta calibration” is conceptually assimilable to a type IIIb non-fluorescent standard. In a few words, the procedure consists in: 1. Determining the median side scatter intensity and the rCV of the peak of each microbead subset of the standard. 2. Drawing a regression line between the median scatter intensity and the indicated size of each subset. 3. Calculating for each subset the fraction of light scattered into the detector; this step requires knowledge of several details regarding the cytometer used in the test and the solution of a series of equations based on Lorenz–Mie scatter theory. 4. Obtaining, through a computational approach and for each population of standards under analysis, the scaling factor F, i.e., the relationship between the crosssectional area expressed in square nanometers and the intensity of the light scattered into the detector. 5. Applying the scale factor F to the side scatter values of the analyzed events to derive their dimensions. The standard package, marketed by Exometry (https://www.exometry.com/ products/rosetta-calibration), comes with a special software allowing its customization with different instruments. The second system, known as “Megamix,” also relies on a commercial standard consisting of multiple subsets of fluorescent microbeads with known dimensions. It is mainly used to standardize the microparticle quantitative analysis and check the reproducibility of the measurement and the instrumental consistency. Megamix is made commercially available from Stago (https://www.stago.com/) and exists in different versions optimized for different instruments, including the CytoFlex (Spittler 2015). Finally, other standards made up of different subsets of microbeads with known sizes are (1) the Size Calibration Standards marketed by Polysciences and (2) the Flow Cytometry Size Calibration kit marketed by Thermo Fisher Scientific. Last but not least, software for light scatter calibration (FCMPASS) has been recently made available (Welsh et al. 2020) and is freely downloadable from https:// nano.ccr.cancer.gov/fcmpass/.
13.5
Instrument Performance and Its Control
13.5 13.5.1
229
Instrument Performance and Its Control Linearity
Linearity refers to a directly proportional relationship between two different quantities and their measured values. Thus, linearity plays a role in performing quantitative measurements and compensating for the spillover between channels; it is noteworthy that both these procedures are negatively affected by the heteroskedasticity of the signal. Lack of linearity strongly influences the measurement accuracy and affects the value of the spillover spreading produced by the compensation procedures. Linearity can be kept under control by monitoring a primary performance parameter extrapolated from the calibration curve. This parameter consists of the average of the absolute deviations (Average Residual Percentage, or ARP), or the average deviation percentages between MESF (or ABC) values indicated for the standards and the values experimentally determined based on the calibration curve. In the absence of problems related to the calibrators, this parameter’s high values suggest perturbations of the measurement’s linearity generally due to circuitry problems. In this context, the average residual percentage or average percentage of absolute deviations (ARP) is satisfactory when less than 3% for a calibration curve calibrated in MESF and less than 5% for a calibration curve calibrated in ABC (Schwartz et al. 1998). An empirical method to monitor linearity relies on controlling the ratio value between the different fluorescence intensities produced by doublets and singlets detected during the analysis of the DNA content of a sample of nucleated chicken red blood cells (CRBC) stained with Propidium iodide and excited at 488 nm. In this type of test, the data analysis must be carried out on the pulse’s Area (A) and not on its Height (for further information on this topic, see Sect. 8.1). Other methods exist, devised for analog instrumentations, which rely on the measurement of standards, and are fit both for linear and logarithmic amplifiers (Schmid et al. 1988; Bagwell et al. 1989).
13.5.2
Accuracy
Accuracy consists of the proximity between the value produced by the measurement and the “true” value. Like in Clinical Chemistry, a fluorescent daily internal control allows the Levey-Jennings control charts’ construction; in this case, the procedure relies on the daily monitoring of a series of variables, including the peak channel produced by the analysis of the standard and the voltage supplied to the photodetector (or the gain applied to its output) to keep the peak channel stable over time. Thanks to Internal Quality Controls (ICQs) and External Quality Assessment (EQA) procedures, accuracy can be constantly monitored in the Clinical Cytometry Laboratory. For further information on this topic, see also Sect. 13.1.3.
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Carry-over
The carry-over is the contamination of a sample by the previous sample; this issue is well-known in continuous flow systems; it is mostly seen when a sampler device is used in flow cytometers. For hematology analyzers, there are standardized procedures for evaluating the phenomenon (CLSI 2010; Vis and Huisman 2016), according to which the carryover is determinable by reading a high concentration sample in triplicate first (A1, A2, and A3), then a low concentration sample (B1, B2, and B3), and then applying the formula: carry over% ¼ ððB1 B3Þ=ðA3 B3Þ 100ÞÞ: This approach can also be applied to flow cytometers; acceptable values for normal operative conditions are below 1% (Wood et al. 2013). However, one should consider that in Flow Cytometry: 1. The staining procedures vary the volume of the sample empirically to maintain an antibody excess and, consequently, tend to restrict the range of the variability of samples concentration so that in practice, there is no—or there should not be— occurrence of high concentration samples before low concentration samples. 2. High concentration samples should be considered as such not based on the total number of cells but on the number of cells belonging to the subset of interest. 3. The assessment system applicable to hematology analyzers is conceptually inadequate in cases in which the carry-over assumes critical importance, as in the evaluation of the minimal residual disease (MRD), where the achievement of a limit of detection ranging between 105 and 106 requires specific methodological and empirical approaches (Gross et al. 1993; Donnenberg and Donnenberg 2007). 13.5.2.2
Count Inaccuracy at High Speed
A particular feature of accuracy in Flow Cytometry is counting a total number of events per arbitrary volume unit close to the number of events present in that volume. A certain degree of inaccuracy is a well-known problem, but it is not relevant since the cytometric analysis is often qualitative. However, accuracy can be very important in particular situations, such as cell sorting or absolute counts on a volumetric basis. The problem generally occurs for particularly high analysis rates and depends on the circuitry characteristics, i.e.: 1. In analog-type systems, it is due to the dead time of electronics that cannot analyze a new event when managing a previous one. 2. In digital systems, it is generally due to the difficulty encountered by the FPGA (field-programmable gate array) in judging as separate two distinct but close pulses due to relatively too low sampling frequencies.
13.5
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The difficulty encountered by the FPGA depends on two variables. The first is the value assigned to the Window’s Gate, which, if excessive, tends to increase the percentage of aborted pulses because of the overlapping of adjacent windows (Verwer 2002; Beckman Coulter 2015) (for further information on this topic, see Sect. 8.2.2.1). The second is the analysis threshold level, whose increase determines a better resolution between consecutive pulses but can hinder the distinction between low-value pulses (Duggan 2014). The counting inaccuracy at high speeds can be evaluated by creating a reference sample appropriately diluted to generate, in standard conditions, a counting rate of about 5000 events per second. Through centrifugation of various aliquots of this reference sample and their resuspension in appropriate volumes, it is possible to create samples with known and increasing concentrations, to analyze under the same flow rate conditions. The correlation between the number of counts per second declared by the instrument and the number of counts expected as a function of the resuspension volume allows for monitoring the possible loss of counts. In an FCS file, the key $LOST reports the number of events lost because the computer is performing other tasks; the key $ABRT also reports the number of lost events, but with a subtle difference: the lost events reported by the key $ABRT are due to coincidences.
13.5.3
Resolution
The resolution is the ability to detect small changes in the physical quantity under analysis. The resolution of a cytometric measurement depends on many factors, among which the number of intervals available to the measurement plays a pivotal role; this number, often but improperly called “bit density,” is a function of the efficiency of the digitization process, i.e., the number of bits available to the analogdigital converter (ADC). The resolution should not be confused with sensitivity but is related to it, as it will be made clearer in the next section. Indeed, the term “resolution limit” is often used to indicate the sensitivity of a cytometer, which is not entirely improper because, strictly speaking, the resolution limit refers to the ability to detect the smallest possible variation beyond the limit of detection (LOD). An empirical but very informative approach to evaluating the resolution of a cytometer can be made by visually observing a type III standard’s behavior, paying particular attention to the less fluorescent microbeads’ behavior in the first few decades of the scale (Fig. 13.6). The mutual distinction of the histograms produced by the less fluorescent microbeads indicates a satisfactory resolution, while the total or partial overlap of the histograms in question suggests a low resolution in that scale range. Depending on the instrument, a multicolor type IIIb standard does not always resolve all the populations in every channel; it follows that the visual histogram interpretation must compare with data previously produced in known optimal conditions by the same standard in the same channel.
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Another more reproducible approach involves the analysis of a type 0 standard (Y) combined with a dim positive standard (X); the procedure is aimed at the evaluation of the best gain to apply to an APD’s output, and the resolution is expressed with the formula (Bhowmick et al. 2020) res ¼ Median FI of X Median FI of Y =rSD of Y, where X is the dim positive standard and Y is the type 0 standard.
13.5.4
Sensitivity
From a strictly metrological point of view, a measurement system’s sensitivity consists of the relationship between the change in the indication produced by the measuring instrument and the corresponding change in the measured value (Working Group 2 of the Joint Committee for Guides in Metrology (JCGM/WG 2) 2008). In observance of this definition, the sensitivity can also be defined as the degree of the slope of the calibration curve, as it is clear that greater inclinations produce higher value ratios between the indication change and the corresponding value change, with a greater range of possible values corresponding to greater values of inclination (Fig. 13.11). Given that the change in the indication also depends on the system intrinsic capacity to detect small variations, it is evident how the sensitivity also depends on the instrument’s resolution (Working Group 2 of the Joint Committee for Guides in Metrology (JCGM/WG 2) 2008). Therefore, it is correct to state that a cytometer’s
Fig. 13.11 Schematic representation of two different calibration curves characterized by different slopes (a and a0 ). The \ curve in panel a shows a greater slope (a > a0 ) than panel b’s curve. The test referred to in panel a’s calibration curve is more sensitive than that to which the calibration curve in panel b refers, and its range R of measurable values is greater than the range R0
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sensitivity also depends on the number of bits available to its analog-to-digital converter (ADC), acknowledging that sensitivity and resolution are two intertwined but different concepts not to be confused. From an “epidemiological” point of view, sensitivity corresponds to a test’s ability to identify as positive the sick subjects in a population; in this context, sensitivity is the opposite of specificity, which corresponds instead to the capacity of a test to identify as negative the healthy subjects. Consequently, and by analogy, in Flow Cytometry, sensitivity could be seen as the ability to produce a signal in the presence of a positive event. In contrast, specificity could be seen as the ability not to produce signals in the absence of positive events. This concept is of extraordinary importance for assessing the sensitivity of analyzes to determine rare events and developing the concepts of the limits of blank (LOB), detection (LOD), and quantification (LOQ) in this context (for further information on this topic, see Sect. 13.5.5). From a strictly electronic point of view, and for an apparatus for counting photons, sensitivity can also be defined as detecting the lowest number of photons possible beyond the background signal. This definition is of the utmost importance, as it introduces the background concept. In eukaryotic cells, this sensitivity is somewhat limited by the background signal constituted by autofluorescence. Nevertheless, there is experimental evidence that it is possible to recognize the signal produced in water from a single Allophycocyanin molecule (APC) even in a background of autofluorescence and Raman scattering (Doornbos et al. 1997).
13.5.4.1
Q, Qr, Stain Index, and Other Indexes
Before proceeding further, it is important to point out that a fluorochrome’s brightness is generally defined with Q, i.e., the number of photons produced by a known quantity of excited molecules. However, to evaluate the brightness of a fluorochrome in a given instrument, what is important is not only the number of Q photons entering the transducer but the number of photoelectrons that comes out, defined by the unit of measurement Qr (relative brightness, also known as optoelectronic efficiency), which results from the ratio between the number of photons produced by the fluorescent molecule and the number of the photoelectrons produced by the detector. Therefore, Qr and not Q is the measuring unit determining the fluorochromes’ brightness in a given instrument. Moreover, in the evaluation of the sensitivity of a cytometer, for each fluorochrome under evaluation, it is important not only the optoelectronic Qr efficiency of its specific detector but also the total background, or Btot, which defines the lower limit of the dynamic range relative to each detector under consideration (Wood and Hoffman 1998). The practical application of these concepts leads to the formulation of the so-called Stain Index (SI), which can be calculated in more or less complex ways, including the simple empirical equation
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Fig. 13.12 The Stain Index (SI), or resolution index, depends on the relative brightness Qr of the fluorochrome, i.e., the distance between the negative population mode X and the positive population mode Y to be corrected according to the background. Consequently, the formula that determines SI considers the standard deviation of the negative population X, and in some formulations, that of the positive population Y, often overlooked as being usually low and constant. The figure in question shows the variables necessary for calculating the Stain Index in its simplest form SI ¼ Qr/ Btot (see text)
SI ¼ Qr=Btot ¼ ðMedian FI of the pos Median FI of the negÞ=ðSD of the neg 2ÞÞ, or the equation RI ¼ ðXi XoÞ=
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SDi2 þ SDo2 ,
where Xi is the average intensity of the population with the highest intensity, and Xo is the average intensity of the population with the lowest intensity, Sdi is the standard deviation of the population with the highest intensity, and Sdo is the standard deviation of the population with the lowest intensity (Brando and Sommaruga 1993). The Stain Index considers both the brightness of the fluorochrome and the background influence (Fig. 13.12); however, as it intrinsically depends on the instrument’s characteristics, it cannot compare different instruments. The Stain Index allows comparing in a given instrument the performance of different Mabs targeting the same antigen and conjugated with the same fluorochrome, which can be of the greatest importance in evaluating a reagent during purchase procedures. The comparison between different instruments is only possible by measuring Qr and Bcal in a way independent of the instrumentations under evaluation; this operation relies on a measurement unit called SIps (panel-specific separating index), which only is capable of objectively comparing the performance of different instruments (Perfetto et al. 2014) (for further information on this topic, see Sect. 13.4.4).
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Fig. 13.13 The graph represents the reciprocal relationships between the fluorescence intensity of the negative and positive components of a test in the presence of increasing concentrations of the conjugated antibody. The increase in fluorescence intensity (MFI) of the positive (in red in the picture) does not improve beyond a certain concentration of the conjugate, while the increase in fluorescence intensity (MFI) of the negative (in blue in the picture) tends to increase progressively. The theoretical concentration to be adopted corresponds to the greater distance (maximum value of Z) between the two curves (red arrow in the picture)
13.5.4.2
Antibody Titration
Stain Index optimization requires Mabs titration. Titration is a procedure that ensures an antibody dilution such as that the staining takes place in conditions of excess of antibody without this excess being so high as to introduce an increase in the background due to the establishment of non-specific bonds with the negative component of the test (Hulspas 2010) (Fig. 13.13). The monoclonal antibodies used in Flow Cytometry are generally available on the market already titrated or accompanied by instructions concerning the optimal ratio between the number of cells to be stained and the antibody volume to be used. Nevertheless, it is often desirable to proceed with titration to optimize the reaction conditions. Like the Stain Index, titration allows comparing different clones targeting the same antigen and conjugated with the same fluorochrome; it also allows the formulation of hypotheses on the products’ actual concentration. It may be interesting to consider how the Mab affinity can affect the maintenance of saturation conditions. A high-affinity antibody can be used at relatively low concentrations since all its molecules tend to bind the target; however, an increase in the number of cells to be stained can be critical due to the absence of unbound Mab molecules; in this case, the loss of saturation conditions manifests rapidly with the production of an inaccurate MFI. On the other hand, a low-affinity antibody
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requires high concentrations to reach saturation, with relatively high amounts of unbound antibody available in the case of an increase in the number of targets.
13.5.5
Limits of Blank (LOB), Detection (LOD), and Quantification (LOQ)
In the study of sensitivity, the concepts of Limit of Blank (LOB), Limit of Detection (LOD), and Limit of Quantification (LOQ) take on significant importance (NCCLS 2004b; Armbruster and Pry 2008; Wood et al. 2013). Even if they are the two faces of the same coin, the sensitivity of measurement in Flow Cytometry can split in two ways, i.e., (1) sensitivity in the context of detection of low signal intensities, and (2) sensitivity in the context of rare events counting. It follows that the concepts of the limit of the LOB, LOD, and LOQ, yet remaining conceptually the same, must be adapted to different situations.
13.5.5.1
LOB, LOD, and LOQ in the Detection of Weak Signals
In the context of low signal intensity detection, the concepts of the LOB, LOD, and LOQ can be taken from Clinical Chemistry and adapted as follows: 1. LOB (Limit of Blank) is the limit under which 95% of the blank signals falls and corresponds to the highest signal produced by a blank, i.e., by an aliquot of the unstained cells suspension, or else a fluorescence-minus-one (FMO) control (Wood et al. 2013). 2. LOD (Limit of Detection) is the limit over which 95% of the measured signals fall above the blank level and corresponds to the lowest specific signal produced by a dim positive cell suspension that a cytometer can tell from the limit of blank. 3. LOQ (Limit of Quantification) is the limit corresponding to the lowest specific signal that a cytometer can tell from the limit of blank under predetermined conditions of total error (bias or imprecision).
LOB (Limit of Blank) In the presence of a Gaussian distribution of the results, the LOB is the limit under which 95% of the blank values fall (Fig. 13.14); as such, LOB is equal to the mean of the blank values plus its standard deviation multiplied by 1.645 according to the following formula: LOB ¼ ½ðblank meanÞ þ ð1:645 SD of blankÞ:
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Fig. 13.14 Curve A represents the distribution of the results obtained from the analysis of the blank sample. The LOB is in red; since 95% of the blank results fall on its left, there is a small area α on the right comprising the LOB exceeding values, including the false-positive events whose value is higher than the LOB
Fig. 13.15 Curve A represents the distribution of the results obtained from the blank analysis, while curve B represents the distribution of the results obtained from the dim positive sample analysis. The LOD (green bar) is equal to the LOB plus x, i.e., the SD of the distribution of the dim positive sample results multiplied by 1.645. The β area to the LOB’s left includes the false-negative events whose value is lower than the LOB
For calculating the LOB in non-Gaussian distributions, see the specific documents (CLSI 2010).
LOD (Limit of Detection) In a sample expressing the marker at a low intensity, the LOD is the limit over which 95% of the measured signals fall above the blank level (Fig. 13.15). A LOD value can be obtained either by replicating the blank counts and calculating LOD as its mean + 2 SD or by replicating a dimly positive sample’s measurement (Armbruster et al. 1994). In the latter case, which provides more accurate values, LOD can be derived from the following formula:
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LOD ¼ ½ðLOBÞ þ ð1:645 SD of the dim sample valuesÞ: The LOD is a function of the system’s analytical sensitivity, as it expresses the ability to distinguish a low-level stained population from the same unstained population.
LOQ (Limit of Quantification) The limit of quantification, or LOQ, or LLOQ (lower limit of quantification) corresponds to the lowest signal whose measurement in a series of repeated counts displays a total error value (bias plus imprecision) equal to or lower than a predetermined threshold (Fig. 13.16). The LOQ is usually calculated by analyzing replicates and verifying they comply with an acceptable or predetermined imprecision level (Wood et al. 2013). The lowest quantification level (LOQ) can approach or coincide with the lowest detection level (LOD) but cannot be less and varies according to the predetermined value assigned to the total error.
13.5.5.2
LOB, LOD, and LOQ in Rare Event Analysis
As anticipated in Sect. 13.5.5, in Flow Cytometry, the term “sensitivity” is also referred to as the ability to detect and enumerate a cellular subpopulation present at a very low frequency in a major population. This condition occurs by definition in the so-called rare event analysis, of which the search for minimal residual disease (MRD) is the most notable example (Hedley and Keeney 2013; Wood et al. 2013).
Fig. 13.16 Curve A represents the distribution of the results of the blank, the curve B represents the distribution of the results of a very dim positive sample, and the curve C represents the distribution of the results of another sample, which is also weak, but whose measurement is characterized by a total error (bias plus imprecision) equal to a threshold pre-set by the operator. Under these conditions, LOQ corresponds to the curve’s average that describes the population C, and if the total error of C is equal to the total error of B, then LOQ and LOD coincide
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This definition of sensitivity agrees perfectly with that which defines sensitivity as identifying true positive events as positive. It is hardly necessary to observe how it is complementary to the definition of specificity, understood as identifying true negative events as negative. In rare event analysis, the concepts of the LOB, LOD, and LOQ need redefining based on the number of positive counts performed by the instrument versus the total number of counted events. A way to obtain a sample for the LOQ (and LOD) determination in this special context consists of creating various samples spiking different amounts of a positive sample in negative samples (Wood et al. 2013).
LOB (Limit of Blank) According to the premises, the LOB (Limit of Blank) becomes the highest number of counts produced by analyzing an unstained cell population, i.e., the number of events counted in the negative control tube. This number should be subtracted from the total number of events counted in the test tube, provided that the number of total events counted is the same in both runs.
LOD (Limit of Detection) Following a series of observations and mathematical considerations aimed at containing the error inherent in the counts (Gross et al. 1995; Subira et al. 2002; Arroz et al. 2016), a near-universal consensus has been reached. According to this general agreement, a total count of 30 target events constitutes the minimum necessary and sufficient count to declare the presence of a defined cell population in a sample. It follows that 30 target events are the detection limit (LOD) of a cell population in this context (Arroz et al. 2016), but other authors are more stringent and use 100 as the minimum number of counts (Barnett et al. 2000). On the other hand, the difficulty of achieving the detection of 30 events in particularly unfavorable situations has currently led to further lowering of the detectability limit, which can sometimes be set at 20 total elements (Illingworth et al. 2018). Given that sensitivity is the percentage of the counted population compared to the whole analyzed population, the LOD can be defined by the ratio between the value of 30 counted target cells (or 20 if the lowest limit is adopted) and the total number of counted cells multiplied by 100 that is LOD% ¼ ½ð30 ðor 20Þ rare events=number of total cells countedÞ 100:
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LOQ (Limit of Quantification) With regards to the LOQ, the threshold value has been raised to 50 target events, based on the fact that this number is considered sufficient to allow the reproducible characterization of a cell population in a sample analyzed under the same conditions (Arroz et al. 2016). It follows that LOQ% ¼ ½ð50 rare events=total number of cells countedÞ 100: It is crucial considering that the information provided by LOD and LOQ are different and indicate in the former the sensitivity necessary to define the undeniable but still unquantifiable presence of a cell subset in a given sample, while the latter is the sensitivity needed to carry out its quantification.
LOD and LOQ and Minimal Residual Disease (MRD) In practice, in the search for the Minimal Residual Disease with cytometric techniques, once the counted events in the unlabeled sample (LOB) have been subtracted, 1. A count of pathological events less than 30 is equivalent to the absence of MRD. 2. A count of pathological events between 30 and 49 makes it possible to demonstrate its presence without being able to report its quantification. 3. A count of pathological events equal to or greater than 50 makes it possible to demonstrate its presence with certainty and report its quantification. It is possible to compare the sensitivity of different counts thanks to the formulas LOD% and LOQ%, which place the number of total events counted in the denominator.
13.5.6
Precision
Precision consists of the proximity between the different values obtained by repeated measurements. The precision can be divided into “intraseries precision,” if determined within the same experiment, or “interseries precision,” between different experiments. The evaluation of precision over time contributes to the study of reproducibility. The degree of precision of an analytical series is expressed using a spread measurement such as the coefficient of variation or CV. The coefficient of variation is a function of the natural variability of the measured parameter and the measurement error. In Flow Cytometry, the measurement error and hence the coefficient of variation depends on a series of fundamental factors, including the stability of the power supply, the stability of the light source, the photoelectron statistics, the speed of the flow, the electric noise of the circuitry, and the binning error.
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In immunophenotyping, a coefficient of variation of 10% is acceptable, to be raised to 20% or more in the case of rare events (Barnett et al. 2000). In the analysis of DNA content (bare nuclei stained with PI), a CV close between 1 and 2% is expected in a G0 population (Shapiro 1983; Thomas et al. 2001). Precision monitoring is an important tool in assessing an instrument’s stability and managing internal quality control (IQC). This purpose relies on comparing coefficients of variation obtained from the repetition of the same sample analysis within the same series or in different series. The number of replicates required for the determination of the precision in a flow cytometer has not yet been unequivocally defined, but there is evidence that they may be much lower than those provided by the CLSI guidelines for soluble analytes and may consist of no more than three or four replicates in most cases (Davis et al. 2013).
13.5.7
Specificity
Among the many possible definitions, the specificity of a test can be seen as its ability to identify as negative the true negative events. In Flow Cytometry, the specificity of the measurements depends on the control of all the pre-analytical and analytical variables influencing the global analytical quality, with special attention concerning the staining procedures (for further information on this topic, see also Chap. 20). The main factors affecting the specificity of a phenotypic analysis performed in Flow Cytometry have been reviewed and identified as (Hulspas et al. 2009): 1. The non-specific bonds existing between the tail of the antibody molecules and the Fc receptors expressed by cells other than the targets 2. The non-specific bonds that the “weak forces” can establish between the fluorochrome and the cell membrane or between the antibody and the cell membrane 3. The presence of dead cells, which non-specifically bind the fluorescent probes To these, the presence of unexpected complementarity between cell antigen receptors and fluorochromes can be added; this last occurrence is exceedingly rare but still reported in the literature (Tabary et al. 2008; Smith et al. 2021).
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Chapter 14
Fluorochromes: Overview
Fluorochromes are a group of molecules capable of Fluorescence. Under certain conditions, these molecules can absorb a photon; this absorption can make an electron reach a transient state of excitation, from which it comes back to the ground state, emitting another photon characterized by less energy. These transitions, represented in the Jablonski diagram depicted in Fig. 14.1, have previously been dealt with in Chap. 3. From a structural point of view, fluorochromes are organic molecules consisting of cyclical structures or polyene systems with double and triple bonds, which, because of their molecular configuration, have an outer shell of mobile electrons, also called delocalized electrons, or π electrons, particularly susceptible to absorbing a quantum of energy and passing from the ground state to an excited state (Williams and Bridges 1964). Different atoms in the rings, including N, O, and S, can modulate this trend in one direction or another. Fluorochromes’ denomination should comply with the nomenclature promoted by the International Union of Pure and Applied Chemistry (IUPAC). IUPAC nomenclature is often replaced by a common name or a CAS number, i.e., a progressive number that the Chemical Abstract Service (hence CAS) of the American Chemical Society assigns to each new synthetic molecule to identify each compound without the possibility of misunderstanding. An extensive list of existing fluorochromes and their features (besides isotopes for mass cytometry and chromogens) can be found in the ISAC Probe Tag Dictionary (Blenman et al. 2020). Information on the spectral behavior of virtually all the fluorochromes used in Flow Cytometry, accompanied by information on the most suitable light sources and filters for signal detection, can be obtained using several utilities freely accessible on the Internet. These utilities include: 1. “BD Spectrum Viewer” by Becton Dickinson (accessible at https://www. bdbiosciences.com/en-us/resources/bd-spectrum-viewer). 2. “Cytek Full Spectrum Viewer” by Cytek (accessible at https://spectrum. cytekbio.com/); this utility has been devised for Spectral Flow Cytometry (SFC) © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_14
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Fig. 14.1 Jablonski diagram, which schematically describes the possible energy states of a molecule. The figure shows the ground state (S0), two different singlet states (S1 and S2), and two different triplet states. (T1 and T2). The figure briefly describes the fluorescence phenomenon, understood as the passage (in blue) of an electron from the ground state S0 to a higher energy state S1, with the return (in red) to the ground state S0 and emission of a photon. The return (in gray) from S1 to S0 through the triplet state is also described. E energy, S singlet state, T triplet state, ISC inter-system crossing, ex excitation, em emission
3. 4.
5.
6.
7. 8.
and represents the emission spectra as detected in the spectral intervals managed by the multichannel sensors implemented in spectral flow cytometers manufactured by Cytek. “Fluorescence spectra viewer” by Miltenyi (accessible at https://www. miltenyibiotec.com/US-en/resources/tools/spectra-viewer.html#gref). “Fluorescence SpectraViewer—Multicolor Design Tool” by Thermo Fisher Scientific (accessible at https://www.thermofisher.com/order/fluorescencespectraviewer). “Fluorescence Spectrum Analyzer for Flow Cytometry” by Beckman Coulter (accessible at https://www.beckman.tw/flow-cytometry/fluorescence-spectrumanalyzer). “FPBase Fluorescence Spectra Viewer,” accessible at https://www.fpbase.org/ spectra/; this utility is a public open-source non-commercial resource encompassing information on virtually all the existing Fluorescent Proteins and many other fluorescent probes (btw, this utility has been exploited in the creation of the figures of this book). “Novus Spectra Viewer” by Novus (accessible at https://www.novusbio.com/ spectraviewer). “SearchLight™” by Semrock (accessible at https://searchlight.semrock.com/); this utility has been devised for Fluorescence Microscopy and reports the spectral behavior of several LEDs.
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Spectral Behavior of Fluorescent Molecules
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9. “Spectra Analyzers,” by Biolegend (accessible at https://www.biolegend.com/ en-us/flow-cytometry-tools); this tool encompasses two different utilities, i.e., the first (Conventional Spectra Analyzer) that display the probes’ spectral behavior in the traditional way (excitation and emission spectra, plus light sources and suggested filters), and the second, devised for Spectral Flow Cytometry (SFC), that represents the emission spectra as detected in the spectral intervals managed by the multichannel sensors implemented in the spectral flow cytometer Aurora manufactured by Cytek. 10. “Spectra Viewer” by Chroma (accessible at https://www.chroma.com/spectraviewer). 11. “Spectrum Viewer” by AATBIO (accessible at https://www.aatbio.com/ spectrum/). 12. “Spectra Viewer for Flow Cytometry” by Fluorofinder (accessible at https://app. fluorofinder.com/ffsv/svs/b70695105a76013a7396576b754ae561), including a suppletive utility for Spectral Flow Cytometry (SFC) (accessible at https://app. fluorofinder.com/ffsv/svs/12c9c5306119013af6eb4dd8e5cd5210) and a Fluorescence Dye Directory (accessible at https://app.fluorofinder.com/dyes).
14.1
Spectral Behavior of Fluorescent Molecules
Besides their structure or their molecular weight, it is possible to classify fluorochromes based on the features of their spectral behavior, among which we must remember: 1. The excitation peak (ex peak) (Fig. 14.2), which defines the band of the spectrum in which the fluorescent molecule absorbs; sometimes, the terms “absorption” or “extinction” are used as synonymous with “excitation,” but an important difference exists, since, even if the wavelength is the same, excitation is usually followed by photon emission, whereas absorption does not necessarily behave that way. 2. The emission peak (em peak) (Fig. 14.2), which defines the band of the spectrum in which the fluorescent molecule emits. 3. The Stokes shift (Fig. 14.2), that is, the distance between the emission peak and the excitation peak. 4. The extinction coefficient (E), expressed as cm 1M 1, which defines the quantity of light absorbed at a given wavelength from a given concentration of the substance under examination; the extinction coefficient is a function of the probability that a molecule absorbs a photon. 5. The emission coefficient, or quantum yield (Q), which expresses the number of photons emitted for each absorbed photon and is a function of the probability that an excited molecule emits a photon. 6. The fluorescence lifetime, i.e., the time elapsing between the excitation of a fluorochrome and the emission of fluorescence.
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CY5
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Fig. 14.2 Schematic representation of the spectral behavior of a fluorescent molecule, specifically CY5. Excitation and emission peaks are shown; the distance between the peaks is called the Stokes shift. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/ spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277). Courtesy of FPbase
7. The brilliance (B), i.e., the product of the quantum yield (Q) for the extinction coefficient (E). A fluorochrome’s relationships with the range of electromagnetic radiations define its spectral behavior, whose graphic representation gives us information on its excitation and emission characteristics (Fig. 14.2). In particular, the excitation spectrum of a fluorochrome gives us information on the distribution of its probability of absorbing a photon with a given energy, i.e., electromagnetic radiation of a given wavelength. In contrast, the emission spectrum gives us information on its probability of producing, in turn, a photon with a given energy, i.e., electromagnetic radiation of a given wavelength. The amount of energy given back by an excited molecule during the phase of return to the fundamental state depends 1) on the starting energetic level among the many theoretically associated with that energetic state and 2) on the non-radiative processes that dissipate part of the energy received before its return in the form of light radiation. Since these conditions are not the same at the same time in all the excited molecules, it follows that the energy given back, i.e., the wavelength of the emission, distributes within a consistent range of values depending on the individual conditions of the molecules interested by the phenomenon. That is why fluorochromes do not emit in lines, as in the lasers, but in bands that occupy a more or less wide range of the electromagnetic spectrum.
14.1
Spectral Behavior of Fluorescent Molecules 1
2
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Fig. 14.3 The figure shows the excitation spectrum (in black) and the emission spectra (numbered 1–9) of the Quantum Dot available for conjugation with antibodies. It is immediately noticeable how the excitation spectrum is drastically different from the emission spectra, configuring itself as a continuous sloping absorption with the wavelength increase. This behavior is completely different from that expected in small organic molecules because Quantum Dots are not organic molecules but semiconductor nanocrystals. Figure obtained thanks to the FPbase Spectra Viewer program (https:// www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
As already stated in Sect. 3.1, the emitted light displays a wavelength greater than that absorbed; this phenomenon is because the energy emitted is equal to the energy absorbed minus the component dissipated in a series of thermal, vibrational, and translational non-radiative processes, which occur between the moment of absorption and the moment of emission. Notable exceptions to this principle are the two-photon excitation (for further information on this topic, see Sect. 3.1) and the up-converting nanoparticles (for further information on this topic, see Sect. 15.4.2). Another feature of the fluorochromes’ spectral behavior concerns the similarity between the shapes of the excitation and the emission spectra, which appear specular; in other words, the excitation spectrum appears slightly asymmetrical with a tail placed on its left, while the emission spectrum appears slightly asymmetrical with a tail placed on its right (Fig. 14.2), which constitutes the primary cause of the intralaser spillover. The specularity between the excitation and the emission spectrum applies to almost all the fluorochromes belonging to the small organic molecules group but not to upconverting nanoparticles and Quantum Dots. In particular, Quantum Dots display an emission spectrum with a low CV and a range of excitation that practically consists of a degrading continuum between 300 and 500 nm (Fig. 14.3). This particular behavior can be ascribed to their particular molecular structure since the Quantum Dots behave like extremely small semiconductors, and their excitation occurs with mechanisms different from those operating in organic molecules. A new method to evaluate the spectral features of a fluorescent probe is exploited in Spectral Flow Cytometry (SFC). This method considers the probe behavior across the whole spectrum according to the spectral intervals managed by the sensors depending on the lasers implemented in the instrument (Fig. 14.4). This approach is very useful since it allows investigating the emission of each probe across the whole spectrum, pointing out the differences able to distinguish molecules with the same emission peak (Fig. 14.4).
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Fig. 14.4 Spectral behavior of APC (panel a) and AF647 (panel b). The percentages of normalized excitation/emission of the two fluorochromes (Y-axis) are represented as detected by the multichannel sensors implemented in an RBV three-laser instrument (Aurora, Cytek) (X-axis). Despite the overlapping emission peaks, the two probes can be distinguished based on the signal in the violet-depending detectors (panel a, arrow). Figure obtained by courtesy of Biolegend thanks to the “Spectra Analyzers” utility made available on the site https://www.biolegend.com/en-us/flowcytometry-tools. ©2022 Reprinted with permission from BioLegend, Inc. All Rights Reserved
In this chapter, the fluorochromes are considered based on their use in Flow Cytometry and consequently distinguished in: 1. 2. 3. 4.
Fluorochromes used in conjugating antibodies or other proteins Fluorochromes used in the nucleic acid analysis Fluorochromes used in the evaluation of cellular features and functions Fluorochromes used in the study of transfection and gene transcription processes
The molecules mentioned in point 4, known as Fluorescent Proteins, should not be confused with the Phycobiliproteins, which are fluorescent proteins commonly
14.2
Relationships with the Environment
253
involved in the respiratory chain of some microorganisms and are exploited in Flow Cytometry as fluorescent probes conjugated to antibodies.
14.2
Relationships with the Environment
The relationships between the fluorochrome and the environment’s molecules can significantly affect the fluorochrome’s spectral behavior. These effects take the name of bathochromic, hypsochromic, hyperchromic, hypochromic, and solvatochromic effects. To these must be added other phenomena known as extinction (quenching), photodestruction (photobleaching or fading), and non-radiative transfer of energy (FRET, Förster Resonance Energy Transfer).
14.2.1
Spectral Effects
14.2.1.1
Bathochromic Effect
By bathochromic effect, we mean the shift of the absorption range toward regions of the spectrum characterized by longer wavelengths (red); an example of a bathochromic effect of cytometric relevance is represented by the behavior of Pironin Y when it intercalates in a double-stranded nucleic acid (Kapuscinski and Darzynkiewicz 1987).
14.2.1.2
Hypsochromic Effect
By hypsochromic effect, we mean the shift of the absorption range toward regions of the spectrum characterized by shorter wavelengths (blue); an example of a hypsochromic effect of cytometric relevance is represented by the behavior of Pironin Y when it binds to a single-stranded nucleic acid (Kapuscinski and Darzynkiewicz 1987).
14.2.1.3
Hyperchromic Effect
By hyperchromic effect, we mean an increase in absorbance, which generally causes an increase in the emission; an example of a hyperchromic effect of cytometric relevance is represented by the behavior of Propidium iodide (PI) when intercalates in a double-stranded nucleic acid (Arndt-Jovin and Jovin 1989).
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Fluorochromes: Overview
Hypochromic Effect
By hypochromic effect, we mean a decrease in absorbance, which generally causes a decrease in emission. A hypochromic effect has been observed in the analysis of ploidy of human melanoma cells marked with Mithramycin (Cunningham et al. 1982).
14.2.1.5
Solvatochromic Effect
By solvatochromic effect, we mean the ability of a molecule to modify its excitation and emission spectrum as a function of the polarity of the solution in which it is located (Buncel and Rajagopal 1990); examples of a solvatochromic effect of cytometric relevance are the behavior of BODIPY-derived fluorochromes in the tracking and analysis of unfixed cells (Pakhomov et al. 2017) and Nile Red in lipid staining (Lampe et al. 2008).
14.2.2
Other Effects
Other phenomena resulting from interaction with the environment and able to affect the fluorescence emission are i) extinction or quenching, ii) photodestruction or photobleaching, and iii) the non-radiative transfer of energy or FRET (Förster Resonance Energy Transfer).
14.2.2.1
Extinction or Quenching
Extinction, also known as quenching or dumping, reduces the fluorescence emission due to interactions between the fluorochrome and other substances present in the surrounding environment, such as oxygen, heavy metals, ions, or other complex molecules absorbing energy. This phenomenon can be affected by the system’s temperature and complies with the principle that low temperatures favor the phenomenon of Fluorescence. This phenomenon also depends on the intermolecular distance and occurs in a range of values slightly greater than that required for the non-radiative transfer of energy, or FRET (see below).
14.2.2.2
Photodestruction or Photobleaching
Photodestruction, or photobleaching, damages the fluorochrome by the incident radiation, which, instead of exciting it, induces structural modifications that result in the failed fluorescence emission. This phenomenon is due to a series of events,
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Relationships with the Environment
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including breaking the covalent bonds and the fluorophore reaction with various other groups (Song et al. 1995, 1996). Photobleaching is typical of molecules irradiated with high energies, and its appearance varies according to the molecule considered. The phenomenon is much more relevant in Fluorescence Microscopy than in Flow Cytometry, where the irradiation times are much shorter. Even though other interpretations of the phenomenon exist, a molecule considered to be particularly sensitive to photobleaching is PerCP, whose quantum efficiency progressively decreases when irradiated in the blue with energies beyond 50–80 mW (Greimers et al. 1996, Shapiro 2012) (for further information on this topic, see Sect. 15.1.2). The terms photobleaching and fading are often used interchangeably, but this is incorrect. Fading, a progressive signal weakening, recognizes photobleaching as one of its most important causes, but other factors contribute to the phenomenon, including extinction and returning to the ground state through a triplet state.
14.2.2.3
Non-radiative Transfer of Energy (FRET)
The non-radiative transfer of energy, also known as Förster Resonance Energy Transfer (FRET) , is a phenomenon that occurs between two fluorescent molecules, the first called Donor and the second called Acceptor (Foerster 1965). The phenomenon occurs when the Donor emission range overlaps the Acceptor absorption range, and the two molecules reside at a mutual distance lower than 100 Å (10 nm). A non-radiative energy passage occurs between the excited Donor and the Acceptor when these conditions are met, resulting in an acceptor’s fluorescence emission (Fig. 14.5). The donor-acceptor complex behaves like a single functional structure with the Donor’s excitation characteristics and the Acceptor’s emission characteristics. Unlike photobleaching and quenching, which generally represent an undesired event, energy transfer is very useful in Flow Cytometry and constitutes the principle on which Tandem Fluorochromes rely (for further information on this topic, see Sect. 15.5. It also allows studies to explore the relationships between structures in mutual proximity, such as receptors expressed on the same cell’s membrane (Szollosi et al. 1998, 2006). In such studies, the spatial relationships between different molecule pairs are identified by marking the molecules under analysis with antibodies conjugated with fluorochromes differentially behaving as Donors and Acceptors. The persistence of the Donor signal argues against close spatial relations between the two antigens. In contrast, the reduction of its emission, accompanied by the appearance of the signal generated by the Acceptor, stands for the opposite hypothesis (Szollosi et al. 1987a, b, 1989).
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Fig. 14.5 Non-radiative transfer of energy (FRET), according to Jablonski. Molecule A, which has absorbed a photon, does not give back another photon of lesser energy but transfers the acquired energy to molecule B in a non-radiative way. It follows that molecule B, coming back to its ground state, emits a photon whose energy depends on its structural features but is lower than the energy of the photon exciting molecule B
14.3
Accessory Groups
The Fluorophore is the molecular structure responsible for Fluorescence and is the most important component of a fluorescent molecule. Nevertheless, other molecular structures can be of the utmost importance, among which those that determine the molecule’s functional behavior and modulate its spectral features in certain circumstances, such as the presence of a particular analyte. These molecular structures include: 1. Sulfonate groups, which make the molecule negatively charged and hydrophilic. 2. Aliphatic lipophilic accessory chains, which mediate the location of the molecule in the context of the cytoplasmic membrane (Merocyanines, PKH molecules). 3. Acetic, ethyl, or acetoxymethyl esters, able to allow the transmembrane passage of the fluorochrome; these groups are generally eliminated by intracellular esterases so that the molecules entered into the cell remain irreversibly trapped inside. 4. Molecular structures able to determine the total charge of the molecule, regulating its distribution in the various cell compartments or its affinity with particular substrates as a function of its cationic or anionic nature (cyanines and oxonols as probes for the membrane potential). 5. Molecular structures able to bind the analyte, such as the macrocyclic structures specific for Sodium and Potassium operating in the SBFI and PBFI fluorochromes (see Sects. 17.11.1 and 17.12.1). Another very important family of molecular structures includes the reactive groups that make conjugation possible with antibodies or other proteins (Ernst et al. 1989; Mujumdar et al. 1989, 1993, 1996; Southwick et al. 1990; Mao and Mullins 2010;
References
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Shrestha et al. 2012). The role of these reactive groups is critical since the conjugation must be as stable as possible, must not affect the complex’s water solubility, and must not affect the epitopes involved in the antigen recognition, under the penalty of affecting the antibody affinity or—in the worse scenario—specificity. The classic methods for antibody conjugation rely on adding to the probe some functional groups active in establishing covalent bonds with complementary moieties present on the protein's amino acid sequence (Shrestha et al. 2012). These groups include: 1. Sulfonyl chlorides, isothiocyanate, and hydroxysuccinimidyl groups, reactive with the protein amino moieties 2. Chloromethyl, iodoacetamide, maleimide, and methyl iodoacetate groups, reactive with the protein thiols 3. Hydrazide groups, reactive with the previously oxidized protein carbohydrates
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Pakhomov AA, Deyev IE, Ratnikova NM, Chumakov SP, Mironiuk VB, Kononevich YN, Muzafarov AM, Martynov VI (2017) BODIPY-based dye for no-wash live-cell staining and imaging. BioTechniques 63(2):77–80. https://doi.org/10.2144/000114577 Shapiro HM (2012) Anyone looked at this? – “Software simulates laser-based measurements for flow cytometry – Laser Focus World”. Purdue Cytometry Discussion List. https://lists.purdue. edu/pipermail/cytometry/2012-August/044018.html. Accessed 21 July 2015 Shrestha D, Bagosi A, Szöllősi J, Jenei A (2012) Comparative study of the three different fluorophore antibody conjugation strategies. Anal Bioanal Chem 404(5):1449–1463. https:// doi.org/10.1007/s00216-012-6232-z Song L, Hennink EJ, Young IT, Tanke HJ (1995) Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy. Biophys J 68(6):2588–2600. https://doi.org/10.1016/ s0006-3495(95)80442-x Song L, Varma CA, Verhoeven JW, Tanke HJ (1996) Influence of the triplet excited state on the photobleaching kinetics of fluorescein in Microscopy. Biophys J 70(6):2959–2968. https://doi. org/10.1016/s0006-3495(96)79866-1 Southwick PL, Ernst LA, Tauriello EW, Parker SR, Mujumdar RB, Mujumdar SR, Clever HA, Waggoner AS (1990) Cyanine dye labelling reagents-carboxymethylindocyanine succinimidyl esters. Cytometry 11(3):418–430 Szollosi J, Damjanovich S, Goldman CK, Fulwyler MJ, Aszalos AA, Goldstein G, Rao P, Talle MA, Waldmann TA (1987a) Flow cytometric resonance energy transfer measurements support the association of a 95-kDa peptide termed T27 with the 55-kDa Tac peptide. Proc Natl Acad Sci U S A 84(20):7246–7250 Szollosi J, Matyus L, Tron L, Balazs M, Ember I, Fulwyler MJ, Damjanovich S (1987b) Flow cytometric measurements of fluorescence energy transfer using single laser excitation. Cytometry 8(2):120–128 Szollosi J, Damjanovich S, Balazs M, Nagy P, Tron L, Fulwyler MJ, Brodsky FM (1989) Physical association between MHC class I and class II molecules detected on the cell surface by flow cytometric energy transfer. J Immunol 143(1):208–213 Szollosi J, Damjanovich S, Mátyus L (1998) Application of fluorescence resonance energy transfer in the clinical laboratory: routine and research. Cytometry 34(4):159–179 Szollosi J, Damjanovich S, Nagy P, Vereb G, Matyus L (2006) Principles of resonance energy transfer. Curr Protoc Cytom Chapter 1:Unit1 12 Williams RT, Bridges JW (1964) Fluorescence of solutions. A review. J Clin Pathol 17(4):371–394
Chapter 15
Fluorochromes Suitable for Antibody Conjugation
Depending on the parameters adopted for their classification, the fluorescent molecules selected for antibody conjugation can be grouped in many different ways, but generally, they are split into four main categories according to their structure, namely: 1) 2) 3) 4)
Large protein molecules Small organic molecules Polymeric molecules Nanocrystals
A fifth group must be added to these four traditional groups comprising the so-called tandem fluorochromes, resulting from the association of two or more different molecules; tandem fluorochromes are treated separately due to their unique structural and spectral characteristics. A final observation must be reserved for the AmCyan fluorochrome, which, despite being a CFP (cyan fluorescent protein), is included in the section dedicated to large protein molecules because it is used to tag monoclonal antibodies.
15.1
Large Protein Molecules
The large protein molecules used in Flow Cytometry include Phycobiliproteins and the Peridinin–Chlorophyll–Protein complex, or PerCP. The AmCyan molecule belongs to the Fluorescent Proteins family but is included in this group because it is conjugated with Mabs and used in immunophenotypic studies.
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Phycobiliproteins
The phycobiliproteins are proteins present in certain organisms, including cyanobacteria, red algae (Rhodophyta), and cryptomonad algae (Cryptophyta) (Apt et al. 1995; MacColl 1998). Phycobiliproteins can be divided into three main families: phycocyanins, allophycocyanins, and phycoerythrins. Each family consists of numerous similar molecules, which vary slightly according to the donor species, the protein component’s structural characteristics, and the chromophores’ structural characteristics (Apt et al. 1995; MacColl 1998). The phycobiliproteins combine to form the phycobilisomes, complex structures transporting electrons along the respiratory chain. This function relies on chromophores’ functional groups linked to the protein structure that acts as a scaffold (Glazer 1982). These chromophores are generally bilins, constituted by open chains of pyrroles belonging to the same family of Heme catabolism products (Fig. 15.1) (Brown et al. 1990). There have been some attempts to use the whole phycobilisomes as fluorescent probes (Telford et al. 2001c). In these attempts, a whole organelle, called PBXL-1, was conjugated with avidin or with a secondary anti-mouse antibody and used in Laser Scanning Cytometry, where it was shown to have an excitation band in the green at around 540 nm and an emission band in the red around 660 nm (Morseman et al. 1999). Due to their physical-chemical peculiarities, the phycobiliproteins behave very well when conjugated to antibodies (Kronick and Grossman 1983); nonetheless, despite their many favorable characteristics, only a few have found a regular use in Flow Cytometry. Among these are R-Phycoerythrin (R-PE) and Allophycocyanin B (APCB). Besides, even though for historical reasons only, other molecules can be mentioned, among which are B-phycoerythrin (B-PE), phycocyanin C (PC), and other Phycobiliproteins extracted from Criptomonads and commercially known as Cryptofluor molecules (Telford et al. 2001b). These molecules were used in the past to increase the number of fluorochromes simultaneously manageable.
Fig. 15.1 Chain of pyrroles, the basic structure of chromophores present in large protein molecules
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Phycobiliproteins are difficult to conjugate with IgM immunoglobulins because they form poorly soluble complexes in water. This problem is the primary reason IgM antibodies, such as those directed towards glycolipid or polysaccharide antigens (CD15, CD57, CD65, et cetera), are generally available only conjugated with FITC or other small organic molecules (Loken 1990).
15.1.1.1
R-Phycoerythrin (R-PE)
The phycoerythrins are a family of phycobiliproteins present in the Phycobacteria and all Red Algae phycobilisomes (Rhodophyta), including three main groups, known as R-Phycoerythrin, B-Phycoerythrin, and C-Phycoerythrin (GallandIrmouli et al. 2000). The only phycoerythrin currently used in Flow Cytometry is Phycoerythrin R (R-PE), so that, unless stated otherwise, the generic term phycoerythrin, or its abbreviation PE, from now on exclusively refers to this molecule. However, it should be noted that even within R-phycoerythrin, there is a considerable degree of heterogeneity, which can modulate the molecule’s quantum efficiency and spectral behavior. This heterogeneity affects the features of the various commercial products available, so it is theoretically possible that the different PE-conjugated antibodies (or standards) are not always spectrally consistent with each other. From a cytometric point of view, PE, which weighs 250 kDa, possesses a series of highly desirable characteristics since it is a stable molecule (White and Stryer 1987), is very bright because it has numerous chromophores characterized by high quantum efficiency, and is easily conjugated to antibody molecules (Oi et al. 1982). Furthermore, PE lends itself easily to being a donor in tandem molecules (Glazer and Stryer 1983). From a spectral point of view, PE has a double excitation peak, which allows its management both in the blue (excitation peak at 488 nm) and in green and yellowgreen (from 514 up to 560 nm) (Fig. 15.2). The absorption in green is greater than in blue; when excited by a green line, PE and its tandems emit more photons and generate brighter signals with reduced coefficients of variation. Satisfactory use of PE requires awareness of a few limitations and possible artifacts; it has been reported that: 1. Under certain conditions, PE can partially extinguish the signal emitted by Fluorescein (Chapple et al. 1988). 2. The signal emitted by PE can be irremediably extinguished by the presence of Cu + (Glazer 1988); this effect can be exploited for the preparation of a cytometric test aimed at determining the ions of this metal. 3. In at least one case of lymphoproliferative disease, PE has been found selectively able to bind the neoplastic B lymphocytes; this event has been interpreted with the expression of surface immunoglobulins able to recognize and bind the fluorescent molecule (Tabary et al. 2008).
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Fig. 15.2 Spectral characteristics of R-phycoerythrin. Note the presence of two distinct absorption peaks in the blue and green regions. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
4. In mice, 0.1% of B lymphocytes (Pape et al. 2011) and 0.02–0.04% of T lymphocytes with gamma delta receptor (Zeng et al. 2012) bind PE via the antigen receptor. 5. In humans, 0.02–0.04% of T lymphocytes with gamma delta receptor bind PE through the antigen receptor (Zeng et al. 2012); as in the point before, the low frequency of these populations makes the phenomenon practically irrelevant, but a clonal expansion of these elements is always theoretically possible, which in the cytometric analysis would bind all Mabs conjugated with PE or with PE-based tandems. 6. PE can aspecifically bind activated murine B cells, and, after permeabilization, the plasma cells of mouse small intestine lamina propria (Kim and Kim 2013). 7. PE can establish non-specific bonds in mice with Fcγ receptors at intermediate (CD32) and low affinity (CD16), resulting in inappropriate staining of macrophages, monocytes, neutrophils, mast cells, NK cells, and T-cell subsets (Takizawa et al. 1993). 8. A case has been reported in which human granulocytes activated with Dexamethasone were able to aspecifically bind the NKG2D specific Mab clone 149810 conjugated with PE, but not (1) the same clone conjugated with APC, nor (2) the same unconjugated clone recognized with a PE-conjugated goat anti-mouse antibody, nor (3) another specific Mab for NKG2D (clone 1D11) also conjugated with PE (Chitadze et al. 2021); the artifact, whose mechanism is unknown, appears due to the combined presence of that clone and that fluorochrome, but to neither of the two factors separately.
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PE performs quite well in staining intracytoplasmic antigens in human hematopoietic cells despite its large size. Studies have shown PE to be superior to other fluorochromes in the intracellular determination of MPO, TdT, and CD22 (Kappelmayer et al. 2000). If excited by a 488 nm line, the PE signal is generally collected via a band-pass filter like BP570/20. If the excitation occurs in green or yellow-green, it may be useful to place a band-pass filter with a range slightly shifted towards longer wavelengths (for example, BP590/20) on the dedicated detector to exclude the entry into the sensor of any excitation radiation tails.
15.1.1.2
Allophycocyanin (APC)
Allophycocyanins are a family of proteins present in virtually all Cyanobacteria, including Spirulina platensis, which contains it in large quantities (Jung and Dailey 1989). Accordingly, Spirulina-based food supplements commonly sold in pharmacies have been proposed as a possible commercial source for this Phycobilin (Jung and Dailey 1989). The only Allophycocyanin currently used in Flow Cytometry is Allophycocyanin B (APCB) (ex633/em670), so that, unless otherwise stated, the generic term Allophycocyanin, or its abbreviation APC, refers exclusively to this molecule. APC exists at pH 7 as a trimeric complex with a molecular weight of approximately 100 kDa (Bryant et al. 1976). Like PE, APC is a stable molecule (White and Stryer 1987), is very bright because of numerous chromophores characterized by high quantum efficiency, can be easily conjugated with antibody molecules, and can be an efficient donor in tandem molecules. From a spectral point of view, APC features a wide absorption region variable from about 570 to 670 nm and beyond (Fig. 15.3), which makes it excitable with the lines produced by a dye laser loaded with Rhodamine 6G (ex530/em590), from a Helium-Neon laser, a Krypton laser, and orange, short-red and full-red solid-state lasers. However, the excitation of APC with orange lasers is suboptimal. It has been reported that, in mice, 0.02% of B lymphocytes (Pape et al. 2011) bind APC via their antigen receptor; the low frequency of these populations makes the phenomenon practically irrelevant, but a clonal expansion of these elements is always theoretically possible, which in the cytometric analysis would bind all the Mabs conjugated with APC or with APC-based tandem. In this regard, it is interesting to note that a case of B-CLPD was recently reported in a patient whose neoplastic cells bound any monoclonal antibody conjugated with APC (Smith et al. 2021). A non-better-defined APC from Anacystis nidulans would be capable of binding genomic DNA; no further details have been offered in this regard (Kuddus and Ramteke 2009).
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Fig. 15.3 Spectral characteristics of Allophycocyanin B. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
15.1.1.3
B-Phycoerythrin (B-PE)
Some attention has also been paid to B-Phycoerythrin (B-PE), which has different spectral characteristics than R-phycoerythrin, as it is less efficiently excitable in blue. B-phycoerythrin is a photostable molecule (White and Stryer 1987), weighing about 240 kDa and composed of three different subunits; B-phycoerythrin emits in orange around 570 nm and has two major absorption peaks in the green-yellow at 545 and 563 nm, accompanied by a minor region at 488 nm (Glazer and Hixson 1977). B-phycoerythrin was used in conjunction with R-phycoerythrin in a dual laser system, which used a line at 488 nm to excite the former, and a line at 532 nm to excite the latter. Despite the need to correct the spillover, the two fluorochromes’ signals could be efficiently distinguished (Hoffman et al. 1993).
15.1.1.4
Phycocyanin-C (PC)
Phycocyanins are present in virtually all Cyanobacteria, including Spirulina platensis, which contains them in large quantities (Jung and Dailey 1989). Their function is to transport energy along the respiratory chain (Scott and Berns 1965). Phycocyanin-C is a particularly photostable molecule (White and Stryer 1987) and exists at pH 7 as a trimeric complex with a molecular weight of approximately 100 kDa (Bryant et al. 1976); according to others, the molecular weight of Cyanine C is around 37 kDa (Oi et al. 1982), but this last data may refer to the single monomeric component. For a time, Phycocyanin-C was considered likely to become the second fluorochrome excitable by a secondary red laser (Hoffman et al. 1987)
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since a BP650/7 filter could collect its emission in combination with a BP685/10 filter intended for the APC signal. Phycocyanin-C has also been proposed as a substitute for ethidium bromide in staining genomic DNA in electrophoretic lanes on agarose gel (Singh et al. 2011).
15.1.1.5
Cryptofluor™ Molecules
The family of CryptoFluor molecules includes some molecules extracted from Cryptomonads (unicellular algae). These molecules have a particularly low molecular weight (60 kDa), a feature considered desirable in the execution of intracytoplasmic labeling (Telford et al. 2001b), and some of them have been used in Flow Cytometry tests (Telford et al. 2001b). The molecules belonging to this group are: 1. Phycocyanin 645, with different excitation peaks between 575 and 640 nm and emission peak at 658 nm, renamed CryptoFluor-2 and marketed as Cryptofluor™ Crimson 2. Phycoerythrin 555 (ex555/em580), renamed CryptoFluor-4 3. Phycoerythrin 566 (ex565/em600), renamed CryptoFluor-5 and marketed under the name of Cryptofluor™ Gold by Sigma Aldrich (https://www.sigmaaldrich. com/)
15.1.2
Peridinin–Chlorophyll–Protein (PerCP)
Peridinin–Chlorophyll–Protein (PerCP) (ex488/em670) is a 35 kDa fluorescent protein complex active in the chlorophyll photosynthesis of Dinoflagellates (Dinophyta) (Song et al. 1976; Afar et al. 1991). Although functionally similar to a phycobiliprotein, PerCP must be considered separately, as it does not contribute to the formation of phycobilisomes. From a structural point of view, PerCP consists of a trimer, each component containing eight molecules of the peridinin chromophore and two chlorophyll molecules; its function is to transfer to the chlorophyll the sunlight energy absorbed by peridinin (Song et al. 1976). PerCP is a fluorochrome with a large Stokes shift, with an excitation peak in the blue around 488 nm and an emission peak in the red around 670 nm (Afar et al. 1991). PerCP also absorbs in the violet, making difficult the combined use of PerCP and red-emitting Qdots (Herman 2008), and according to its spectrum, also in the green and yellow-green (Fig. 15.4); moreover, PerCP displays another little absorbance peak in the red (633 nm) due to the presence of chlorophyll chromophores. In the early 1990s, PerCP was mostly used as a third fluorochrome excited by the primary blue laser in four-colour dual laser cytometers of the FACSCalibur type; this choice would rely on the very low inter-laser spillover evoked by the secondary red
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Fig. 15.4 Spectral characteristics of the PerCP molecule. The molecule is excited at 488 nm and is re-excited very little at 633 nm, with minimal inter-laser spillover. It can also be efficiently excited in violet and green. Figure obtained thanks to the FPbase Spectra Viewer program (https://www. fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
laser in the APC channel (Afar et al. 1991). PerCP was also used in immunofluorescence experiments on formalin-fixed paraffin-embedded tissues (Niki et al. 2004). The increased availability of other fluorescent probes has reduced the importance of PerCP, but the molecule is still successfully in use despite being susceptible to generating inter-laser spillover if excited by a violet laser. The correct use of the PerCP requires knowledge of some of its specific features, including: 1. The low brightness, which requires it to be used to stain highly expressed antigens, or in cases with low background conditions. 2. The reduced quantum efficiency if excited at high intensity, which impedes the use in stream-in-air platforms (Greimers et al. 1996). 3. The sensitivity to some permeabilization procedures, which suggests caution in its adoption in multi-parameter analyzes, including intracellular staining (Horvatinovich et al. 1994; Schmid 2000); this effect does not occur if the Lysing Solution produced by Becton Dickinson is exploited as a permeabilizing solution (Horvatinovich et al. 1994). The reduced quantum efficiency of PerCP in response to the high excitation powers can be explained in two ways. The first way postulates the destruction of the molecule due to the high energetic stress received (photobleaching), with a consequent reduction in the number of molecules able to emit. In contrast, the second way predicts that the increase of the excitation’s power also increases the probability that the excited PerCP molecule does not return directly to the ground state but passes through a triplet state, with refractoriness to further excitations and delay in the
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emission of the photon corresponding to the relative quantum leap (Bishop et al. 2000). PerCP behaves as a donor in the PerCP-Cy5.5 tandem, which has good brilliance. This particular brilliance would depend on the fact that the single state of this molecule, when excited, would transfer its energy to the acceptor more efficiently than its triplet state (Bishop et al. 2000).
15.1.3
AmCyan and AmCyan 100
As anticipated, the AmCyan molecule (ex458/em489) is a cyan fluorescent protein (CFP) obtained from the cnidarian Anemonia majano, and as such, it should be considered in the chapter on Fluorescent Proteins; nevertheless, it is discussed in this section because it is used in the conjugation of antibodies. Although its absorption peak is in deep blue, it is excited quite effectively in violet (Fig. 15.5), and before the appearance of the BV series fluorochromes, it was often used as a second “violetdriven” fluorochrome emitting to the right of Pacific Blue even though not particularly brilliant. The AmCyan molecule generates a certain amount of inter-laser spillover in the channels managed by the 488 nm line; consequently, the AmCyan 100 molecule (ex395/em500) was synthesized by genetic manipulation to reduce the spillover. The new CFP maintains the emission peak around 500 nm but moves the excitation peak towards the violet, thus acquiring spectral characteristics more suitable for contemporary use with FITC in multilaser systems with excitation lines in blue and violet (Guryev et al. 2012).
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Fig. 15.5 Spectral characteristics of the AmCyan Fluorescent Protein. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
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In polychromatic analyses, the compensation of the spillover due to AmCyan has been reported as not always satisfactory when performed with capture beads (DiGiuseppe and Cardinali 2011).
15.2
Small Organic Molecules
The small organic molecules used in protein conjugation are particularly numerous and complex. The fluorochromes belonging to this group encompass pyrene derivatives, coumarins, xanthenes, and cyanines. A further sub-group (Miscellany) collects the fluorochromes whose structural formulas are not included in the abovementioned categories or have not yet been undisclosed for commercial reasons. The BODIPY-based dyes are not mentioned in this chapter because of their limited use in Mab conjugation due to their solubility and stability problems and their tendency to bind proteins aspecifically. The succinimidyl esters of some small organic molecules, including Pacific Blue (ex410/em455), AF488 (ex488/em520), AF700 (ex702/em723), and AF750 (ex749/ em775), are used not only in the conjugation of antibodies but also in the so-called “barcoding” procedure, i.e., in the direct staining of the cells’ membranes performed with special methods. Usually, the barcoding procedure consists of marking multiple batches of cells, each with a spectrally different probe; the batches are then pooled together, stained, and acquired. Given that each batch has been variously “barcoded,” a barcoding-based gating strategy allows separate analysis of the batches even if stained and acquired together. This operative approach simplifies the execution of screening procedures, prevents batch-to-batch analytical differences, and reduces the overall costs (Krutzik and Nolan 2006; Krutzik et al. 2011).
15.2.1
Pyrene Derivatives
The pyrene derivatives group includes the molecule AF405 (ex401/em421) (Fig. 15.6) and the molecule Cascade Blue (CB) (ex390/em415), or pyrenyl oxy-trisulfonic acid, used in the 1980s as a fluorochrome excited by the violet line of a Krypton laser; both AF405 and CB (Telford et al. 2006) are effectively excited by a diode laser with 405 nm emission. CB can also be used as a basis for the synthesis of a series of different fluorescent molecules (Whitaker et al. 1991). CB (ex390/em415) was also used in a series of multicolor cytometric analyses (Beavis and Pennline 1994; Roederer et al. 1996b; Anderson et al. 1998; Bigos et al. 1999), including the analysis of murine hematopoietic stem cells (Donahue et al. 1999). The simultaneous use of Cascade Blue (CB) (ex390/em415) and Cascade Yellow (CY) (ex405/em550) must be carried out with caution, as it has been
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Fig. 15.6 Spectral properties of AF405. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a communityeditable fluorescent protein database. Nat Methods, 2019; 16: 277)
reported that the two fluorochromes have a tendency, albeit low, to bind each other (Roederer 2001).
15.2.2
Pyrydil-Oxazole Derivatives
An example of a fluorochrome belonging to the group of pyrydil-oxazole derivatives is Cascade Yellow (CY) (ex405/em550), a molecule characterized by a remarkable Stokes shift, used in the 1980s as an immunophenotyping fluorochrome in a series of multicolor cytometric analyses (Anderson et al. 1998; Bigos et al. 1999). The CY molecule (ex405/em550) was excited alternatively or in association with CB (ex390/em415) from a violet line originating from a Krypton laser but can also be excited by a 406 nm diode laser (Telford et al. 2006). Currently, CY is often used to stain oligonucleotides. CY is akin to the molecule PyMPO, structurally (1-(3-(Succinimidyloxycarbonyl) Benzyl)-4-(5-(4-Methoxyphenyl) Oxazol-2-yl) Pyridinium Bromide) (Fig. 15.7). The PyMPO molecule (ex415/em560–580) was used in the fluorescent Gramicidin synthesis required to study ion channel functioning (Lougheed et al. 2001).
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Fig. 15.7 Panel (a): Structural formula of the fluorescent molecule Cascade Yellow. Observe the oxazole group (in the red frame) between benzene (in the blue frame) and pyridine (in the green frame) and the extreme similarity with the PyMPO molecule (panel b), from which it differs for the presence of a bound sulphonic group with benzene. Notice in the black frame the succinimidyl group necessary for protein binding
15.2.3
Coumarin Derivatives
The coumarins are compounds characterized by the presence of the coumarinspecific benzopyranic structure, a plant-derived molecule. Depending on their structural characteristics, they are generally characterized by UV, NUV, and violet excitation, while their emission ranges vary from blue to blue-green. Coumarin compounds of cytometric relevance are the following: 1. The amino-methyl-coumarin-acetate (AMCA) (ex355/em450), used in the 1980s as a fluorophore for immunophenotyping excited by the UV line of a Krypton or high-power Argon laser (Khalfan et al. 1986; Aubry et al. 1990; Delia et al. 1991) 2. The AMCA sulfonated counterpart 7-Amino-4-methyl-6-sulfo-coumarin-3acetic acid (AMCA-S), also called AF350 (ex346/em442) (Leung et al. 1999; Panchuk-Voloshina et al. 1999) 3. A heterogeneous group including the molecules Horizon VH450 (ex405/em450) (Abrams et al. 2009), Horizon VH500 (ex415/em500) (Abrams et al. 2013), Marina Blue (ex360/em460), Pacific Blue (PB) (ex410/em455) (Sun et al. 1998), and DyLight 350 (ex353/em432) recently used in cell barcoding procedures (Krutzik et al. 2011) The Marina Blue fluorochrome (ex360/em460) can also be excited by a NUV laser consisting of an Indium-Gallium nitride diode (Telford 2004).
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Coumarins are also exploited in the synthesis of some members of the Pdots family (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2).
15.2.4
Xanthene Derivatives
The molecules of the xanthene derivatives group, mainly Fluorescein and Rhodamine derivatives, are characterized by a common scaffold consisting of an organic heterocyclic aromatic structure made of three benzene rings called Fluorone (Fig. 15.8). Depending on their structural features, xanthene molecules are generally excited in a region ranging from blue to green-yellow, while the emission ranges from green to yellow-orange. Xanthene molecules with deep red and infrared emissions are also known but have not yet been used in conjugation with protein molecules (Sun et al. 2015; Niu et al. 2016). Xanthenes are also exploited in the synthesis of some members of the Pdots family (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2).
15.2.4.1
Blue Excited Xanthenes (FITC and Others)
The molecules of this group used in conjugation with monoclonal antibodies are essentially Fluorescein and other structurally related molecules less frequently used, among which are AF488 (ex488/em520), Oregon Green 488 (ex488/em523), AF430 (ex439/em541), and DyLight 490 (ex491/em515). Before starting this section, it can be useful to consider some terminological issues. Two different molecules exist, i.e., Fluorescin and Fluorescein, which must not be confused with each other (Fig. 15.8). To worsen the matter, the terms “fluoresceinated” and “fluorescinated” can be indifferently found in the literature to define Fluorescein-conjugated Mabs.
Fig. 15.8 Structural formulas of Fluorescin (panel a), Fluorescein (panel b), and Rhodamine (panel c). Note the shared xanthene structure (fluorone, red frame)
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Fluorescein Fluorescein (CAS number 2321-07-5) (Fig. 15.8) is a fluorescent molecule used to conjugate streptavidin and Mabs and is the base from which fluorescent derivatives are obtained, to be also exploited in fluorescence Flow Cytometry (for further information on Fluorescein analogs, see Sects. 15.2.4.1 and 17.10.2). Fluorescein is also used in Ophthalmology for retinal angiography and can interfere with immunophenotyping involving Fluorescein-conjugated Mabs in patients undergoing this procedure (for further information on this topic, see Sect. 20.3.2). Fluorescein is a small molecule weighing 390 Da, which absorbs at 488 nm and emits at 520 nm (Fig. 14.3). The Fluorescein isothiocyanate (FITC), is one of the most frequently used molecules in immunofluorescence methods. Although it risks being replaced by molecules with more advantageous spectral and chemicophysical characteristics, it is still one of the most popular fluorochromes. The reason for this preeminence lies in a series of positive features, including the low cost, the easy conjugability with antibody molecules (Spendlove 1966; Freeman and Crissman 1975), and the spectral compatibility with the 488 nm line emitted by an Argon laser or an equivalent solid-state laser (Fig. 14.3). FITC also exhibits a few negative characteristics, which must be considered. These include: 1) Limited brilliance, which in some cases can constitute a contraindication to its use in stream-in-air cell-sorter (Wadley 2014) 2) The tendency to undergo fading in the event of prolonged excitation (Schauenstein et al. 1980), which may limit its use in fluorescence microscopy or image cytometry techniques 3) Sensitivity to non-physiological pH, which may limit its use in particular techniques (Haugland 1996) The overall electric charge of the FITC is negative, and it is possible that this characteristic influences the behavior of the monoclonal antibodies to which it is conjugated. It is known that the Mabs QBEnd10 and 8G12 unsatisfactorily recognize the CD34 antigen when conjugated with FITC (Siena et al. 1991; Ortuno et al. 1997; Gratama et al. 1998) and that the Mab T3, specific for the CD3 ε chain, recognizes all T lymphocytes when conjugated to PE, but only those with αβ TCR when conjugated with FITC (Mullersman et al. 1991). In the latter case, the behavior has been attributed to greater glycosylation of the CD3 ε chains expressed by T lymphocytes with γδ TCR, which could configure electrostatic incompatibility between the epitope and the fluorochrome (Krangel et al. 1987). It has been reported that FITC can non-specifically bind to eosinophils (Efthimiadis et al. 1996; Bedner et al. 1999), and the exploitation of this feature has been proposed in a method for their cytometric counting (Efthimiadis et al. 1996). FITC tends to bind naturally to proteins, especially in the basic environment and is useful not only to conjugate avidin and antibodies (either monoclonal or polyclonal) but also as a probe to determine the total protein content of cycling cells together with their DNA content evaluated with Propidium iodide (Crissman et al. 1976, 1985; Crissman and Steinkamp 1994).
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Fig. 15.9 Structural formulas of Fluorescein (panel a), Oregon Green (panel b), and Alexa Fluor 488 (panel c). Note the shared xanthene structure (fluorone, red frame)
The signal produced by FITC is generally presented to the PMT by a band-pass filter centered at 525 nm (BP525/30). However, in case of the simultaneous presence of a green DPSS laser with emission at 532 nm, it can be necessary to adopt a bandpass filter centered more to the left (for example, a BP515/20) or introduce a notch filter to prevent the entry into the PMT of green laser stray radiation (for further information on notch filters see Sect. 6.3.2). Fluorescin Strictly speaking, Fluorescin (CAS number 518-44-5) (Fig. 15.8) should not be comprised among fluorochromes because it is a non-fluorescent molecule. Fluorescin can be converted by oxidation into the fluorescent molecule Fluorescein, which can be reverted to Fluorescin by reduction. In FCM, Fluorescin is mainly used in the oxidative burst evaluation as dichlorofluorescein diacetate (DCF-DA) (ex495/ em529) (Bass et al. 1983) (for further information about this topic, see Sect. 17.9.2). Fluorescin can also be used as a forensic tool because Heme oxidizes it into Fluorescein, allowing bloodstain demonstration (Li et al. 2015). Alexa Fluor 488 The Alexa Fluor 488 molecule, or AF488 (ex488/em520), has a structural formula very similar to FITC (Fig. 15.9) but is characterized by modifications credited to improve its brilliance and resistance to fading. It is commercially proposed as a substitute for FITC in the conjugation of different Mabs and possesses similar excitation and emission characteristics (Panchuk-Voloshina et al. 1999). Like the molecules Oregon Green 488 (ex488/em523) and Oregon Green 514 (ex489/em526), the AF488 molecule was also used in the study of fluorescence anisotropy in protein–protein and protein–nucleic acid interactions (Rusinova et al. 2002). AF488 molecule has also been used to synthesize nanosensors (PEBBLEs, Probe Encapsulated By Biologically Localized Embedding) capable of reacting to the presence of free iron (Sumner and Kopelman 2005). Oregon Green 488 Another compound very similar to FITC is Oregon Green 488 (ex488/em523) (Fig. 15.9), characterized by modifications that allow replacing FITC in situations
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characterized by unfavorable pH. The Oregon Green 488 molecule has also been used for other purposes, including: 1. Evaluation of intracellular Calcium (this function requires conjugation with BAPTA, a polycarboxylic amino acid able to chelate Calcium specifically) (Lock et al. 2015) 2. The study of fluorescence anisotropy in protein–protein and protein–nucleic acid interactions (Rusinova et al. 2002) 3. The differential staining of bacteria, together with Hexidium iodide (HI) (ex518/ em600) (Gunasekera et al. 2003; Holm and Jespersen 2003) 4. Imaging applications (Delmotte and Delmas 1999) Other derivatives Fluorescein is the basis for synthesizing a very high number of molecules mainly used to explore cell features (Lavis 2017); they share its excitation and emission and are discussed in the respective sections in Chap. 17. For the sake of completeness, at least the following molecules should be mentioned: 1. The fluorescein-diacetate (FDA) (ex495/em520), used in the study of the membrane fluidity by evaluation of the depolarization (Dimitropoulos et al. 1988) 2. The 5-chloromethyl fluorescein-diacetate (CMFDA) (ex488/em530), used in the determination of sulfhydryl groups (Zurgil et al. 1999) and peroxynitrite (ONOO) (Balaguer et al. 2017) 3. Other fluorescein esters, which consist of molecules produced by esterification with a series of different groups and include: (a) Succinimidyl esters of carboxyfluorescein (CFDA-SE, or CFSE), used mainly in the evaluation of cell viability (Rotman and Papermaster 1966; Martel et al. 1974; Persidsky and Baillie 1977; Weaver 1998) and cell proliferation (Hasbold et al. 1999; Parish 1999; Lyons 2000) (for further information on this topic, see Sect. 17.3.3.1) (b) The acetoxymethyl ester of bis-2-carboxyethyl-carboxyfluorescein (BCECFAM) (ex505/em545), primarily used in the evaluation of intracellular pH (Thomas 1986; Franck et al. 1996; Ozkan and Mutharasan 2002) and free intracellular Potassium (Balkay et al. 1997) (for further information on this topic, see Sect. 17.7.2) 4. The Calcein acetoxymethyl ester (ex496/em520), used mainly in the evaluation of viability, cell proliferation (Parish 1999), and Multidrug Resistance (MDR) (Hollo et al. 1994; Karaszi et al. 2001) (for further information on this topic, see Sect. 17.3.3.2 for viability and cell proliferation, and Sect. 17.18.1 for MDR)
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15.2.4.2
275
Green and Yellow-Green Excited Xanthenes (TRITC and Others)
This group of molecules, especially Tetramethylrhodamine (TMR) (ex554/em576), have played a major role in fluorescence microscopy. However, they have not been successful in Flow Cytometry due to their poor excitation by the blue lasers initially implemented in cytometers. However, it should be noted that this situation is susceptible to change due to the recent availability of solid-state lasers with greenyellow and orange emissions (Kapoor et al. 2008). Tetramethylrhodamine (TMR, TRITC) The best known of the molecules belonging to this group is Tetramethylrhodamine (TMR) (ex554/em576) in its various formulations, among which Tetramethylrhodamine isothiocyanate (TRITC) (ex550/em570) (Fig. 15.10). This molecule would require differing filter combinations depending on the excitation wavelength used; for example, with excitation of 532 nm, a BP570/20 would be required, whereas, with excitation of 561 nm, a BP590/20 or a BP593/40 would be needed. TRITC is a historically important molecule, most used as the second color in fluorescence microscopy. Although its use has been occasionally documented in Flow Cytometry (Loken et al. 1977), it has never been popular since its excitation characteristics are poorly compatible with FITC, thus making the combined use of these two fluorochromes in a single laser platform difficult. Texas Red (TR) Texas Red (TR) (ex589/em615) consists of the sulfonyl-chloride derivative of Sulforhodamine 101 (ex586/em605) (Titus et al. 1982; Lefevre et al. 1996). This molecule has experienced some historical success in multicolored cytometry but
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Fig. 15.10 Spectral properties of TRITC. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a communityeditable fluorescent protein database. Nat Methods, 2019; 16: 277)
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Fig. 15.11 Structural formulas of rhodamine derivatives Alexa Fluor 594 (panel a) and Texas Red (panel b)
rapidly lost favor due to its spectral characteristics, which require excitation wavelengths close to 600 nm. These wavelengths, easily obtainable at present with solidstate lasers, could at that time be produced only with the help of a dye laser loaded with Rhodamine 6G (ex530/em590) or a Krypton laser (Weichel et al. 1985). TR is currently used as an acceptor in the tandem PE-TR (commercially known in the past as ECD, or Duochrome, or Red613) and is akin to the AF594 (ex590/em617) molecule, which shares similar spectral characteristics (Fig. 15.11). Other Rhodamines The Rhodamines of cytometric interest consist primarily of the following: 1. Lissamine Rhodamine (RB 200) (ex572/em590), which had been a candidate to replace FITC in the conjugation of antibodies, but whose suitability rested on the use of a microphotometric system based on a Xenon lamp (McKay et al. 1981) 2. Rhodamine 123 (RH123) (ex507/em529), used in the evaluation of the topographical distribution of mitochondria (Johnson et al. 1980), oxidative burst (Rothe et al. 1988; Vowells et al. 1995), mitochondrial membrane potential (Scaduto and Grotyohann 1999; Lugli et al. 2007), multidrug resistance (MDR) (Eytan et al. 1997; Forster et al. 2012), and concentration of peroxynitrite (ONOO) (Crow 1997) 3. Sulforhodamine B (SRB) (ex565/em586), used in imaging techniques as a global cell dye (Han et al. 2012), is “closed if not identical” to the fluorochrome used in the CASP-Glow Red kit used in the determination of apoptosis (Shapiro 2003) 4. Sulforhodamine 101 (ex586/em605), used in the synthesis of Texas Red (ex589/ em615) (Lefevre et al. 1996), in imaging techniques as a probe for oligodendrocytes (Hill and Grutzendler 2014) and mouse astrocytes (Nimmerjahn et al. 2004), and in the cytometric determination of total cellular protein content (Engelhard 1997) (it should be noted that in Engelhard’s paper, Sulforhodamine 101 is declared as excitable at 488 nm, feature not supported by a review of its spectral behavior)
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5. The methyl ester (TMRM, or T668) (ex548/em574) and the ethyl ester (TMRE, or T669) (ex552/em574) of Tetramethyl Rhodamine, used in the evaluation of mitochondrial membrane potential (Floryk and Houstek 1999) (Scaduto and Grotyohann 1999) 6. The molecules of the Mitotracker group, except for Mitotracker Green (ex490/ em516) which is a cyanine 7. Rhodamine 6G (ex530/em590), used in the evaluation of multidrug resistance (Eytan et al. 1997) and as an active medium in laser dyes destined for the excitement of Texas Red (laser emission range 570–660)
15.2.4.3
Red and NIR Excited Xanthenes (Vita Blue and Others)
The in-the-red excitable xanthene molecules are currently not used in Flow Cytometry but for functional or imaging studies. These molecules include: 1. AF633 (ex632/em647), a derivative sulfonate of Rhodamine (Berlier et al. 2003), conjugated with increasing frequency to monoclonal antibodies to be used in imaging techniques but also Flow Cytometry (Berlier et al. 2003) 2. Vita Blue (ex633/em660), a derivative of Fluorescein, whose esters have been used in the determination of intracellular viability and pH (Lee et al. 1989a, b) 3. Rhodamine 700 (ex641/em661), used as an active medium for dye lasers (under the name LD700) and as a specific fluorochrome for nucleic acids in various fields, including the study of cell kinetics (Shapiro and Stephens 1986) and detection of Plasmodium DNA in parasitized red blood cells in rodents (Gerena et al. 2011) 4. Rhodamine 800 (ex685/em705), also known as R800 or as MitoFluor Far Red 680 dye, used in determining the membrane potential of mitochondria and as a specific fluorochrome for nucleic acids in various areas, including the study of cellular kinetics (Shapiro and Stephens 1986) and the detection of Plasmodium berghey DNA in rodent red blood cells (Gerena et al. 2011) 5. Rhodamines in which the Oxygen atom in the center of the xanthene structure has been replaced with a Phosphorus (Chai et al. 2015) or a Silicon atom (Kushida et al. 2015); these molecules demonstrate IR emission and are candidates for fluorescence imaging techniques in this spectral region; a carboxylated siliconRhodaminee has been linked with HO33342 to form a complex known as SiRHoechst (ex652/em674) (commercial name SiR-DNA marketed by Thermo Fisher Scientific) (Lukinavicius et al. 2015) (for more information on this topic, see Sect. 16.4).
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Cyanines
The cytometric relevant cyanines consist, with some exceptions, of molecules characterized by the presence of a polymethine bridge [¼ CH (CH ¼ CH) n-] of variable length that binds one or two distinct heterocyclic molecular structures containing one Nitrogen atom each (Ernst et al. 1989; Mujumdar et al. 1989; Southwick et al. 1990) (Fig. 15.12). The cyanines in which the bridge links two heterocyclic molecules are known as Dimeric Cyanines and further divide into Symmetrical, in which the cyclic molecules are the same, and Asymmetrical, in which the heterocyclic molecules differ from each other. The cyanines with only one heterocyclic molecule take the name of Monomeric Cyanines (aka Hemocyanines). Cyanines are a highly heterogeneous group of molecules whose names depend on the polymethine bridge length and the nature of the heterocyclic structures bound together (Sims et al. 1974; Ernst et al. 1989). The heterocyclic molecules can consist of indole (Indocyanine), imidazole, pyrrole, thiazole, benzothiazole, pyridine rings, and quinolines (Carbocyanines). If a ketonic group is present, the cyanine molecule takes the name of Merocyanine (Ernst et al. 1989). The heterocyclic structures of cyanines can bind a variable number of different groups, thus modulating certain features of the whole molecule, such as water solubility (Ernst et al. 1989; Mujumdar et al. 1989), photostability (Sims et al. 1974), spectral behavior, and conjugability with proteins (Ernst et al. 1989; Mujumdar et al. 1989). In Flow Cytometry, dimeric cyanines play a leading role in several tasks, including antibody conjugation, nucleic acid staining, protein content determination, and cell function evaluation. In contrast, monomeric cyanines are mostly exploited in nucleic acid staining (for further information on this topic, see Sect. 16.6.2). Cyanines are also useful in imaging techniques and theranostic procedures; Cy3 and Cy5 are markers for oligonucleotide detection in microarray gene expression techniques (Agbavwe and Somoza 2011). Cyanines are also exploited in the synthesis of some members of the Pdots family (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2).
Fig. 15.12 The structural formula of a symmetrical dimeric cyanine. Note the two heteroaromatic structures (red frame) and the polymethine bridge that joins them (blue frame)
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Fig. 15.13 An example of a cyanine characterized by an extra benzene ring (red frame) added to each heteroaromatic structure. This modification allows shifting to the right the spectral capabilities of the molecule
Fig. 15.14 The structural formula of the IR-820 fluorochrome, a cyanine with spectral characteristics markedly shifted towards the infrared (ex710/em820). Note the long polymethine chain, the doubling of the benzene rings on heteroaromatic structures, and the presence of an extra cyclic structure on the polymethine chain (red frame); all these modifications are capable of moving the spectral features to the right. The sulfonate groups (green frame) make the fluorochrome negatively charged and hydrophilic
The spectral properties of cyanines depend essentially on the length of the polymethine bridge and tend to move towards the right with an increase in the number of carbon atoms (Southwick et al. 1990; Lipowska et al. 1993; Mujumdar et al. 1993); it has been shown that the addition of a Carbon atom shifts the spectral characteristics of the molecule by approximately 100 nm towards the right (Ernst et al. 1989). The addition of a benzene ring to each of the aromatic structures further shifts the spectral characteristics of the molecule to the right; a Cyanine X modified in this way takes the name of Cyanine X.5 (Fig. 15.13). Another way to move the excitation and emission peaks to the right is to insert an extra aromatic structure in the polymethine chain (Fig. 15.14). The following sections group the Cyanines according to their different spectral characteristics.
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Cyanine 2 (CY2) and Cyanine CY2-Like
Cyanine 2 (CY2) (ex489/em506) is a cyanine characterized by a polymethine chain consisting of two carbon atoms. CY2 has been tested in immunofluorescence microscopy, where it was inferior to FITC, DTAF (dichlorotriazinylaminofluorescein) (ex492/em516), BODIPY (dipyrrometheneboron difluoride) (ex493/em503), and Rhodol Green (ex492/em516). The latter was superior in fluorescence intensity and photobleaching resistance (Benchaib et al. 1996). CY2 is currently not used in cytometric techniques.
15.2.5.2
Cyanine 3 (CY3) and Cyanine CY3-Like
Cyanine 3 (ex550/em570) can be excited with increasing efficiency at 514 and 532 nm but is optimally coupled with the 546 nm line of a mercury arc lamp (Mujumdar et al. 1993); in Flow Cytometry, it is not used alone but as an acceptor in the BV570 tandem (Chattopadhyay et al. 2012). CY3 can be bound to oligonucleotides and is used in microarray gene expression techniques (Agbavwe and Somoza 2011). The CY3-like cyanine group includes AF555 (ex555/em565) (Berlier et al. 2003). According to a non-peer-reviewed report available on the Internet, CY3 and DiIC1(3), also known as Hexamethylindocarbocyanine and used to determine membrane potential, share the same chromophore group (Shapiro 1997).
15.2.5.3
Cyanine 3.5 (CY3.5) and Cyanine CY3.5-Like
Cyanine 3.5 (CY3.5, ex581/em594) is a trimethine cyanine that distinguishes itself from cyanine 3 because of an extra benzene ring in the two cyclic molecules at the two ends of the methine bridge, whose presence moves the spectral characteristics of the molecule further to the right. A molecule with spectral characteristics compatible with this group is the AF594 (ex594/em617), a carbocyanine derivative with excitation and emission characteristics similar to the Texas Red molecule (ex589/em615) but with the emission peak slightly shifted to the right. AF594 is used as an acceptor in PE-AF594 tandem, but it is frequently conjugated to monoclonal antibodies tailored for imaging techniques exploiting the recently available laser diodes with emission in the yellow and orange spectral regions (Berlier et al. 2003; Kapoor et al. 2008).
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Cyanine 5 (CY5) and Cyanine CY5-Like
Cyanine 5 (CY5) (ex625/em670) emits in the same range as the APC and is excited by the lines in red emitted by Helium-Neon lasers (633 nm), Krypton lasers (647 nm) (Fig. 15.15) (Mujumdar et al. 1993), and diode lasers either “short red” (620 and 625 nm) and “full red” (635 and 650 nm). Cyanine 5 was used in direct antibody conjugation, but it is now only available as an acceptor in PE-CY5 tandems because it increases background noise by aspecifically binding the high-affinity IgG receptor (CD64) (van Vugt et al. 1996). Together with CY3 (ex550/em570), CY5 is used in microarray gene expression tests but is negatively affected by environmental ozone, which significantly reduces its emission intensity (Fare et al. 2003). Conjugation with cyclooctatetraene (COT) or with 4-nitrobenzyl alcohol (NBA) can drastically increase its photostability (Altman et al. 2011), which is of practical value in microarray gene expression tests (Branham et al. 2007). The CY5-like cyanine group includes AF647 (ex650/em665), which showed sensitivity to ozone similar to that found in CY5 (Fare et al. 2003). According to a non-peer-reviewed report available on the Internet, CY5 and DiIC1(5), also known as Hexadimethylindocarbocyanine, marketed under the name of MiTO-Probe ™ and used in the determination of membrane potential, share the same chromophore group (Shapiro 1997).
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Fig. 15.15 Spectral properties of Cyanine 5. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a communityeditable fluorescent protein database. Nat Methods, 2019; 16: 277)
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Cyanine 5.5 (CY5.5) and Cyanine CY5.5-Like
Cyanine 5.5 (CY5.5, ex633/em695) is a pentamethyne cyanine (Lee et al. 1995) which is distinguished from CY5 by the fact that the two cyclic molecules placed at the two ends of the methine sequence are fused with an extra benzene ring, which moves further to the right the spectral characteristics of the molecule. CY5.5 is currently used as an acceptor in the PE-CY5.5 tandem, the APC-CY5.5 tandem, and the PerCP-CY5.5 tandem. The CY5.5-like cyanine group includes AF700 (ex 702/em723), which can be used as an acceptor in the APC-AF700 Tandem but can also be directly conjugated to antibodies, although the peak of excitation is quite far from the emission of red lasers, and consequently its brilliance is not very high. Nevertheless, it can play an important role as a third fluorochrome manageable by a 633 nm line.
15.2.5.6
Cyanine 7 (CY7) and Cyanine CY7-Like
Cyanine 7 (CY7, ex743/em767) is a heptamethyne arylsulfonate cyanine (Lee et al. 1995) that is used as an acceptor in the PE-CY7 (Roederer et al. 1996a) and APC-CY7 tandems (Beavis and Pennline 1996; Roederer et al. 1996a). The CY7-like cyanine group includes the molecules AF750 (ex749/em775) and HiLyte 7, a CY7 stabilized by a further bridge between the two halves connected by the methine chain. HiLyte 7 is generally used as an acceptor in the APC-H7 tandem molecule and should be more stable than APC-CY7.
15.2.5.7
Other Near-Infrared (NIR) Emitting Cyanines
Cyanine molecules with spectral characteristics further shifted towards the nearinfrared can be obtained in different ways, including the insertion of pyridinium ions on the polymethine chain’s central methine. The first cyanines with emission in the IR tested in Flow Cytometry are probably the heptamethyne cyanines BHDMAP (ex785/em800) and BHMP (ex785/em800) (Lee et al. 1995), effectively excited by a diode at 785 nm. AF790 (ex784/em814) and CY7.5 (ex788/em808) are additional molecules classed in this category. Another cyanine with excitement in the infrared is Indocyanine Green (ex780/ em812), a non-toxic tricarbocyanine formerly used in Cardiology to determine cardiac output. Given that the liver selectively takes up Indocyanine Green, there are experiences in which the unconjugated fluorochrome has been used for “physiological” labeling of hepatocytes; in this scenario, the molecule was excited with a line at 785 nm, while the signal was collected with a BP832/36 (Yoshie et al. 2012). Similar molecules are the heptamethine cyanine dyes IR-775, IR-780, IR-783, IR-797, IR-806, and IR-808 (IR-X molecules) (Deng et al. 2018), whose main field of application is in vivo imaging and theranostic procedures (Shi et al. 2016).
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The molecules that make up this group share particular spectral characteristics: (1) an absorption peak between 770 and 810 nm and (2) two distinct emission peaks, the first between 790 and 810 nm and the second between 920 and 950 nm (Deng et al. 2018). The IR-783 molecule has demonstrated a favorable behavior in that it is not toxic in vivo imaging procedures (Xing et al. 2016), while the IR-806 molecule (ex824/em837) has been used in silica particles to investigate encapsulation-related phenomena (Auger et al. 2011). The molecules IR-786 (ex770/em805) and IR-820 (ex710/em820) (Prajapati et al. 2009) should be added to this group, despite not being expressly mentioned in the group of IR-X molecules. Molecule IR-786 was linked to Hoechst 33258 utilizing a polyethylene glycol linker and used as a probe for the extracellular DNA produced by apoptotic processes (Leung 2004) (for further information on this topic, see Sect. 16.4.1), while molecule IR-820 was used as a contrast medium in a series of analyses in vivo (Prajapati et al. 2009). Finally, the Promofluor series should be considered, consisting of cyanines marketed already conjugated with a carboxylic acid, succinimidyl esters, or maleimide and therefore intended for conjugation with proteins, and theoretically usable as viability markers. This group includes the molecule PF840 (ex843/em884), sometimes used in multicolor Flow Cytometry together with the molecule AF790 (ex782/em805) (Irwan et al. 2016).
15.2.6
Proprietary Molecules
Many commercial companies continuously synthesize new fluorochromes and offer their customers panels of antibodies conjugated with proprietary molecules. In most cases, these fluorochromes are derived from already known molecules, and modified by various procedures. Their producers promote them as possessing advantages such as greater brilliance, lower spectral spillover, greater resistance to fading, lower tendency to form non-specific bonds, and greater stability. Since it is possible to patent new names but not old molecules, names may correspond to molecules not disclosed by the producer but already long known. Even if the structural formulas are undisclosed, it is often possible to hypothesize the class to which they belong since a review of the molecular structures published so far shows that, among the small fluorescent organic molecules used in the conjugation of antibodies: 1. Almost all the molecules excited in the UV and the violet belong to the Coumarin or the Pyrene group 2. Almost all the molecules excited in blue-green are derivatives of Fluorescein 3. Almost all the molecules excited in orange are derivatives of Rhodamine 4. Almost all the molecules excited in red and IR belong to Cyanines or molecules of similar structure, except for AF633 (ex632/em647), a Rhodamine derivative (Berlier et al. 2003)
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These molecules are sold already conjugated with antibodies or streptavidin or with the groups needed for subsequent conjugation with proteins (hydroxysuccinimidyl esters, iodoacetamide, et cetera). Together with their specific characteristics, their availability can be explored by consulting the producers’ websites. Nevertheless, further information is available in the next sections for the reader’s convenience.
15.2.6.1
Alexa Series
The Alexa series includes several compounds originally synthesized by Richard Paul “Dick” Haugland. These compounds, originally called “Alexa Fluor” in honor of the son Alex, are named after AF or “Alexa,” followed by a number corresponding to the excitation peak wavelength. At present, at least 19 different molecules are commercially available from AF350 to AF790 (Fig. 15.16), whose absorption peaks cover and exceed the visible range, granting the possibility of a perfect spectral coupling with any emission laser line available (Panchuk-Voloshina et al. 1999) (Table 15.1). The most relevant Alexa molecules in Flow Cytometry currently are: 1. AF488 (ex488/em520), which is a xanthene molecule structurally and spectrally akin to FITC (Fig. 15.9) but credited with being less affected by photobleaching. 2. AF594 (ex590/em617), which is a carbocyanine molecule spectrally similar to Texas Red (ex589/em615), used as an acceptor in the PE-AF594 tandem.
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Fig. 15.16 Emission peaks of 19 molecules of the Alexa Fluor series. From left to right 1) Alexa Fluor 350, 2) Alexa Fluor 405, 3) Alexa Fluor 430, 4) Alexa Fluor 488, 5) Alexa Fluor 514, 6) Alexa Fluor 532, 7) Alexa Fluor 546, 8) Alexa Fluor 555, 9) Alexa Fluor 568, 10) Alexa Fluor 594, 11) Alexa Fluor 610, 12) Alexa Fluor 633, 13) Alexa Fluor 635, 14) Alexa Fluor 647, 15) Alexa Fluor 660, 16) Alexa Fluor 680, 17) Alexa Fluor 700, 18) Alexa Fluor 750, 19) Alexa Fluor 790. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/ spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
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Table 15.1 Main features of Alexa Fluor molecules Name AF350 AF405 AF430 AF488 AF500 AF514 AF532 AF546 AF555 AF568 AF594 AF610 AF633 AF635 AF647 AF660 AF680 AF700 AF750 AF790
Excitation peak (nm) 346 401 439 488 502 517 532 546 555 578 590 612 632 633 650 663 679 702 749 782
Emission peak (nm) 442 421 541 520 525 542 554 573 565 603 617 628 647 647 665 690 702 723 775 805
Chemical group Coumarin Pyrene Xanthene Xanthene Xanthene Xanthene Xanthene Xanthene Cyanine Xanthene Xanthene Cyanine Xanthene Cyanine Cyanine Cyanine Cyanine Cyanine Cyanine Cyanine
3. AF610 (ex612/em628), which is a carbocyanine molecule spectrally similar to Texas Red (formerly 589/em615), used as an acceptor in the PE-AF610 tandem. 4. AF647 (ex650/em665), which is a cyanine spectrally equivalent to APC used as an acceptor in the PE-AF647 tandem; AF647 is also excited at slightly higher wavelengths and has an emission tail toward 720–730 nm, which can cause intralaser spillover issues (Telford 2015b); there is a suggestion in the literature regarding its possible use together with AF660 if both excited at 620, 630 nm (Telford 2015b). 5. AF660 (ex663/em690), which is a cyanine very well excited by a series of lines ranging from 620 up to 685 nm (Telford 2015b); there is a non-peer-reviewed suggestion on the Internet regarding its possible use at once with AF633 if both excited at 633 (Shlomchik 2002). 6. AF700 (ex702/em723), which is a cyanine spectrally equivalent to Cy5.5, used alone and as an acceptor in the APC-AF700 tandem; AF700 is sometimes excited quite nicely by a 633 nm line but displays an emission tail towards 750 nm, susceptible to create spillover issues with other NIR emitting cyanines (Telford 2015b). According to a couple of isolate reports, (1) AF700 was susceptible to bind non-specifically the Mab anti-PD-L1 clone 29E.2A3 (Hughes et al. 2020), and (2) the AF700-conjugated anti NKG2D Mab clone 149810 was able to bind aspecifically human granulocytes activated with Dexamethasone (Chitadze et al. 2021).
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7. AF750 (ex749/em775), which is a cyanine spectrally equivalent to Cy7, mainly used as an acceptor in the APC-AF750 tandem. Interest is also increasing around the NIR-excited AF790 (ex782/em805), which is a cyanine spectrally equivalent to CY7.5; its excitation is centered at 782 nm but is satisfactorily managed even by a 730 nm line, which is particularly useful since laser emitting at higher wavelengths are more expensive (Telford 2015b).
15.2.6.2
Other Series
The fluorochromes included in these series are mainly derived from molecules belonging to the pyrenes, xanthenes, and cyanines, both symmetrical and asymmetrical, depending on the case. Many of these molecules have been synthesized for live-cell imaging and super-resolution, confocal, and two-photon microscopy, but they have been mentioned in this section, both for completeness’ sake and because the availability of new excitation lines can allow their future use in Flow Cytometry. These compounds can be available conjugated with primary or secondary antibodies, other proteins (avidin, phalloidin), or various reactive groups (maleimide) for further custom conjugations. With the exclusion of the previously cited Alexa series, other series of molecules commercially available at the release of this book include: 1. 2. 3. 4. 5. 6.
7. 8. 9. 10. 11. 12. 13.
14.
The ATTO series (marketed by Atto-tech) The CF series (marketed by Biotium) The cFluor™ series (marketed by Cytek) The ColorWheel® series (marketed by Sigma-Aldrich) The CoraLyte® series (marketed by Proteintech) The DyLight series, including, but not limited to, molecules mostly used in microscopy, also includes plenty of NIR-excited molecules (marketed by Dyomics) The eFluor series, specifically designed for use in Flow Cytometry (marketed by Thermo Fisher Scientific) The Fire series, including molecules used as acceptors in tandem fluorochromes (marketed by BioLegend) The Fluoprobes series (marketed by Interchim) The HiLyte series (marketed by Anaspec) The iFluor series (marketed by Aatbio) The IRDye series, including, but not limited to, NIR-excited molecules, mainly intended for imaging (marketed by Licor) The Janelia series (marketed by Tocris), consisting of xanthene molecules modified through the addition of azetidine groups and claimed to possess better features of brilliance and stability (Grimm et al. 2017) The Promofluor series, specifically designed for use in Flow Cytometry (marketed by Promocell) (for further information on these molecules, see Sect. 15.2.5.7)
15.2
15. 16. 17. 18. 19.
Small Organic Molecules
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The Seta series (marketed by Setabiomedicals) The Spark series (marketed by BioLegend) The NIR-excited TRACY dyes (marketed by Sigma Aldrich) The VioDyes series (marketed by Miltenyi Biotec) The red- and NIR-excited VivoTag series (marketed by Perkin Elmer)
Further information on these molecules can be found on the respective manufacturers’ site and in the Fluorescence Dye Directory by Fluorofinder (accessible at https://app.fluorofinder.com/dyes).
15.2.6.3
Miscellany
Small organic molecules not belonging to any already cited series are Pacific Green (PG) (ex410/em500), Pacific Orange (PO) (ex410/em551), and Krome Orange (KO) (ex398/em528) (Preijers et al. 2011), all excitable in the violet at 405 nm, and BYG584P (ex563/em583), excitable in the yellow-green and marketed for custom conjugation by Becton Dickinson. Another interesting molecule, also recently marketed by Becton Dickinson, is BD Horizon RealYellow™ 586 (RY586) (ex565/em586), which can be excited by a yellow-green but not by a green laser. The structural differences with BYG584P, if any, have not been disclosed. Other interesting molecules are the Dazzle 594 molecule, used as an acceptor in the PE-Dazzle 594 tandem (ex488/em610), the R700 molecule, used as an acceptor in the APC-R700 tandem (ex652/em704), and the Horizon R 718 molecule (ex695/ em718), credited as a more spectrally efficient version of AF700 (Becton Dickinson 2014c). For the sake of completeness, mention should be made of the molecule Lucifer Yellow (LY) (ex428/em536), reportedly used in Fluorescence Microscopy for intracellular (Hanani 2012) and retrograde staining of neurons (Stewart 1981), and in Flow Cytometry for anion transport evaluation through the cell membrane (Dinchuk et al. 1992), free thiol groups evaluation on the cell surface (Archer et al. 1995), and immunophenotyping procedures (Anderson et al. 1998). ELF-97 Finally, the ELF-97 molecule (ex365/em520), a quinazolinone classifiable in a large group of heteroaromatic molecules, should be mentioned. This molecule is not conjugated to antibodies but is a fluorogenic substrate that is not fluorescent by itself but is transformed into insoluble fluorescent alcohol by the action of the enzyme phosphatase (Paragas et al. 1997). Although the absorption spectrum of ELF-97 does not seem to allow excitation at 405 nm (Fig. 15.17), there are reports based on its excitation in the violet (Telford et al. 2003), which, together with its emission in green, makes ELF-97 an interesting molecule. ELF-97 can be used not only in the determination of endogenous phosphatase (Telford et al. 1999) but also to detect the successful binding of a phosphataseconjugated antibody (Telford et al. 2001a), both in Fluorescence Microscopy
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Fig. 15.17 Spectral properties of the ELF-97 molecule. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
(Paragas et al. 1997, 2002) and in Flow Cytometry procedures (Telford et al. 1999, 2001a). ELF-97 has also been used to synthesize a specific fluorogenic substrate for peroxidase (Cai et al. 2015).
15.3 π-Conjugated Organic Polymers (Brilliant Violet and Others) π-Conjugated polymer molecules, also known as organic polymers, consist of an inter-linked organic structural sequence in which the alternation of single and double bonds configures a repeated π orbitals system characterized by delocalized electrons (Williams and Bridges 1964). This structure behaves as a functional unit with peculiar optoelectronic characteristics, explaining the high fluorescence intensity of this class of molecules when excited (Liu and Bazan 2004). The first π-conjugated polymer molecule used in cytometry was the Brilliant Violet 421 molecule (BV421, ex405/em421) (Chattopadhyay et al. 2012), which presents excitation and emission characteristics akin to Pacific Blue (ex410/em455), but shows a particularly high brilliance. The organic polymer molecules represent a breakthrough in fluorochromes because of their brilliance and stability; they are also exploited in the synthesis of the Pdots (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2). Organic polymers currently or shortly available for use in Flow Cytometry can be classified according to their excitation range; at the time of this book’s release, they were:
15.3
π-Conjugated Organic Polymers (Brilliant Violet and Others)
289
1. The Brilliant Ultra Violet 395 molecule BUV395 (ex355/em395), marketed by Becton Dickinson; it is of note that BUV395 molecule (and its tandems) has been devised to be excited with a “classical” UV (355/360) nm laser source, but it can also be excited by a NULV source (Telford 2015a) and, even though sub-optimally, by a 320 nm laser line (Telford et al. 2017); on the other hand, BUV395 and its tandems are not substantially excited by a deep UV line, so that it looks possible managing both DUV and BUV at once without significant interlaser spillover (Telford et al. 2019). 2. Five violet excited molecules, i.e., (1) the aforementioned molecule Brilliant Violet BV421 (ex405/em421) marketed by Becton Dickinson (2015) and BioLegend, (2) the molecule SuperNova V428 (ex405/em428) marketed by Beckman Coulter (2020), (3) the molecule Super Bright 436 (ex414/em436) marketed by Thermo Fisher Scientific, (4) the molecule Brilliant Violet BV480 (ex436/em478) marketed by Becton Dickinson, (5) and the molecule Brilliant Violet BV510 (ex405/em510) also marketed by Becton Dickinson (2016a) and BioLegend; in comparison with BV510, BV480 suffers less inter-laser UV excitation but tends to give a higher however easily manageable inter-laser spillover in the BV421 channel (Becton Dickinson 2016a). 3. A blue excited molecule, i.e., the molecule Brilliant Blue BB515 (ex488/em510) marketed by Becton Dickinson (2014a). Furthermore, there are rumors about the upcoming commercialization of a deep UV (280 nm) excited polymer. The simultaneous incubation of some molecules belonging to the group of polymeric molecules may require the additional presence of proprietary buffers of undisclosed composition. These buffers likely protect the various polymers from mutual interference; some non-peer-reviewed citations indicate that some proprietary reagents may interfere with artificial capture standards routinely used in compensation procedures (Richter 2018).
15.3.1
Organic Polymers-Based Tandems
The organic polymers play an exceptionally important role as donors in a series of tandems, considerably extending the number of parameters that can be investigated simultaneously in the cytometric analysis. Many series encompassing polymeric molecules-based tandems are now or will soon be commercially available; among these, the following can be quoted: 1. A group of BUV395-based tandems (for more information on this topic, see Sect. 15.5.1) 2. A group of BV421-based tandems (for more information on this topic, see Sect. 15.5.2.1) 3. A group of SuperNova V428-based tandems (for more information on this topic, see Sect. 15.5.2.3)
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4. A group of Super Bright 436-based tandems (for more information on this topic, see Sect. 15.5.2.2) 5. A group of BB515-based tandems (for more information on this topic, see Sect. 15.5.3.10) Furthermore, there are rumors about the upcoming commercialization of a group of not yet disclosed tandems based on a deep UV (280 nm) excited polymer, encompassing molecules able to emit in a range spanning from 360 nm to 750 nm. For more information on tandems, see Sect. 15.5.
15.3.2
Narrow-Band Emissive Chromoforic Polymer Dots (Pdots)
Narrow-band emissive chromophoric polymer dots, also known as chromophoric nanoparticles or chromophoric polymer dots (from now on Pdots), are fluorescent molecular complexes characterized by a particularly narrow emission band, very useful in Polychromatic Flow Cytometry to limit spillover. Pdots are complex structures made of one or more chromophoric polymeric backbones with delocalized p-electrons variously joined with a number of narrowband emissive units acting as energy acceptors. The nature of the chromophoric polymers and the emissive units can be varied to obtain the desired spectral properties. The absorption peaks depend on the chromophoric polymers and can be tuned at 266, 355, 405, 450, 488, 532, 560, 635, 655, 700, 750, 800, 900, 980, and 1064 nm (Chiu et al. 2012, 2018); the emission peaks depend on the molecules in the narrowband emissive units, which can include different fluorochromes encompassing metal complexes and BODIPY, squaraine, porphyrin, metalloporphyrin, phthalocyanine, Lanthanide complexes, perylene, cyanine, rhodamine, coumarin, xanthene, and their derivatives. Even though no deep-ultraviolet or infrared-driven Pdots are commercially available, the synthesis of such probes is feasible, allowing the exploiting of spectrum areas hitherto left unused. Even though no technical details have been disclosed, it is likely that the fluorochromes marketed by Bio-Rad as StarBright Dyes belong—all or part—to this group of molecules. According to the manufacturer, StarBright Dyes have favorable characteristics of brilliance and stability and display narrow emission spectra, which would reduce intra-laser issues. StarBright Dyes divide into three groups, i.e., (1) a first group driven by a UV line, including eight molecules with emission peaks at 400, 445, 510, 575, 605, 665, 740, and 795 nm, (2) a second group driven by a violet line, including 8 molecules with emission peaks at 440, 475, 515, 570, 610, 670, 710, and 760 nm, and (3) a third group driven by a blue line, including seven molecules with emission peaks at 510, 580, 611, 675, 705, 763, and 808 nm, respectively.
15.4
Nanocrystals
15.4
291
Nanocrystals
Nanocrystals are a particular group of microstructures not attributable to any of the other categories of fluorescent molecules mentioned in this book. They constitute a heterogeneous group but share a crystalline structure and excitation and emission mechanisms that drastically differ from conventional fluorophores and are conceptually similar to diode lasers. In Flow Cytometry, the most interesting types are the Quantum Dots (Qdots) and the so-called “upconverting nanoparticles.” Although both have extremely promising features, their use in flow cytometry is currently documented only for Quantum Dots.
15.4.1
Quantum Dots (Qdots)
Quantum Dots consist of semiconductor nanocrystals encompassing a core made of CdSe and a shell made of ZnS. They display a size ranging from 3 to 10 nm and can emit in the visible region when excited by UV or violet light (Ibanez-Peral et al. 2008). Qdots emission wavelength is directly proportional to their size, and Qdots’ size increases with their synthesis time; it follows that it is possible to obtain Qdots with different emissions simply by adjusting their synthesis duration (Riegler and Nann 2004). Qdots find their main use in several industrial applications, including highdefinition television screens. Their particular spectral characteristics have also been used in fluorescence microscopy and Flow Cytometry, where they were reported among the first commercial molecules excitable with a violet laser (Bruchez et al. 1998). To be used as biological tracers, Qdots are coated with an organic layer, allowing solubility in water and carrying the functional groups needed to bind proteins as streptavidin or antibodies (Chattopadhyay et al. 2010). The final diameter of the resulting nanostructure is around 10–15 nm (Watson et al. 2003). From a cytometric point of view, the most important characteristic of the Qdots in Flow Cytometry is their excitation spectrum (Fig. 15.18). Unlike the other fluorochromes discussed so far, the shape of their excitation spectrum does not approximate the inverse of the emission but is a dwindling continuum from 300 to 500 nm. This behavior is because Qdots are not fluorophores but extremely small semiconductors and get excited by other mechanisms than organic molecules. Come as it may, it follows that Qdots are re-excited by the other light sources present in an instrument and cause an important inter-laser spillover, whose magnitude is inversely proportional to the wavelength of the ‘inappropriate’ excitation. Qdots are used in platforms equipped with violet lasers, but ultraviolet and blue lines can also manage them; moreover, Qdots have been referred to as the only fluorochromes manageable by a deep UV source (280 nm) (Telford et al. 2019). A non-peer-
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Fig. 15.18 Spectral properties of Quantum Dots. The excitation range is practically common, while the emission peaks of Qdot 525 (1), Qdot 545 (2), Qdot 565 (3), Qdot 585 (4), Qdot 605 (5), Qdot 625 (6), Qdot 655 (7), Qdot 705 (8), Qdot 800 (9) are shown from left to right. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
reviewed report also exists, according to which a red line (635 nm) was able to excite Qdot 655 (Dimmick 2008). As said before, the emission of the Qdots depends on their size and displays a particularly low CV. The Qdots currently available for cytometric analysis are Qdot 525, Qdot 545, Qdot 565, Qdot 585, Qdot 605, Qdot 625, Qdot 655, Qdot 705, and Qdot 800, where the number refers to the emission peak (Thermo Fisher Scientific). Given their spectral properties, Qdots may cause lesser spillover spreading than organic molecules in compensated systems (Chattopadhyay et al. 2010). Besides these positive features, Qdots can also display some less desirable characteristics, i.e., 1. Sensitivity to the ions of some metals, such as Iron, Zinc, and particularly Copper, which reduces their quantum efficiency (Chattopadhyay et al. 2010; Zarkowsky et al. 2011) 2. Unsatisfactory behavior in intracytoplasmic staining procedures, due either to their dimensions and sensitivity to permeabilizing solutions (Chattopadhyay et al. 2010) 3. Intra-batch spectral variability, requiring spillover assessment over time (Wu et al. 2007b; Chattopadhyay et al. 2010) 4. Variable behavior from batch to batch (Wu et al. 2007a) 5. Unpredictable stability (Chattopadhyay et al. 2010) Qdots can also be exploited in the synthesis of some members of the Pdots family (Chiu et al. 2012, 2018) (for further information on this topic, see Sect. 15.3.2). Qdots have also been proposed as a secondary standard for defining calibrators in MESF (Buranda et al. 2011) (for further information on this topic, see Sect. 13.4.2).
15.5
Tandem Fluorochromes
293
Quantum dots are currently marketed by Thermo Fisher Scientific; Quantum dots have also been marketed under the name of eFluor® Nanocrystals, not to be confused with eFluor® Organic Dyes (see also Sect. 15.2.6.2).
15.4.2
Upconverting Nanoparticles (UCNPs)
Up-converting nanoparticles (UCNPs) are semiconductor nanocrystals made up of Ytterbium or Erbium, which behave as activators, and rare earth elements (Holmium, Thulium, et cetera), which dwell in the crystal’s center and emit the light. One of their most interesting features is to be excited in the IR and emit in the visible spectrum’s region. UCNPs are excited in the near-infrared region (NIR) with a preference for the 975 nm line and emit at 365, 475, 538 545, and 804 nm depending on their composition (Table 15.2) (Sigma Aldrich n.d.; Zheng et al. 2019). UCNPs have been exploited in bio-imaging (Nyk et al. 2008; Wang et al. 2009; Kwon et al. 2016) and in diffuse in vivo Flow Cytometry (DIFC) (Bartosik et al. 2020), an experimental technique that analyzes circulating cells through diffuse light excitation. Antibodies and streptavidin conjugated with UCNPs are marketed by CD Creative Diagnostics® (https://www.creative-diagnostics.com/).
15.5
Tandem Fluorochromes
Tandem fluorochromes are artificial molecules formed by the union of two or more different fluorescent molecules assembled to constitute a single functional structure in which the excited molecule is the donor while the acceptor is the emitting molecule. The principle behind the tandem fluorochromes is the non-radiative transfer of energy (FRET) . The molecules in tandems are selected so that the donor’s emission
Table 15.2 Composition and spectral behavior of upconverting nanoparticles
Crystal formula NaYF4, Yb, Tm LiYF4, Yb, Tm NaYF4, Yb, Ho NaYF4, Yb, Er NaYF4, Yb, Er, Mn NaYF4, Yb, Tm LiYF4, Yb, Tm, Mn
Absorption 975 nm 975 nm 975 nm 975 nm 975 nm 975 nm 975 nm
Emission 365 and 475 nm 475 and 800 nm 538 and 660 nm 545 and 660 nm 545 and 660 nm 804 nm 804 nm
Er Erbium, Ho Holmium, Li Lithium, Mn Manganese, Na Sodium, Tm Thulium, Yb Ytterbium, YF Yttrium fluoride
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a PE EM PEAK
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WAVELENGTH
Fig. 15.19 Schematic representation of a tandem fluorochrome, consisting of a donor (in this case, Phycoerythrin) and an acceptor (in this case, Cyanin 5). Due to the non-radiative transfer of energy from donor to acceptor, allowed by the spectral compatibility between the donor’s emission and the acceptor’s excitation (black frame in panel a), the tandem behaves as a single molecule (panel b), displaying the donor’s excitation spectrum and the acceptor’s emission spectrum (panel c). Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
range overlaps the acceptor’s excitation range or the excitation range of a “bridge” molecule able to transfer the donor’s excitation into the final acceptor. The short distance (660) is permeant, binds with DNA intercalating in the double helix, and is generally used for a rapid evaluation of the ploidy or viable cells kinetics during multiparametric immunophenotypic analysis (Plander et al. 2003; Yuan et al. 2004; Yuan and Yang 2008), but it has been successfully exploited in the staining of Escherichia coli DNA (Silva et al. 2010). DRAQ5 has been used in determining the nucleic acid of Plasmodium berghei (Billker et al. 2004) and, together with SYTOX® Blue, in human milk stem cell determination (Keller et al.
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2018). DRAQ5 was also used in the evaluation of chromatin condensation in a new method called ChromEM tomography (ChromEMT) (Smith et al. 2018). It has been suggested that, together with Pyronin Y, DRAQ5 can selectively inhibit the binding of Pyronin with DNA but not with RNA (Leif 2003). The simultaneous presence of BODIPY-labeled compounds hinders the intake of DRAQ5 by the cells, probably due to the formation of a complex between the two molecules (Snyder and Garon 2003). The DRAQ7 molecule (ex488-633/em> 660) shares all the characteristics and peculiarities of DRAQ5 but is impermeant and is generally used as a probe for cell viability (Edward 2012), particularly in situations where its non-toxicity is of particular relevance (Akagi et al. 2013). DRAQ7 can also be useful in evaluating ploidy or kinetics in fixed cell populations. DRAQ5 and DRAQ7 should not be confused with DRAQ9™ (ex655/em697), a permeant molecule with an undisclosed structure reported staining the cytoplasm and the perinuclear structures (Golgi, endoplasmic reticulum, et cetera).
16.8.2.2
CyTRAK Orange
CyTRAK Orange (ex510/em610) is a permeant anthraquinone molecule that binds both to the cytoplasm and to the nucleus of eukaryotic cells, demonstrating an affinity for both the double helix of DNA and, to a lesser extent, for the double helix of cytoplasmic RNA (Pieper et al. 2016). CyTRAK Orange is efficiently excited at 488 nm or 532 nm and emits in red-orange, with an emission peak located at 610 nm (Pieper et al. 2016); CyTRAK Orange has been used in Flow Cytometry during polychromatic immunophenotypic analysis to exclude non-nucleated events from the analysis (Mathis et al. 2013). Similar to LDS-751, CyTRAK Orange covariates with CD45 expression (Pieper et al. 2016).
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Thornthwaite JT, Thomas RA (1990) High resolution DNA measurement using the nuclear isolation medium, DAPI, with the RATCOM flow cytometer. In: Darzynkiewicz Z, Crissman HA (eds) Flow cytometry, Methods cell biology, vol 33. Academic Press, San Diego, pp 111–120 Tiendrebeogo RW, Adu B, Singh SK, Dodoo D, Dziegiel MH, Mordmüller B, Nébié I, Sirima SB, Christiansen M, Theisen M (2014) High-throughput tri-colour flow cytometry technique to assess Plasmodium falciparum parasitaemia in bioassays. Malar J 13:412. https://doi.org/10. 1186/1475-2875-13-412 Traganos F, Darzynkiewicz Z (1994) Lysosomal proton pump activity: supravital cell staining with acridine orange differentiates leukocyte subpopulations. In: Darzynkiewicz Z, Robinson JP, Crissman HA (eds) Flow cytometry, Methods in cell biology, part A, 2nd edn. Academic Press, San Diego, pp 185–194 Traganos F, Kapuscinski J, Darzynkiewicz Z (1991) Caffeine modulates the effects of DNA-intercalating drugs in vitro: a flow cytometric and spectrophotometric analysis of caffeine interaction with novantrone, doxorubicin, ellipticine, and the doxorubicin analogue AD198. Cancer Res 51(14):3682–3689 Trask BJ, van den Engh GJ, Elgershuizen JH (1982) Analysis of phytoplankton by flow cytometry. Cytometry 2(4):258–264. https://doi.org/10.1002/cyto.990020410 Urbanova J, Lubal P, Slaninova I, Taborska E, Taborsky P (2009) Fluorescence properties of selected benzo[c]phenantridine alkaloids and studies of their interaction with CT DNA. Anal Bioanal Chem 394(4):997–1002. https://doi.org/10.1007/s00216-009-2601-7 van Asten I, Blaauwgeers M, Granneman L, Heijnen HFG, Kruip M, Beckers EAM, Coppens M, Eikenboom J, Tamminga RYJ, Pasterkamp G, Huisman A, van Galen KPM, Korporaal SJA, Schutgens REG, Urbanus RT (2020) Flow cytometric mepacrine fluorescence can be used for the exclusion of platelet dense granule deficiency. J Thromb Haemost 18(3):706–713. https:// doi.org/10.1111/jth.14698 Van Bockstaele DR, Peetermans ME (1989) 1,3'-Diethyl-4,2'-quinolylthiacyanine iodide as a “thiazole orange” analogue for nucleic acid staining. Cytometry 10(2):214–216. https://doi. org/10.1002/cyto.990100213 van den Engh G, Trask B, Lansdorp P, Gray J (1988) Improved resolution of flow cytometric measurements of Hoechst- and chromomycin-A3-stained human chromosomes after addition of citrate and sulfite. Cytometry 9(3):266–270. https://doi.org/10.1002/cyto.990090313 Van Der Pol MA, Broxterman HJ, Westra G, Ossenkoppele GJ, Schuurhuis GJ (2003) Novel multiparameter flow cytometry assay using Syto16 for the simultaneous detection of early apoptosis and apoptosis-corrected P-glycoprotein function in clinical samples. Cytometry 55B (1):14–21 Van Dilla MA, Langlois RG, Pinkel D, Yajko D, Hadley WK (1983) Bacterial characterization by flow cytometry. Science 220(4597):620–622 Van Hove L, Goossens W, Van Duppen V, Verwilghen RL (1990) Reticulocyte count using thiazole orange. A flow cytometry method. Clin Lab Haematol 12(3):287–299 van Vianen PH, van Engen A, Thaithong S, van der Keur M, Tanke HJ, van der Kaay HJ, Mons B, Janse CJ (1993) Flow cytometric screening of blood samples for malaria parasites. Cytometry 14(3):276–280 Vermes I, Haanen C, Steffens-Nakken H, Reutelingsperger C (1995) A novel assay for apoptosis. Flow cytometric detection of phosphatidylserine expression on early apoptotic cells using fluorescein labelled Annexin V. J Immunol Methods 184(1):39–51 Verwer B (2002) BD FACSDiVa options. White Paper Becton Dickinson. Available at http://www. bdbiosciences.com/ds/is/others/23-6579.pdf. Last accessed 8 Jan 2021 Wall JE, Buijs-Wilts M, Arnold JT, Wang W, White MM, Jennings LK, Jackson CW (1995) A flow cytometric assay using mepacrine for study of uptake and release of platelet dense granule contents. Br J Haematol 89(2):380–385 Ward DC, Reich E, Goldberg IH (1965) Base specificity in the interaction of polynucleotides with antibiotic drugs. Science 149(3689):1259–1263
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Zimmer C, Wahnert U (1986) Nonintercalating DNA-binding ligands: specificity of the interaction and their use as tools in biophysical, biochemical and biological investigations of the genetic material. Prog Biophys Mol Biol 47(1):31–112 Zipper H, Brunner H, Bernhagen J, Vitzthum F (2004) Investigations on DNA intercalation and surface binding by SYBR Green I, its structure determination and methodological implications. Nucleic Acids Res. https://doi.org/10.1093/nar/gnh101 Zuliani T, Duval R, Jayat C, Schnebert S, Andre P, Dumas M, Ratinaud MH (2003) Sensitive and reliable JC-1 and TOTO-3 double staining to assess mitochondrial transmembrane potential and plasma membrane integrity: interest for cell death investigations. Cytometry A 54(2):100–108. https://doi.org/10.1002/cyto.a.10059 Zurek-Biesiada D, Kedracka-Krok S, Dobrucki JW (2013) UV-activated conversion of Hoechst 33258, DAPI, and Vybrant DyeCycle fluorescent dyes into blue-excited, green-emitting protonated forms. Cytometry A 83(5):441–451. https://doi.org/10.1002/cyto.a.2226
Chapter 17
Fluorochromes for the Study of the Cell Features
The fluorochromes included in this chapter are generally used to study extrinsic parameters related to individual cellular features, such as mitochondrial potential, membrane permeability, intracytoplasmic Calcium concentration, and intracellular pH. Cellular functions modulate these parameters, and their study allows obtaining important information on more complex processes such as activation, communication, apoptosis, autophagy, and proliferation. The fluorochromes commonly used in studying cellular characteristics are very numerous and heterogeneous, although observation of their molecular structure highlights that many are attributable to two main structural models, i.e., Xanthenes and Cyanines. The classification of these fluorochromes can be performed based on a series of different parameters, including molecular structure, mechanism of action, or the parameter explored, and is made even more complex by the fact that the same probe can explore different cellular characteristics; moreover, different probes can evaluate the same phenomenon. Moreover, to make matters worse, many molecules with the same structure are marketed with different names, and many other molecules have unpublished molecular structures. This chapter follows a classification based on the investigated parameters for practical reasons. It also encompasses molecules whose use in Flow Cytometry has not yet been described, both for completeness’ sake and the availability of solid-state lasers allowing the use of molecules previously exploited so far with imaging techniques only. More details on the structure and functions of the molecules discussed in this chapter can be obtained from the consultation of dedicated monographs and commercial companies’ catalogs (Molecular Probes 2010; Sabnis 2015). In studying cell features, two different mechanisms can provide the information generated by a fluorescent probe. In the first case, the substrate concentration does not modulate the probe’s spectral behavior, and the signal is directly or inversely proportional to the concentration of the substrate. In the second case, the substrate concentration modulates the probe’s spectral behavior. The latter situation can configure two different scenarios, requiring a different analytical approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_17
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382
Fig. 17.1 The layout of an optical bench for ratio analysis of a probe that modulates its wavelength emission depending on the substrate concentration. Information on the concentration is provided by the ratio between emission intensities elicited by the same laser but measured by two different PMTs in two different and properly chosen spectral regions. There is only one laser in the configuration shown, and both the detectors depend on it. The two detectors collect the two signals, each of them through a spectrally different and properly chosen filter array. PMT photomultiplier, DM dichroic mirror, BP band-pass. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
The substrate concentration affects the probe emission wavelength in the first scenario. It follows that information on the substrate concentration is provided by the ratio between emission intensities elicited by the same laser at the same wavelength and measured in two different and properly chosen spectral regions through different filter arrays (Fig. 17.1). The substrate concentration affects the probe excitation wavelength in the second scenario. It follows that information on the substrate concentration is provided by the ratio between the emission intensities, which are (1) elicited by two different lasers, each tuned at a different and properly chosen wavelength, and (2) collected through two identical properly chosen filter arrays (Fig. 17.2). From a general perspective, ratio techniques can provide results independent of the probe’s concentration variations occurring among different cells. From an instrumental perspective, the ratio techniques require a different approach depending on the cytometer’s circuitry; in digital cytometers, the ratio is performed computationally, while dedicated circuits are needed for analog cytometers.
17.1
Protein Content
Protein content has been studied to investigate changes induced by cell cycle progression. The study was made possible by directly staining the analyzed cells with a solution of FITC (ex488/em520) and PI (Crissman et al. 1976; Crissman and
17.1
Protein Content
383
Fig. 17.2 The layout of an optical bench for ratiometric analysis of a probe that modulates its wavelength emission depending on the substrate concentration. Information on the concentration is provided by the ratio between emission intensities elicited by two different lasers and measured by two different PMTs through two identical, properly chosen filter arrays. PMT photomultiplier, DM dichroic mirror, BP band-pass. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
Steinkamp 1982; Crissman and Steinkamp 1994). In these experiments, the bivariate analysis of the fluorescence emitted by FITC and Propidium iodide allowed the demonstration of the increase in the total protein content during the phase of DNA synthesis (phase S). Another fluorochrome used in the determination of the total protein content is sulforhodamine 101 (SR101) (ex586/em605) (Heiden et al. 1990), used mainly in confocal microscopy in astrocyte and oligodendrocyte staining (Nimmerjahn et al. 2004; Hill and Grutzendler 2014). Over time, many fluorochromes have been devised to bind proteins. Most of them belong to the group of cyanines and precisely to: 1. Merocyanines (SYPRO Red (ex300-550/em630), SYPRO Orange (ex300-470/ em570), SYPRO Tangerine (ex300-490/em640), and Coomassie Fluor Orange (ex300-470/em570)) (Yarmoluk et al. 2011) 2. Carbocyanines (Lucy 506 (ex504/em515), Lucy 565 (ex565/em584) and Lucy 569 (ex565/em581)) (Yarmoluk et al. 2011) 3. Sulfoindocarbocyanines (Epicocconone, also known as Deep Purple (ex405-488532/em620 when bound to proteins)) (Mackintosh et al. 2003; Choi et al. 2006; Yarmoluk et al. 2011) Other frequently used molecules are SYPRO Ruby (ex280-450/em610), a Ruthenium chelate, and Flamingo (ex512/em535) (Yarmoluk et al. 2011), whose formula is currently undisclosed. These compounds have been exploited to visualize proteins in electrophoretic gels, and none of them has been allegedly used in flow cytometry, with only one exception known to the author, i.e., the use of SYPRO Red in the evaluation of the
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total protein content of bacterial cells isolated from seawater samples (Zubkov et al. 1999).
17.2
Nucleic Acid Content and Chromatin Organization
Nucleic acid content determination is functional in the study of cell kinetics. It is based on the principle of stoichiometry, which, in a cytometric context, means (1) that the number of photons emitted by a fluorochrome is directly proportional to the number of fluorochrome molecules binding the substrate and (2) that the number of fluorochrome molecules binding the substrate is directly proportional to the number of substrate molecules. Some DNA-specific fluorochromes show a non-stoichiometric behavior, depending on several variables, including the degree of chromatin condensation. These fluorochromes, which include proflavine, quinacrine, berberine, PY, LDS-751, and compounds of the SYTO® series (Cowden and Curtis 1981; Frey 1995), preferentially diminish their signal when bound to nuclei characterized by more condensed chromatin, presumably because of the increased self-quenching of the signal, due to the greater mutual proximity the molecules establish under these conditions (Frey 1995). Consequently, some fluorochromes, such as SYTO®16 and LDS-751, have been used to explore apoptosis, which is a process characterized by a decrease in chromatin condensation (Walker and Sikorska 1994). Chromatin condensation has also recently been studied using a new method, called ChromEM tomography (ChromEMT), based on the use of the DRAQ5 probe (ex488-633/ em>660) (Smith et al. 2018). The fluorochromes used in determining the content of nucleic acids in general and chromatin condensation have been extensively discussed in Chap. 16 of this book, to which the reader is referred.
17.3
Cell Viability
Cell viability is a parameter often but not exclusively explored through the study of membrane permeability. Apart from the specific impermeant molecules for nucleic acids, already discussed in Chap. 16, the most frequently used fluorophores in the study of membrane permeability are the amino dyes, which being impermeant, penetrate only into dead cells, and some molecules structurally related to Fluorescein, which selectively bind to viable cells. Some attempts to evaluate cell viability were also performed with Calcofluor White (Berglund et al. 1987) and Trypan Blue (Avelar-Freitas et al. 2014).
17.3
Cell Viability
17.3.1
385
DNA Impermeant Probes
The impermeant DNA probes most frequently used in evaluating cell viability are PI, 7-AAD, and DRAQ7. These probes and others less frequently used have been discussed in Chap. 16 of this book, to which the reader is referred.
17.3.2
Amine Reactive Dyes
There are situations, such as the determination of dead cells in samples subsequently fixed, in which the use of specific molecules for DNA is not desirable since these molecules, which bind under equilibrium, tend to redistribute themselves to the fixed cells that were vital before fixation. In such cases, Amine Reactive Dyes can be useful. These molecules bind irreversibly to amino groups in dead cells’ cytoplasm, which they enter thanks to abnormal membrane permeability. The behavior of these compounds is akin to the impermeant nucleic acids probes, with the difference that, even after fixation, they remain selectively linked to the cells they have previously labeled (Perfetto et al. 2006, 2010). These molecules, whose structures have not been disclosed, exist in different versions with spectral characteristics covering the whole spectrum. In particular, the Amine Reactive Dyes encompass: 1. The group of LIVE/DEAD® Fixable molecules (marketed by Thermo Fisher), including (1) LIVE/DEAD® Fixable Violet (ex416/em451), (2) LIVE/DEAD® Fixable Lime (ex405/em506), (3) LIVE/DEAD® Fixable Aqua (ex367/em526), (4) LIVE/DEAD® Fixable Blue (ex350/em450), (5) LIVE/DEAD® Fixable Green (ex495/em520), (6) LIVE/DEAD® Fixable Olive (ex480/ em557), (7) LIVE/DEAD® Fixable Yellow (ex400/em575), (8) LIVE/DEAD® Fixable Orange (ex580/602), (9) LIVE/DEAD® Fixable Red (ex595/em615), (10) LIVE/DEAD® Fixable Far Red (ex650/em665), (11) LIVE/DEAD® Fixable Scarlet (ex700/em723), and two NIR emitting molecules, encompassing (12) LIVE/DEAD® Fixable NIR (ex750/em775) and (13) a second LIVE/ DEAD® Fixable NIR with spectral properties more shifted in the IR (ex840/ em876) (according to the Manufacturer’s specifications, LIVE/DEAD® Fixable Scarlet and LIVE/DEAD® Fixable NIR (ex750/em775) are also excited by a red line, while the 876 nm emitting molecule requires an 808 nm line). 2. The group of Zombie™ molecules (marketed by BioLegend) (Fig. 17.3), including (1) Zombie UV™ (ex362/em459), (2) Zombie Violet™ (ex400/em423), (3) Zombie Aqua™ (ex382/em510), (4) Zombie Green™ (ex491/em515), (5) Zombie Yellow™ (ex396/em572), (6) Zombie Red™ (ex600/em624), (7) and Zombie NIR™ (ex719/em746) (according to the manufacturer’s specifications, the Zombie NIR™ molecule would also be excitable from a line at 633 nm).
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1
400
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2
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6
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600
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Fig. 17.3 Emission spectra of some molecules of the Zombie group molecules. In particular: (1) Zombie UV, (2) Zombie Violet, (3) Zombie Green, (4) Zombie Yellow, (5) Zombie Red, and (6) Zombie NIR. Figure obtained thanks to the FPbase Spectra Viewer program (https://www. fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
3. The group of Phantom Dyes (marketed by Proteintech), including (1) Phantom Dye UV 450, (2) Phantom Dye Violet 450, (3) Phantom Dye Violet 510, (4) Phantom Dye Blue 515, (5) Phantom Violet 540, (6) Phantom Red 710, and (7) Phantom Dye Red 780. 4. The group of Ghost Dyes (marketed by Tonbo Biosciences), including (1) Dye™ Blue 516 (ex488/em516), (2) Dye™ Red 710 (ex633/em710), (3) Dye™ Red 780 (ex633/em780), (4) Dye™ UV 450 (ex355/em450), (5) Dye™ Violet 450 (ex405/em450), (6) Dye™ Violet 510 (ex405/em510), and (7) Dye™ Violet 560 (ex405/em537). 5. The group of “Viobility™ Fixable Dye” (marketed by Miltenyi), including (1) Viobility™ Fixable Dye (ex405/em452), (2) Viobility™ Fixable Dye (ex405/em520), and (3) Viobility™ Fixable Dye (ex488/em520). Because of their reactivity for amino groups, in the case of multiparametric analysis, it is recommended to stain cells first with the desired Reactive Amino Dye and then proceed with normal labeling, using a washing solution containing proteins (for example, BSA) to prevent further unwanted reactions of the amine dyes, which could bind to the antibodies used in the staining. Any problems related to the spillover compensation produced by these dyes can be solved using appropriate standards, consisting of microbeads conjugated with amino groups selectively binding the Reactive Amino Dye in question (Viability Dye Compensation Standard, marketed by Polysciences).
17.3
Cell Viability
17.3.3
387
Fluorescein Derivatives
Fluorescein esters used to determine cell viability passively diffuse in all cells, living or dead, due to their hydrophobic ester groups. Subsequently, Fluorescein esters are selectively retained inside living cells thanks to aspecific esterases, inactive in dead cells, which trap them in living cells and make them intensely fluorescent. The molecules belonging to this group include: 1. The succinimidyl ester of Carboxyfluorescein (or CFSE) (ex495/em519) 2. The succinimidyl diacetate ester of Carboxyfluorescein (or CFDA-SE) 3. The acetoxy-methyl ester of Calcein (ex 496/em520), a derivative of fluorescein also known as fluorexone All these three molecules have approximatively the same spectral behavior (ex495/em515); CFSE and Calcein are also useful in the evaluation of cell proliferation and intercellular communication (Lyons and Parish 1994; Parish 1999; Fonseca et al. 2006; Begum et al. 2013).
17.3.3.1
Carboxyfluorescein Esters
CFSE (succinimidyl carboxyfluorescein ester) and CFDA-SE) (succinimidyl carboxyfluorescein diacetate ester) (Fig. 17.4) are two different molecules often confused with each other because of similar structure and mechanism of action. It should be noted that, while CFSE is a naturally fluorescent molecule, CFDA-SE only becomes fluorescent due to the action of the intracellular esterases. Both molecules enter vital cells by direct diffusion, a process in which CFDA-SE appears more efficient due to acetate groups’ presence. Once entered into cells,
Fig. 17.4 The figure shows the molecular structure of CFSE (succinimidyl ester of Carboxyfluorescein) (panel A) and CFDA-SE (succinimidyl ester of Carboxyfluorescein diacetate) (panel B)
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17 Fluorochromes for the Study of the Cell Features
CFDA-SE is converted into CFSE by intracellular esterases; subsequently, the CFSE binds covalently to the available amino groups thanks to its succinimidyl group. The spectral characteristics of the CFSE (ex495/em519) make it suitable for detection with the same optical array set up for FITC, of which it is indeed a derivative. However, a non-peer-reviewed report on the Internet claims that the spectral characteristics expressed by the molecule in cells fixed and permeabilized with formaldehyde are not necessarily the same as only fixed cells (Hicks 2008). CFSE in immunophenotype procedures must be used with caution because, according to another non-peer-reviewed report available on the Internet, it potentially interferes with the epitopes recognized by the monoclonal antibodies used for staining (Johnson 2008). Carboxyfluorescein esters are also used in the evaluation of cell proliferation (Hasbold et al. 1999; Lyons 2000; Quah et al. 2007; Parish et al. 2009; Lyons et al. 2013) (for further information about this topic, see 17.17.1).
17.3.3.2
Calcein
Calcein (ex496/em520) (Fig. 17.5), also known as Fluorexone, is used in the study of cell proliferation (Parish 1999) and the evaluation of Multidrug Resistance, as it is an excellent substrate for pumps involved in foreign molecule extrusion, including MDR1 (Multidrug Resistance Transporter) 1) and MRP1 (Multidrug Resistanceassociated Protein) (Glavinas et al. 2004; Karaszi et al. 2001). Calcein displays a curious behavior, as it tends to stain the lymphocytes in a heterogeneous way, separating them into a “Calcein low” first population that recirculates in all the lymphoid organs and a “Calcein bright” second population, which does not recirculate in the lymph nodes, the entrance into which depends on the presence of high endothelial venules (Weston and Parish 1992). Unlike Carboxyfluorescein, Calcein (ex 496/em520) is equipped with groups capable of chelating Calcium and Magnesium and some metals, including Cobalt, Niobium, Copper, Iron, and Manganese. Since the bond with these metals can downmodulate the emission intensity of Calcein at a physiological pH, this probe, under controlled conditions, could be used to monitor the intracellular concentration of these substances.
Fig. 17.5 Molecular structure of Calcein
17.3
Cell Viability
389
Fig. 17.6 Molecular structure of Calcofluor White
Calcein has also been used in the flow cytometric absolute counting of Mycobacteria (Barr et al. 2021).
17.3.4
Calcofluor White
Calcofluor White (Fig. 17.6), also called CFW or Fluorescence Brightener 28, is a stilbene derivative excitable in UV and violet, which emits in the blue and demonstrates a specific affinity with polysaccharides such as cellulose and chitin (Herth and Schnepf 1980). It is used in everyday life as a whitening component of laundry detergents, and it can be used in the laboratory to determine cell viability thanks to its impermeant behavior, which allows its entry only into dead cells (Berglund et al. 1987). As a result of its affinity with chitin, Calcofluor White is useful in the study of Mycetes with microscopy techniques (Hoch et al. 2005), where it displays a behavior similar to Trypan Blue (Liesche et al. 2015); Calcofluor White is also useful in the study of Dinoflagellates (Fritz and Triemer 1985). In a non-peer-reviewed report available on the Internet, an artifact was reported some years ago consisting of the random appearance of signal flares in the channel managed by the UV and violet lasers (Hogarth 2017) (Fig. 20.6); this artifact has been attributed to wiping the sample tube with Calcofluor treated fabric (Galbraith 2017).
17.3.5
Trypan Blue
Trypan Blue is an impermeant dye that binds to proteins, historically used in fluorescence microscopy for counterstaining preparations. Trypan Blue is also widely used in cell culture for observations by an inverted microscope (Fig. 17.7). Trypan Blue absorbs in blue, green, and orange; it emits in a spectral range from 600 to 700 nm and can be used as a probe to highlight apoptotic (Reno et al. 1997) and dead cells, both with microscopic (Kim et al. 2011) and cytometric techniques
17 Fluorochromes for the Study of the Cell Features
390
Fig. 17.7 Molecular structure of Trypan Blue
(Avelar-Freitas et al. 2014). Trypan Blue has also proved useful in reducing autofluorescence because it improves the signal-to-noise ratio, allowing the evaluation of low-intensity signals (Mosiman et al. 1997; Srivastava et al. 2011). A further report has confirmed this effect but has restricted its utility to the green autofluorescence induced at 488 nm in permeabilized cells, underlining the importance of evaluating its use case by case (Shilova et al. 2017). Trypan Blue has also been used in some phagocytosis studies based on quenching techniques. (Kumaratilake and Ferrante 2000; Busetto et al. 2004; Nuutila and Lilius 2005; Santos et al. 2014). In these techniques, designed to increase the signal specificity and the measurement accuracy, Trypan Blue in the suspension would quench the signal from the events adhering to the phagocytes membrane without affecting the signal of the internalized events. Finally, Trypan Blue was also used in the study of Mycetes, where it behaves like the Calcofluor molecule (Liesche et al. 2015).
17.4
Membrane Potential
In eukaryotic cells, the membrane potential ΔΨ consists of the potential difference between the globally negative intracellular compartment and the globally positive extracellular compartment. This phenomenon is due to several factors, among which the most important is the action of the Na/K pumps present in the membrane. In eukaryotic cells, the membrane potential varies from 10 to about 90 mV and is considered a parameter able to provide information about a series of important processes and cellular conditions such as viability, activation, metabolic activity, and the energetic state (Shapiro et al. 1979; Shapiro 1981; Stelzer and Robinson 1988; Shapiro 2000). It is important to keep in mind that eukaryotic cell membrane potential is determined both by the plasmatic and mitochondrial membrane potential, which is predominant. The selective study of the mitochondrial membrane potential in whole cells favorably uses cationic probes that selectively concentrate in the mitochondria. In contrast, the selective study of the plasma membrane potential can rely on oxonol molecules, which, being anionic, are excluded.
17.4
Membrane Potential
391
The fluorochromes most frequently used in the study of the membrane potential belong to the following groups: (1) carbocyanine derivatives, (2) oxonol derivatives, and (3) xanthene derivatives.
17.4.1
Carbocyanines
Carbocyanines are cyanines characterized by the presence of alkyl chains, which can link one (carbocyanine) or two (dicarbocyanine) heterocyclic molecules, generally but not necessarily constituted by indoles (Fig. 17.8). If one of the aromatic nuclei’s heteroatoms is an Oxygen, a Sulfur, or an additional Nitrogen atom, the cyanine takes the name of oxacarbocyanine, thiacarbocyanine, or benzimidazolyl carbocyanine, accordingly. Alternatively, if the Carbon atom expected in that position binds two methyls (group C(CH3)2), the molecule is called an indocarbocyanine (Sims et al. 1974). As a rule, in cyanine derivatives, the spectral properties of the molecules shift to the right as a function of the length of the polymethine bridge (Wiegand and Vanysek 1988); the characteristics of greater or lesser lipophilicity depend on the length of the lateral alkyl chains (Novo et al. 1999).
Fig. 17.8 The figure shows the molecular structures of some cyanine derivatives frequently used to evaluate membrane potential and relative absorption and emission peaks. From top to bottom, the molecules DiOC6(3) (panel A), DiOC5(3) (panel B), and DiIC1(5) (panel C)
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17 Fluorochromes for the Study of the Cell Features
Table 17.1 Main features of Carbocyanines exploitable in membrane potential determination Abbreviated Name DiOC1(3) DiOC2(3) DiOC4(3) DiOC5(3) DiOC6(3) DiOC7(3) DiIC3(3) DiIC5(3) DiIC6(5) DiIC1(3) DiIC1(5)
Ex Peak (nm) 479 482 481 481 481 481 548 505 633 488 638
Em Peak (nm) 510 500 511 502 498 495 599 577 675 532 658
DiSC3(5) DiSC6(5)
500 632
705 664
Name dimethyloxycarbocyanine diethoxycarbocyanine dibutyloxycarbocyanine dipentyloxycarbocyanine diesyloxycarbocyanine dieptyloxycarbocyanine dipropyltetramethylindocarbocyanine dipentylindocarbocyanine dihexylindocarbocyanine hexamethylindocarbocyanine hexadimethylindocarbocyanine (also MiTOProbe™) dipropylthiodicarbocyanine dihexylthiodicarbocyanine
The carbocyanine derivatives theoretically usable in the study of membrane potential pass through the cell membrane and divide between extracellular and intracellular compartments with a ratio that is a function of the membrane potential. This ratio is defined by the Nernst equation E ¼ ½C in =½Cout ¼ e‐n FE=RT , where E is the cell potential, [C]in is the intracellular concentration of the molecule, [C]out is its extracellular concentration, e is the Euler number, n is the charge of the molecule, F is the Faraday constant, E is the potential in Volts, R is the gas constant, and T is the temperature in Kelvins. In determining the membrane potential with carbocyanine derivatives, the intensity of the signal produced is directly proportional to the analyzed cell’s membrane potential, but in particular situations, it is also possible to perform ratiometric tests (Novo et al. 1999). Theoretically exploitable for studying the membrane potential, Carbocyanine derivatives consist of approximately thirteen highly lipophilic cationic compounds (Table 17.1). Some of these compounds have also been tested to evaluate bacterial membrane potential (Novo et al. 1999). The probe most frequently used in the study of eukaryotic cells is DiOC6(3) (ex481/em498) (Shapiro 1994), also used as a lipophilic dye in the automated counting of quail leukocytes (Uchiyama et al. 2005). Other molecules used experimentally include: 1. The molecule DiOC1(3) (ex482/em510), used jointly with Hoechst 33342 in the study of the membrane potential of Plasmodium vinckei parasitized red blood
17.4
2.
3.
4.
5.
6.
7.
8.
9.
Membrane Potential
393
cells (Jacobberger et al. 1983) and alone in the study of reticulocytes (Jacobberger et al. 1984). The molecule DiOC2(3) (ex482/em500), mainly used in the study of bacteria (Novo et al. 1999) and their sensitivity to antibiotics (Novo et al. 2000), as well as Multidrug Resistance (MDR) (Marcelletti et al. 2018). The molecule DiOC5(3) (ex481/502), used in many studies, including the evaluation of the activation of B lymphocytes (Yokoyama et al. 1988), the evaluation of the response of Leishmania and Staphylococcus to drugs (Azas et al. 1997; Ordonez and Wehman 1993), the evaluation of the polymorphonuclear response to formyl-methionyl-leucyl-phenylalanine (fMLP) (Lazzari et al. 1990), and as a simple lipophilic dye in the automated counting of quail leukocytes (Uchiyama et al. 2005). The molecule DiOC7(3) (ex481/em495), originally described as a probe for membrane potential but also used as a probe of penetration into the study of cells from disaggregated spheroids (Olive and Durand 1987). The molecule DiIC1(3) (ex488/em532), used to evaluate the mitochondrial potential in cultures of mouse cells deficient for the E2A gene (Engel and Murre 1999); according to a non-peer-reviewed report available on the Internet, this molecule is the same chromophore as Cyanine 3 (Shapiro 1997). The molecule DiIC1(5) (ex638/em658), sold under the name of MiTO-Probe™ DiIC1(5); according to a non-peer-reviewed report available on the Internet, this molecule is the same chromophore as Cyanine 5 (Shapiro 1997). The molecule DiIC3(3) (ex548/em599), used in many studies, including the evaluation of the polymorphonuclear response to formyl-methionyl-leucyl-phenylalanine (fMLP) (Lazzari et al. 1990). The molecule DiIC6(5) (ex633/em675), used to determine the mitochondrial membrane potential during exposure to the cytostatic arabinose-furanosyl-cytosine (ara-C) (Backway et al. 1997); being excited by a red line, DiIC6(5) can be a useful alternative to DiOC6(3), leaving the blue line available for other probes (Shapiro 1994). The molecule DiSC3(5) (ex500/em705), used in the determination of the membrane potential of platelets (Pipili 1985) and yeast mitochondria (Farrelly et al. 2001).
Some of these compounds have also been tested to evaluate bacterial membrane potential (Novo et al. 1999). The availability of adequate light sources often determines the choice of the right cyanine. With this in mind, it is worth remembering that: 1. The lines in blue can excite DiOC5(3) and DiOC6(3), which require the same instrumental setting as FITC. 2. The lines in blue and green can excite DiIC1(3), which requires the same instrumental setting as PE. 3. The lines in red can excite DiIC1(5), sold as MiTO-Probe™ DiIC1(5), which requires the same instrumental setting as APC (Shapiro 1994).
394
17.4.2
17 Fluorochromes for the Study of the Cell Features
Oxonol Derivatives
Like carbocyanine derivatives, oxonol derivatives are highly lipophilic molecules capable of crossing the cell membrane and dividing between the extracellular compartment and the intracellular compartment with a ratio that is a function of the membrane potential (Shapiro 2000). Unlike carbocyanine derivatives, oxonol derivatives are anionic molecules that tend to be redistributed in the extracellular compartment so that the signal’s intensity is inversely proportional to the analyzed cell’s membrane potential, an opposite way to what happens with cationic carbocyanine derivatives. It should also be considered that the oxonol derivatives are selectively extruded from the mitochondria (Maftah et al. 1993), which makes them particularly useful in the selective evaluation of the plasma membrane potential. The most frequently used oxonol derivatives in this type of determination are the following: 1. DiBAC4(3) (bis- (1,3-dibarbiturate) -trimethine v) (ex493/em516) (Yamada et al. 2001; Klapperstuck et al. 2009; Hernlem and Hua 2010). 2. DiBAC4(5) (bis- (1,3-dibutylbarbiturate) -trimethine oxonol) (ex590/em615) (Hernlem and Hua 2010). 3. DiSBAC2(3) (bis- (1,3-diethyltiobarbiturate) -trimetine oxonol (ex535/em560), used in numerous studies including the evaluation of the polymorphonuclear response to formyl-methionyl-leucyl phenylalanine (fMLP) (Lazzari et al. 1990).
17.4.3
Xanthene Derivatives
The most frequently used xanthene derivatives in the measurements of membrane potential are the following: 1. Rhodamine 123 (ex507/em529) (Scaduto and Grotyohann 1999; Lugli et al. 2007). 2. Tetramethylrhodamine methyl ester (TMRM, T668) (ex488/em575) (Floryk and Houstek 1999; Scaduto and Grotyohann 1999). 3. Tetramethylrhodamine ethyl ester (TMRE, T669) (ex488/em574) (Floryk and Houstek 1999; Scaduto and Grotyohann 1999). 4. The molecules belonging to the group of MitoFluor molecules, and in particular, MitoFluor Far Red 680 (ex685/em705), also known as Rhodamine 800; this molecule has also been used in the determination of nucleic acids (Shapiro and Stephens 1986; Gerena et al. 2011). Like cyanine derivatives, rhodamine derivatives concentrate in the mitochondria with a concentration directly proportional to their polarization; however, the signal produced by the rhodamine derivatives is inversely proportional to their concentration since the high proximity between molecules due to the high concentration generates a self-quenching phenomenon (Scaduto and Grotyohann 1999).
17.5
Mitochondrial Membrane Potential
17.5
395
Mitochondrial Membrane Potential
Since the cationic probes concentrate a thousand times more in the mitochondria than in the cytoplasm, the mitochondrial potential largely affects the plasma membrane potential (Yousif et al. 2009). Nonetheless, it may be desirable to study the mitochondrial potential by itself, adopting probes that selectively concentrate in the mitochondria. The preferential distribution of these probes can be documented experimentally through controls based on the use of substances known to inhibit the mitochondrial membrane potential selectively (Cossarizza et al. 1993). The molecules exploited in the determination of the mitochondrial potential include: 1. DiIC1(5) (ex638/em658), a cyanine also known as MiTO-Probe™ DiIC1(5) (marketed by Thermo Fisher Scientific). 2. DiOC2(3) (ex482/em500), a cyanine also known as MiTO-Probe™ DiOC2(3) (marketed by Thermo Fisher Scientific). 3. JC-1 (ex488/em527-590), a cyanine also known as MiTO-Probe™ JC-1 (marketed by Thermo Fisher Scientific). 4. Rhodamine 800 (ex685/em705), also known as R800 or MitoFluor Far Red 680 dye (marketed by Thermo Fisher Scientific). 5. MitoStatus Red (marketed by BD Pharmingen) (ex633/em670), whose structure has not been disclosed. 6. MitoFluor Red 594 (ex598/em630) (marketed by Thermo Fisher Scientific), whose structure has not been disclosed.
17.5.1
JC-1
The JC-1 molecule (tetrachloro-tetraethyl benzimidazole carbocyanine) (Fig. 17.9) is peculiar because it forms aggregates and modulates its spectral characteristics at low membrane potential values. JC-1 is excited at 488 nm, but a 405 line seems
Fig. 17.9 The figure shows the molecular structures of the oxonol derivative DiBAC4(3) (panel A) and the cyanine derivative JC-1 (panel B)
396
17 Fluorochromes for the Study of the Cell Features
Fig. 17.10 Possible configuration of the optical bench for the ratiometric analysis of JC-1. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
preferable because a violet-driven JC-1 produces less spillover in the orange channel (Perelman et al. 2012). JC-1 emits a peak at 527 nm in its monomeric form, but if polymerized, it shifts its emission towards red with an emission peak at approximately 590 nm (Reers et al. 1991). It follows that a ratiometric study or a bivariate analysis of the emission of JC-1, detected simultaneously in the green (using detector settings optimized for FITC) and orange (using detector settings optimized for PE), can produce much more significant information than simple quantitative signal variations (Cossarizza et al. 1993; Salvioli et al. 1997; Cossarizza and Salvioli 2001) (Fig. 17.10). The creation of a completely depolarized (and therefore only green) control through the use of carbonyl-cyanide-m-chlorophenyl-hydrazine (CCCP) (Perelman et al. 2012) is strongly suggested to optimize the compensation between the two channels. JC-1 was used in the study of the membrane potential of Plasmodium sp. parasitized red blood cells (Ch’ng et al. 2010) and, in combination with HO33342 and HO34580, in the study of the membrane potential of the Plasmodium chabaudi adami parasitized murine red blood cells (Lelliott et al. 2015). Together with TOTO3, JC-1 can tell apoptotic cells from living and necrotic cells (Zuliani et al. 2003). Finally, JC-1 was also used in the study of the membrane potential of bacteria, but with less satisfying results (Shapiro 1994).
17.6
Mitochondrial Mass
17.6
397
Mitochondrial Mass
Mitochondrial mass is a parameter frequently studied in cell biology, as it constitutes a marker of cellular integrity and provides information on the progress of some phenomena that occur during apoptosis (Petit et al. 1995; Ferlini et al. 1996; Ferlini and Scambia 2007; King et al. 2007); furthermore, its evaluation is pivotal in the study of mitochondrial biogenesis and during the establishment of the mitophagy process (Mauro-Lizcano et al. 2015). An essential characteristic of the fluorochromes used in this area is establishing a link with the mitochondria independently from their energetic status. The fluorochromes used in the mitochondrial mass study are Nonyl Acridine Orange (NAO), the Mitotracker molecules, and some molecules of the MitoFluor series.
17.6.1
Nonyl Acridine Orange (NAO)
Although there are dissenting opinions (Jacobson et al. 2002; Puleston 2015), it is generally agreed that (NAO) (ex495/em522) binds to mitochondria independently from their potential (Maftah et al. 1989; Sweet and Singh 1999; Thomas et al. 1999), probably establishing high-affinity bonds with cardiolipin (Garcia Fernandez et al. 2004; Leung et al. 2014). NAO has also been exploited in the flow cytometric evaluation of mitochondrial cardiolipin peroxidation, an early event of apoptosis (Castedo et al. 2002; Garcia Fernandez et al. 2004). Finally, NAO is useful for studying multidrug resistance (MDR) (Kessel et al. 1991) (for further information on this topic, see Sect. 17.18).
17.6.2
Mitotracker Molecules
The molecules belonging to the Mitotracker® group of fluorochromes are (1) Mitotracker® Green (ex490/em516), (2) Mitotracker® Orange CMTMRos (ex551/em576), (3) Mitotracker® Red CMXRos, also known as chloromethyl-X rosamine (ex578/em599) (Macho et al. 1996; Pendergrass et al. 2004), and (4) Mitotracker® Deep Red (ex644/em665). Except for Mitotracker® Green and perhaps Mitotracker® Red CMXRos (Gilmore and Wilson 1999), these molecules are fixable, i.e., they maintain the bond with the mitochondria even after fixation. This behavior occurs because these molecules are oxidized and sequestered into the mitochondria; their chloromethyl groups react with the thiol groups (Kholmukhamedov et al. 2013), forming a complex further fixed by aldehydes. The molecule Mitotracker® Green (ex490/em516), also known as Benzoxazolium (Puleston 2015), is an asymmetric cyanine whose chloromethyl groups react with the free thiol groups belonging to the cysteine residues of
398
17 Fluorochromes for the Study of the Cell Features
CH2Cl
A
B
CH2 Cl
N
O CH=CH-CH=
Cl
N+
Cl-
CH2
N CH3
420
440
460
480
500
520
540
560
580
WAVELENGTH
CH2Cl
Fig. 17.11 Molecular structure (panel A) and spectral behavior (panel B) of Mitotracker green molecule, consisting of an asymmetric dimeric cyanine. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
A
B (CH3)2N
O
CH2Cl
+ N(CH3)2
480
500
520
540
560
580
600
620
640
WAVELENGTH
Fig. 17.12 Molecular structure (panel A) and spectral behavior (panel B) of the Mitotracker Orange molecule, containing a xanthene structure. Note the chloromethyl groups capable of reacting with the thiols of peptides and proteins. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
mitochondrial proteins (Presley et al. 2003) (Fig. 17.11). Mitotracker Green was used in the study of mitochondrial mass (Puleston 2015), autophagy (Warnes 2015; Xiao et al. 2016), and in the study of endothelial microparticles (EMP) within a protocol which identified the EMPs with the phenotype events CD45-/CD146+/ CD34+/DRAQ5-/MTG+ (Lanuti et al. 2012). Mitotracker® Green is a substrate of P-glycoprotein (P-gp/MDR1) associated with multidrug resistance (MDR), and its expression should be followed up in studies exploiting Mitotracker® Green to distinguish the changes due to mitochondrial mass variations from those due to its extrusion by the membrane pumps (Marques-Santos et al. 2003). The other molecules of the Mitotracker® group are rhodamine derivatives (Fig. 17.12).
17.7
Intracellular pH
399
Mitotracker® Deep Red (ex644/em665) was used in seminological analysis (Pena et al. 2018) and in the study of mitophagy (Mauro-Lizcano et al. 2015; EstebanMartinez et al. 2017), where it is particularly useful because it can evaluate both classical mitophagy and the formation of mitochondrial vesicles (mitochondriaderived vesicles, or MDVs) (Mauro-Lizcano et al. 2015; Canonico et al. 2016).
17.6.3
Mitofluor Molecules
The MitoFluor molecules group is heterogeneous both from a structural and a functional point of view. It consists of four different compounds, namely: 1. 2. 3. 4.
MitoFluor Green (ex490/em516), a cyanine derivative. MitoFluor Red 589 (ex588/em622), whose molecular structure is undisclosed. MitoFluor Red 594 (ex598/em630), whose molecular structure is undisclosed. MitoFluor Far Red 680 (ex685/em705), a rhodamine derivative (rhodamine 800).
Structurally, MitoFluor Green is akin to Mitotracker® Green but without the latter’s chloromethyl groups; consequently, it does not irreversibly bind to the thiols of the mitochondrial proteins and is lost by the cells after fixation. Loss after fixation is common to all probes of the MitoFluor group. MitoFluor Green (MFG) and MitoFluor Red 589 have been considered independent of the membrane potential, consequently capable of estimating the mitochondrial mass, but according to some authors, MFG behavior would also be affected by the membrane potential (Keij et al. 2000).
17.7
Intracellular pH
The intracellular pH is a powerful regulator of cellular enzymatic activities, and in many cell types, it tends to vary following the activation (Busa 1986; Moolenaar 1986). The evaluation of the cytoplasmic pH is also useful in the study of the metabolism of neoplastic cells of solid tumors, which can respond to hypoxic conditions with the production of lactic acid and by entering into a quiescence state resistant to chemo- and radio- treatments (Musgrove et al. 1986). The most frequently used fluorochromes for determining the intracellular pH are: 1. DCH (ex365/em440-488 nm), a quinone derivative (Valet et al. 1981; Cook and Fox 1988). 2. BCECF (ex505/em545), a xanthene derivative belonging to the group of Carboxyfluoresceins (Musgrove et al. 1986). 3. SNARF (ex488/em580-640), a xanthene derivative belonging to the group of naphtho-Carboxyfluoresceins (van Erp et al. 1991).
400
17.7.1
17 Fluorochromes for the Study of the Cell Features
DCH
DCH (2,3-dicyano-hydroquinone) (ex365/em440-488) is a derivative of ADB (1,4-diacetoxy-2,3-dicyanobenzene), a non-fluorescent molecule that quickly enters cells, is transformed into fluorescent DCH by cytosolic esterases, and remains sequestered in the cytoplasm (Valet et al. 1981; Cook and Fox 1988). As the pH changes, DCH shifts its emission from blue (440 nm) to blue-green (488 nm) (Musgrove et al. 1986; Cook and Fox 1988; Musgrove and Hedley 1990). Consequently, the ratio between the intensity of emission in the blue-green and blue is a function of the intracellular pH, and comparing the obtained values with those from a reference curve allows for determining the pH in the cells under analysis. This test’s limitations consist mainly of the necessity of a source in the UV (Cook and Fox 1988). Compared to SNARF, DCH is more suitable for measuring pH values under 7 but has the disadvantage of leaking more quickly from the cells (Fox 2000).
17.7.2
Bcecf-Acetoxymethyl Ester
Like CFDA/CFSE (ex495/em519), BCECF-AM (ex505/em525-620) (Fig. 17.13) diffuses passively into cells, where the action of intracellular esterases traps it. When pH increases, BCECF, excited at 488, shifts its emission from green (520 nm) to red (620 nm) (Franck et al. 1996; Nilsson et al. 2003). Consequently, the ratio between the intensity of emission in the red and green is a function of the intracellular pH, and comparing the obtained values with those from a reference curve allows for determining the pH in the cells under analysis (Musgrove and Hedley 1990; Wang et al. 1990) (Fig. 17.14). BCECF-AM has also been used as a probe in studies on fluorescence polarization (Gelman-Zhornitsky et al. 1997).
Fig. 17.13 Molecular structure of the BCECF-AM molecule, containing a xanthene structure
17.7
Intracellular pH
401
Fig. 17.14 Possible optical bench configuration for the ratiometric analysis of BCECF. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
Fig. 17.15 Possible optical bench configuration for the ratiometric analysis of SNARF-1 (Fox 2000). Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
17.7.3
SNARF-1 Acetoxymethyl Ester
Like CFDA/CFSE (ex495/em519) and BCECF-AM (ex505/em525-620), SNARF1-AM (ex514/em525-620) diffuses passively into cells, where the action of intracellular esterases traps it (Fox 2000). The excitation peak of SNARF-1 is at 514 nm, but it can be excited, even if sub-optimally, by a 488 nm line. When pH increases, SNARF-1 shifts its emission from orange (~570 nm) to red (~640 nm) (Fig. 17.15). Consequently, the ratio between the intensity of emission in the red and orange is a function of the intracellular pH, and comparing the obtained values with those from a
402
17 Fluorochromes for the Study of the Cell Features
reference curve allows for determining the pH in the cells under analysis (Wieder et al. 1993; Fox 2000). Compared to DCH, SNARF-1 is more suitable for measuring pH values between 7 and 8; moreover, it remains trapped in the cells longer than DCH (Fox 2000).
17.8
Lysosomal Mass and Lysosomal pH
The study of acidic vesicular organelles (AVOs)), mainly represented by lysosomes and autolysosomes, is progressively gaining importance as it allows the study of autophagic processes, apoptotic processes (Yuan et al. 2002; Canonico et al. 2014), and of lysosomal thesaurismosis (Canonico et al. 2016). Acidic vesicular organelles (AVOs) can be studied thanks to two groups of molecules known as LysoTracker® and LysoSensor™ (marketed by Thermo Fisher Scientific), whose structural formulas have not yet been disclosed. A series of alternative compounds are also commercially available, known as LysoHunt (marketed by Setareh Biotech), presumably similar to the LysoTracker® series. Lysotracker® Red can also be utilized in fluorescence confocal microscopy (Pierzynska-Mach et al. 2014). Acidic vesicular organelles (AVOs) can also be studied thanks to the use of AO both by Flow Cytometry and fluorescence microscopy (Traganos and Darzynkiewicz 1994); similar experiments based on the same principles have been performed with quinacrine in confocal microscopy (Pierzynska-Mach et al. 2014).
17.8.1
Lysotracker® and Lysohunt Molecules
The molecules belonging to the LysoTracker® or LysoHunt groups are permeant molecules that penetrate the target organelles thanks to their acidotropism and maintain their spectral characteristics regardless of the environmental pH. The LysoTracker® series of molecules include (1) LysoTracker® Blue DND-22 (ex373/em422), (2) LysoTracker® Yellow HCK-123 (ex465/em535), (3) LysoTracker® Green DND-26 (ex504/em511), and (4) LysoTracker® Red DND- 99 (ex577/em590), while those of the LysoHunt series include (1) LysoHunt Blue DND-22 (ex373/em422), (2) LysoHunt Yellow HCK-123 (ex465/em535), (3) LysoHunt Green DND-26 (ex504/em511), and (4) LysoHunt Red DND-99 (ex577/590) (Fig. 17.16). Fig. 17.16 Molecular structure of the LysoHunt Red molecule. Note the BODIPY structure (red frame)
17.8
Lysosomal Mass and Lysosomal pH
403
Fig. 17.17 Molecular structure of the PDMPO molecule, also known as LysoSensor™ Yellow/ Blue DND-160 or LysoSens Yellow/Blue DND-160
These compounds have been widely used in the study of autophagic (Chikte et al. 2014; Warnes 2014, 2015) and apoptotic processes (Pierzynska-Mach et al. 2014).
17.8.2
Lysosensor™ Molecules
The molecules belonging to the LysoSensor™ group (marketed by Thermo Fisher Scientific) are permeant aromatic molecules with a positive charge, which bind to their targets thanks to their acidotropism. They increase their quantum yield with the decrease of the environmental pH, so they can be used to selectively evaluate the pH of the organelles in which they concentrate (Padh et al. 1989; Traganos and Darzynkiewicz 1994; Paglin et al. 2001). Lysosomal pH is an important cellular parameter, as there is evidence that some of its alterations are associated with a transformed state both in mice and humans (Jiang et al. 1990) and other pathological conditions such as cystic fibrosis (Barasch et al. 1991). Molecules belonging to the LysoSensor™ group include (1) LysoSensor™ Blue DND-167 (ex373/em422), (2) LysoSensor™ Green DND-189 (ex443/em505), (3) LysoSensor™ Green DND-153 (ex442/em505), and (4) LysoSensor™ Yellow/ Blue DND-160, also known as PDMPO (ex384/em540 in an acid environment, and ex329em/em440 in a neutral environment (Diwu et al. 1999) (Fig. 17.17). To these molecules, the LysoSens Yellow/Blue DND-160 molecule should be added, a compound marketed by Setareh Biotech, which displays the same spectral behavior as LysoSensor™ Yellow/Blue DND-160. Although its excitation peak is in the UV, LysoSensor™ Yellow/Blue DND-160 (or PDMPO) is excited efficiently even at 405 nm and changes the wavelength of its emission as a function of pH, emitting in the blue when it is in a neutral environment, and in yellow when it is in an acidic environment (Diwu et al. 1999). Consequently, it is possible to set up a ratiometric test based on the use of the commonly used bandpass filters for the combined analysis of Pacific Blue (ex410/em455) and Pacific Orange (ex410/em551), or equivalent (Fig. 17.18). Excited in the violet region, LysoSensor™ Yellow/Blue lends itself very well to multiparametric analyses that include probes excited in the blue with emission in the green, such as FITC, GFP, Fluo-8, Calcein, et cetera. Fluorochromes belonging to the LysoTracker and LysoSensor groups are also available conjugated with dextran (Thermo Fisher Scientific) to improve their entering into target organelles.
404
17 Fluorochromes for the Study of the Cell Features
Fig. 17.18 Possible configuration of the optical bench for the ratiometric analysis of PDMPO. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
17.8.3
Acridine Orange (AO)
Acridine Orange diffuses passively into the acidic vesicular organelles (AVOs), where it is protonated and trapped, and changes its spectral behavior shifting emission from green to red because its high concentration induces the formation of stacked dye aggregates. The ratio between red and green is a function of AVOs total volume and acidity (Traganos and Darzynkiewicz 1994). Studies on AVOs using acridine orange have been performed both by Flow Cytometry and confocal fluorescence microscopy (Paglin et al. 2001; Pierzynska-Mach et al. 2014; Thome et al. 2016). For the exploitation of AO as a nucleic acid probe, see Sect. 16.7.1.1.
17.9
Free Radicals (Oxidative Burst)
Free radicals are a family of molecules, including hydroxide, peroxide, hypochlorite, and nitrogen monoxide anions produced by a cellular activity known as the oxidative burst. The oxidative burst is a physiological process in neutrophils, eosinophils, monocytes, and macrophages to destroy previously phagocytosed microorganisms (bacteria and fungi). The killing of pathogens is made possible by the direct action exerted on them by the products of a chemical reaction catalyzed by an enzyme known as NADPH oxidase, which reduces free oxygen to a superoxide anion. Thanks to superoxide dismutase (SOD) and myeloperoxidase (MPO) enzymes, the superoxide anion combines with the available substrates to generate a series of highly reactive free radicals, which irreparably damage the microorganisms’ structures. The NADPH enzyme is multimeric, consisting of various components known
17.9
Free Radicals (Oxidative Burst)
405
Fig. 17.19 Rhodamine 123 signal in the neutrophils of a patient affected by CGD and in the neutrophils of his parents. The patient’s neutrophils do not oxidize the non-fluorescent DHR in the fluorescent Rhodamine 123 (panels 1, 2, 3), whereas the father’s neutrophils do (panels 4, 5, 6). The neutrophils of the mother, the carrier of the disease, show a characteristic bimodal (or mosaic) pattern due to the X chromosome lyonization (panels 7, 8, 9). (DHR: dihydrorhodamine 123)
as gp91-phox, p22-phox, p40-phox, p47-phox, p67-phox, Rap 1A, and Rac2. The existence of defects in the genes that code for these components is the basis of a primary immunodeficiency (PID), known as Chronic granulomatous disease (CGD). The CGD diagnosis is based on the functional study of oxidative burst, performed in vitro using a series of tracers able to change spectral characteristics in the presence of free radicals produced by the in vitro reaction triggered by E. coli and phorbol myristate acetate (PMA) (Gomes et al. 2005) (Fig. 17.19). The molecules of choice for the functional study of the oxidative burst are dihydroethidium (DHE) (ex535/em610) (Rothe et al. 1991; Georgiou et al. 2005), 20 70 -dichlorofluorescin diacetate (DCF-DA) (Bass et al. 1983), and dihydrorhodamine 123 (DHR) (Rothe et al. 1988; Vowells et al. 1995). Experiments have also been performed with other molecules, including tetrazolium nitroblue (NBT), Fluo-3 (Koizumi et al. 1995), and 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) (Sieracki et al. 1999). Of note, a probe also exists selectively able to detect hypochlorous acid (HOCl) at a cell level; this probe, called PQI, is synthesized by joining a HOCl-sensitive thioether with a phenothiazine-quinolinium scaffold. PQI can be excited by a 488 nm line and is managed in the same channel intended for FITC (Feng et al. 2018).
406
17.9.1
17 Fluorochromes for the Study of the Cell Features
Dihydroethidium (DHE)
Dihydroethidium (DHE) (also known as Hydroethidine) (ex535/em610) is a non-fluorescent permeant molecule that enters the cells, where it is converted into ethidium by the presence of superoxide anions (Rothe and Valet 1990; Peshavariya et al. 2007). Ethidium is a fluorescent molecule that intercalates into DNA, increasing its quantum yield. Consequently, once excited in the blue, the cells in which superoxide radical production occurs emit in the red (Perticarari et al. 1991).
17.9.2
Dichlorofluorescin Diacetate (DCF-DA)
Dichlorofluorescin-diacetate (DCF-DA) diffuses passively into the cells and it is hydroxylated into 20 , 70 dichloro-dihydro-fluorescin (DCFH). Neither DCF-DA nor DCFH is fluorescent, but as an effect of the oxidative burst, DCFH is transformed into 20 , 70 dichlorofluorescein (DCF) (ex495/em529), a fluorescent molecule (Bass et al. 1983); consequently, the development of fluorescence attests for the presence of the oxidative reaction (Bass et al. 1983; Zeller et al. 1989; Rothe and Valet 1990). However, some authors say the fluorescence produced is not always satisfactory; the compound tends to diffuse from the stained cells, so defining the difference between negative and positive events is sometimes problematic (Vowells et al. 1995).
17.9.3
Dihydrorhodamine 123 (DHR123)
Dihydrorhodamine 123 (DHR123) is a permeant dye that enters living cells and localizes in mitochondria, where it is oxidized to Rhodamine 123 (RH123) (ex507/ em529) (Rothe et al. 1991). Rhodamine 123 (RH123), excited at 488 nm, emits a signal proportional to the oxidative burst’s magnitude.
17.9.4
Tetrazolium Derivatives
Tetrazolium nitroblue (NBT), a non-fluorescent molecule, is reduced by NADPH’s action to a blue precipitate known as Formazan. Due to this characteristic, it has been adopted to evaluate cellular respiratory activity by conventional optical microscopy (van der Valk and Herman 1987). NBT is mentioned in this section for two reasons. First, its use in evaluating NADPH activity is theoretically detectable with an imaging flow or axial extinction-based cytometer. Second, a cytometric method based on Formazan exists; this method consists of (1) the transformation of neutrophils into fluorescent cells by incubation with FITC-
17.10
Calcium Content
407
conjugated Concanavaline A (Con-A), (2) their subsequent incubation with tetrazolium nitroblue and phorbol myristate, and (3) the cytometric analysis of their signal excited in the blue and detected in the fluorescein channel. This method’s rationale is based on the fact that the Formazan absorbs in the range of emission of the FITC and extinguishes its signal; it follows that non-fluorescent PMNs are normal, while those fluorescent carry a deficit in the activity of the enzyme (Fattorossi et al. 1990). Moreover, another cytometric method exists, based on using 5-cyano-2,3-ditolyl tetrazolium chloride (CTC); the method is based on the fact that CTC is transformed by NADPH into a fluorescent product with excitation at 488 nm and emission over 630 nm (Sieracki et al. 1999).
17.10
Calcium Content
Cytoplasmic Calcium determination is a biological parameter of paramount importance, involved in a myriad of physiological and pathological processes, among which cell activation. The first probe used in Flow Cytometry for cytoplasmic Calcium evaluation was Quin2 (ex339/em490-500) (Tsien and Pozzan 1989), a Calcium chelator replaced later by a series of molecules with better spectral behavior. Currently, the intracellular content of Calcium can be determined in Flow Cytometry with the use of a series of different probes, among which the most frequently used are: 1. Indo-1 acetoxymethyl ester (ex365/em405-485) 2. A group of xanthene derivatives consisting of Fluo-3 (ex488/em526), Fluo-4 (ex488/em516), Oregon Green (ex488/em523), Calcium Green-1 (ex506/ em531), Calcium Orange (ex550/em575), and Calcium Crimson (ex590/em610) 3. The Fura group molecules 4. The BTC molecule (ex400-488/em530) From a cytometric point of view, it is important to note that the relationship between Calcium concentration and signal behavior depends on the molecule (s) exploited in the test. As Calcium concentration increases, the signal can (1) shift its emission from blue to violet (Indo-1), (2) decrease its emission intensity (Fura Red), (3) increase its emission intensity (Fluo-3, Fluo-4, Oregon Green, Calcium Green-1, Calcium Orange, and Calcium Crimson), or (4) shift its emission from orange to green (Fura Red plus Fluo-3). Finally, some green Fluorescent Proteins, named “Chameleons,” have been engineered to behave like “in vivo” Calcium indicators (Miyawaki et al. 1997; Demaurex and Frieden 2003; Hara et al. 2004).
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17 Fluorochromes for the Study of the Cell Features
Fig. 17.20 Molecular structure of the Indo-1 molecule
17.10.1
INDO-1 Acetoxymethyl Ester
The most popular probe for evaluating intracellular Calcium is probably Indo-1 acetoxymethyl ester (ex365/em405-485), a Quin-2-like molecule with better general characteristics (Grynkiewicz et al. 1985) (Fig. 17.20). This molecule passively enters cells by diffusion and is then subjected to cellular esterases causing it to remain trapped in the cytoplasm where it binds to Calcium resulting in modifications to its spatial conformation. The change of the structural configuration resulting from the bond with Calcium modifies the molecule’s spectral characteristics, shifting its emission from blue to violet. This behavior is proportional to the concentration of Calcium, so an evaluation of the ratio between the emissions measured at the two different wavelengths allows the calculation of its intracellular concentration (Fig. 17.21). The main problem regarding the use of Indo-1 is the need for a UV excitation, which, excluding the arc sources (Mercury lamps), should consist of: 1. The 320 nm or 355 nm line delivered by a solid-state UV laser (Telford et al. 2017). 2. The 325 nm line delivered by a Helium-Cadmium laser (Shapiro 1993). 3. The UV line delivered by a Krypton (337–356 nm) or high-power Argon laser (351–356 nm) (June et al. 1986; Rabinovitch et al. 1986). The use of solid-state NUV sources has proved to be unfeasible (Telford 2004; Telford 2015). Regarding the use of a Helium-Cadmium laser, it should be noted that it is suitable for this purpose despite the unsatisfactory CVs of its emission, as the determination of Calcium uses a ratiometric index, whose terms are evenly affected by the emission power variations.
17.10
Calcium Content
409
Fig. 17.21 Possible configuration of the optical bench for the ratiometric analysis of Indo-1. Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
The analysis of Calcium concentration is performed with a two-channel detection system consisting of a dichroic mirror LP450, which splits the different wavelengths of emitted light and delivers them to the two detectors, one dedicated to the violet emission using a BP405/20 filter and the second dedicated to the green emission using a BP530/30 filter (Roederer et al. 1995; Bailey and Macardle 2006) (Fig. 17.21). Other instrumental layouts are also possible, which sample the Calcium free molecule emission in the blue region thanks to a BP490/15 filter; this configuration requires an LP420-460 dichroic mirror to separate the two emissions and an LP380 filter at the beginning of the optical path to block stray laser light and the autofluorescence elicited by the UV excitation (Grynkiewicz et al. 1985; Osborne and Waring 2000).
17.10.2
Xanthene Derivatives (Fluo Molecules and Others)
Except for the Calcium Orange (ex550/em575), Calcium Crimson (ex590/em610), and Rhod-5N (ex557/em581) probes, all the fluorochromes included in this group can be managed by the typical instrument configuration for Fluorescein (excitation at 488 nm, BP 530/30). Fluo-3 (ex488/em526) consists of a xanthene nucleus conjugated with Chlorine atoms, which increases its emission in the presence of Calcium (Minta et al. 1989). Fluo-3 was also used in tests to evaluate the oxidative burst (Koizumi et al. 1995). Fluo-4 (ex488/em516) is a close relative of Fluo-3, in which Fluorine atoms have replaced Chlorine atoms (Fig. 17.22). The result is a probe with the same spectral features but increased brilliance.
17 Fluorochromes for the Study of the Cell Features
410
Fig. 17.22 Molecular structure of the xanthene derivative Fluo-4
Oregon Green 488 (ex488/em523), Calcium-Green-1 (ex488/em530), Calcium Orange (ex550/em575), and Rhod-5N (ex557/em581) are analogs of Fluo-3; the first two reproduce its spectral features, while Calcium Orange and Rhod-5N have a more complex structure still derived from the xanthene ring, and consequently display peaks of excitation and emission more displaced to the right (Paredes et al. 2008). The Calcium Crimson probe (ex590/em610) is a molecule of even greater structural complexity and displays spectral characteristics further shifted towards the regions with greater wavelength (Paredes et al. 2008).
17.10.3
Fura Molecules
The Fura molecule group includes the Fura-2 molecule (Fura Red) and some of its derivatives, variously conjugated with fluorine atoms (Fura-4F, Fura-5F, and FuraFF). The Fura-2 molecule (Fura Red) (Fig. 17.23) emits in the red at approximately 650 nm and has a wide excitation band with an absorption peak in the blue (June and Rabinovitch 1994). From a spectral point of view, the Fura Red molecule behaves peculiarly, as unlike the xanthene derivatives considered so far, it tends to decrease its intensity of expression with the increase in Calcium concentration (June and Rabinovitch 1994). The joined use of Fura Red and Fluo-3, both excited at 488 but with different emission peaks, allows the establishment of a ratiometric method in which the Calcium content is determined from the ratio between the signal produced by Fluo-3 into the PMT 1 equipped with a BP525/20 filter and the signal produced by Fura Red into the PMT 2 equipped with a BP670/20 filter (Novak and Rabinovitch 1994); a first separation between the two emissions is carried out by an LP610 dichroic mirror (Fig. 17.24). A similar method also exists based on the combined use of Fura Red and Fluo-4 (Assinger et al. 2015).
17.10
Calcium Content
411
Fig. 17.23 Molecular structure of the Fura Red molecule
Fig. 17.24 Possible configuration of the optical bench for the ratiometric analysis of a combination of Fura Red and Fluo-3 molecules (Novak and Rabinovitch 1994). Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
The Fura Red molecule has another peculiar spectral characteristic, as its excitation peak changes according to the Calcium concentration, moving towards the right side of the spectrum. Consequently, in the absence of Calcium, Fura Red absorbs at 405 nm but not at 532 nm, while in the presence of Calcium, it decreases its absorbance at 405 nm and becomes excitable at 532 nm. This peculiarity allows a second ratiometric method in which the Fura Red molecule is excited by two different laser lines, the first at 405 nm and the second at 532 nm. Accordingly, the Calcium content is determined from the ratio between the two signals, the first detected by the PMT depending on the green laser (filter set: LP685 + BP710/50),
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17 Fluorochromes for the Study of the Cell Features
Fig. 17.25 Possible optical bench configuration for the Fura Red molecule’s ratiometric analysis with excitation at two different wavelengths (Wendt et al. 2015). Redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company
and the second detected by the PMT depending on the violet laser (filter set: LP630 + BP660/20) (Wendt et al. 2015) (Fig. 17.25). Fura-2 emission can be quenched by some metal ions, whose presence is infrequent in normal analytical conditions but can be removed through the use of special chelators, such as TPEN (tetrakis(2-pyridylmethyl)ethylenediamine) (Roe et al. 1990).
17.10.4
BTC
BTC (ex400-488/em530) is a benzothiazole derivative of coumarin, whose tetraacetoxymethyl ester enters cells, where its spectral characteristics are modulated as a function of Calcium concentration, varying its excitation peak from 480 to 400 nm (Iatridou et al. 1994). This peculiarity allows the establishment of a ratiometric method in which the BTC molecule is excited by two different laser lines, the first at 488 nm and the second at 405 nm. The Calcium content is determined from the ratio between the signal detected by the PMT depending on the violet laser and the signal detected by the PMT depending on the blue laser. This molecule presents (1) absorption peaks complementary to excitation lines available in most cytometers (blue + violet), (2) low sensitivity to the presence of Magnesium, and (3) independence from fluctuations in probe concentration thanks to the ratiometric nature of the test. Disadvantages include pH dependence, limited dynamics, and the tendency to bind other molecules, including Cadmium, Gadolinium, and especially Zinc, with consequent negative modulation of its quantum yield (Hyrc et al. 2000). BTC is particularly useful in evaluating high Calcium levels, a condition in which other probes become saturated.
17.11
Sodium Content
413
Some BTC derivatives have been modified to produce probes sensitive to the concentration of α-ketoglutarate, a metabolite involved in the pathogenesis of non-alcoholic hepatic steatosis (nonalcoholic fatty liver disease, NAFLD) (Gan et al. 2017); at present, the use of these probes does not yet seem documented in Flow Cytometry.
17.11
Sodium Content
The determination of intracellular sodium content can be performed with several probes, including SBFI and Sodium Green, belonging to the benzofuran family.
17.11.1
SBFI
The SBFI molecule (sodium-binding benzofuran isophthalate) is a permeant molecule difficult to manage, as it requires (1) the evaluation of the ratio between the emissions obtained in a spectral range spanning from 450 to 550 nm induced by two different excitations, the first in the 330–345 and the second in the 370–390 nm range (Minta and Tsien 1989), or (2) the evaluation of the ratio between the emission at 410 nm and that at 590 nm induced by excitation at 340 nm (Baartscheer et al. 1997). No reports exist on the use of SBFI in Flow Cytometry.
17.11.2
Sodium Green
The Sodium Green probe is another benzofuran-derived compound available both as a permeant molecule (tetra-acetate) and an impermeant molecule (tetramethylammonium salt). From a spectral point of view, the Sodium Green probe has much more favorable spectral characteristics than SBFI, as it is excited at 488 nm and emits at approximately 540 nm, being detectable in an optical bench suitable for FITC. Given that Sodium Green emission intensity increases with Sodium concentration, a flow cytometric method has been devised in which Sodium concentration is proportional to the ratio between the Forward Scatter and the Sodium Green emission signal, calculated for each cell and expressed as a histogram (Amorino and Fox 1995).
17 Fluorochromes for the Study of the Cell Features
414
Fig. 17.26 Molecular structures of SBFI (panel A) and PBFI (panel B) molecules. Notice how the two molecules are closely similar, differing only in the macrocyclic unit destined to bind with the electrolyte
17.12
Potassium Content
The probes currently available for the measurement of intracellular Potassium consist of PBFI (Potassium-binding benzofuran isophthalate) (ex340/em500) (Jezek et al. 1990), BCECF (ex505/em545), and Potassium Green (ex526/em546) (Rimmele and Chatton 2014).
17.12.1
PBFI
The spectral behavior of PBFI is similar to SBFI since it requires the evaluation of the ratio between the emissions obtained in a spectral range spanning from 450 to 550 nm induced by two different excitations, the first in the 330–345 and the second in the 370–390 nm range (Meuwis et al. 1995). This analogy is due to the structural similarity between the two probes, which differ only in the groups responsible for binding with the electrolyte (Fig. 17.26). A report exists in which Flow Cytometry evaluated PBFI; in this report, the probe was excited by a multiline UV output of an
17.14
Magnesium Content
415
Argon laser, and the emission was registered via a 408 nm cutoff filter (Laskay et al. 1995).
17.12.2
BCECF
There is also an alternative method, based on the use of the BCECF molecule (ex505/em545), usually used to determine intracellular pH (Balkay et al. 1997). In this protocol, Potassium is not measured as such but as a function of the variations in emission of BCECF under pH conditions made stable with Nigericine, an antibiotic produced by Streptomyces hygroscopicus acting as an ionophore and favoring the transport of the Hydrogen ions. This action stabilizes the Hydrogen and Potassium concentration ratio between the intra- and extra-cytoplasmic compartments. Under these conditions, the intracellular Potassium concentration can be derived from the intracellular pH measurement provided by BCECF (Balkay et al. 1997).
17.13
Chloride Content
The determination of intracellular Chloride content can be performed using methoxy-N-sulfopropyl quinolinium (SPQ) (ex344/em443) (Pilas and Durack 1997), in which the emission intensity behaves inversely to the concentration of intracellular Chloride. Another probe used to determine the intracellular content of Chloride is N-methoxyquinolyl acetoethyl ester (MQAE) (ex350/em460) (Amorino and Fox 1995).
17.14
Magnesium Content
The determination of the intracellular content of Magnesium can be performed using the Magnesium Green™ molecule (ex 506/em531) or Mag Green, which exists both as an impermeant version as a Potassium salt and a permeant version as an acetoxymethyl ester. In the presence of magnesium, the probe increases its fluorescence emission. Magnesium Green™ has been used in Flow Cytometry to determine Magnesium presence in platelets (Fox et al. 2007).
416
17.15
17 Fluorochromes for the Study of the Cell Features
Glutathione Content
Glutathione is a reducing tripeptide consisting of glycine, cysteine, and glutamate, which plays a fundamental role in detoxification. Its dosage can provide important information concerning different areas, including the cell cycle (Poot et al. 1991) and the mode of action of antiblastic drugs (Arrick and Nathan 1984; Coleman et al. 1988; Hedley 1993). The cytometric determination of Glutathione can be performed using Monochlorobimane, able to bind to Glutathione by glutathione-transferase action, or using a group of fluorescent molecules that bind to sulfhydryl groups in a non-enzymatic way (Hedley and Chow 1994). Monochlorobimane (MCB) (ex360/ em490) has the disadvantages of requiring a UV excitation source, as well as having a low affinity with the glutathione-transferase of humans, which limits its field of application. The molecules capable of binding to Glutathione in a non-enzymatic way include monobromobimane (MBB) (ex394/em490) (Durand and Olive 1983), Mercury Orange (MO) (ex488 line/emLP570) (O’Connor et al. 1988), ortho-phthalaldehyde (OPA, OPT) (ex340/em455) (Treumer and Valet 1986), 5-chloromethylfluoresceindiacetate (CMFDA) (ex488/em530), and 5-chloromethyleosin-diacetate (CMEDA) (ex514/em>540) (Poot et al. 1991). A common problem with these molecules, except ortho-phthalaldehyde, is binding to sulfhydryl groups other than glutathione, generating background noise that must be subtracted from the measurement result.
17.16
Heavy Metals Content
Many probes are commercially available to determine the intracytoplasmic content of some heavy metals. These probes are infrequently used in Flow Cytometry but are mentioned in this chapter for completeness. Among the available probes is the PhenanGreen FL molecule (ex492/em517) (also known as Phen Green™ FL), a phenanthroline derivative that can bind to Cadmium, Iron, Mercury, Niobium, Lead, and Copper, and decreases its emission intensity in proportion to the concentration of the metal ion (Petrat et al. 1999; Chavez-Crooker et al. 2001; Du et al. 2015). Lead can be selectively explored with the Leadmium Green molecule (ex488/ em520) (Singh et al. 2019), which is also suitable for the determination of Cadmium (Malaiyandi et al. 2016); Leadmium Green is not influenced by Calcium concentration. Zinc intracellular concentration has been explored with a series of compounds, among which (1) Zinquin (TSQ) (ex365/em495) (methoxy-quinolyl-p-toluene sulfonamide), a derivative of 8-hydroxyquinoline used in the determination of zinc in spermatozoa (Andrews et al. 1995), (2) Zinpyr-1 (ZP1) (ex515/em525), a molecule
17.17
Cell Proliferation
417
Fig. 17.27 Polaric molecular structure
manageable with a 488 nm line (Malavolta et al. 2006), and (3) FluoZin-3 (ex494/ em515) (Haase et al. 2006). Iron can also be selectively evaluated using nanosensors (PEBBLEs, or Probe Encapsulated By Biologically Localized Embedding) containing the molecule AF488 (ex488/em520) (Sumner and Kopelman 2005).
17.17
Cell Proliferation
The study of cell proliferation can be carried out in many ways, mostly based on the measurement of DNA distribution in the proliferating population, possibly associated with the study of the incorporation of DNA precursors such as BrdU or other halogenated pyrimidines. Another way to explore cell proliferation relies on cell trackers, which irreversibly bind to cell membranes. The rationale for their use is that, in cell division, the daughter cells divide the molecules present in the mother cell. Consequently, the fluorescent molecules bound to a mother cell progressively halve their concentration in each daughter cell so that the successive generations differ from each other based on the gradually dwindling fluorescence intensity values (Wallace and Muirhead 2007; Wallace et al. 2008). The cell trackers used in this methodological approach must bind themselves permanently to the cell membrane without compromising their ability to proliferate. They can belong to various groups, among which the most commonly used are the esters of Fluorescein (Hasbold et al. 1999; Lyons 2000; Quah et al. 2007; Parish et al. 2009; Lyons et al. 2013), Calcein (ex496/em520) (Parish 1999), the molecules belonging to the PKH group (Parish 1999), some molecules belonging to the group of lipophilic carbocyanines, and the molecules belonging to the CellVue® group (Bantly et al. 2007; Tario et al. 2007; Wallace and Muirhead 2007). Some attempts have also been made with the SNARF-1 molecule (ex488/em570-670) (Magg and Albert 2007); particularly satisfactory results, especially as in vivo cell trackers, seem possible with Polaric molecules, a group of amphiphilic dyes with a high affinity for cell membranes (Maishi et al. 2013) (Fig. 17.27). Cell trackers can also be useful when it is important to document the fusion between membranes belonging to different cells, as in the study of trogocytosis or the relationships between monocytes and neoplastic cells. In this case, the cells are loaded separately with spectrally different cell trackers: the transfer of material between different events will be demonstrated by the appearance of double-labeled
418
17 Fluorochromes for the Study of the Cell Features
events (Spotl et al. 1995; Bermudez-Fajardo et al. 2007; Gertner-Dardenne et al. 2007; Canonico et al. 2012). Appropriate algorithms are then applied to the cell proliferation histograms obtained with cell trackers to measure the number of division cycles, the fraction of cells that divide, and the number of times each cell population divides. It is also possible to estimate the number of precursors in a heterogeneous population containing cycling and resting cells (Givan et al. 1999).
17.17.1
Fluorescein Esters (CFSE)
Fluorescein esters include CFSE and CFDA-SE, already discussed in Sect. 17.3.3 dedicated to cell viability. As anticipated in the introduction, the daughter cells contain half of the CFSE molecules (ex491/em518) present in the mother cell. The different cellular generations following the proliferative stimulus not only can be distinguished based on the progressive halving of fluorescence intensity values (Hasbold et al. 1999; Lyons 2000; Quah et al. 2007; Parish et al. 2009; Lyons et al. 2013) but may be subjected to the simultaneous analysis of other antigens relevant for the study (Fazekas de St Groth et al. 1999; Fulcher and Wong 1999). Unfortunately, CFSE has been alleged to possess cellular toxicity and is likely to provide inaccurate results (Last’ovicka et al. 2009).
17.17.2
Lipophilic Carbocyanines
The lipophilic carbocyanines exploited as cell trackers are characterized by long alkyl hydrocarbon tails on each Nitrogen of their two aromatic moieties. These long alkyl tails bind the cell membrane and persist for a long period (up to several weeks) (Slezak and Horan 1989 Horan and Slezak 1989; Teare et al. 1991). The lipophilic carbocyanines commercially available are the molecules DiO, DiI, DiD, and DiR, and the group of the PKH molecules. Some of these probes, like DiO, have also been exploited as lipid probes based on their high hydrophobicity (Cirulis et al. 2012) (see Sect. 17.20).
17.17.2.1
DiO, DiI, DiD, AND DiR Molecules
This group includes the following probes: DiO (DiOC18(3) (ex484/em501), DiI (DiIC18(3) (ex551/em566), DiD (DiI18(5) (ex644/em663), and DiR (DiIC18(7) 748/780), marketed by AAT (formerly ABD), Bioquest, and Thermo Fisher Scientific. These probes are mainly used in fluorescence microscopy, but DiIC18(3) has been used in flow cytometry and precisely in the study of the gap junction-mediated intercellular communication (GJIC) (Fonseca et al. 2006) and DID in the study of
17.17
Cell Proliferation
419
tumor proliferation in vivo (Yumoto et al. 2014). Despite a suboptimal spectral matching, DIC can be excited by a 488 nm line and managed in the channel intended for PE (García-Fojeda et al. 2019); as for DID, it could be excited by a 633 nm line and managed in the channel intended for APC (Yumoto et al. 2014). DiO, also marketed as Vybrant DiO-C18 by Thermo Fisher Scientific, can be excited by a 488 nm line and managed in the channel intended for FITC and has been used in flow cytometry in studies on the lipid content of green Algae (Cirulis et al. 2012).
17.17.2.2
PKH Molecules
The PKH molecules are a group of lipophilic cationic carbocyanine derivatives (Parish 1999), named after their inventor P. K. Horan (Horan and Slezak 1989). Molecules belonging to this group are PKH2, PKH26, and PKH67.
PKH2 The PKH2 molecule (ex488/em514) is less used than in the past as it has shown binding instability (Ashley et al. 1993) and the ability to reduce the viability of lymphocytes to which it binds (Samlowski et al. 1991). Furthermore, its use as a cell tracker in experimental biology studies in vivo interferes with lymphocyte circulation in lymph nodes, probably due to the down-regulation of CD62L (Samlowski et al. 1991).
PKH26 The PKH26 molecule (ex488/em570) (Fig. 17.28) is excited in blue, green, and green-yellow (488 nm, 514 nm, and 543 nm) and emits at approximately 570 nm. Its use is spectrally incompatible with phycoerythrin, tandems with Texas Red as acceptor, PerCP, and some Fluorescent Proteins with yellow and red emission. The spectral characteristics of the PKH26 molecule are particularly interesting, as they allow the use of fluorescein and all the molecules derived from it in the simultaneous monitoring of the proliferation of two different cell populations in the same experiment (Parish 1999); moreover, the simultaneous use of PKH26 and PKH67 (Schutz et al. 2009) is also possible.
Fig. 17.28 Molecular structure of PKH26 molecule. Note the aliphatic chains (red frame in the picture) that allow stable positioning of the probe in the cell membrane
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PKH67 The PKH67 molecule is excited by blue lines (457 and 488 nm) and emits at approximately 502 nm in the blue-green. Its use is spectrally incompatible with Fluorescent Proteins with emission in green-yellow and fluorescein, from which it derives. The PKH67 molecule did not affect the viability of the labeled cells. It was used in the study of the membrane recycling mechanisms (Rousselle et al. 2001), in the study of leukemic cell lines resistant to Daunorubicin (Barbier et al. 2005), in the study of exosomes and microvesicles (Pospichalova et al. 2015), and in the study of trogocytosis and cellular interactions (Arkwright et al. 2010; Canonico et al. 2012; Valgardsdottir et al. 2017). As already specified, the simultaneous use of PKH67 and PKH26 is possible (Schutz et al. 2009).
17.17.3
CellVue® Series Molecules
The molecules belonging to the CellVue® series (marketed by Sigma Aldrich and Thermo Fisher Scientific) are compounds with undisclosed formulas consisting of an aromatic nucleus linked to a long aliphatic chain, which binds irreversibly to the lipid regions of the cell membrane. The use of these molecules is similar to PKH molecules or fluorescein esters and includes investigations on trogocytosis and cell proliferation (Bantly et al. 2007; Gertner-Dardenne et al. 2007; Tario et al. 2007; Wallace and Muirhead 2007). CellVue® molecules are available in the following different versions, characterized by different spectral configurations, most of which are used in image cytometry: (1) CellVue® Lilac (ex423/em471), (2) CellVue® Lavender (ex425/em461), (3) CellVue® Jade (ex478/em508), (4) CellVue® Red (ex567/em588), (5) CellVue® Maroon (ex647/em667), (6) CellVue® Plum (ex652/em671), (7) CellVue® Claret (ex655/em675), (8) CellVue® Burgundy (ex683/em707), (9 CellVue® NIR780 (ex745/em776), and (10) CellVue® NIR815 (ex786/em814). One of the most used molecules is CellVue® Claret, which has a spectral behavior similar to APC, and is spectrally incompatible with CY5 tandems, APC, DRAQ5 (ex488-633/em> 660), TO-PRO-3 (ex642/em661), and some Fluorescent Proteins with emission in red and deep red. The CellVue® Claret molecule provides equivalent results to those produced by labeling with Fluorescein esters or PKH molecules (Bantly et al. 2007). The CellVue® NIR780 molecule is excited by a 633 nm line and is detectable with the flow cytometer configuration used for APC-CY7 and equivalents (BP780/60 or else LP750).
17.18
17.18
Multidrug Resistance
421
Multidrug Resistance
Multidrug resistance (MDR) is a cell’s ability to extrude unrelated chemical compounds (xenobiotics). This activity is mediated by a series of membrane pumps that transport multiple substances, including ions, sugars, amino acids, steroids, and other molecules. This activity is ubiquitous in the animal kingdom, as it has been detected both in eukaryotes and in bacteria. Proteins belonging to this family perform a host of physiological functions in humans, including maintaining the blood-brain barrier (Cox et al. 2001), the placental barrier (Allikmets et al. 1998), and several detoxification processes. In oncology, multidrug resistance is of particular importance, as it extrudes from the target cells the drugs used in anticancer therapy, thus contributing to the mechanisms of resistance. In humans, the membrane pumps responsible for these transport activities belong to a super-family known as the ABC protein (ATP binding cassette) and consist of approximately thirty different proteins variously represented in the different cytotypes (Klein et al. 1999). The most important and most studied are P-gp1 (P-glycoprotein 1, subsequently renamed MDR1), identified in 1976 (Juliano and Ling 1976), and MRP1 (also known as ABCC1); these two proteins are interesting because they are primarily responsible for the MDR of neoplastic cells (Klein et al. 1999). Despite the theoretical importance of MDR in the context of cancer therapy, the results of the studies devoted to the phenomenon have proved to be rather disappointing from a clinical point of view (Cripe et al. 2010; Kolitz et al. 2010; Libby and Hromas 2010). There is evidence that the same mechanisms active in MDR enable the so-called “side population” to modify the intracellular content of Hoechst 33342, making it detectable by Flow Cytometry in particular experimental conditions (Goodell et al. 1996) (for further information on this topic, see Sect. 16.4.2.1). Similar to myeloperoxidase (MPO), the evaluation of MDR relies both on functional tests and the demonstration of the membrane proteins responsible for this phenomenon (Krishan et al. 1991; Tiirikainen et al. 1992). The demonstration of these molecules can rely both on Immunohistochemistry (IHC) (Ohsawa et al. 2005) and Flow Cytometry; the most frequently used Mabs are the Mab MRPm6, specific for the MRP1/ABCC1 protein, and the Mab 4E3, specific for the P-gp1/MDR1 protein, also known as CD243. The P-gp1/MDR1 and MRP1/ABCC1 proteins can extrude various exogenous molecules, including many fluorescent dyes. It follows that their activity can be studied by Flow Cytometry on cells in suspension, evaluating the decrease of the fluorescence signal, i.e., the efflux over time; this also allows comparison of the ability of inhibitors, such as Verapamil, to modulate the phenomenon. The probes used in the functional evaluation of MDR are numerous and used to study the phenomenon in solid tumors and hematological malignancies (Dorn-Beineke and Keup 2009). The molecules mostly exploited are Rhodamine 123 (RH123) (ex507/em529), which can be considered a specific substrate of P-gp1/MDR1 (Feller et al. 1995), and
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17 Fluorochromes for the Study of the Cell Features
Calcein acetoxymethyl ester (ex496/em520), which has proved to be an excellent substrate of both the P-gp1/MDR1 protein and the MRP1/ABCC1 protein (Glavinas et al. 2004; Karaszi et al. 2001). Many experiments have been also carried out with a series of other compounds including (1) NAO (Kessel et al. 1991) (ex495/em522), (2) Daunorubicin (ex488/ em575) (Hart et al. 1993; Van Acker et al. 1993), (3) JC-1 (ex 405-488/em527-590) (Legrand et al. 2001), (4) Fura-2 (ex405-532/em> 600) (Homolya et al. 1993), (5) Fluo-3 (ex488/em526) (Homolya et al. 1993), (6) Indo-1 (ex365/em405-485) (Homolya et al. 1993), (7) BCECF (ex505/em545) (Homolya et al. 1993), (8) Mitotracker Green (ex490/em516) (Marques-Santos et al. 2003), (9) SYTO®16 (ex488/em518) (Van Der Pol et al. 2003), (10) DyeCycle violet (DCV) (ex365/ em440) (Nerada et al. 2016), (11) HO33342 (ex365/em450) (Morgan et al. 1989) (Feuring-Buske and Hogge 2001), and (12) DiOC2(3) (ex482/em500) (Marcelletti et al. 2018). The MDR activity can influence the behavior displayed by many of these molecules in functional tests, so the utmost care should be paid in these cases to the standardization of experimental conditions.
17.18.1
Rhodamine 123 (RH123)
Compared to Calcein, the determination of MDR by RH123 (ex507/em529), also known as Rhodamine Efflux Assay, is based on the ability of a cell to extrude a permeant fluorescent dye that tends to flow freely into it. In this case, the cell fluorescence is inversely proportional to the ability to extrude it compared to a negative control consisting of cells of the same type treated with a P-gp1/MDR1 inhibitor (Verapamil). This approach is likely common to all MDR determinations employing fluorescent permeant probes (Frey et al. 1995).
17.18.2
Calcein Acetoxymethyl Ester
The determination of MDR by Calcein (ex496/em520), also known as Calcein Influx Assay, is based on Calcein acetoxymethyl ester (Calcein-AM), a non-fluorescent molecule, which freely crosses the cell membrane but is actively extruded from the pumps responsible for the MDR (Karaszi et al. 2001). The intracellular esterases transform non-fluorescent Calcein-AM into fluorescent Calcein; it follows that the cell fluorescence is inversely proportional to the ability to extrude the Calcein (Szakacs et al. 1998). Given that Calcein-AM and Calcein are substrates for MRP1 but not for Pgp (Szakacs et al. 1998), selective inhibitors for one or the other membrane pumps make it possible to collect separate information on the activity of each of them (Hollo et al. 1994). Moreover, the comparison with a control consisting of cells of the same type
17.19
Membrane Fluidity
423
Fig. 17.29 Molecular structure of Merocyanine 540. Note the benzoxazole group (blue frame in the picture), the ketone groups, typical of merocyanines (red frame in the picture), and the aliphatic chains that allow their positioning in the membrane (green frames in the picture)
treated with a P-gp1/MDR1 inhibitor (Verapamil) allows quantitating the phenomenon (Karaszi et al. 2001).
17.19
Membrane Fluidity
The determination of membrane fluidity is generally carried out with the molecule Merocyanine 540 (ex555/em578, excitable at 488 nm), a heterodimeric symmetrical cyanine equipped with accessory groups consisting of long aliphatic chains that mediate their bond with cell membranes (Fig. 17.29). Merocyanine 540, long studied due to its preferential affinity for immature and leukemic hematopoietic cells (Valinsky et al. 1978; Belloc et al. 1988), binds more efficiently to cell membranes in which structural alterations have occurred. These alterations consist of exposure to the outside of phosphatidylserine (PS) groups or changes in the organization of the membrane lipids (McEvoy et al. 1988); consequently, Merocyanine 540 is useful in the study of apoptosis (Laakko et al. 2002), where the first stages of the process include re-organization of the cell membrane (Mower et al. 1994). Merocyanine 540 is used in veterinary andrology to evaluate capacitation, i.e., those structural and metabolic changes necessary for spermatozoa to achieve full fertilization capacity (Petrunkina and Harrison 2011). Increased bonds between Merocyanine 540 and spermatozoa correlate with capacitation in a series of different species, including horses (Rathi et al. 2001), macaques (Baumber and Meyers 2006), and dogs (Steckler et al. 2015), but not humans (Muratori et al. 2004). In human andrology, Merocyanine 540 is useful to highlight positive M540 bodies, which are small rounded bodies wrapped in a plasma membrane that binds the dye, are found in the ejaculate of sub-fertile subjects and are interpreted as apoptotic bodies escaped from the active phagocytosis in testicular and epididymal sites (Marchiani et al. 2007). Annexin V is another molecule that can see these membrane alterations; annexin V is not a fluorescent molecule but can be conjugated to a suitable fluorochrome.
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17 Fluorochromes for the Study of the Cell Features
Finally, experimental reports correlate membrane fluidity to the depolarization of fluorescence signals resulting from cellular activation (for further information on this topic, see Sect. 3.1.1). The fluorochromes exploited in the study of membrane fluidity by evaluation of the depolarization include (1) BCECF-AM (ex505/ em545) (Gelman-Zhornitsky et al. 1997), (2) CFSE (ex495/em519) (Cohen-Kashi et al. 1997), (3) diphenyl-hexatriene (DPH) (ex350/em452) (Schaap et al. 1984) (Fox and Delohery 1987), (4) Fluorescein diacetate (FDA) (ex495/em520) (Dimitropoulos et al. 1988), and (5) trimethylammonium-diphenyl-hexatriene (TMA-DPH) (ex350/em452) (Schaap et al. 1984).
17.20
Lipid Content
Flow cytometry allows the analysis of the lipid content on a single-cell basis (Hyka et al., 2013). The most often used lipid-binding probes include (1) Nile Red (ex488/ em570), (2) BODIPY493/503 (ex493/em503), (3) BODIPY505/515 (ex505/ em515), (4) and DiO (for further information on DiO, see Sect. 17.17.2.1). These probes have been exploited (1) in flow cytometry in a series of studies on Algae and (2) in confocal and fluorescence microscopy in a series of studies on pluricellular organisms such as Caenorhabditis elegans.
17.20.1
NILE RED
Nile Red (NR) is a benzophenoxazone dye that displays high hydrophobicity and lipids affinity (Fowler and Greenspan 1985), but great attention should be paid to interpreting the results because it has been shown to bind non-lipidic structures as well (O’Rourke et al. 2009). Nile Red is a highly solvatochromic probe (Lampe et al. 2008), which moves both excitation and emission towards red spectral regions depending on the polarity of the environment. From a flow cytometric point of view, NR can be excited by a 488 nm line and managed in the same channel intended for PE (Cirulis et al. 2012); slight changes in the band-pass filter might be required to optimize the signal detection, given its solvatochromic properties. NR has been widely used in many flow cytometric studies concerning the lipid content of microalgae (Hyka et al. 2013; Rumin et al. 2015).
17.20
Lipid Content
17.20.2
425
Bodipy and Its Derivatives
The fluorochromes belonging to the group of BODIPY molecules (abbreviation for Boron-Di-Pyrrole) share a structure consisting of a molecule of Dipyrrometheneboron difluoride (Fig. 17.30). These fluorochromes display high insolubility in water, making them unsuitable for conjugation with antibodies for immunofluorescence techniques, even though some try has been carried out conjugating BODIPY with streptavidin (Benchaib et al. 1996). BODIPY molecules are most often used as probes for lipids based on their great affinity for this group of molecules due to their high hydrophobicity and are mainly used in the study of microalgae. The alkylation and arylation of the central nucleus in different positions generate a series of similar compounds but with different spectral characteristics. Among the many synthesized forms, those mostly used in Flow Cytometry are: 1. BODIPY493/503 (Benito et al. 2014; Qiu and Simon 2016; Oliveira et al. 2018), also evaluated in its streptavidin-conjugated form as a probe for immunofluorescence techniques (Benchaib et al. 1996); BODIPY493/503 is a cell membranepermeable dye which can be excited by a 488 nm line and managed in the same channel intended for FITC (Qiu and Simon 2016); 2. BODIPY505/515, mainly exploited in the analysis of microalgae but also used in the analysis of murine lymphocytes (Pacella et al. 2020); BODIPY505/515 is a cell membrane-permeable dye that can be excited by a 488 nm line and managed in the same channel intended for FITC (Brennan et al. 2012; Cirulis et al. 2012; Rumin et al. 2015; Oliveira et al. 2018); 3. BODIPY581/591, exploited in the synthesis of C11-BODIPY581/591 (read further) (Drummen et al. 2002). The simultaneous presence of DRAQ5 hinders the intake of BODIPY-labeled compounds by the cells, probably due to a complex between the molecules (Snyder and Garon 2003). The BODIPY core can be conjugated with particular moieties, providing probes to perform special tasks. One of the most interesting is C11-BODIPY581/591, which is a lipid-specific probe exploited in the study of lipid peroxidation (Cheloni and Slaveykova 2013; Peluso et al. 2013) (for further information on this compound, see Sect. 17.20.1). Another interesting compound is BODIPY aminoacetaldehyde (BAAA), marketed by StemCell™ (https://www.stemcell.com/) under the name of Aldefluor™. BAAA is obtained by linking BODIPY to an aminoacetaldehyde Fig. 17.30 Molecular structure of BODIPY
426
17 Fluorochromes for the Study of the Cell Features
moiety (Storms et al. 1999); it acts as a fluorogenic substrate for the aldehyde dehydrogenase (ALDH) contained in the hematopoietic precursors and cancer cells, allowing their identification with flow cytometry techniques (Dolle et al. 2015). BAAA can be excited by a 488 nm line and managed in the same channel intended for FITC (Storms et al. 1999). Lysotracker (Thermo Fisher Scientific) and Lysohunt (Setareh Biotech) Green and Red are BODIPY derivatives as well. BODIPY molecules have also been used in the production of hard-dyed microbeads to be used as a standard in compensation procedures (Zhang et al. 1998); moreover, they are also exploited in the synthesis of some members of the Pdots family (Chiu et al. 2012; Chiu et al. 2018) (for further information on this topic, see Sect. 15.3.2).
17.21
Lipid Peroxidation
Lipid peroxidation is an oxidative degradation of lipids that plays a role in the etiopathogenesis of several degenerative conditions (Halliwell 1987; Farber 1994) and cell response to cancer chemotherapeutic agents (Babson et al. 1981; Vile and Winterbourn 1990). Flow cytometry allows the analysis of lipid peroxidation on a single-cell basis. The most often used lipid-binding probes are the cis-parinaric acid and the BODIPY derivative C11-BODIPY581/591.
17.21.1
Cis-Parinaric Acid
Cis-parinaric acid (ex320/em420) is a long-chain fatty acid that belongs to the rare group of non-aromatic fluorescent molecules due to the presence of conjugated double bonds. It behaves as a lipid-specific probe and binds the cell membrane, and its fluorescence emission intensity is inversely proportional to the state of lipid peroxidation (Kuypers et al. 1987). Cis-parinaric acid has been exploited in the cytometric study of lipid peroxidation in Chinese hamster ovary cells (Hedley and Chow 1992).
17.21.2
C11-BODIPY581/591
C11-BODIPY581/591, marketed by Thermo Fisher Scientific, is a lipid-specific probe that can be excited by a 488 nm line. Its emission peak can move from green (520 nm) in oxidized lipids to orange (595 nm) in non-oxidized ones (Drummen et al. 2002); it follows that information about the oxidative status of the events under analysis can be provided by a change of the green signal intensity (Cheloni and Slaveykova 2013) or by the ratio between the signals detected in the
References
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green and orange regions of the spectrum (Guthrie and Welch 2007; Peluso et al. 2013).
17.22
Endoplasmic Reticulum (ER) Labeling
The in-vivo labeling of the endoplasmic reticulum (ER) may be performed with Dapoxyl (DPX) or with fluorochrome-conjugated Glibenclamide (Molecular Probes 2005). Dapoxyl (aminoethyl sulfonamide) is a small molecule that is excited in UV, can emit from violet to red depending on the environmental conditions (Molecular Probes 2005), and displays a high affinity for ER, at least in Mycetes (Cole et al. 2000). Glibenclamide binds the sulphonyl-urea receptors present in ER (but also in Golgi apparatus); this molecule can be conjugated with BODIPY® FL (em504/ ex511), marketed as ER-tracker Green, or BODIPY® TR (ex587/em615), marketed as ER-tracker Red. Fluorochrome-conjugated Glibenclamide is usually exploited in imaging techniques, but recently, ER-tracker Green has been used in the imageenabled cell sorting (ICS) of HeLa cells (Schraivogel et al. 2022) (for more information on ICS, see Sect. 22.1.2).
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Uchiyama R, Moritomo T, Kai O, Uwatoko K, Inoue Y, Nakanishi T (2005) Counting absolute number of lymphocytes in quail whole blood by flow cytometry. J Vet Med Sci 67(4):441–444 Valet G, Raffael A, Moroder L, Wunsch E, Ruhenstroth-Bauer G (1981) Fast intracellular pH determination in single cells by flow-cytometry. Naturwissenschaften 68(5):265–266 Valgardsdottir R, Cattaneo I, Klein C, Introna M, Figliuzzi M, Golay J (2017) Human neutrophils mediate trogocytosis rather than phagocytosis of CLL B-cells opsonized with anti-CD20 antibodies. Blood 129(19):2636–2644. https://doi.org/10.1182/blood-2016-08-735605 Valinsky JE, Easton TG, Reich E (1978) Merocyanine 540 as a fluorescent probe of membranes: selective staining of leukemic and immature hemopoietic cells. Cell 13(3):487–499. https://doi. org/10.1203/00006450-199501000-00013 Van Acker KL, Van Hove LM, Boogaerts MA (1993) Evaluation of flow cytometry for multidrug resistance detection in low resistance K562 cells using daunorubicin and monoclonal antibodies. Cytometry 14(7):736–746 Van Der Pol MA, Broxterman HJ, Westra G, Ossenkoppele GJ, Schuurhuis GJ (2003) Novel multiparameter flow cytometry assay using Syto16 for the simultaneous detection of early apoptosis and apoptosis-corrected P-glycoprotein function in clinical samples. Cytometry 55B (1):14–21 van der Valk P, Herman CJ (1987) Leukocyte functions. Lab Invest 56(2):127–137 van Erp PE, Jansen MJ, de Jongh GJ, Boezeman JB, Schalkwijk J (1991) Ratiometric measurement of intracellular pH in cultured human keratinocytes using carboxy-SNARF-1 and flow cytometry. Cytometry 12(2):127–132. https://doi.org/10.1002/cyto.990120205 Verwer B (2002) BD FACSDiVa options. White Paper Becton Dickinson. Available at http://www. bdbiosciences.com/ds/is/others/23-6579.pdf. Last accessed 8 Jan 2021 Vile GF, Winterbourn CC (1990) dl-N,N0 -dicarboxamidomethyl-N,N0 -dicarboxymethyl-1,2diaminopropane (ICRF-198) and d-1,2-bis(3,5-dioxopiperazine-1-yl)propane (ICRF-187) inhibition of Fe3+ reduction, lipid peroxidation, and CaATPase inactivation in heart microsomes exposed to adriamycin. Cancer Res 50(8):2307–2310 Vowells SJ, Sekhsaria S, Malech HL, Shalit M, Fleisher TA (1995) Flow cytometric analysis of the granulocyte respiratory burst: a comparison study of fluorescent probes. J Immunol Methods 178(1):89–97 Walker PR, Sikorska M (1994) Endonuclease activities, chromatin structure, and DNA degradation in apoptosis. Biochem Cell Biol 72(11-12):615–623 Wallace PK, Muirhead KA (2007) Cell tracking 2007: a proliferation of probes and applications. Immunol Invest 36(5-6):527–561 Wallace PK, Tario JD Jr, Fisher JL, Wallace SS, Ernstoff MS, Muirhead KA (2008) Tracking antigen-driven responses by flow cytometry: monitoring proliferation by dye dilution. Cytometry A 73(11):1019–1034 Wang ZH, Chu GL, Hyun WC, Pershadsingh HA, Fulwyler MJ, Dewey WC (1990) Comparison of DMO and flow cytometric methods for measuring intracellular pH and the effect of hyperthermia on the transmembrane pH gradient. Cytometry 11(5):617–623. https://doi.org/10.1002/ cyto.990110509 Warnes G (2014) Measurement of autophagy by flow cytometry. Curr Protoc Cytom 68:9.45.41-10. https://doi.org/10.1002/0471142956.cy0945s68 Warnes G (2015) Flow cytometric assays for the study of autophagy. Methods 82:21–28. https:// doi.org/10.1016/j.ymeth.2015.03.027 Wendt ER, Ferry H, Greaves DR, Keshav S (2015) Ratiometric analysis of fura red by flow cytometry: a technique for monitoring intracellular calcium flux in primary cell subsets. PLoS One. https://doi.org/10.1371/journal.pone.0119532 Weston SA, Parish CR (1992) Calcein: a novel marker for lymphocytes which enter lymph node. Cytometry 13(7):739–749 Wieder ED, Hang H, Fox MH (1993) Measurement of intracellular pH using flow cytometry with Carboxy-SNARF-1. Cytometry 14(8):916–921
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Chapter 18
Fluorescent Proteins
Fluorescent Proteins (FPs) are proteins with unknown biological significance present in Cnidarians, Coelenterates, Crustaceans, Chordates, and Elasmobranchs (Gruber et al. 2016). Fluorescent proteins are naturally fluorescent when excited at certain wavelengths without requiring cofactors, substrates, catalysts, or coenzymes. The first isolated protein of this group was the green fluorescent protein (GFP), discovered in the Aequorea victoria jellyfish in 1961 (Shimomura et al. 1962) and cloned in 1992 (Prasher et al. 1992). The fluorescence of GFP and all fluorescent proteins results from a series of translational modifications of the protein structure, which occur spontaneously in the presence of oxygen (Tsien 1998). This phenomenon is made possible by the chromophore configuration, which appears highly preserved in fluorescent proteins belonging to other species. In particular, a triplet of amino acids (serine, tyrosine, and glycine, located respectively in positions 65, 66, and 67) remains confined within a hydrophobic environment defined by a tubular protective structure consisting of 11 β sheets (Yang et al. 1996). The relationships between serine and glycine residues lead to a heterocyclic ring formation, which combines with the imidazole ring of tyrosine to form the chromophore. Modifications of this arrangement, i.e., amino acid substitutions in the triplet and the tubular protective structure, lead to the synthesis of proteins possessing new and different spectral behaviors covering the visible range from blue to NIR (Fig. 18.1). The classification of the Fluorescent Proteins adopted in this chapter relies on their emissions, which allows their division into: 1. 2. 3. 4. 5. 6.
Blue Fluorescent Proteins (BFP) Cyan Fluorescent Proteins (CFP) Green Fluorescent Proteins (GFP) Yellow Fluorescent Proteins (YFP) Red Fluorescent Proteins (RFP) Infrared Fluorescent Proteins (iRFP)
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_18
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Fig. 18.1 Emission spectra of some Fluorescent Proteins. In particular, (1) m-Wasabi, (2) EBFP2, (3) AmCyan-1, (4) Citrine, (5) m-Banana, (6) d-Tomato, (7) m-Strawberry, (8) m-Cherry, (9) E2-Crimson, (10) iRFP 670, (11) iRFP 682, (12) iRFP 713, (13) iRFP 720. Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
The names of the FPs reflect their spectral characteristics (emerald, citrine), their structure, or the animals’ name from which they derive (Ptilosarcus sp., Renilla sp.). Accordingly: 1. The prefix “Turbo” suggests a particularly rapid chromophore maturation time. 2. The prefix “e” or “E” stands for “enhanced,” i.e., improved after engineering. 3. The prefixes “m,” “d,” and “t” suggest the type of quaternary structure (monomer, dimer, tetramer). 4. Other prefixes recall the donor species, as in the RFP DsRed, where Ds stands for Discosoma striata, or in the RFP HcRed, where Hc stands for Heteractis crispa; et cetera. Mutations in the sequence of Fluorescent Proteins have been artificially induced to search for other more favorable characteristics as biological trackers. These features consist of: 1. 2. 3. 4. 5. 6.
Spectral characteristics more suited to the currently available light sources Spectral characteristics more suitable for FRET between different FPs Greater brilliance and persistence of the emission Faster maturation rate of the chromophore Lower tendency to form oligomers, potentially harmful to cellular integrity Greater resistance to certain micro-environmental conditions, such as resistance to a very low pH, a desirable characteristic in the study of lysosomes and other acid organelles
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7. The development of particular spectral behaviors exploitable in biosensing different environmental features The success of Fluorescent Proteins in biological research is because a gene coding for one of them can be joined with a gene to be transfected (Kobayashi et al. 2013). As a result of this procedure, the transfected sequence is encoding both its specific product and the fluorescent protein, which, being very small, does not interfere with the function of the transcript but signals its presence, providing information on both successful transfection and transcription (Chalfie et al. 1994). A few manufacturers, including Takara Bio inc., market fluorescent Protein plasmids; moreover, non-profit plasmid repositories exist, including Addgene (https://www.addgene.org). Given that some Fluorescent Proteins have been engineered to modulate their spectral behavior as a function of some microenvironment’s features, it is possible to exploit them as biosensors to demonstrate various cellular parameters in physiological conditions without other probes. In this regard, there are Fluorescent Proteins that modify their spectral behavior as a function of environmental features such as the presence of Hydrogen peroxide, the concentration of Calcium, the state of phosphorylation, the membrane potential, the redox state, the cell pH, and others (Miyawaki et al. 1997; Demaurex and Frieden 2003; Hara et al. 2004; Chudakov et al. 2010; Piatkevich and Verkhusha 2011; Zhao et al. 2011; Kost et al. 2017; Pakhomov et al. 2017). Some Fluorescent Proteins have been used with FRET techniques in co-localization studies (Day and Davidson 2012); others, called photoactivable proteins (PAFPs), behave as probes that can modify their spectral properties when excited with light radiation of a given wavelength or intensity (Lukyanov et al. 2005). The methods of use and Fluorescent Proteins application constitute a welldefined, highly specialized topic whose exhaustive treatment exceeds this chapter’s aims, mainly dedicated to the relationships between Fluorescent Proteins and Flow Cytometry. Further information on the characteristics of these proteins and their role in biological research can be derived from the reading of dedicated publications (Stepanenko et al. 2008; Day and Davidson 2009; Chudakov et al. 2010; Piatkevich and Verkhusha 2011), and consulting an FP-dedicated public database including a “spectrum viewer” utility accessible at the address https://www.fpbase.org/about/.
18.1
Fluorescent Proteins and Flow Cytometry
Fluorescent Proteins are mainly used in imaging techniques aimed at the topographical demonstration of the fluorescent molecules. In Conventional Flow Cytometry, the conditions are different. Conventional Flow Cytometry demonstrates the fluorescent protein’s presence with an all-or-nothing logic and does not provide “topographical” information; its role is to assess the number of transfected cells in a heterogeneous population and evaluate the number of copies in each transfected cell as a signal intensity function.
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The flow analysis of FPs is subjected to a series of limitations, including the short excitation time and the need for excitation lines other than the classic RBV lines usually available. Even one of the great advantages of Flow Cytometry, i.e., multiparametric analysis, is challenged in this context, as many Fluorescent Proteins have highly overlapping emission spectra, both among themselves and with other “classic” fluorochromes used in staining procedures. All the conditions potentially affecting the sensitivity are critical, and the attempts to optimize optical benches are crucial. All these premises explain why the Fluorescent Proteins preferentially used in Flow Cytometry techniques constitute a fraction of those used with imaging techniques.
18.1.1
Green Fluorescent Proteins (GFPs)
Fortunately, a point mutation, consisting of the replacement of a serine at position 65 with a threonine, generates the appearance of an excitation peak at 488 nm, perfectly coupled with the 488 nm line of an Argon or equivalent solid-state laser (Cormack et al. 1996). The mutated protein, known as enhanced green fluorescent protein (EGFP) (ex488/em510) (Fig. 18.2), is one of the most widely used Fluorescent Proteins in Flow Cytometry and features many favorable characteristics. The commercial availability of standards calibrated in MESF makes the quantitative evaluation of EGFP feasible (Becton Dickinson 2002). The GFP group includes (1) mEmerald (ex487/em509), also obtained from the Aequorea victoria and considered a good alternative to EGFP, and a series of other proteins including: (2) AcGFP (ex480/em505), (3) amphiGFP (ex488/em515) obtained from the chordate Branchiostoma floridae, (4) anm1GFP1 (ex475/
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Fig. 18.2 Excitation and emission spectra of EGFP (enhanced Green Fluorescent Protein). Figure obtained thanks to the FPbase Spectra Viewer program (https://www.fpbase.org/spectra/) by TJ Lambert (Lambert TJ FPbase: a community-editable fluorescent protein database. Nat Methods, 2019; 16: 277)
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em495) and (5) anm1GFP2 (ex490/em504) obtained from Anthomedusae sp., (6) Azami Green (ex492/em505) obtained from a coral, (7) CopGFP (ex482/ em502) obtained from a copepod, (8) hrGFP (ex502/em505) and (9) hrGFP II (ex502/em505) obtained from an anthozoan (Renilla reniformis), (10) laesGFP (ex491/em506) obtained from Labidocera aestiva, (11) mNeonGreen (ex506/ em517), (12) Monster Green (ex505/em518) obtained from a coral, (13) pmeaGFP1 (ex489/em504) and (14) pmeaGFP2 (ex487/em502) obtained from Pontella meadi, (15) ppluGFP1 (ex480/em500) and (16) ppluGFP2 (ex482/em502) obtained from Pontellina plumata, (17) Ptilosarcus GFP (ex495/em508) obtained from an anthozoan, (18) TagGFP (ex482/em505) and (19) TagGFP2 (ex483/em506) obtained from Aequorea macrodactyla, (20) Renilla GFP (ex472/em540) obtained from an anthozoan, (21) TurboGFP (ex482/em502) obtained from a copepod, (22) ZsGreen (ex493/em505), and (23) ZsGreen1 (ex493/em505) obtained from a coral (Baumann et al. 2008; Ilagan et al. 2010; Telford et al. 2012; Shaner et al. 2013). The molecules of this group are generally excited by the 488 nm line; according to some authors, the use of bandpass filters with a narrow range and peak transmission as close as possible to the emission peak of the GFP (BP510/20, BP512/20 and BP517/20) would allow optimizing the signal of this fluorescent protein, drastically lowering the signal-to-noise ratio (Vorobjev et al. 2012). If GFP has to be used together with a yellow fluorescent protein (YFP), it is better to excite them separately. Nevertheless, it is possible to excite them with a single 488 nm line, collecting the GFP signal through a BP510/20 filter plus an LP505 dichroic mirror and the YFP signal through a BP550/30 filter plus an LP525 dichroic mirror (Lybarger et al. 1998).
18.1.2
Blue Fluorescent Proteins (BFPs)
When histidine replaces tyrosine 66 in GFP, the emission shifts to 450 nm and beyond. This fluorescent protein group is well excited by NUV and violet sources. Among the BFPs currently available, the most suitable, for use in Flow Cytometry, are (1) Azurite (ex384/em450), (2) eBFP2 (ex383/em445) and (3) TagBFP (ex399/em456) (Telford et al. 2012); other Fluorescent Proteins belonging to the BFP group include (4) EBFP2 (ex383/em445), (5) mAmetrine (ex 406/ em526), (6) mTagBFP (ex399/em456), (7) Sapphire (ex399/em511) and (8) T-Sapphire (ex399/em511). Sapphire and T-Sapphire could be classified in the GFP group, as they have an exceptionally long Stokes shift. Another fluorescent protein of particular interest is (9) Sirius (ex355/em425), which displays acid pH resistance (Tomosugi et al. 2009).
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Cyan Fluorescent Proteins (CFPs)
When tryptamine replaces tyrosine 66 in GFP, the emission shifts to approximately 480 nm. This group is well excited by violet (405 nm) and deep blue lasers depending on the protein studied. Among the CFP currently considered the most suitable for Flow Cytometry are (1) Cerulean (ex434/em476) and (2) eCFP (ex439/em476) (Telford et al. 2012). Other molecules belonging to the CFP group are (3) AmCyan1 (ex458/em489), (4) TagCFP (ex458/em480) and (5) CoralHue Cyan (ex472/em495) (Thomas 2007). The AmCyan molecule has also been used in conjugation with antibody molecules to increase the number of probes that can be managed with a violet laser to perform immunophenotyping (for further information on this topic, see Sect. 15.1.3).
18.1.4
Yellow Fluorescent Proteins (YFPs)
When tyrosine replaces threonine 203 in GFP, a further emission shift occurs to the right of the spectrum. This feature characterizes a series of proteins known as yellow Fluorescent Proteins (YFP), of which EYFP (enhanced YFP) (ex515/em530) is the base molecule. Among the YFPs currently considered the most suitable for use in Flow Cytometry are (1) Citrine (ex516/em529), and (2) Venus (ex515/em527) (Telford et al. 2012); the YFP group also includes (3) mBanana (ex540/em553), (4) TagYFP (ex508/em524), (5) Topaz (ex514/em527), (6) TurboYFP (ex525/em538) and (7) ZsYellow1 (ex529/em539) proteins. The fluorescent protein mBanana belongs to the group of Fluorescent Proteins synthesized by Tsien et al., also known as “fruit proteins” due to their names, inspired by various fruit species (Shaner et al. 2004). The molecules of this group are generally excited by the 514 nm line produced by an Argon laser or by the 532 nm line produced by a DPSS laser. It is also possible to carry out the simultaneous analysis of GFP and YFP on a single-laser cytometer with 488 nm excitation, putting a BP510/20 filter before the GFP PMT and putting a BP550/30 filter before the YFP PMT (for further information on yellow-green lasers, see Sect. 5.2.2.5).
18.1.5
Orange Fluorescent Proteins (OFPs)
An emission shifted beyond yellow is particularly desirable in imaging techniques both in vitro and in vivo because, in this spectral region, auto-fluorescence is low or absent, and signal penetration into tissues is particularly useful. Nevertheless, the production of Fluorescent Proteins with orange and red emissions was initially somewhat difficult because it could not be obtained with the simple genetic
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manipulation of GFP. Still, it required the identification of new Fluorescent Proteins in different organisms from the Aequorea victoria species and their subsequent engineering. A series of advancements occurred due to the discovery of Fluorescent Proteins in other marine organisms, among which the most interesting was the anthozoan Discosoma striata. The orange Fluorescent Proteins most suitable for use in Flow Cytometry are (1) tdTomato (ex554/em581), (2) dTomato (ex554/em581), (3) DsRed (ex558/ em583) and its variants (4) DsRed Express (T1) (ex557/em579) and (5) DsRed2 (ex563/em582) (Telford et al. 2012). This subgroup also includes (6) Kusabira Orange (ex548/em559), (7) Kusabira Orange 2 (ex551/em565), (8) mOrange (ex548/em562), (9) mOrange2 (ex549/em565), (10) mTangerine (ex568/em585), (11) TagRFP (ex555/em584), (12) TagRFP-T (ex555/em584), (13) Turbo RFP (ex553/em574), (14) AsRed2 (ex576/em592), and (15) mStrawberry (ex574/ em596). Broad excitation spectra and good efficiency characterize the proteins of this group. Comparative studies (Telford et al. 2012) have shown that: 1. The optimal excitation of dTomato (ex554/em581), DsRed (ex558/em583), and mStrawberry (ex 574/em596) is in the green-yellow (532 nm or 561 nm depending on the molecule). 2. DsRed (ex558/em583) has an excitation spectrum more shifted to the left than the other molecules of this group, and it can even be managed by a 488 nm line, which in no way constitutes a recommended option.
18.1.6
Red Fluorescent Proteins (RFP)
The red Fluorescent Proteins most suitable for use in Flow Cytometry are (1) mCherry (ex587/em610), (2) HcRed1 (ex588/em618), (3) mRFP1 (ex584/ em607), (4) mRuby (ex558/em605), and (5) TurboFP602 (ex574/em602) proteins (Telford et al. 2012). Another interesting Red Fluorescent protein is (6) mBeRFP (ex446/em615), which presents an exceptionally long Stokes shift, good brightness, and good photostability; this RFP is particularly suited to imaging techniques (Yang et al. 2013). The mCherry fluorescent protein (ex587/em610) is often used as a reporter in the CRISPR-Cas9 technique (Witte 2016). Other red Fluorescent Proteins with an emission further moved toward red and IR are (1) mKate (ex 588/em635), mKate2 (ex588/em635), (2) Katushka (ex588/ em635, synonyms TurboFP635 and Red635) (Telford et al. 2012), (3) HcRedTandem (ex590/em637), (4) E2 Crimson (ex611/em646), (5) mPlum (ex 590/ em649), (6) TagFP657 (ex611/em657), (7) eqFP670 (ex605/em670). Broad excitation spectra and good efficiency characterize the proteins of this group. Comparative studies (Telford et al. 2012) have shown that:
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1. The 561 nm line provides the best results and can satisfactorily excite HcRed (ex 592/em625), mKate (ex588/em635), Katushka (ex 588/em635), and mPlum (ex590/em649). 2. The optimal excitation of HcRed (ex592/em625), mKate (ex588/em635), and Katushka (ex588/em635) is a line in the yellow or in yellow-orange (580 nm or 592 nm). 3. The excitation of E2 Crimson (ex611/em646), eqFP670 (ex605/em670), and TagFP657 (ex611/em657) can benefit from a “short red” laser, but it remains satisfactory even at 633 nm. In cases characterized by a high concentration of the molecule, it is sometimes possible to detect an RFP even with sub-optimal excitations, and there is a non-peerreviewed report available on the Internet that documents the sorting of cells positive for mCherry (ex587/em610) with excitation at 488 nm (Witte 2016). Nevertheless, it should be emphasized that, as a rule, the RFPs benefit from excitation in greenyellow, or a longer wavelength, depending on the molecule.
18.1.7
Infrared Fluorescent Proteins (iRFPs)
Fluorescent Proteins with infrared emission (iRFP) are obtained from phytochromes present in some photosynthetic bacteria, including Deinococcus radiodurans and Rhodopseudomona palustris (Giraud et al. 2005; Shu et al. 2009; Rockwell and Lagarias 2010). From a strictly taxonomic point of view, iRFPs are not true Fluorescent Proteins, as their fluorescence is not due to a fluorophore formed by the intramolecular reaction of the side chains of the involved amino acids but depends on the presence of additional groups of tetrapyrrolic nature that bind to some specific protein domains (Telford et al. 2015). However, this dependence is not of practical importance, given that one of the possible tetrapyrroles, biliverdin IXa (BV), is a product of Heme catabolism universally present in mammalian systems. The study of iRFPs in systems other than mammalian cells, such as bacterial cells, is possible thanks to the simultaneous transfection of plasmids encoding hemeoxygenases capable of producing biliverdin in the presence of rhamnose-inducible promoters (Piatkevich et al. 2013; Shcherbakova and Verkhusha 2013). The iRFPs utilized so far in Flow Cytometry are (1) iRFP670 (ex643/em670), (2) iRFP682 (ex663/em682), (3) iRFP702 (ex673/em702), (4) iRFP713 (ex690/ em713), and (5) iRFP720 (ex702/em720) (Telford et al. 2015). Currently, the experiences made with iRFPs are quite limited, both in image cytometry and Flow Cytometry. Regarding the latter, there is evidence in the literature that: 1. The iRFP670 protein is optimally excited by the lines at 620, 633, and 637 nm and is detected with a BP680/30 filter configuration. 2. The iRFP682 protein is optimally excited by the lines at 620, 633, 637, and 685 nm and is detected with a BP680/30 or BP710/50 filter configuration.
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3. The iRFP702, iRFP713, and iRFP720 proteins are excited by the lines at 685 and 705 nm and are detected in a region spanning 710–780 nm.
References Baumann D, Cook M, Ma L, Mushegian A, Sanders E, Schwartz J, Yu CR (2008) A family of GFP-like proteins with different spectral properties in lancelet Branchiostoma floridae. Biol Direct. https://doi.org/10.1186/1745-6150-3-28 Becton Dickinson (2002) BD FACS™ EGFP calibration beads. White paper. Available at http:// www.ebiotrade.com/emgzf/clo2002apr/FACS-EGFP.pdf. Last accessed 3 February 2022 Chalfie M, Tu Y, Euskirchen G, Ward WW, Prasher DC (1994) Green fluorescent protein as a marker for gene expression. Science 263(5148):802–805 Chudakov DM, Matz MV, Lukyanov S, Lukyanov KA (2010) Fluorescent proteins and their applications in imaging living cells and tissues. Physiol Rev 90(3):1103–1163. https://doi.org/ 10.1152/physrev.00038.2009 Cormack BP, Valdivia RH, Falkow S (1996) FACS-optimized mutants of the green fluorescent protein (GFP). Gene 173 (1 Spec No):33–38 Day RN, Davidson MW (2009) The fluorescent protein palette: tools for cellular imaging. Chem Soc Rev 38(10):2887–2921. https://doi.org/10.1039/b901966a Day RN, Davidson MW (2012) Fluorescent proteins for FRET microscopy: monitoring protein interactions in living cells. BioEssays 34(5):341–350. https://doi.org/10.1002/bies.201100098 Demaurex N, Frieden M (2003) Measurements of the free luminal ER Ca(2+) concentration with targeted “cameleon” fluorescent proteins. Cell Calcium 34(2):109–119 Giraud E, Zappa S, Vuillet L, Adriano JM, Hannibal L, Fardoux J, Berthomieu C, Bouyer P, Pignol D, Vermeglio A (2005) A new type of bacteriophytochrome acts in tandem with a classical bacteriophytochrome to control the antennae synthesis in Rhodopseudomonas palustris. J Biol Chem 280(37):32389–32397. https://doi.org/10.1074/jbc.M506890200 Gruber DF, Loew ER, Deheyn DD, Akkaynak D, Gaffney JP, Smith WL, Davis MP, Stern JH, Pieribone VA, Sparks JS (2016) Biofluorescence in Catsharks (Scyliorhinidae): fundamental description and relevance for elasmobranch visual ecology. Sci Rep 6:24751. https://doi.org/10. 1038/srep24751 Hara M, Bindokas V, Lopez JP, Kaihara K, Landa LR Jr, Harbeck M, Roe MW (2004) Imaging endoplasmic reticulum calcium with a fluorescent biosensor in transgenic mice. Am J Physiol Cell Physiol 287(4):C932–C938. https://doi.org/10.1152/ajpcell.00151.2004 Ilagan RP, Rhoades E, Gruber DF, Kao HT, Pieribone VA, Regan L (2010) A new bright greenemitting fluorescent protein--engineered monomeric and dimeric forms. FEBS J 277(8): 1967–1978. https://doi.org/10.1111/j.1742-4658.2010.07618.x Kobayashi M, Watanabe M, Matsunari H, Nakano K, Kanai T, Hayashida G, Matsumura Y, Kuramoto M, Sakai R, Arai Y, Umeyama K, Watanabe N, Onodera M, Nagaya M, Nagashima H (2013) 20 generation and characterization of transgenic-cloned pigs expressing the far-red fluorescent protein monomeric plum. Reprod Fertil Dev 26(1):124–125. https://doi.org/10. 1071/RDv26n1Ab20 Kost LA, Nikitin ES, Ivanova VO, Sung U, Putintseva EV, Chudakov DM, Balaban PM, Lukyanov KA, Bogdanov AM (2017) Insertion of the voltage-sensitive domain into circularly permuted red fluorescent protein as a design for genetically encoded voltage sensor. PLoS One. https://doi. org/10.1371/journal.pone.0184225 Lukyanov KA, Chudakov DM, Lukyanov S, Verkhusha VV (2005) Innovation: Photoactivatable fluorescent proteins. Nat Rev Mol Cell Biol 6(11):885–891. https://doi.org/10.1038/nrm1741
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Lybarger L, Dempsey D, Patterson GH, Piston DW, Kain SR, Chervenak R (1998) Dual-color flow cytometric detection of fluorescent proteins using single-laser (488-nm) excitation. Cytometry 31(3):147–152 Miyawaki A, Llopis J, Heim R, McCaffery JM, Adams JA, Ikura M, Tsien RY (1997) Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388(6645): 882–887. https://doi.org/10.1038/42264 Pakhomov AA, Martynov VI, Orsa AN, Bondarenko AA, Chertkova RV, Lukyanov KA, Petrenko AG, Deyev IE (2017) Fluorescent protein Dendra2 as a ratiometric genetically encoded pH-sensor. Biochem Biophys Res Commun 493(4):1518–1521. https://doi.org/10.1016/j.bbrc. 2017.09.170 Piatkevich KD, Verkhusha VV (2011) Guide to red fluorescent proteins and biosensors for flow cytometry. Methods Cell Biol 102:431–461. https://doi.org/10.1016/b978-0-12-374912-3. 00017-1 Piatkevich KD, Subach FV, Verkhusha VV (2013) Far-red light photoactivatable near-infrared fluorescent proteins engineered from a bacterial phytochrome. Nat Commun 4. https://doi.org/ 10.1038/ncomms3153 Prasher DC, Eckenrode VK, Ward WW, Prendergast FG, Cormier MJ (1992) Primary structure of the Aequorea victoria green-fluorescent protein. Gene 111(2):229–233 Rockwell NC, Lagarias JC (2010) A brief history of phytochromes. Chemphyschem A European Journal of Chemical Physics and Physical Chemistry 11(6):1172–1180. https://doi.org/10.1002/ cphc.200900894 Shaner NC, Campbell RE, Steinbach PA, Giepmans BN, Palmer AE, Tsien RY (2004) Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol 22(12):1567–1572. https://doi.org/10.1038/nbt1037 Shaner NC, Lambert GG, Chammas A, Ni Y, Cranfill PJ, Baird MA, Sell BR, Allen JR, Day RN, Israelsson M, Davidson MW, Wang J (2013) A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat Methods 10(5):407–409. https://doi.org/10. 1038/nmeth.2413 Shcherbakova DM, Verkhusha VV (2013) Near-infrared fluorescent proteins for multicolor in vivo imaging. Nat Methods 10(8):751–754. https://doi.org/10.1038/nmeth.2521 Shimomura O, Johnson FH, Saiga Y (1962) Extraction, purification and properties of aequorin, a bioluminescent protein from the luminous hydromedusan, Aequorea. J Cell Comp Physiol 59: 223–239 Shu X, Royant A, Lin MZ, Aguilera TA, Lev-Ram V, Steinbach PA, Tsien RY (2009) Mammalian expression of infrared fluorescent proteins engineered from a bacterial phytochrome. Science 324(5928):804–807. https://doi.org/10.1126/science.1168683 Stepanenko OV, Verkhusha VV, Kuznetsova IM, Uversky VN, Turoverov KK (2008) Fluorescent proteins as biomarkers and biosensors: throwing color lights on molecular and cellular processes. Curr Protein Pept Sci 9(4):338–369 Telford WG, Hawley T, Subach F, Verkusha V, Hawley RJ (2012) Flow cytometry of fluorescent proteins. Methods 57(3):318–330. https://doi.org/10.1016/j.ymeth.2012.01.003 Telford WG, Shcherbakova DM, Buschke D, Hawley TS, Verkhusha VV (2015) Multiparametric flow cytometry using near-infrared fluorescent proteins engineered from bacterial phytochromes. PLoS One 10(3):0122342. https://doi.org/10.1371/journal.pone.0122342 Thomas N (2007) Fluorescent proteins and engineered cell lines. In: Lansing Taylor D, Haskins JR, Giuliano KA (eds) High content screening. Powerful approach to systems cell biology and drug discovery. Human methods in biology. Humana Press, Totowa, New Jersey, pp 165–187 Tomosugi W, Matsuda T, Tani T, Nemoto T, Kotera I, Saito K, Horikawa K, Nagai T (2009) An ultramarine fluorescent protein with increased photostability and pH insensitivity. Nat Methods 6(5):351–353. https://doi.org/10.1038/nmeth.1317 Tsien RY (1998) The green fluorescent protein. Annu Rev Biochem 67:509–544. https://doi.org/10. 1146/annurev.biochem.67.1.509
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Chapter 19
Spillover and Compensation
19.1
Spillover
The term spillover means the entrance of a signal produced by one fluorochrome into a detector intended for another fluorochrome. This phenomenon can be mitigated with a wise choice of fluorochromes, filters, mirrors, and instrumental setups. Still, it cannot be eliminated due to the fluorochromes’ spectral features (see Chap. 14). Depending on the case, the spillover can be intra-laser or inter-laser. The two phenomena can coexist; indeed, as a rule, they do. Intra-laser spillover means that the probe signal, besides the detector intended for that probe, also affects the detectors intended for other probes whose excitation depends on the same laser. Inter-laser spillover, also called crossbeam spillover, means that the probe signal, besides the detector intended for that probe, also affects detectors intended for other probes whose excitation depends on other lasers. It may also happen that the scattered emission from a laser directly reaches one or more sensors. This unexpected signal, also known as “stray light,” is generally fixed by plugging appropriate bandpass or notch filters before the PMT that receives the unwanted, inappropriate wavelengths (Mazel 2015).
19.1.1
Intra-Laser Spillover
Depending on its spectral characteristics, the intra-laser spillover generated by the emission of a fluorochrome can, in turn, split into two different types. In the first type, definable as spillover to the right, the phenomenon affects the detectors committed to wavelengths lower than the fluorochrome’s emission peak responsible for the spillover.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_19
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In the second type, definable as a spillover to the left, the phenomenon affects the detectors committed to wavelengths shorter than the fluorochrome’s emission peak responsible for the spillover.
19.1.1.1
Intra-Laser Spillover to the Right
The intra-laser spillover to the right occurs because the emission spectrum is asymmetric and tails to the right, in compliance with the Frank–Condon principle. Because of this phenomenon, in a multi-fluorescence analytical system, a fraction of a fluorochrome’s signal can reach the sensors intended for other fluorochromes excited by the same laser and featuring emission peaks located on the right (longer wavelengths, i.e., lower frequencies). An example of this can be easily shown by considering the behavior of a FITC-only stained event in a simple dual fluorescence system (FITC + PE). Although known to emit in green, FITC also emits at decreasing intensity in the region of yellow and orange (Fig. 19.1). It follows that in a system that simultaneously analyzes FITC and PE, a FITConly stained event behaves as if it were also stained with PE since the tail of its FITC emission is yellow and gets sampled by the PE detector, programmed to sample precisely in that region of the spectrum (Fig. 19.1). According to these premises, in a cytogram representing the two fluorescences’ distribution, the cluster of positive events for FITC alone is located in an inappropriate dot-plot region, which in compensated systems should be reserved for events also stained with PE (Fig. 19.2).
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Fig. 19.1 Example of intra-laser spillover to the right, i.e., affecting another detector tributary of the same laser intended to detect a lower frequency. The FITC emission (green line) displays a tail to the right of its emission peak, which the PE-intended PMT can sample through its BP575/26 filter
19.1
Spillover
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Fig. 19.2 Graphic representation of a FITC-only positive cluster in a NON-compensated FITC vs. PE bivariate system. Histograms depict the inappropriate position of the FITC-only stained subset (panel A) and the representation of the inappropriate signal generated by the spillover of FITC into the PE channel (panel B). Compare with Fig. 19.3
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Fig. 19.3 Graphic representation of a FITC-only positive cluster in a compensated FITC vs. PE bivariate system. The location measurement of the negative and positive components of the signal are the same (panel A), and no FITC spillover occurs into the PE channel (panel B). Note the compensated FITC-only positive cluster’s spreading due to photoelectron statistics (red in the picture). Compare with Fig. 19.2
In a compensated system, the FITC-only positive cluster is aligned with the negative component of the signal, or better said, the location measurement of the negative and positive components of the signal are the same (Fig. 19.3).
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19.1.1.2
Spillover and Compensation
Intra-Laser Spillover to the Left
The intra-laser spillover to the left occurs because the emission spectrum usually also displays, even though to a much lesser extent, a left tail as well. Because of this phenomenon, in a multi-fluorescence analytical system, a fraction of a fluorochrome’s signal can reach the sensors intended for other fluorochromes excited by the same laser and featuring emission peaks located on the left (shorter wavelengths, i.e., higher frequencies). An example of this can be easily shown by considering the behavior of a PE-only stained event in a simple dual fluorescence system (FITC + PE). Although known to emit in yellow-orange, PE also emits a weak signal in the green region (Figs. 19.3 and 19.4). It follows that in a system that simultaneously analyzes FITC and PE, a PE-only stained event behaves as if it were also weakly stained with FITC since a tail of its PE emission is green and gets sampled by the FITC detector, intended to sample precisely in that region of the spectrum (Fig. 19.4). According to these premises, in a cytogram representing the two fluorescences’ distribution, the cluster of positive events for PE alone is located in an inappropriate dot-plot region, which in compensated systems should be reserved for events also stained with FITC (Fig. 19.5). As a rule of thumb, whose exceptions are dealt with below, the intra-laser spillover amount appears much smaller to the left than to the right. This effect is because, as said before, a fluorochrome emission tends to be skewed to the right of the spectrum.
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Fig. 19.4 Example of intra-laser spillover to the left, i.e., affecting another detector tributary of the same laser intended to detect a higher frequency. The PE emission (green line) displays a tail to the left of its emission peak, which the FITC-intended PMT can sample through its BP530/20 filter
19.1
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Fig. 19.5 Graphic representation of a PE-only positive cluster in a NON-compensated FITC vs. PE bivariate system. Histograms depict the inappropriate position of the PE-only stained subset (panel A) and the representation of the inappropriate signal generated by the spillover of PE into the FITC channel (panel B). Compare with Fig. 19.6 Tube_001 5
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Fig. 19.6 Graphic representation of a PE-only positive cluster in a compensated FITC vs. PE bivariate system. The location measurement of the negative and positive components of the signal are the same (panel A), and no PE spillover occurs into the FITC channel (panel B). Compare with Fig. 19.5
In a compensated system, the PE-only positive cluster is aligned with the negative component of the signal, or better said, the location measurement of the negative and positive components of the signal are the same (Fig. 19.6). A particular case of intra-laser spillover to the left is due to the tandem fluorochromes, in which the non-radiative energy transfer between the Donor and the Acceptor may not take place completely. In this case, besides emitting into the
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expected emission range, tandem fluorochromes may also emit unexpectedly in the Donor range (Fig. 15.20) (for further information on this topic, see Sect. 15.5).
19.1.2
Inter-Laser Spillover
The inter-laser spillover, also called crossbeam spillover, occurs between detectors depending on different lasers because a fluorochrome is unexpectedly re-excited by another laser line intended for other fluorochromes (Fig. 15.22). Consequently, that fluorochrome generates a signal in detectors intended for other probes and depending on other lasers. This completely inappropriate signal depends on a series of factors, including the spectral features of the fluorochrome, the layout of the optical bench, and the light source’s power. This phenomenon becomes particularly evident with tandem fluorochromes, in which the acceptor is almost always susceptible to re-exciting by one of the secondary lasers, with a probability directly proportional to the number of available lasers. Consider analyzing PE-CY5-stained events in a blue-red dual laser system. PE-CY5 is excited as expected by the blue laser at 488 nm, but its CY5-acceptor fluorochrome is re-excited by the second red laser at 633 nm. It follows that an event only marked with PE-CY5 generates a completely artifactual signal in the APC channel, i.e., in a detector not dependent on the blue laser (Fig. 19.7). In addition to tandem fluorochromes, generating inter-laser spillovers is also typical of Quantum Dot molecules, which can be excited in ultraviolet, violet, and blue.
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Fig. 19.7 Graphic representation of a PE-CY5-only positive cluster (panel A) in a NON-compensated PE-CY5 vs. APC bivariate system. Cytogram in panel B depicts the inappropriate position of the PE-CY5-only stained subset due to inter-laser spillover into the APC channel
19.1
Spillover
19.1.3
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The spillover is quantified by the spillover coefficients, which define, for a given channel, the signal percentage entering from probes intended for other channels. The spillover coefficients are ascertained thanks to specific standards, consisting of cells or capture microbeads stained with the same fluorochromes used in the labeling (for further information on this topic, see Sect. 13.1.2.3). The calculation of the spillover coefficients relies, in turn, on linear regression algorithms, but robust linear regression algorithms have also been proposed (AutoSpill) (Roca et al. 2021) (for further information on this topic, see Sect. 12.4.1.3). The $SPILLOVER optional keyword, covered in FCS 3.1 version, records the spillover coefficients in a table in which: 1. Columns refer to the channel/probe from which the unwanted signals come. 2. Rows refer to the channel into which the unwanted signals enter. 3. Cells report the percentages of the unintended signal (specified in the column) to be subtracted from a given channel (specified in the row). These percentages are the spillover coefficients, and the table takes the name of the spillover matrix (Fig. 19.8). In analog instruments, compensation is set via hardware before acquiring the data, which are stored already compensated. The signal percentages subtracted between compensated channels can be memorized in the keywords of the different
Fig. 19.8 Example of a spillover matrix generated by a ten-color analysis. Note the presence of values greater than 100% in the red frame (Alexa Fluor 700 vs. PerCP-CY5.5)
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versions of the FCS format but have only informative value, and their removal does not change the representation or analysis of the data. On the contrary, knowing these percentages is pivotal in digital instruments because data are stored “raw,” i.e., uncompensated. In this case, without the spillover matrix, it might not be possible to represent or analyze the results correctly. The matrix can be exported, archived, modified, or re-imported as a separate file. These possibilities give the operator full control of the analytical conditions. To be applied to a data set, a spillover matrix must be converted into the compensation matrix, which reports the compensation coefficient, i.e., the percentage of the unwanted signal that must be subtracted from the signal related to a probe to obtain the true signal’s value of that probe. The compensation matrix is the inverse of the spillover matrix and reports the coefficients which the compensation algorithm applies to the data set (Bagwell and Adams 1993; Roederer 2002; Verwer 2002; Hahne et al. 2009; O’Neill et al. 2013; Denovo 2014). Thankfully enough, this operation is performed by software.
19.2
Compensation
Compensation is the set of procedures aimed at correcting the spillover. These procedures, as described in this section, should only be necessary for Conventional Flow Cytometry because: 1. Lifetime Cytometry is free from the problem by definition because it samples the signals independently from one another (for further information on this topic, see Sect. 22.4). 2. In Mass Cytometry (MC), the narrow band of the isotopes’ atomic weights prevents the overlapping of adjacent tag signals. 3. Spectral Flow Cytometry (SFC) distinguishes between overlapping fluorochromes’ emissions through a computational approach known as “spectral unmixing” (Roederer 2019) (for further information on this topic, see Sect. 22. 2.2). However, strictly speaking, it must be considered that: 1. Although in a somewhat limited way, spillover also affects Mass Cytometry (MC) due to variations in abundance sensitivity, unsatisfactory isotope purity, oxide formation, and other confounding signals (Leelatian et al. 2015; Keller et al. 2016; Olsen et al. 2019) (for further information on this topic, see Sect. 22. 3.2). 2. As for Spectral Flow Cytometry (SFC), also according to some non-peerreviewed authors’ opinions available on the Internet, spectral unmixing can be considered a special case of compensation (Roederer 2019; Niewold et al. 2020). In Conventional Flow Cytometry, compensation is a linear algebraic procedure (Spidlen et al. 2010), consisting of applying the compensation coefficients stored in
19.2
Compensation
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Fig. 19.9 Graphic representation of a bivariate system FITC vs. PE in conditions of achieved compensation. The compensated clusters’ location measurements (in this case, the medians) are the same between the compensated clusters P3 and P4 and their negative control P2. The scale of the cytogram is logarithmic in panel A and hyperlogarithmic in panel B. Note that (1) only hyperlogarithmic transformation (panel B) allows ascertaining the clusters’ distribution, and (2) logarithmic transformation (panel A) erroneously suggests that the system is hypo-compensated
the compensation matrix to the fluorescence data stored in the data set, i.e., subtracting from each channel the amount of signal from probes other than the probe intended for that channel (Bagwell and Adams 1993; Roederer 2002; Verwer 2002; Hahne et al. 2009; O’Neill et al. 2013; Denovo 2014). In constant conditions (choice of fluorochromes, instrument setup, optical bench layout, laser output power, et cetera), the amount of signal to be subtracted is constant over time and, as such, represents a fixed percentage of the signal managed by the “front-end” electronics of each channel. Because of the need to correct each channel against all the others, the number of corrections to be carried out is equal to the squared number of channels minus the number of channels (n2-n), which, in a 12-color system, is equivalent to 132 individual corrections. In some cases, compensation may not be needed, as in the case of the FITC spillover in the APC channel, which cannot take place; however, this does not change the rationale of the approach, as in such cases, the compensation coefficient is set to zero. Compensation is only achieved when the location measurement’s value (preferably the median) of each compensated population corresponds to the location measurement’s value of its negative control (Roederer 2001b) (Fig. 19.9). Once compensation is achieved, the instrument setup should not be changed anymore. If the power supplied to the PMT is changed, it is necessary to adjust the compensation again; however, in digital platforms equipped with avalanche photodiodes (APDs), the detector linearity can be compatible with the automatic recalculation of the compensation matrix (Beckman Coulter 2019). In all cases, it is always possible to
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modify the variables related to the intrinsic parameters, whose signals do not intervene in the spillover matrix (Roederer 1999, 2001a, 2001b, 2002). Compensation can be performed by hardware or software. Compensation by hardware, also known as on-line or analog compensation, is usually implemented in analog instruments. In contrast, compensation by software, also known as off-line or digital compensation, is generally applied computationally to the “raw” data sets produced by digital instruments.
19.2.1
Paradoxical Effects
The graphical representation of compensated data may present some paradoxical effects, which are not artifactual but are consequent to the laws governing photoelectrons statistics. These effects include: 1. An increase of the clusters’ spreading due to an increase in their statistical dispersion (spillover spreading) 2. The possible perturbation of the negative clusters due to discrepancies between the background and signal distribution It is important to realize that it is not the compensation that generates the error but the measurement; the compensation only makes it visible.
19.2.1.1
Spillover Spreading (SS)
The reasons for the spillover spreading (SS), also known as post-compensation spreading or spectral spreading, are plural, different, and intertwined. It is of note that: 1. Compensation is a combination of measurements; given that in a combination of measurements, variances behave additively, it follows that the error of a compensated population is affected by the combination of the errors of all the measurements involved in the process (Nguyen et al. 2013). 2. The photoelectron counts comply with the Poisson distribution (Prescott 1966; Coates 1972; Lachs 1974); it follows that the linear algorithms underlying the compensation procedures are likely to increase the variance of the result (Niewold et al. 2020). 3. In a flow cytometer, the signal is heteroskedastic (for further information on this topic, see Sect. 1.3); it follows that the variance of the signal increases with its intensity (Futamura et al. 2015; Gondhalekar et al. 2018). Consequently, the spillover spreading of a compensated population is greater than before compensation and tends to increase with the intensity of the signal (Niewold et al. 2020) (Fig. 19.10).
19.2
Compensation
467
Fig. 19.10 The figure shows the behavior of a Rainbow standard reported for the bivariate PE-CY7 vs. PE in a system in which all the PE spillover has been completely subtracted from the PE-CY7 channel, as it can be deduced from the seven population location measurements (red line). Note that the events’ coefficient of variation increases with the signal’s intensity; the phenomenon can be easily appreciated in hyperlogarithmic transformed data (panel A) but not in the logarithmictransformed ones (panel B) that pile up in the first channels, crowding along the ordinate axis
The extent of this phenomenon is variable and depends on a series of factors, including the extent of each spillover to be corrected, the number of measurements involved in the compensation process, and the error inherent to each measurement (counting error), which, according to the photoelectron statistics, is, in turn, directly proportional to the emission wavelength and the square root of the signal intensity (Nguyen et al. 2013). Of note, for the reasons mentioned above, the photoelectron statistics make the errors greater in signals from red- and NIR-emitting probes. Knowing the extent of spillover spreading can be very important because it allows you to optimize the choice of fluorochromes in the preparation of polychromatic analyzes; moreover, it allows the comparison between different instruments. Over time, at least two methods have been published to meet these needs. In the first case, the method consists of the creation of a spillover spreading matrix (SSM) aggregating the spillover spreading measured empirically or calculated based on the comparison of the variances between compensated and uncompensated populations; the spillover matrix constitutes the necessary tool to evaluate the behavior of spreading spillovers over time and between different instruments. (Nguyen et al. 2013). In the second case, the method consists of the creation of an index, called Spread Quantification Index (SQI), calculated as the difference of MFI between the 99th and the 55th percentile of the population measured after compensation in the channel interested by the spillover under analysis. Compared to the method based on the spillover spreading matrix, the SI method does not depend on the detectors’ sensitivity or the extent of the dynamic range.
468
19
Spillover and Compensation
The spillover spreading extent may be reduced by performing compensation with algorithms based on robust linear regression like AutoSpill (Roca et al. 2021) (for further information on this topic, see Sect. 12.4.1.3).
19.2.1.2
Perturbation of the Negative Clusters Distribution
Compensation procedures can produce other effects, which become especially evident in polychromatic analyzes when the experimental conditions exasperate the issues connected to the photoelectron statistics. It may happen that, when compensation is reached, the bivariate image of the distribution of the negative assumes unusual patterns, taking, for example, a “banana” shape or other unexpected appearance (Fig. 19.11). This phenomenon is because the distribution of the (auto)fluorescence in the negative cluster is not necessarily related to the distribution of fluorescence in the positive cluster; in compliance with photoelectron statistics, the very low signal values in the first decade give dominance to the measurement error in general and electrical noise in particular. The events represented in the “banana” cluster are negative for the parameters represented in the cytogram but are positive for a series of other non-represented parameters; it follows that the compensation of the spillover related to the non-represented parameters disturbs the negative distribution, making its graphic representation different from the expected “cloud” model (Roederer 2016).
[C] CD19 A700 / CD3 PC5.5
103
CD3
A
CD16
B
102
CD16 ECD
102
CD3 PC5.5
[C] CD3 PC5.5 / CD16 ECD
103
101 5
101
100
0 −5
CD19 −2
0
2
101
CD19 A700
102
CD3
0 103
−2
0
101
102
103
CD3 PC5.5
Fig. 19.11 Representation of some cytograms related to a peripheral blood polychromatic analysis. When compensation is reached, the negatives cluster related to the cytogram CD19 vs. CD3 (violet cluster in panel A) assumes an unusual needle-like shape (red arrows). The phenomenon depends on the fact that the negative elements for CD3 and CD19 mostly consist of the intensely positive neutrophils for CD16 PE-TR (panel B), whose spillover has to be compensated against that of all the other fluorochromes present in the staining, disturbing the distribution of the negative cluster for the parameters represented in panel A
19.2
Compensation
19.2.2
469
Negative Values and their Management
Compensation is a subtractive procedure and, as a consequence, can generate negative values, whose probability increases with the number of parameters compensated together (Bagwell 2005). Far from being a rare occurrence, the appearance of negative values after compensation is quite frequent, and it has even been said that “if you don’t get any compensated measurement values below zero, then your compensation is probably wrong.” (Roederer 2010). These negative values result from the correction algorithms and do not reflect the magnitude of the phenomenon, as it is evident that a “negative” fluorescence cannot occur. Nevertheless, they pose a problem if logarithmically amplified in analog machines, as voltages close to zero confuse the circuitry, or logarithmically transformed in digital machines, as the log transformation cannot apply to the negative values registered in the data set. In both cases, low or negative values crowd in the first histogram channels. This artifact leads to the loss of information regarding the signal distribution and the appearance of an artifactual pseudo-bimodality in the negative data distribution (Fig. 19.12). Consequently, the graphical representations of the distribution of parameters explored under experimental conditions, which are particularly “demanding” from the photoelectron statistics point of view, can assume configurations that only hyperlogarithmic or similar transformations allow interpreting correctly (Fig. 19.13) (for further information on this topic, see Sect. 10.2).
Fig. 19.12 Graphical representation of data related to a population of negative events transformed in a logarithmic manner. Note the overcrowding (piling up) of a high number of counts in the first channels (region P3) encompassing more than half of the analyzed events and giving the histogram a completely artifactual bimodal distribution
470
19
Spillover and Compensation
Fig. 19.13 Graphic representation of a compensated APC vs. AF700 bivariate analysis. The logarithmic transform (panel 1) causes the overcrowding of the negative events in the first channels of the cytogram and makes the interpretation of the data problematic. The hyperlogarithmic transform (panel 2) allows the unequivocal identification of the distribution of the represented clusters
19.2.3
Compensation by Hardware
Compensation by hardware, also known as analog or on-line compensation, occurs by correcting the spillover between detectors through circuits’ intervention. For simplicity, consider a two-channel analog model, A and B. A double-action is necessary to compensate between the two channels: first, a circuit interpolated in the output line of the B detector subtracts from it a percentage of the output signal sampled from the A detector, and second, another circuit interpolated in the output line of the A detector subtracts from it a percentage of the output signal sampled from the B detector. Since it is necessary to compensate each channel with every other channel, the number of an instrument’s circuits devoted to this task should be equal to n2 – n, where n is the number of channels. The circuits most often used in hardware compensation are differential amplifiers performing addition or subtraction between signals, and the amount of the signal to be subtracted is regulated through a potentiometer. It is of note that: 1. The circuits performing the task increase the total electrical noise of the system. 2. As already stated, given that compensation circuits stand before the amplifiers, any negative or close to zero value confuses the logarithmic amplifiers. 3. Since the compensation occurs between analog signals, i.e., pulses, any shape difference between pulses is likely to increase the total error.
19.2
Compensation
471
4. Since the signals must be synchronous for compensation to occur, dedicated circuits must synchronize the signals from non-collinear lasers, also increasing the electrical noise. 5. Since compensation values must be set in advance, the operator must choose each appropriate value by a “trial and error procedure” before the analytical run starts, which can be practically unfeasible in polychromatic cytometry.
19.2.4
Compensation by Software
Compensation by software, also known as off-line compensation, is usually performed computationally on uncompensated linearly amplified data acquired by digital instruments but can also be applied to data acquired by analog platforms. In this last case, some limitations occur (see below).
19.2.4.1
In Digital Instruments
This approach does not require logarithmic amplifiers or dedicated compensation circuits (both missing in the digital architecture) and reduces the system’s general electric noise. The compensation in digital instruments consists of calculating the inverse of the spillover matrix and applying the result (compensation matrix) to the stored data set. The algorithms of the analytical software perform the whole procedure. Since digital compensation concerns numbers and not signals, it follows that it can be applied or modified regardless of the pulses’ shape and time, and even with spillover coefficients higher than 100%, which could not be applied with analog circuitry. The operator acquiring a sample sees the clusters on the screen as if they were compensated; this effect is due to the DAQs, which emulate the previously chosen spillover values to allow the operator to check the analysis and sorting procedures in realtime.
19.2.4.2
In Analog Instruments
Since off-line compensation consists of applying an algorithm to a data set, nothing prevents it in principle from applying it to a data set obtained from analog instrumentations. In this case, off-line compensation consists of: 1. Performing the linear back-transformation of logarithmically transformed data 2. Performing their compensation by computing 3. Performing again the logarithmic transformation of the resulting computationally compensated values
472
19
Spillover and Compensation
However, this approach is not optimal since the behavior of logarithmic amplifiers, far from the theoretical one, introduces a considerable inaccuracy error; additionally, any negative value possibly resulting from the corrections can not be managed by the logarithmic transformation. In any case, data must not have been previously compensated because this approach can increase their compensation but cannot revert them to their original uncompensated status; in other words, this approach can correct off-line an under-compensated acquisition but can only worsen an over-compensated acquisition. A digital shortcut to compensation was implemented some years ago in an analog machine in which the following processes would occur (Shapiro 2001): 1. 2. 3. 4. 5.
Sampling the logarithmically amplified signals directly from the integrators Performing their digitization with a dedicated 16-bit ADC Performing their linear back-transformation Performing their compensation by computing Performing again the logarithmic transformation of the computationally compensated values
Apart from the noise due to dedicated circuits, this approach leaves unfixed the inaccuracy flawing the logarithmic amplifiers because, in the analog architecture, integrators stand after the amplifiers.
References Bagwell CB (2005) Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64A(1):34–42 Bagwell CB, Adams EG (1993) Fluorescence spectral overlap compensation for any number of flow cytometry parameters. Ann N Y Acad Sci 677:167–184 Beckman Coulter (2019) Gain independent compensation. White paper. available at https://media. beckman.com/-/media/pdf-assets/flyers/flow-cytometer-cytoflex-gain-independent-compensa tion-flyer.pdf?la¼en-us&hash¼99767EDA4C94CDD473FF36A6A0EC0FB51461E956. Last accessed 4 October 2021 Coates PB (1972) Photomultiplier noise statistics. J Phys Appl Phys 5(5):915–930 Denovo (2014) FCS express version 4 RUO edition users manual. White paper. Available at https:// fcsexpressdownloads.s3.amazonaws.com/manual/FCSExpress4Manual.pdf. Last accessed 3 February 2022 Futamura K, Sekino M, Hata A, Ikebuchi R, Nakanishi Y, Egawa G, Kabashima K, Watanabe T, Furuki M, Tomura M (2015) Novel full-spectral flow cytometry with multiple spectrallyadjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement. Cytometry A 87(9):830–842. https://doi.org/10.1002/cyto.a.22725 Gondhalekar C, Rajwa B, Patsekin V, Ragheb K, Sturgis J, Robinson JP (2018) Alternatives to current flow cytometry data analysis for clinical and research studies. Methods 134-135:113– 129. https://doi.org/10.1016/j.ymeth.2017.12.009 Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R (2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-10-106
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Keller BC, Presti RM, Byers DE, Atkinson JJ (2016) Significant interference in mass cytometry from medicinal iodine in human lung. Am J Respir Cell Mol Biol 55(1):150–151. https://doi. org/10.1165/rcmb.2015-0403LE Lachs G (1974) The statistics for the detection of light by nonideal photomultipliers. IEEE J Quant Electr 10(8):590–596 Leelatian N, Diggins KE, Irish JM (2015) Characterizing phenotypes and signaling networks of single human cells by mass cytometry. Methods Mol Biol 1346:99–113. https://doi.org/10. 1007/978-1-4939-2987-0_8 Mazel S (2015) Analyzer with 445nm laser. Purdue cytometry discussion list. Available at https:// lists.purdue.edu/pipermail/cytometry/2015-December/049514.html. Last accessed 2 January 2019 Nguyen R, Perfetto S, Mahnke YD, Chattopadhyay P, Roederer M (2013) Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A 83(3):306–315. https://doi.org/10.1002/cyto.a.22251 Niewold P, Ashhurst TM, Smith AL, King NJC (2020) Evaluating spectral cytometry for immune profiling in viral disease. Cytometry A 97(11):1165–1179. https://doi.org/10.1002/cyto.a.24211 Olsen LR, Leipold MD, Pedersen CB, Maecker HT (2019) The anatomy of single cell mass cytometry data. Cytometry A 95(2):156–172. https://doi.org/10.1002/cyto.a.23621 O'Neill K, Aghaeepour N, Spidlen J, Brinkman R (2013) Flow cytometry bioinformatics. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1003365 Prescott JR (1966) A statistical model for photomultiplier single-electron statistics. Nucl Instrum Meth 39(1):173–179 Roca CP, Burton OT, Gergelits V, Prezzemolo T, Whyte CE, Halpert R, Kreft Ł, Collier J, Botzki A, Spidlen J, Humblet-Baron S, Liston A (2021) AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data. Nat Commun. https://doi.org/ 10.1038/s41467-021-23126-8 Roederer M (1999) Compensation in flow cytometry. Curr Protoc Cytom 22:1.14.11–11.14.19 Roederer M (2001a) Compensation is not dependent on signal intensity or on number of parameters. Cytometry 46(6):357 Roederer M (2001b) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45(3):194–205 Roederer M (2002) Compensation in flow cytometry. Curr Protoc Cytom Chapter 1: Unit 1 14. https://doi.org/10.1002/0471142956.cy0114s22 Roederer M (2010) Mean/geometric mean/fold calculations. Purdue cytometry discussion list. Available at https://lists.purdue.edu/pipermail/cytometry/2010-September/039818.html. Last accessed 21 October 2018 Roederer M (2016) Distributions of autofluorescence after compensation: be panglossian, fret not. Cytometry A 89(4):398–402. https://doi.org/10.1002/cyto.a.22820 Roederer M (2019) Shiny spectral baubles. Purdue cytometry discussion list. Available at https:// lists.purdue.edu/pipermail/cytometry/2019-May/054076.html. Last accessed 11 April 2021 Shapiro HM (2001) Digital flow electronics? Purdue cytometry discussion list. Available at https:// lists.purdue.edu/pipermail/cytometry/2001-May/019595.html. Last accessed 8 January 2021 Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Gorombey P, Hyun B, Hubbard M, Lange S, Lefebvre R, Leif R, Novo D, Ostruszka L, Treister A, Wood J, Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR (2010) Data file standard for flow cytometry, version FCS 3.1. Cytometry A 77A(1):97–100 Verwer B (2002) BD FACSDiVa options. White paper - Becton Dickinson. Available at http:// www.bdbiosciences.com/ds/is/others/23-6579.pdf. Last accessed on 8/1/2021
Chapter 20
Artifacts
20.1
Escapees
Escapees are events that bind monoclonal antibodies specific to lymphocytes but present physical parameters greater than lymphocytes (Fig. 20.1). In cases positive for escapees, a back-gating performed on events positive for a lymphocyte-specific antigen shows a subpopulation of events with physical parameters that “escape” from the area of the cytogram typical for lymphocytes (Prince et al. 1994; Gratama et al. 1997). The formation of escapees seems particularly favored by IgG1 isotype Mabs, FITC conjugated Mabs, and lysing solutions based on ammonium chloride; the escapees can be counteracted with the use of lysing solutions containing formaldehyde or paraformaldehyde (Prince et al. 1994). Dual-labeling and sorting experiments have shown that escapees consist of aggregates of lymphocytes complexed with myeloid cells or thrombocytes (Prince et al. 1994; Gratama et al. 1997). The formation of escapees is mostly due to the CD32 IgG Fc receptor expressed by neutrophils, monocytes, and platelets, which bind to the tail of the Mab legitimately bound to lymphocytes (Gratama et al. 1997). The CD32 antigen is polymorphic, and the ability to form escapee appears to be predominantly related to the allotype characterized by the presence of a 131 arginine (Gratama et al. 1997). In some cases, anticoagulation with EDTA can also facilitate the formation of aggregates (Juneja et al. 1992). Escapee formation can be inhibited in many ways, including: 1. Blocking the receptor responsible for the artifact using a non-conjugated antiCD32 Mab 2. Pre-incubating the sample with mouse serum (Gratama et al. 1997) 3. Pre-incubating the sample with a commercially available Fc blocking solution (for further information on this topic, see Sect. 20.3.2.2) The addition of formaldehyde to the lysing solution, or the adoption of commercial solutions containing formaldehyde, can also curb the phenomenon (Prince et al. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_20
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Artifacts
Fig. 20.1 Example of escapee (in black in the picture)
Fig. 20.2 Escapees generally consist of aggregates made of lymphocytes complexed with other cells or platelets. The analysis of the FSC pulse reveals their nature as aggregates
1994). In cases where escapees result from the presence of cellular aggregates, their exclusion from the analysis is possible through the combined analysis of the height (H) and area (A) of the FSC pulse (Fig. 20.2) (for further information on this topic, see Sect. 8.2.3.1).
20.3
Other Artifacts
20.2
477
Debris
The debris consists of events with very low physical parameters. In an FSC vs. SSC cytogram, debris is placed on the immediate left of the lymphocytes and can partially overlap them depending on their size. Debris generally consists of red blood cell fragments (Stass et al. 1979; Bessman 1988; Hammerstrom 1992) but can also include platelet aggregates, ghosts of red blood cells (Mellors et al. 1995), and foreign bodies occurring in the sheath or sample suspension. The presence of an abnormal amount of debris can be due (1) to causes inherent to the sample, such as a particular fragility of the analyzed cells (Ito et al. 2010) or an incomplete RBC lysis, frequent in microcytemic patients (Booth and Mead 1983), (2) to preanalytical causes, such as an increased lysis time, and (3) to causes related to analytical conditions, such as the presence of foreign bodies in the sheath fluid, such as the dust shed by some latex gloves usually worn during the manipulation of the biological samples. When the debris is present, it may be useful to centrifuge the sample using a density gradient. Alternatively, using permeant DNA dyes like LDS-751 makes it possible to tell the nucleated elements from background noise (Ito et al. 2010) (Fig. 16.8). An important cause of debris is a too vigorous resuspension or centrifugation at a too high speed. These conditions are particularly relevant in samples from leukemic patients, where blasts feature higher fragility than normal cells.
20.3 20.3.1
Other Artifacts Due to Anticoagulants
EDTA is the anticoagulant of choice for immunophenotyping, even if good results can also be obtained with other coagulants commonly used in a clinical setting. However, it should be remembered (1) that EDTA exerts its coagulating action thanks to its Calcium chelating activity and (2) that some conformational epitopes are kept in their configuration thanks to the action of divalent cations. Therefore, a Calcium-chelating anticoagulant may modify the steric configuration of these epitopes, and Mabs specific for these epitopes may not recognize the antigen or recognize it with lower affinity. In this regard, it has been shown that Mab D12, which recognizes a conformational epitope on CD11b, has difficulty recognizing the antigen in EDTA anticoagulated samples (Leino and Sorvajarvi 1992). It is interesting to note that there is a report in the literature on the expression of myeloid antigens in a case of B-CLPDs in which the expression of CD11b was demonstrated on neoplastic cells in bone marrow (anticoagulated with heparin), but not in peripheral blood (anticoagulated with EDTA) (Emery and Cleveland 1995).
478
20.3.2
20
Artifacts
Due to Fluorochromes
Besides the generic issues due to steric hindrance or energy transfer between two excessively close fluorescent molecules (Chapple et al. 1988, 1990; Waggoner 1990; Matos 2020; Khenine et al. 2021), it is well known that some fluorochromes can cause recurring and characteristic artifacts. This behavior can occur for many reasons, including: 1. The influence of the fluorochrome on the binding capacity of the antibody, which causes impaired or missing antigen recognition 2. The interaction of the fluorochrome with the Fc receptor, which turns out in a non-specific binding with cells negative for the antigen being tested 3. The interaction of the fluorochrome with an antigen receptor that cross-reacts with it 4. Interaction of the fluorochrome with an antigen other than those considered in the previous points 5. Unknown mechanisms, which can be attributed to the combination of a particular clone with a particular fluorochrome; this behavior is very rare and often goes unexplained
20.3.2.1
Influence on Mab Binding Capacity
FITC can modify some antibody behavior, probably increasing their total negative charge (Gratama et al. 1998). This phenomenon is known to affect some Mabs, like (1) QBEnd10 and 8G12 Mabs, which unsatisfactorily recognize the CD34 antigen when conjugated with FITC (Siena et al. 1991; Ortuno et al. 1997; Gratama et al. 1998), and (2) the T3 clone specific for the CD3 epsilon chain, which recognizes all the T lymphocytes when conjugated to PE, but only those with TCR alpha/beta when conjugated with FITC (Mullersman et al. 1991).
20.3.2.2
Interaction with Fc Receptors
Another frequent cause of an unexpected signal is the non-specific binding between the Fc receptors on the cells under analysis and the Fc fragment of the Mab exploited in the staining. This non-specific binding also plays a fundamental role in escapee formation and satellitism between leukemic blasts and platelets; in this last case, the blasts can appear positive for platelet-associated antigens, mistakenly suggesting a diagnosis of megakaryoblastic leukemia (AML-M7) (Betz et al. 1992). These artifacts occur especially with tandem fluorochromes and Mabs of IgG2a and IgG3 isotype (for more information on tandem behavior, see Sect. 15.5). In a human model, they can be prevented or mitigated by a series of procedures, including:
20.3
Other Artifacts
479
1. Preincubation with diluted mouse serum 2. Preincubation with a protein solution (AB plasma, bovine serum albumin (BSA), fetal calf serum (FCS)) 3. Preincubation with purified human IgG (Andersen et al. 2016; Smith and Chattopadhyay 2016) 4. Preincubation with commercially available blocking reagents (Kristensen et al. 2021) Commercially available blocking reagents include Fc Block rat anti-mouse CD16/CD32 (marketed by Becton Dickinson), Anti-Mouse CD16/CD32 (marketed by eBioscience), Human Fc Receptor Binding Inhibitor (marketed by eBioscience), True-Stain Monocyte Blocker (marketed by BioLegend), TruStain fcX™ (antimouse CD16/32) (marketed by Biolegend), Human TruStain FcX™ (Fc Receptor Blocking Solution) (marketed by Biolegend), and Oligo-Block (phosphorothioateoligodeoxynucleotides) (marketed by Sigma-Aldrich), to name a few. In a murine model, possible interferences are counteracted by preincubation of the sample with blocking antibodies, i.e., i) the rat Mab 2.4G2, which recognizes an epitope common to FcγRIIb and FcγRIII, and interacts with FcγRI via its Fc tail, and ii) the hamster Mab 9E9, which recognizes FcγRIV (Biburger et al. 2015).
20.3.2.3
Interaction with Antigen Receptors
Interaction with antigen receptors has been reported for some fluorochromes, among which: 1. APC, which is recognized by the 0.02% of murine B lymphocytes (Pape et al. 2011); moreover, a case of human B-CLPD is known whose surface immunoglobulins bound specifically the fluorochrome (Smith et al. 2021). 2. PE, which is recognized by the 0.1% of murine B lymphocytes0.02–0.04 (Pape et al. 2011) and by the 0.02–0.04% of murine and human T lymphocytes with gamma/delta receptor (Zeng et al. 2012); a case of human B-CLPD is known whose surface immunoglobulins bound specifically the fluorochrome (Tabary et al. 2008). 20.3.2.4
Interaction with Antigens Other Than those Considered in the Previous Points
Interaction with other antigens has been rarely reported for other fluorochromes, among which: 1. The tandem PE-CY5, which, in addition to binding the Fc gamma receptor, binds the murine cells positive for CD72c as well; CD72c is an allelic form of CD72 expressed by the murine B lymphocytes of NOD, SJL, and MRL/lpr, but not by those of the C57BL/6, DBA/2, SWR, 159/Sv, and BALB/c strains (Doucet et al. 2001).
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2. The tandem PE-CY5.5, which binds the murine (but not human) cells positive for CD205, a multi-lectin receptor of dendritic cells (Park et al. 2012); the effect would also be present at a lesser extent in PerCP-Cy5.5 and APC-Cy5.5, but not in PE, PE-Cy5, Cy5, FITC, or AF488. Of note, in both cases, the artifact was due to the whole tandem molecule and not its separate moieties.
20.3.2.5
Unknown Mechanisms
Unexplained behavior has been anecdotally reported for AF700, which was susceptible to non-specifically bind the Mab anti-PD-L1 clone 29E.2A3 (Hughes et al. 2020); moreover, an AF700-conjugated anti-NKG2D Mab clone 149,810 was referred to non-specifically bind the human granulocytes activated with Dexamethasone (Chitadze et al. 2021). Finally, a case of human B-CLPD is also known whose neoplastic cells bound specifically the fluorochrome PerCP-CY5.5 but not PerCP alone. No evidence has been reported about the molecular structure responsible for the phenomenon, even though BCR seems the most probable culprit (Shi et al. 2022).
20.3.3
Due to Serum Factors
Artifacts exist due to the presence of serum factors binding together monoclonal antibodies conjugated with different fluorochromes, thus simulating the presence of double-labeled cells or inadequate compensation (Ekong et al. 1993; Nicholson et al. 1994; Bukowska-Straková et al. 2006). It is also possible that serum factors induce doublets formation, which can be recognized thanks to the FSC pulse analysis. These serum factors may consist of: 1. Complement components such as C1q (Wood and Levin 2006). 2. Anti-mouse antibodies, present in subjects treated with murine antibodies for therapeutic purposes (Fritschi and Arnold 1996). 3. Anti-PEG antibodies, present in subjects for known or unknown reasons (Hong et al. 2020); they react with the pegylated fluorophore-conjugated antibodies (BioLegend 2021), and since the RNA-based vaccines can contain polyethylene glycol (PEG) (Garvey and Nasser 2021), their detection is likely to increase in the future. 4. Non-well-defined factors present in a minority of subjects (Bukowska-Straková et al. 2006). These artifacts are commonly solved by prewashing the sample or by its preincubation with mouse serum (Frengen et al. 1994; Nicholson et al. 1994; Fritschi and Arnold 1996); in case anti-PEG antibodies are the cause, the addition of PEG to the staining buffer can prevent the phenomenon (BioLegend 2021). Nonetheless, in
20.3
Other Artifacts
481
the case reported by Bukowska-Straková (Bukowska-Straková et al. 2006), the artifact was not prevented by washing and was reproduced in previously unaffected samples by transferring the serum of affected patients.
20.3.4
Anecdotal Reports
Isolated phenomena reported in the literature include: 1. The presence of non-specific binding between murine IgM isotypes and galactocerebrosides (Hofstetter et al. 1985). 2. The presence of an unexpected high autofluorescence of all the analyzed cells, elicited in the blue and detected in the FITC channel; this phenomenon has been described in patients who previously undergone fluoroangiography (Kaur et al. 2002; Burgisser et al. 2007). 3. The presence of an unexpected high autofluorescence of all the analyzed cells, elicited in the violet and found at approximately 450 nm (BP438/24) in the Pacific Blue channel; this phenomenon has been described in samples previously stored in Eppendorf-type polypropylene tubes sterilized with gamma rays and has been hypothesized as due to the absorption of an unidentified fluorescent molecule generated by the irradiation of polypropylene (Roederer 2013). 4. The presence of sporadic, unexpected signals (flares) at the end of the analytical run, due to the excitation at 405 nm and found at approximately 450 nm (BP438/ 24) in the Pacific Blue channel (Hogarth 2017) (Fig. 20.3); this artifact has been
Fig. 20.3 The appearance of unexpected signals in a tributary channel of the violet laser. The signals, which appear toward the end of the sample acquisition in the PB channel (panel A), are presumably due to the sample’s contamination with a fluorescent molecule (Calcofluor White) present as a bleach in the fabric used to wipe the sampler needle. Courtesy of Phil Hogarth
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20
Artifacts
attributed to wiping the sample tube with Calcofluor White treated fabric (Galbraith 2017). 5. The appearance of weak positive subsets in definitely negative samples; the phenomenon has been described in some digital platforms and is possibly attributable to grounding problems. In some cases, it could be fixed by unplugging and re-plugging the cable connecting the PMT to its front-end circuitry (Bigos 2013).
References Andersen MN, Al-Karradi SN, Kragstrup TW, Hokland M (2016) Elimination of erroneous results in flow cytometry caused by antibody binding to fc receptors on human monocytes and macrophages. Cytometry A 89(11):1001–1009. https://doi.org/10.1002/cyto.a.22995 Bessman JD (1988) Red blood cell fragmentation. Improved detection and identification of causes. Am J Clin Pathol 90(3):268–273 Betz SA, Foucar K, Head DR, Chen IM, Willman CL (1992) False-positive flow cytometric platelet glycoprotein IIb/IIIa expression in myeloid leukemias secondary to platelet adherence to blasts. Blood 79(9):2399–2403 Biburger M, Trenkwald I, Nimmerjahn F (2015) Three blocks are not enough--blocking of the murine IgG receptor FcgammaRIV is crucial for proper characterization of cells by FACS analysis. Eur J Immunol 45(9):2694–2697. https://doi.org/10.1002/eji.201545463 Bigos M (2013) False positives fixed by a laying on of hands. Purdue cytometry discussion list. Available at https://lists.purdue.edu/pipermail/cytometry/2013-February/044832.html. Last accessed 6 January 2021 BioLegend (2021) BioLegend communication regarding unusual fluorescent signal in a subset of donor samples. White paper - technical note. Available at https://www.biolegend.com/Files/ Images/BioLegend/faqs/Fluorescence_Signal_Observations_06102021.pdf. Last accessed 27 June 2021 Booth F, Mead SV (1983) Resistance to lysis of erythrocytes containing haemoglobin C--detected in a differential white cell counting system. J Clin Pathol 36(7):816–818 Bukowska-Straková K, Baran J, Gawlicka M, Kowalczyk D (2006) A false expression of CD8 antigens on CD4+ T cells in a routine flow cytometry analysis. Folia Histochem Cytobiol 44(3): 179–183 Burgisser P, Vaudaux J, Bart PA (2007) Severe interference between retinal angiography and automated four-color flow cytometry analysis of blood mononuclear cells. Cytometry A 71(8): 632–636 Chapple MR, Johnson GD, Davidson RS (1988) Fluorescence quenching of fluorescein by R-phycoerythrin. A pitfall in dual fluorescence analysis. J Immunol Methods 111(2):209–217 Chapple MR, Johnson GD, Davidson RS (1990) Fluorescence quenching; a practical problem in flow cytometry. J Microsc 159(Pt 3):245–253 Chitadze G, Lettau M, Peters C, Luecke S, Flüh C, Quabius ES, Synowitz M, Held-Feindt J, Kabelitz D (2021) Erroneous expression of NKG2D on granulocytes detected by phycoerythrinconjugated clone 149810 antibody. Cytometry B Clin Cytom. https://doi.org/10.1002/cyto.b. 22001 Doucet M, Soussi N, Crain-Denoyelle AM, Gendron MC, Sanchez P (2001) R-phycoerythrincyanine 5 tandem discerns CD72 polymorphism. Immunogenetics 53(4):307–314 Ekong T, Gompels M, Clark C, Parkin J, Pinching A (1993) Double-staining artefact observed in certain individuals during dual-colour immunophenotyping of lymphocytes by flow cytometry. Cytometry 14(6):679–684
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Mellors I, McArdle P, Bell D (1995) Pseudoleucocytosis due to incomplete erythrocyte lysis. Clin Lab Haematol 17(4):347–348 Mullersman JE, White G, Tung KS (1991) Differential staining of human alpha/beta and gamma/ delta T cells by the fluorescein conjugate of an anti-CD3 monoclonal antibody. Clin Exp Immunol 84(2):324–328 Nicholson JK, Rao PE, Calvelli T, Stetler-Stevenson M, Browning SW, Yeung L, Marti GE (1994) Artifactual staining of monoclonal antibodies in two-color combinations is due to an immunoglobulin in the serum and plasma. Cytometry 18(3):140–146 Ortuno F, Ferrer F, Lozano ML, Heras I, Moraleda JM, Vicente V (1997) Differences in phycoerythrin- or fluorescein-isothiocyanate conjugated 8G12 on CD34+ cell evaluation. Haematologica 82(3):334–335 Pape KA, Taylor JJ, Maul RW, Gearhart PJ, Jenkins MK (2011) Different B cell populations mediate early and late memory during an endogenous immune response. Science 331(6021): 1203–1207. https://doi.org/10.1126/science.1201730 Park CG, Rodriguez A, Steinman RM (2012) PE-Cy5.5 conjugates bind to the cells expressing mouse DEC205/CD205. J Immunol Methods 384(1–2):184–190. https://doi.org/10.1016/j.jim. 2012.07.011 Prince HE, York J, Kuttner DK (1994) Reduction of escapee formation in flow cytometric analysis of lymphocyte subsets. J Immunol Methods 177(1–2):165–173 Roederer M (2013) Warning: increased autofluorescence when using certain “bullet” tubes. Purdue cytometry discussion list. Available at https://lists.purdue.edu/pipermail/cytometry/2013February/044826.html. Last accessed 4 January 2019 Shi M, Timm MM, Howard MT, Jevremovic D, Yuan J, Greipp PT, Peterson JF, Roh DJ, Horna P, Olteanu H (2022) Spurious CD34 expression in B-cell lymphoma due to non-specific binding to PerCP-Cy5.5 fluorochrome conjugates: a rare phenomenon and a diagnostic pitfall. Cytometry B Clin Cytom. https://doi.org/10.1002/cyto.b.22079 Siena S, Bregni M, Brando B, Belli N, Lansdorp PM, Bonadonna G, Gianni AM (1991) Flow cytometry to estimate circulating hematopoietic progenitors for autologous transplantation: comparative analysis of different CD34 monoclonal antibodies. Haematologica 76(4):330–333 Smith PJ, Chattopadhyay PK (2016) Re-visiting fc-receptor blocking maneuvers in man. Cytometry A 89(11):975–977. https://doi.org/10.1002/cyto.a.22998 Smith W, Rodriguez A, Patry CA, Schammel C, Knight J (2021) CLL/SLL specifically binding to the APC fluorochrome: a previously undescribed phenomenon. Cytometry B Clin Cytom. https://doi.org/10.1002/cyto.b.22006 Stass S, Holloway M, Peterson V, Creegan W, Gallivan M, Schumacher H (1979) Cytoplasmic fragments causing spurious platelet counts in the leukemic phase of poorly differentiated lymphocytic lymphoma. Am J Clin Pathol 71(1):125–128 Tabary T, Staal-Viliare A, Rault JP, Didion J, Latger-Cannard V, Reveil B, Cohen JH, Rio Y (2008) Unusual direct phycoerythrin labeling of B-cells from a splenic marginal zone lymphoma. Cytometry Part B: Clin Cytom 74B(3):189–193 Waggoner AS (1990) Fluorescent probes for cytometry. In: Melamed MR, Lindmo T, Mendelsohn ML (eds) Flow cytometry and sorting, 2nd edn. Wiley-Liss, New York, pp 209–226 Wood BL, Levin GR (2006) Interactions between mouse IgG2 antibodies are common and mediated by plasma C1q. Cytometry B Clin Cytom 70(5):321–328 Zeng X, Wei YL, Huang J, Newell EW, Yu H, Kidd BA, Kuhns MS, Waters RW, Davis MM, Weaver CT, Chien YH (2012) γδ T cells recognize a microbial encoded B cell antigen to initiate a rapid antigen-specific interleukin-17 response. Immunity 37(3):524–534. https://doi.org/10. 1016/j.immuni.2012.06.011
Chapter 21
Cell Sorting
From a general point of view, the separation of events of interest from a heterogeneous population can be carried out through a series of different methods (Davies 2007), including (1) cell filtration, (2) cell affinity methods (Baran et al. 1982; Greenberg et al. 1985), (3) fractionation (Weiskirchen and Gressner 2005), and (4) centrifugal elutriation (Merrill 1998). Another way to sort cells is by staining them with antibodies conjugated with ferromagnetic beads, followed by exposition to a magnetic field (Miltenyi et al. 1990). Nevertheless, this chapter only deals with the separation procedures carried out by flow cytometers explicitly designed for this purpose. According to the implemented technologies, these cytometers divide into fluid switching, electrostatic, and pneumatic sorters. A separate word should also be spent for the so-called “sorters on-chip.” They encompass a host of microfluidic devices relying on an astonishing variety of technological solutions; some of them are commercially available but, in most cases, have to be docked to a larger platform (see below). A general agreement exists to call cell sorter this type of instrument, but it is important to stress that cells are not the only objects which could undergo sorting procedures; consequently, in this chapter, the term “cell” will be used as a synonym of “event.”
21.1
Fluid Switching Sorters
As can be deduced from their name, the fluid switching sorters are instruments in which the volume of fluid containing the cell of interest is temporarily diverted from the mainstream and collected separately. The identification of the cells of interest occurs at the interrogation point, and their separation relies upon a transient modification of the fluidic path triggered by the system (Orfao and Ruiz-Arguelles 1996). In the past, some models have been marketed, including a model relying on a lateral © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_21
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catcher moving to the center of the flow to capture the cell of interest (Becton Dickinson 2003), and others relying on the action of a piezoelectric driven valve (Partec PAS-III, and Dako Galaxy PRO) (Picot et al. 2012; Chapman 2000). The fluid switching sorters commercially available today are microfluidic cartridge-based sorters and include: 1. MACSQuant® Tyto® Sorter, marketed by Miltenyi (https://miltenyibiotec.com), in which a magnetic-driven sort valve diverts the elements of interest (Beuk et al. 2018). 2. On-chip Sort, marketed by On-chip Biotechnologies (https://www.on-chipbio. com/), in which a fluidic pulse diverts the cells of interest (Watanabe et al. 2014). 3. Wolf® Cell sorter, marketed by Biosystems (https://il-biosystems.com/de-en/). The fluid switching technology is also exploited in some experimental sorters based on Imaging Flow Cytometry (IFC) (Nitta et al. 2018).
21.1.1
Pros and Cons of Fluid Switching Sorters
Cell sorting occurs in a closed system, without droplet or aerosol generation, which is highly desirable when running infective samples. Moreover, the sorting procedure is far gentler than in electrostatic sorters, which stress the cells with shocks either mechanic (exit from the nozzle under pressure) or electrostatic (drop charging). Finally, the underlying technology is far simpler (and cheaper) than in electrostatic sorters, and in the past, sorting modules could be easily implemented in some pre-existing analytical-only instruments. Nevertheless, a series of drawbacks affect the fluid switching sorters (Orfao and Ruiz-Arguelles 1996). The sorting is slower than in electrostatic sorters and only can affect one type of cell at a time, whereas stream-in air sorters can sort up to six different subsets at once, depending on the sorter type. Moreover, fluid switching sorters can not base sorting decisions on the adjacent drops’ features (sorting envelope). Finally, the sorted sample was highly diluted in some models and often had to be concentrated further.
21.2
Electrostatic Sorters
Almost all the sorters operating worldwide are electrostatic cell sorters. Currently, many manufacturers are marketing these instruments, including: 1. Beckman Coulter, which sells a choice of conventional instruments (MoFlo XDP, MoFlo Astrios, and CytoFlex SRT). 2. Becton Dickinson, which sells a choice of conventional instruments (FACSMelody™, FACSAria™ III, FACSAria™ Fusion, FACSymphony™ S6).
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3. Bio-Rad, which sells a choice of conventional instruments (S3e cell sorter in its different versions). 4. Cytek, which sells a spectral sorter (Aurora CS). 5. Sony, which sells a choice of microfluidic-based conventional sorters (MA900, FX500, and SH800S). 6. Thermo Fisher Scientific, which sells a spectral sorter (Bigfoot). Unlike the fluidic sorters, in the electrostatic sorters, also called “in-air” or “stream-in-air” sorters, the cell suspension is injected downwards into the air by a nozzle at a typical speed of some meters per second to reach a catcher connected to the drain tank. The following Bernoulli’s eq. V ¼ ð2P=ρÞ1=2 determines the speed of the stream, where P is the pressure and ρ is the density of the liquid (Peters et al. 1985). The nozzle is usually a conic or flat device with an orifice displaying a 50 to 150 microns diameter. As a rule of thumb, the nozzle diameter should be roughly three times that of the event to be sorted; larger gauges up to 400 microns are available for particular tasks (Chapman 2000). In electrostatic sorters, the interaction between the event and the light radiation occurs immediately after the nozzle’s exit, thanks to the laser’s direct focus on the liquid stream, which behaves like a cylindrical lens (Lindmo et al. 1990). In some more recent in-air sorters (FACSMelody™, FACSAria™ III, FACSAria™ Fusion, FACSymphony™ S6), the laser spot is focused not on the liquid jet but on the conical nozzle’s final portion, which is transparent and behaves as a cuvette. This technical solution makes the interrogation occur inside the cuvette and allows sorting without waiving the advantages of the in-cuvette analysis. A manufacturer is also known that, instead of the nozzle, exploits a disposable microfluidic chip behaving as a cuvette (Sony). When sorting is needed, the nozzle holder begins vibrating at a given frequency thanks to a piezoelectric device and breaks the fluid’s jet into a series of drops that travel straight towards the waste aspirator, also known as the catcher. The point at which the drops detach from the stream takes the name of “breakoff point,” and the time between the analysis point and breakoff point takes the name of drop delay or time delay, not to be confused with the time delay existing between the interrogation points managed by parallel lasers. Before the breakoff point, a fluid neck exists between the future drops; it follows that after the breakoff point, small droplets called satellites occur between the drops to merge with them later. The drop-driving frequency, the speed of the stream, which depends on the sheath pressure, and the nozzle diameter are three variables of the following equation. f ¼ v=4:5d,
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where f is the frequency in kHz, v is the speed of the jet in mm/sec, and d is the diameter of the nozzle in micrometers (mμ) (Pinkel and Stovel 1985). The driving amplitude and the phase between the charging pulse and droplet generation are other variables affecting drop formation. A stroboscopic light enables drop visualization and allows the operator to check the drops’ presence, shape, and position. It is noteworthy that the technology at the basis of electrostatic sorting derives from electrostatically deflected inkjet printers (Sweet 1965). The system electrically charges the drops to be sorted, which contain the cells of interest; it follows that, while passing through an electrostatic field provided by two charged plates, the charged drops (negative or positive according to the sorting criteria) deviate from the central flow axis deflecting to the left or right towards a collecting tube. It is essential that each charging pulse is always synchronized, i.e., in phase with the drop generation. The system can also charge the drops immediately after and before the one expected to contain the cell of interest; the group of charged cells can also take the name of “drop envelope.” The capability of charging more than a drop at once is of the utmost importance in optimizing the sorting procedures. Cell sorters require a high flow rate to separate the most significant number of events in the shortest possible time and keep stable the fragmentation of the stream (Kachel et al. 1990). Unfortunately, the high transit speed through the interrogation point generates a series of undesired effects, including (1) the difficulty of measuring the crossing time (W of the pulse), (2) a reduced lighting time of the event, and (3) a reduced collection time of the light signal (Zucker et al. 1990). Furthermore, other conditions are not favorable to the signal’s collection. These conditions depend on the model and can include (1) the presence of the Raman scattering increasing the background, (2) the incident radiation’s diffraction from the interface between the stream’s surface and the air, (3) the refraction affecting the events’ light coming out of the stream before entering the lens of the optical bench, and (4) the possible presence of obscuration bars (for further information on this topic, see Sect. 6.2). The distance between lens and interrogation point, kept quite long to avoid the lens contamination with accidental aerosols generated by partial nozzle occlusions, worsens the matter since it hinders lenses with high numerical aperture, which could mitigate the problem. These conditions explain why some in-air cell sorters are sometimes inefficient in analyzing the less brilliant fluorochromes and require more powerful light sources than those used in analyzers.
21.2.1
Sorting Procedures
Sorting procedures generally share several common steps, even though some details vary depending on the sorter model (Fig. 21.1). After defining the gates containing the events to sort, the operator switches on the drop-driving. After jet stabilization, the operator:
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Electrostatic Sorters
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Fig. 21.1 Schematic representation of an electrostatic cell sorter with drop driving and plate charging switched off (panel A), with drop driving switched on (panel B), and with drop driving and plate charging both switched on (panel C)
1. Measures the space between the interrogation spot and the droplet breakoff point; this space is called “drop delay” and is critical since its value dictates the system when to charge the drop 2. Enters the drop delay value to the system 3. Powers the two metallic plates located at the sides of the stream, creating an electrostatic field the stream is forced to pass through 4. Decides the modality of sorting (purity, recovery, et cetera) 5. Feeds the sample to be sorted and switches on the sorting procedure If an event fulfills predetermined criteria (i.e., inclusion in the sorting gate), the system charges the droplet containing it just before its breakoff point. However, it is essential to realize that the system does not know its actual position but only presumes it based on the drop delay; because of the fluctuations in the stream stability, there is a slight possibility that the event is trapped not in the expected drop but in that immediately preceding or following. It follows that it can sometimes be helpful to sort more than a drop together, usually three of them at once. The system can modulate the charge intensity, which affects the drops path through the electrostatic field; it follows that modern sorters can sort more than two different cells subset at once, in a number ranging from four to six, depending on the model. The theoretical sorting rate SR is given by the formula.
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SR ¼ DDF Z where DDF is the drop driving frequency, and Z is the percentage of the drops containing a cell.
21.2.2
Sorting Modalities
The sorting procedures can be tweaked to favor recovery, i.e., collecting the maximum possible number of events of interest or purity, i.e., the presence in the sorted sample of the minimum number of events other than those of interest. It is also possible to activate a procedure to ensure that each sorted drop contains one and only one event of interest; this last mode is used in particular situations such as cloning or sorting in multiwell plates (Davies 2007). All the sorting modes are affected by the probability of cell appearance at the breakoff point. Given that the events proceed randomly, their arrival probability complies with Poisson’s law (Lindmo and Fundingsrud 1981), but sometimes other distributions are possible due to interactions between the cells and the tip of the sample injection tube (Lindmo and Fundingsrud 1981). Come as it may, two or more different cells can arrive at the breakoff point very close to each other. It follows that: 1. A single drop can contain more than an event, one of which can be of no interest. 2. An unwanted event can dwell in an extremity of a drop close to the drop containing the event of interest. This occurrence depends on the drop and flow rates and affects both (1) purity, since an unwanted event can be sorted together with one of interest, and (2) recovery, since the system aborts very close events because of the dead time. A relatively high frequency of aborts is typical of analog sorters, but they can also occur in digital sorters, albeit to a much lower extent. The random arrival of the events can confound the system, which presumes the event position only based on the drop delay; moreover, to make the matter worse, the events can dwell at any point inside the drop, thus increasing the general uncertainty. Digital cell sorters rely upon two tricks to overcome this ambiguity. The first trick considers the adjacent drops’ status in making sort decisions. These cells are usually three and take the name of “sorting envelope”; they can divide into (1) the leading drop (drop before the interrogated one), (2) the interrogated drop (primarily eliciting the sort decision), and (3) the trailing, or lagging, drop (drop after the interrogated one). The second trick resolves each stream segment intended to form a drop in an array of slices, called a “sorting mask“or simply “mask,” which allows considering the cell’s position in the drop. The slices relevant to the sorting procedures are those at the mask extremities, as the sorter may consider the presence of cells in those slices to make sorting decisions (Fig. 21.2).
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Electrostatic Sorters
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Fig. 21.2 Panel A displays the schematic representation of a sorting mask, i.e., the resolution in virtual slices of a stream segment bound to generate a drop. The segment is sliced into 32 slots, 8 of which reside facing the next segment (leading) and 8 facing the past segment (lagging). Panel B shows the correspondence between stream segments and drops; after the drop driver activation, the stream segments change into drops, and the events flowing along the stream dwell in the drops in positions known to the system. Panel C displays the three drops making up the sorting envelope (red frame) and the events inside them. In the shown example, the system will sort the drops 2, 3, and 4 (recovery mode), or 3 and 4 (purity mode), or only drop 3 (single-cell mode) depending on the sorting mode chosen
In-depth treatment of sorting procedures and mask tweaking is beyond the scope of this book, but further information is available in some papers to which we refer (Verwer 2002; Arnold and Lannigan 2011; Becton Dickinson 2012). Nevertheless, the following sections will contain some more details for the sake of completeness.
21.2.2.1
Purity Mode
By purity P, we mean the ratio between all the sorted events of interest S and all the sorted events T(P ¼ S/T ). The purity mode aims to reduce the percentage of unwanted events in the sorted fraction at the cost of not sorting all the possible events of interest. In purity mode, only one drop is sorted at a time, and the following conditions must also be satisfied (Davies 2007) (Fig. 21.3): 1. The sorted drop must contain one or more events of interest, but no unwanted events must be present in the sorted drop. 2. The drop containing the event of interest is not sorted if an unwanted event is in the sorting masks of the adjacent drops.
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Fig. 21.3 Purity mode. The figure displays the sorting choices exploited in purity mode. The sorted drops (in the frame) only must contain events of interest. The presence of an unwanted event in the same drop or the sorting masks of an adjacent drop aborts the sorting
The operator can select the number of the masks’ slices to consider to make the procedure more stringent. Increasing the number of slices increases the purity but still risks reducing the number of sorted events.
21.2.2.2
Recovery Mode
By recovery R, we mean the ratio between the sorted events of interest S and all the events of interest satisfying the sort decision E (R ¼ S/E), whereas, by yield Y, we mean the ratio between the sorted events of interest S and all the events of interest N (Y ¼ S/N ) (Davies 2007). According to the Poisson distribution, the theoretical yield is given by the formula. Y ¼ exp :ðð1sÞrT Þ , where s is the fraction to sort, r the event rate, and T the drop envelope time. The recovery mode aims to maximize the recovery of the events of interest at the cost of separating unwanted events as well. In recovery mode, the following conditions must be satisfied (Davies 2007) (Fig. 21.4): 1. The sorted drop can contain more than one event of interest. 2. The sorted drops can also contain an unwanted event besides the event of interest. 3. If the event of interest dwells in one of the drop masks, the system also sorts the adjacent drop.
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Electrostatic Sorters
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Fig. 21.4 Recovery mode. The figure displays the sorting choices exploited in recovery mode. The sorted drops (in the frame) may contain more than one event of interest and are sorted even if an unwanted event is present besides the event of interest. The presence of an event of interest in the sorting mask triggers the sorting of the adjacent drop
Fig. 21.5 Single-cell mode. The figure displays the sorting choices exploited in single-cell mode. The sorted drops (in the frame) may contain only one event of interest; the drops containing the event of interest are not sorted if another event of interest is present in the sorting mask of an adjacent drop
21.2.2.3
Single-Cell Mode
In some procedures like cloning or sorting in multiwell plates, the sorted drops must contain one and only one event of interest. In single-cell mode, the following conditions must be satisfied (Davies 2007) (Fig. 21.5):
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1. The sorted drop must contain only one event of interest; unwanted events are not allowed. 2. If the event of interest dwells in one of the drop masks, then the system aborts the sorting of the drop and that of the other mask-adjacent drop.
21.2.3
High-Pressure Systems
All other things being equal, the only way to increase the number of cells collected per unit of time is to increase the number of drops produced per unit of time. However, since the number of drops is related to the drop drive frequency, which is related to the flow velocity, it follows that the only way to increase the final harvest is to increase the pressure delivered to the hydraulic system (Ibrahim and van den Engh 2003). The application to the sheath of pressure regimens up to 60 psi causes a very high transit speed at the breakoff point (up to 100 Km/h) and allows the production of up to 100.000 drops per second (Arnold and Lannigan 2011). This technological solution must ensure a high enough differential pressure on the sample to inject it into the core. Unfortunately, it solicits the sorted events mechanically and, in some cases, can jeopardize cell viability (Suh et al. 2005). Moreover, in high-speed systems, the electronics must be fast enough to process the high number of signals generated in the time unit.
21.3
Pneumatic Sorters
Particular situations require the analysis and the sorting of large events (20–1500 microns) such as adipocytes, spheroids, or even embryos or organisms. The pressures typically implemented in the conventional sorters are likely to damage these events making their separation impossible (Eisenstein 2006). For this type of analysis, specialized platforms are available characterized by low pressures fluidics (around 4 or 5 psi), in which a gentle puff of air pushes the event of interest out of the flow (Picot et al. 2012; Union Biometrica 2015). These systems are also equipped with special optics (Watson et al. 2009; Delgado-Ramos et al. 2014; HernandoRodriguez et al. 2018) because they are affected by a series of issues in the signal’s analysis since the events display dimensions that exceed the interrogation point several times. A low-pressure pneumatic sorter is marketed by Union Biometrika (https://www.unionbio.com/) under the name COPAS™ (Complex Object Parametric Analyzer and Sorter) (Union Biometrica 2015).
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Chapter 22
Non-Conventional Flow Cytometry
Other flow techniques than Conventional Flow Cytometry have appeared on the cytometry stage in recent years, primarily differing on the exploited signals. These “non-conventional” flow techniques rely on different technological solutions, some of which are scarcely represented in laboratories worldwide or not even commercially available, while others are thriving and even rapidly expanding. The most relevant “non-conventional” flow techniques encompass (1) Imaging Flow Cytometry (IFC), (2) Spectral Flow Cytometry (SFC), (3) Mass Cytometry (MC), (4) Lifetime Cytometry, and (5) Raman Cytometry. All of them are covered in this chapter for completeness’ sake; a final section is devoted to the so-called microfluidic devices or “labs-on-chips,” representing a turbulent evolutive branch of Flow Cytometry.
22.1
Imaging Flow Cytometry
Like in Conventional Flow Cytometry, in Imaging Flow Cytometry (IFC), the events in suspension are moved in a fluid to intercept one or more light lines. The result, or one of the results of this interaction, is creating a digitized image associated with each analyzed event. Depending on the models, the implemented technology can rely on CCDs, high-speed brightfield cameras, or PMTs. The instruments commercially available are very different from each other, both in performance and structural layout, and encompass: 1. Two CCD-based Imaging Flow Cytometers (FlowSight® and Image Stream®X MKII, marketed by DiaSorin), which can detect up to two brightfield images, two darkfield images, and eight fluorescence-related images, depending on the model. 2. A conventional analyzer (Attune CytPix Flow Cytometer, marketed by Thermo Fisher Scientific), which besides pulse analysis, can also capture high-resolution brightfield images thanks to a high-speed brightfield camera. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_22
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3. A module based on fluorescence imaging using radiofrequency-tagged emission (FIRE, also known as BD CellView™ Image Technology, marketed by Becton Dickinson), which can transform a PMT-based conventional cell sorter (e.g., the BD FACSMelody™ system) into an image-enabled cell sorter (ICS) capable of detecting axial extinction, FSC, SSC, and four fluorescence signals.
22.1.1
CCD-Based Imaging Flow Cytometry
In the CCD-based Imaging Flow Cytometry (IFC), the image of each analyzed event is detected by a CCD sensor and resolved in a pixel matrix (George et al. 2004; Basiji 2005; Basiji et al. 2007). Every pixel intensity value is digitalized and stored in an array of data associated with each event (Fig. 22.1). These actions allow the achievement of four primary goals, i.e., (1) to retrieve and directly observe the image of the event and the topographical distribution of the signals, (2) to evaluate the event shape, calculating all the parameters made available by the image analysis algorithms, like shape, perimeter, and elongatedness, (3) to evaluate the texture of the image, understood as the signal modulation in the event’s context, and (4) to calculate for each event the total signal strength, obtained by summing all the pixels
Fig. 22.1 The figure shows the result of a flow image analysis of a sample of peripheral blood carried out for physical parameters (darkfield intensity vs. brightfield area), DNA content (Propidium iodide), and oxidative activity (Dihydrorhodamine). The dot plot representation (panel A) allows the distinction between the main cell types and the oxidative activity evidenced by the transformation of the non-fluorescent Dihydrorhodamine into the fluorescent Rhodamine 123 (panel B). On the right, the gallery (panel C) demonstrates the image features of the five selected elements, including the bright field image, the rhodamine 123 emission, the shape of the nucleus, and the combination of the two last parameters
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Imaging Flow Cytometry
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intensities. It is hardly necessary to observe that the total signal strength is equivalent to the peak value obtained by Conventional Flow Cytometry; it follows that the results of an IMC analysis can also be stored in FCS format, as far as it pertains to the total signal strength values.
22.1.1.1
Optical Bench in CCD Based Imaging Flow Cytometry
Even though the details can vary depending on the models, in CCD-based IFC, the optical bench spectrally resolves each event’s signals through a dichroic filter stack. It leads them to different sections of the CCD spectrally selected by an array of bandpass filters allowing the separate transmission of adjacent sections of the spectrum. In this way, an event generates several spectrally different images, each filtered by a different band-pass interpolated before the detector; the integration of these images allows the topographical recognition of all the signals in the event’s context. In 12-channel commercial instruments, the twofold optical bench creates at once the following images, i.e., 1. Two brightfield images (one for each bench) elicited by an array of colored LED lights. 2. Two darkfield images (one for each bench), conceptually equivalent to the side scatter, elicited by a 785 nm laser and collected through a 740–800 nm band-pass. 3. Up to eight fluorescence-related images (four for each bench), which can be increased to ten if you exploit the 785 nm line to elicit fluorescence instead of scattering signals. 22.1.1.2
Compensation Issues in CCD-Based Imaging Flow Cytometry
In CCD-based IFC, compensation procedures are not exceedingly far from Conventional Flow Cytometry; still, significant differences exist. Given that this technique records each event image as a pixel matrix, the compensation procedure applies the compensation matrix to each pixel of each image related to each analyzed event (Ortyn et al. 2006).
22.1.1.3
Format Issues in CCD-Based Imaging Flow Cytometry
The result of an analysis performed by CCD-based IFC is usually saved by the acquisition applicative (InSpire) in a file in rif (raw image file) format, containing the raw data (pixel intensity data and uncorrected image data) and the instrument set-up data. The rif file is subsequently analyzed by an image analysis program (Ideas) which in turn creates daf (data analysis file) and cif (compensated image file) format files. Daf files contain the calculated feature values, the graphs, and the statistics, while cif files contain the compensated data.
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The result of an analysis performed by Imaging Flow Cytometry (IFC) can also be saved as an FCS format file; this file is unsuitable for being re-analyzed by the Ideas software, having lost the data needed for image analysis.
22.1.1.4
Pros and Cons in CCD-Based Imaging Flow Cytometry
One of the direct advantages of taking events’ digitalized images is the capability of performing their morphometric analysis, i.e., the evaluation of parameters other than photometric, such as cell area, perimeter, aspect ratio, texture, spot counts, cell centroid, gradient intensity, spatial frequency, and others (George et al. 2004). As extensively said before, the second great asset of CCD-based IFC is the possibility of seeing the morphology of the analyzed events and the co-localization of the probes adopted in the analysis. In other words, and as an example, it is possible to decide if a target is associated with the membrane, the nucleus, or other structures dwelling in the cytoplasm. This capability assumes the utmost importance in studying a series of biological phenomena, including apoptosis, autophagy, cytotoxicity, phagocytosis, trogocytosis, and NETosis. The main drawback of CCD-based IFC is its low speed, which does not usually exceed 1000 events/sec (Mikami et al. 2020). Another drawback is its exquisitely analytical nature, which does not allow separating the events of interest.
22.1.2
PMT-Based Imaging Flow Cytometry
A PMT-based image-enabled cell sorter (ICS) is a cell sorter displaying a conventional architecture in which, besides the other conventional laser lines, an optical module based on radiofrequency-tagged emission (FIRE) is implemented (Schraivogel et al. 2022). The FIRE module, also known as BD CellView™ Image Technology, consists of a blue laser whose emission is modulated by a couple of radiofrequency-driven acousto-optic deflectors (AOD) into an array of roughly 100 beamlets, each carrying a sinusoidal modulation at a different frequency (Diebold et al. 2013). The array of spots interacts with the event traversing the interrogation point, and each parameter under analysis gives out a pulse whose shape displays a series of modulations depending on the radio-frequency modulations applied by the AODs to the beamlets (Schraivogel et al. 2022). Each pulse, transduced by a conventional PMT, is sampled at a very high frequency (180 MHz), digitized by a 16 bit ADC, and pipelined into an array of digital signal processors that compute its A, H, W, and time-to-peak values, and manage the Fourier transform-based extraction of the morphology-related details. For each event, all these data are compensated and assembled in an information bulk called “event packet” (EP) and fed to further FPGA responsible for the sorting decisions, based both on traditional pulse components and on extracted image
22.2
Spectral Flow Cytometry (SFC)
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parameters (i.e., eccentricity, max intensity, size, radial moment, the correlation between two imaging channels, and delta center of mass) (Schraivogel et al. 2022). The short time (not exceeding 400 μsec) required by the whole operation allows for sorting procedures and makes possible an event rate of close to 15.000 events per second (Schraivogel et al. 2022).
22.1.2.1
Compensation and Format Issues in PMT-Based Imaging Flow Cytometry
In PMT-based IFC, the compensation procedures rely on traditional linear unmixing (Schraivogel et al. 2022). The final output of the analytical run consists of (1) image data encompassing a multi-plane floating-point TIFF file, (2) a conventional data set in FCS 3.1 standard, and (3) an event table including scatter, intensity, and image-derived parameter values. Non-FCS format values can be re-analyzed outside the native program thanks to plug-ins made on purpose (Schraivogel et al. 2022).
22.2
Spectral Flow Cytometry (SFC)
Spectral Flow Cytometry (SFC) has long been the subject of research and development (Wade et al. 1979; Gauci et al. 1996; Robinson 2004; Leavesley et al. 2005; Robinson et al. 2004, 2005a, 2005b, 2007; Grégori et al. 2012; Nolan and Condello 2013). Still, its full realization has been long hampered by a series of issues related to the inadequacy of the technologies needed to realize it. The term “Spectral Flow Cytometry” defines a variant of Flow Cytometry that relies on a new way of detecting fluorescent light signals since it does not detect the emission peak of each fluorochrome like in Conventional Flow Cytometry, but its total emission over the whole visible range of the spectrum. It follows that in Spectral Flow Cytometry, it is possible to distinguish between the signals of fluorochromes sharing the same emission peak, provided their emissions differ in another region of the spectrum. Spectral Flow Cytometers and Sorters are currently marketed by Cytek Biosciences, Sony, and Thermo Fisher Scientific. A module for spectrally enhanced cytometric analysis has been recently made available by Becton Dickinson for their instrument FACSymphony™ A5 SE (for further information on this topic, see Sect. 6.4.4.3).
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Optical Bench in Spectral Flow Cytometry
In a spectral cytometer, the light signal generated in the interrogation point is resolved along the entire spectrum by a device consisting of an array of prisms, gratings, or coarse WDM (CWDM) demultiplexers (Cytek Biosciences 2019) (for further information on this topic, see Sect. 6.3.7). The resulting signals are detected by a single multichannel sensor, which may consist of a CCD camera, a multianode (multichannel) PMT, or an array of APDs (Grégori et al. 2012; Nolan and Condello 2013; Feher et al. 2016; Cytek Biosciences 2019) (for further information on this topic, see Sect. 7.1.4). More complex models rely on multiple lasers; in that case, lasers are separated in space, and the signals evoked by each rely on a separate optical pathway, encompassing a dispersion optic and a multichannel sensor. The number of channels in the multichannel sensors depends on the laser eliciting the signals and decreases as the laser emission shifts towards red; it follows that the total number of channels available in a given instrument varies according to the characteristics of the optical bench.
22.2.2
Spectral Unmixing in Spectral Flow Cytometry
In Spectral Flow Cytometry (SF), spillover management has been considered not conceptually different from Conventional Flow Cytometry (Roederer 2019). Still, compensation procedures differ in that the inappropriate signals are not subtracted from the detected peak of the signal to be compensated. Instead, the signal relative to each parameter is computationally reconstructed through a procedure known as “spectral unmixing” by subtracting the inappropriate signals channel by channel for the entire spectral range. From a computational point of view, this procedure can rely on different mathematical models, including least-squares methods (LSM), multivariate curve resolution (MCR), principal component analysis (PCA), and probabilistic spectrum analysis (PSA), or a combination of them. The choice of the model can depend on different factors, among which are the availability of negative controls (i.e., the knowledge of the single components’ spectra) and the linearity of the light dispersion (Nolan and Condello 2013; Futamura et al. 2015; Feher et al. 2016; Niewold et al. 2020). One of the most successful computational models is the least-squares method (LSM), which carries out a linear unmixing procedure based on the assumption that the different detectors’ noise level displays the same statistical behavior (Nolan and Condello 2013). It is noteworthy that spectral unmixing can also subtract an autofluorescence signal, provided that such a control is available (Nolan and Condello 2013; Novo 2022). It follows that spectral Flow Cytometry can brilliantly resolve a dim positivity in the context of a highly autofluorescent population, as often occurs in the analysis of cell lines or special samples like the bronchoalveolar lavage fluid (BALF).
22.2
Spectral Flow Cytometry (SFC)
22.2.3
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Format Issues in Spectral Flow Cytometry
In Conventional Flow Cytometry, the quantitative expression of a single parameter is stored in the data set as the peak value detected by the detector devoted to its measurement. Still, in Spectral Flow Cytometry (SFC), all the available detectors measure the parameter expression along the whole spectrum. It follows that the quantitative expression of a single parameter could be stored as a string of different quantitative values, each of them related to the channel managed by the detector responsible for that definite spectral span (Watson et al. 2008). From a theoretical point of view, this approach is still compliant with the FCS format but generates files of cumbersome and unmanageable dimensions and breaks the continuity of the measured spectrum, particularly when the measurement is performed by a CCD (Nolan and Condello 2013). Since raw data produced by spectral cytometers can also be stored as spectra in ASCI format (Nolan et al. 2013), a solution has been devised in which a zipped archive encompasses a file in ASCII format containing the streamed data together with a related FCS format-compliant file (Nolan et al. 2012). In reality, the files produced by commercial spectral cytometers are stored in proprietary formats specifically designed by the manufacturers, which can still be translated into the FCS format and read by third-party applications. By computational means, it is possible to apply virtual spectral filters to the spectra generated by each event to analyze the signals relating to defined wavelength ranges. This approach recreates the situation existing in conventional cytometers, in which the spectral ranges are defined by band-pass, and allows the performance of traditional ratio and compensation procedures (Nolan et al. 2013). Moreover, this approach is fully compliant with the FCS 3 format since it reproduces the “one fluorochrome-one detector” mantra of Conventional Flow Cytometry and assigns only one value (the peak value) to each event; nevertheless, even though technically possible, it nullifies all the advantages connected to the spectral measurement. Still, switching between this approach and the full spectral one allows a comparison between conventional compensation and spectral unmixing algorithms on the same data set (Grégori et al. 2012). In this regard, it is interesting to note that spectral unmixing and compensation procedures produce substantially similar results (Niewold et al. 2020).
22.2.4
PROs and CONs in Spectral Flow Cytometry
Spectral Flow Cytometry (SFC) can simultaneously manage fluorochromes with the same emission peak, provided their emissions differ in other spectrum regions. This capability allows the simultaneous use of more fluorochromes than conventional flow cytometry techniques; actually, the highest number of extrinsic parameters
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Fig. 22.2 Increase over time of total parameters detected in Flow and Mass Cytometry according to the literature at the publication of this book. Of course, the data are provisional and likely to increase shortly
measured by SFC so far is 43 (Sahir et al. 2020), against 28 measured by FCM (Liechti and Roederer 2019a, 2019b) (Fig. 22.2). Another advantage of Spectral Flow Cytometry (SFC) is getting rid of the autofluorescence signal by computational spectral unmixing techniques (Novo 2022). Finally, it should be noted that the circuitry at the basis of the spectral flow cytometry allows fast multiparametric spectral-based cell sorting. Regarding the technique’s sensitivity, there is a non-peer-reviewed debate on the Internet which objects that, compared to Conventional Flow Cytometry, the greater number of detectors available in Spectral Flow Cytometry would be susceptible to decreasing the amount of light detected by each detector with an increase of the noise and a consequent increase in the signal to noise ratio. This theoretical claim has been refuted because the total Poisson noise would be the same in both cases (Novo 2019). Moreover, it has also been observed that the total spectral range of the optical filter system used in Spectral Flow Cytometry is larger than in Conventional Cytometry; consequently, it should detect more photons under the same experimental conditions, allowing “more accurate measurements and hence better data” (Novo 2022).
22.3
Mass Cytometry (MC)
Mass Cytometry (MC) differs from fluorescence-based Flow Cytometry because of the nature of the detected signal. Instead of fluorochromes, in Mass Cytometry, Mabs are conjugated with metal isotopes whose mass is detected by time-of-flight
22.3
Mass Cytometry (MC)
505
inductively coupled plasma mass spectrometry (TOF-ICPMS) (Bandura et al. 2009; Ornatsky et al. 2010; Bjornson et al. 2013). The detected isotopes, absent in biological materials, include the Lanthanides Cerium (140Ce, exploited for the verification of the plasma state (Ornatsky et al. 2010)), Praseodymium (141Pr), Neodymium (142Nd, 143Nd, 144Nd, 145Nd, 146Nd, 148Nd, 150Nd), Samarium (147Sm, 149Sm, 152Sm, 154Sm), Europium (151Eu, 153Eu), Gadolinium (155Gd, 156Gd, 158Gd, 160Gd), Terbium (159 Tb), Dysprosium (161Dy, 162Dy, 163Dy, 164Dy), Holmium (165Ho), Erbium (166Er, 167Er, 168Er, 170Er), Thulium (169Tm), Ytterbium (171Yb, 172Yb, 173Yb, 174Yb, 176Yb), and Lutetium (175Lu) (Dusoswa et al. 2019). Other metals exploited in tagging are Palladium (102Pd, 104Pd, 105Pd, 106Pd, 108Pd, 110Pd, mainly used in barcoding procedures), Platinum (194Pt, mainly used as Cisplatin in viability assessment), Rhodium (mainly used coupled with an intercalating molecule for DNA detection), and Iridium (191Ir and 193Ir, also mainly used coupled with an intercalating molecule for DNA detection/dead cells identification). Finally, further exploited metals are Indium (113In and 115In) (Grenier et al. 2020) and Yttrium (Han et al. 2018), to which Bismuth, Cadmium, Hafnium, Iodine, Niobium, Osmium, Ruthenium, Silver, Tantalum, Tellurium, Tin, and Zirconium have been anecdotally added (Han et al. 2018; Dusoswa et al. 2019; Devine et al. 2021). Another significant difference with Conventional Flow Cytometry is the absence of a hydraulic component. Instead of being transported by the flow, the suspension containing the tagged events is nebulized, prewarmed, and injected into an inductively coupled plasma (ICP) torch that ionizes each event. The resulting ion clouds are (1) sampled by a three-aperture plasma-vacuum interface, (2) deflected by a dc quadrupole which leaves photons and neutral elements undeflected, and (3) led to the time-of-flight (TOF) analyzer by a radiofrequency quadrupole ion guide which gets rid of low-mass ions deriving from organic moieties (Bandura et al. 2009; Ornatsky et al. 2010; Bendall et al. 2011; Olsen et al. 2019). Every 13 μs, a push-out plate pushes the ions into a TOF analyzer, determining the ions’ mass according to their time of flight (Lee and Rahman 2019). At the end of the procedure, the values of each event’s spectra are digitalized and recorded. The very narrow band of the signals detected by Mass Cytometry allows reaching two very important intertwined goals, i.e., a very high number of parameters to be analyzed simultaneously and the virtual absence of spillover between different signals. Cytometers based on mass detection are currently produced and commercialized by Fluidigm (https://www.fluidigm.com/).
22.3.1
Format Issues in Mass Cytometry
In Mass Cytometry, data are recorded in an integrated mass data (IMD) format file, which contains, for every channel, the ion counts related to each ion cloud shoved every 13 microseconds towards the TOF. An IMD format file is usually very heavy
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(even some tenths of Gigabytes), and it is usually converted into an FCS file (Newell et al. 2012; Lee and Rahman 2019; Olsen et al. 2019), which contains an integrated value for every event instead, and can be read by the common analysis programs. Note that in the transition from IMD to FCS format, only are translated the clouds whose signals comply with the requisites preset by the operator, while the other clouds are not. It follows that the content of the translated FCS file depends on the criteria set by the operator, and it is good practice, when possible, not to rid of the IMD file, which can be converted again according to different criteria.
22.3.2
PROs and CONs in Mass Cytometry
22.3.2.1
Pros
The main asset of Mass Cytometry is its multiparametricity. From a theoretical point of view, up to 135 parameters can be simultaneously detected (Han et al. 2017); nevertheless, the relative scarcity of available metal tags prevents reaching theoric proficiency (Schulz et al. 2017). At the time of publication of this book, the highest number of signals simultaneously analyzed with this method was 48 (Rodriguez et al. 2020); in this regard, it should be remembered that the largest number of parameters currently analyzed with Fluorescence Cytometry techniques (Spectral Flow Cytometry) was 45, encompassing two scatter and 43 fluorescence signals related to antigen expression (Sahir et al. 2020) (Fig. 22.2). The second most important advantage is the elimination of spillover. Since the isotopes’ atomic weights are unequivocally separated, no compensation should be needed in Mass Cytometry. Nevertheless, despite these premises, spillover issues are not entirely eliminated since some crosstalk can still exist due to variations in abundance sensitivity, unsatisfactory isotope purity, oxide formation adding to the tag the 16 mass units of the Oxygen atom (16O) (Olsen et al. 2019), and other confounding signals (Leelatian et al. 2015; Lu et al. 2015; Keller et al. 2016; Rahman et al. 2018). Computational procedures have been published to overcome this problem (Chevrier et al. 2018; Miao et al. 2020). Finally, the absence of autofluorescence drastically reduces the instrumental background.
22.3.2.2
Cons
One of the main limitations of Mass Cytometry is the missed detection of the light scattering signals. In Mass Cytometry, the cell is recognized as such based on three main criteria, i.e.: 1. The presence of DNA, detected thanks to intercalating molecules conjugated with Platinum and Iridium (Fienberg et al. 2012) or with Iridium and Rhodium (Ornatsky et al. 2010); the use of Iridium and Rhodium allows the recognition
22.4
Lifetime Cytometry
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of nucleated events (Iridium +) and the distinction between living (Iridium + Rhodium -) and dead cells (Iridium + Rhodium +). 2. The value of a parameter called “cell length” or “event length,” which does not correlate with the size of the event but is related to the ion cloud size and the overall metal ion content (Stern et al. 2017). 3. The value of a parameter called “lower convolution threshold,” i.e., the total signal intensity across all acquisition channels, which should exceed a preset threshold (Olsen et al. 2019). In Conventional Flow Cytometry, the term “TOF” (time of flight) corresponds to the width of the pulse and is somehow proportional to the size of the event, but in Mass Cytometry, it does not provide any information on the physical parameters since it defines the time that the ions take to travel from the torch to the detector (Tanner et al. 2013). Anyway, there are some attempts to detect parameters co-varying with cell size; these consist of the detection of wheat germ agglutinin (WGA) or Osmium tetroxide (OsO4) (Stern et al. 2017). WGA staining intensity correlates well with both cell size and FSC and, in Mass Cytometry, can be detected through staining the cells with AF488-conjugated WGA followed by incubation with a Holmium-conjugated anti AF488 antibody. Osmium tetroxide, widely used in electron microscopy sample fixation, binds proportionally to plasma membrane lipids and can be detected as such because of its atomic weight. Another attempt was made to correlate SSC with the expression of VAMP-7 in hematological samples (Tsai et al. 2020). Other significant disadvantages of Mass Cytometry are: 1. The definitive unfeasibility of sorting procedures due to the ionization of the events. 2. The inability to detect cellular function usually detected by fluorescent probes, except for anecdotal reports concerning (1) a proliferation test allowed by the use of an anti-FITC CFSE-crossreacting 172Yb-conjugated Mab (Good et al. 2019) and (2) alternative methods of viability determination based on Hafnium, Niobium, Platinum, and Zirconium (Devine et al. 2021). 3. The inability to perform the autofluorescence-based analysis of phytoplankton. 4. The inability to detect Fluorescent Proteins, except for a couple of anecdotal reports tracing GFP thanks to a GFP-specific 139La- (Ajami et al. 2018) or 169Tm-conjugated Mab (Chen et al. 2020). 5. The low speed of analysis, which currently stands at around 1000 events per second (Bendall et al. 2012) against 30.000 and more attainable in Conventional Fluorescence Cytometry.
22.4
Lifetime Cytometry
The Fluorescence Lifetime (τ) is the time elapsing between the excitation of a fluorochrome and the emission of fluorescence, i.e., the time an excited fluorochrome spends before coming back to the fundamental state. The Fluorescence
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Lifetime generally varies from 100 picoseconds to 15 nanoseconds, depending on the structural characteristics of the molecule (Lakowicz 2006). Consequently, it may happen that two different molecules, characterized by the same emission peak, have different lifetimes and can be mutually distinguished based on this parameter. Moreover, modulation of fluorescence lifetime can be induced in the same molecule by variously modifying the molecule or its environment; consequently, the study of this parameter has been exploited in the label-free evaluation of a series of conditions, including the status of residual chromatin in apoptotic cells (Sailer et al. 1998b), the type of nucleic acid hosting an intercalating probe (Sailer et al. 1998a; Cui et al. 2003), the cell pH (Islam et al. 2013), bacterial phagocytosis (Li et al. 2017), leukemic blast recognition (Lee et al. 2018), integrin activation (Sambrano et al. 2018), apoptosis (Alturkistany et al. 2019; Nichani et al. 2020), FRET phenomena (Sailer et al. 1997a, 1997b; Steinkamp et al. 1999, 2000; Nedbal et al. 2015), and others (Sailer et al. 1996, 1997b, 1997a, 1998a, 1998b; Keij et al. 1999; Steinkamp 2001; Cui et al. 2003; Gohar et al. 2013; Sands et al. 2014). The technologies available for measuring this parameter belong to two different groups, i.e., frequency-resolved techniques and time-resolved techniques (Bitton et al. 2021). The frequency-resolved techniques rely on an excitation sinusoidally modulated in intensity at a high frequency; given that the emission will also be modulated in this case, these techniques measure the phase shift between the two signals, which is directly proportional to the fluorescence lifetime (Steinkamp et al. 1999). Another ingenious frequency-resolved technique evaluates the phase shift between the fluorescence signal and the side scatter signal (Houston et al. 2010). The time-resolved techniques rely on serial measurements of the fluorescence emission resulting from a very short pulsed excitation; these techniques encompass different methods, including time-correlated single-photon counting (TCSPC), time gating, and pulse sampling, also known as direct waveform recording. A particular lifetime cytometry exploiting a technology belonging to the latter group is the Fluorescence-lifetime Excitation Cytometry by Kinetic Dithering (FLECKD). In FLECKD, the exciting laser oscillates (dithers) transversely to the events’ direction, delivering a series of very brief excitation pulses lasting about 25 ns. The fluorescence lifetime can be devised thanks to multiple detectors evaluating the gap between the appearance of the side scatter signals and the fluorescence-related signals, whose serial sampling allows detection of the decay curves, which can be further deconvoluted computationally (Li et al. 2014; Vacca and Houston 2015). The fluorescence lifetime-based distinction of the emission of multiple fluorochromes sharing the same emission peak makes feasible three particularly advantageous objectives: 1. The possibility of increasing the number of probes to be exploited simultaneously in a multiparametric analysis. 2. The elimination of the spillover phenomenon, given that the emissions are mainly resolved based on fluorescence lifetime and not wavelength.
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Raman Cytometry
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3. The elimination of autofluorescence, whose signal is also resolved based on its lifetime and can be selectively subtracted. The principles of lifetime cytometry have also been experimentally applied to cell sorting (Houston et al. 2010). The existing fluorescence lifetime-based cytometers are exquisitely analytical instruments, but an experimental project has considered an instrument with sorting capabilities equipped with SiPMs (Rocca et al. 2016). A fluorescence lifetime-based flow cytometer is currently commercially available from Kinetic River Corporation (https://www.kineticriver.com/).
22.5
Raman Cytometry
Besides Rayleigh scattering, a molecule hit by a monochromatic incident light emits another scattering signal known as Raman scattering (for further information on this topic, see Sect. 2.3). Raman scattering displays different peaks whose wavelengths differ from incident light and depend on the molecule’s vibrational modes, which, in turn, depend on several features, among which the presence of double bonds. The Raman scattering behaves as the spectral signature of a molecule, and its detection can be exploited in an analytical technique known as “Raman spectroscopy,” which can be applied to Flow Cytometry (Watson et al. 2008; Nolan and Sebba 2011; Nolan et al. 2012). In a way similar to Spectral Fluorescence Cytometry, in Raman Cytometry, the scattering is spectrally resolved by a prism, a grating, or an equivalent device and led to a detector consisting of a multi-anode PMT or a high-density array detector such as a CCD. A condition that hampers the detection of Raman scattering in Flow Cytometry is the very low intensity of the signal. A series of technical solutions can overcome this problem, allowing the molecules under analysis to adhere to a metal nanoparticle surface that amplifies the signal. This phenomenon is known as surface-enhanced Raman scattering (SERS) (Kerker 1983). Raman cytometry can be theoretically exploited according to two different approaches. In the first approach, the technique analyzes label-free cells to gather information about their intrinsic parameters (metabolic features), et cetera; in the second, the technique analyzes cells labeled with Raman scattering probes, whose signal has been amplified by nanoparticles. SERS-tagged antibodies and ligands have been increasingly used to detect cell surface markers or intracellular antigens (Pilot et al. 2019). In this particular case, the Raman scattering allows the exploration of extrinsic parameters, similarly to what happens with the conventional scattering elicited by antibodies conjugated with colloidal gold (Bohmer and King 1984a, b). No Raman scattering-based flow cytometers are commercially available; experimental devices have been devised based on a microfluidic architecture (Lau et al. 2008; Zhang et al. 2015).
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Microfluidic Devices (Labs on Chips)
In-depth treatment of Microfluidics Devices is beyond the scope of this book, and further information on the subject can be obtained from the consultation of a series of reviews and monographs to which we refer (Hughes and Hoettges 2010; Yu et al. 2014; Warkiani et al. 2015; Tang et al. 2019; Witek et al. 2020). Nevertheless, this article will cover the subject briefly for completeness’ sake. Microfluidics Devices are a heterogeneous family of small flow cytometers, relying on different technological solutions. They are generally very small in size, with a fluidic system featuring conduits with a diameter lesser than 1 mm, and are often collectively referred to as “lab on chips”. Microfluidics Devices are currently built at a reasonable low cost by engraving the hydraulic circuit with computer-assisted design (CAD) techniques, printing a mask with photolithographic techniques, and embedding it in a chip made of glass, silicone, or other polymers (Huh et al. 2005; Chinn 2010; Wolfe et al. 2010). They can display a series of different capabilities, including: 1. Management of very small samples (tens of nanoliters). 2. System integration (for example, labeling and detection on the same chip) (Shields et al. 2017; Witek et al. 2020). 3. Modularity by docking to more complex instrumentation (Watanabe et al. 2014a; Futamura et al. 2015). 4. The exploitation of signals other than Lorenz-Mie scattering and fluorescence, i.e., axial extinction, impedance by Coulter effect, impedance by electrorotation (Mansor and Ahmad 2015), dielectrophoretic (DEP) crossover frequency (Cheung et al. 2010), and direct dielectric features (Gawad et al. 2001; Holmes and Gawad 2010; Sun and Morgan 2010). 5. Sorting capabilities (Witek et al. 2020). Moreover, their low cost makes them disposable, a highly desirable feature in managing infectious materials. Microfluidic Devices have been designed for many different purposes, and samples have been built dedicated to a series of tasks, among which (1) bacterial detection and analysis (Bernabini et al. 2011), (2) bacterial counts (Bernabini et al. 2011), (3) bacterial counts in milk (Fernandes et al. 2014), (4) circulating tumor cells counts, enrichment and phenotyping (Teo et al. 2017; Watanabe et al. 2018; Abdulla et al. 2020; Xu et al. 2020), (5) detection and isolation of circulating myelomatous plasma cells (Qasaimeh et al. 2017), (6) evaluation of CD64 expression on neutrophils (Hassan et al. 2017), (7) exosomes analysis (Kanwar et al. 2014), (8) identification of leukemic blasts (Lee et al. 2018), (9) determination of RBC volume and Hb content (Schonbrun et al. 2014), (10) sorting of motile spermatozoa (Schuster et al. 2003), (11) evaluation of cell density (Grover et al. 2011) (12) evaluation of cytoplasmic viscosity (Wang et al. 2020), (13) evaluation of membrane potential (Farinas et al. 2001), and (14) human white blood cell (WBC) counts (Peng et al. 2021).
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Microfluidic Devices (Labs on Chips)
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Microfluidic Devices are still largely experimental and not commercially available (Volpatti and Yetisen 2014) except for isolated cases, which include (Shields et al. 2017): 1. Disposable special microfluidic sorting cartridges to be docked to a dedicated conventional platform, including: (a) The SH800 Cell Sorter marketed by Sony (https://www.sonybiotechnology. com/). (b) The Wolf® Sorter marketed by Biosystems (https://il-biosystems.com/de-en/ ). (c) The MACSQuant® Tyto® Sorter, marketed by Miltenyi (Beuk et al. 2018). (d) The On-chip Sort, marketed by On-chip Biotechnologies. 2. CellSearch® (Watanabe et al. 2014b; Hardingham et al. 2015; Watanabe et al. 2018) and DEPArray™ (Di Trapani et al. 2018) microdevices, marketed by Menarini for the identification and separation of circulating tumor cells (CTC) (https://siliconbiosystem.com/). 3. ZellSafe™ Chip microdevice (Teo et al. 2017), marketed by Canopy Biosciences (https://canopybiosciences.com/) for CTC characterization.
22.6.1
Hydraulic Issues in Microfluidic Devices
The most important feature of this class of fixtures is the conditions in which the flow occurs. Due to miniaturization, the laws of hydraulics, while keeping to be respected, take on different importance because inertial effects become negligible, and viscosity, density, and surface tension take over (Holmes and Gawad 2010). It follows that the Reynolds number collapses, and the flow keeps being laminar in virtually all situations; consequently, the displacements of solvents, solutes, and particles can only occur by diffusion. Nonetheless, even the microfluidic devices have to face a couple of the greater cytometers’ most important hydraulic issues, i.e., (1) the regulation of the fluid progression in the conduits and, except for the sheathless models, (2) the focalization of the sample in the center of the conduit. Besides the classical application of a pressure gradient (hydrodynamic flow), the progression of the fluid in the conduits is often entrusted to an electric field, which generates an electro-osmotic flow due to the migration of the fluid’s electrolytes (Slater et al. 2010; Shields et al. 2015). The fluid profile, usually parabolic if pressure-driven, in electro-osmotic-driven flow displays a flat head that is very useful to minimize the effect of the event’s position on signal generation (Slater et al. 2010). The focalization of the sample in the center of the conduit can be obtained by 3D hydrodynamic thanks to the injection of the sample flow in the center of a wider orthogonally driven sheath flow (Golden et al. 2012; Daniele et al. 2015; Testa et al.
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2015; Zhao and You 2016), but in sheath-less fluidics, other technical solutions are needed, like acoustic focusing (Piyasena et al. 2012; Wang et al. 2021), dielectrophoretic focusing (Yu et al. 2005), inertial focusing (Gou et al. 2018), and magnetic focusing (Zeng et al. 2012).
22.6.2
Sorting Procedures in Microfluidic Devices
Microfluidic devices can often sort events, relying on many different technologies. Due to their particular structural features, the events of interest can not be sorted by stream-in-air electrostatic sorting; their separation is made possible by a series of different separation methods divided into active and passive separation. Active separation is conceptually similar to “traditional” sorting since it requires the preliminary staining of the population of interest and its subsequent diversion from the main flow. In microfluidic devices, the separation can be achieved by a series of different methods, including: 1. Immunocapture (Mab attached to the conduit/microchannel surface) (Sheng et al. 2012). 2. Immunoseparation by magnetophoresis (through magnetic-bead-conjugatedMabs adhering to the cells) (Plouffe et al. 2012) and other magnetic-based techniques (Magnetic, Magnetotactic, and Magnetoresistance Cell Sorting (Zborowski and Chalmers 2011; Witek et al. 2020)). 3. Microvalve sorting (Piyasena and Graves 2014). 4. Electrokinetic switching (Fu et al. 2004). 5. Spark-generated microbubble cavitation (Zhao and You 2018). Three microfluidic active separation-based cell sorters are commercially available that rely on disposable micro-cartridges to be docked to a more or less conventional platform (for further information on this topic, see Sect. 21.1). Passive separation is of particular interest because it is label-free. In passive separation, events separate, so to speak, by themselves, i.e., only based on the interaction they establish with the conduit walls and with the microstructures possibly contained in it. These relationships can require direct contact, as in mechanical filtration (Tan et al. 2009; Zheng et al. 2011; Qin et al. 2015; Sarioglu et al. 2015; Hvichia et al. 2016; Kim et al. 2017), but in other cases can exploit the event deformability without requiring direct contact. The contactless passive separation techniques are called hydrodynamic sorting and include 1. Inertial microfluidics (Sun et al. 2012b; Sollier et al. 2014; Abdulla et al. 2018) 2. Pinched flow fractionation (Yamada et al. 2004; Lu and Xuan 2015) 3. Deterministic lateral displacement (DLD) (Huang et al. 2004; Loutherback et al. 2012; Salafi et al. 2019; Witek et al. 2020) 4. Deterministic cell rolling (Choi et al. 2012) 5. Lift-force cell sorting (Witek et al. 2020)
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Some hydrodynamic sorting techniques may require the application of an external force, consisting of an electric field or acoustic wave; the most frequent separation techniques belonging to this group are: 1. Dielectrophoresis (DEP) (Kang et al. 2006; Sun et al. 2012a) 2. Electrorotation (ROT) (Mansor and Ahmad 2015) 3. Acoustophoresis (Warkiani et al. 2015; Yang and Soh 2012; Li et al. 2015; Ding et al. 2013; Olm et al. 2021) 4. Electrokinetic switching (Fu et al. 2004) Hybrid devices exploiting both active and passive sorting techniques have been devised (Yan et al. 2017). Moreover, microdevices are known for combining microfluidic architecture with non-conventional microscopic techniques, such as a lab-on-chip relying on fluorescence lifetime imaging microscopy (FLIM) devised to identify leukemic blasts (Lee et al. 2018). Other, much simpler, require microscopic observation as a final step (Hassan et al. 2017). Further information on this topic can be found in the excellent review by Tang and colleagues (Tang et al. 2019).
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Watson DA, Brown LO, Gaskill DF, Naivar M, Graves SW, Doorn SK, Nolan JP (2008) A flow cytometer for the measurement of Raman spectra. Cytometry A 73(2):119–128 Witek MA, Freed IM, Soper SA (2020) Cell separations and sorting. Anal Chem 92(1):105–131. https://doi.org/10.1021/acs.analchem.9b05357 Wolfe DB, Qin D, Whitesides GM (2010) Rapid prototyping of microstructures by soft lithography for biotechnology. In: Hughes MP, Hoettges KF (eds) Microengineering in biotechnology, Methods in molecular biology, 2009/09/19 edn, vol 81–108. Humana Press, Hatfield, Hertfordshire, pp 81–108. https://doi.org/10.1007/978-1-60327-106-6_2 Xu M, Zhao H, Chen J, Liu W, Li E, Wang Q, Zhang L (2020) An integrated microfluidic Chip and its clinical application for circulating tumor cell isolation and single-cell analysis. Cytometry A 97(1):46–53. https://doi.org/10.1002/cyto.a.23902 Yamada M, Nakashima M, Seki M (2004) Pinched flow fractionation: continuous size separation of particles utilizing a laminar flow profile in a pinched microchannel. Anal Chem 76(18): 5465–5471. https://doi.org/10.1021/ac049863r Yan S, Zhang J, Yuan D, Li W (2017) Hybrid microfluidics combined with active and passive approaches for continuous cell separation. Electrophoresis 38(2):238–249. https://doi.org/10. 1002/elps.201600386 Yang AH, Soh HT (2012) Acoustophoretic sorting of viable mammalian cells in a microfluidic device. Anal Chem 84(24):10756–10762. https://doi.org/10.1021/ac3026674 Yu C, Vykoukal J, Vykoukal DM, Schwartz JA, Shi L, Gascoyne PR (2005) A three-dimensional dielectrophoretic particle focusing channel for microcytometry applications. J Microelectromech Syst 14(3):480–487 Yu ZT, Aw Yong KM, Fu J (2014) Microfluidic blood cell sorting: now and beyond. Small (Weinheim an der Bergstrasse, Germany) 10(9):1687–1703. https://doi.org/10.1002/smll. 201302907 Zborowski M, Chalmers JJ (2011) Rare cell separation and analysis by magnetic sorting. Anal Chem 83(21):8050–8056. https://doi.org/10.1021/ac200550d Zeng J, Chen C, Vedantam P, Brown V, Tzeng TR, Xuan X (2012) Three-dimensional magnetic focusing of particles and cells in ferrofluid flow through a straight microchannel. J Micromech Microeng. https://doi.org/10.1088/0960-1317/22/10/105018 Zhang Q, Zhang P, Gou H, Mou C, Huang WE, Yang M, Xu J, Ma B (2015) Towards highthroughput microfluidic Raman-activated cell sorting. Analyst 140(18):6163–6174. https://doi. org/10.1039/c5an01074h Zhao J, You Z (2016) A microflow cytometer with a rectangular quasi-flat-top laser spot. Sensors (Basel, Switzerland). https://doi.org/10.3390/s16091474 Zhao J, You Z (2018) Spark-generated microbubble cell sorter for microfluidic flow cytometry. Cytometry A 93(2):222–231. https://doi.org/10.1002/cyto.a.23296 Zheng S, Lin HK, Lu B, Williams A, Datar R, Cote RJ, Tai YC (2011) 3D microfilter device for viable circulating tumor cell (CTC) enrichment from blood. Biomed Microdevices 13(1): 203–213. https://doi.org/10.1007/s10544-010-9485-3
Chapter 23
Statistics: A Cytometric Point of View
A full understanding of some topics treated in this book benefits from some knowledge of the basic principles of Statistics. Even though further information can be derived from the consultation of monographs dedicated to the topic (Altman 1999; Skinner 2018), some concepts will be recalled in this appendix for the reader’s convenience.
23.1
Concept of Distribution
By distribution, we mean how a phenomenon occurs over time. Even though the treatment of this subject far exceeds the aims of this section, it is important to remember that the behavior of some phenomena studied in Flow Cytometry is described by particular distributions, whose knowledge makes it easier to understand the particularities of this technique. It is also important to remember that according to the central limit theorem, each distribution tends to become Gaussian with the increase of the counts.
23.1.1
Log-Normal Distribution
The log-normal curve distribution is skewed to the right or left; in the first case, mean > median > mode, while the opposite holds up if the curve is skewed to the left. The distribution of the phenomena occurring in nature is often log-normal (Heath 1967; Sweet et al. 1981; Gandler and Shapiro 1990; Limpert et al. 2001; Diwakar 2017), and it has been specifically affirmed that the membrane antigens expression (Parks and Herzenberg 1984) and the absolute counts of most of the blood cell types (except basophils) are log-normal distributed (Bain et al. 1984). Of note, the log © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_23
523
524
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Statistics: A Cytometric Point of View
transformation of a log-normal distribution is a normal (Gaussian) distribution (Diwakar 2017).
23.1.2
Normal (Gaussian) Distribution
In a normal distribution, the curve is symmetrical, and the Median, Mean, and Mode coincide. The normal distribution describes common events occurring around a mean and decreasing rapidly in frequency at the symmetrical tails. This distribution is of great importance in Flow Cytometry because it describes the behavior of many fundamental phenomena in this area, including some background components, such as the noise produced by the pre-amplifiers (van Kempen and van Vliet 2000). Moreover, in immunophenotypic analyzes, the distribution of the log-transformed fluorescence histograms tends to be normal, with log-normal being the underlying distribution of the measured parameters. This behavior often deviates from the expected (Coder et al. 1994), which can be proved by observing that the Mean (arithmetic average), Mode, and Median no longer coincide.
23.1.3
Poisson Distribution
In contrast with the normal distribution, Poisson distribution describes the behavior of rare events (rare in comparison to a potentially infinite underlying series, as it happens by counting white blood cells in one limited volume drawn from the whole peripheral blood) (CLSI 2010). For this very reason, the Poisson distribution applies by definition to counts (Shapiro et al. 1998; Gratama et al. 1999; CLSI 2010), and every time the following three conditions are met, namely: 1. When the probability of an event occurring in a given time interval is proportional to the interval itself. 2. When two events cannot occur at the same instant (i.e., in the same sub-interval as small as you like, either exactly one event occurs or no event occurs). 3. When each event is independent of previous or subsequent events. The Poisson distribution has the utmost importance in Flow Cytometry, first because, as already said, it applies by definition to the counts, but above all, because it describes the behavior of a series of phenomena of interest, including: 1. The generation of photoelectrons by the photocathode (Chase and Hoffman 1998; Hamamatsu 2007) 2. The detection of photoelectrons (Lachs 1974; Wood 1998) 3. The generation of photoelectrons by successive dynodes (Hamamatsu 2007) 4. The behavior of some background components, among which the fluctuation of the dark current brought up by the APDs (Excelitas 2011), et cetera
23.2
Location Measurements
525
Furthermore, since it describes the probabilities of the occurrence of random events (Arnold and Lannigan 2011), the Poisson distribution allows for predicting the time sequence of cells passing through the sensing region of a flow cytometer (Lindmo and Fundingsrud 1981) and the occurrence of the coincidences (Pinkel and Stovel 1985), contributing to defining the theoretical efficiency of sorting. Moreover, it can predict the volume of the sample to be drawn to accurately detect and enumerate a very small number of events (Tibbe et al. 2007; Allan and Keeney 2010). A fundamental corollary of the Poisson distribution is that the precision of the counts increases with the number of events counted, and this effect on photon and photoelectron counts can be empirically verified by observing some recurrent behaviors in certain typical Flow Cytometry situations. For example, in the analysis of a class III standard, the histograms generated by the less bright populations have a higher CV than those generated by the brightest populations (Fig. 13.4). The phenomenon relies on the fact that the less bright microbead populations emit fewer photons than the brightest populations, which results in counts with a broader CV. By the same token, PE-labeled populations’ analysis produces histograms with smaller coefficients of variation when excited in green instead of blue, as PE is more effectively excited in this region of the spectrum and consequently produces a higher number of photons.
23.2
Location Measurements
In Flow Cytometry, a location measurement aims to provide information on the expression intensity in a population. The choice of the most appropriate location measurement (Mean, Median, Mode, et cetera) may vary case by case depending on the characteristics of the population of interest. In all cases, it requires a critical evaluation of the results since it is not always possible to describe a heterogeneous population by location measurement. In Cytometry, the most often used location measurements are the arithmetic Mean, the Mode, and the Median. One of the typical uses of the location measurements is evaluating the so-called Mean Fluorescence Intensity (MFI) of a population of events (for further information on this topic, see Sect. 12.1.2.2). In the evaluation of the MFI, the chosen index should be declared: if the Mean is used, the term MFI should be expressed as MeanFI, while in the case of the Median, the term MFI should be expressed as MedianFI.
526
23.2.1
23
Statistics: A Cytometric Point of View
Mean (Am)
The Mean (aM), also known as average or arithmetic Mean or arithmetic average, is a location measurement corresponding to the sum of the values x of the events divided by the number of events n. x ¼ ðx1 þ x2 þ . . . þ xn Þ=n: The Mean is strongly affected by extreme values and is considered unsuitable for managing log-transformed data but can be helpful in the case of linearly amplified data. The arithmetic Mean is particularly representative in heterogeneous populations; however, as already observed, a location measurement cannot always describe a heterogeneous population satisfactorily.
23.2.2
Geometric Mean (gM)
The geometric Mean (gM) is a location measurement corresponding to the nth root of the product of the values of n events. gM ¼
p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n x1 x2 ⋯xn
Some authors define the geometric Mean as “log friendly” because it is based on the multiplication of the values and not their addition; the geometric Mean helps evaluate the MFI between populations whose values have been log-transformed. Unfortunately, the geometric mean is useless in the presence of zero or negative values (a single zero makes the geometric Mean equal to zero regardless of the number and value of the other events), which is a possible occurrence in cytometric data sets due to compensation procedures or baseline restorer’s too aggressive activity (Wood 1998). The formula for the geometric Mean used by some software to analyze cytometric data consists of the antilogarithmic transformation of the arithmetic mean of the logarithmic values of the channel numbers (Becton Dickinson 2007).
23.2.3
Truncated Mean
The truncated Mean, also known as the “trimmed” Mean, is calculated as ordinary arithmetic mean, eliminating 5% of the data in the two distribution queues. The truncated Mean is less sensitive to the presence of extreme values.
23.3
Spread Measurements
23.2.4
527
Mode (v0)
The Mode (v0) corresponds to the most represented value; consequently, it coincides with the so-called “peak channel” in a cytometric histogram. Within a flow cytometric histogram, more than one Mode can be present. Interestingly, in immunophenotyping, a histogram with more modes suggests the presence of biologically distinct cellular populations, each of which is related to one of the modes in the histogram. The Mode is perhaps the best location measurement in evaluating nominal data (male/female, red/black, et cetera). However, it is not generally helpful in representing a given population’s fluorescence except as s gross indicator of its intensity.
23.2.5
Median (Me)
The Median (Me) corresponds to the value or class of values dividing the data into two equal groups, with 50% of the data falling below and 50% above the Median. In the evaluation of cytometric data, such as in the calculation of the ratio between mean fluorescence intensities (MFI) or in compensation procedures, the Median is preferred to the Mean because it is more representative of the tendency expressed by the analyzed population; moreover, is more robust, i.e., less likely to be influenced by roundings, and more resistant to the presence of outliers and minor variations of the experimental conditions. The Median can be helpful in all cases of non-Gaussian distribution.
23.3
Spread Measurements
The spread measurements are a function of the Variance; in Flow Cytometry, their practical purpose is twofold, i.e., 1. To represent with a number the variability of a parameter in a given population (biological variability) 2. To provide information on the measurement’s precision As for the second point, it is of the utmost importance to realize that, in basal conditions, the Variance of the electric signal is the main source of the instrumental background known as Bcal (for further information on this topic, see Sect. 8.1 on the background and Sect. 13.3.1 on the SDen).
528
23.3.1
23
Statistics: A Cytometric Point of View
Variance (Var)
In the Gaussian distribution, the Variance (Var) corresponds to the arithmetic Mean of the squared deviations (ξ) from the Mean M, according to the formula. Var ¼ Σðx M Þ2 =ðn 1Þ ¼ ΣðξÞ2 =ðn 1Þ , where x is the value of every single event, and n is the number of analyzed events (CLSI 2010).
23.3.2
Standard Deviation (SD)
The Standard Deviation (SD) SD ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X 2ffi 2 ðξÞ =N 1:
corresponds to Variance’s square root in the Gaussian distribution. For reasons whose explanation goes beyond the scope of this book, in the Poisson distribution, the SD of the observations is equal to the square root of their number instead (Grégory 2004; CLSI 2010) SD ¼ √ðnÞ, where n is the average number of events in a given interval. Since it follows that the analysis precision depends on the number of analyzed cells, this corollary becomes very important in the rare event analysis, where it defines the minimum number of events to be acquired as that number whose Variance is equal to or smaller than that previously established by the operator (Allan and Keeney 2010).
23.3.3
Median Absolute Deviation (MAD)
The Variance is a function of the distance of the deviations from the Mean; if we consider the distance of the deviations from the Median instead of the Mean, we obtain the Median Absolute Deviation (MAD). MAD ¼ Median ðjxi ‐MedianjÞ
23.3
Spread Measurements
529
The use of MAD to estimate the DS presupposes an underlying more or less Gaussian distribution (unimodal and symmetric) contaminated by outliers in the tails.
23.3.4
Robust Standard Deviation (rSD)
The robust Standard Deviation is a spread measurement that is more resistant to outliers than the Standard Deviation. Based on a series of calculations not reported here, the following relationship exists between MAD and rSD: rSD ¼ MAD 1:4826: In Flow Cytometry, the robust standard deviation often finds application in some data analysis programs and instrumental control. For example, the rSD of a non-fluorescent standard (class 0 standard) is a function of the instrumental background (Bcal), allowing its follow-up over time (Perfetto et al. 2014).
23.3.5
Coefficient of Variation (CV)
The Coefficient of Variation, or CV, or Relative Standard Deviation, is the ratio between the standard deviation (SD, or σ) and the absolute value of the arithmetic Mean (μ). As such, it is independent of the measurement unit involved in the analysis. The formula CV% ¼ ððSD=μÞ 100Þ, defines its percentage CV%. In Flow Cytometry, some data analysis programs calculate the CV referring not to the basis of the entire histogram but its width measured in the middle of the peak (Half Peak Coefficient of Variation, or HPCV); in comparing data, it is essential to distinguish the two separate measurements, as they are not equivalent. By its nature, the CV does not apply to populations in which negative values and positive values coexist; its use is also not recommended in assessing a parameter’s dispersion when the population average has a value very close to zero. Notably, this behavior often occurs in populations subjected to spillover correction.
530
23.3.6
23
Statistics: A Cytometric Point of View
Robust Coefficient of Variation (rCV)
Similarly to what is already reported for the robust standard deviation (rSD), some programs use the robust Coefficient of Variation (rCV) defined by the formula rCV ¼ rSD=Median rCV is an especially sensitive marker in determining an instrument’s proficiency (Perfetto et al. 2012).
References Allan AL, Keeney M (2010) Circulating tumor cell analysis: technical and statistical considerations for application to the clinic. J Oncol 2010:10. https://doi.org/10.1155/2010/426218:426218 Altman DG (1999) Practical statistics for medical research. Chapman & Hall/CRC, Boca Raton, FL Arnold LW, Lannigan J (2011) Practical issues in high-speed cell sorting. Curr Protoc Cytom Chapter 1:Unit 1 24 21-30 Bain B, Seed M, Godsland I (1984) Normal values for peripheral blood white cell counts in women of four different ethnic origins. J Clin Pathol 37(2):188–193 Becton Dickinson (2007) BD CellQuest™ Pro Software Reference Manual. White Paper. Available at http://facilities.igc.gulbenkian.pt/flowcytometry/docs/software_cellquest_long_guide.pdf. Last accessed 12 September 2021 Chase ES, Hoffman RA (1998) Resolution of dimly fluorescent particles: a practical measure of fluorescence sensitivity. Cytometry 33(2):267–279 CLSI (2010) Validation, verification, and quality Assurance of Automated Hematology Analyzers; Approved standard - second edition. CLSI document H26-A2. CLSI, Wayne, PA Coder DM, Redelman D, Vogt RF (1994) Computing the central location of immunofluorescence distributions: logarithmic data transformations are not always appropriate. Cytometry 18(2): 75–78 Diwakar R (2017) An evaluation of normal versus log-normal distribution in data description and empirical analysis. Practical Assessment, Research & Evaluation. Available at http://pareonline. net/getvn.asp?v¼22&n¼13. Last accessed 18 June 2021 Excelitas (2011) Avalanche photodiodes (APDs) - a user guide. White paper. Available at https:// www.photonicsonline.com/doc/avalanche-photodiode-a-users-guide-0001. Last accessed 15 July 2019 Gandler W, Shapiro H (1990) Logarithmic amplifiers. Cytometry 11(3):447–450 Gratama JW, Bolhuis RL, Van’t Veer MB (1999) Quality control of flow cytometric immunophenotyping of haematological malignancies. Clin Lab Haematol 21(3):155–160 Grégory G (2004) Flow cytometry data handling and analysis. MARBEF advanced course 3 3-6 November 2004. Available at https://cupdf.com/document/flow-cytometry-data-handling-andanalysis-gerald-gregori-phd-laboratory.html. Last accessed 3 February 2022 Hamamatsu (2007) Photomultiplier tubes. Basics and applications (Edition 3a). White Paper. Available at https://www.hamamatsu.com/resources/pdf/etd/PMT_handbook_v3aE.pdf. Last accessed 23 June 2019 Heath DF (1967) Normal or log-normal: appropriate distributions. Nature 213(5081):1159–1160 Lachs G (1974) The statistics for the detection of light by nonideal photomultipliers. IEEE J Quant Electr 10(8):590–596
References
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Limpert E, Stahel WA, Abbt M (2001) Log-normal distributions across the sciences: keys and clues. Bioscience 51(5):341–352 Lindmo T, Fundingsrud K (1981) Measurements of the distribution of time intervals between cell passages in flow cytometry as a method for the evaluation of sample preparation procedures. Cytometry 2(3):151–154. https://doi.org/10.1002/cyto.990020303 Parks DR, Herzenberg LA (1984) Fluorescence activated cell sorting: theory, experimental optimization, and application in lymphoid cell biology. Methods Enzymol 108:197–241 Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay PK, Roederer M (2012) Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat Protoc 7(12):2067–2079. https://doi.org/10.1038/nprot.2012.126 Perfetto SP, Chattopadhyay PK, Wood J, Nguyen R, Ambrozak D, Hill JP, Roederer M (2014) Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels. Cytometry A 85(12):1037–1048. https://doi.org/10.1002/cyto.a.22579 Pinkel D, Stovel H (1985) Flow chambers and sample handling. In: Van Dilla MA, Dean PN, Laerum OD, Melamed MR (eds) Flow cytometry: instrumentation and data analysis. Academic, Orlando, Florida, pp 77–128 Shapiro HM, Perlmutter NG, Stein PG (1998) A flow cytometer designed for fluorescence calibration. Cytometry 33(2):280–287 Skinner J (2018) Statistics for immunologists. Curr Protoc Immunol 122(1):54. https://doi.org/10. 1002/cpim.54 Sweet R, Parks D, Nozaki T, Herzenberg L (1981) A 3 1/2 decade logarithmic amplifier for cell fluorescence data (abstract). Cytometry 2(2):130 Tibbe AG, Miller MC, Terstappen LW (2007) Statistical considerations for enumeration of circulating tumor cells. Cytometry A 71(3):154–162 van Kempen GM, van Vliet LJ (2000) Mean and Variance of ratio estimators used in fluorescence ratio imaging. Cytometry 39(4):300–305 Wood JC (1998) Fundamental flow cytometer properties governing sensitivity and resolution. Cytometry 33(2):260–266
Correction to: Fluorochromes That Bind Nucleic Acids
Correction to: Chapter 16 in: C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_16 The original version of the book was inadvertently published with the incorrect figure legend for Fig. 16.7, which has been updated as below. The reference cited in the figure caption has been added to the reference list. Fig. 16.7: Cytometric analysis of the Side Population present in mouse bone marrow, performed by HO33342 staining. Panel A shows the optical bench necessary for the analysis, while panel B shows the results, i.e., a population dimly emitting blue and red (in the red frame). This optical bench configuration can also be used without modification in adopting DyeCycle Violet (DCV) instead of HO33342. Panel A was redrawn from the white paper “BD FACSDiva Option” (Verwer 2002), courtesy of © Becton, Dickinson and Company. Panel B was redrawn from Petriz J (2013) “Flow cytometry of the side population (SP)”. Curr Protoc Cytom Chapter 9:Unit 9 23; courtesy of Wiley Reference: Petriz J (2013) Flow cytometry of the side population (SP). Curr Protoc Cytom 64:9.23.1–9.23.20. https://doi.org/10.1002/0471142956.cy0923s64 The corrections have been carried out in the chapter and the updated chapter has been approved by the author.
The updated original version for this chapter can be found at https://doi.org/10.1007/978-3-031-10836-5_16 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5_24
C1
Index
A ABC protein (ATP binding cassette) family, 421 Absolute counts, 45 Absorption, 249 Absorption filters, 83 Accuracy, 229 count inaccuracy at high speed, 230, 231 Acidic vesicular organelles (AVOs), 402 Acoustic focusing, 48 Acoustophoresis in microfluidic devices, 513 Acridine Orange (AO), 353, 404 Acridines, 353 AF488, 417 Alcian Blue, 30 Aldefluor™, 425 Alexa series, 284 AF350, 270 AF405, 268 AF488, 273, 284 AF555, 280 AF594, 276, 280, 284 AF610, 285 AF633, 277 AF647, 281, 285 AF660, 285 AF700, 282, 285 artifacts due to, 480 AF750, 286 AF790, 282, 283, 286 ALLO-7, 307 Allophycocyanin B (APCB), 260, 263 artifacts due to, 479 Allophycocyanins, 260
Alveolar macrophages autofluorescence in, 26 AMCA-S, 270 AmCyan, 267 AmCyan 100, 267 Amine Reactive Dyes, 385 7-Amino-actinomycin-D (7-AAD), 357 Amino-methyl-coumarin-acetate (AMCA), 270 Analog-to-digital converters (ADCs), 115 bit number, 112 clock number, 113 ANALYSIS segment, 142 Analysis Window, 127 Anthraquinones, 362 Antibody Binding Capacity (ABC), 225 Antibody titration, 235 Anti-mouse antibodies, artifacts due to, 480 Anti-PEG antibodies, artifacts due to, 480 Anti-Stokes scatter, 17 APC-AF700, 307 APC-CY5.5, 307 artifacts due to, 480 APC-CY7, 307 APC-H7, 308 APC-750, 308 APC-800, 308 APC-830, 308 Archival Cytometry Standard (ACS), 137 Arc lamps, 54 Area scaling, 128 Area scaling factor, 128 Argon ion lasers, 58 Artificial standards, 207 ATTO series, 286
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Ortolani, Flow Cytometry Today, https://doi.org/10.1007/978-3-031-10836-5
533
534 Autofluorescence, 25 reduction, 26 Avalanche photodiodes (APDs), 98 Average residual percentage (ARP), 217, 229 Axial extinction, 30
B Backbone-linked fluorophores, 309 Background, 121, 209, 233 experimental background (Bsos), 122 instrumental background (Bcal), 122, 220, 226 Band-pass filters (BP), 84 Baseline restorers, 105 Basophils side scatter in, 15 Bathochromic effect, 253 BB515, 289 BB515 based tandems, 306 BB700, 306 BCECF, 400, 415, 422 BCECF-AM, 424 Beam splitters, 87 Benzophenanthridine alkaloids, 344 Benzoxazolium, 397 Berberine (BRB), 361 BHDMAP, 282 BHMP, 282 Bioconductor, 197 Bisbenzimydazole derivatives group, 328 Bit density, 231 Blocking reagents, 479 Blue emitting SSLs, 67 Blue fluorescent proteins (BFPs), 449 Azurite, 449 eBFP2, 449 EBFP2, 449 mAmetrine, 449 mTagBFP, 449 Sapphire, 449 Sirius, 449 TagBFP, 449 T-Sapphire, 449 BOBO-1, 345 BOBO-3, 345, 347 BODIPY molecules, 425 BODIPY aminoacetaldehyde (BAAA), 425 BODIPY493/503, 425 BODIPY505/515, 425 BODIPY581/591, 425 C11-BODIPY581/591, 426 Boolean gating, 187
Index B-Phycoerythrin (B-PE), 264 BrdU incorporation, 185 Breakoff point, 487, 490 Brewster windows, 58 Brilliance (B), 250 Brilliant Ultra Violet 395 molecule (BUV395), 289 Brilliant Violet 421 molecule (BV421), 288 Broadened polynomials computational method, 183 BTC, 412 BUV395 BUV395 based tandems, 299 BUV496, 299 BUV563, 299 BUV661, 299 BUV737, 299 BUV805, 299 BV421 BV421 based tandems, 300 BV570, 300 BV605, 300, 301 BV650, 300, 301 BV711, 300, 301 artifacts due to, 300 BV750, 300 BV785, 300, 301 BV480, 289 BV510, 289 BYG584P, 287
C Calcein, 388 Calcein acetoxymethyl ester, 274, 387, 422 Calcein Influx Assay, 422 Calcium Crimson, 409, 410 Calcium-Green-1, 410 Calcium Orange, 409 Calcofluor White, 384, 389 Calcofluor White treated fabric, artifacts due to, 482 Calf thymocytes, 207 Calibration, 223 Capacitation determination, 423 Capture beads, 212 Carbocyanines, 383, 391 Carbonyl-cyanide-m-chlorophenyl-hydrazine (CCCP), 396 Carboxyfluorescein succinimidyl diacetate ester (CFDA-SE), 387, 418 succinimidyl ester (CFSE), 387, 418, 424
Index Carry-over, 230 Cascade Blue (CB), 268 Cascade Yellow (CY), 269 C11-BODIPY581/591, 425, 426 CCD-based Imaging Flow Cytometry, 498 CD4+ lymphocytes absolute counts, 45 Cell proliferation determination, 417 Cell viability determination, 384 CellVue® series, 420 CellVue® Burgundy, 420 CellVue® Claret, 420 CellVue® Jade, 420 CellVue® Lavender, 420 CellVue® Lilac, 420 CellVue® Maroon, 420 CellVue® NIR780, 420 CellVue® NIR815, 420 CellVue® Plum, 420 CellVue® Red, 420 CF series, 286 cFluor™ series, 286 Channel-by-channel subtraction method, 179 Channel number, 158 Charged-Coupled Devices (CCDs), 102 Chelerythrine (CHE), 344 Chelilutine (CHL), 344 Chelirubine (CHR), 344 Chicken red blood cells (CRBC), 206, 229 Chloromethyleosin-diacetate (CMEDA), 416 Chloromethylfluorescein-diacetate (CMFDA), 274, 416 Chloromethyl-X rosamine, 397 Chromatin condensation determination, 384 ChromEM tomography (ChromEMT), 384 Chromomycin A3 (CA3), 361 Chromophoric nanoparticles, 290 Chromophoric polymer dots, 290 Chronic granulomatous disease (CGD), 405 Cis-parinaric acid, 426 Coarse WDM (CWDM), 88 Coefficient of Response (CR), 217 Collinear laser configuration, 91 ColorWheel® series, 286 Comparators, 105 Compensation analog compensation, 466, 470 compensation by hardware, 466, 470 compensation by software, 471 compensation coefficient, 464 compensation matrix, 464 off-line compensation, 471 on-line compensation, 466, 470 post-compensation spreading, 466
535 Compensation performing circuits, 106 Complement components (C1q), artifacts due to, 480 Continuous collinear lasers, 93 Contour plot representation, 165 linear density, 165 logarithmic density, 167 probability method, 167 Coomassie Fluor Orange, 383 CoraLyte® series, 286 Core, 41 Coriphosphine O (CO), 355 Coulter counter, 28 Coulter effect, 29 Coumarin derivatives group, 270 Coupled Dye, 303 CRC segment, 143 Crossbeam spillover, 462 CryptoFluor molecules, 265 Cumulative frequency subtraction plus ratio analysis of means, 179 Cumulative subtraction method, 179 Custom keywords, 147 Cuvette, 42, 47 Cyan fluorescent proteins (CFPs), 450 AmCyan1, 450 Cerulean, 450 CoralHue Cyan, 450 eCFP, 450 TagCFP, 450 Cyanine 2 (CY2), 280 Cyanine 3 (CY3), 280 Cyanine 3.5 (CY3.5), 280 Cyanine 5 (CY5), 281 Cyanine 5.5 (CY5.5), 282 Cyanine 7 (CY7), 282 Cyanine 7.5 (CY7.5), 282 Cyanines, 278, 328, 344, 351 Cyano-ditolyl tetrazolium chloride (CTC), 407 CyCHROME, 303 Cytobank, 197 Cyto-Cal™ beads, 216, 227 Cytograms, 162, 176 Cytoplasmic Calcium determination, 407 CyTRAK Orange, 364
D DAPI, 330 Dapoxyl (DPX), 427 Data processing programs, 198 DATA segment, 141 Daunorubicin, 422 Dazzle 594, 287
536 DC restorers, 109 DCH, 400 Debris, 477 Deep Purple, 383 Depolarized fluorescence, 25 Depolarized scatter, 18 DETC (or DEQTC), 356 Detection Threshold (DT), 209, 217 Deterministic cell rolling in microfluidic devices, 512 Deterministic lateral displacement (DLD) in microfluidic devices, 512 DiBAC4(3), 394 DiBAC4(5), 394 Dichloro-dihydro-fluorescin (DCFH), 406 Dichlorofluorescein (DCF), 406 Dichlorofluorescin-diacetate (DCF-DA), 406 Dichroic mirrors (DM), 84 DiD (DiI18(5)), 418 Dielectrophoresis (DEP) in microfluidic devices, 513 Diffraction gratings, 88 Digital signal processors (DSPs), 116 Dihydroethidium (DHE), 339, 406 Dihydro-imidazol-phenyl-indole (DIPI), 330 Dihydrorhodamine 123 (DHR123), 406 DiI (DiIC18(3)), 418 DiIC1(3), 280, 393 DiIC1(5), 393, 395 DiIC3(3), 393 DiIC6(5), 393 DiO (DiOC18(3)), 418 DiOC1(3), 392 DiOC2(3), 393, 395, 422 DiOC5(3), 393 DiOC6(3), 392, 393 DiOC7(3), 393 Diode-pumped solid-state lasers (DPSSLs), 62 Diphenyl-hexatriene (DPH), 424 Dipicolylamine in the evaluation of acute bacterial infections, 27 DiR (DiIC18(7)), 418 DiSBAC2(3), 394 DiSC3(5), 393 Discriminators, 105 Dispersion optics, 88 D12 Mab, 477 Dot plot representation, 163 Doublets, 130 DRAQ5, 363, 384 DRAQ7, 364 DRAQ9, 364 Drop delay, 489
Index Drop envelope, 488 Dumping, 254 Duochrome, 276, 303 DyeCycle violet (DCV), 338, 422 Dye-lasers, 72 DyLight 350, 270 DyLight series, 286 Dynamic range, 131
E EDTA, artifacts due to, 477 Effective resolution (ER), 132 eFluor® Nanocrystals, 293 eFluor series, 286 8G12 Mab, 478 Electrokinetic switching in microfluidic devices, 512, 513 Electronic circuitry analog model, 104 digital model, 113 hybrid model, 117 Electronic noise standard deviation (SDen), 122, 220 Electrorotation (ROT) in microfluidic devices, 513 Electrostatic cell sorters, 486 ELF-97, 287 Elliptical lenses, 80 Emission Coupled Dye (ECD), 276 Endoplasmic reticulum (ER), 427 Eosinophils autofluorescence in, 26 depolarized scatter in, 18 Epicocconone, 383 Equivalent number of Reference Fluorophore (ERF), 225 ER-tracker Green, 427 ER-tracker Red, 427 Escapees, 475 Ethidium bromide (EB), 339, 344 Ethidium monoazide (EMA), 339 Excitation, 249 External Quality Assessments (EQAs), 207, 218 Extinction, 249, 254 Extinction coefficient (E), 249 Extrinsic parameters, 5, 11, 29
F Fading, 255 FAL format, 138 False-color representation, 167
Index FCMPASS software for light scatter calibration, 228 Fc receptors, artifacts due to, 478 FCS format, 138 Fiber lasers, 72 Field-programmable gate arrays (FPGAs), 115 Fire series, 286 Flamingo, 383 Flow cell, 42 Flow chamber, 42 Flow cytochemistry, 30 Flow rate control, 43 Fluid switching sorters, 485 Fluo-3, 409, 422 Fluo-4, 409 Fluoprobes series, 286 Fluorescein, 272 Fluorescein diacetate (FDA), 274, 424 Fluorescein esters, 274 Fluorescein isothiocyanate (FITC), 272 artifacts due to, 478 Fluorescence, 23 Fluorescence Intensity Unit (FLU), 209, 226 Fluorescence lifetime, 24, 249, 507 Fluorescence Resonance Energy Transfer (FRET), 255, 293 Fluorescent proteins (FPs), 445 Fluorescin, 273 Fluoroangiography, artifacts due to, 481 FluoZin-3, 417 Förster Resonance Energy Transfer (FRET), 255, 293 Forward Scatter (FSC), 14 Fura Red, 410 Fura Red and Fluo-3, combined use, 410 Fura Red and Fluo-4, combined use, 410 Fura-2, 410, 422 Fura-4F, 410 Fura-5F, 410 Fura-FF, 410
G Gaintration, 223 Galileian beam expanders, 81 Gates, 186 Gating-ML standard, 137 Geiger mode, 97 Ghost Dyes, 386 Dye™ Blue 516, 386 Dye™ Red 710, 386 Dye™ Red 780, 386 Dye™ UV 450, 386
537 Dye™ Violet 450, 386 Dye™ Violet 510, 386 Dye™ Violet 560, 386 G-quadruplexes (GQs), 328 Green fluorescent proteins (GFPs), 448 AcGFP, 448 amphiGFP, 448 anm1GFP1, 448 anm1GFP2, 449 Azami Green, 449 CopGFP, 449 Enhanced green fluorescent protein (EGFP), 448 hrGFP, 449 hrGFP II, 449 laesGFP, 449 mEmerald, 448 mNeonGreen, 449 Monster Green, 449 pmeaGFP1, 449 pmeaGFP2, 449 ppluGFP1, 449 ppluGFP2, 449 Ptilosarcus GFP, 449 Renilla GFP, 449 TagGFP, 449 TagGFP2, 449 TurboGFP, 449 ZsGreen, 449 ZsGreen1, 449 Green and Yellow-Green emitting SSLs, 68
H HEADER segment, 140 Helium-Cadmium ion lasers, 61 Helium-Neon atom lasers, 60 Helium-Silver metal vapor lasers, 62 Hemozoin deposition in phagocytes, 18 Heterocyclic aromatic compounds, 328 Hexidium iodide (HI), 339 High numerical aperture (NA) lenses, 82 High-speed sorters, 494 HiLyte series, 286 Histograms, 157, 173 HO33258, 333, 334 HO33258 in chromosome analysis, 335 HO33342, 333, 335, 422 HO33342 in side population analysis, 337 HO34580, 333, 338 Hoechst tagging, 333 Horizon VH450, 270
538 Horizon VH500, 270 Hydrodynamic focusing, 39 Hydroethidine, 406 Hydroxystilbamidine, 330 Hyperchromic effect, 253 Hyperlog algorithm, 152 Hypochromic effect, 254 Hypsochromic effect, 253
I iFluor series, 286 Imaging Flow Cytometry (IFC), 1, 497 Immunocapture in microfluidic devices, 512 Impedance, 28 Indo-1, 422 Indo-1 acetoxymethyl ester, 408 Indocyanine Green, 282 Inertial microfluidics in microfluidic devices, 512 Infrared (IR) emitting SSLs, 71 Infra-Red fluorescent proteins (RFPs) iRFP670, 452 iRFP682, 452 iRFP702, 452 iRFP713, 452 iRFP720, 452 Integrators, 110 Inter System Crossing (ISC), 28 Interference filters, 83, 86 Internal Quality Controls (IQCs), 207, 217 Intracellular Chloride content determination, 415 Intracellular Glutathione content determination, 416 Intracellular Heavy Metals content determination, 416 Intracellular Magnesium content determination, 415 Intracellular pH determination, 399 Intracellular Potassium content determination, 414 Intracellular Sodium content determination, 413 Intrinsic parameters, 5, 11 IR-775, 282 IR-780, 282 IR-783, 282 IR-786, 283 IR-797, 282 IR-806, 282 IR-808, 282 IR-820, 283 IRDye series, 286 ISHAGE protocol, 187, 189, 357
Index J Jablonski diagram, 23 Janelia series, 286 JC-1, 395, 422 Jittering, 134 JOJO-1, 345 JO-PRO-1, 347
K Keplerian beam expanders, 81 Kiravia Blue 520™, 309 Kolmogorov-Smirnov test, 181 Krome Orange (KO), 287 Krypton ion lasers, 60
L Laminar flow, 38 LDS-751, 359, 384, 477 Leadmium Green, 416 Levey-Jennings control charts, 229 Lifetime Cytometry, 464, 507 Lift-force cell sorting in microfluidic devices, 512 Light emitting diodes (LEDs), 74 Light loss, 30 Limit of Blank (LOB), 217, 233 in rare event analysis, 239 in the detection of weak signals, 236 Limit of Detection (LOD), 233 in rare event analysis, 239 in the detection of weak signals, 236 Limit of Quantification (LOQ), 233 in rare event analysis, 240 in the detection of weak signals, 236 Linear amplifiers, 107 Linear channel, 158 Linearity, 229 Linear unit, 160 Lipid content determination, 424 Lipid peroxidation determination, 426 Lipophilic carbocyanines, 418 Liquid state lasers (dye lasers), 72 Lissajous figures, 16, 219 Lissamine Rhodamine (RB 200), 276 ListMode modality, 141 LIVE/DEAD® Fixable molecules, 385 LIVE/DEAD® Fixable Aqua, 385 LIVE/DEAD® Fixable Blue, 385 LIVE/DEAD® Fixable Far Red, 385 LIVE/DEAD® Fixable Green, 385 LIVE/DEAD® Fixable Lime, 385 LIVE/DEAD® Fixable LimScarlet, 385
Index LIVE/DEAD® Fixable NIR, 385 LIVE/DEAD® Fixable Olive, 385 LIVE/DEAD® Fixable Red, 385 LIVE/DEAD® Fixable Violet, 385 LIVE/DEAD® Fixable Yellow, 385 LMD format, 138 Location measurements, 525 geometric Mean (gM), 526 Mean (aM), 526 Median (Me), 527 Mode (v0), 527 truncated Mean, 526 Logarithmic amplifiers, 108, 161 Logarithmic transformation, 151, 161 Logicle algorithm, 152 Log-like transformation, 152, 162 Log-normal distribution, 523 LOLO-1, 345 Long-pass filters (LP), 84 Lorenz-Mie’s theory, 12, 14, 15 Low-pressure pneumatic sorters, 494 LST tube, lymphoid screening tube, 195 Lucifer Yellow (LY), 287 LysoHunt molecules, 402 LysoHunt Blue DND-22, 402 LysoHunt Green DND-26, 402 LysoHunt Red DND-99, 402 LysoHunt Yellow HCK-123, 402 LysoSensor™ molecules, 403 LysoSensor™ Blue DND-167, 403 LysoSensor™ Green DND-153, 403 LysoSensor™ Green DND-189, 403 LysoSensor™ Yellow/Blue DND-160, 403 Lysosomal mass determination, 402–404 Lysosomal pH determination, 402–404 LysoTracker® molecules, 402 LysoTracker® Blue DND-22, 402 LysoTracker® Green DND-26, 402 LysoTracker® Red DND- 99, 402 LysoTracker® Yellow HCK-123, 402
M Macarpine (MA), 344 Magnetophoresis in microfluidic devices, 512 Malaria, 18 Marina Blue, 270 Mass Cytometry (MC), 1, 464, 504 Mast cells side scatter in, 15 Maximum positive difference method, 179 M540 bodies, 423 Mean fluorescence intensity (MFI), 174
539 Megamix calibration system, 228 Membrane fluidity determination, 423 Membrane potential determination, 390 Mercury Orange (MO), 416 Mercury vapor lamps, 54 Merocyanine 540, 423 Merocyanines, 383 Mesenchymal stem cells autofluorescence in, 26 Methoxyquinolyl acetoethyl ester (MQAE), 415 Methoxy-sulfopropyl quinolinium (SPQ), 415 Methyl Green (MG), 329 Microfluidics devices, 510 Microglia cells autofluorescence in, 26 Microvalve sorting in microfluidic devices, 512 Minimal residual disease (MRD), 194, 240 Mithramycin (MTH), 361 Mitochondrial mass determination, 397 Mitochondrial membrane potential determination, 395 MitoFluor molecules, 394, 399 MitoFluor Far Red 680, 394, 399 MitoFluor Green, 399 MitoFluor Red 589, 399 MitoFluor Red 594, 395, 399 MitoStatus Red, 395 Mitotracker® molecules, 397 Mitotracker® Deep Red, 397, 399 Mitotracker® Green, 397, 422 Mitotracker® Orange CMTMRos, 397 Mitotracker® Red CMXRos, 397 Mixed-gas ion lasers, 60 Molecules of Equivalent Soluble Fluorochrome (MESF), 207, 225 Monobromobimane (MBB), 416 Monochlorobimane (MCB), 416 Multi-anode PMTs, 101 Multichannel PMTs, 101 Multidrug resistance (MDR) determination, 421
N Nanocrystals, 291 Nanoparticle-encapsulated fluorophores, 308 Narrow-band emissive chromophoric polymer dots, 290 Natural standards, 206 Near Ultraviolet (NUV) emitting SSLs, 65 Negative controls, 171 fluorescence-minus-one (FMO) control, 173 isoclonic control, 173
540 Negative controls (cont.) isotype control, 172 unstained control, 173 Neon-Copper metal vapor lasers, 62 Network Common Data Form format (NetCDF), 137 Neutral density filters (ND), 86 Nigericine, 415 Nile Red (NR), 424 Non-heterocyclic aromatic compounds, 328 Non-standard keywords, 147 Nonyl acridine orange (NAO), 397, 422 Normal (Gaussian) distribution, 524 Notch filters, 84 NovaFluor series, 309 NovaBlue510, 309 NovaBlue530, 309 NovaBlue555, 309 NovaBlue585, 309 NovaBlue610, 309, 310 NovaBlue660, 309, 310 NovaRed660, 310 NovaRed685, 310 NovaRed700, 310 NovaRed720, 310 NovaYellow570, 309 NovaYellow610, 309 NovaYellow660, 309, 310 NovaYellow690, 310 NovaYellow700, 310 NovaYellow720, 310 Nozzle, 487 Nozzle holder, 487 NTB520, 309 NTB660, 309 NTB700, 309 NTB780, 309 Numerical aperture, 82 Nyquist-Shannon theorem, 113, 158
O Obscuration bar, 81 Olivomycin (OL), 361 Optical bench setup, 218 Optically pumped semiconductor lasers (OPSLs), 62 Optoelectronic efficiency, 233 Orange emitting SSLs, 70 Orange fluorescent proteins (OFPs), 450 AsRed2, 451 DsRed, 451 DsRed Express (T1), 451
Index DsRed2, 451 dTomato, 451 Kusabira Orange, 451 Kusabira Orange 2, 451 mOrange, 451 mOrange2, 451 mStrawberry, 451 mTangerine, 451 TagRFP, 451 TagRFP-T, 451 tdTomato, 451 Turbo RFP, 451 Oregon Green 488, 273, 410 Oregon Green 514, 273 Organic polymers, 288 Ortho-phthalaldehyde (OPA, OPT), 416 OTHER segment, 142 Overplotting, 164 Oxazine 750, 355 Oxazines, 355 Oxazole Yellow (YO), 347 Oxidative burst determination, 404 Oxonol derivatives group, 394
P Pacific Blue (PB), 270 Pacific Green (PG), 287 Pacific Orange (PO), 287 Panel Specific Separating Index (SIps), 226 Parallel laser configuration, 91 PBXL-1, 260 PDMPO, 403 Pdots, 290 PE-750, 306 PE-800, 306 PE-830, 306 Peak detectors, 109 Peak reflect method, 183 PE-AL647, 304 PE-CF594, 303 PE-CY5, 303 artifacts due to, 479 PE-CY5.5, 304 artifacts due to, 480 PE-CY7, 305 PE-DAZZLE 594, 303 PerCP-CY5.5, 304, 480 Peridinin–Chlorophyll–Protein (PerCP), 265 PE-TEXAS RED (PE-TR), 303 PF840, 283 Phantom Dyes, 386 Phantom Dye Blue 515, 386
Index Phantom Dye Red 780, 386 Phantom Dye UV 450, 386 Phantom Dye Violet 450, 386 Phantom Dye Violet 510, 386 Phantom Red 710, 386 Phantom Violet 540, 386 PharRed, 307 PhenanGreen FL, 416 Phenanthridine derivatives group, 328 Phenylindole derivatives group, 328 Phosphorescence, 28 Photobleaching, 254 Photocathode, 99 Photodestruction, 254 Photodetectors’ setup, 220–223 Photodiodes (PDs), 97 Photomultipliers (PMTs), 99 Phycobiliproteins, 260 Phycobilisomes, 260 Phycocyanin-C, 264 Phycocyanins, 260 Phycoerythrin R (R-PE), 260, 261 artifacts due to, 479 Phycoerythrins, 260 Picket fence phenomenon, 133 PicoGreen (PG), 352 π-Conjugated polymer molecules, 288 Pie-shaped optical bench, 94 Pinched flow fractionation in microfluidic devices, 512 PKH molecules, 419 PKH2 molecule, 419 PKH26 molecule, 419 PKH67 molecule, 420 PMT-based Imaging Flow Cytometry, 500 Poiseuille law, 40 Poisson distribution, 524 Polaric molecules, 417 Polarizing filters, 87 Polymethine derivatives group, 328 Polynomial transformation, 154 POPO-1, 345 POPO-3, 345, 347 PO-PRO-1, 347 PO-PRO-3, 347 Potassium-binding benzofuran isophthalate (PBFI), 414 Precision, 240 Pre-processing programs, 197 Primary performance parameters (PPPs), 209, 213, 216 Prisms, 88 Probe encapsulated by biologically localized embedding (PEBBLEs), 273, 417
541 Proflavine, 354 Promofluor series, 283, 286 Propidium iodide (PI), 339 in DNA content evaluation, 341 in membrane permeability evaluation, 342 Protein content determination, 382 Pseudo-three-dimensional representation, 168 Pulse analysis, 123 in analog systems, 125 in digital systems, 126 event larger than the interrogation point, 124 event smaller than the interrogation point, 124 practical applications, 129 Pulse processing, 111 Pulse processing synchronization, 111 Pulsed collinear lasers, 94 Purity sorting mode, 491 PyMPO, 269 Pyrene derivatives group, 268 Pyronin Y (PY), 358, 359 Pyrydil-oxazole derivatives group, 269
Q QBEnd10 Mab, 478 QIFIKit®, 226 Quantitative absorption cytometer (QAC), 30 Quantum dots, 251, 291 Qdot 525, 292 Qdot 545, 292 Qdot 565, 292 Qdot 585, 292 Qdot 605, 292 Qdot 625, 292 Qdot 655, 292 Qdot 705, 292 Qdot 800, 292 Quantum yield (Q), 250 Quantum™ Simply Cellular®, 226, 227 Quenching, 254 Quinacrine, 354
R Rainbow beads, 216, 221 Raman cytometry, 509 Raman scattering, 16 Ratio between fluorescence intensities (RFI), 175 Ratio performing circuits, 106 Rayleigh’s theory, 12 Recovery sorting mode, 492 Rectangle graphic method, 183
542 Red emitting SSLs, 70 Red fluorescent proteins (RFPs) E2 Crimson, 451 eqFP670, 451 HcRed1, 451 HcRed-Tandem, 451 Katushka, 451 mBeRFP, 451 mCherry, 451 mKate, 451 mKate2, 451 mPlum, 451 mRFP1, 451 mRuby, 451 TagFP657, 451 TurboFP602, 451 Red613, 276, 303 Red670, 303 Reflection type optical bench, 88, 90 Regions, 186 Relative brightness, 233 Relative fluorescence intensity (RFI), 175 Relative median fluorescence (RMF), 175 Resolution, 231 Reynolds number, 38 Rhod-5N, 409 Rhodamine 123 (RH123), 276, 394, 406, 422 Rhodamine 6G, 277 Rhodamine 700, 277 Rhodamine 800, 277, 394 Rhodamine Efflux Assay, 422 Rosetta calibration system, 228
S Sample differential, 43 Sanguilutine (SL), 344 Sanguinarine (SA), 344 Sanguirubine (SR), 344 Satellitism, 478 Sensitivity, 232 Seta series, 287 Sheath, 40 Sheathless systems, 50 Short-pass filters (SP), 84 Side scatter (SSC), 15 Signal heteroskedasticity, 6, 220 Signal-to-noise ratio (SNR), 131 Silicon photomultiplier (SiPMs), 101 Single-cell sorting mode, 493 Simple FITting (SFIT) computational method, 183 Single-photon avalanche diodes (SPADs), 101
Index Single-platform counts, 46 SiR-Hoechst, 277, 334 SNARF-1, 401 Sodium azide (NaN3), 40 Sodium-binding benzofuran isophthalate (SBFI), 413 Sodium Green, 413 Solid-state lasers (SSLs), 62 Solvatochromic effect, 254 Soret band, 31 Sorting envelope, 490 Sorting mask, 490 Spark-generated microbubble cavitation in microfluidic devices, 512 Spark series, 287 Specificity, 241 Spectral flow cytometry (SFC), 1, 251, 464, 501 Spectral unmixing, 502 Spectrally enhanced optical benches, 95 SpectralRed, 303 Spherical lenses, 80 Spillover spectral spreading, 466 spillover coefficients, 463 spillover inter-laser, 295, 462 spillover intra-laser, 457, 458, 460 spillover matrix, 463 spillover spreading (SS), 466 spillover spreading matrix (SSM), 467 Spread Quantification Index (SQI), 467 Spread measurements Coefficient of Variation, 529 Median Absolute Deviation (MAD), 528 robust Coefficient of Variation (rCV), 530 robust Standard Deviation, 529 Standard Deviation (SD), 528 Variance (Var), 528 Stain Index (SI), 233 Standard optional keywords, 145 Standard required keywords, 144 StarBright Dyes series, 290 Stilbene derivatives group, 328 Stokes scatter, 17 Stokes shift, 24, 249 Sulfoindocarbocyanines, 383 Sulforhodamine 101, 276 Sulforhodamine B (SRB), 276 Sum of Gaussian curves computational method, 183 Super Bright 436, 289 Super Bright 436 based tandems, 301 Super Bright 600, 301 Super Bright 650, 301
Index Super Bright 702, 301 Super Bright 780, 301 Supercontinuum white light emitting SSLs, 72 SuperNova V428, 289 SuperNova V428-based tandems, 301 SuperNova V605, 301 SuperNova V786, 301 Surface-enhanced Raman scattering (SERS), 509 SYBR series, 351 SYBR 14, 352 SYBR Gold, 352 SYBR Green I, 351 SYBR Green II, 351 SYPRO molecules SYPRO Orange, 383 SYPRO Red, 383 SYPRO Ruby, 383 SYPRO Tangerine, 383 System Dynamic Integrating Window, 127 SYTO® series, 350, 384 Blue Fluorescent SYTO® Dyes, 350 Green Fluorescent SYTO® Dyes, 350 Orange Fluorescent SYTO® Dyes, 350 Red Fluorescent SYTO® Dyes, 350 SYTO RNASelect, 326 SYTO®12, 350 SYTO®13, 351 SYTO®16, 350, 351, 384, 422 SYTO®61, 351 SYTOX® series, 349 SYTOX® Blue, 349 SYTOX® Green, 349 SYTOX® Orange, 349 SYTOX® Red, 349
T Tandem fluorochromes, 293 blue-excited, 301 drawbacks, 295 green-yellow-excited, 306 red-excited, 307 UV-excited, 297 violet excited, 300 Tetramethylrhodamine (TMR), 275 Tetramethylrhodamine ethyl ester (TMRE), 394 Tetramethylrhodamine isothiocyanate (TRITC), 275 Tetramethylrhodamine methyl ester (TMRM), 394 Tetrazolium nitroblue (NBT), 406 Texas Red (TR), 275 TEXT segment, 140
543 Thiazole Orange (TO), 347, 355 Threshold, 106 Time, 6 Time delay, 43, 92 T3 Mab, 478 TMRE, 277 TMRM, 277 TO-PRO series, 347 TO-PRO-1, 347 TO-PRO-3, 347, 349 TO-PRO-5, 347, 349 TOTO series, 345 TOTO-1, 345 TOTO-3, 345, 347 TRACY dyes, 287 Trans-impedance amplifiers (TIAs), 102 Transmission type optical bench, 88, 89 Transparency, 30 Transverse Electromagnetic Modalities, 57 Triaryl derivatives group, 328 Tricolor, 303 Tricyclic antibiotics, 361 Trimethylammonium-diphenyl-hexatriene (TMA-DPH), 424 Trout nucleated red blood cells (TRBC), 206 True three-dimensional representation, 169 True Volumetric Absolute Counting (TVAC), 46 TrueRed, 304 Trypan Blue, 384, 389 Turbulent flow, 38
U Ultraviolet (UV) emitting SSLs, 64 Up-converting nanoparticles (UCNPs), 293
V Vanishing beads phenomenon, 46 Verapamil, 422, 423 Viobility™ Fixable Dyes, 386 VioDyes series, 287 Violet emitting SSLs, 66 Vita Blue, 277 VivoTag series, 287 Vlog algorithm, 152, 160 Voltration, 223
W Wavelength Division Multiplexing (WDM), 88 Weak positive sample with a negative component, 178
544 Weak positive sample without a negative component, 178 Window’s extension, 93, 127 Window’s gate, 127, 231
X Xanthene derivatives group, 271, 394 Xenon vapor lamps, 54
Y Yellow fluorescent proteins (YFPs), 450 Citrine, 450 mBanana, 450 TagYFP, 450 Topaz, 450 TurboYFP, 450 Venus, 450 ZsYellow1, 450
Index YO-PRO series YO-PRO-1, 347 YO-PRO-3, 347 YO-YO series YOYO-1, 345, 347 YOYO-3, 345
Z Zero channel value (ZCV), 217 Zinpyr-1 (ZP1), 416 Zinquin (TSQ), 416 Zombie™ molecules, 385 Zombie Aqua™, 385 Zombie Green™, 385 Zombie NIR™, 385 Zombie Red™, 385 Zombie UV™, 385 Zombie Violet™, 385 Zombie Yellow™, 385