Multiplexed Imaging: Methods and Protocols (Methods in Molecular Biology, 2350) 1071615920, 9781071615928

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Methods in Molecular Biology 2350

Eli Zamir Editor

Multiplexed Imaging Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Multiplexed Imaging Methods and Protocols

Edited by

Eli Zamir Department of Cellular Biophysics, Max Planck Institute for Medical Research, Stuttgart, Baden-Württemberg, Germany

Editor Eli Zamir Department of Cellular Biophysics Max Planck Institute for Medical Research Stuttgart, Baden-Wu¨rttemberg, Germany

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1592-8 ISBN 978-1-0716-1593-5 (eBook) https://doi.org/10.1007/978-1-0716-1593-5 © Springer Science+Business Media, LLC, part of Springer Nature 2021, Corrected Publication 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, 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. Cover Illustration: Composite scheme illustrating multiplexed imaging of PKA activity, ERK activity, Ca2+ level and cAMP level in living cells. Contributed by Jeremiah Keyes, Sohum Mehta and Jin Zhang. See Chapter 1 for more information. This Humana imprint is published by the registered company Springer Science+Business Media, LLC part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Multiplexed imaging microscopy consists of methods that enable co-imaging of multiple, different, components in a specimen. Such components can be, for example, the local levels of distinct proteins, their states (e.g., post-translational modifications), and their interactions within cells and tissues. Additionally, multiplexed imaging approaches can include the imaging of biophysical parameters (e.g., biomechanical forces) and combining them with the imaging of molecular components. In the era of systems biology, multiplexed imaging increasingly becomes an essential strategy for advancing the study of cells and tissues. The main reason for this is that biological properties arise from the collective action of multiple distinct biochemical components affecting each other. Furthermore, these biochemical networks are often organized in spatiotemporal patterns which are fundamental for the emergence of their biological functions. Due to intracellular and intercellular variabilities, it is usually infeasible to infer the spatiotemporal relations between different components simply by imaging separately each of them in different specimens. Multiplexed imaging provides the ultimate solution for this fundamental challenge, through the co-imaging of multiple components of interest in the same specimen. Multiplexed imaging of biochemical networks in intact cells and tissues is fundamentally challenging. The main challenges here are how to label specifically each of the multiple components of interest in a given specimen and how to image specifically and efficiently the signal of each of the labels. Fortunately, the frontiers of multiplexed imaging are constantly advancing thanks to rapid developments in labeling and imaging approaches. These developments synergize with the rise of systems biology and personalized medicine, which motivate multiplexed imaging for basic research and clinical diagnostics. This volume of the Methods in Molecular Biology series provides a collection of stateof-the-art approaches covering key aspects of multiplexed imaging. Hence, this book should be helpful for researchers interested in implementing multiplexed imaging or in developing new, cutting-edge multiplexed labeling and imaging techniques. I am grateful to all the authors for their valuable contributions, providing detailed protocols and insightful descriptions of their methods. I also would like to thank Dr. John M. Walker for his helpful advice along the way. I wish the readers a rewarding use of this book and successful multiplexed imaging. ¨ rttemberg, Germany Stuttgart, Baden-Wu

Eli Zamir

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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 Strategies for Multiplexed Biosensor Imaging to Study Intracellular Signaling Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeremiah Keyes, Sohum Mehta, and Jin Zhang 2 Six-Color Confocal Immunofluorescence Microscopy with 4-Laser Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ hr, Lukas Amon, Ana-Suncˇana Smith, Lukas Heger, Jennifer J. Lu Nathalie Eissing, and Diana Dudziak 3 Multiplexed Imaging of Posttranslational Modifications of Endogenous Proteins in Live Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuko Sato and Hiroshi Kimura 4 Multiplex Imaging of Rho GTPase Activities in Living Cells. . . . . . . . . . . . . . . . . . ¨ lsemann, Polina V. Verkhusha, Ravi M. Bhalla, Maren Hu Myla G. Walker, Daria M. Shcherbakova, and Louis Hodgson 5 Multicolor Localization-Based Super Resolution Microscopy . . . . . . . . . . . . . . . . . Leila Nahidiazar and Rolf Harkes 6 Multiplexed Tissue Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evan H. Phillips, David Scholten, Amy C. Flor, Stephen J. Kron, and Steve Seung-Young Lee 7 Multicolor 3D Confocal Imaging of Thick Tissue Sections . . . . . . . . . . . . . . . . . . . Leo Kunz and Daniel L. Coutu 8 Multiphoton Deep-Tissue Imaging of Micrometastases and Disseminated Cancer Cells Using Conjugates of Quantum Dots and Single-Domain Antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alyona Sukhanova, Fernanda Ramos-Gomes, Patrick Chames, Pavel Sokolov, Daniel Baty, Frauke Alves, and Igor Nabiev 9 Multiplexed Imaging for Immune Profiling on Human FFPE Material . . . . . . . . Artur Mezheyeuski and Carina Strell 10 Method for Multiplexed Dynamic Intravital Multiphoton Imaging . . . . . . . . . . . Asylkhan Rakhymzhan, Andreas Acs, Ruth Leben, Thomas H. Winkler, Anja E. Hauser, and Raluca A. Niesner 11 Fourier Multiplexed Fluorescence Lifetime Imaging . . . . . . . . . . . . . . . . . . . . . . . . Leilei Peng 12 Bimolecular Fluorescence Complementation (BiFC) and Multiplexed Imaging of Protein–Protein Interactions in Human Living Cells. . . . . . . . . . . . . . Yunlong Jia, Franc¸oise Bleicher, Jonathan Reboulet, and Samir Merabet

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Out-of-Phase Imaging after Optical Modulation (OPIOM) for Multiplexed Fluorescence Imaging under Adverse Optical Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Raja Chouket, Ruikang Zhang, Agne´s Pellissier-Tanon, Annie Lemarchand, Agathe Espagne, Thomas Le Saux, and Ludovic Jullien 14 Multicolor Bioluminescence Imaging of Subcellular Structures and Multicolor Calcium Imaging in Single Living Cells. . . . . . . . . . . . . . . . . . . . . . Kazushi Suzuki, Md Nadim Hossain, Tomoki Matsuda, and Takeharu Nagai 15 Nanoparticles for In Vivo Lifetime Multiplexed Imaging . . . . . . . . . . . . . . . . . . . . Erving Ximendes, Emma Martı´n Rodrı´guez, Dirk H. Ortgies, Meiling Tan, Guanying Chen, and Blanca del Rosal 16 Versatile on-Demand Fluorescent Labeling of Fusion Proteins Using Fluorescence-Activating and Absorption-Shifting Tag (FAST) . . . . . . . . . . Arnaud Gautier, Ludovic Jullien, Chenge Li, Marie-Aude Plamont, Alison G. Tebo, Marion Thauvin, Michel Volovitch, and Sophie Vriz 17 UltraPlex Hapten-Based Multiplexed Fluorescent Immunohistochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matt Levin, Amy C. Flor, Helen Snyder, Stephen J. Kron, and David Schwartz 18 Multimodal Approach for Cancer Cell Investigation . . . . . . . . . . . . . . . . . . . . . . . . Alexandre Berquand and Jeroˆme Devy 19 Multiplexed Fourier Transform Infrared and Raman Imaging . . . . . . . . . . . . . . . . Guillermo Quinta´s, Bayden R. Wood, Hugh J. Byrne, and David Perez-Guaita 20 Multiplexed Imaging Mass Spectrometry of Histological Staining, N-Glycan and Extracellular Matrix from One Tissue Section: A Tool for Fibrosis Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cassandra L. Clift, Anand Mehta, Richard R. Drake, and Peggi M. Angel 21 Multiplexed Raman Imaging in Tissues and Living Organisms . . . . . . . . . . . . . . . Travis M. Shaffer and Sanjiv S. Gambhir Correction to: Out-of-Phase Imaging after Optical Modulation (OPIOM) for Multiplexed Fluorescence Imaging under Adverse Optical Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors ANDREAS ACS • Division of Genetics, Department of Biology, Nikolaus-Fiebiger-Center for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany FRAUKE ALVES • Max Planck Institute for Experimental Medicine & University Medical Center, Go¨ttingen, Germany LUKAS AMON • Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nu¨rnberg, Erlangen, Germany PEGGI M. ANGEL • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, USA DANIEL BATY • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France ALEXANDRE BERQUAND • Laboratoire de Recherche en Nanosciences LRN EA4682 and NanoMat’ platform, Universite´ de Reims Champagne-Ardenne, Reims, France RAVI M. BHALLA • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA FRANC¸OISE BLEICHER • Institut de Ge´nomique Fonctionnelle de Lyon, UMR5242, Universite´ Lyon 1, CNRS, Ecole Normale Supe´rieure de Lyon, Lyon Cedex 07, France HUGH J. BYRNE • FOCAS Research Institute, Technological University Dublin, Dublin, Ireland PATRICK CHAMES • Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, Marseille, France GUANYING CHEN • School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China RAJA CHOUKET • PASTEUR, De´partement de Chimie, E´cole normale supe´rieure, PSL University, Sorbonne Universite´, CNRS, Paris, France CASSANDRA L. CLIFT • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, USA DANIEL L. COUTU • Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada; Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada; Division of Orthopaedic Surgery, The Ottawa Hospital, Ottawa, ON, Canada BLANCA DEL ROSAL • ARC Centre of Excellence for Nanoscale Biophotonics, RMIT University, Melbourne, VIC, Australia JEROˆME DEVY • CNRS UMR 7369, Matrice Extracellulaire et Dynamique Cellulaire (MEDyC), UFR Sciences Exactes et Naturelles, Universite´ de Reims Champagne-Ardenne (URCA), Laboratoire SiRMa - Campus Moulin de la Housse, Reims Cedex, France RICHARD R. DRAKE • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, USA DIANA DUDZIAK • Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nu¨rnberg, Erlangen, Germany; Deutsches Zentrum Immuntherapie (DZI), Erlangen, Germany; Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg

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Contributors

(CCC ER-EMN), Erlangen, Germany; Medical Immunology Campus Erlangen, Erlangen, Germany NATHALIE EISSING • Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nu¨rnberg, Erlangen, Germany AGATHE ESPAGNE • PASTEUR, De´partement de Chimie, E´cole normale supe´rieure, PSL University, Sorbonne Universite´, CNRS, Paris, France AMY C. FLOR • Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, IL, USA SANJIV S. GAMBHIR • Department of Radiology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Bio-X Program, Molecular Imaging Program at Stanford (MIPS), Canary Center for Early Cancer Detection, Stanford, CA, USA ARNAUD GAUTIER • Sorbonne Universite´, E´cole Normale Supe´rieure, Universite´ PSL, CNRS, Laboratoire des Biomole´cules, LBM, Paris, France; Institut Universitaire de France, Paris, France ROLF HARKES • Netherlands Cancer Institute, Amsterdam, Netherlands ANJA E. HAUSER • Immundynamics, Deutsches Rheumaforschungszentrum - a Leibniz Institute, Berlin, Germany; Immundynamics and Intravital Microscopy, Department of Rheumatology and Clinical Immunology, Charite´ - Universit€ atsmedizin Berlin, Corporate Member of Freie Universit€ a t Berlin, Humboldt-Universit€ at zu Berlin, and Berlin Institute of Health, Berlin, Germany LUKAS HEGER • Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nu¨rnberg, Erlangen, Germany LOUIS HODGSON • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA MD NADIM HOSSAIN • SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Osaka, Ibaraki, Japan MAREN HU¨LSEMANN • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA YUNLONG JIA • Institut de Ge´nomique Fonctionnelle de Lyon, UMR5242, Universite´ Lyon 1, CNRS, Ecole Normale Supe´rieure de Lyon, Lyon Cedex 07, France LUDOVIC JULLIEN • PASTEUR, Department of Chemistry, E´cole Normale Supe´rieure, Universite´ PSL, Sorbonne Universite´, CNRS, Paris, France JEREMIAH KEYES • Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA HIROSHI KIMURA • Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan STEPHEN J. KRON • Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, IL, USA; Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA LEO KUNZ • Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Roche Innovation Center Zurich, Pharmaceutical Research & Early Development (pRED), Schlieren, Switzerland

Contributors

xi

RUTH LEBEN • Biophysical Analytics, Deutsches Rheumaforschungszentrum - a Leibniz Institute, Berlin, Germany STEVE SEUNG-YOUNG LEE • Department of Pharmaceutical Sciences, The University of Illinois at Chicago, Chicago, IL, USA ANNIE LEMARCHAND • Laboratoire de Physique The´orique de la Matie`re Condense´ e (LPTMC), Sorbonne Universite´, Centre National de la Recherche Scientifique (CNRS), Paris, France MATT LEVIN • Cell IDx, San Diego, CA, USA CHENGE LI • PASTEUR, Department of Chemistry, E´cole Normale Supe´rieure, Universite´ PSL, Sorbonne Universite´, CNRS, Paris, France; Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China JENNIFER J. LU¨HR • Department of Physics, Nano-Optics, Sandoghdar Division, Max Planck Institute for the Science of Light, Erlangen, Germany EMMA MARTI´N RODRI´GUEZ • Nanomaterials for BioImaging Group, Instituto Ramon y Cajal de Investigacion Sanitaria IRYCIS, Madrid, Spain; Fluorescence Imaging Group, Departamento de Fı´sica Aplicada, Facultad de Ciencias, Universidad Autonoma de Madrid, Madrid, Spain TOMOKI MATSUDA • SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Osaka, Ibaraki, Japan ANAND MEHTA • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC, USA SOHUM MEHTA • Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA SAMIR MERABET • Institut de Ge´nomique Fonctionnelle de Lyon, UMR5242, Universite´ Lyon 1, CNRS, Ecole Normale Supe´rieure de Lyon, Lyon Cedex 07, France ARTUR MEZHEYEUSKI • Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden IGOR NABIEV • Laboratoire de Recherche en Nanosciences, LRN-EA4682, Universite´ de Reims Champagne-Ardenne, Reims, France; Laboratory of Nano-Bioengineering, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia TAKEHARU NAGAI • SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Osaka, Ibaraki, Japan LEILA NAHIDIAZAR • Netherlands Cancer Institute, Amsterdam, Netherlands RALUCA A. NIESNER • Biophysical Analytics, Deutsches Rheumaforschungszentrum - a Leibniz Institute, Berlin, Germany; Dynamic and Functional in vivo Imaging, Institute for Veterinary Physiology, Veterinary Medicine, Freie Universit€ at Berlin, Berlin, Germany DIRK H. ORTGIES • Nanomaterials for BioImaging Group, Instituto Ramon y Cajal de Investigacion Sanitaria IRYCIS, Madrid, Spain; Nanomaterials for BioImaging Group, Departamento de Fı´sica de Materiales, Facultad de Ciencias, Universidad Autonoma de Madrid, Madrid, Spain AGNE`S PELLISSIER-TANON • PASTEUR, De´partement de Chimie, E´cole normale supe´rieure, PSL University, Sorbonne Universite´, CNRS, Paris, France LEILEI PENG • Wyant College of Optical Science, University of Arizona, Tucson, AZ, USA DAVID PEREZ-GUAITA • FOCAS Research Institute, Technological University Dublin, Dublin, Ireland

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Contributors

EVAN H. PHILLIPS • Department of Pharmaceutical Sciences, The University of Illinois at Chicago, Chicago, IL, USA MARIE-AUDE PLAMONT • PASTEUR, Department of Chemistry, E´cole Normale Supe´rieure, Universite´ PSL, Sorbonne Universite´, CNRS, Paris, France GUILLERMO QUINTA´S • Health & Biomedicine, LEITAT Technological Center, Barcelona, Spain ASYLKHAN RAKHYMZHAN • Biophysical Analytics, Deutsches Rheumaforschungszentrum - a Leibniz Institute, Berlin, Germany FERNANDA RAMOS-GOMES • Max Planck Institute for Experimental Medicine & University Medical Center, Go¨ttingen, Germany JONATHAN REBOULET • Institut de Ge´nomique Fonctionnelle de Lyon, UMR5242, Universite´ Lyon 1, CNRS, Ecole Normale Supe´rieure de Lyon, Lyon Cedex 07, France YUKO SATO • Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan THOMAS LE SAUX • PASTEUR, De´partement de Chimie, E´cole normale supe´rieure, PSL University, Sorbonne Universite´, CNRS, Paris, France DAVID SCHOLTEN • Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, IL, USA DAVID SCHWARTZ • Cell IDx, San Diego, CA, USA TRAVIS M. SHAFFER • Department of Radiology, Stanford University, Stanford, CA, USA DARIA M. SHCHERBAKOVA • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA ANA-SUNCˇANA SMITH • Physics Underlying Life Sciences Group, Institute for Theoretical Physics, Friedrich-Alexander-University Erlangen-Nu¨rnberg, Erlangen, Germany; Group for Computational Life Sciences, Division of Physical Chemistry, Institute Rua˛er Bosˇkovic´, Zagreb, Croatia HELEN SNYDER • Cell IDx, San Diego, CA, USA PAVEL SOKOLOV • Laboratory of Nano-Bioengineering, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia CARINA STRELL • Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden ALYONA SUKHANOVA • Laboratoire de Recherche en Nanosciences, LRN-EA4682, Universite´ de Reims Champagne-Ardenne, Reims, France KAZUSHI SUZUKI • Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan; SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Osaka, Ibaraki, Japan MEILING TAN • School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China ALISON G. TEBO • Sorbonne Universite´, E´cole Normale Supe´rieure, Universite´ PSL, CNRS, Laboratoire des Biomole´cules, LBM, Paris, France; Janelia Farms Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA MARION THAUVIN • Center for Interdisciplinary Research in Biology (CIRB), Colle`ge de France, CNRS, INSERM, Universite´ PSL, Paris, France POLINA V. VERKHUSHA • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA

Contributors

xiii

MICHEL VOLOVITCH • Center for Interdisciplinary Research in Biology (CIRB), Colle`ge de France, CNRS, INSERM, Universite´ PSL, Paris, France; Department of Biology, E´cole Normale Supe´rieure, Universite´ PSL, Paris, France SOPHIE VRIZ • Center for Interdisciplinary Research in Biology (CIRB), Colle`ge de France, CNRS, INSERM, Universite´ PSL, Paris, France; Faculty of Science, Universite´ de Paris, Paris, France MYLA G. WALKER • Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY, USA THOMAS H. WINKLER • Division of Genetics, Department of Biology, Nikolaus-FiebigerCenter for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany BAYDEN R. WOOD • Faculty of Science, Centre for Biospectroscopy, School of Chemistry, Monash University, Clayton, VIC, Australia ERVING XIMENDES • Nanomaterials for BioImaging Group, Instituto Ramon y Cajal de Investigacion Sanitaria IRYCIS, Madrid, Spain; Nanomaterials for BioImaging Group, Departamento de Fı´sica de Materiales, Facultad de Ciencias, Universidad Autonoma de Madrid, Madrid, Spain JIN ZHANG • Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA RUIKANG ZHANG • PASTEUR, De´partement de Chimie, E´cole normale supe´rieure, PSL University, Sorbonne Universite´, CNRS, Paris, France

Chapter 1 Strategies for Multiplexed Biosensor Imaging to Study Intracellular Signaling Networks Jeremiah Keyes, Sohum Mehta, and Jin Zhang Abstract Signal transduction processes are a necessary component of multicellular life, and their dysregulation is the basis for a host of syndromes and diseases. Thus, it is imperative that we discover the complex details of how signal transduction processes result in specific cellular outcomes. One of the primary mechanisms of regulation over signaling pathways is through spatiotemporal control. However, traditional methods are limited in their ability to reveal such details. To overcome these limitations, researchers have developed a variety of genetically encodable, fluorescent protein-based biosensors to study these dynamic processes in real time in living cells. Due to the complexities and interconnectedness of signaling pathways, it is thus desirable to use multiple biosensors in individual cells to better elucidate the relationships between signaling pathways. However, multiplexed imaging with such biosensors has been historically difficult. Nevertheless, recent developments in designs and multiplexing strategies have led to vast improvements in our capabilities. In this review, we provide perspectives on the recently developed biosensor designs and multiplexing strategies that are available for multiplexed imaging of signal transduction pathways. Key words Biosensor, Signal transduction, Multiplexed imaging, Fluorescent protein, Signaling

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Introduction For life to function, cells need to be able to sense and respond to their environment. Furthermore, multicellular life requires communication between cells, tissues, and organs. These critical sensing and communication abilities occur through complex, tightly controlled signal transduction circuits that enable individual cells, tissues, and organs to respond appropriately to stimuli and meet the needs of the organism. When these pathways become dysregulated, illnesses such as cancer, diabetes, cardiovascular disease, and neurodegenerative disorders may develop [1–3]. This fact underscores the critical need to be able to understand these complex circuits in order to prevent and treat disease and improve human health. Traditional methods to study these pathways, such as immunohistochemical and in vitro assays, have provided significant insight

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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into signal transduction mechanisms, but they are vastly limited in providing quantitative spatiotemporal information. Furthermore, these traditional methods are labor- and time-intensive and reflect the population average, which may not reflect the heterogenous responses of individual cells [4]. Since the advent of fluorescent protein (FP) utilization to study biological processes [5, 6], there has been an explosion of new FPs and the creation of genetically encodable, FP-based biosensors: engineered proteins that alter their fluorescence properties in response to a given trigger, which we have recently reviewed in detail [7]. A major advantage of using biosensors to study signal transduction networks is that these tools can illuminate signaling processes in real time, vastly increasing our ability to determine the strict spatiotemporal dynamics of specific signaling pathways. Furthermore, biosensor imaging allows researchers to study these signaling events in the context of single living cells, resolving previously unseen details such as spatiotemporal dynamics or cell heterogeneity. Thus far, individual biosensors are often used in isolation. While this approach has led to a richer and deeper understanding of signal transduction networks, signaling pathways do not operate in seclusion. When these FP-based biosensors are used to study isolated processes, researchers are unable to discern the interrelationship between two or more pathways, such as whether they are synergistic, antagonistic, upstream, or downstream from one another. Thus, there is much to be gained by using multiple biosensors to simultaneously examine multiple pathways in the same cell, as this will yield a richer understanding of these complex signal transduction circuits. Multiplexed imaging is an advantageous approach to study these important signaling networks, but it is not without its own caveats. The challenge of multiplexed imaging with biosensors arises from two important facts: the visible spectrum is a finite resource, and FPs have relatively large spectral footprints (Fig. 1). These two facts mean that attempting to multiplex with multiple FPs can easily lead to overlapping spectra, masking our ability to simultaneously detect multiple signaling events. However, the unique advantages of genetically encodable biosensors have led researchers to continually develop creative methods to bypass the issue of spectral overlap and reduce the spectral footprints of current imaging techniques. Here, we first provide a general overview of common biosensor designs, followed by a discussion of several methods that have been developed to enable multiplexed imaging of fluorescent biosensors.

Strategies for Multiplexed Biosensor Imaging to Study Intracellular. . .

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Fig. 1 Spectral overlap between fluorescent proteins. Excitation and emission spectra of various fluorescent proteins (FPs) that span the visible spectrum. While numerous FPs have been developed with distinct excitation and emission peaks that give rise to their characteristic “colors” (e.g., “green” or “red” FPs), all FPs have broad (>100 nm) spectral footprints that pose a challenge for multiplexed imaging. (a) Cyan (CFP) and yellow (YFP) FPs are commonly paired as the FRET donor and acceptor, respectively, as are green (GFP) and red (RFP) FPs. However, considerable overlap between the CFP, GFP, and YFP spectra precludes the use of these FRET pairs for multiplexed imaging. (b) Orange (OFP) and red (RFP) FPs have been used as an orthogonal FRET pair with CFP and YFP. Nevertheless, spectral contamination is likely between OFP and RFP, as well as between OFP and YFP, due to their substantial spectral overlap, which can diminish biosensor signals. (c) The novel FRET pair of mIRFP670 and miRFP720 shows much better spectral separation from CFP and YFP and may therefore enable more robust multiplexing of FRET-based sensors

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Overview of Biosensor Design Extensive efforts have been made to develop FP-based biosensors in order to visualize signal transduction activities in real time in living cells. These FP-based biosensors all share a similar overall design, in which one or more FPs serve as a “reporting unit” in conjunction with a “sensing unit” specifically engineered or derived from endogenous protein sequences that detect a specific signaling event (Fig. 2), such as second messenger binding or phosphorylation by a specific kinase. Coupling of the sensing and reporting units allows the signaling event to induce a change in the fluorescence readout from the biosensor, and how this coupling is implemented defines the nature of the fluorescence change.

2.1 Modulating FRET between a Pair of FPs

Fo¨rster resonance energy transfer (FRET) is a photophysical phenomenon where an excited “donor” FP nonradiatively transfers its excited-state energy to a nearby “acceptor” FP via dipole-dipole interactions (see Greenwald et al. [7], Newman et al. [8], and Cardullo et al. [9] for a more detailed overview). The efficiency of this nonradiative energy transfer is strongly dependent on distance: only when the two fluorophores are in close proximity (2%) as it will lead to faster bleaching of the tissue. Use the range indicator to prevent saturation of the signal. 7. Use the tile scan function (or a similar function using other imaging software) to analyze larger areas of the tissue (e.g., B and T cell zones with surrounding tissue; see Note 10). 8. To improve the signal-to-noise ratio, adjust the scan speed so that the pixel dwell time is longer than 3 μs.

4

Notes 1. Attach the surface of the sliced tissue to the bottom of the cryomold. Make sure that the block contains no air bubbles and that the area of interest in the tissue is in parallel to the bottom of the cryomold. 2. Panel design: Weakly expressed antigens should be stained with bright fluorochromes (e.g., Cy3, Brilliant Violet 421), while dim fluorochromes should be used for strongly expressed antigens (e.g., CD3, CD8). 3. Since some fluorochromes are either prone to bleaching or relatively dim, amplification steps with secondary and tertiary antibodies are necessary. As most available antibodies are rather coupled with unstable (PE) or dim (FITC) fluorochromes, we established amplification protocols by using a secondary goat anti-PE and tertiary donkey anti-goat-Cy3 antibody for PE- as well as a secondary Alexa Fluor® 488-coupled mouse antiFITC antibody for FITC-coupled antibodies. Therefore, at least three separate staining steps are necessary, when using PE-coupled primary antibodies.

Six-Color Confocal Immunofluorescence Microscopy

29

4. The incubation time for the staining steps can be increased (staining can be also performed overnight at 4  C). However, to avoid unspecific binding, the slices should be washed longer/more often. 5. The first drop of the mounting medium should be discarded to avoid air bubbles in the mounting medium. This will improve the microscopy results. 6. After the coverslip has been lowered, it should be fixed until the mounting medium is evenly distributed, to avoid air bubbles and/or damage to the tissue. 7. To allow for high magnification and resolution of the images, objectives with 63
or 100
magnification and a high numerical aperture (NA) should be used (e.g., 63
/1.3 NA oil immersion objective) as high NA leads to a brighter signal. 8. Spectral detectors enable easy determination of the range in which fluorescence signals are measured. However, also combinations of short, long, and band pass filters can be used as described before [9]. Shortly, for the 405 nm laser, the fluorescence signals can be detected with a short-pass (590 nm. Fluorochromes that are excited with a 488 nm laser line can be detected with a band-pass (490–555 nm) filter (Alexa Fluor® 488, FITC) in combination with a long-pass (640 nm) filter due to a far red-shifted emission (PerCP-eFluor® 710, PerCPCy5.5). For PE/Cy3 antibodies, a 490–635 nm band-pass filter can be used. Alexa Fluor® 647-labeled antibodies can be detected with a long-pass (>640 nm) filter. 9. Tissue slices stained with single fluorochrome-coupled antibodies should be used to test for spillover into the other channels after the different tracks have been setup at the microscope for the described protocol. 10. For imaging of larger areas of tissue slices (>1 mm2) with more than 10
10 tiles, objectives with lower magnification (e.g., 40
/1.2 NA) can be used to reduce the acquisition time and thereby lower the risk of loss of focus due to heating or shaking.

Acknowledgments We thank S. Beck for technical support. We are grateful for the support by the Medical Immunology Campus Erlangen (MICE) and the Optical Imaging Center Erlangen (OICE). This work was partly supported by grants from the German Research Foundation

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[Deutsche Forschungsgemeinschaft (DFG)] to D.D. (CRC1181TPA7), D.D., and A.-S.S. (RTG1962). D.D. and A.-S.S. are funded by the Emerging Fields Initiative BIG-THERA of the Friedrich-Alexander University Erlangen-Nu¨rnberg. L.H. was supported by Erlanger Leistungsbezogene Anschub-finanzierung und Nachwuchsfo¨rderung (ELAN) (DE-17-09-15-1-Heger). D.D. received support from Interdisziplin€ares Zentrum fu¨r Klinische Forschung (IZKF) (IZKF-A65). References 1. Combs CA (2010) Fluorescence microscopy: a concise guide to current imaging methods. Curr Protoc Neurosci 79:2.1.1–2.1.25. https://doi.org/10.1002/0471142301. ns0205s00 2. Dekkers JF, Alieva M, Wellens LM et al (2019) High-resolution 3D imaging of fixed and cleared organoids. Nat Protoc 14:1756–1771. https://doi.org/10.1038/s41596-019-01608 3. Sheppard CJR (1988) Depth of field in optical microscopy. J Microsc 149:73–75. https://doi. org/10.1111/j.1365-2818.1988.tb04563.x 4. Perfetto SP, Chattopadhyay PK, Roederer M (2004) Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol 4:648–655. https://doi.org/10. 1038/nri1416 5. Lichtman JW, Conchello J-A (2005) Fluorescence microscopy. Nat Methods 2:910–919. https://doi.org/10.1038/nmeth817 6. Biggs DSC (2010) 3D deconvolution microscopy. Curr Protoc Cytom 52:12.9.1–12.9.20. https://doi.org/10.1002/0471142956. cy1219s52 7. Jonkman J, Brown CM (2015) Any way you slice it—a comparison of confocal microscopy

techniques. J Biomol Tech 26:54–65. https:// doi.org/10.7171/jbt.15-2602-003 8. Kaliman S, Jayachandran C, Rehfeldt F, Smith AS (2016) Limits of applicability of the voronoi tessellation determined by centers of cell nuclei to epithelium morphology. Front Physiol 7:551. https://doi.org/10.3389/fphys.2016. 00551 9. Eissing N, Heger L, Baranska A et al (2014) Easy performance of 6-color confocal immunofluorescence with 4-laser line microscopes. Immunol Lett 161:1–5. https://doi.org/10. 1016/j.imlet.2014.04.003 10. Heidkamp GF, Sander J, Lehmann CHK et al (2016) Human lymphoid organ dendritic cell identity is predominantly dictated by ontogeny, not tissue microenvironment. Sci Immunol 1:40–50. https://doi.org/10.1126/ sciimmunol.aai7677 11. Heger L, Balk S, Lu¨hr JJ et al (2018) CLEC10A is a specific marker for human CD1c+ dendritic cells and enhances their tolllike receptor 7/8-induced cytokine secretion. Front Immunol 9:1–16. https://doi.org/10. 3389/fimmu.2018.00744

Chapter 3 Multiplexed Imaging of Posttranslational Modifications of Endogenous Proteins in Live Cells Yuko Sato and Hiroshi Kimura Abstract Posttranslational histone modifications are associated with the regulation of genome function. Some modifications are quite stable to maintain epigenome states of chromatin, and others can exhibit dynamic changes in response to internal and external stimuli. To track the local and global changes in histone modifications, multiplexed imaging in living cells is beneficial. Among live cell probes for detecting histone modifications, genetically encoded modification-specific intracellular antibodies, or mintbodies, are convenient and suitable tools for this purpose. We here describe the mintbody-based methods to monitor the changes in histone modification levels induced by histone methyltransferase and deacetylase inhibitors. By measuring the nuclear to cytoplasmic intensity ratios of mintbodies in living cells, changes in histone H4 lysine 20 methylation states and the increase in histone H3 acetylation were detected. Key words Histone modifications, Modification-specific intracellular antibody, Live-cell imaging

1

Introduction In eukaryotic nuclei, DNA is wrapped around a histone octamer to form a nucleosome, which is a basic structural unit of chromatin [1]. A histone octamer consists of two copies of H2A, H2B, H3, and H4, which are called core histones. These core histones, particularly H3 and H4, bind stably with DNA, and the nucleosomes generally inhibit the binding of transcription factors to the target DNA sequence [2, 3]. Therefore, genome functions, such as transcription, DNA replication, and repair, can be regulated at the chromatin level, and so nucleosome reorganization is critical for activation and inactivation of these reactions. Posttranslational modifications on histones are often associated with the state of chromatin. In particular, modifications on lysine residues play critical roles in transcription regulation as epigenetic marks [4, 5]. The amino group of lysine residues, which is positively charged under physiological pH conditions, can be acetylated, mono-, di-, and tri-methylated, and ubiquitylated. Lysine acetylation is in general

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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associated with gene activation, through neutralizing the positive charge to reduce histone-DNA interaction and generating a platform for acetylated lysine-specific binding proteins, such as bromodomain proteins. In contrast, lysine methylation is in general associated with gene silencing, although the role depends on the site for methylation. Histone H3 lysine 27 trimethylation is found on the promoter regions of transcriptionally silent genes, whereas H3 lysine 4 trimethylation is enriched in active gene promoters. Some of these modifications can be maintained over cell generations to be epigenetic marks, and others can be dynamically added and removed depending on internal and external stimuli. Genome-wide localization of histone modifications has been revealed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). However, the dynamic changes in modifications at single-cell levels have not been well demonstrated because singlecell epigenome profiling has been challenging [6, 7], and only a few techniques are available to track the modifications in living cells [8– 12]. Among the methods for live-cell modification tracking, fluorescent probes derived from modification-specific antibodies appear more suitable for multiplexed imaging than Fo¨rster/fluorescenceresonance energy transfer (FRET)-based probes, which require two fluorescent proteins/dyes for a donor and an acceptor in a single probe [9, 10]. Antibody-based probes can be fluorescent dye-conjugated antigen-binding fragments (Fabs) or genetically encoded intracellular antibodies [8]. The Fab-based live endogenous modification labeling (FabLEM) methods can be applied to multiplexed imaging, by using three different Fabs conjugated with different fluorescent dyes (like Alexa Fluor™ 488, Cy3™, and Cy5™) [13, 14]. Histone modification dynamics during zygotic genome activation in zebrafish embryos have recently been revealed using multiplexed FabLEM [15]. The detailed protocol for Fab preparation, dye-conjugation, loading into cells, and evaluation of dye’s suitability have been described previously [13, 14, 16, 17]. In contrast to a limited period of imaging by FabLEM due to the dilution and degradation of the loaded protein, a genetically encoded intracellular antibody probe, i.e., single-chain variable fragment (scFv) tagged with a fluorescent protein, is suitable for long-term and in vivo imaging. In addition, loading DNA into cells by transfection is also much more convenient than loading proteins, although it is not straightforward to express a functional probe. Genetically encoded modification-specific intracellular antibodies are termed “mintbodies” [18–21]. By fusing with different fluorescent proteins (like EGFP, mCherry, or iRFP) or tags that can be labeled with fluorophores (like Halo Tag® and SNAP-tag®), multiplexed imaging using mintbodies is also possible (Fig. 1). Simultaneous

Multiple Imaging of PTMs in Live Cells

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visualization of two mintbodies could reveal the dynamics of different modifications in the same cells. We describe the usage of mintbodies to detect changes in modifications in response to inhibitors of histone deacetylase and methyltransferase.

2 2.1

Materials Plasmids

1. pEGFP-13C7 (H3K9ac): a pEGFP-based plasmid vector that expresses the EGFP version of H3K9ac-mintbody [18] (see Note 1). 2. PB510-15F11 (H4K20me1)-mCherry: a PB510-based plasmid vector that expresses the mCherry version of H4K20me1-mintbody [19]. PB510 is a PiggyBac Transposon system vector (System Biosciences). 3. PB533-2E2 (H4K20me2)-sfGFP: a PB533-based plasmid vector that expresses the sfGFP version of H4K20me2-mintbody (manuscript in preparation). PB533 is a PiggyBac Transposon system vector (System Biosciences). 4. PB210PA-1: a super PiggyBac Transposase expression vector (System Biosciences). 5. pEGFP-13C7 (H3K9ac)-SNAP: a pEGFP-based plasmid vector that express the SNAP-tag® version of H3K9ac-mintbody.

34

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Yuko Sato and Hiroshi Kimura

Cell Lines

1. Mouse embryonic carcinoma MC12 cells [22]: cells harboring one or two inactive X chromosomes fused with a part of chromosome 2 near the telomere region. The active X chromosome does not have such a translocation. Near-diploid and near-tetraploid cells respectively harbor one and two copies of both inactive and active X chromosomes. 2. Human cervical carcinoma HeLa cells: commonly used cells.

2.3 Transfection Reagents

Here, the following reagents were used for transfection: 1. FuGENE® HD (Promega). 2. Opti-MEM® I (ThermoFisher Scientific).

2.4 Cell Culture and Inhibitors

1. Culture medium: Dulbecco’ modified Eagle’s medium, highglucose (DMEM, high-glucose; Nacalai Tesque), supplemented with 10% fetal bovine serum, 100 U/mL streptomycin and 100 μg/mL penicillin. 2. Low-fluorescent medium: FluoroBrite™ DMEM (ThermoFisher Scientific), supplemented with 10% fetal bovine serum, 100 U/mL streptomycin and 100 μg/mL penicillin. 3. Stock solution of 50 mg/mL G418 (dissolved in water). 4. Dimethylsulfoxide (DMSO; Molecular Biology Grade). 5. Stock solution of 10 mM Trichostatin A (TSA; dissolved in DMSO): a histone deacetylase inhibitor [23]. 6. A-196: a selective inhibitor of Suv420H1 and Suv420H2, which are methyltrasferases converting monomethylation on H4 lysine 20 (H4K20me1) to di- and tri-methylation (H4K20me2 and H4K20me3) [24]. 7. Stock solution of 60 μM SNAP-Cell® TMR Star (New England Biolabs; dissolved in DMSO): a cell-permeable SNAP ligand with tetramethylrhodamine. 8. Stock solution of 10 mg/mL Hoechst33342 (dissolved in water). 9. 35 mm Glass Base Dish (Glass 12φ, No. 1-S).

2.5 Microscopy and Image Analysis

1. A spinning disk confocal system (Yokogawa Electric; CSU-W1) attached to an inverted fluorescence microscope equipped with a Plan Apo VC 100
Oil (NA 1.4) lens, an electron multiplying charge-coupled device (EM-CCD), a laser unit (405, 488, and 561 nm), and a heat-stage chamber. 2. Operating and analysis software: NIS Element ver. 4.3 (Nikon). 3. Fiji-Image J (https://fiji.sc/).

Multiple Imaging of PTMs in Live Cells

35

4. Excel (Microsoft). 5. BoxPlotR (http://shiny.chemgrid.org/boxplotr/).

3

Methods

3.1 Establishing Cell Lines Stably Expressing Mintbodies

1. Plate MC12 cells on a 6-well plate at ~20% confluency in 2 mL Culture medium on the day before transfection. 2. For transfection using FuGENE® HD, aliquot 100 μL of OptiMEM® I into an Eppendorf tube. 3. Add each 0.8 μg of two mintbody expression plasmids and 0.4 μg of transposase expression vector to Opti- MEM® I in a tube, mix well using a Vortex mixer, and briefly spin down. 4. Add 6 μL FuGENE® HD to Opti-MEM-DNA mixture, mix well using a Vortex mixer, and briefly spin down. 5. Leave the mixture for 5–20 min at room temperature, and drop on to a well where cells were plated. 6. After an incubation for a day, trypsinize cells and plate them in a 100 mm dish in DMEM containing 1 mg/mL G418 (see Note 2). 7. After 1–2 weeks, pick up single colonies and transfer to wells in a 96-well plate using tips with a micropipette. 8. Select the clones that show both green and red fluorescence under a fluorescence microscope (see Note 3).

3.2 Microscopic Observation to Analyze the Effect of a Suv420H1/2 Inhibitor on Histone H4 Lysine 20 Methylation States

1. Plate MC12 cells that express H4K20me1-mintbody (mCherry) and H4K20me2-mintbody (sfGFP) on two (or more) 35 mm glass base dishes at 10–20% confluency at least 4 h before the addition of an inhibitor. 2. Replace the medium with low-fluorescent medium containing 1 μM A-196 in one dish and the medium containing vehicle (DMSO) in another dish, and incubate for 16–17 h. 3. Set the dish onto a heated stage on an inverted microscope with a confocal system. Acquire 2D or 3D images using 488- and 561-nm laser lines for sfGFP (H4K20me2) and mCherry (H4K20me1) (Fig. 2a). 4. To evaluate the effect of A-196 on H4K20me1 and H4K20me2 levels, measure the nuclear to cytoplasmic intensity ratios. In the case of manual measurement using Image J (see Note 4), use “ROI manager” and “Polygon selections” tool to choose and register the whole cell and nuclear areas, as well as the background area without cells. Measure the Area, Mean Gray Value (Mean Intensity), and Integrated Density (Total Intensity) using “Multi Measure” tool in “More” option in “ROI manager”, and export the data to Excel.

36

Yuko Sato and Hiroshi Kimura

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Fig. 2 Monitoring the distribution and relative levels of H4K20me1 and H4K20me2 in living cells. Mouse MC12 cells that express H4K20me1-mintbody (mCherry; magenta) and H4K20me2-mintbody (sfGFP; green) were treated with 1μM A-196 or vehicle (DMSO) for 16 h. Fluorescence images in living cells were acquired using a confocal microscope. (a) Single optical section images. H4K20me1-mintbody is concentrated on inactive X chromosomes [18]. H4K20me2-mintbody is distributed like DNA staining because H4K20me2 is a highly abundant modification in mammalian cells [25]. As chromatin-unbound mintbodies can diffuse out to the cytoplasm, the nucleus/cytoplasm intensity ratio can be a measure of the target modification level. H4K20me1-mintbody in the cytoplasm appears higher in the control (DMSO-treated) cells than in A-196-treated cells. In contrast, H4K20me2-mintbody in the cytoplasm appears opposite. These views were supported by quantified data in (b). Bar ¼ 10 μm. (b) Quantification. The nucleus/cytoplasm intensity ratios were measured for both H4K20me1- and H4K20me2-mintbodies in single cells (N ¼ 10 each condition). The relative values to the mean of the control sample are box-plotted. Center lines show the medians; box limits indicate the 25th and 75th percentiles; and whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles

5. Calculate the cytoplasm intensity by dividing the cytoplasm total intensity (“Integrated Density of the whole cell” – “Integrated Density of the nucleus”) by the cytoplasm area (“Area of the whole cell” – “Area of the nucleus”). After subtracting the background intensity from both the nuclear and cytoplasm intensities, calculate the nucleus/cytoplasm intensity ratio by dividing the nuclear intensity with the cytoplasm intensity. A convenient web tool (BoxPlotR) can be used to plot the ratios of individual cells, the median, and confidence intervals (Fig. 2b). The nucleus/cytoplasm ratios of H4K20me1-mintbody and H4K20me2-mintbody were higher and lower, respectively, in A-196-treated cells, consistent with the inhibition of Suv420H1 and Suv420H2.

Multiple Imaging of PTMs in Live Cells

3.3 Time-Course Analysis for Evaluating the Effect of a Histone Deacetylase Inhibitor

37

1. Plate MC12 cells that express H3K9ac-mintbody (EGFP) and H4K20me1 (mCherry) on a 35 mm glass base dish at 10–20% confluency on the day before observation. 2. Replace the medium to 2 mL low-fluorescent medium, and set the dish on to a heated stage on an inverted microscope with a confocal system. 3. Set appropriate conditions for time-lapse imaging using 488and 561-nm laser lines for EGFP (H3K9ac) and mCherry (H4K20me1) (see Note 5). 4. During the time-lapse imaging, typically after collecting images a few time points under normal conditions, add 0.5 mL low-fluorescent medium containing 5
concentration of an inhibitor (Fig. 3). For administrating TSA at a final concentration of 0.1 μM, add 0.5 mL of low-fluorescent medium containing 0.5 μM TSA to 2 mL medium in the glass base dish (see Note 6). 5. To evaluate changes in H3K9ac levels, measure the nuclear to cytoplasmic intensity ratios, as described above (Subheading 3.2) at each time point, and calculate the nucleus/cytoplasm intensity ratios over time (Fig. 3). The nucleus/cytoplasm ratios of H3K9ac increased gradually, and inactive X chromosomes (Xi) depicted by H4K20me1-mintbody enrichment became decondensed, consistent with the increase in global acetylation by TSA.

3.4 Mintbodies Fused with a Fluorescent Protein and SNAP-Tag® for Multiplexed Imaging

1. A transient expression system is described here. Plate HeLa cells on a 35 mm glass base plate at 10–20% confluency on the day before transfection. 2. Transfect H3K9ac-mintbody (EGFP) and H3K9ac-mintbody (SNAP-tag®) using FuGENE® HD as described in Subheading 3.1. 3. Incubate cells in 60 nM SNAP-Cell® TMR Star for >0.5 h. 4. (Optional) Incubate cells in 100 ng/mL Hoechst33342 for 0.5 h [26]. 5. Discard the medium, wash 3 times with 2 mL low-fluorescent medium, and add 2 mL low-fluorescent medium. 6. Set the dish onto a heated stage on an inverted microscope with a confocal system for image acquisition using 405-nm (Hoechst33342), 488-nm (EGFP), and 561-nm (SNAPTMR) laser lines (see Note 7). Both H3K9ac-mintbodies (EGFP and SNAP-TMR) distributed similarly, distinct from Hoechst-dense chromatin (Fig. 4).

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Fig. 3 Monitoring the changes in chromatin structure and acetylation level by a histone deacetylase inhibitor. Fluorescence images of mouse MC12 cells that express H3K9ac-mintbody (EGFP; green) and H4K20me1mintbody (mCherry; magenta) were acquired every 20 min using a confocal microscope. TSA was added at a final concentration of 0.1 μM during the filming. (a) Single optical section images before (0 h) and 13 h after the addition of TSA. Without TSA (0 h), H4K20me1-mintbody was concentrated on condensed inactive X chromosomes in interphase nuclei (yellow arrowheads; left panel) and during mitosis (a yellow arrow; left panel), where H3K9ac-mintbody was depleted. After 13 h in TSA, inactive X chromosomes in interphase nuclei became more decondensed (yellow arrowheads; right panel), and H3K9ac-mintbody became associated with inactive X chromosomes (yellow arrow: right panel). Bar ¼ 10 μm. (b) Quantification over time. Nucleus/ cytoplasm intensity ratios of H3K9ac-mintbody are plotted. Left: individual cells. Right: the averages with the standard deviations (N ¼ 10). The increase in H3K9ac-mintbody nucleus/cytoplasm ratio represents the increase in acetylation level

4

Notes 1. The function of scFv is often affected by the fusion partner protein. Among the fluorescent and tag proteins we tested, including EGFP, sfGFP, mCherry, sfCherry, iRFP, HaloTag, and SNAP-tag, sfGFP has been a superb fusion partner for all mintbodies. The distribution of H3K9ac-mintbody became fuzzy when fused with mCherry, compared with its fusion with EGFP and sfGFP, probably due to unstable folding. 2. By co-transfection using the PiggyBac system, more than one plasmids harboring the transposon target sequences are often integrated simultaneously. Therefore, it is not necessary to construct two plasmids with different selection marker genes (like G418 and puromycin). Both plasmids can have G418resistant gene, or if one plasmid harbors G418-resistant gene, another does not need any marker gene as long as it contains the transposon target sequences.

Multiple Imaging of PTMs in Live Cells

Hoechst

H3K9acEGFP

H3K9acSNAP

EGFP Hoechst

SNAP Hoechst

EGFP SNAP

39

Fig. 4 Comparison between EGFP- and SNAP-tagged H3K9ac-mintbody. HeLa cells were transfected with expression vectors for EGFP- and SNAP-tagged H3K9ac-mintbodies. Cells were incubated with 60 nM SNAPCell® TMR to label SNAP-tag (and 0.1 μM TSA to increase the acetylation level) for 15 h. DNA was stained with Hoechst33342 for 0.5 h. After washing out these reagents, living cells were imaged using a confocal microscope. Single optical sections are shown with their merged images. EGFP- and SNAP-tagged H3K9ac mintbodies distributed similarly in Hoechst33342-poor regions. Bar ¼ 10 μm

3. The level of fluorescence generally varies in different clones. Brighter fluorescent clones that show expected mintbody distribution are convenient for a further use in microscopy, but it is not recommended to choose exceptionally bright ones as a large excess of mintbody may block the target modification and/or increase diffuse background. 4. More sophisticated platforms can be used to automatically track the nucleus when cells are not moved much during the time intervals, and the signal-to-noise ratio is sufficiently high to draw the thresholds [18]. 5. The condition of image acquisition depends on the purpose and brightness of fluorescence. To see the effect of histone deacetylase inhibitor on the global changes in modifications, imaging a single plane with intervals with 5–30 min should be adequate. When the distribution on chromosomes during mitosis is needed to be investigated, z-stack images should be taken. If fluorescence is brighter, the image acquisition condition becomes more flexible, because higher quality images can be obtained with a lower intensity of illumination light. However, highly overexpressing mintbody probes is not recommended as described in Note 3.

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Yuko Sato and Hiroshi Kimura

6. Adding a small volume, like 2 μL to 2 mL medium using 1000
solution, needs an extensive mixing by pipetting to make a homogenous concentration. Pre-dilution to 5
helps mixing. A drawback of adding a large volume is a temperature shift, which results in focus drift. Therefore, preheating the solution at 37  C is desired. The focus drift problem can be compensated for by an auto-focus system of a microscope. 7. Chemical fluorescent dyes like TMR have better stability than mCherry. Therefore, it is a good idea to choose Halo Tag® or SNAP-tag® for combining with GFP.

Acknowledgments We thank D. Barsyte-Lovejoy and W.N. Pappano for A-196 and T.J. Stasevich for pEGFP-13C7-SNAP construct. The author’s work was supported by JSPS KAKENHI JP17KK0143 and JP20K06484 (to Y.S.) and JP17H01417 and JP18H05527 (to H.K.) and JST-CREST JPMJCR16G1 (to H.K.) and JPMJCR20S6 (to Y.S.). References 1. Zhou K, Gaullier G, Luger K (2019) Nucleosome structure and dynamics are coming of age. Nat Struct Mol Biol 26:3–13. https:// doi.org/10.1038/s41594-018-0166-x 2. Kimura H, Cook PR (2001) Kinetics of core histones in living human cells: little exchange of H3 and H4 and some rapid exchange of H2B. J Cell Biol 153:1341–1353. https:// doi.org/10.1083/jcb.153.7.1341 3. Fenley AT, Anandakrishnan R, Kidane YH, Onufriev AV (2018) Modulation of nucleosomal DNA accessibility via charge-altering posttranslational modifications in histone core. Epigenetics Chromatin 11:11. https://doi.org/ 10.1186/s13072-018-0181-5 4. Bannister AJ, Kouzarides T (2011) Regulation of chromatin by histone modifications. Cell Res 21:381–395. https://doi.org/10.1038/ cr.2011.22 5. Kimura H (2013) Histone modifications for human epigenome analysis. J Hum Genet 58:439–445. https://doi.org/10.1038/jhg. 2013.66 6. Schwartzman O, Tanay A (2015) Single-cell epigenomics: techniques and emerging applications. Nat Rev Genet 16:716–726. https:// doi.org/10.1038/nrg3980 7. Harada A, Maehara K, Handa T, Arimura Y, Nogami J, Hayashi-Takanaka Y, Shirahige K,

Kurumizaka H, Kimura H, Ohkawa Y (2019) A chromatin integration labelling method enables epigenomic profiling with lower input. Nat Cell Biol 21:287–296. https://doi. org/10.1038/s41556-018-0248-3 8. Kimura H, Hayashi-Takanaka Y, Stasevich TJ, Sato Y (2015) Visualizing posttranslational and epigenetic modifications of endogenous proteins in vivo. Histochem Cell Biol 144:101–109. https://doi.org/10.1007/ s00418-015-1344-0 9. Kimura H, Sato Y (2015) Histone modification sensors in living cells. In: Zang J, Mehta S, Schultz C (eds) Optical probes in biology. CRC Press, Boca Raton, FL, pp 317–331. https://doi.org/10.1201/b18007 10. Sasaki K, Yoshida M (2016) The exploitation of FRET probes to track bromodomain/histone interactions in cells for bromodomain inhibitors. Drug Discov Today Technol 19:51–56. https://doi.org/10.1016/j.ddtec.2016.06. 001 11. Lungu C, Pinter S, Broche J, Rathert P, Jeltsch A (2017) Modular fluorescence complementation sensors for live cell detection of epigenetic signals at endogenous genomic sites. Nat Commun 8:649. https://doi.org/10.1038/ s41467-017-00457-z

Multiple Imaging of PTMs in Live Cells 12. Delachat AM, Guidotti N, Bachmann AL, Meireles-Filho ACA, Pick H, Lechner CC, Deluz C, Deplancke B, Suter DM, Fierz B (2018) Engineered multivalent sensors to detect coexisting histone modifications in living stem cells. Cell Chem Biol 25:51–56.e6. https://doi.org/10.1016/j.chembiol.2017. 10.008 13. Hayashi-Takanaka Y, Yamagata K, Wakayama T, Stasevich TJ, Kainuma T, Tsurimoto T, Tachibana M, Shinkai Y, Kurumizaka H, Nozaki N, Kimura H (2011) Tracking epigenetic histone modifications in single cells using Fab-based live endogenous modification labeling. Nucleic Acids Res 39:6475–6488. https://doi.org/10.1093/ nar/gkr343 14. Hayashi-Takanaka Y, Stasevich TJ, Kurumizaka H, Nozaki N, Kimura H (2014) Evaluation of chemical fluorescent dyes as a protein conjugation partner for live cell imaging. PLoS One 9:e106271. https://doi.org/ 10.1371/journal.pone.0106271 15. Sato Y, Hilbert L, Oda H, Wan Y, Heddleston JM, Chew T-L, Zaburdaev V, Keller P, Lionnet T, Vastenhouw N, Kimura H (2019) Histone H3K27 acetylation precedes active transcription during zebrafish zygotic genome activation as revealed by live-cell analysis. Development 146:dev179127. https://doi. org/10.1242/dev.179127 16. Kimura H, Yamagata K (2015) Visualization of epigenetic modifications in preimplantation embryos. Methods Mol Biol 1222:127–147. https://doi.org/10.1007/978-1-4939-15941_10 17. Sato Y, Stasevich TJ, Kimura H (2018) Visualizing the dynamics of inactive X chromosomes in living cells using antibody-based fluorescent probes. Methods Mol Biol 1861:91–102. https://doi.org/10.1007/978-1-4939-87665_8 18. Sato Y, Mukai M, Ueda J, Muraki M, Stasevich TJ, Horikoshi N, Kujirai T, Kita H, Kimura T, Hira S, Okada Y, Hayashi-Takanaka Y, Obuse C, Kurumizaka H, Kawahara A, Yamagata K, Nozaki N, Kimura H (2013) Genetically encoded system to track histone modification in vivo. Sci Rep 3:2436. https:// doi.org/10.1038/srep02436 19. Sato Y, Kujirai T, Arai R, Asakawa H, Ohtsuki C, Horikoshi N, Yamagata K, Ueda J, Nagase T, Haraguchi T, Hiraoka Y, Kimura A, Kurumizaka H, Kimura H (2016)

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A genetically encoded probe for live-cell imaging of H4K20 monomethylation. J Mol Biol 428:3885–3902. https://doi.org/10.1016/j. jmb.2016.08.010 20. Sato Y, Kimura H (2017) Semi-quantitative analysis of H4K20me1 levels in living cells using mintbody. Bio-protocol 7:e2276. https://doi.org/10.21769/BioProtoc.2276 21. Tjalsma SJD, Hori M, Sato Y, Bousard A, Ohi A, Raposo AC, Roensch J, Saux AL, Nogami J, Maehara K, Kujirai T, Handa T, Bage´s-Arnal S, Ohkawa Y, Kurumizaka H, de Rocha ST, Z˙ylicz JJ, Kimura H, Heard E (2021) H4K20me1 and H3K27me3 are concurrently loaded onto the inactive X chromosome but dispensable for inducing gene silencing. EMBO Rep 22: e51989. https:// doi.org/10.15252/embr.202051989 22. Abe K, Takagi N, Sasaki M (1988) Nonhistone nuclear proteins specific to certain mouse embryonal carcinoma clones having an inactive X chromosome. Exp Cell Res 179:590–594. https://doi.org/10.1016/0014-4827(88) 90297-2 23. Yoshida M, Kijima M, Akita M, Beppu T (1990) Potent and specific inhibition of mammalian histone deacetylase both in vivo and in vitro by trichostatin A. J Biol Chem 265:17174–17179 24. Bromberg KD, Mitchell TR, Upadhyay AK, Jakob CG, Jhala MA, Comess KM, Lasko LM, Li C, Tuzon CT, Dai Y, Li F, Eram MS, Nuber A, Soni NB, Manaves V, Algire MA, Sweis RF, Torrent M, Schotta G, Sun C, Michaelides MR, Shoemaker AR, Arrowsmith CH, Brown PJ, Santhakumar V, Martin A, Rice JC, Chiang GG, Vedadi M, Barsyte-Lovejoy D, Pappano WN (2017) The SUV4-20 inhibitor A-196 verifies a role for epigenetics in genomic integrity. Nat Chem Biol 13:317–324. https:// doi.org/10.1038/nchembio.2282 25. Pesavento JJ, Yang H, Kelleher NL, Mizzen CA (2008) Certain and progressive methylation of histone H4 at lysine 20 during the cell cycle. Mol Cell Biol 28:468–486. https://doi. org/10.1128/MCB.01517-07 26. Haraguchi T, Kaneda T, Hiraoka Y (1997) Dynamics of chromosomes and microtubules visualized by multiple-wavelength fluorescence imaging in living mammalian cells: effects of mitotic inhibitors on cell cycle progression. Genes Cells 2:369–380. https://doi.org/10. 1046/j.1365-2443.1997.1280326.x

Chapter 4 Multiplex Imaging of Rho GTPase Activities in Living Cells Ravi M. Bhalla, Maren Hu¨lsemann, Polina V. Verkhusha, Myla G. Walker, Daria M. Shcherbakova, and Louis Hodgson Abstract Fo¨rster resonance energy transfer (FRET) biosensors are popular and useful for directly observing cellular signaling pathways in living cells. Until recently, multiplex imaging of genetically encoded FRET biosensors to simultaneously monitor several protein activities in one cell was limited due to a lack of spectrally compatible FRET pair of fluorescent proteins. With the recent development of miRFP series of nearinfrared (NIR) fluorescent proteins, we are now able to extend the spectrum of FRET biosensors beyond blue-green-yellow into NIR. These new NIR FRET biosensors enable direct multiplex imaging together with commonly used cyan-yellow FRET biosensors. We describe herein a method to produce cell lines harboring two compatible FRET biosensors. We will then discuss how to directly multiplex-image these FRET biosensors in living cells. The approaches described herein are generally applicable to any combinations of genetically encoded, ratiometric FRET biosensors utilizing the cyan-yellow and NIR fluorescence. Key words Near-infrared fluorescent protein, FRET biosensor, Rho GTPases, Multiplex imaging

1

Introduction Through advances in fluorescent biosensor technologies, we are now able to directly observe cellular posttranslational modifications and protein–protein interactions within their native subcellular microenvironments in living cells. The current state of the art in fluorescent biosensor imaging techniques is simultaneous interrogation and/or perturbations of multiple protein activity status in single living cells. These multiplex approaches are enabled by new FRET-based probes that are spanning increasingly wider range of fluorescence spectrum, into far-red and NIR. We have recently developed the first fully NIR FRET biosensor for a member of the p21 Rho-family of small GTPase, Rac1 [1]. The NIR FRET biosensor uses a FRET pair of genetically encoded fluorescent proteins that fluoresce at NIR wavelengths,

Ravi M. Bhalla, Maren Hu¨lsemann, Polina V. Verkhusha, and Myla G. Walker contributed equally to this work. Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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including the miRFP670 [2] (donor) and the miRFP720 (acceptor) [1]. The fully NIR spectral characteristics of this biosensor system enabled us to simultaneously utilize it with a traditional cyan-yellow fluorescent protein-based FRET biosensor. This approach enabled the first, direct visualization of RhoA GTPase and Rac1 GTPase activities in single living cells at the same time, allowing to observe their antagonism at the cell edge [1]. In this work, we will detail the methods employed to prepare appropriate cell lines for multiplex imaging of a cyan-yellow and a NIR FRET biosensors. We will then detail technical aspects and considerations important during the multiplex FRET biosensor imaging experiments and the subsequent data processing and analysis procedures. We will use RhoA-Rac1 FRET biosensor pair as an example system; however, any ratiometric, single-chain cyan-yellow and NIR FRET biosensor systems may be multiplexed by the methodology described herein.

2 2.1

Materials Cell Culture

1. Mouse embryonic fibroblast (MEF) with stable tet-OFF (Clontech) (see Notes 1 and 2). 2. tet-OFF MEF stably harboring a CFP-YFP RhoA FRET biosensor [3]. 3. tet-OFF MEF stably harboring a CFP-YFP Rac1-GDI FRET biosensor [4]. 4. GP2-293 cells (see Note 3) (Clontech). 5. DMEM supplemented with 10% certified tetracycline-free fetal bovine serum (FBS; Atlanta Biological), 2 mM GlutaMax (Invitrogen), and 100 μg/mL penicillin (100 IU)/ streptomycin. 6. DPBS, calcium, and magnesium free: 0.2 g/L KCl, 0.2 g/L KH2PO4, 8 g/L NaCl, 1.15 g/L Na2HPO4 (anhydrous). 7. Trypsin (0.05%)—EDTA. 8. Poly-L-lysine (molecular weight: 70,000–150,000 Da) 0.001% solution in sterile DPBS. 9. G418/neomycin (100 mg/mL stock solution). 10. Puromycin (Puro) (10 mg/mL stock solution). 11. Hygromycin (Hygro) (50 mg/mL stock solution). 12. Doxycyclin (Dox) (10 mg/mL stock solution). 13. Soybean trypsin inhibitor 0.05% in DPBS. 14. 35 mm, 6 cm, and 10 cm cell culture dishes and 6-well tissue culture plates.

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15. 1.5 (conical) and 2.0 (round bottom) mL tubes. 16. 15- and 50-mL polypropylene tubes. 2.2 Transfection, Retrovirus Production, and Transduction

1. Opti-MEM (Invitrogen). 2. DMEM supplemented with 10% certified tetracycline-free FBS (Atlanta Biological) and 2 mM GlutaMax (Invitrogen). 3. Polyethylenimine (PEI) 25kD linear, 1 μg/mL in sterile water (Polysciences) [5]. 4. Plasmids: pRetro-X-hygro-DEST-NIR-Rac1 (wildtype) biosensor [1], pGag-Pol, and pVSVg (Clontech). pQCXIN-tetOFF-advanced (see Note 4). 5. Retro-X concentrator solution (Clontech). 6. Hexadimethrine bromide (Polybrene) (8 mg/mL stock solution). 7. 0.45 μm surfactant-free cellulose acetate, PVDF, or polyethersulfone syringe filter (see Note 5). 8. Biliverdin hydrochloride Dimethylsulfoxide).

2.3 Imaging Experiments

(25

mM

stock

solution

in

1. 25 mm round coverslips #1.5 thickness. 2. 1 N HCl. 3. 200-proof ethanol. 4. Ultrasonicator bath. 5. Coverslip carrier rack. 6. Glass jar. 7. Fibronectin from bovine plasma. 8. Ham’s F12K medium without phenol red (Crystalgen) for live cell experiments [6], supplemented with 2 mM GlutaMax (Invitrogen) and 3% certified tet-free FBS. 9. Live cell imaging chamber [7–9], Attofluor chamber system (Invitrogen) or other compatible live-cell chamber system that can accommodate 25 mm coverslips, or CellView glass bottom culture dishes (Greiner-Bio-one), or Mattek dishes (Mattek. com). 10. Mineral oil. 11. Silicone vacuum grease. 12. Argon gas. 13. Oxyfluor reagent (Oxyrase.com). 14. Sodium DL-lactate 60% solution. 15. 40
1.3 numerical aperture (NA), differential interference contrast (DIC)—Oil immersion objective lens.

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Fig. 1 Schematic drawing of the inverted microscope used for live cell, multiplex imaging of Rho GTPase activities. The microscope as depicted is configured for simultaneous CFP-YFP FRET biosensor imaging and DIC/NIR imaging setup. Filterwheel 1: switches the neutral density filters; Filterwheel 2: switches the excitation band-pass filters; Filterwheel 3: switches the emission band-pass filters. Microscope is equipped with Olympus Zero Drift Compensation (ZDC) autofocus mechanism using a 794 nm laser source. The main fluorescence turret of the microscope is equipped with an 80/20 (transmittance/reflection) mirror (Chroma). Details of the construction and specification of this microscope can be found elsewhere [9]

16. 60
1.45 NA, DIC—Oil immersion objective lens. 17. A multi-channel fluorescence inverted microscope system capable of timelapse imaging of Fo¨rster resonance energy transfer (FRET) biosensors [9] (Fig. 1). Appropriate light source and camera considerations are also important (see Note 6). 18. Microscope calibration slides: Mounted multispectral beads (Invitrogen) and a rectilinear grid patterned slide (Photometrics). 19. Matlab software ver. 2011a (Mathworks). 20. Metamorph software ver. 7.8.13.0 (Molecular Devices).

Multiplex FRET Biosensors for Rho Family GTPases

3

47

Methods

3.1 Production of Double-Stable and Inducible NIR-Rac1 FRET Biosensor Cell Lines

3.1.1 Production of Retrovirus

NIR FRET biosensors can be transduced into cells that already harbor a CFP-YFP-FRET biosensor under the tet-inducible system, such as our Rho family GTPase biosensors [3, 10–14], or any other FRET biosensors that utilize the cyan-yellow fluorescent proteins (see Note 7). The following procedure requires the stable tet-OFF cell line (see Notes 2 and 4). 1. (Day 1) Treat 5
10 cm cell culture dishes (see Note 8) with 5 mL of poly-L-lysine solution (0.001% in DPBS) at room temperature for 15 min (see Note 9). Aspirate this solution; do not rinse dishes with DPBS but immediately proceed to plating cells. Plate GP2-293 cells on these treated 10 cm cell culture dishes at 6.5
106 cells/dish (see Note 10). 2. (Day 2) Prepare PEI transfection mix. In 5 mL of OptiMEM in a 15 mL polypropylene tube, add 12 μg of pVSVg DNA, 12 μg of pGag-Pol DNA, and 48 μg of pRetro-X-hygro-DEST-NIRRac1 FRET biosensor DNA (see Note 11), vortex for 10 s; add 288 μL PEI solution and vortex for 10 s followed by 1 min centrifugation at room temperature at 300
g. Incubate at room temperature for 15 min. 3. While the transfection mix incubates, rinse cells once with DPBS and add 4 mL/dish of DMEM with 10% FBS but without any antibiotic supplementation. 4. Following incubation, add 1 mL of the transfection mix dropwise to each plate. Swirl gently to mix. 5. Incubate the plates for 24 h at 37  C and 5% CO2. 6. (Day 3) After the overnight transfection, gently add 3 mL/dish of serum-free DMEM without antibiotics, and transfer the cell plates to an incubator set at 32  C and 5% CO2, and incubate for an additional 48 h (see Note 12). 7. (Day 5) Collect the medium from the dishes and centrifuge at room temperature at 300
g for 5 min to pellet any cell debris. Filter the supernatant through a 0.45 μm syringe filter and collect into a 50 mL tube (see Note 13). Mix this together with the Retro-X concentrator solution, following the manufacturer’s protocols (see Note 14). Store this mixture at 4  C overnight (see Note 15). 8. (Day 6) Centrifuge the viral supernatant-concentrator mixture at 1500
g for 45 min at 4  C. Aspirate the supernatant, leaving a white pellet at the bottom of the tube. Resuspend the pellet into 300 μL, in serum- and antibiotic-free DMEM (see Note 16). This solution can be used directly to infect cells

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at this time, frozen at 80  C, or stored for a short time (1 week) at 4  C (see Note 17). 3.1.2 Infection and Selection of Cells

1. (Day 1: this step should coincide with the step 7 of Subheading 3.1.1) Plate MEF at 2.4
104 cells/dish onto a 6 cm tissue culture dish (see Notes 18 and 19), and incubate overnight prior to infection. 2. (Day 2: first cycle of infection) In the morning, aspirate cell medium and dropwise add 150 μL of the concentrated virus solution. If using a 6 cm tissue culture dish, add complete culture medium to make the total volume to 2 mL. If using a 35 mm tissue culture dish, make to 1 mL total volume. Supplement the medium with polybrene at 8 μg/mL, and incubate 6 h at 37  C and 5% CO2. 3. (Day 2: second cycle of infection) In the evening, aspirate the medium, and add dropwise 150 μL of the concentrated virus solution, followed by addition of complete medium, supplement with 8 μg/mL polybrene, and incubate overnight at 37  C and 5% CO2. 4. (Day 3) If any more concentrated virus solution is available and if cells appear unstressed at this point, it is possible to perform one additional cycle of infection. Alternatively, check infected cells for fluorescence using an epifluorescence microscope (see Note 20). Aspirate medium, and replace with normal culture medium. If fluorescence is visible, then supplement the medium with Dox at 1 μg/mL. If fluorescence is not visible, then continue incubation in normal culture medium and conditions for an additional 24 h and check again for fluorescence. 5. (Day 5) Start antibiotic selection at 48 h following the media change in step 4. Always maintain Dox at 1 μg/mL. For Hygro-resistance, start the selection at 125 μg/mL. Increase the concentration by doubling the antibiotic concentration until the final concentration of 500 μg/mL is reached (see Note 21). For Puro-resistance, start the selection at 1 μg/ mL. Increase the concentration by doubling the antibiotic concentration until the final concentration of 10 μg/mL is reached. 6. After completion of the antibiotic selection, the new biosensor cell line should be expanded and then frozen down for storage (see Notes 22 and 23).

3.2 Multiplex Imaging of GTPase Activities Using Cyan-Yellow and NIR FRET Biosensors

MEF cells stably harboring two biosensors will be used to image two FRET biosensor activities in single living cells. Here we describe imaging of cyan-yellow RhoA biosensor [3] and NIR Rac1 biosensor [1]. We also demonstrated simultaneous imaging of cyan-yellow Rac1-GDI interaction biosensor [4] and NIR Rac1

Multiplex FRET Biosensors for Rho Family GTPases

A

49

B

10

Difference from background fluorescence

Difference from background fluorescence

C

1

0.1 0.1

1

10

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1000

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1

0.1 0.1

10

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1

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miRFP670 Intensity (a.u.) FRET-535nm Emission Cerulean Emission Venus Emission

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miRFP670 Emission FRET-miRFP720 Emission

Difference from background fluorescence

Difference from background fluorescence

miRFP670 Emission FRET-miRFP720 Emission

0.1 0.1

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Cerulean-FP Intensity (a.u.)

Venus-FP Intensity (a.u.)

10000

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0.1 0.1

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miRFP720 Intensity (a.u.) FRET-535nm Emission Cerulean Emission Venus Emission

Fig. 2 Multiplexing of NIR FRET Rac1 and CFP-YFP FRET RhoA biosensors, reproduced from Shcherbakova et al. 2018 Nature Chemical Biology [1]. (a) Cartoon of the two biosensors multiplexed in single living cells. The NIR Rac1 biosensor uses miRFP670 [2] and miRFP720 [1] as the FRET pair, modulated by activity of C-terminally attached full length wildtype Rac1 GTPase interacting with a p21-binding domain (PBD) derived from p21 activated kinase 1. Affinity of the PBD toward Rac1 interaction is tuned appropriately by incorporating a dimerization-based autoinhibitory motif through a 2nd PBD containing GTPase-binding deficient mutations [12]. The NIR Rac1 biosensor can be imaged together with any cyan-yellow FRET-based biosensors

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biosensor [1]. Other combinations are possible in which NIR-FRET biosensors are combined with any other cyan-yellow FRET biosensors. This is made possible due to clean spectral separation achieved between the cyan-yellow versus NIR fluorescent proteins (Fig. 2). Here, we will focus on using a pair of single-chain biosensors in which equimolar distribution of the FRET donor and acceptor is guaranteed everywhere within a cell. This assumption is critical for making the simplification in the subsequent ratiometric data processing (see Notes 24 and 25). 3.2.1 Coverslip Preparation for Imaging

1. (Day 1) Ultrasonicate for 1 h in 1 N HCl, 25 mm #1.5 round coverslips, placed onto coverslip holders in a glass jar. Following ultrasonication, leave the coverslips in 1 N HCl solution overnight. 2. (Day 2) Drain the HCl solution, rinse the coverslips with water, and place them back into the glass jar filled with 50:50 ethanolwater mixture. Ultrasonicate for 1 h. 3. Replace the solution with 70:30 ethanol-water mixture. Ultrasonicate for 1 h. 4. Replace the solution with 200-proof ethanol. Ultrasonicate for 1 h. 5. Transfer the cleaned coverslips to holding jars containing 200-proof ethanol. 6. (Day of the experiment) Place cleaned coverslips into 6-well plates, containing 4 mL DPBS per well (see Note 26). Aspirate and replace with 10 μg/mL fibronectin in 1 mL DPBS, and incubate at room temperature for 1 h prior to plating cells.

3.2.2 Induction of Biosensor Expression

1. (Day 1) MEFs harboring two biosensors, propagated in 10 cm tissue culture dishes, will be used. Wash cells 2
in DPBS and trypsinize to detach cells (see Note 27). Collect detached cells in a 15 mL tube by resuspending in 10 mL normal complete growth medium; centrifuge at room temperature at 300
g for 3 min. Aspirate carefully to remove as much medium as possible. Resuspend into 10 mL of normal growth medium

ä Fig. 2 (continued) including that for RhoA also depicted here [3]. (b) The excitation spectra of miRFP670 (FRET donor) and miRFP720 (FRET acceptor) are shown together with the bandpass filter used to excite the cyanyellow FRET biosensor (magenta shaded region, ET436/20
Chroma Technology). The spectra are normalized to the peaks at ~400 nm at the “Soret” band. (c) Bleedthrough characterization between different channels of the microscope used for multiplex imaging. Top-Left: Purified Venus FP fluorescence. Top-Right: Purified Cerulean FP fluorescence. Bottom-Left: Purified miRFP670 fluorescence. Bottom-Right: Purified miRFP720 fluorescence. In all panels, the first data point (yellow square) is the vehicle control (water) where no FP was included. Intensity range useful for live-cell imaging is shown as a diagonally shaded box

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Fig. 3 Western blot showing the inducible expression of RhoA biosensor in MDA-MB231 tet-OFF cell line, detected using anti-RhoA GTPase antibody. Lane 1: Cell lysate from cell culture maintained with 2 μg/mL Dox; Lane 2: Cell lysate from biosensor induced cells that underwent single cycle of trypsinization; Lane 3: Cell lysate from biosensor induced cells that underwent two cycles of trypsinization. Endogenous RhoA expression levels are similar in all cases

without any selection antibiotics and without Dox, and plate 5
10 cm tissue culture dishes at 1:20 dilution (see Note 28). 2. (Day 2) Wash plated cells from Day 1, 2
in DPBS and trypsinize to detach cells. Resuspend the cells in 10 mL of fresh complete growth medium (without Dox or any of the selection antibiotics), and immediately put them back down onto the same tissue culture dish. The second trypsinization significantly improves the extent of biosensor expression; thus, it may be optional depending on the expression levels desired (Fig. 3). 3. Add biliverdin to media at 25 μM to improve miRFP maturation (see Note 29). 4. (Day 3) Wash cells 2
in DPBS and trypsinize to detach cells. Resuspend cells in 10 mL of soybean trypsin inhibitor in DPBS, and centrifuge at room temperature at 300
g for 3 min. Resuspend cells in complete culture medium, and plate cells at 4
104 cells/well on prepared 25 mm coverslips placed into 6-well dishes. Allow 2–4 h for cells to attach and spread before imaging. At this point, withhold biliverdin from media. 3.2.3 Imaging of Biosensors in Living Cells

1. (3 h prior to imaging) Prepare imaging medium. Ham’s F-12K without phenol red supplemented with glutamine is used for imaging as it has low background fluorescence in all

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Table 1 Bandpass filter specifications used for multiplex imaging of NIR FRET and CFP-YFP FRET biosensors in single living cells. “ET” designation indicates the filters were from Chroma Technology, Inc. “FF” designation indicates the filters were from Semrock, Inc. Channel ID

Excitation

Emission

CFP (donor)

ET436/20


ET480/40M

FRET (CFP-YFP)

ET436/20


ET535/30M

miRFP670 (donor)

FF01-628/32

FF02-684/24

FRET (670-720)

FF01-628/32

FF01-794/160

fluorescence channels used. 2 mL of imaging medium is prepared per coverslip of cells to be imaged. Take appropriate volume plus 1 mL excess of the medium into a 15 mL tube. Gently bubble argon gas through the medium for approximately 1 min (see Note 30) to displace dissolved oxygen from media. In 2 mL round bottom Eppendorf tubes, add 20 μL of Oxyfluor reagent, 4 μL of sodium-DL-lactate solution, 60 μL of FBS, and 2 mL of argon gas-treated Ham’s F-12K medium (see Note 31). Incubate the imaging medium at 37  C until ready to mount the coverslips (see Note 32). Do not supplement imaging media with biliverdin. 2. Mount the coverslips containing cells onto live-cell imaging chamber (the sealed chamber system [9], or Attofluor chamber). Alternatively, CellView or Mattek dishes can be used as well (see Note 33). 3. Using a 40
magnification objective lens (40
DIC N/A 1.30), set up for Ko¨hler illumination and differential interference contrast (DIC) imaging (see Note 34). 4. Check and adjust the hardware autofocus settings (see Note 35). 5. Set up for illumination of miRFP670 (Table 1), set short camera exposure times such as 200 ms, and visualize the NIR FRET biosensor expression in cells using the live-view capability of the data acquisition software (see Note 36). Move the stage, identify, and locate a cell of interest in the middle of the field of view (see Note 37). Immediately close the shutter and stop live view observation. Check in each imaging channels of the multidimensional data acquisition scheme, by quickly snapping single-frame images in DIC, CFP, FRET (CFP-YFP), miRFP670, and FRET (miRFP670-miRFP720), and confirm that the exposure times and the relative illumination intensities are within the optimal camera digitization range (see Notes 38– 40).

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Fig. 4 Alignment of multiple imaging channels based on a priori calibration. Top panels: A field of multispectral beads, taken using the two cameras for CFP and cyan-yellow FRET channels (Cameras 1 and 2, as depicted in Fig. 1). The red arrows indicate the point used for the manual alignment of two fields of view. White bar ¼ 20 μm. Middle left: Centroid locations of the calibration beads from the top panels as shown were extracted (Matlab routines incorporating particle-tracking methods by J.C. Crocker and D.G. Grier [24] were used) and overlaid, indicating a significant misalignment as a function of the location within the field. Middle right: The morphed alignment indicates correction applied to the FRET channel field of view to bring the two channels into register. The red arrows show the corresponding positions indicated in the top panels. Bottom panels: Zoomed in views of the data overlay showing the original and the morphed bead images. The zoomed region is indicated in the middle panels. White bar ¼ 20 μm

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6. Start timelapse experiments (see Note 41). We typically acquire in the following order: (1) CFP-YFP FRET simultaneously, (2) miRFP670, (3) miRFP670-miRFP720 FRET, and (4) DIC (see Note 42). 7. Acquire control images, consisting of field shading, camera noise, and alignment calibration images [7]. Field shading image for each channel is acquired by taking 10 different cellfree fields of view at the appropriate focal plane, exposure, and light intensity conditions for each color (including DIC) (see Note 43). The acquired 10-frame image stacks are then median-filtered to produce single-frame images, free from random fluorescent noise and debris. Camera noise images are acquired in complete absence of illumination (see Note 44). For camera noise images, 10-frame averaging is performed to smooth out the stochastic fluctuation in noise. Field alignment calibration images are taken using mounted multispectral beads for the two side camera channels in CFP and FRET. To match the side port images to the bottom port images, we use rectilinear grid pattern for calibration (see Note 45). The calibration image data are immediately tested for computational convergence (Fig. 4) prior to concluding the imaging experiments for the day in order to ascertain that the nonlinear coordination transformation calculations work appropriately [7]. 3.2.4 Processing of Multiplex Image Data to Determine GTPase Activity

Details of ratiometric calculations and data processing procedures can be found in a number of other sources [6–8, 15]. However, we will describe herein specific details associated with processing of multiplex imaging data of two single-chain biosensors in a single living cell (see Note 46). 1. Establish the reference fluorescence channel in the data set. We normally set the CFP channel (side-port camera 2; Fig. 1) as the reference channel, and align all other channels to it during data processing. First, process CFP-YFP FRET ratiometric data set, followed by NIR FRET data set, then the DIC channel, and finally correct for stage drift in all channels using the DIC image set. The following will be described in terms of “donor” and “FRET” channels to be applicable to both CFP-YFP and NIR-FRET systems. 2. Flat-field correct the donor and FRET channels by dividing the foreground images by the field shading images acquired in Subheading 3.2.3, step 7. Here, appropriate camera noise images should be subtracted from all images (both foreground and the shading images) prior to division by shading images (see Note 47). In addition, we also process the shade-corrected images by a 3
3 median filter to remove any hot pixel spots [7] (see Note 48).

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Fig. 5 Grid-based alignment of side port channel (CFP) to the bottom port views. Comparison of the original unmodified bottom port view (Top left, Camera 3 in Fig. 1) shows significant deviation from the reference image obtained from the CFP channel (Top right) at the side camera (Camera 2 in Fig. 1). Coordinate transformation based on manual determination of control points within the grid image brings the bottom port view partially into register (Bottom left). Additional X-Y linear translation shift (51 pixels in X and + 3 pixels in Y) (Bottom right) was required to fully register the bottom port view against the reference CFP channel image (Top right). Red bar ¼ 20 μm

3. For background subtraction, identify a region within the timelapse stack that is free from any foreground features as a function of time and measure average intensity values per unit area within such a region. Subtract this intensity value from every pixel of the corresponding image plane. Then, this process should be repeated at every timepoint in the timelapse stack [7]. Once the processing of the donor channel is complete, transfer the region of interest used to calculate the background fluorescence to the FRET channel data stack, and repeat the measurement and the calculation processes. 4. Coordinate transformation (“morphing”) (see Note 49) [7]. Using the calibration data obtained from multispectral

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Fig. 6 Representative, timelapse panels of RhoA and Rac1 activities imaged in a single living MEF, reproduced from Shcherbakova et al. 2018 Nature Chemical Biology [1]. Top panels: Differential interference contrast. Middle two panels: Rac1 activity (NIR FRET Rac1 biosensor; Upper panels) and RhoA activity (CFP-YFP FRET RhoA biosensor; Lower panels). Bottom panels: Localizations of high Rac1 (yellow) and high RhoA (blue) activities are overlaid, where regions of colocalization are shown in white. Regions of high Rac1 and RhoA activities were defined by intensity thresholding the top 2.5% of pixel ratio values within the image intensity histogram. Regions and features of interest are shown using matching colored arrowheads. White bar ¼ 20 μm. Pseudocolor bar corresponds to ratio limits of 1.0 to 1.55 for Rac1 and 1.0 to 1.32 for RhoA activities (black to red)

beads at the two side cameras, perform the morphing operation using affine transformation in Matlab (see Note 50). 5. Image masking [7]. Manually determine the cell edge using the intensity-thresholding method based on the histogram distribution of pixel-intensities. Produce binary masks in 16-bit at every time point, and multiply into the cell images to produce segmented timelapse data stacks. Some previously described considerations include selection of dimmer structures versus brighter cell body regions, proper identification of the cell edge at thinner lamellipodia regions, and the out-of-focus light stemming from the shape and the height of a cell in three dimensions [6, 7, 15]. 6. X-Y translational alignment. Using a cross-correlation-based approach, perform X-Y linear translational alignment optimization [7, 16] (see Note 51). 7. Calculate ratio of FRET/donor, taking into consideration floating point issues [6, 7, 15]. 8. Perform photobleach correction calculations [17] (see Note 52).

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9. Produce the channel alignment parameters between the side port (grid image obtained at Camera 2; Fig. 1) and the bottom port (grid image obtained at Camera 3; Fig. 1), using the rectilinear grid-based calibration data obtained in Subheading 3.2.3, step 7 (see Note 45). Using the determined parameters, perform the morphing calculations (affine transformation in Matlab) on the same calibration grid image set, by morphing the bottom port grid image into the reference channel grid image (the side port CFP channel). Then determine the number of X-Y linear pixel shifts in the morphed bottom port grid image that are necessary to bring the two grid images into correct alignment with each other (Fig. 5) (see Note 53). 10. Using the morphing parameters and X-Y linear pixel shift parameters determined in step 9, process the NIR FRET ratio images so that they are in alignment with the CFP-YFP FRET ratio images. 11. Process DIC channel images. Flat-field correct the DIC images (see Note 47). Perform morphing and X-Y linear shift operations using the parameters determined in step 9. 12. Stage-drift compensation (see Note 54). Identify a small feature within the corrected and aligned DIC channel that describes the motion of the field of view by positional drift of the stage and/or live-cell chamber system (see Note 55), i.e., a speck of debris, imperfections in the coverslip, etc. Using such a feature, produce a stack of binary masks that describe the motion of such a feature. Determine the centroid location of the binary mask at every time point, and calculate the pixel displacement in X-Y required to compensate for such a motion; then apply this correction to all FRET/donor ratio channels and the DIC channel. Examples of timelapse panels of two biosensor activities showing RhoA and Rac1 GTPase activities in a single living cell are shown in Fig. 6. 13. In order to more accurately align the two FRET/donor ratio channels (following the manual X-Y registration that was performed in step 10), perform a cross-correlational X-Y alignment optimization between the two FRET images at every time point [7, 16].

4

Notes 1. We use tet-OFF system as opposed to tet-ON. We find that tetracycline/doxycycline has strong autofluorescence in the blue-green spectrum. The problem arises from differential levels of autofluorescence that occurs from Dox into the CFP-donor versus CFP-YFP FRET channels upon

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donor excitation. We found that this results in unpredictable artefacts when ratiometric calculations are performed in presence of the internalized Dox in cells. Due to spectral compatibility, NIR FRET biosensor systems do not suffer from this problem and are able to take advantage of the tet-ON system. However, when producing double-stable and inducible cell lines with cyan-yellow and NIR FRET biosensors, tet-OFF system is useful. 2. In addition to commercially available tet-OFF MEF cells, we have produced several tet-OFF cell lines in house, using the second-generation tet-OFF transactivator (tTA) system. To do this, we first prepare and infect cells using retrovirus harboring the tet-OFF tTA and select for stable integration by culturing cells in G418/neomycin (1 mg/mL) selection. Upon establishing the stable line, we then proceed to the second round of infection using retrovirus harboring the biosensor gene cassette under the tet-inducible promoter and containing a different selection marker than G418/neomycin (Puro, Hygro, etc.) 3. We have also successfully used HEK293T cells available from ATCC. However, we find that the freshness of cells used here matters greatly; thus, we maintain 293 cells in culture for the viral production purposes only for about 3–4 weeks (10–12 passages). 4. pQCXIN-tet-OFF-advanced is used for producing tet-OFF cell lines prior to infection with virus harboring the biosensor expression cassette (see Note 2). 5. The syringe filter is used to filter cellular membrane and other debris away from the viral supernatant. As such, the filter membrane material must not adsorb protein/virus or otherwise damage the virus as they are being filtered through. Nitrocellulose filter has been known to impact viral yield due to higher adsorption of viral surface proteins [18, 19]. 6. A microscope system used for imaging of NIR fluorescent proteins and biosensors should be equipped with appropriate light source and camera. The light source should produce sufficient light power in NIR. Xenon lamp produces greater light output in red and NIR wavelengths, while producing significantly less light in blue-green-yellow wavelengths compared to a mercury arc lamp. Metal-halide lamp is also suitable for imaging; however, its power is usually lower than arc lamps, resulting in significantly longer exposure times being required. Also make sure that the infrared-blocking filter usually installed in the light housing does not block the longer wavelengths light. If such a filter is installed, it can be removed or replaced with a filter with wavelength cut-off at 780 nm. Because a blueshifted excitation wavelength (628/32 nm) for miRFP670

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(FRET donor) was required to minimize cross-excitation of miRFP720 (FRET acceptor), we used a mercury arc lamp for our imaging without significant issues. Lastly, the camera sensitivity should be considered for imaging of NIR fluorescence. We used Photometrics Coolsnap HQ2 operating in alternate-normal sensitivity mode of the sensor to boost the red and NIR wavelength sensitivity while sacrificing slightly the quantum efficiency in the blue-green wavelength range. 7. If a cell line only carrying the NIR-biosensor is required, then the parental tet-OFF MEF can be transduced with the retrovirus harboring the inducible, NIR FRET biosensor cassette. Similarly, tet-inducible cell lines can be produced for any other cell lines as required (see Note 2). 8. We have found that between 4 and 6
10 cm tissue culture dishes per a type of biosensor construct yield optimal virus concentration for infection. This can be scaled up or down depending on the efficiency of the viral transduction observed. 9. Be sure to let the poly-L-lysine/DPBS solution fully cover and wet the entire surface of the culture vessel. This is critical for obtaining good cellular adhesion and viral yield. The poly-Llysine/DPBS solution can be reused 2–3 times, so collect the used fraction into an empty sterile bottle and reuse as required. 10. It is important that the cells be monodispersed when plated for transfection to achieve maximum transfection efficiency. GP2-293 cells do not attach to the culture vessels strongly; thus they have the tendency to slough off in multicellular clusters even when trypsinized. Ensure that trypsinization is complete and that cells are fully resuspended and monodispersed prior to plating. 11. If producing virus for tet-OFF cell line production, replace pRetro-X-hygro-DEST-NIR-Rac1 with pQCXIN-tet-OFFadvanced and proceed as written. 12. We normally process murine leukemia virus-based retroviral production at 32  C and at 5% serum concentration. While there are considerations of the half-life and viral titer/yield based on different temperatures [20], we have found the conditions given here to produce excellent retroviral infectivity in our hands. As an aside, we process human immunodeficiency virus-based lentivirus production at 37  C and at 5% serum concentration with excellent results. 13. When using a syringe filter, it is important to not be forceful or rapid when pressing on the syringe plunger. The virus in suspension is fragile and can be damaged by shear forces acting on them in solution from too much force and speed being exerted onto the syringe plunger. A good rate of speed is when the

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liquid being filtered comes out dropwise. If the filter feels clogged at any time during filtration, it should be replaced. 14. Retro-X concentrator is a 4
solution, divide the total volume of the viral supernatant by 3, and add this amount of the concentrator solution. The concentrator is a highly viscous solution: Slower pipetting is required to dispense the full volume in the pipette. Mix thoroughly by shaking. 15. To achieve the highest infectivity, one should proceed immediately to cell infection following the viral concentration. To do this, the target cells to be infected are plated at the same day as the start of the overnight incubation of the viral concentrator solution. This allows the first round of infections to take place on the following morning when the viral precipitation is completed. Cells to be plated and infected may be directly propagated from those already in culture; alternatively, we have had success by directly defrosting cells the evening before at relatively sparse cell density and then proceed to infect those cells the following morning. The cell density is critical to achieve the best results: It should be sparse; 10–20% confluence is ideal at the time of the first infection. 16. The viral resuspension must be performed very gently. Shear stress by pipetting too vigorously may damage the virus and impact infectivity. 17. Freshly prepared virus is the most infective virus. While we have stored viral supernatant at 4  C for up to 1 week, we have observed significant reduction in infectivity as a function of time stored. The freeze-thaw cycle is to be avoided if at all possible. Each freeze-thaw cycle may impact the infectivity by as much as a factor of 50–70% in our hands. 18. In some cell lines, relative passage numbers are used as an important measure of the consistency of cell assay results. In these cases, the passage numbers of cells to be infected and made stable must be conserved as much as possible. To do this, we defrost the early passage-number cells the evening before infection at a very sparse cell density (see Note 15), so that multiple rounds of infections and at least the first few days of the antibiotic selection may be performed without having to pass these cells unnecessarily. This will take some trial and error to determine the optimal cell seeding density to allow proceeding to initial stages of selection without having to split the culture. 19. We have found that using smaller cell culture vessels (i.e., 35 mm round dishes) conserves the viral supernatant and achieves good results. One drawback, however, is that it may require additional cell passages to expand to larger culture vessels prior to cell freezing, adding to the passage numbers.

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20. For NIR biosensors, it may be difficult to observe the fluorescence on a microscope at this point unless the media is supplemented with Biliverdin at 25 μM for 6 h to overnight. Addition of Biliverdin improves the fluorescence brightness of NIR fluorescent proteins by up to two orders of magnitude. At this point, if the fluorescence levels are low without addition of exogenous biliverdin, proceed to antibiotic selection and evaluate at a later stage after the completion of the selection process. 21. For all antibiotics, we add gradually greater amounts to reach the final selection concentrations. For MEFs treated with G418, the starting concentration is 125 μg/mL. This concentration is increased by doubling when cells appear to be no longer under stress and are growing normally. The final concentration is 2 mg/mL (the normal maintenance concentration is 1 mg/mL). G418 is antagonized by Pen/Strep in culture; however, we have routinely produced stable cells with G418 resistance even in the presence of Pen/Strep in culture. For Puro, the starting concentration is 1 μg/mL. The concentration is doubled until reaching the final concentration (10 μg/mL). 22. When defrosting stable biosensor cell lines, be sure to defrost into medium containing the full strengths of antibiotic selection (G418, Puro, Hygro, plus Dox for biosensor repression). This ensures that only those cells stably incorporating the gene expression cassettes are propagated. Depending on how quickly the clonal selection process may outcompete in a particular cell line, it is possible to withhold the selection antibiotics after a while (Dox is always required). The duration of time for which this is possible should be determined empirically for individual cell lines as necessary. 23. Stable biosensor cell line may be FACS sorted to obtain population of cells that express similar amounts of biosensor. This is particularly useful when producing double-stable cell lines. We normally produce the stable cyan-yellow biosensor cells first, followed by FACS sorting to obtain tighter population distributions of the expression levels. This is then followed by a second infection and production of a double-stable cell line containing also the NIR biosensor and a second round of FACS sorting. Additionally, it is important also to determine how much exogenous biosensor is expressed compared to the endogenous protein levels. 24. Check the biosensor expression profile using Western blot and FACS. These analyses should point to possible issues that may be present if any single-color populations (FACS) or truncated fragments (Western blot) are visible. In these situations, ensure

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that the single-chain biosensor uses the synonymous modified codons [21] which prevent these spurious expression issues. 25. For single-chain biosensor systems, the relative concentrations of the FRET donor and acceptor moieties are everywhere equal to each other in cells because the FRET moieties are physically linked to one another in a single molecule. In dual-chain biosensor systems, this is more complex because the two FRET moieties are not linked; thus, differential concentrations and accessibilities exist within subcellular compartments. Due to differential localization and accessibility, the sensitivity of the biosensor will change as a function of the concentrations of the FRET donor/acceptor moieties within a given subcellular compartment: Fluctuations in concentrations of either the donor or the acceptor act to change the forward or the reverse rate of the binding reaction (i.e., Le Chatelier’s principle), dynamically altering the biosensor sensitivity. Furthermore, we induce biosensor expression for 48 h, which ensures steady-state levels of biosensors are attained in cells to minimize any potential effects from differential maturation times of different fluorescent proteins. 26. Coverslips tend to float on top of DPBS. Use sterile pipette tips to push them down under the surface of DPBS in wells. 27. Trypsinization is important for biosensor induction. In cases where only EDTA or Accutase (both non-tryptic cell-detachment agents) was used, biosensor expression was not fully achieved. We think that trypsinization and associated tryptic damage to the cellular receptors may result in translational activation, driving biosensor expression also from the bystander effects of such activation. 28. Relatively high dilution factor of plating during biosensor induction is important. We think that this (1) helps to dilute out Dox from cells into excess media and (2) drives stressrelated gene programs from sparse plating, resulting in co-activation of biosensor translation due to bystander effects. 29. miRFP requires biliverdin as the chromophore. This is a natural heme oxidation product and should be available in FBS; however, the amount available in cell culture medium is at limiting levels. We supplement the medium with exogenous biliverdin during induction for at least 6 h up to overnight at 25 μM to achieve up to two orders of magnitude improvement in brightness of the miRFP-based constructs. 30. We attach a clean glass Pasteur pipette at the end of the hose from the Argon gas tank which is set at very low pressures. The Pasteur pipette is then used to bubble the gas into the media. 31. To remove any sources of air (oxygen), we also back fill the underside of the caps of Eppendorf tubes with argon gas

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purged medium to expel as much air as possible from the imaging medium preparations. 32. There are situations in which reactive oxygen species are required for cellular signaling pathways, including cell motility. To determine the requirement, it is important to perform control experiments in which non-deoxygenated imaging medium is used and the readouts be directly compared against one another. 33. In the case of open chamber systems, mineral oil can be layered onto the imaging medium to protect it from evaporation and gas exchange. The sealed chamber system does not require the oil because it uses a clean coverslip to seal the upper side of the chamber [9]. 34. Higher magnifications are also possible. Calculate the Nyquist sampling limit of the magnification based on the particular dimensions of the camera pixels. Increased magnification above the Nyquist limit does not gain further spatial resolution but simply results in loss of brightness. 35. Hardware autofocus system detects the interface position between the glass and the water, using the difference in the refractive index of water vs. glass. As such, the best focus positions for cells are usually found to be further away from this glass-water interface by a specific offset distance, which depends also on the specific cellular features of interest and the wavelength of the probes being used. Therefore, it is important to determine the focus offsets for each of the wavelength to be used in the acquisition sequence so that the autofocus system may be pre-programed with such offsets for each channel during timelapse imaging. In the absence of the hardware autofocus capabilities, software-based autofocus using DIC can be employed [16, 22, 23]. 36. NIR biosensor is not readily visible by naked eye through the objective lens due to their far-red wavelengths characteristics. Instead, live-view function through the camera is used to find cells in the field of view that expresses the biosensor. 37. The speed at which one scans through the specimen and determines the ideal field of view, adjusts the exposure conditions, and initiates the timelapse imaging is very important to minimize photobleaching. If/when a good cell is found, do not keep looking at it, and immediately turn off the live-view illumination. If further stage adjustments are necessary, perform these operations in DIC to minimize photobleaching of fluorescence. 38. The fluorescence intensity of the acquisition should be adjusted so that pixel intensity histogram in each image frame should fill approximately 80% of the total digitization range of

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the scientific camera. This should be adjusted by changing the neutral density filter and the camera exposure times accordingly. 39. When acquiring a pair of FRET and donor images using a single camera, it is preferable to keep the same neutral density filter for both and only adjust the camera exposure times to make the acquired intensity ranges approximately the same in both channels. Once this “ratio” of exposure times is determined for each biosensor, always maintain this ratio of exposure times between the donor and the FRET channels. When using a dual camera approach to acquire simultaneously both the donor and the FRET emissions, the camera exposure times may or may not be independently adjustable depending on the software controller platform specifications. If camera exposure times are not independently adjustable (i.e., Metamorph), we insert a neutral density filter in the emission light pathway of the brighter channel to adjust the total light flux to be approximately the same in both donor and FRET channels. 40. To minimize photo-damage to cells during live cell imaging, the brightness of the excitation light and the duration of camera exposure should be carefully adjusted. Cells tolerate better dimmer illumination for a longer duration of acquisition as opposed to shorter acquisition times under brighter illumination. We attenuate the intensity of excitation light using neutral density filters so that each fluorescence channel takes approximately 600–1200 ms to reach 80% histogram fill level of the digitization range of the camera. 41. Timelapse experiments for MEFs usually span 40 min at 10 s intervals of acquisitions, for a total of 240 consecutive frames. Sometimes, an even longer imaging is possible depending on the photobleach mitigation and with appropriate cell health monitoring. 42. It is important to consistently maintain the order of image acquisition the same. However, it is also important to reverse the order of acquisition as control experiments to determine if specific biosensor features visible in cells are due to some sort of artefacts from the specific order of image acquisition used. The switching of the order of acquisition can also be used to effectively determine the extent of motion artefact present within the data acquisition time series. 43. When acquiring flat-field/shading control image sets, it is important to maintain the acquisition conditions as identical to the actual timelapse experiments as possible. This includes the amount of immersion oil used, amount of media used in a live-cell chamber, the order of data acquisition, and the

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appropriate focal plane for each channel (i.e., predetermined offsets from the glass-water interface at each wavelengths). 44. To achieve complete isolation from any stray light, close the hardware shutters on the microscope manually. 45. If multispectral beads do not extend far into NIR wavelengths, calibration beads may be custom produced to cover those wavelengths. We also find that the large relative shifts and differences in magnifications between the side port and the bottom port often make automated tracking-based calibration approaches more cumbersome. Thus, we find the use of gridbased calibration to be more straightforward by manually setting the control points for morphing (Fig. 5). 46. Dual-chain-based FRET biosensor data processing will require additional controls and processing routines that are beyond the scope of the current work. Additional details can be found elsewhere [7, 15]. 47. The flat-field correction is achieved by the following equation: n o ½IMAGERaw  ½DC þ ½DC
½SF n o ½IMAGECorrected ¼ ð1Þ ½SHADE  ½DC þ ½DC The components necessary for this correction are as follows: (1) data image stack for the donor and FRET ([IMAGE]), (2) corresponding shading images in donor and FRET channels ([SHADE]), (3) noise image set for the exposure times used in donor and FRET acquisitions ([DC]; ½DC denotes average pixel intensity value of the [DC] image), and (4) a scaling factor ([SF]) to account for floating point calculation issues. 48. Whenever two images are divided by one another, take into consideration floating point calculation issues. Floating point issues arise from how computers operate—binary operations, calculating in integers. The problem here is that when two images of similar intensities are divided, the pixel values in the resulting image scale to numbers close to 1.0. These numbers are then either rounded up or down due to the integer operation requirement of computing. Thus, a scaling factor is required to move the decimal point during the calculation so that the pixel intensity range in the resulting image will resemble that of the original image set. In some image processing platforms, scaling factors are automatically set to the maximum pixel value found in the original input image. This would be problematic because the scaling factors will be different every time images of different intensities are processed. We circumvent this problem by specifying a scaling factor of 1000 when performing any image-to-image division in 16-bit depth.

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49. Morphing is critical for dual-camera setup (Fig. 1; Cameras 1 and 2). This stems from imperfect mounting of two cameras, dichromatic mirrors, and bandpass filters in the optical beam splitter pathway. If only a single camera is used, this is usually not necessary; however, it may still be useful to test for the extent of lateral chromatic aberration and correct if required. Mounted multispectral beads are used to obtain calibration fields of views. Matlab routine based on a particle tracking algorithm package by J. C. Crocker and D.G. Grier [24] is used to extract the positional parameters, followed by affine transformation to perform the alignment. 50. We maintain the CFP channel as the global reference channel and align all other channels to it during image processing. 51. X-Y translational alignment is needed even after performing the morph operations. Morphing is a field-based operation that uses calibration patterns to determine the transform matrix for the coordinate transformation. We use affine transformation, which is a linear functional transformation followed by a translation. Depending on the morphing parameters, the resulting morphed image channel may be rotated at some angle compared to the reference image channel, which is then fitted into a rectangular matrix. Thus, the affine transformation we use does not preserve the location of the origin, as well as the morphed image may also become larger or smaller compared to the original. We account for this motion of the X-Y origin location by performing an additional X-Y translocation alignment procedure [16]. 52. The photobleach correction algorithm that we use [17] is based on an important biological assumption regarding the Rho family GTPases. The assumption is that the great majority (>95%) of cellular Rho GTPases are in complex with the guanine nucleotide dissociation inhibitor (GDI) and inactive at any given time [25]. This makes it possible to model the photobleach kinetic as a double-exponential decay function of inactive, non-responsive biosensor fluorescence in solution. This assumption will not necessarily hold for other molecules and biosensor probes that do not behave similarly to the Rho family GTPases in living cells. 53. X-Y pixel shift values can be approximate at this point. Whole pixel values are usually determined and applied. Additional fine-resolution alignment may be performed later using a cross-correlation-based approach [16]. 54. We have observed that, depending on the live-cell chamber system used, there will be a significant and continual shift in the X-Y position as a function of time in the field of view. The problem is also exacerbated when multistage position

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acquisitions are performed. The source of this shift for our system has been determined as the autofocus system that is installed on our Olympus microscope (the first-generation Zero Drift Compensation system [ZDC]). This system continuously moves the objective lens during the focus search routine, which transfers the motion onto the live-cell chamber causing small fluctuations in X-Y position. 55. Make sure that the motion of the speck of dirt/debris is independent from the motion of a cell from protrusions and general motility. It is easy to determine this by running the movies back and forth and see if the motion scales to that of the background coverslip or to the cell.

Acknowledgments This work was supported by National Institutes of Health grant GM136226 to LH. LH is an Irma T. Hirschl Career Scientist. RMB was supported by the Mamaroneck High School Original Science Research Program. MGW was supported by the EinsteinMontefiore Summer High School Research Program of the Albert Einstein College of Medicine, Graduate Division of Biomedical Sciences. NIR-Rac1 FRET biosensor was originally engineered, in part, by contributions from Tsipora M. Huisman, MD, and Natasha Cox Cammer [1]. Biosensor cDNA constructs and Matlab codes to enable processing of FRET data are available upon request. References 1. Shcherbakova DM, Cox Cammer N, Huisman TM, Verkhusha VV, Hodgson L (2018) Direct multiplex imaging and optogenetics of Rho GTPases enabled by near-infrared FRET. Nat Chem Biol 14(6):591–600. https://doi.org/ 10.1038/s41589-018-0044-1 2. Shcherbakova DM, Baloban M, Emelyanov AV, Brenowitz M, Guo P, Verkhusha VV (2016) Bright monomeric near-infrared fluorescent proteins as tags and biosensors for multiscale imaging. Nat Commun 7:12405. https://doi.org/10.1038/ncomms12405 3. Pertz O, Hodgson L, Klemke RL, Hahn KM (2006) Spatiotemporal dynamics of RhoA activity in migrating cells. Nature 440 (7087):1069–1072 4. Hodgson L, Spiering D, Sabouri-Ghomi M, Dagliyan O, DerMardirossian C, Danuser G, Hahn KM (2016) FRET binding antenna reports spatiotemporal dynamics of GDI-Cdc42 GTPase interactions. Nat Chem

Biol 12(10):802–809. https://doi.org/10. 1038/nchembio.2145 5. Longo PA, Kavran JM, Kim MS, Leahy DJ (2013) Transient mammalian cell transfection with polyethylenimine (PEI). Methods Enzymol 529:227–240. https://doi.org/10.1016/ B978-0-12-418687-3.00018-5 6. Hodgson L, Pertz O, Hahn KM (2008) Design and optimization of genetically encoded fluorescent biosensors: GTPase biosensors. Methods Cell Biol 85:63–81 7. Spiering D, Bravo-Cordero JJ, Moshfegh Y, Miskolci V, Hodgson L (2013) Quantitative ratiometric imaging of FRET-biosensors in living cells. Methods Cell Biol 114:593–609. https://doi.org/10.1016/B978-0-12407761-4.00025-7 8. Bravo-Cordero JJ, Moshfegh Y, Condeelis J, Hodgson L (2013) Live cell imaging of RhoGTPase biosensors in tumor cells. Methods Mol Biol 1046:359–370. https://doi.org/ 10.1007/978-1-62703-538-5_22

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9. Spiering D, Hodgson L (2012) Multiplex imaging of rho family GTPase activities in living cells. Methods Mol Biol 827:215–234. https://doi.org/10.1007/978-1-61779-4421_15 10. Donnelly SK, Cabrera R, Mao SPH, Christin JR, Wu B, Guo W, Bravo-Cordero JJ, Condeelis JS, Segall JE, Hodgson L (2017) Rac3 regulates breast cancer invasion and metastasis by controlling adhesion and matrix degradation. J Cell Biol 216(12):4331–4349. https://doi. org/10.1083/jcb.201704048 11. Miskolci V, Wu B, Moshfegh Y, Cox D, Hodgson L (2016) Optical tools to study the isoform-specific roles of small GTPases in immune cells. J Immunol 196(8):3479–3493. https://doi.org/10.4049/jimmunol. 1501655 12. Moshfegh Y, Bravo-Cordero JJ, Miskolci V, Condeelis J, Hodgson L (2014) A trio-Rac1Pak1 signalling axis drives invadopodia disassembly. Nat Cell Biol 16(6):574–586. https:// doi.org/10.1038/ncb2972 13. Hanna S, Miskolci V, Cox D, Hodgson L (2014) A new genetically encoded singlechain biosensor for Cdc42 based on FRET, useful for live-cell imaging. PLoS One 9(5): e96469. https://doi.org/10.1371/journal. pone.0096469 14. Zawistowski J, Sabouri-Ghomi M, Danuser G, Hahn K, Hodgson L (2013) A RhoC biosensor reveals differences in the activation kinetics of RhoA and RhoC in migrating cells. Plos One 8 (11):e79877 15. Hodgson L, Shen F, Hahn K (2010) Biosensors for characterizing the dynamics of rho family GTPases in living cells. Curr Protoc Cell Biol Chapter 14:Unit 14.11.1–Unit 14.1126 16. Shen F, Hodgson L, Rabinovich A, Pertz O, Hahn K, Price JH (2006) Functional

proteometrics for cell migration. Cytometry A 69(7):563–572. https://doi.org/10.1002/ cyto.a.20283 17. Hodgson L, Nalbant P, Shen F, Hahn K (2006) Imaging and photobleach correction of Mero-CBD, sensor of endogenous Cdc42 activation. Methods Enzymol 406:140–156 18. Kostenbader KD Jr, Cliver DO (1983) Membrane filter evaluations using poliovirus. J Virol Methods 7(5–6):253–257 19. Wallis C, Henderson M, Melnick JL (1972) Enterovirus concentration on cellulose membranes. Appl Microbiol 23(3):476–480 20. Beer C, Meyer A, Muller K, Wirth M (2003) The temperature stability of mouse retroviruses depends on the cholesterol levels of viral lipid shell and cellular plasma membrane. Virology 308(1):137–146 21. Wu B, Miskolci V, Sato H, Tutucci E, Kenworthy CA, Donnelly SK, Yoon YJ, Cox D, Singer RH, Hodgson L (2015) Synonymous modification results in high-fidelity gene expression of repetitive protein and nucleotide sequences. Genes Dev 29(8):876–886. https://doi.org/10.1101/gad.259358.115 22. Shen F, Hodgson L, Hahn K (2006) Digital autofocus methods for automated microscopy. Methods Enzymol 414:620–632 23. Shen F, Hodgson L, Price JH, Hahn KM (2008) Digital differential interference contrast autofocus for high-resolution oil-immersion microscopy. Cytometry A 73(7):658–666 24. Crocker JC, Grier DG (1996) Methods of digital video microscopy for colloidal studies. J Colloid Interface Sci 179:298–310 25. Del Pozo MA, Kiosses WB, Alderson NB, Meller N, Hahn KM, Schwartz MA (2002) Integrins regulate GTP-Rac localized effector interactions through dissociation of Rho-GDI. Nat Cell Biol 4(3):232–239

Chapter 5 Multicolor Localization-Based Super Resolution Microscopy Leila Nahidiazar and Rolf Harkes Abstract Super Resolution (SR) microscopy has become a powerful tool to study cellular architecture at the nanometer scale. Single molecule localization microscopy (SMLM) is a method in which fluorophore labels repeatedly switch On and Off (“blink”). Their exact locations are estimated by computing the centers of individual blinks. Therefore, the image quality depends on the density of the detected labels, as well as the accuracy of the estimation of their location. Both are influenced by several factors. Here we present a stepby-step method that optimizes many of these factors to facilitate multicolor imaging. Key words Super resolution, Multicolor, SMLM, OxEA, Microscopy, Fluorescence, Imaging buffer

1

Introduction The discovery of single molecule localization microscopy (SMLM) [1–3] has sparked an ever increasing interest in fluorescence microscopy beyond the diffraction limit. In this technique most fluorophores are pushed to a reversible Off-state where they cannot emit light. Only a limited number of fluorophores return stochastically to the On-state and their emission can be registered on a camera. The spatially well-separated emission from individual fluorophores will be fitted and their exact location can be estimated with a precision up to two orders of magnitude better than the diffraction limit. By measuring multiple frames, many individual fluorophores can be localized. These localizations are used to reconstruct a super resolution image. The extension of SMLM to antibody staining using conventional cyanine-based and rhodamine-based dyes [4] made the technique readily available to biologists. The reversible Off-state of these dyes is induced by a chemical reaction to a quencher. Therefore, the chemical environment greatly influences the blinking properties of the dyes and the precise concentration of quenchers and oxygen is important to obtain optimal blinking

The authors Leila Nahidiazar and Rolf Harkes contributed equally. Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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[5]. Especially, the presence of oxygen in the buffer has a very detrimental effect on the blinking properties. It can react with the dyes and increase the bleaching rate. To remove oxygen from the buffer it is common to use an oxygen scavenging system consisting of glucose oxidase and catalase, also known as gloxy. Cyanine-based dyes (e.g., Cy5 and Alexa 647) perform well in gloxy buffer [5]. However, rhodamine-based dyes (e.g., Alexa 555 and Alexa 488) do not blink well in gloxy. Conversely, in buffers that make rhodamine-based dyes blink, cyanine-based dyes do not blink well. This makes finding a good buffer for multicolor experiments challenging, since dyes in different spectral regions must be used. While most of the dyes in red and far red spectrum belong to the cyaninebased dyes, the rest of the dyes are rhodamine-based. Additionally, the pH of gloxy is not stable due to the production of gluconic acid. This limits the imaging time in gloxy to 1 h. We have developed a novel buffer system called OxEA [6] that is ideally suited for both cyanine and rhodamine-based dyes and enables multicolor SMLM (Fig. 1).

Fig. 1 An example of a three color super resolution image. In red Keratin stained with Alexa 488, in green Actin stained with Alexa 647, in blue Integrin β4 stained with Alexa 532. Scalebar is 500 nm [7]

Multicolor Localization-Based Super Resolution Microscopy

2

71

Materials

2.1 OxEA Imaging Buffer

OxEA imaging buffer (500 μL) is made of: 100 μL DL-Lactate, 3 μL 5M NaOH, 15 μL OxyFluor, 50 μL Cysteamine (see Note 1) and 332 μL PBS. For preparing OxEA imaging buffer, Stock solutions are: 5M NaOH, PBS (pH 7.4), 15 μL aliquots of OxyFluor (Oxyrase, Inc.) stored at 20∘C, 50 μL aliquots of 0.5M Cysteamine hydrochloride stored at 20∘C, DL-Lactate stored at 4∘C (see Note 2). The following stock solutions are prepared in advance to speed up the daily preparation of OxEA: 1. 5M NaOH 2. PBS (pH 7.4) 3. 15 μL aliquots of OxyFluor (Oxyrase, Inc.) stored at

20∘C

4. 50 μL aliquots of 0.5M Cysteamine hydrochloride (pH 8.0) stored at 20∘C (see Note 3). 5. DL-Lactate stored at 4 ∘C 2.2 Imaging Chambers

It is important to mount samples for imaging in a way that minimizes drift. Two methods work in our hands: 1. 25 mm round #1.5 ultraclean coverslips placed in a 6-well plate for cell and tissue imaging. Individual coverslips are mounted in a magnetic coverslip holder (see Note 4). 2. Glass bottom dishes optimized for SR (see Note 5). Using a sealed glass bottom dish decreases dye bleaching rate tremendously by preventing oxygen diffusion into the sample. This improves the sample lifetime to a month, enabling image stitching in 2D and 3D [7].

2.3 Ultra Clean Coverslips

Ultracleaning the coverslips remove grease and dust from the glass surface and increases image quality. There are several methods for cleaning coverslips, and the following works well in our hands. Immerse coverslips in the following solutions and rinse them with milli-Q in between each step (see Note 6): l

2 h 2M HCl

l

2 h 0.1M Na2B4O7 (pH8.5)

l

2 h 2M NaOH

l

12 h 20% H3PO4

Then leave Coverslips in 2M HCl overnight. Coverslips can be stored in 70% Ethanol for later use.

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Table 1 Dyes that blink in OxEA Name

Laser

Alexa Fluor 647

642

Alexa Fluor 555

532

Alexa Fluor 532

532

Alexa Fluor 488

488

CF680

642

CF660C

642

CF500

488

CF514

488

CF488A

488

Table 2 Filters and Lasers in a typical Leica GSDIM Laser

Excitation filter

Dichroic

Emission filter

488 nm/300 mW

ZET405/488x

t405_488rpc

et555/100m + ET505LPa

532 nm/500 mW

ZET405/532x

t405_532rpc

et600/100m + ET550LP

642 nm/500 mW

ZET405/642x

t405_642rpc

et710/100m + ET650LP

See Note 7

a

2.4

Antibodies

Several dyes have been reported to blink well in various solutions. Table 1 is a non-exhaustive list of 6 dyes conjugated to secondary antibodies that all show good blinking in OxEA.

2.5

Microscope

SMLM requires a sensitive camera with a quantum efficiency of at least 0.8. The microscope must have a sufficiently large magnification to spread the point spread function over at least 3x3 pixels of the camera. It also must be stable for prolonged periods of time, and have a laser with sufficient power to achieve at least 3kW/cm2 illumination intensity. The microscope we use is a Leica GSDIM with a 160 oil immersion objective and the Andor Ixon DU-897 EMCCD camera. Table 2 lists the laser lines and filters that are available in a typical Leica set up. A Leica astigmatic lens can be placed in the emission path for three-dimensional localization [8]. Multicolor fluorescent beads are required to correct the chromatic aberration of the microscope. We use 0.1 μ TetraSpeck microspheres from ThermoFisher.

Multicolor Localization-Based Super Resolution Microscopy

2.6

3

ThunderStorm

73

The microscope data contains many blinking events. These need to be identified and fitted to find the location of the fluorophores. For implementation in an automated analysis pipeline we recommend ThunderStorm [9]. This plugin for ImageJ [10] automates the identification of blinking events and can fit different types of point spread functions to the microscope data (see Note 8).

Methods

3.1 Sample Preparation

1. Grow the cells on ultraclean coverslips or in WilcoWells. 2. Fix, permeabilize, and incubate with primary and secondary antibodies at RT (see Note 9).

3.1.1 Staining of the Cells

3. Wash extensively after each step.

3.1.2 Staining of the Tissue Sections

1. Freeze the tissue in optimal cutting temperature (OCT) compound and cut them in thin slices (not thicker than 5 μm). 2. Coat the ultraclean coverslips with 10% poly-L-lysine and dry them for 1 h at RT. 3. Add the tissue sections to the coverslips, and let them stick to the surface for 15 min. 4. Wash them with PBS for 5 min, fix them in 2% paraformaldehyde for 10 min, and block them with 2% BSA in PBS. 5. Incubate the sections on the coverslips with primary and secondary antibodies as described for cells [7].

3.1.3 Buffer Preparation

To prepare 500 μL OxEA in one Eppendorf tube: 1. Add 100 μL DL-Lactate (see Note 10). 2. Add 3 μL 5M NaOH. 3. Add 15 μL OxyFluor. 4. Add 50 μL Cysteamine. 5. Add 332 μL PBS. The OxEA imaging buffer is ready for use 20 min after mixing and is stable for at least 6 h.

3.2

Measurement

For each image, acquire between 10.000 and 100.000 frames at 100 Hz, 10 ms exposure time. The exact number of frames is a trade-off: more frames increase the total time required for a measurement. Consequently, the risk of drift increases and more time is needed for analyzing registered localizations. Nevertheless, higher numbers of frames yield more localizations and thus, a higher sampling density. The low bleaching rate of dyes in OxEA buffer allows users to increase the number of imaging frames. The optimal number of frames depends on the required density of detected labels to answer the biological question.

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3.3 Calibration for the Correction of Chromatic Aberration

Different wavelengths of light will have slightly different paths through the optical system due to dispersion. Therefore they will end up at slightly different locations on the camera even if originating from the same point. This is commonly known as chromatic aberration. The following steps will calibrate the setup to correct for this effect: 1. Measure multicolor fluorescent beads at three different excitations: 488 nm, 532 nm, and 642 nm. 2. Record 100–1000 frames at multiple fields of view (typically 5), and concatenate the movies in order to gather a high bead density. 3. Group the fitted emissions that originate from the same bead. This can be done using ImageJ. The goal is to retrieve paired coordinates using multispectral beads for all colors to define the transformations. On our setup the aberration does not change over time, so the procedure only needs to be repeated when you make alterations to the optical system. From this dataset you can calculate two affine transforms using a custom ImageJ plugin (see Note 11) that maps the localizations of the beads at 488 nm and 532 nm onto the corresponding locations at 642 nm.

3.4

Post-Processing

The post-processing of the imaging data consists of the following steps: 1. Background subtraction: As background estimation you can use a sliding window temporal median [11] with a window of 501 frames, calculated using a custom-made ImageJ plugin (see Note 12). 2. Fit the data using the ImageJ [10] plugin ThunderStorm [9]. 3. Correct drift and merge localizations using ThunderStorm. 4. Correct for chromatic aberration using calibrated affine transforms (see Subheading 3.3). 5. Render the localization data using ThunderStorm. You can perform all post-processing steps in ImageJ. A macro that automates these steps is available online (see Note 13).

4

Notes 1. Cysteamine is also known as β-Mercaptoethylamine, β-MEA, or MEA. The powder is hygroscopic, so it must be stored in a sealed container at + 4∘C. Dissolving Cysteamine powder in milli-Q water should be accompanied by pH compensation between 8 and 8.5.

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2. The DL-Lactate is stored at 4∘C to prevent decomposition. Make sure it is not out of the fridge for more than 5 min. 3. OxyFluor and Cysteamine cannot stand multiple thawing procedure. Freeze and keep them in aliquots at 20∘C. An hour before imaging, thaw a small volume at RT (not in a heat bath). Repetitive freezing/thawing is not recommended. 4. Magnetic holders for 25 mm coverslips are acquired from Chamlide CMB with product number CM-B25-1. 5. Here, special imaging chambers from Willco Wells (GWSB3512-N) were used. 6. Clean coverslips with milli-Q water in between the steps makes sure that acids and bases are not mixed so that they can be stored and used for several cleaning rounds. 7. When doing multicolor super resolution with Alexa 488 and either Alexa 532 or Alexa 555, the excitation at 488 nm will also excite Alexa 555 and Alexa 532. Therefore use an additional emission bandpass filter of 500/30 to pass only the emission of Alexa Fluor 488. 8. The threshold for distinguishing blinks from background noise is very important for the final quality of the image. It is dependent on the signal to noise ratio (SNR) of the blinks. The original ThunderStorm paper 9. In methanol fixation most of the proteins are precipitated and therefore it is suitable for only cytoskeletal proteins. PFA fixation is usable for most of the other proteins. Other fixation methods include glutaraldehyde and PEM fixation. 10. DL-Lactate is very viscose. To pipet 100 μL you can cut off the top of a yellow pipette tip diagonally. 11. The plugin and source code for correction and calibration of chromatic aberration are found here: https://github.com/ rharkes/Chromatic-Aberration-Correction [9] recommends to filter blinks from background using the wavelet filter with a B-spline basis function of the third order with a scaling factor of 2. After this step we find that for low SNR blinks it is optimal to use as a threshold std(Wave.F1), for high SNR use 2*std (Wave.F1). Based on the optical setup other parameters can also be adjusted. 12. The plugin and source code for temporal median background subtraction are found here: https://github.com/rharkes/Tem poral-Median-Background-Subtraction 13. Macros for automatic analysis of super resolution data are found here: https://github.com/rharkes/ImageJ-Macros

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References 1. Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S, Bonifacino JS, Davidson MW, Lippincott-Schwartz J, Hess HF (2006) Science 313(5793):1642. https://doi.org/10. 1126/science.1127344 2. Hess ST, Girirajan TP, Mason MD (2006) Biophys J 91(11): 4258. https://doi.org/10. 1529/biophysj.106.091116 3. Rust MJ, Bates M, Zhuang X (2006) Nature Methods 3(10):793. https://doi.org/10. 1038/nmeth929 4. Heilemann M., van de Linde S, Schu¨ttpelz M, Kasper R, Seefeldt B, Mukherjee A, Tinnefeld P, Sauer M (2008) Angew Chem Int Ed 47(33):6172. https://doi.org/10. 1002/anie.200802376 5. Dempsey GT, Vaughan JC, Chen KH, Bates M, Zhuang X (2011) Nature Methods 8(12):1027 (2011). https://doi.org/10. 1038/nmeth.1768 6. Nahidiazar L, Agronskaia AV, Broertjes J, van den Broek B, Jalink K (2016) PLOS ONE 11

(7):e0158884. https://doi.org/10.1371/jour nal.pone.0158884 7. Nahidiazar L, Kreft M, van den Broek B, Secades P, Manders EMM, Sonnenberg A, Jalink K (2015) J Cell Sci 128(20):3714. https://doi.org/10.1242/jcs.171892 8. Simons CAJ, Lam HT (1977) Apparatus for reading an optically readable reflecting information structure 9. Ovesny´ M, Krˇ´ızˇek P, Borkovec J, Sˇvindrych Z, Hagen GM (2014) Bioinformatics 30 (16):2389. https://doi.org/10.1093/bioin formatics/btu202 10. Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW (2017) BMC Bioinformatics 18(1):1–26. https://doi. org/10.1186/s12859-017-1934-z 11. Hoogendoorn E, Crosby KC, Leyton-Puig D, Breedijk RMP, Jalink K, Gadella TWJ, Postma M (2017) Sci Rep 4(1). https://doi.org/10. 1038/srep03854

Chapter 6 Multiplexed Tissue Tomography Evan H. Phillips, David Scholten, Amy C. Flor, Stephen J. Kron, and Steve Seung-Young Lee Abstract Multiplexed tissue tomography enables comprehensive spatial analysis of markers within a whole tissue or thick tissue section. Clearing agents are often used to make tissue transparent and facilitate deep tissue imaging. Many methods of clearing and tissue tomography are currently used in a variety of tissue types. Here we detail a workflow known as transparent tissue tomography (T3), which builds upon previous methods and can be applied to difficult to clear tissues such as tumors. Key words Tomography, Microscopy, Tissue clearing, Multiplex imaging, Immunofluorescence, Antibody

1

Introduction Three-dimensional imaging or tomography is desirable in many tissue samples. Samples with structures that have complex geometry, such as tumor microvasculature, have cellular markers with distribution patterns that will vary with depth. Unfortunately, there are limits in the depth that laser light excitation and fluorescence emission propagate in tissue. Absorption and scattering of light do not allow for deep tissue imaging without some prior treatment to the tissue to make it less opaque. For this reason, tissue tomography has remained difficult or unfeasible for many biological imaging applications. Tissue clearing is the replacement of the aqueous medium within cells with a solution that better matches the refractive index of the tissue. Cleared tissue with a surrounding medium of a similar refractive index has higher transparency and less refraction and reflection of light at the surface [1]. Over the past century, several innovative methods have been tested for clearing different types of tissues. Easy to clear tissues, such as brain and heart, are the most common tissue types, while tissues with abundant extracellular matrix, such as tumors, have proven to be difficult [1]. In 2013,

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Chung et al. pioneered a method involving lipid extraction (CLARITY), which provides exceptionally detailed tomographic data of neural connections within intact or non-sectioned brains [2]. This approach however requires about a week for sample preparation and relies upon special embedding and clearing materials (Table 1) as well as electrophoresis equipment. Conventionally, the two main approaches to clearing are with a solvent or an aqueous medium (Table 1), each of which presents potential drawbacks. Using a solvent means that the tissue needs to be dehydrated first. Unfortunately, tissue shrinkage [3, 4] and possibly fluorescence quenching will occur through this process. Clearing with aqueous medium is thought to be much slower and make imaging difficult, but several advances have been made recently. Ke et al. developed an aqueous clearing agent known as SeeDB (Table 1), a high concentration solution of fructose (80% weight/ volume) with 0.5% α-thioglycerol [5]. It is based on older applications of sucrose solutions to imaging brain slices. Importantly, SeeDB has a refractive index that closely matches that of lipids, and it does not cause fluorescence quenching or tissue shrinkage. SeeDB is therefore a clearing method with some key advantages over other aqueous media. Transparent tissue tomography (T3) is a newly developed workflow for multiplexed three-dimensional tissue analysis and relies on clearing with aqueous medium [6]. Prior to imaging, stained tissue sections are made optically transparent after incubation in an ascending gradient of D-fructose solutions containing α-thioglycerol [5]. This method is relatively fast (a few hours) as compared to other methods and avoids fluorescence quenching and the use of organic solvents [3, 4]. Animal models [6] and human tissues [7] have been tested by T3. The workflow (Figs. 1 and 2) starts with intact fresh tissues and yields a three-dimensional multiplex image dataset that can be quantitatively analyzed (Fig. 3). Standard lab equipment and supplies are used, and the entire procedure can be carried out in 2 days or less. It is also non-destructive, permitting further downstream analysis of the same tissue. At the start of the workflow, fresh tissue is lightly fixed. A lighter fixation treatment allows for antigen preservation while obviating the need to perform harsh antigen retrieval procedures, which are often required for tissues fixed using heavier fixation treatments. Conventional immunofluorescence (IF) exploits secondary antibodies conjugated to fluorescent dyes to amplify the detection of primary antibody-antigen interactions. However, finding secondary antibodies that do not have cross-reactivity with other primary antibodies being used or with endogenous IgG limits the number of multiplex combinations. This issue is avoided in the T3 workflow by only using primary antibodies and tailoring the combination of a specific fluorescent dye with each antibody. If tissue sectioning is necessary, a vibratome

Ce3D

Yes LNm; musclem; PFA; clearing solution of bonem N-methylacetamide with Histodenz™, Triton X-100, and 1-thioglycerol

Bladder Methanol; clearing solutions Not cancerm; tested with THF, (DCM), and DBE FFPE tumor samplesh

iDISCO/DIPCO

Confocal Two photon

LSFM

Wide-field fluorescence Confocal two photon

Confocal Open-top light sheet

Multiplex capability Imaging tested

Hydrogel solution with Yes acrylamide and PFA; SDS

Materials needed

PFA; clearing solutions with Yes Mammary THF, DCM and DBE glandm; LNm; spleenm; lung tumorm

Brainm,h; breast tumorm,h; breasth; prostateh

Tissue tested

3DISCO

Clarity

Method

Table 1 Currently used methods for tissue tomography

[2, 8–11]

(continued)

[14, 15]

>10 days [4, 12, 13]

[3] Few hours (small tissues) or 1 day (large tissues)

8 days

Duration Refs

B220; CD3; CD4; 7 days CD8; CD11c; CD25; CD44; CD169; LYVE-1

LYVE-1

MHCII; D11c; CX3CR1

CD3

Immune markers

Multiplexed Tissue Tomography 79

PFA; clearing solutions with Yes fructose

Glioblastomam PFA; clearing solution with Yes urea and Triton X-100

Breast tumorm, PFA; clearing solution with Yes h fructose, urea, and glycerol

Embryom; brainm; Breast tumorm; head and neck core needle biopsyh

Confocal Two photon SRS

Confocal Multiphoton

Confocal Two photon

Confocal Confocal; LSFM

Confocal; LSFM

Multiplex capability Imaging tested

PFA; clearing solutions with Not tested N-butyldiethanolamine, Triton X-100, nicotinamide, and antipyrine PFA; clearing solutions with quadrol, urea, and sucrose

Materials needed

Not tested

Not tested

CD3; CD4; CD8; CD45

Not tested Not tested

Not tested

Immune markers

>4 days

3 days

3 days

3 days >3 days

>3 days

[23, 24]

[22]

[5–7, 20, 21]

[17, 18] [18, 19]

[16]

Duration Refs

Abbreviations: m (mouse); h (human); DBE (dibenzyl ether); DCM (dichloromethane); FFPE (formalin-fixed paraffin-embedded); LN (lymph node); LSFM (light sheet fluorescence microscopy); PFA (paraformaldehyde); SDS (sodium dodecyl sulfate); SRS (stimulated Raman scattering microscopy); THF (tetrahydrofuran)

Scale derivative

FUnGI

SeeDB

Various tumorsm; whole body metastasesm

CUBIC

Lung tumorm Various organsh; LNh

Tissue tested

Method

Table 1 (continued)

80 Evan H. Phillips et al.

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Fig. 1 T3 workflow with macrosectioning. (a) Harvesting whole tumors followed by light tissue fixation; (b) tissue embedding in 2% agarose gel; (c) collecting thick tissue sections (macrosections) from a vibrating microtome; (d) staining with a cocktail of fluorescent primary antibodies; (e) optical clearing of the macrosections using D-fructose solutions; (f) three-dimensional confocal imaging of multiple fluorophores; (g) image reconstruction of whole tumors by concatenating image data; (h) 3D analysis of markers throughout whole tumor. (Reproduced from Lee et al., 2017 with permission from Nature (ref. [6]))

can be used to collect macrosections (i.e., sections on the order of several hundred micrometers thick). After three-dimensional imaging of each macrosection, the whole tissue can be reconstructed with good fidelity due to collection of thicker tissue sections rather than thin serial sections which are more likely to become distorted. Here we present a generalized workflow for T3. From the point of tissue collection to imaging, we highlight the key steps and considerations in performing fixation, embedding, sectioning, staining, clearing and image reconstruction. Citations to key references are also provided for further information on materials, methods, and image analysis resources.

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Fig. 2 T3 workflow without macrosectioning. (a) Collection of a tissue cylinder (core) followed by light fixation. (b) Placement of core in a pre-cast agarose well. (c) Staining with a cocktail of fluorescent primary antibodies followed by washing and fixation. (d) Optical clearing using D-fructose. (e) Confocal imaging of both sides of the core. (f) Fusion of half-cylinder images and reconstruction of the whole core; (g) 3D spatial analysis of multiple markers. (h) Removal of D-fructose and tissue fixation; (i) 2D chromogenic immunohistochemistry (IHC) of each marker; (j) Correlation between 2D and 3D image data. (Reproduced from Lee et al., 2018 with permission from Nature (ref. [7]))

2 2.1

Materials Tissue Collection

2.1.1 Whole Tumor Excision

1. Asphyxiation chamber. 2. Fine tip forceps, curved or straight depending on user preference. 3. Fine tip scissors. 4. Storage container, such as a 12-well plate or conical tube of appropriate size.

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Fig. 3 3D mapping of multiple markers in the microenvironment of a mouse tumor. Top left: 3D rendering of a reconstructed mouse tumor revealing Her2+ cancer cells, CD45+ immune cells, proliferating cells (Ki-67), CD31+ endothelial cells, and PD-L1+ cells. Top left inset shows the appearance of the tumor mass after excision. Top right inset shows a representative 2D image with Her2+ cells. Top right: Lateral view of the reconstructed tumor shows the depth of the whole tumor. Bottom: 3D (left) and 2D (right) individual channel images for each of the five markers. (Adapted from Lee et al., 2017 with permission from Nature (ref. [6])) 2.1.2 Core Needle Biopsy Collection

1. Isoflurane. 2. Anesthesia chamber. 3. Automated core needle biopsy device. 4. 12-well plate. 5. P-1000 pipette and pipette tips. 6. RPMI media.

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Fixation

1. 16% paraformaldehyde (PFA). 2. 12-well plate. 3. Fine tip forceps. 4. 1 PBS (pH 7.4).

2.3 Tissue Embedding

1. Agarose gel solution: 2% agarose dissolved in distilled water.

2.3.1 Agarose Gel Embedding for Whole Tumors

3. Fine tip forceps.

2.3.2 Agarose Cassette Preparation for a Core Needle Biopsy

1. Agarose gel solution: 2% agarose dissolved in distilled water.

2. 12-well plate. 4. Microwave or hot plate. 5. Immersible thermometer (0–100  C).

2. 2 cm diameter glass bottom dish. 3. Glass scintillation vial cap. 4. Ice bucket with ice. 5. 2 cm long needle of a gauge matched to the core biopsy needle used (see Note 1) 6. Single edged razor blade. 7. Fine tip forceps. 8. Ring tip forceps. 9. Microwave or hot plate. 10. Immersible thermometer (0–100  C).

2.4

Macrosectioning

1. Double edged stainless steel razor blades. 2. 12-well plate. 3. Fine tip forceps. 4. Agarose gel plug containing tumor sample. 5. “Super Glue”. 6. Vibrating microtome. 7. 1 PBS (pH 7.4) 8. Ice. 9. RPMI media.

2.5 Antibody-Dye Conjugation

1. Primary antibodies of interest (see Note 2). 2. Fluorescent dyes for conjugation (see Note 3). 3. N,N-Dimethylformamide (DMF). 4. 1 PBS (pH 8.0). 5. Dialysis cassettes (10,000 molecular weight cut-off). 6. 1 mL syringes. 7. 18G needles.

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8. 1 L plastic buckets. 9. 1 PBS (pH 7.4). 10. Aluminum foil. 11. Shaker. 12. Magnetic stir plates and stir bars. 2.6 Antibody Staining

1. Staining buffer (kept on ice): RPMI media with 1% BSA (does not need to be IgG-free) and 0.3% (for mouse) or 0.1% (for human) Triton X-100. 2. Fluorescent dye-conjugated monoclonal antibodies (stored at 4  C, in the dark). 3. P-1000 and P-100 pipettes and pipette tips. 4. 12-well plate. 5. Fine tip forceps. 6. Parafilm. 7. Aluminum foil. 8. Shaker.

2.7

Optical Clearing

1. 16% PFA. 2. 1 PBS (pH 7.4). 3. Phosphate buffer solution: Add 800 mL of distilled water to suitable container. Add 20.209 g Na2HPO4l7H2O and 3.394 g NaH2PO4lH2O and adjust pH to 7.8 Add distilled water until volume is 1 L. Store at room temperature. 4. Clearing solutions: Add 8.0 g D-Fructose to appropriate container. Fill with phosphate buffer solution until volume is 40 mL (20% D-Fructose, w/v). Vortex solution well. For macrosection samples, repeat with 20.0 g and 32.0 g D-fructose for 50% and 80% (w/v) solutions, respectively. For core biopsy samples, repeat with 20.0 g and 40.0 g D-fructose for 50% and 100% (w/v) solutions, respectively. After D-fructose is dissolved, add 0.5% α-thioglycerol to each solution. These solutions need to be made at least one day before performing tissue clearing. Shake overnight at room temperature to completely dissolve. 5. Fine forceps. 6. Glass scintillation vials with caps. 7. Shaker.

2.8

Imaging

1. Confocal microscope (see Note 4). 2. Microscope cover glasses (24  50 mm; #1 or #1.5 thickness, dependent on microscope objective). 3. Tweezers.

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Methods

3.1 Antibody-Dye Conjugation

1. Solubilize amine-reactive fluorescent dye in DMF, according to manufacturer instructions. 2. Dilute the primary antibody in 1 PBS (pH 8.0) and add excess fluorescent dye for conjugation reaction. Calculate the volumes required according to manufacturer instructions. As a guideline, a 30:1 dye:antibody molar ratio is typically acceptable. 3. Allow the antibody and dye conjugation reaction to proceed overnight at 4  C with gentle agitation. 4. Carefully inject the fluorescent dye-conjugated antibody mixture into a dialysis cassette. Place the cassette into a 1 L container immersed in 1 PBS (pH 7.4) with a magnetic stir bar, protected from light with aluminum foil (see Note 5). 5. Allow dialysis to proceed on a magnetic stir plate overnight at 4  C. 6. Change the dialysis buffer twice, once on each of the following 2 days. 7. After 3 days, unreacted dye will have been removed into the buffer solution. 8. Remove the fluorescent dye-conjugated antibody mixture from the cassette. 9. Perform degree of labeling (DOL) quantitation (see Note 6). 10. If conjugate DOL is in acceptable range (3–7 fluorophores per IgG), store protected from light at 4  C.

3.2

Tissue Collection

3.2.1 Whole Tumor Excision

1. Sacrifice animal in anesthesia chamber using a humane method such as a CO2 asphyxiation. 2. Using fine tip scissors, make a small incision near the tumor site. Insert closed scissor into incision and open to begin clearing the area around the tumor. 3. Using scissors and forceps, carefully remove connective tissue from around tumor area to free tumor from the animal body. Remove any residual fur and skin (see Note 7). 4. Using forceps, transfer tumor tissue to appropriate storage container, such as a 12-well plate, and cover with PBS.

3.2.2 Core Needle Biopsy Collection

1. Anesthetize animals using 2% vaporized isoflurane. 2. Using an automated core needle biopsy device, collect a sample through the middle of the tumor, avoiding necrotic areas. 3. Collect core needle biopsy sample in 12-well plate by gently pipetting RPMI media over sample in specimen notch until it is released from the device.

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1. Dilute 16% PFA to 2% PFA in 1 PBS (pH 7.4). 2. Transfer sample to an empty well of 12-well plate and cover with 2% PFA. Fix at room temperature for 5 min. 3. Wash tumor in PBS at room temperature 3 times, for 10 min each.

3.3 Tissue Embedding 3.3.1 Agarose Gel Embedding and Macrosectioning for Whole Tumors

1. Make agarose gel solution and microwave for 1 min or until agarose has completely dissolved. 2. Let molten agarose cool to 37  C (see Note 8). 3. Pour molten agarose into well of 12-well plate, and using forceps transfer the tumor to the well containing agarose. 4. Cover well plate and let agarose completely solidify. 5. After agarose has solidified, remove gel plug containing tumor using fine tip forceps (see Note 9). 6. Using “Super Glue”, affix bottom of gel plug to vibrating microtome removable stage. 7. Cut 400 μm thick macrosections of tumor tissue. Collect in a chilled PBS bath on the vibrating microtome, and then transfer to a 12-well plate containing RPMI media.

3.3.2 Agarose Cassette Preparation for a Core Needle Biopsy

1. Pour about 700 μL of molten 2% agarose solution into a 2 cm glass bottom dish. 2. Place an appropriately sized needle into dish. Make sure needle is completely submerged in agarose. 3. Cover agarose and needle with the top of the glass scintillation vial cap. Place on ice until agarose has completely cooled. 4. Remove cap and use fine tip forceps to remove any agarose that came out from under the cap. 5. Using the edge of a razor blade, gently remove agarose cassette from glass bottom dish. Using fine tip forceps, remove the needle from the agarose by pulling it up through the cassette so that it forms a well. Do not push the needle through the cassette, forming a tunnel. 6. Using ring forceps, gently transfer the core needle biopsy sample to the well of the agarose cassette. Open and then close the cassette by gently bending it to capture the tumor sample in the well.

3.4 Antibody Staining

1. Add fluorescent dye-conjugated antibodies to staining buffer at user-determined optimal titrations (see Note 10). 2. Add 250 μL of staining buffer to each tumor macrosection in a 12-well plate, or enough to cover. 3. Seal plate with parafilm and cover with aluminum foil. 4. Gently shake for at least 18 h at 4  C (see Note 11).

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Optical Clearing

1. On a shaker, wash macrosections in cold PBS 3 to 5 times for 10 min each. 2. Dilute 16% PFA to 2% PFA in 1 PBS (pH 7.4). 3. Fix the macrosections in 2% PFA at room temperature for 10 min. 4. Pour 10 mL of 20% D-fructose clearing solution into a glass scintillation vial. Carefully transfer the tumor macrosections to the vial. 5. Gently shake at room temperature in the dark for at least 20 minutes. 6. Repeat steps 4 and 5 with 50% D-fructose clearing solution. 7. Pour 10 mL of 100% D-fructose (core needle biopsy samples) or 80% D-fructose (macrosections) clearing solution into a glass scintillation vial. Add tumor macrosections to the vial. 8. Gently shake at room temperature in the dark until tumor is optically cleared, at least 1 h. 9. If desired, verify tissue clearing using a spectrophotometer (see Note 12).

3.6

Imaging

1. Using tweezers, remove optically cleared tissue from 100% D-fructose clearing solution and place on a microscope cover glass. 2. Gently place a second microscope cover glass on top of the tissue, taking care to avoid creating bubbles. 3. Transport tissue sections to a confocal microscope. 4. Image tissue sections on a confocal microscope. Antibody-dye conjugates that have not been validated should be tested for specificity and staining depth. Use a high-resolution objective (40) to visualize the sample at the single-cell level and confirm targets were stained as expected. Acquire digital images as desired. 5. For most microscopes, up to 5 fluorescent dye channels may be used (488, 550, 594, 633, and 680 nm). However, each microscope is different, and the T3 antibody panel used should be tailored to the microscope that is available. 6. To evaluate the depth of staining, use the Z-stage functionality of the microscope to scan vertically through the tissue until no more staining is observed. Using the image software, digitally mark this position as the lower edge of the tissue. Next, scan up through the tissue until no more staining is observed. Digitally mark this position as the upper edge of the tissue. Additionally, some microscopes are equipped with “xzy” cross-sectional imaging functionality. This can be utilized to view the staining

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depth in a straightforward manner. Top to bottom staining depth should be close to the actual tissue thickness in micrometers. 7. Imaging of whole tissues should be carried out with a 10 objective, while specific regions of interest should be imaged using a higher magnification objective.

4

Notes 1. For selection of needles for core biopsy and preparation of the core agarose cassette, the gauge of the needle used for making the cassette is dependent on the gauge of the core needle biopsy device. The gauge of the needle used for preparing the cassette should thus be equal to the gauge of the biopsy device needle. Further, the length of the cassette needle should be equal to the diameter of the glass bottom dish used for cassette preparation. 2. Primary antibodies and fluorescent dye combinations must be chosen carefully. For primary antibodies, it is recommended to use strictly monoclonal antibodies for enhanced target specificity and tissue penetration. When purchasing monoclonal antibodies, clones are preferable that have been cited in multiple publications for immunofluorescence assays. If there is not a commercial antibody that meets these criteria, it is then recommended to obtain several different clones and test each on tissue known to be antigen-positive, in order to select an optimal antibody for use. Optimal antibody titration should be user-determined, although if available, manufacturer recommended titration can be used as a starting point. 3. A wide variety of commercial fluorescent dyes (e.g., Alexa Fluor or DyLight) may be used for antibody conjugation. Various conjugation chemistries may be used, but our group has mostly utilized amine-reactive NHS dyes. Dyes should be selected which have excitation and emission spectra that are compatible with the optical capabilities of the confocal microscope that will be used for imaging. Alternatively, commercially available conjugated fluorescent antibodies may be acceptable (and a time saver) if they are available as monoclonals, have the desired fluorescent dye, and are determined by the user to perform well for T3. 4. There are many different models of confocal microscopes available. Microscopes equipped with a white line laser offer the greatest flexibility in fluorescent dye excitation. If a white line laser is not available, separating lasers that can excite all fluorescent dyes to be used in the T3 experiment is necessary.

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Microscopes should have adjustable XY functionality to capture the entire XY area of tissue, as well as a galvometric Z-stage to image the depth of the tissue. For imaging Z-stacks, a 7 μm step size is sufficient to capture all cells. Regarding microscope objectives, the working distance of the objective in use must be greater than the thickness of the tissue being imaged. Otherwise, the objective will not be able to image the entire Z-depth of the tissue. While this is not an issue for most standard highquality objectives between 10 and 40 magnification, which often have working distances sufficient for most macrosections, high-magnification objectives (60 to 100) often have reduced working distances and may not be suitable. 5. Be careful not to puncture the side of the dialysis cassette membrane when inserting the needle into the cassette. Different antibodies conjugated to the same fluorescent dye may be dialyzed in the same PBS bath container, but never dialyze antibodies conjugated to different dyes in the same container to avoid cross reactions. 6. Test the fluorescent dye degree of labeling (DOL) to make sure antibody conjugation was successful. Most dye manufacturers should include a short protocol for testing DOL in the respective manual. Many protocols for DOL are available online as well. For T3 experiments, DOL should be in the range of 3–7 fluorophores per IgG molecule. However, some antigens that are more ubiquitous in the tissue may stain sufficiently using a conjugate with DOL 700 μm in the far-red. Although the method was initially developed for bone tissue imaging, we have successfully applied it to several other tissue types. Key words In situ, Bone marrow, Multicolor, Confocal, 3D imaging, Fluorescence, Tissue clearing, Quantitative microscopy, Single cell

1

Introduction Tissue and organ physiology, development, homeostatic maintenance, and repair all require an integrated crosstalk between different cell types. This crosstalk can be mediated through cell-cell contact or secreted molecules (cytokines, chemokines, extracellular matrix, exosomes, etc.) as well as chemical and physical/mechanical cues [1]. The spatial organization of these cells and molecules is of critical importance during development, for tissue homeostasis and tissue regeneration. While some organs are composed of relatively small and repetitive functional units (e.g., intestine, lymph node, spleen etc.) others, such as brain or bone marrow, require a holistic approach to fully grasp their structure and function [2, 3]. At the same time, developmental and regenerative processes can involve rare stem cells or their progeny, which are scattered throughout the tissue and difficult to identify among more abundant cell types [4, 5]. For all these reasons, several new tissue processing and

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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imaging techniques that allow multicolor fluorescence imaging of large tissue samples at a single-cell resolution have been developed [6]. Moreover, identifying rare stem cells with complex phenotypes and understand their relationship with their microenvironment in large tissue samples requires specific computational approaches for quantitative image analysis [7–9]. These image analysis techniques however can only be used efficiently if the source data is of high quality and, as much as possible, devoid of autofluorescence and staining artefacts. To establish a highly multiplexed fluorescence imaging experiment, the choice of appropriate animal models and reagents is key. The use of fluorescent reporters can help visualize protein or gene expression when a specific antibody is not available or incompatible with other reagents of the tissue preparation process [10, 11]. Of note, one must take care while interpreting non-fusion fluorescent reporters as florescent protein levels do not always correlate with protein production. Similarly, the use of fluorescent dyes (nuclear and lipophilic dyes, biosensors, etc.) should be maximized whenever possible, to simplify the antibody staining scheme [12–14]. If an antigen is highly abundant in the tissue of interest, using fluorescent primary antibodies also helps multiplexing while avoiding complex blocking steps that require several rounds of optimization. For imaging of highly multiplexed thick and large tissue samples, the choice of appropriate fixative, buffer, and clearing or mounting medium is important. Fixative and tissue processing buffers and conditions should avoid the creation of imaging artefacts, maintain cell and tissue morphology, and be compatible with immunostaining [15, 16]. The ideal medium should be compatible with immunostaining reagents and all the fluorophores used. It should also preserve tissue architecture and cell morphology (to allow correct evaluation of cell to cell/structure distances) and have a refractive index matching that of the objective lens used on the microscope. There are several optical clearing methods described in the literature, and all have intrinsic pros and cons and should be carefully evaluated and tested for each fluorophore and tissue of interest (reviewed in [6]). These methods typically fall into four categories based on the chemistry used to achieve optical clearing. Solvent-based methods rely on delipidation of the tissue using solvents and subsequent refractive index matching by immersion into a final mounting medium [17–19]. Aqueous-based methods normally use simple immersion for refractive index matching [4, 7, 20–23]. Alternatively, hyperhydration methods rely on delipidation using long incubations in detergents and partial denaturation of proteins (hyperhydration) [24–27]. Finally, more complex techniques use hydrogel embedding of the tissue followed by a combination of the methods just described [2, 28]. The protocol described here uses an aqueous-based simple immersion technique to match the refractive index of glycerol or

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oil and thus provide a clearing effect. Although this protocol was initially developed for bone and bone marrow imaging [7], it provides a high signal-to-noise ratio in all tissues tested, while being limited in its clearing efficiency in brain tissue and spleen. It preserves tissue architecture and cellular morphology very well and is compatible with all fluorophores tested. Combined with an appropriate confocal microscope for image acquisition, this protocol allows simultaneous detection of up to eight different fluorescent dyes in a single specimen (Fig. 1).

2 2.1

Materials Reagents

1. 70% ethanol. 2. 4% formaldehyde, methanol free (see Note 1). 3. Phosphate-buffered saline (PBS). 4. 10% EDTA, pH ¼ 8. 5. 4% low-temperature gelling agarose in PBS (high gel strength, >1200 g/cm2). 6. Tris-base and Tris–HCl. 7. NaCl. 8. NaOH and HCl to adjust pH. 9. Normal donkey serum. 10. Avidin/biotin blocking kit (optional) (see Note 2). 11. Cyanoacrylate glue (see Note 3). 12. Triton-X, DMSO, and Tween 20 (see Note 4). 13. Nuclear counterstain: e.g., DAPI (2 mg/mL stock). 14. Clearing and mounting medium (e.g. 2–20 -thiodiethanol [TDE], glycerol, etc.) (see Note 5). 15. Silicone cement (e.g., aquarium silicone). 16. Primary antibodies and compatible fluorophore-/biotinlabeled secondary antibodies (raised in donkey, see Note 6). 17. Normal immunoglobulins and monovalent F(ab) fragments raised in donkey for immunostaining using primary antibodies of the same species. 18. N-propyl gallate (NPG).

2.2

Equipment

1. Dissection tools. 2. Magnetic stir plate, stir bar. 3. Paraffin tissue embedding plastic cassettes. 4. Plastic embedding molds. 5. Water bath and thermometer.

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Fig. 1 Example of an eight-color imaging of a large tissue sample. A femur from a 12-week-old Sp7- (osterix-) GFP mice was dissected, fixed, and decalcified. Sections (250 μm thick) were generated using a vibratome. A first round of immunostaining was performed to detect GFP (chicken anti-GFP with donkey anti-chickenAlexaFluor 488), GFAP (rabbit anti-GFAP with donkey anti-rabbit-biotin and streptavidin-Chromeo494), Sca1 (rat anti-Sca1 with donkey anti-rat monovalent F(ab)-AlexaFluor647), and IL1R1 (goat anti-IL1R1 with donkey anti-goat-QDot605). Blocking was then performed with rat, rabbit, and goat IgGs and donkey anti-rat, antirabbit, and anti-goat monovalent F(ab)s. A second round of staining detected nuclei (DAPI), collagen 1a1 (rabbit anti-col1a1 with donkey anti-rabbit-CF680), c-Kit (goat anti-c-Kit with donkey anti-goat-Dylight594), and CD105 (rat anti-CD105 with donkey anti-rat-AlexaFluor555). Appropriate controls were generated to verify staining specificity and to adjust microscope settings (lasers power, detectors bandwidth and gain). Sections were cleared with TDE, mounted, and imaged using a Leica SP5 confocal microscope with oil immersion. Objective lens used was 20 with 0.7NA and 0.680 mm free-working distance

6. Disposable 3 mL plastic transfer pipets. 7. 6-well plates. 8. Vibratome. 9. Low-profile, injector-type ceramic blades, or sapphire blades, or razor blades. 10. Histology brushes (e.g., Camel hair brushes no 1 and 2). 11. Microscope slides (non-coated) and coverslips (see Note 7).

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12. Silicone spacers (see Note 8). 13. pH-meter and pH-indicator strips (see Note 9). 14. Refractometer (recommended). 15. Confocal microscope (see Note 10). 16. Bottle-top filter units (0.45 μm).

3

Methods

3.1 Preparation of Reagents

Just before tissue harvest, prepare 4% (para-)formaldehyde in PBS and keep on ice until used.

3.1.1 Fixative 3.1.2 TBS-T

All buffers (blocking, staining, washing) should have a final concentration of 0.1 M Tris, 0.15 M NaCl, and 0.05% Tween-20 (pH ¼ 7.5). We recommend preparing 12.5 stock solutions, one using Tris-base and another using Tris–HCl. These are then mixed until pH reaches 7.5 (Tris-base solution is added to Tris– HCl solution). For 500 mL, use 75.71 g of Tris-base or 98.5 g of Tris–HCl in ddH2O and add 54.79 g NaCl and 3.2 mL Tween-20 (or 6.25 mL Triton-X). Complete volume to 500 mL using ddH2O. Stir at room temperature until dissolved. Filter using 0.45 μm bottle-top filter unit.

3.1.3 Permeabilization, Blocking, Washing, and Staining Buffers

Dilute 12.5X TBS-T stock solution (pH ¼ 7.5) 1:10 to reach 1.25 and add 20% DMSO. The TBS-T should now be 1. Use this buffer for washing steps. For permeabilization, blocking, and staining, add 5–10% (v/v) normal donkey serum.

3.1.4 Mounting Media

For glycerol mounting, prepare a graded series of glycerol (20, 40, 60, 80%) making sure the final TBS concentration remains 1 in the final solutions (detergents should be removed at this point). Prepare a final mounting medium consisting of 80% glycerol and 20% 1 TBS (use Tris-base here). Add 0.1 M n-propyl gallate and mix for 5 h to overnight at room temperature until dissolved. Store at 20  C. Before using, equilibrate at room temperature and adjust pH to 8.5 using NaOH and pH indicator stripes. Verify refractive index of the immersion liquid used on the microscope and your mounting medium using a refractometer and adjust mounting medium if necessary, using glycerol or TBS. For TDE mounting, follow the same procedure but replace glycerol with TDE and bring samples up to 97% TDE. For the final mounting medium, dissolve Tris-base, NaCl, and n-propyl gallate directly into 97% TDE, mixing overnight at room temperature. Store at -20  C. Before using, equilibrate at room temperature and adjust pH to 8.5 using NaOH and pH indicator stripes. Verify

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refractive index using a refractometer and adjust if necessary, using TDE or TBS. 3.2 Femur Dissection and Processing

1. Sacrifice mice by CO2 inhalation or cervical dislocation. 2. Spray the fur with 70% ethanol. 3. Remove the skin from the hindlimbs; remove the tail. 4. Dissect out femurs or any other bones. 5. Place bones in cold PBS 5–10 min (optional). 6. Carefully remove muscles and tendons from bones (optional). 7. Fix bones 16–24 h at 4  C in cold 4% formaldehyde using 20–50 times the volume of the tissue with gentle rotation (see Note 11). 8. Wash 2–3 1 h in a large volume of PBS. 9. Place bones in a 250 mL flask filled with cold 10% EDTA. 10. Decalcify at 4  C in EDTA for 2 weeks on a magnetic stirrer. Refresh EDTA twice a week (see Note 12).

3.3 Vibratome Sectioning of Femurs

1. Wash 2 5 min in PBS (see Note 13). 2. Prepare 4% low-gelling agarose and place it in a hot water bath, no warmer than 50  C (see Note 14). 3. Dry bones and place them in embedding mold (see Note 15). 4. When the agarose reaches 50  C, add 2–3 mL melted agarose to the mold containing one femur. 5. Let agarose gel 30–60 min on ice. 6. Section bones using a vibratome (see Note 16). 7. Add PBS to a 6-well plate to collect sections. 8. Remove agarose block from mold and trim excess agarose. 9. Fix the block on the specimen holder using cyanoacrylate glue (see Note 17). 10. Start sectioning. 11. Lift sections using a histology brush and clean microscope slide and transfer to the 6-well plate. 12. Proceed to immunostaining as soon as possible (section can be stored for several weeks in the presence of sodium azide but intensity of, e.g., fluorescent proteins should be evaluated).

3.4 Immunostaining of Femur Sections

1. Transfer the sections to permeabilization/blocking buffer on clean microscope slides mounted with silicone spacers. 2. Use a graphite pencil to label slides (some inks are fluorescent). 3. Permeabilize/block sections 45–60 min or more at room temperature.

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4. Block with Avidin/Biotin blocking kit (30 min blocking steps with 15 min washes) (optional). 5. Dilute primary antibodies (see Note 18) in blocking buffer (200–400 μL is usually sufficient to cover sections, depends on size of silicon spacers). 6. Incubate overnight at room temperature in a humidified chamber (larger and thicker samples may require longer incubations, up to 3–5 days). 7. Wash 5 for 30–60 min. 8. Add secondary antibodies, nuclear counterstain, and/or streptavidin diluted in blocking buffer (see Note 18). 9. Incubate overnight at room temperature in a humidified chamber. 10. Wash 5 for 30–60 min. 11. If using primary antibodies of the same species, block with species-specific IgGs overnight at room temperature in a humidified chamber, wash 5, block overnight at room temperature in a humidified chamber with monovalent F(ab) fragments raised in donkey against the IgGs used for blocking, and wash 5 (see Note 19). 12. Perform second immunostaining round by repeating steps 5– 10. 3.5 Mounting Femur Sections

1. Treat samples with a graded series of glycerol or TDE, 4 1 h each step at room temperature. 2. Replace the final solution with fresh mounting medium and add coverslip. Use silicone cement to seal coverslip. 3. Invert slides (if using an inverted microscope); allow silicone to cure and sections to settle overnight at 4  C (optional). 4. Proceed to imaging as soon as possible (slides can be stored at 4  C for up to 1 month, but signal strength in comparison to freshly mounted samples on a per staining scheme basis is recommended).

4

Notes 1. Methanol is often used to stabilize commercial formaldehyde or formalin solutions. It is important to avoid methanol or other types of alcohols during sample processing as they will affect tissue morphology, subcellular localization, and detection of antigens and increase autofluorescence of the tissues. We recommend using freshly prepared 4% paraformaldehyde or a formaldehyde solution prepared under an inert atmosphere.

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2. If using a biotin-streptavidin detection system, it is strongly recommended to block endogenous biotins or avidins before starting the staining. 3. A non-toxic, low odor, fast curing contact glue such as Loctite® 401 is recommended. 4. Typically, Tween-20 and DMSO are used for permeabilization. For staining of nuclear antigens, Triton-X (1%) and DMSO are used. 5. The choice of clearing agent and mounting medium depends on the tissue type and available objective lenses at the microscope. 80% glycerol and 97% TDE give good results for bone sections, using glycerol and oil immersion, respectively. In all cases, the mounting medium should contain 0.1 M Tris, 0.15 M NaCl, and 0.1 M n-propyl gallate. Adjust the pH to 8.5. It is preferable to treat the samples with a graded series of the mounting medium before the final mounting (e.g., 20%, 50%, 75%, etc.) 6. Secondary antibodies raised in donkey are preferred to avoid potential cross-reactivity between reagents. Nonspecific binding of these antibodies is blocked using normal donkey serum. 7. Most microscope lenses are corrected for size 1.5 coverslips. However, if using TDE clearing and mounting with oil immersion, the coverslip thickness can be varied since the refractive indices of the oil, glass, and TDE are exactly matched. 8. Silicone spacers with adhesive on one or both sides are preferred. Shape and thickness should be selected based on the tissue specimen to be processed and its dimensions. Samples should not be compressed between slide and coverslip, and volume should be sufficient to hold sample and immunostaining reagents. 9. The pH-meter is used to adjust pH of the buffer solutions. Mounting media are typically viscous and incompatible with many types or standard electrodes. The use of pH-indicator stripes is recommended. 10. For highly multiplexed immunostaining of large tissue sections, the choice of an appropriate microscope is key. Important parameters that should be considered are as follows: (1) the presence of a motorized stage, (2) working distance of the stage and objective lenses, (3) number of laser lines, (4) spectral detection with virtual filters, (5) field of view size, and (6) capacity to perform mosaic tile scans. 11. Using Image and open the autofluorescent image(s).

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Fig. 4 Manual Analysis view of the inForm™ software, Configurate option. Used to select the needed analytical steps for the image analysis protocol

5. Navigate to File - > Open - > Image and open several Field/ MSI images from your cohort. These images shall be representative and reflect the heterogeneity of the cohort (see Note 8). 6. When all images are loaded into the “project,” set Spectral Library Source to “inForm.” 7. With Select Flours function, select the spectral library, created at Subheading 3.3; make sure all needed reference spectra are selected (Fig. 5). 8. Navigate to the auto-fluorescent image(s), activate the AF pipet tool, and select pixels with brightest autofluorescence signal. You can select autofluorescence from more than one region by holding Shift on the keyboard. Do not encircle regions: only the pixels, covered by the AF tool line, will be used as reference autofluorescence. 9. Run Prepare All. 10. Move to Segment Tissue step. Create tissue classes you want to distinguish. In a current example, there are three tissue categories: tumor, stroma, and blanc. 11. Select staining, to be used for the Tissue Segmentation. From our experience, it is cytokeratin staining (Opal 690), DAPI, and Autofluorescence.

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Fig. 5 Manual Analysis view of the inForm™ software, the initial step of the image analysis protocol settings. With the Select Flours function, reference fluorophores are selected

12. Set other parameters as shown on Fig. 6. Use these settings as starting point and modify if needed according to your requirements. 13. Select manually a number of regions to provide to software with the examples of the different tissue categories (Fig. 6). You may switch to pseudo-brightfield visualization with one of the channels converted to brown staining and DAPI converted into hematoxylin-like staining (Fig. 7). It is better to select cytokeratin (Opal 690) for region sampling. 14. Train Tissue Segmented. Make sure the training quality reached 90% (or higher). If the level of 90% is not reached during several minutes, stop the training and select more tissue region examples (see Note 9). 15. Apply by Segment All. 16. Control the result. If not sufficient, add more manually selected examples of tissue regions, especially from the areas, which seemed most problematic. 17. Repeat until segmentation results are appropriate. 18. Move to Segment Cells step. 19. Set the parameters as shown on Fig. 8. Use these settings as starting point and modify if needed according to your requirements.

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Fig. 6 Manual Analysis view of the inForm™ software, the Segment Tissue step of the image analysis protocol settings. Manually created regions colored with red (for cancer tissue), green (for stroma), and yellow (for tissue-free area) are seen. The tissue is visualized as multicolor picture after the signal unmixing using reference spectral library

Fig. 7 Manual Analysis view of the inForm™ software, the Segment Tissue step of the image analysis protocol settings. The illustration is identical to Fig. 6, but tissue is visualized as double-color picture with DAPI and cytokeratin staining

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Fig. 8 Manual Analysis view of the inForm™ software, the Segment Cell step of the image analysis protocol settings. Nuclei segmentation is visualized as green masks on the false-hematoxylin staining (DAPI staining converted to false-brightfield image). The settings for the nuclei segmentation are seen on the left panel

20. Move to Phenotype Cells step. Create phenotypes for the markers you want to distinguish and phenotype for negative staining: CD1a, CD208, CD15, CD123, CD68, panCK, negative (Fig. 9). 21. Train phenotype classifier by activating Train Classifier and apply the phenotyping to all scans by Phenotype All. 22. Move to “Export” step. 23. Check the Tissue Segmentation Data in Tables to Export. 24. Set Export directory. 25. Run Export for All. 3.9 Batch Analysis and Image Control

1. Open the inForm™ software and navigate to Batch Analysis. 2. From the Batch Algorithm or Project, select the established project. 3. Choose the output image format (its normally enough to have jpeg if you are not planning additional analysis). Select export directory. 4. In the field Image Output Format, check the views to be exported. It may depend on your needs for future analysis, but we recommend exporting RGB, RGB with Tissue Segmentation Map, RGB with Cell Segmentation Map, Composite Image (Fig. 10).

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Fig. 9 Manual Analysis view of the inForm™ software, the Phenotype Cells step of the image analysis protocol settings. Note phenotypes manually created (left panel, upper part). The phenotyping tool is activated for the selected cell (right part of the view, the false-DAB brown staining), and the phenotype from the drop-down menu can be manually chosen. The number of cells with certain annotation is shown on the left panel, middle part

5. In a field Tables To Export, check the Tissue segmentation Data and Cell Segmentation Data. 6. By Add Images, select images from the cohort and apply batch analysis. 7. When analysis is performed, navigate to Review/Merge mode. Every case would be represented as four views: RGB—the view of the scanned image as it looks after imaging with the filters; Composite Image—the view with spectrally unmixed signals; Tissue Segmentation Image—showing the overlay of the tissue segmentation, applied by the algorithm, established in the project; and Cell Segmentation Image—reflecting the segmentation of the nuclei. It is important to control every scan for artefacts, necrosis, etc. One need also to control the accuracy of tissue segmentation. Finally, control the quality of nuclear segmentation. Remove regions of inappropriate quality or exclude whole scan if necessary. Then press Re-process. 8. When all images are controlled and corrected (if needed), merge the data using Merge function.

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Fig. 10 Batch Analysis menu of the inForm™ software 3.10 Data Structure and Data Analysis

Data analysis is going beyond current chapter. Here we provide a short description of the analytical pipeline established in out lab. Most of the described procedures are performed in R™. The .txt tables produced on the step Image analysis project establishment contain cell-vise expression data of the markers and phenotype, assigned to the cell, based on the training and confidence level of assigned phenotype. This data is used to generate intensity thresholds for the marker expression. 1. Select only cells with maximal confidence level (100%). 2. In each phenotype, collect the data of the expression levels of the relevant marker. For example, for the cells, phenotyped as CD1a, the data of the expression of Opal 520 in the cytoplasmic region of the cells needs to be collected. In ideal scenario, because these cells are considered as having CD1a phenotype with the confidence of 100%, one could use the lowest expression as a cut-off value. We would recommend, however, to investigate the data in more detail by looking at the distribution of the expression values to make a final decision about the threshold.

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Fig. 11 A simplified view of the merged .txt file with merged_cell_seg_data from the Batch analysis and image control. Only columns relevant for the current discussion are shown. The data is represented as cell-vise output (only six cells are shown on the figure for simplicity reasons). File contains the information about the path in the PC of the original analyzed image, the sample name, the tissue category from which each cells is originating (tissue category is a result of the Segment Tissue algorithm), Cell X and Y position, and the expression levels of the markers

Fig. 12 A simplified view of the merged .txt file with merged_cell_seg_data from the Batch analysis and image control after applying the thresholds for each of the marker, to define cell positivity or negativity to this marker

The merged .txt file with merged_cell_seg_data from the Batch analysis and image control contains cell-wise expression data of the markers (Fig. 11). In this file, we apply thresholds for each of the markers and each of the cells, thus defining if cell is positive or negative with regard to every marker. A simplified example of final output is shown on Fig. 11 and after applying thresholds on Fig. 12. Using marker co-expression data, one can assign immune cell subclass. Note that cell coordinates are traced and can be used to analyze cell spatial localization. The merged .txt file with merged_tissue_seg_data_summary from the Batch analysis and image control contains information about the region sizes of the analyzed sample. This information is needed, if one wants to produce “cell density” data.

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Notes 1. The ImmPRESS polymer system is used in current protocol when increased signal amplification is needed. It can be replaced by other peroxidase detection systems, and in case of low expression, higher concentration of primary antibody may be needed. 2. Antigen retrieval procedure will require heating the Opal Slide Processing Jar filled with pH 9 or pH 6 retrieval buffer and slides. The temperature shall be made higher as quickly as possible (maximal power of the microwave oven) and then held for 15 min around the boiling point. The power settings

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could differ for different microwave oven models and depending on how many Opal Slide Processing Jars are used for the antigen retrieval procedure. We therefore encourage forehead testing for the optimal power settings selection. We recommend Panasonic microwave NN-GD452W, 1000 W, with inverter technology. However, other microwave systems can be used. Alternatively, pressure cooker or water bath can be also used for antigen retrieval. The antibody concentration for such protocol may differ. 3. The cocktail of pan-cytokeratin antibodies (each of whose itself is a cocktail of several anti-cytikeratin antibodies) and anti-Ecadherin antibody is used to ensue that even highly de-differentiated cancer cells, which may lose some of cytokeratins, still can be stained. Also, different cytokeratin expression differs in different tumor types. For tumors with non-epithelial origin, one can use other markers, for example, S-100 for melanoma. 4. Some markers are very rarely presented in the tissue (e.g., CD1a cells are very few and may be not enough for the reference Opal 520 signal), and therefore it may be complicated to make a reference slide with needed amount of signal. In this case, one can use a more abundant marker (e.g., CD68) with relevant secondary amplification system, but use Opal 520 dye for visualization. Such slide can be used as reference for Opal 520 dye for the library. 5. The exact filter settings may differ on different Vectra systems. 6. The function Table Overview (see on Fig. 2) is making the bright-field scanning of the slides on the loaded carrier and displays it at low resolution on the slide icons. It is not necessary to activate, but may be very helpful for finding the tissue on the slide, especially if the tissue sample is small. 7. The Autofocus function may not work if the focus is too far from optimal. One would need to find the focus manually and then press Autofocus function to refine it. 8. Too many images in the project may slower down the software significantly. While working on the project, the images got loaded into RAM-memory, thus having large RAM in your PC system, is important. 9. Training quality percentage does not reflect quality of the segmentation, but rather capacities of the algorithm to separate and distinguish provided tissue phenotypes. The final quality of the segmentation shall always be controlled by the operator. In most of cases, the best result will be in some compromise, which gives appropriate quality of the segmentation. Note that inappropriately segmented regions, if they appear after batch analysis, can be manually removed. Alternatively, for

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such cases (e.g., having too high background of cytokeratin staining or, opposite, too low cytokeratin expression in epithelial tissue), an alternative project with special tissue segmentation settings may be needed. References 1. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Hassel JC, Rutkowski P, McNeil C, Kalinka-Warzocha E, Savage KJ, Hernberg MM, Lebbe C, Charles J, Mihalcioiu C, Chiarion-Sileni V, Mauch C, Cognetti F, Arance A, Schmidt H, Schadendorf D, Gogas H, Lundgren-Eriksson L, Horak C, Sharkey B, Waxman IM, Atkinson V, Ascierto PA (2015) Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 372(4):320–330. https://doi. org/10.1056/NEJMoa1412082 2. Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, Daud A, Carlino MS, McNeil C, Lotem M, Larkin J, Lorigan P, Neyns B, Blank CU, Hamid O, Mateus C, Shapira-Frommer R, Kosh M, Zhou H, Ibrahim N, Ebbinghaus S, Ribas A, KEYNOTE-006 Investigators (2015) Pembrolizumab versus Ipilimumab in Advanced Melanoma. N Engl J Med 372(26):2521–2532. https://doi.org/10.1056/NEJMoa1503093 3. Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, Tykodi SS, Sosman JA, Procopio G, Plimack ER, Castellano D, Choueiri TK, Gurney H, Donskov F, Bono P, Wagstaff J, Gauler TC, Ueda T, Tomita Y, Schutz FA, Kollmannsberger C, Larkin J, Ravaud A, Simon JS, Xu LA, Waxman IM, Sharma P, CheckMate I (2015) Nivolumab versus Everolimus in advanced renal-cell carcinoma. N Engl J Med 373(19):1803–1813. https://doi.org/10. 1056/NEJMoa1510665 4. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E, Barlesi F, Kohlhaufl M, Arrieta O, Burgio MA, Fayette J, Lena H, Poddubskaya E, Gerber DE, Gettinger SN, Rudin CM, Rizvi N, Crino L, Blumenschein GR Jr, Antonia SJ, Dorange C, Harbison CT, Graf Finckenstein F, Brahmer JR (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med

373(17):1627–1639. https://doi.org/10. 1056/NEJMoa1507643 5. Ferris RL, Blumenschein G Jr, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C, Worden F, Saba NF, Iglesias Docampo LC, Haddad R, Rordorf T, Kiyota N, Tahara M, Monga M, Lynch M, Geese WJ, Kopit J, Shaw JW, Gillison ML (2016) Nivolumab for recurrent squamouscell carcinoma of the head and neck. N Engl J Med 375(19):1856–1867. https://doi.org/10. 1056/NEJMoa1602252 6. Sharma P, Retz M, Siefker-Radtke A, Baron A, Necchi A, Bedke J, Plimack ER, Vaena D, Grimm MO, Bracarda S, Arranz JA, Pal S, Ohyama C, Saci A, Qu X, Lambert A, Krishnan S, Azrilevich A, Galsky MD (2017) Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275): a multicentre, single-arm, phase 2 trial. Lancet Oncol 18(3):312–322. https://doi.org/10.1016/ S1470-2045(17)30065-7 7. El-Khoueiry AB, Sangro B, Yau T, Crocenzi TS, Kudo M, Hsu C, Kim TY, Choo SP, Trojan J, Welling THR, Meyer T, Kang YK, Yeo W, Chopra A, Anderson J, Dela Cruz C, Lang L, Neely J, Tang H, Dastani HB, Melero I (2017) Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an openlabel, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389 (10088):2492–2502. https://doi.org/10. 1016/S0140-6736(17)31046-2 8. Ogino S, Giannakis M (2018) Immunoscore for (colorectal) cancer precision medicine. Lancet 391(10135):2084–2086. https://doi.org/10. 1016/S0140-6736(18)30953-X 9. Mezheyeuski A, Bergsland CH, Backman M, Djureinovic D, Sjoblom T, Bruun J, Micke P (2018) Multispectral imaging for quantitative and compartment-specific immune infiltrates reveals distinct immune profiles that classify lung cancer patients. J Pathol 244(4):421–431. https://doi.org/10.1002/path.5026

Chapter 10 Method for Multiplexed Dynamic Intravital Multiphoton Imaging Asylkhan Rakhymzhan, Andreas Acs, Ruth Leben, Thomas H. Winkler, Anja E. Hauser, and Raluca A. Niesner Abstract Intravital two-photon microscopy enables monitoring of cellular dynamics and communication of complex systems, in genuine environment—the living organism. Particularly, its application in understanding the immune system brought unique insights into pathophysiologic processes in vivo. Here we present a method to achieve multiplexed dynamic intravital two-photon imaging by using a synergistic strategy combining a spectrally broad range of fluorophore emissions, a wave-mixing concept for simultaneous excitation of all targeted fluorophores, and an effective unmixing algorithm based on the calculation of spectral similarities with previously acquired fluorophore fingerprints. Our unmixing algorithm allows us to distinguish 7 fluorophore signals corresponding to various cellular and tissue compartments by using only four detector channels. Key words Multiphoton microscopy, Intravital imaging, Optical parametric oscillator, Two-color two-photon excitation, Similarity unmixing, Fluorescing proteins, Near-infrared fluorescent dyes

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Introduction Multiplex imaging techniques are of highest importance to understand the spatial complexity of biological phenomena, in general, and of immune responses, in particular [1]. Studies performed on fixed (static) samples are based on repeated analysis of the same sample, under exactly the same circumstances. Among the fluorescence-based technologies, multi-epitope-ligand cartography—MELC— [2, 3] allows imaging of around 100 markers within one sample, by using repeated cycles of staining, image acquisition, and photobleaching. Similar results can be achieved by Imaging Mass Cytometry in a shorter time but without sample preservation [4]. Nonetheless, such approaches are not suitable for intact, live tissue, which is highly dynamic on both cellular and molecular level. Two-photon microscopy is therefore the method of choice for in vivo applications; however, it is limited in terms of

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_10, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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the number of parameters that can be detected. Currently, it allows the simultaneous visualization of up to four different cellular subsets or other tissue structures over time [1, 5, 6]. In order to account for the cellular and tissue complexity in living organisms, in a dynamic manner, further development of the state-of-the-art technology is necessary, i.e., optimizing the excitation strategy, expanding the fluorescence range towards the near-infrared (NIR), and improving the spectral unmixing algorithms [5]. Extending the spectral range of fluorophore emission to the near infrared (NIR) region not only reduces autofluorescence, absorption of water and hemoglobin, and light scattering in tissue but also increases the number of available fluorophores making multiplexed deep tissue imaging feasible [7, 8]. Several approaches to achieve two-photon excitation of a broad range of fluorophores within the sample have been developed and applied: sequential single excitation [9, 10] by a short-pulsed laser (typically, Ti:Sa); dual excitation [11] by two colocalized shortpulsed lasers (Ti:Sa and OPO); triple excitation using wavelength mixing of two lasers, i.e., Ti:Sa and OPO, leading to an additional two-color-two-photon excitation [5, 12]; as well as excitation with an electromagnetic wave with broad continuous spectrum, e.g., super-continuum lasers based on photonic crystal fibers [13]. Among these strategies, the triple excitation with two laser beams synchronized in time and space ensures effective, specific, and simultaneous excitation of all fluorophores and signals of interest. In order to resolve multiple fluorophore signals, linear spectral unmixing algorithms are typically employed, which require an equal number of detectors to the number of signals to be distinguished. Similarity approaches which rely on reference spectra are predicted to allow a superior separation quality and to be able to distinguish more signals than the available detection channels [14]. In this protocol, we describe a multiplexed dynamic fluorescence imaging method [5], which allowed us simultaneous detection of seven fluorophore signals corresponding to seven cellular and tissue compartments in murine popliteal lymph nodes using only four PMT detectors. The method is based on the optimal selection of chromophores and optically nonlinear signals, an adaptation of state-of-the-art two-photon microscopes for effective triple two-photon excitation, and an effective signal discrimination algorithm based on the principle of similarity unmixing, called SIMI-block. The here described method, applicable with most available multiphoton microscopes, is a versatile tool able to monitor complex dynamic processes in vivo, applicable to the investigation of any organ, in which the motion patterns and communication of various cell populations define tissue function.

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2.1 Choosing the Optimal Set of Fluorophores

We chose the combination of fluorophores to be simultaneously imaged by multiplexed two-photon microscopy (see Note 1), i.e., fluorescent proteins expressed in animals (mice), fluorescent dyes used to stain cells to be transferred to the imaged organism, and fluorescently labeled antibodies injected to highlight-specific tissue compartments, in an emission range covering approx. 300 nm. 1. Fluorescent reporter mice as previously described in [5]: F1 mice from a breeding of Rosa26-Brainbow2.1 mice [15] with Rosa26-CreERT2 mice [16], expressing either CFP or GFP or YFP or dsRed in lymphocytes. 2. HEK cells transfected with eCFP or eGFP or YFP or dsRed. 3. Hoechst 33342 was used for nuclear staining of cells, subsequently transferred by i.v. injection as previously described [17]. 4. CD21/35 labeling of cellular structures in the murine popliteal lymph node were labeled by foot pad injection of CD21/35Fab-Atto680 [17]. 5. Splenocytes labeled either by Hoechst33342 or Atto680 [5, 17]. 6. 200 nm red fluorescent polystyrene beads (emission maximum at 605 nm) embedded in agarose gel (2%) at a concentration of 0.0012%. 7. Potassium dihydrogen phosphate (KDP) powder placed on a microscope slide and air-tight covered with a cover slip.

2.2 Imaging and Anesthesia

Anesthesia and the surgical preparation need to be adapted to the question of interest. For a detailed description of the popliteal node imaging to monitor germinal center reactions consult [17]. 1. Mixture of Ketamin/Xylazin dosed corresponding to the mouse weight. 2. Custom-build imaging stage for imaging the popliteal lymph node of mice [17].

2.3 Two-Photon Laser-Scanning Microscope Setup

State-of-the-art two-photon laser-scanning microscope with additional customized features (Fig. 1): 1. Two excitation sources, i.e., near-infrared laser Ti:Sa laser tunable in the range 690–1080 nm and infrared laser—optical parametric oscillator (OPO) tunable in the range 1050–1600 nm (see Note 2). The Ti:Sa and OPO beams, both linearly polarized, were combined in the scan head using a dichroic mirror with a cut-off wavelength at 1040 nm. Ti:Sa

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Fig. 1 (a) Schematics of the state-of-the-art multiphoton microscope extended with customized features to achieve spectrally multiplexed dynamic imaging in deep tissue. These additional features are (1) two synchronized lasers, i.e., Ti:Sa and OPO; (2) piezo-motorized delay stage, which allows temporal overlapping of the two lasers pulse trains and generating a third, virtual excitation laser line—asymmetric two-photon excitation (ATPE); (3) pulse compression for both lasers; (4) adjustable mirrors and telescopes to allow accurate spatial overlap of the laser beams under the objective lens; and (5) variably adjustable spectrally resolving detection. (b) Schematics of the triple two-photon excitation of molecules with corresponding spontaneous emissions

laser was used as a pump laser for the OPO. Hence both lasers have exactly the same repetition rate. 2. Piezo-motorized delay stage positioned in the OPO beam path to vary the length of the OPO optical path. Custom-build stage made of two prisms and a piezo-table to achieve steps of 15 nm in space, corresponding to 0.05 fs in time domain. The accuracy of our measurements was 1 fs, i.e., 300 nm (see Note 3). 3. Custom-built single-prism compressor for the OPO beam path was used for pulse-compression [5]. 4. Water-immersion objective lens (20
, NA 1.0, PlanApochromat) was used to focus both laser beams into the sample. 5. Interference filters and dichroic mirrors are to be placed in front of the PMT detectors and separate fluorescence, SHG, SFG and wavelength mixing signals prior to detection. All PMTs were assembled in a detection system with different optical channels, where every channel was determined by individual fluorescence filter and a set of dichroic mirrors (see Note 4).

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6. Power control was achieved using a system made of λ/2 plates and polarizing beam-splitter cube (see Note 5). 2.4

Data Analysis

1. Fiji/ImageJ—open source software. 2. Custom-build plug-in block-SIMI for Fiji/ImageJ to perform similarity spectral unmixing (available from the authors).

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Methods The chosen combination of fluorophores and signals is cyan fluorescing protein (CFP); enhanced green fluorescing protein (eGFP); yellow fluorescing protein (YFP); dsRed—red fluorescing protein (single color expression in lymphocytes); Hoechst33342 (nuclear staining of transferred otherwise non-fluorescent naı¨ve B cells); Atto680 (CD21/35 staining); second harmonic generation (SHG) of collagen fibers; and autofluorescence of macrophages. 1. Search for two-photon excitation spectra and emission spectra of the chosen fluorophores in common data bases and literature. If certain two-photon excitation spectra are not available, measure them as described in Note 6.

3.1 Acquiring the Fingerprints of the Fluorophores and Signals Combination

2. Chose the combination of Ti:Sa and OPO wavelengths for optimal triple two-photon excitation of all fluorophores and the dichroic and interference filters to spectrally analyze the emission. 1. Acquire fluorescence images of HEK-293 T cells expressing single colors (eCFP, eGFP, YFP, and dsRed) by using the two-photon microscope at the corresponding optimal excitation wavelength using all 4 PMT detectors (detection at 466  30 nm, 525  25 nm, 593  20 nm and 655  20 nm). For each single-labeled fluorophore, extract the ratio of relative intensities in the four PMT channels, i.e., the finger-print. This will serve as the criterion for the spectral unmixing analysis. 2. Similar to 1, acquire fluorescence images of splenocytes isolated from C57Bl/6 mice and prepared and labeled either with Hoechst 33342 or Atto680 at the corresponding optimal excitation wavelength with all 4 PMT detectors. Extract the corresponding fingerprints. 3. Calculate the fingerprint of SHG and SFG. Due to their quantum mechanical nature, these signals are spectrally as narrow as the bandwidth of the generating laser source and appear only in one detection channel. The detection of SHG appears at exactly half of the excitation wavelength for both Ti:Sa and OPO. For SFG 1/λSFG ¼ 1/λTi:Sa + 1/λOPO holds true, with λindex wavelength of Ti:Sa, OPO, or SFG.

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4. Generate fingerprints of autofluorescence of macrophages in situ using the same setup used for the actual experiments. Macrophages are identified based on their specific morphology (see Note 7). 3.2 Microscope Setup Preparation

1. Couple into the two-photon microscope both the Ti:Sa and the OPO laser beams using routing mirrors, and control their divergence using telescopes, e.g., two-lens telescopes (Fig. 1). Adjust the beam paths to achieve full visual overlap (see Note 8). 2. Suspend 200 nm fluorescent polystyrene beads in agarose and place them on a microscope slide. 3. Acquire 50
50
10 μm3 3D images of fluorescent beads at exactly the same position using: (a) only Ti:Sa excitation, (b) only OPO excitation, and (c) Ti:Sa and OPO excitation. 4. Verify the spatial overlap of the Ti:Sa and OPO beams in these images in all three dimensions. A shift between images (a) and (b) indicates that the full overlap of the Ti:Sa and OPO beams with an accuracy at the diffraction limit was not achieved. In this case, smearing of the single beads in image (c) will appear. 5. If Ti:Sa and OPO beams do not overlap yet, keep one laser beam path fixed and change only the other. We kept the Ti:Sa beam path fixed and changed only the OPO beam path. Use the routing mirrors to move the beam laterally (xy-direction, perpendicular to the optical axis of the microscope) and the telescope to control the divergence of the OPO beam (see Note 9). 6. Repeat 3, 4, and 5 until full spatial overlap of Ti:Sa and OPO beams is achieved. 7. Install the piezo-motorized delay stage in the OPO beam path. 8. Measure the lengths of the Ti:Sa and OPO beam paths, respectively, starting from the laser exit and ending at the back aperture of the objective lens. Make sure the difference between the length of the Ti:Sa beam path and that of the OPO beam path is not larger than the range of the piezomotorized delay stage. 9. Acquire the SHG and SFG signals of KDP (potassium dihydrogen phosphate) powder using simultaneous Ti:Sa (850 nm) and OPO (1230 nm) excitation. SHG of Ti:Sa is detected at 425 nm (channel 440  20 nm), SHG of OPO at 615 nm (channel 617  35 nm), and SFG at 503 nm (channel 525  25 nm). Change the length of the OPO beam path by changing the position of the delay stage to maximize the SFG signal. In this case, full overlap in time between the Ti:Sa and OPO pulse trains is achieved.

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1. Prepare the popliteal lymph node of living mice as described in Ulbricht et al. (2017). (Note: All animal experiments need to be approved by and performed in accordance with institutional, state and federal guidelines.) 2. Repeatedly acquire 3D images with a field of view of 500μm
500μm and a digital resolution of 1024
1024 pixel over 50μm depth (z-step 2μm) every 20 s, over 30 to 60 min. Use therefore the microscope setup previously described in Subheading 3.2.

3.4 Spectrally Unmixing the Imaging Data

1. Open the imaging data (as 2D/3D data) with the Fiji/ImageJ software, in which you previously installed the modified unmixing PlugIn SIMI for block similarity unmixing (custom-written code available from the authors). 2. Based on the fingerprints acquired in Subheading 3.1, generate the mixing matrix A by using the routine “Measure Mixing Matrix,” which is located in a submenu of SIMI PlugIn. In our case (Fig. 2), the matrix A is:

with each column representing the relative contribution of each channel to the respective (fluorescent or non-fluorescent) signal (see Note 10). 3. Run the routine “Unmix” in the submenu of SIMI PlugIn. The corresponding mixing matrix A will be required to determine in each pixel of the images the maximum similarity with a measured fingerprint. The software will attribute the pixel to the corresponding signal. 4. Repeat 1–3 for all 2D/3D images of the data set to generate spectrally unmixed 4D data as shown in Fig. 2. 5. Perform object (typically cells) segmentation and tracking of the newly generated images using existing plugins in programs such as Fiji/ImageJ or CellProfiler.

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Notes 1. In order to choose the optimal combination of fluorophores, a previous knowledge of the emission spectra of the fluorophores is necessary. For most fluorescent proteins and dyes used in life sciences, the emission spectra and the fluorescence quantum yields are known and can be found in open-access databases.

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A

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Fig. 2 Example of a 3D fluorescence image reconstruction as raw data (a) and similarity unmixed data (b) displaying B lymphocytes expressing CFP, eGFP, YFP, or dsRed, collagen fibers (SHG), macrophages (autofluorescence), and transferred naive B cells (Hoechst33342) in the murine popliteal lymph node. By using only four detection channels, we were able to resolve seven different cellular subsets and tissue structures. Scale bar ¼ 100μm

The fluorescent dyes should be chosen in such a way that their emission spectra minimally overlap with the emission spectra of the fluorescent proteins expressed in the available reporter mice. 2. The optimal combination of excitation wavelengths, i.e., Ti:Sa wavelength (λ1), OPO wavelength (λ2), and the virtual excitation wavelength resulting out of the two (2/λ3 ¼ 1/λ1 + 1/λ2) must allow a similar excitation efficiency for all chosen fluorophores with similar fluorescence quantum yields. The excitation efficiency of each fluorophore is given by two-photon excitation spectra. In contrast to emission spectra, two-photon excitation spectra and absolute values of two-photon excitation cross-sections measured in cellular environment are known and published only for a limited number of fluorophores. For fluorophores with higher quantum yields, a lower excitation efficiency is acceptable. 3. Since the Ti:Sa laser is used as a pump laser for the OPO, their pulse trains are synchronized but not in phase. By using the custom-build delay line, the Ti:Sa and OPO pulse trains are brought in phase and an accurate time-overlap of their pulses is achieved. 4. The number of detectors and the combination of dichroic mirrors and interference filters (Fig. 1) were selected in such a way that for each fluorophore in the chosen combination a specific and unique spectral fingerprint is generated. In our case, we chose 4 PMT detectors.

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5. In all imaging experiments, we used an maximum laser power of 10 mW for both Ti:Sa and OPO to avoid photodamage. The pulse width of Ti:Sa was 180 fs, for OPO 160 fs. 6. We typically record two-photon spectra of fluorophores in live cells for a better comparability with the in vivo situation. Therefore, 2D images are acquired with the two-photon microscope, without any additional filtering of emitted light before detection [18, 19]. The images are acquired in a wide wavelength range by the means of both Ti:Sa (760  λTi: Sa  1040 nm) and OPO (1060  λOPO  1300 nm). For normalizing the emission intensities at different wavelengths, the row data are corrected for background signal and peak photon flux, which includes squared laser power (measured simultaneously by reflecting about 4% of laser beams into a photodiode), photon energy in pulse peak, pulse width in focus (measured by an external auto-correlator), repetition rate of lasers, and excitation volume at each excitation wavelength. To avoid saturation and photobleaching, we keep the laser power at moderate values. In order to completely exclude the effect of photobleaching, we acquire emission images twice, by increasing and by decreasing the excitation wavelength. 7. Typically, the autofluorescence fingerprint is spectrally broad, i.e., with significant contributions from all detection channels. 8. The spatial overlap achieved visually is typically not accurate enough. Spatial overlap at the diffraction limit must be reached in order to perform triple two-photon excitation. 9. By changing the divergence of a beam, the position of the focus will be shifted along the optical axis of the microscope. In this way, the spatial overlap of the Ti:Sa and OPO beams in z-direction, along the optical axis can be achieved. 10. If a fingerprint cannot be acquired from a single-labeled fluorophore sample, it can be determined in situ from unique structure features, such as collagen fibers, directly on mixed color samples. The conventional way of SIMI unmixing is to apply the whole mixing matrix A at once for all fluorophores but for higher number of colors, i.e., more than four, a different approach should be applied to achieve more accurate unmixing results. This approach has a well-known concept from linear algebra, where more precise color separation can be achieved by reducing the number of independent equations, in our case the rank reduction of the mixing matrix A. This situation occurs often when the blue and green fluorophores have zero value elements of fingerprints in red and far red channels as well as the opposite, when red and far red markers have zero value elements of fingerprint in blue and green channels (Fig. 3). Such a situation will lead to obvious rank

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Fig. 3 (a) Mixing matrix for resolving Hoechst 33342, CFP, YFP, dsRed, Atto680, and SHG using four detector channels (466, 525, 593, 655). The yellow rectangle indicates that only Hoechst 33342, CFP, and YFP contribute to the detection channels 466 and 525, whereas the red rectangle indicates the exclusive contribution of dsRed, SHG from OPO, and Atto680 to the channels 593 and 655. Ordering the similarity matrix in the described manner (see Note 10) improves the accuracy and the efficiency of spectral unmixing as depicted in (b). (b) Raw 3D fluorescence images (upper row) and unmixed images (lower row) using the mixing matrix described in (a) displaying B lymphocytes expressing CFP, YFP, or dsRed, collagen fibers (SHG), follicular dendritic cells (CD21/35-Fab-Atto680), and transferred naive B cells (Hoechst33342) in the murine popliteal lymph node. Scale bar ¼ 100μm

decomposition, where the system of 4 channels and 6 fluorophores will be transformed to two independent subgroups (blocks) of 2 channels and 3 fluorophores. More accurate spectral unmixing can be achieved by using the system rank reduction with SIMI-block unmixing as shown in Fig. 4.

Multiplexed Multiphoton Microscopy

B

A

C

Hoescht CFP YFP DsRed SHG OPO Atto680

466 525 593 655

Raw image

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SIMI unmixed image

Hoescht CFP YFP DsRed SHG OPO Atto680

SIMI-block unmixed image

Fig. 4 The raw data (a) and results of similarity unmixing with an un-grouped mixing matrix (b, conventional similarity algorithm [5]) as compared to results of the SIMI-block unmixing algorithm (c). The 3D fluorescence images display B lymphocytes expressing CFP, YFP, or dsRed, collagen fibers (SHG), follicular dendritic cells (CD21/35-Fab-Atto680), and transferred naive B cells (Hoechst33342) in the murine popliteal lymph node. The SIMI-block unmixing algorithm performs better than the conventional similarity unmixing algorithm in assigning the correct fluorophore to all pixels within a cell. Overview images (upper row) scale bar ¼ 100μm. Zoomin images (lower row) scale bar ¼ 30μm

Acknowledgments We thank Robert Gu¨nther and Peggy Mex for excellent technical support. Financial support from the German Research Council (DFG) under grant TRR130 (C01 to R.N., C01, P17 to A.E.H. and P11, C03 to T.H.W.), FOR2165/2 (NI1167/4-2 to R.N. and HA5354/6-2 to A.E.H.), and HA5354/8-1 (SPP1937) to A.E.H. is greatly acknowledged. References 1. Tang JY, van Panhuys N, Kastenmuller W, Germain RN (2013) The future of immunoimaging - deeper, bigger, more precise, and

definitively more colorful. Eur J Immunol 43:1407–1412 2. Schubert W, Bonnenkoh B, Pommer AJ et al (2006) Analyzing proteome topology and

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function by automated multidimensional fluorescence microscopy. Nat Biotechnol 24 (10):1270–1278. https://doi.org/10.1038/ nbt1250 3. Holzwarth K, Ko¨hler R, Philipsen L et al (2018) Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections. Cytometry A 93(9):876–888. https://doi.org/10.1002/ cyto.a.23526 4. Giesen C, Wang HA, Schapiro D et al (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11(4):417–422. https://doi. org/10.1038/nmeth.2869 5. Rakhymzhan A, Leben R, Zimmermann H et al (2017) Synergistic strategy for multicolor two-photon microscopy: application to the analysis of germinal center reactions in vivo. Sci Rep 7(1):7101. https://doi.org/10. 1038/s41598-017-07165-0 6. Hauser AE, Junt T, Mempel TR et al (2007) Definition of germinal-center B cell migration in vivo reveals predominant intrazonal circulation patterns. Immunity 26(5):655–667 7. 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 8. Shcherbo D, Shemiakina II, Ryabova AV et al (2010) Near-infrared fluorescent proteins. Nat Methods 7(10):827–829. https://doi.org/10. 1038/nmeth.1501 9. Entenberg D, Wyckoff J, Glicorijevic B et al (2011) Setup and use of a two-laser multiphoton microscope for multichannel intravital fluorescence imaging. Nat Protoc 6:1500–1520. https://doi.org/10.1038/ nprot.2011.376 10. Ricard C, Debarbieux FC (2014) Six-color intravital two-photon imaging of brain tumors and their dynamic microenvironment. Front

Cell Neurosci 8:57. https://doi.org/10. 3389/fncel.2014.00057 11. Herz J, Siffrin V, Hauser AE et al (2010) Expanding two-photon intravital microscopy to the infrared by means of optical parametric oscillator. Biophys J 98:715–723. https://doi. org/10.1016/j.bpj.2009.10.035 12. Mahou P, Zimmerley M, Loulier K et al (2012) Multicolor two-photon tissue imaging by wavelength mixing. Nat Methods 9:815–818. https://doi.org/10.1038/nmeth.2098 13. Tu H, Boppart SA (2013) Coherent fiber supercontinuum for biophotonics. Laser Photon Rev 7(5):628–645. https://doi.org/10. 1002/lpor.201200014 14. Zimmermann T (2005) Spectral imaging and linear unmixing in light microscopy. Adv Biochem Eng Biotechnol 95:245–265 15. Snippert HJ, van der Flier LG, Sato T et al (2010) Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143:134–144. https://doi.org/10.1016/j. cell.2010.09.016 16. Seibler J, Zevnik B, Ku¨ter-Luks B et al (2003) Rapid generation of inducible mouse mutants. Nucleic Acids Res 31(4):e12 17. Ulbricht C, Lindquist RL, Tech L, Hauser AE (2017) Tracking plasma cell differentiation in living mice with two-photon microscopy. Methods Mol Biol 1623:37–50. https://doi. org/10.1007/978-1-4939-7095-7_3 18. Niesner R, Roth W, Gericke KH (2004) Photophysical aspects of single-molecule detection by two-photon excitation with consideration of pulsed illumination. ChemPhysChem 5 (5):678–687 19. Niesner R, Gericke KH (2006) Quantitative determination of the single molecule detection regime in fluorescence fluctuation microscopy by means of photon counting histogram analysis. J Chem Phys 124(13):134704

Chapter 11 Fourier Multiplexed Fluorescence Lifetime Imaging Leilei Peng Abstract Fluorescence lifetime imaging microscopy (FLIM) is a widely used functional imaging method in bioscience. Fourier multiplexed FLIM (FmFLIM), a frequency-domain lifetime measurement method, explores the principle of Fourier (frequency) multiplexing to achieve parallel lifetime detection on multiple fluorescence labels. Combining FmFLIM with a confocal scanning microscope allows multiplexed 3D lifetime imaging of cells and tissues. FmFLIM can also be integrated with the scanning laser tomography imaging method to perform 3D multiplex lifetime imaging of whole embryos and thick tissues. Key words Lifetime imaging, Fo¨rster resonance energy transfer, Multiplexed imaging

1 1.1

Introduction Background

Fluorescence lifetime imaging microscopy (FLIM) [1] captures the intensity and the emission decay lifetime of fluorescent samples in the same time. Compared with typical intensity-only imaging methods, FLIM offers additional information about the fluorescence emission decay, which can indicate the microenvironment of the fluorophore and provide functional information about the biological sample [2]. FLIM is particularly useful for measuring Fo¨rster resonant energy transfer (FRET) between donor fluorophores and acceptor fluorophores, where the donor lifetime decreases when the distance between the donor and the accepter decreases [3]. FLIM-FRET is widely used to detect protein interactions and confirmation changes [4]. FLIM methods can be grouped into two categories: frequency-domain methods and time-domain methods [5]. The frequency-domain method excites fluorophores with an intensitysinusoidal-modulated excitation source to imprint modulation in the sample emission, whose amplitude and phase lag are indicators of the speed of emission decay. The time-domain method excited the sample with a pulsed source and directly measures the emission decay with a fast detector. Both methods can be implemented in the

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_11, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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point-scanning mode or in the full-frame imaging mode with modulated or gated cameras. Full-frame FLIM instruments generally have faster frame rates than point-scanning FLIM, but modulated or gated cameras have higher costs. Point-scanning FLIM is compatible with confocal and two photon microscopes, which have depth sectioning capabilities and enable 3D lifetime imaging. Both the frequency-domain and time-domain FLIM methods have their pros and cons. The time-domain method in theory is more sensitive because it needs less photon counts to achieve the same lifetime accuracy [6]. However, time-domain FLIM instruments have higher costs because of the need for femto- or picosecond-pulsed laser source and sub-nanosecond time-resolved detector. Time-correlated single-photon counting (TCSPC) detection, which is used in point-scanning time-domain FLIM [7], has a low count rate, which sets a limit on the imaging speed. The frequency-domain FLIM instruments can use low-cost modulated continuous wave laser or light emitting diode as the excitation source. The detector can be in either the single-photon counting mode or analog mode. The latter mode does not have the countrate bottleneck. The main limitation in frequency-domain FLIM is that there is no easy way to scan the measurement through multifrequencies quickly. Existing frequency FLIM instruments typically only operate at one or two modulation frequencies, have poor lifetime accuracy, and are insufficient for analyzing complex decay behavior [8]. Multifrequency FLIM can be performed with pulsed excitation by utilizing higher-order harmonics of the fundamental pulse frequencies. However, such maneuver has a higher cost due to the pulsed excitation source [6, 9]. Differing from intensity-imaging fluorescence microscopes, which are typically equipped with multiple lasers and filter channels for multi-label imaging, most FLIM instruments can only image single-labeled samples. FLIM imaging with multiple labels typically requires multiple excitation wavelengths, each designated to excite one label and analyze its decay lifetime. Although it is possible to use labels with large Stokes shifts, so that a single excitation source could excite multiple labels of different emission color, most large Stokes shift fluorescent labels, especially large Stokes shift fluorescence proteins, are inferior in brightness and photostability. In intensity-imaging fluorescence imaging, multiplexing of excitation wavelengths is typically carried out by time-sharing, in which one excitation wavelength is switched one at a time and multichannel images are captured sequentially. Time-sharing significantly decreases the total image acquisition speed. FLIM needs higher photon counts to extract quantitative information on the fluorescence decay. Since the speed of single-channel FLIM is already slower than intensity-only imaging, slowing further down the imaging speed through time-sharing will affect FLIM’s ability to study live samples.

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Fourier multiplexing can be implemented in frequency-domain FLIM. The first demonstration of the concept used multiple electro-optical modulators, each modulating an excitation laser at a unique frequency, to perform parallel FLIM over several channels [10]. However, the instrument, being a single-frequency FLIM system, has limited lifetime accuracy. This chapter introduces a Fourier multiplexing FLIM (FmFLIM) method that uses a scanning interferometer to generate multiplexed laser modulation and fast frequency sweep. The method allows multifrequency FLIM being performed over several labels simultaneously [11–13]. 1.2

FmFLIM Method

The core of FmFLIM is a Michelson interferometer [14], which generates wavelength-specific intensity modulation on the excitation laser (Fig. 1). The interferometer has a stationary mirror arm and a scanning mirror arm, which consist of a polygon-mirror scanner, a lens, and a mirror. The optical delay d of the scanning mirror arm is a parabolic function of the polygon rotational angle, given by D ¼ 2Rθ2, where R is the radius of the mirror array and θ is the incident angle of the laser on the polygon mirror facet. The polygon spins at a constant angular speed ω; thus the optical delay scans at a linearly varying speed dD/dt ¼ 4Rω2t. At the output of the interferometer, interference between laser beams reflected off the stationary arm and the scanning arm generates intensity modulations. The modulation frequency is linear to the delay scan speed and inversely linear to the wavelength of the laser as f ðλÞ ¼ 2 dD=dt t ¼ 4Rω λ , which changes linearly in time and allows multifreλ quency lifetime measurements. When the laser beam incidents normally at the center of the mirror facet, the instantaneous frequency is zero. When the facet spins away from the center, the instantaneous frequency is at the highest. When a mix of multiple laser wavelength lines is used, modulations of individual wavelengths are different in frequency. Emission signals associated with multiple laser wavelengths can be differentiated by frequency analysis of the detector signal, even if a single detector is used to capture all emissions. The linear frequency sweeping from one facet needs to reach 200 MHz in order to measure lifetimes of commonly used fluorophores, which typically range from a fraction of nanosecond to few nanoseconds. A high-speed air-bearing polygon scanner (Lincoln SA24, Cambridge Technology) can run at 55,000 rpm (ω ¼ 5.8  103rad/s). Double passing a delay scanner with a 48-facet polygon mirror (Fig. 2) can achieve the desired frequency sweeping. The frequency sweeping is repeated as all facets of the polygon scanner spin across the beam, allowing repeated lifetime measurements. Combined with a point-scanning imaging optical system, for example, confocal scanning imaging microscope, FmFLIM can run at a pixel rate of 44,000 pixels per second. A

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Fig. 1 Schematic of the polygon mirror scanning Michelson interferometer. The moving arm of the interferometer consists of a polygon mirror optical delay scanner. The scanner generates optical path distance scans as a parabolic function of time

Fig. 2 3D optical setup of a Michelson interferometer with a double-passed polygon delay scanner in one arm and a fixed arm. The double passing eliminates beam position drifts due to the polygon mirror rotation and vibration. To match the wave front from the scanning arm, the fixed arm consists of identical optical layout, except the polygon scanner is replaced with a stationary mirror. (Reprint from reference [11])

typical 512-by-512-pixel image takes 6 s to acquire. Double passing the delay scanner also completely eliminates residue beam position shifting that exists in the single-pass design. Hence, this combined approach is more suitable for imaging applications. FmFLIM can be implemented in any scanning-based image modes. Combining the FmFLIM method with confocal scanning microscopy allows 3D multiplexed FLIM in live sample [11]. It can also be applied to scanning laser optical tomography (SLOT) [15], which allows for volumetric imaging of large tissue samples and small animals [12].

Fourier Multiplexed Fluorescence Lifetime Imaging Multi-line excitation

Michelson Interferometer

Pixel scan clock

Facet scan detector signal Sample

x y

Detector array

Beam pickoff

Objective

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Dichroic 2D galvo scanner

Multi-function DAQ

Multi-channel digitizer

Data analysis

Acquisition trigger

PC

Scanning control

PMT array

RF Mixer RF Mixer RF Mixer RF Mixer

Fig. 3 System schematic of the confocal FmFLIM microscope. (Reprint from reference [11])

Between an FmFLIM confocal microscope and a typical intensity-imaging confocal microscope, there are major differences in the signal processing and data acquisition (Fig. 3). In FmFLIM confocal, a multipoint frequency sweeping signal needs to be recorded at every pixel, whereas in intensity confocal imaging, only the signal strength of the pixel is needed. The multipoint frequency sweeping signal acquisition needs to be acquired in synchronized timing with the facet scan. Furthermore, in FmFLIM, the emission signal, driven by modulated excitation laser, contains high-frequency signals up to 200 MHz. Direct acquisition of the raw emission signal would require GHz digitizer. The data rate would be unsustainable in most PCs. To decrease the data acquisition cost, signals from photomultiplier tubes (PMTs) need to be demodulated in reference to real-time laser modulation signals in a way similar to lock-in detection. To process a lifetime information in a given excitation-emission channel, for example, green emission excited by the 488 nm laser line, the signal of the green emission spectral channel is downmixed with the reference modulation signal of the 488 nm laser line through a radio frequency (RF) signal mixer. In a typical 4-laser confocal system, up to 4-by-4 excitation-emission channels can be acquired simultaneously. A time delay is placed between the 488 nm laser signal and the green emission signal. Because the frequency of the laser modulation is linearly sweeping, the time delay between excitation and emission signals corresponds to a constant frequency offset. By adjusting cable lengths, the

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differential delay can be set as such the frequency offset Δω is at about 100–200 kHz. At the output of the RF mixer, a 1 MHz low-pass filter removes high-frequency signals and relays the 200–300 kHz downmixed signal to the digitizer. The digitizer acquires the downmix signal at 2 MHz, which is 1/100 times less than the original signal frequency. The data rate is sustainable with a typical PC. 1.3 Lifetime Analysis of FmFLIM Image Data

The emission decay of fluorophore can be described in time as a mix of multiple exponential delays: X ð1Þ I ðt Þ ¼ uðt Þ I i e t=τi : The frequency-domain lifetime method measures the Fourier transform of the decay response: e I ðωÞ ¼ F ½I ðt Þ ¼ I 0 mðωÞe iϕðωÞ :

ð2Þ

Acquired data sequences from the FmFLIM system are modulated at the offset frequency Δω and influenced by the frequency e ðωÞ ¼ m 0 ðωÞe iϕ0 ðωÞ as: response of electronic circuit H i ðωÞ ¼ i 0 m 0 ðωÞm ðωÞ sin ½Δωt þ ϕ0 ðωÞ þ ϕðωÞ,

ð3Þ

where ω is linearly scanned during the facet scan. Emission from lifetime standard [16] with known single exponential decay lifetime τs or scattered excitation laser light (lifetime e ðωÞ. A high-quality is(t), obtained τs ¼ 0) can be used to calibrate H by average signals taken from the standard sample, will allow calcue ðωÞ by: lating H e ðωÞ ¼ H

H ½i s ðt Þ ms ðωÞe iϕs ðωÞ

ð4Þ

where H represents Hilbert transform and ms(ω) and ϕs(ω) of the lifetime standard are: ms ðωÞ ¼

1 1 þ ω2 τ s 2

ϕs ðωÞ ¼ ωτw

ð5Þ ð6Þ

e ðωÞ is obtained, the frequency response of the emission Once H can be calculated as: H ½i ðt Þ e I ðωÞ ¼ e ðωÞ H

ð7Þ

The multifrequency emission response can then be analyzed by a wide range of frequency-domain lifetime analysis methods. Nonlinear fitting of a proper decay model is a well-established method for analyzing frequency-domain lifetime measurements [1, 17]. Typically, the single exponential decay model is used, and

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Fig. 4 The phasor representation of multifrequency lifetime measurement resulted from FmFLIM. The dashed half circle is the phasor trajectory of single exponential decay. All phasor trajectories, regardless of decay models, should fall within the gray area surrounded by the dashed half circle. Red areas are imaginary projection areas of the trajectory over a given frequency sweeping span. Green areas are real projection areas. (Reprint from reference [19])

the modulus and the phase of the emission response are fitted with Eqs. 5 and 6. In cases where the emission is weak and the data signal to noise ratio is poor, fitting pixels independently may result in unreliable lifetime results. Averaging over adjacent pixels or global lifetime fitting [17] of the entire image data may be used. Beside iterative fitting methods, phasor analysis of frequencydomain lifetime image data offers faster calculation and real-time lifetime image processing [18]. The method was originally developed for analyzing single-frequency lifetime images. The phasor, which is the complex emission response plotted on 2D graph, moves along a trajectory when the modulation frequency increases. The actual shape of the trajectory is model dependent. However, regardless of what decay model the fluorescent sample follows, the trajectory always falls on or below the trajectory for single exponential decay, which is a half circle between two points: 1 + 0i and 0 + 0i (Fig. 4). For multifrequency data, an average ratio (R-ratio) of the imagery and the real part of the phasor [19] can be calculated as: i R ωmax h e Im I ð ω Þ dω ωmin h i , RðτÞ ¼ R ω ð7Þ max e Re I ð ω Þ dω ωmin

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In the case of single exponential decay, there is:     ln ω2max τ2 þ 1  ln ω2min τ2 þ 1 RðτÞ ¼ 2½ tan 1 ðωmax τÞ  tan 1 ðωmin τÞ

ð8Þ

The R-ratio always increases when the decay constant increases in a single exponential decay or the fraction of a long lifetime component increases in a multi-exponential decay. Practically, the R-ratio can be used as a quantitative indicator of the decay speed without assuming a particular decay model. 1.4 FmFLIM Tomography

FmFLIM can be implemented in any scanning-based image modes. Beside confocal scanning, which offers microscopic imaging of cells, it can also be applied to scanning laser optical tomography (SLOT) [20], which allows for volumetric imaging of large tissue samples and small animals. A FmFLIM-SLOT instrument [12, 13] is mostly identical to a FmFLIM confocal instrument, except the imaging forming method (Fig. 5). In SLOT, a loosely focused laser, instead of a tightly focused laser spot, scans through the entire volume of the sample, fluorophores are excited along the laser path, and the total emission is collected by a concave mirror and a condenser lens, which delivers the emitted light to a PMT [20]. Similar to confocal imaging, SLOT can use dichroic emission filters to split the emission to multiple PMT channels. Unlike confocal imaging, which scans 3D structure in layers, SLOT captures 2D projection images of the entire 3D fluorescent sample while rotating the sample between projects. The 3D image is obtained through tomographic reconstruction. The FmFLIM measurement, which studies fluorescence in the frequency domain, is fully independent of the tomographic reconstruction, which deals with information in the space domain, i.e., transform a set of projection images e I ðx, y, θÞ to a 3D image e I ðx, y, z Þ. The process of generating 3D lifetime images from raw data of FmFLIM-SLOT consists of two steps: Radon e I ðx, y, θ, ωÞ

Lifetime

Transform Analysis e e ) I ðx, y, z, ωÞ ) I ðx, y, z, τÞ

The first step involves organizing raw data into multiple sets of projection images according to their frequency sampling points ω and performing multiple reconstructions on these sets. The core of tomographic reconstruction is the Radon transform, which in most cases is sufficient for reconstructing fluorescence images in SLOT. The second step requires gathering all frequency points of a 3D pixel and performing lifetime analysis using methods discussed in the previous section.

Fourier Multiplexed Fluorescence Lifetime Imaging Projection

Rotation

To PMT

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Reconstruction of 3D volume

Condenser lens

Laser scanning

z

x

θ y

Concave mirror

x

y

Fig. 5 Volumetric imaging with scanning laser optical tomography (SLOT). A loosely focused laser beam penetrates through the sample and excites fluorophores along its path. All fluorescent emission along the laser path is collected as a single-pixel measurement by a concave mirror paired with a condenser lens. The laser is scanned across the sample to form an x-z plane projection. The sample is rotated around the z-axis between scans. Multiple projections are collected at different angles. The 3D volumetric image is reconstructed from 2D projections via inverse Radon transform. (Reprint from reference [12])

The material and method introduced in this chapter provide a guide to setup a four-wavelength-excitation system for multiplexed lifetime imaging.

2

Materials

2.1 Fluorescence Lifetime Standard Slides

1. 1–10 μM fluorescein dissolved in water (4.1 ns lifetime) is needed to calibrate 405 and 488 nm excitation channels. 2. 1–10 μM rhodamine dissolved in water (1.74 ns lifetime) is needed to calibrate 561 nm excitation channels. 3. 1–10 μM Alexa 647 dissolved in water (1 ns lifetime) is needed to calibrate 641 nm excitation channels. 4. To make a lifetime standard slide, place a drop of lifetime standard solution in a slide spacer between a cover glass and a glass slide. Seal the edge of the cover glass with epoxy.

2.2 Tomographic Imaging Sample Mounting

1. Low melting point agarose. 2. Appropriate buffer for maintaining tissue sample or embryos. 3. Clear fluorinated ethylene propylene (FEP) tube. 1. Single-longitudinal-mode lasers at 405, 488, 561, and 640 nm. Each laser needs to have at least 50 mW power.

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2.3 Multiwavelength Excitation Laser Source

2. Isolators to prevent back reflection toward lasers if lasers do not have built-in isolators (see Note 1). 3. Dichroic mirrors for combining four laser beams. Three dichroic mirrors with spectral cutoff edge between 405, 488, 561, and 640 nm are needed. The flatness of these mirrors needs to be laser grade. 4. Fiber delivery system for the excitation laser (optional, see Note 2), consisted of items 5-8. 5. Single-mode fiber launch for FC-connectorized patch cables. 6. Super-achromatic 20 or 40 objective lens for focusing the laser beam on to the fiber tip. 7. Single-mode FC-connectorized fiber cable for visible wavelengths (see Note 3). 8. Reflective collimators, protective silver coating (see Note 4).

2.4

Detection System

1. PMT modules without built-in amplifiers. 2. Multiband emission filter that allows emissions of all labels to pass through. 3. Dichroic mirrors for splitting emission channels (optional, see Note 5). 4. Single-band emission filters for individual emission channels (optional, see Note 5). 5. Broadband transimpedance amplifier with high-frequency cutoff no less than 200 MHz. Recommended amplification gain 40 dB. 6. Broadband-amplified silicon photodetectors with highfrequency cutoff no less than 200 MHz. Recommended gain >1  105 V/W. 7. A low-cost IR laser and an unamplified photodetector for detecting facet spinning (see Subheading 3.4).

2.5 Signal Processing Circuits (All Components Need to Have 50 Ω Impedance)

1. Two-way or four-way RF power splitters with high-frequency cutoff no less than 200 MHz.

2.6 Michelson Interferometer Components

1. Visible beam splitter cube.

2. RF mixer with high-frequency cutoff no less than 200 MHz. 3. DC-2 MHz low-pass filter. 4. BNC and SMA cables.

2. 80 mm F1.8 camera lens (see Note 6). 3. Adaptor for mounting the camera lens on an optical breadboard.

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4. High-speed 48-facet polygon mirror scanner capable of running at approximately 55,000 rpm. 5. Oscilloscope with 200 MHz or better bandwidth. 2.7 Confocal Microscope Components (See Note 7)

1. Inverted microscope frame with a laser port. 2. Beam pickoff. 3. Closed-loop X-Y galvo mirror scanners. 4. Objective lenses. 5. Epi-fluorescence lamp for observing samples.

2.8 Tomography Imaging Components (See Note 8)

1. Motorized rotation stage. 2. Concave mirror, 1 in. diameter, f ¼ 25 mm, silver coating. 3. Achromatic lens, 1 in. diameter, f ¼ 50 mm, visible antireflection coating. 4. Closed-loop X-Y galvo mirror scanners.

2.9 General Optical Components

1. Mirrors, protective silver coating. 2. Visible achromatic lenses. 3. Optical mounts and holders.

2.10

System Control

1. Computer, >8 GB RAM and > 1 TB hard drive. 2. National Instruments (NI) LabVIEW. 3. Multifunction data acquisition card for controlling X-Y galvo scanners (NI DAQ 6115). 4. Multichannel digitizer with FPGA module (NI 5752 and NI FlexRIO module, optional, see Note 9). 5. Control software written in LabVIEW.

3

Methods

3.1 Set up the Multiwavelength Excitation Laser Beam

The multiwavelength laser beam is formed by merging four singlewavelength laser beams into a perfectly co-propagated beam through the following step: 1. Use dichroic mirrors to merge all laser beams to the same propagating direction. 2. Place a pair of irises in the beam path. One locates close the merging point. One locates downstream as far as possible. 3. Adjust the positions and directions of the individual laser beams so that all beams pass through the iris pair with minimal power loss.

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4. To further improve co-propagating alignment, the merged beam could be launched into a single-mode fiber and collimated after propagating through the fiber (see Note 2). 3.2 Set Up the Interferometer

1. Position components of the interferometer according to Fig. 2. 2. With the polygon mirror scanner running, adjust optical distances between the polygon mirror, the 50 mm camera lens, and the following reflective mirror to be 50 mm. When both distances are ideal, the reflected beam, which bounced twice on the polygon mirror, should be collimated and stationary. 3. Use a photodetector and an oscilloscope to observe the laser intensity at the output port. Align optics in the fixed arm of the interferometer so that modulation depths on all wavelengths are as close to 100% as possible. Typically, >80% modulation depths can be achieved. 4. Save oscilloscope traces of individual laser lines. Use fast Fourier transform (FFT) to analyze these traces in short time segments, and obtain frequency sweeping calibration data ω(t) on all laser lines.

3.3 Build the FmFLIM Confocal Microscope (Fig. 3)

1. Split the modulated laser output from the interferometer with a beam pickoff. Direct a small portion of the output to a series of amplified photodetectors, split by dichroic mirrors, to monitor the interference modulations of individual excitation lasers. 2. The majority of the interferometer output is directed to a confocal microscope built on an inverted microscope frame. Like a typical scanning confocal microscope, a pair of closedloop X-Y galvanometer mirrors scan the laser, focused through the objective lens, across the sample, and emission is collected through the same objective lens, separated from the laser path by a dichroic mirror and detected by PMTs through emission filters. The typical excitation power used for live cell imaging is 10–50 μW per laser line measured after the objective lens. Fluorescence emission is spatially filtered by a pinhole and separated from the excitation by a multiband dichroic mirror (see Note 7).

3.4 Imaging Control, Signal Processing, and Data Acquisition

In FmFLIM confocal, a multipoint frequency sweeping signal needs to be recorded at every pixel. The acquisition needs to be acquired in synchronized timing with the facet scan. 1. Place an IR laser at the side of the polygon mirror scanner. Detect the reflection of the IR laser with an IR photodetector behind a small iris. The rising edge of photodetector signal, which tracks the spinning of each facet, serves as the trigger signal of data acquisition.

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2. Feed the IR photodetector signal to NI 6115 as the pixel clock of analog outputs, which drives X-Y galvo mirror to perform raster scan. This allows the X-Y scanner to advance one pixel per facet scan. 3. Feed the IR photodetector signal to NI 6115 or FPGA digitizer as the acquisition trigger. Set the data acquisition (through NI 6115 analog input channels or FPGA digitizer channels) to record 20 μs of emission signal after each trigger, i.e., each polygon facet scan. The recorded signal I(t) is used for lifetime analysis on the pixel. 4. To set up a desired excitation-emission channel, for example, green emission excited by the 488 nm laser line, connect the 488 nm laser excitation monitor signal from the amplified photoreactor to the LO (local oscillator) input connector of a RF mixer. Connect amplified signal from the green channel PMT to the RF input connector. Link the IF signal output of the mixer to an NI 6115 analog input channel or FPGA digitizer channel through an inline low-pass filter. Repeat the process for all desired channels. 5. Adjust PMT gains between 30%- and 0% depending on the sample brightness (see Note 10). Adjust individual laser powers as needed. 6. Perform image scans on samples. 3.5 Calibrate Lifetime Channels with Fluorescence Standards

1. Place a lifetime standard slide on the microscope and perform repeated image scans under identical PMT settings used in experiments (see Note 11). Adjust laser powers or change lifetime standard concentrations as needed. 2. Average all pixels of a lifetime standard into a single-frequency response calibration trace. Use Eq. 4 to calculate the channel’s e ðωÞ. frequency response H 3. Repeat the process for all channels.

3.6 Analyze Lifetime Images

1. Average pixel data as needed. 2. Take a set of time-stamped pixel data I(t). Use pre-calibrated laser frequency data ω(t) to map I(t) into a frequency response data set I(ω). Repeat the process for all channels. 3. Take a set of pixel data; calculate each channel’s frequency response e I ðωÞ according to Eq. 7. e 4. Fit I ðωÞ with an appropriate decay model, or perform R-ratio calculation either within the FPGA or off-line through a processing program. Repeat the process for all channels.

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1. Make lifetime calibration standards for tomographic imaging by siphoning standard dye solutions into a FEP tube. Seal both ends of the tube with plastic cement glue. 2. Embed tissue sample or embryo in a FPE tube with low melting agarose following methods in reference [21]: (a) Mix appropriate buffer for maintaining tissue or embryo with low melting agarose to 0.1% concentration. (b) Make a second agarose solution at 1% concentration. (c) Heat both solutions to melting temperature, and then keep them warm slightly above the setting temperature. (d) Place tissue or embryo in the 0.1% agarose buffer. (e) Siphon the sample tissue or embryo into the FEP tube, and then siphon few mm length of 1% agarose gel, which serves as a plug after setting. (f) Seal two ends of the tube with plastic cement to prevent evaporation and gel sliding. 3. Mount the lifetime standard tube in the scan beam. Follow Subheading 3.5 to calibrate lifetime channels. 4. Mount the sample tube in a rotational stage. Perform projection FmFLIM imaging on the sample at 2 rotation interval. A total of 180 projection images should be collected. 5. Gather all projection images I(x, y, t, θ). Split the data set according to multiple sets of It(x, y, θ), each corresponding a time stamp. 6. Perform tomographic reconstruction on all sets of It(x, y, θ) to obtain It(x, y, z). 7. Gather all sets of It(x, y, z). Reorganize them into a 3D FmFLIM data set I(x, y, z, t). 8. Follow Subheading 3.6 to analyze the 3D lifetime image. 9. Repeat steps 5–8 process in all lifetime channels.

4

Notes 1. The laser beam feeds an interferometer, which generates a back reflection. Isolation on all lasers is needed to protect laser sources. 2. Coupling all beams into the same single-mode fiber is an ideal way to achieve perfect co-propagation. The fiber allows flexible position of the laser unit. This step, however, greatly decreases the laser power delivered to the imaging system and should only been used when the laser power is abundant.

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3. The fiber needs to have an APC (angled physical contact) end and a PC (physical contact) end. The APC end should face the laser to eliminate back reflection. The PC end should be connected to the collimator. 4. Chromatic aberration in lens may degrade co-propagation of multiple wavelength beams. It needs to be well managed. Therefore, a reflective fiber collimator is chosen. All lenses used in the system need to be at least achromatic. Highpower lenses, such as focusing lens and collimating lens for the fiber coupling, need to be super-chromatic corrected. 5. The emission can be detected by a single PMT or further split into multiple spectral bands and detected by multiple photomultiplier tube detectors. When bright labels coexist with dim label, splitting emission colors to multiple PMT allows better performance on dim labels. 6. The lens follows the polygon scanner (Fig. 1) and needs to be a good-quality imaging lens in order to manage aberration. A SLR camera lens, such as Nikon 80 mm F1.8 lens or equivalent, serves the purpose well. The lens needs to be positioned with the image side (the side goes into the camera body) facing the polygon mirror. 7. Methods for setting up a confocal microscope are well documented and not covered here. 8. Please refer to reference [20] on methods of setting up a scanning laser tomography imaging system. 9. FPGA-based data acquisition is optional. It allows real-time lifetime process with the R-ratio method. 10. The FmFLIM method expects a current signal proportional to the photon signal from the PMT. Thus, the PMT gain needs to be set a moderate level. The highest gain setting in a PMT will put the PMT into the photon counting mode, which should be avoided. 11. The frequency response of PMT modules varies between units and changes as the gain setting changes. Thus, the lifetime standard calibration needs to be performed over all channels under same gain settings used for experimental imaging scans. References 1. Lakowicz JR (2006) Principles of fluorescence spectroscopy. Springer, New York 2. Marcu L, French PMW, Elson DS (2014) Fluorescence lifetime spectroscopy and imaging: principles and applications in biomedical diagnostics. CRC Press, Boca Raton

3. Zhang F, Saha S, Kashina A (2012) Arginylation-dependent regulation of a proteolytic product of talin is essential for cell-cell adhesion. J Cell Biol 197(6):819–836. https:// doi.org/10.1083/jcb.201112129 4. Aoki K, Kamioka Y, Matsuda M (2013) Fluorescence resonance energy transfer imaging of cell signaling from in vitro to in vivo: basis of

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biosensor construction, live imaging, and image processing. Dev Growth Differ 55 (4):515–522 5. Gratton E, Breusegem S, Sutin J, Ruan Q, Barry N (2003) Fluorescence lifetime imaging for the two-photon microscope: time-domain and frequency-domain methods. J Biomed Opt 8(3):381 6. Elder A, Schlachter S, Kaminski CF (2008) Theoretical investigation of the photon efficiency in frequency-domain fluorescence lifetime imaging microscopy. J Opt Soc Am A 25 (2):452–462 7. Becker W, Bergmann A, Hink MA, Ko¨nig KK, Benndorf K, Biskup C (2004) Fluorescence lifetime imaging by time-correlated singlephoton counting. Microsc Res Tech 63 (1):58–66. https://doi.org/10.1002/jemt. 10421 8. Hanley QS, SUBRAMANIAM V, Jovin DJA, Jovin TM (2001) Fluorescence lifetime imaging: multi-point calibration, minimum resolvable differences, and artifact suppression. Cytometry 43(4):248–260. https://doi.org/ 10.1002/1097-0320(20010401)43:43.0.co;2-y 9. Chen H, Gratton E (2013) A practical implementation of multifrequency widefield frequency-domain fluorescence lifetime imaging microscopy. Microsc Res Tech 76 (3):282–289. https://doi.org/10.1002/jemt. 22165 10. Carlsson K, Liljeborg A (1998) Simultaneous confocal lifetime imaging of multiple fluorophores using the intensity-modulated multiple-wavelength scanning (IMS) technique. J Microsc 191:119–127 11. Zhao M, Li Y, Peng L (2014) Parallel excitation-emission multiplexed fluorescence lifetime confocal microscopy for live cell imaging. Opt Express 22(9):10221–10232. https://doi.org/10.1364/OE.22.010221 12. Zhao M, Wan X, Li Y, Zhou W, Peng L (2015) Multiplexed 3D FRET imaging in deep tissue of live embryos. Sci Rep 5:13991. https://doi. org/10.1038/srep13991 13. Xu D, Zhou W, Peng L (2017) Cellular resolution multiplexed FLIM tomography with dual-

color Bessel beam. Biomed Opt Express 8 (2):570–578. https://doi.org/10.1364/ BOE.8.000570.v003 14. Zhao M, Peng L (2010) Multiplexed fluorescence lifetime measurements by frequencysweeping Fourier spectroscopy. Opt Lett 35 (17):2910 15. Lorbeer R-A, Heidrich M, Lorbeer C, Ojeda DFR, Bicker G, Meyer H, Heisterkamp A (2011) Highly efficient 3D fluorescence microscopy with a scanning laser optical tomograph. Opt Express 19(6):5419–5430 16. Boens N, Qin W, Basaric´ N, Hofkens J, Ameloot M, Pouget J, Lefe`vre J-PL, Valeur B, Gratton E, vandeVen M, Silva ND, Engelborghs Y, Willaert K, Sillen A, Rumbles G, Phillips D, Visser AJWG, Hoek A, Lakowicz JR, Malak H, Gryczynski I, Szabo AG, Krajcarski DT, Tamai N, Miura A (2010) Fluorescence lifetime standards for time and frequency domain fluorescence spectroscopy. Anal Chem 79:2137–2149 17. Verveer PJ, Squire A, Bastiaens PIH (2000) Global analysis of fluorescence lifetime imaging microscopy data. Biophys J 78(2127):2127 18. Digman MA, Caiolfa VR, Zamai M, Gratton E (2008) The phasor approach to fluorescence lifetime imaging analysis. Biophys J 94(2): L14–L16. https://doi.org/10.1529/ biophysj.107.120154 19. Zhao M, Li Y, Peng L (2014) FPGA-based multi-channel fluorescence lifetime analysis of Fourier multiplexed frequency-sweeping lifetime imaging. Opt Express 22(19):23073. https://doi.org/10.1364/OE.22.023073 20. Lorbeer R-A, Heidrich M, Lorbeer C, Ramı´rez Ojeda DF, Bicker G, Meyer H, Heisterkamp A (2011) Highly efficient 3D fluorescence microscopy with a scanning laser optical tomograph. Opt Express 19(6):5419–5430. https://doi.org/10.1364/OE.19.005419 21. Kaufmann A, Mickoleit M, Weber M, Huisken J (2012) Multilayer mounting enables longterm imaging of zebrafish development in a light sheet microscope. Development 139 (17):3242–3247. https://doi.org/10.1242/ dev.082586

Chapter 12 Bimolecular Fluorescence Complementation (BiFC) and Multiplexed Imaging of Protein–Protein Interactions in Human Living Cells Yunlong Jia, Franc¸oise Bleicher, Jonathan Reboulet, and Samir Merabet Abstract Deciphering protein–protein interactions (PPIs) in vivo is crucial to understand protein function. Bimolecular fluorescence complementation (BiFC) makes applicable the analysis of PPIs in many different native contexts, including human live cells. It relies on the property of monomeric fluorescent proteins to be reconstituted from two separate subfragments upon spatial proximity. Candidate partners fused to such complementary subfragments can form a fluorescent protein complex upon interaction, allowing visualization of weak and transient PPIs. It can also be applied for investigation of distinct PPIs at the same time using a multicolor setup. In this chapter, we provide a detailed protocol for analyzing PPIs by doing BiFC in cultured cells. Proof-of-principle experiments rely on the complementation property between the N-terminal fragment of mVenus (designated VN173) and the C-terminal fragment of mCerulean (designated CC155) and the partnership between HOXA7 and PBX1 proteins. This protocol is compatible with any other fluorescent complementation pair fragments and any type of candidate interacting proteins. Key words BiFC, Multicolor, Living cells, Protein–protein interaction, mVenus, mCerulean

1

Introduction Studying protein–protein interactions (PPIs) is central for the understanding of the molecular mechanisms underlying protein function. These molecular contacts will change from cell to cell, occurring in different places and with various affinities within the cell. Understanding protein function therefore requires capturing underlying PPIs in the correct cell context and in native conditions [1]. Over the past decade, different methods have been developed for PPI visualization in living cells, but the most commonly used hitherto are bimolecular fluorescence complementation (BiFC) and fluorescence resonance energy transfer (FRET), which can be applied in a variety of model organisms [2, 3].

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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+

+ B

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Fig. 1 Principle of BiFC and multicolor BiFC. (a) Diagram of fragments from mVenus [5] fluorescent protein (VN1–172aa and VC155–238aa) and mCerulean [4] (CN1–172aa and CC155–238aa), (b) the different combination of fragments for BiFC. The protein A and protein B are fused to mVenus fragments (VN and VC) or mCerulean fragments (CN and CC). The interaction between proteins A and B induces the fluorescent protein to be reconstituted, leading to emission of fluorescence upon excitation; (c) for multicolor BiFC, the fusions of A-VN, B-CC, and C-CN are constructed. Three-protein interaction is simultaneously visualized by the reconstitution of two different FPs

BiFC is based on the reconstitution of a monomeric fluorescent protein (FP) such as cyan fluorescent protein (CFP) [4] and yellow fluorescent protein (YFP) [5] from two complementary nonfluorescent subfragments (N- and C-terminal subfragments) upon spatial proximity. Interaction between a bait protein and a prey protein fused to such complementary subfragments is sufficient to lead to the reconstitution of the FP, resulting in an emission signal upon excitation (Fig. 1) [6]. BiFC allows not only the detection of PPIs but also has the ability to determine the PPI subcellular location and PPI affinity in the live cell. BiFC is however of irreversible nature, which, unlike FRET, forbids analyzing dynamic complex formation and dissociation [7, 8]. FRET and BiFC have distinct advantages and limitations, which make the two methods complementary. FRET usually

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requires high expression levels of fusion proteins and depends on the very close proximity ( YAAA

HD

HX

HOXA7[dGA]

HOXA7HX[dGA]

RSGYGAGAGAFASTV

HD

YPWM -> YAAA

RSGYGAGAGAFASTV

HX

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HD

Fig. 2 Investigation of interaction properties between HOXA7 and PBX1 proteins. (a) Structure representation of motifs and domains involved in the HOXA7/PBX1 partnership. HOX proteins can use hexapeptide (HX, W-containing) motif to interact with PBX homeodomain (HD, PYP-containing). PBX domains, PBCA and PBCB, are also indicated. In HOX/PBX dimerization, PYP residues of the PBX HD participate in the formation of an hydrophobic pocket that interacts with the W residue of HOX HX motif, as determined by the HOX/PBX crystal structure [14]. (b) Representation of the HOXA7 proteins used in this protocol. Shaded regions represent the domains with mutation Table 1 Fluorescent protein (fragments) used in BiFC assay FP fragments

Dissection point

Ex/Em (filter)

VN173/VC155 (mVenus)

1–172aa; 155–238aa

515/527 (mVenus)

CN173/CC155 (mCerulean)

1–172aa; 155–238aa

433/475 (mCerulean)

VN173/CC155 (mVenus/mCerulean)

1–172aa; 155–238aa

504/513 (mVenus)

mCherry (as BiFC signal normalizer)

–

587/610 (mCherry)

T203Y mutation in mCerulean [15]. However, signals resulting from the VN/CC complementation are efficiently detected, using mVenus setting. Moreover, this combination makes available the

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Fig. 3 Excitation and emission spectra of the different FP fragment combinations. (a) The excitation spectra for each pair of FP fragment, shown as solid lines; (b) the emission spectra for each pair of FP fragment, shown as dashed lines. (Adapted from reference (2))

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Table 2 References of sequences used in BiFC assay Sequence

Reference

pcDNA3 plasmid https://www.addgene.org/vector-database/2092/ [Invitrogen]

Invitrogen

mCherry

https://www.addgene.org/vector-database/6496/ [Clontech]

[16]

mVenus

https://www.addgene.org/browse/sequence_vdb/6524/

[5]

mCerulean

https://www.snapgene.com/resources/plasmid-files/?set¼fluorescent_ [4] protein_genes_and_plasmids&plasmid¼mCerulean

HOXA7 (coding CCDS5408.1 region)

CCDSa

PBX1 (coding region)

CCDS

CCDS1246.1

a

The Consensus CDS (CCDS, https://www.ncbi.nlm.nih.gov/CCDS) project is a collaborative effort to identify a core set of human and mouse protein coding regions that are consistently annotated and of high quality

multicolor BiFC assay to investigate two different PPIs simultaneously (see Note 3). 1. All constructs are cloned into pcDNA3 plasmid. The final plasmids used in BiFC are as follows, with reference sequences listed in Table 2. pcDNA3-VN173-HOXA7s, refer to Fig. 2b, including: (a) pcDNA3-VN173-HOXA7 as positive control. (b) pcDNA3-VN173-HOXA7[dGA] as negative control (see Note 4). (c) pcDNA3-CC155-PBX1. (d) pcDNA3-mCherry, as internal control and signal normalizer (see Note 5). 2. The pcDNA3-VN173-HOXA7s vectors harbor a 2X FLAG epitope tag between VN fragment and HOX protein sequence (see Note 6). For pcDNA3-CC155-PBX1, there are two amino acids separating the CC and PBX1, which are generated from a cloning site, as a pseudo-linker (see Note 7). The details about protein fusion vectors are shown in Fig. 4. 2.2 Cell Culture and Plasmid Transfection

1. HEK-293 cells were obtained from the American Type Culture Collection (ATCC) through LGC Standards Sarl (FR). Cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM-GlutaMAX-I, Gibco by Life Technologies) supplemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS) and 1% (v/v) penicillin-streptomycin (5000 U penicillin and 5 mg streptomycin/mL), incubating at 37
C, in an atmosphere of 5% CO2.

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Fig. 4 Plasmids designed for BiFC. (a) pcDNA3-VN173-HOXA7s plasmid. Different HOXA7 constructs and VN173 fragment are cloned into EcoRI and XhoI sites, the expression being driven by CMV promoter. 2X FLAG epitope tag is used for linking VN and HOX proteins; (b) pcDNA3-CC155-PBX1 plasmid. CC155 fragment is inserted between EcoRI and XhoI sites, followed by PBX1 sequence cloned at XhoI and XbaI sites; (c) pcDNA3mCherry plasmid. mCherry sequence is located between XhoI and XbaI sites, as further internal control

One or 2 weeks before transfection, a stock of HEK-293 cells should be maintained. Cells must remain below 80% confluent (see Note 8). 2. Flat bottom cell culture 6-well plates for cell seeding. 3. Malassez counting chamber or equivalent. 4. Microscope coverslip 22  22 mm for cells growing on, stored in 100% ethanol (see Note 9). 5. JetPRIME (Polyplus transfection, France) was used according to the manufacturer’s instructions for transfecting HEK-293 cells. 2.3 Confocal Imaging

1. A confocal microscope system (see Note 10). In our case, we used Zeiss LSM780 confocal system.

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2. Microscope settings for image collection (see Note 11): Argon as laser source (MBS 488/561/633 nm), using the 561 nm and 488 nm laser lines for the excitation of mCherry and VN/CC BiFC, respectively. Emission filters BP590–630 nm and BP499–543 nm were used to detect mCherry and reconstructed mVenus-like (VN/CC) signals, respectively. Select an image size of 1024  1024 pixels and pinhole of 1.0 AU (see Note 12). Set detector gain as 750 and laser power as 1.0% (mCherry detection) or 1.5% (VN/CC BiFC detection). 3. Microscope slides 76  26 mm for mounting cell-coated coverslip under microscope. 2.4

Data Analysis

1. Mainstream desktop computer. 2. Freely available Fiji imaging software (a distribution of ImageJ, https://imagej.net/Fiji) [17].

3

Methods

3.1 Transfection of BiFC Plasmids

1. In a cell culture hood, prepare the sterile coverslips, and place them on blotting paper for air-drying 15 min. 2. Meanwhile, prepare two 6-well plates, and distribute 2 mL of culture medium for each well. 3. Once coverslips are fully dried, carefully place coverslip on the media surface to each well. Floating coverslip can be pressed down using one 200 μl sterile pipette tip. Ensure that there are no air bubbles between coverslip and well bottom, and then gently shake the plate, and circle around the media to cover the coverslip. 4. Prior to seeding cells, verify stock cell status in the incubator. The cells should have enough confluence (60–80%), and viability of cells must be over 90% using the trypan blue exclusion method. 5. Subsequently, seed 500,000 HEK-293 cells (see Note 13) in each coverslip-containing well. 6. Incubate cells in 6-well plates at 37
C and 5% CO2 for 16–24 h or until cells reach a confluence of 60–80%, which is optimal for transfection. 7. Before transfection, prepare plasmid DNA diluted solution (see Note 14), at the following concentrations: 0.5 μg/μL, pcDNA3-VN173-HOXA7s. 0.5 μg/μL, pcDNA3-CC155-PBX1. 1 μg/μL, pcDNA3-mCherry.

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8. A three-plasmid transfection system is used in BiFC assay for each HOXA7 construct condition. Prepare a 1.5 mL Eppendorf with 200 μL of jetPRIME buffer for each transfection condition. Add 1 μL of each VN/CC/mCherry-containing plasmid solution for a total 2 μg DNA/reaction. Mix by vortexing for 10 s and spin down. 9. Add 4 μL jetPRIME reagent, vortex for 5 s, and spin down briefly. 10. Incubate for 15 min at room temperature. 11. Gently add 200 μL of transfection mix to HEK-293-containing well, and distribute evenly. 12. Incubate the plate for 20 h (see Note 15) at 37
C and 5% CO2, and then image the transfected cells. 3.2 Detect Fluorescent Signals

1. Mount carefully the cell-coated coverslip on a glass slide for image capture under microscope. 2. Image all samples using identical settings (refer to “2.3 Confocal Imaging” in “2. Materials” part) for each FP channel, using a 20 objective. 3. Begin focus on the z-stack that produces the greatest BiFC signal using the fine adjustment knob on the microscope, and then move the focus along the Z-axis to define the first and last sections. 4. Take at least 3 images from different areas of each slide, ensuring that each image includes at least 100 cells (see Note 16).

3.3 Semiquantitative BiFC Analyses

1. Open original images in a software that allows “maximum intensity projection.” In this protocol, we use the ZEN 2.3 SP1 FP1 black edition software as example. 2. In the Processing tab, create a maximum projection (see Note 17) for each image (Method ! Maximum intensity projection); choose Z in Method Parameters. 3. Save all newly created maximum projection images, and then open them in Fiji, choosing Hyperstack, Grayscale, and Autoscale (see Note 18) in Bio-Formats Import Options (Fig. 5a). 4. In the menu bar, go to Analyze and click Set Measurements. Select on Area, Integrated density, Mean gray value, Stack position, and Display label, and keep redirect to “None” in the dropdown list (Fig. 5b, c). This setting will be applied to all image measures. 5. Measure the mean gray value for mVenus (BiFC) and mCherry channels, by pressing “M” on keyboard. The mean values will appear in a new floating window, “Results.”

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Fig. 5 Screenshots showing how to semiquantitate BiFC fluorescence based on acquired images. (a) Import the original images to Fiji. (b, c) Set appropriate measure parameter. (d) Enable brush selection for background mean gray value measure. (e) Background mean gray value measure on mCherry channel. (f) Background mean gray value measure on BiFC channel

6. Then the background fluorescence for each channel needs to be measured, respectively. 7. In the main interface of Fiji, double-click the second graph icon. Tick on Enable selection brush, and set proper pixel size (Fig. 5d). 8. Reselect the image, and on mCherry channel, place the cursor in regions devoid of cells. Holding the “Shift” key on the keyboard, single-click to draw hollow dots as much as possible (20) (Fig. 5e). 9. Measure mean value of selected regions by pressing “M,” as background value of mCherry channel. 10. Roll the middle mouse button to change the channel to BiFC channel; the selected region is fixed as previous location; measure the background value of BiFC channel by pressing “M” (Fig. 5f). 11. Repeat steps 1–10 to measure all images. For each image, four different values are measured, global BiFC mean value, global mCherry mean value, BiFC mean background (BG) value, and mCherry mean BG value.

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Fig. 6 Quantification of BiFC with mutated HOXA7 proteins in HEK-293. Normalized BiFC values were standardized by positive control VN173-HOXA7/ CC155-PBX1 from images of HEK-293 cells with the indicated HOXA7 construct combinations using a 20X objective. Compared to positive control, BiFC signal of negative control HOXA7[dGA]/PBX1 exhibited only about 22% BiFC intensities. Significance is shown relative to BiFC with wild-type HOXA7 and was evaluated by t test (***p < 0.001; ns, nonsignificant). Bars represent mean  SD (n ¼ 3)

12. All values could be gathered in the same “Results” window. 13. Export all data to Microsoft Excel for further analysis. 14. The measured fluorescence should be corrected by subtracting the respective background values from both the mCherry normalizer and BiFC signals. The final normalized BiFC value is calculated by the BiFC/mCherry normalizer fluorescence ratio, according to the following formula: NFB ¼ (FMB  BGMB)/(FMR  BGMR) (see Note 19). where NFB ¼ normalized BiFC fluorescence intensities, FMB ¼ measured BiFC global mean gray value, BGMB ¼ measured BiFC background mean gray value, FMR ¼ measured mCherry global mean gray value, and BGMR ¼ measured mCherry background mean gray value. 15. The final normalized value should be averaged from at least three independent images for each condition. 16. To facilitate the comparison of all conditions, the final BiFC value will be scaled using positive control VN173-HOXA7/ CC155-PBX1 interaction as the reference (Fig. 6).

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a.

HOXA7

PBX1

PBX1

MEIS1

DNA

b.

VN-HOXA7/CC-PBX1

CC-PBX1/CN-MEIS1

Merge

Fig. 7 Bicolor BiFC in HEK-293 cells. (a) Schematic representation of VN-HOXA7/CC-PBX1/CN-MEIS1; (b) illustrative confocal pictures of BiFC between HOXA7/PBX1/MEIS1. Scale bar, 10 mm 3.4 Data Interpretation

In this study, interactions between different HOXA7 constructs and PBX1 were investigated in living HEK-293 cells. Despite the limitation of the transfection system (see Note 20), BiFC could be used to measure different interaction affinities, allowing identification of key HOXA7 residues involved in the interaction with PBX1. In our example, the BiFC signal was reduced by 80% with HOXA7 [dGA] (negative control) compared to the wild type HOXA7. We also demonstrated that HOXA7/PBX1/MEIS1 forms a trimeric complex using bicolor BiFC (see Note 3 and Fig. 7). For statistical significance, results should be obtained from at least three independent experiments, and the different interactions should be measured from the same pool of transfected cells. This kind of consideration allows limiting the fluctuating effect from the transfection efficiency. In addition, using vectors with inducible promoters could allow controlling more tightly the expression level of each construct, which was not the case here with the pcDNA3 vector (with the constitutive CMV promoter).

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Notes 1. The most currently used FPs for BiFC are derived from the GFP, including YFP, Venus, CFP, and Cerulean. The GFP itself is prone to form noncovalent dimers [18], which could lead to incorrect localization and function of the BiFC complex in case of increased expression levels in the cell. FPs used in this protocol inherit an A206K mutation [19], preventing the dimerization, therefore these monomeric FPs are suitable for BiFC. 2. The choice of the fusion topology (fusion with the FP fragment at the N- or C-terminus of the targeted protein) requires prior knowledge of protein characteristics. In many case, these characteristics are not known. Therefore, both N- and C-terminally tagged proteins need to be tested independently. 3. The C-terminal fragments of Venus (VC155) and Cerulean (CC155) display 96% of identity. This allows using one or the other for doing mVenus-like BiFC with the complementary VN173 fragment. In addition, the CC155 fragment can complement with the N-terminal fragment of Cerulean (CN173) for making mCerulean-like BiFC. This property allows visualizing two different PPIs simultaneously by doing mVenus- and mCerulean-like BiFC with three fusion proteins. The CN173/ CC155/VN173 has proven to be the best combination for multicolor BiFC [15]. The multicolor BiFC enables visualization of distinct interactions in the same protein complex [2]. For example, the bicolor BiFC for HOXA7/PBX1/MEIS1 can be done by co-expressing CC-PBX1, CN-MEIS1, and VN-HOXA7 (Fig. 7). The protocol is very similar to classical BiFC, which is detailed in the main text. In the three-plasmid transfection system (see step 8 in “3.1 Transfection of BiFC Plasmids”), replace pcDNA3-mCherry by adding 0.5ug pcDNA3-CNMEIS1 for a total 1.5 μg DNA/reaction. Add 3 μL jetPRIME reagent for final transfection mix. 4. It is important to have a negative control of the BiFC, even when the expression of fusion proteins is tightly controlled in the cell (e.g., by using an inducible promoter). Several negative controls can be used. When possible, proteins with point mutations predicted to disrupt the interaction should be tested: these mutations should affect the BiFC signal when compared to BiFC with the corresponding wild-type proteins under the same parameters of expression. When such knowledge is not available, another good negative control will consist in doing competition against the BiFC complex. In this context, one the two protein partners will be co-expressed with the two fusion

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proteins as a cold competitor: this cold protein partner A will compete with the same fusion protein partner A for interacting with the fusion protein partner B, leading thus to a diminution of the BiFC signal. Unfused FP fragments like CC or VN can be regarded as inappropriate controls for BiFC experiments [20], as the ability of the FP fragments to trigger artificial interaction should be considered in the context of their fusion with the candidate proteins. Despite this fact, they are still frequently seen in the literature [21], especially in BiFC-based screening studies [22– 24]. 5. To compensate for differences in transfection efficiencies across conditions and cell-to-cell variations, an internal control FP with distinct spectral properties is co-expressed with the BiFC fusions and used to normalize BiFC signals [25], thus considered as a signal normalizer. However, for a credible BiFC test, the internal FP should be expressed at least in 50–70% of the cells. 6. For further orthogonal methods to verify the PPI, like Co-IP, an epitope tag is necessary. This implies that one of the protein partners is only fused to a classical epitope tag for co-IP (like HA or FLAG tag), while the other partner remains unchanged (fused to the FP fragment). Revelation of the interactor upon Co-IP with the anti-HA or anti-FLAG antibody can be done with polyclonal anti-GFP antibodies that recognize both N- or C-terminal fragments of Venus and Cerulean. In our lab, rabbit anti-GFP polyclonal antibody (Invitrogen, # A-11122) is frequently used for both CC155 and VN173 immunoassay. 7. To attenuate the effect of the fusion, a short flexible linker is preconized for the construct, which may provide sufficient freedom to the N- and C-terminal FP fragments for complementation. [GGSGG]n linker is one of the most commonly flexible linker that is widely used in fusion protein construct. This linker should not be too long to avoid revealing indirect PPIs. 8. The cell population should be less than 80% confluent and under exponential growth phase. The exponential phase is optimal for efficiency of transfection experiments. Experiments should also be performed with low-passage cell culture (no more than 15 passages) to avoid any possible genetic drift changes in genotype that may affect the final result. 9. When using confocal microscopy, cells must be plated on glass coverslips because high numerical aperture objectives of confocal microscopy will not go through the plastic dishes or glass slides. It is recommended to use dishes with embedded

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coverslips with an optical thickness of 0.17 mm. Coating coverslip could be better for cell attachment. This is however not necessary for HEK-293 cells, which are highly adherent cells. Coating can be done by treating the coverslip with polylysine or collagen prior to adding cells. Classical protocols for sterilizing coverslips are based on ethanol in concentrations ranging from 70% to 100%, either with or without further sterilization steps [26]. Using ethanol alone is convenient for manipulation. In case of potential undesired side effects for the cell culture, other thorough methods like UV or autoclaving can be envisaged. 10. Images can be taken by confocal microscopy or by standard fluorescence microscopy equipped with appropriate excitation and emission filters or laser units to image CFP, YFP, and RFP, with 20 objectives and a CCD camera. 11. The imaging settings for mCherry and BiFC can be different; however, the settings used in each different condition must remain the same for consistent comparison among all the captured images. 12. The size of the pinhole can affect the resolution and light throughput. For a pinhole size of ~1 AU, 86% of the light collected from a point-like source passes through the pinhole. For most applications in confocal microscopy, a pinhole size between 0.8 and 1.0 AU is optimal. Alternatively, using a pinhole greater than 1.0 AU can increase image brightness but reduce image resolution. 13. The seeding cell number may differ from cell types. For example, HEK and MDA-MB-231 could be seeded with 300,000 cells/well, for appropriate confluence after 24 h incubation. 14. Use ultrapure PCR-grade water or jetPRIME buffer to dilute your plasmid solution. The concentration of the plasmid solution should not be too diluted, as it will notably change the transfection mix volume, leading to low transfection efficiency. 15. After transfection, a long time culture will lead to fusion protein overaccumulation. To minimize this effect, a short culture time post-transfection is recommended, usually around 20/24 h for having enough maturation time for BiFC signals. 16. Individual cells express different levels of fusion proteins upon transient transfections. It is therefore essential to measure BiFC signals in large cell populations to dilute cell-to-cell variation. Even if the internal transfection-control plasmid is used to reduce this bias, it is highly recommended to measure BiFC in a high number of fluorescent cells (minimum 100 cells). In addition, images should be captured from at least three different fields in each condition.

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17. Maximum projection means that the algorithm chooses, for each pixel, the highest value found in any of the z sections. ZEN allows “stacking” all z-stacks into a single image. This process will much facilitate the subsequent quantitative analysis. 18. “Autoscale” is an optional choice for image import. It will make the fluorescent signal more clear and visible than original image but without altering underlying values in the image. This option is very useful for weak fluorescence cellular location display. 19. The total measured fluorescence for each channel is calculated as follows: FB ¼ FMB  FSB FR ¼ FMR  FSR where FB ¼ total measured BiFC fluorescence, FMB ¼ measured BiFC global mean gray value, FSB ¼ total measured BiFC area surface, FR ¼ total measured mCherry fluorescence, FMR ¼ measured mCherry global mean gray value, and FSR ¼ total measured mCherry area surface. The total measured fluorescence for background of each channel is calculated as follows: BGFB ¼ BGMB  BGSB BGFR ¼ BGMR  BGSR where BGFB ¼ total measured BiFC background fluorescence, BGMB ¼ measured BiFC background mean gray value, BGSB ¼ total BiFC background area surface, BGFR ¼ total measured mCherry background fluorescence, BGMR ¼ measured mCherry background mean gray value, and BGSR ¼ total mCherry background area surface. The measured fluorescence should be corrected by subtracting the respective background values from both the mCherry normalizer and BiFC signals, according to the following formula: CFB ¼ FB  BGFB CFR ¼ FR  BGFR where CFB ¼ corrected total measured BiFC fluorescence and CFR ¼ corrected total measured mCherry fluorescence. The final formula is generated as follows: NFB ¼ CFB =CFR ¼ ðFB  BGFB Þ=ðFR  BGFR Þ ¼ ðFMB  FSB  BGMB  BGSB Þ=ðFMR  FSR  BGMR  BGSR Þ

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As the total measured fluorescence area is equal to the area surface of background: FSB ¼ BGSB ¼ FSR ¼ BGSR ¼ S thus: NFB ¼ ðFMB  BGMB Þ=ðFMR  BGMR Þ 20. It is difficult to ensure that each fusion protein will be equally expressed in all cells when using three plasmids for transient transfection. More highly expressed proteins will interact more frequently and/or at higher levels, which can induce falsepositive results. Statistical reproduction of the experiment can eliminate these effects that are by definition nonreproducible. Alternatively, stable cell line could be generated for a more constant protein expression [27]. The viral 2A self-cleaving peptide was also applied for combined protein translation in one mRNA [28]. However, the 2A self-cleavage is not always 100% efficient [29].

Acknowledgments Research in the laboratory of S. Merabet is supported by Association pour la Recherche sur le Cancer (ARC, PJA20141202007); Fondation pour la Recherche Me´dicale (FRM, DEQ. 20170336732); Ligue Nationale Contre le Cancer, Centre National de Recherche Scientifique (CNRS); CEFIPRA (5503-2); CNRS; and Ecole Normale Supe´rieure (ENS) de Lyon. We further thank the China Scholarship Council (CSC, File No. 201708070003) for the doctoral grant to Y. Jia. References 1. Kerppola TK (2006) Visualization of molecular interactions by fluorescence complementation. Nat Rev Mol Cell Biol 7:449–456 2. Hu C-D, Kerppola TK (2003) Simultaneous visualization of multiple protein interactions in living cells using multicolor fluorescence complementation analysis. Nat Biotechnol 21:539–545 3. Kerppola TK (2006) Design and implementation of bimolecular fluorescence complementation (BiFC) assays for the visualization of protein interactions in living cells. Nat Protoc 1:1278–1286 4. Rizzo MA, Springer GH, Granada B et al (2004) An improved cyan fluorescent protein variant useful for FRET. Nat Biotechnol 22:445–449

5. Nagai T, Ibata K, Park ES et al (2002) A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications. Nat Biotechnol 20:87–90 6. Bhat RA, Lahaye T, Panstruga R (2006) The visible touch: in planta visualization of proteinprotein interactions by fluorophore-based methods. Plant Methods 2:12 7. Hu C-D, Chinenov Y, Kerppola TK (2002) Visualization of interactions among bZIP and Rel family proteins in living cells using bimolecular fluorescence complementation. Mol Cell 9:789–798 8. Vogel SS, Thaler C, Koushik SV (2006) Fanciful FRET. Sci STKE 2006:re2 9. Hu C-D, Grinberg AV, Kerppola TK (2006) Visualization of protein interactions in living

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cells using bimolecular fluorescence complementation (BiFC) analysis. Curr Protoc Cell Biol Chapter 21:Unit 21.3 10. Magli MC, Largman C, Lawrence HJ (1997) Effects of HOX homeobox genes in blood cell differentiation. J Cell Physiol 173:168–177 11. Moens CB, Selleri L (2006) Hox cofactors in vertebrate development. Dev Biol 291:193–206 12. Passner JM, Ryoo HD, Shen L et al (1999) Structure of a DNA-bound UltrabithoraxExtradenticle homeodomain complex. Nature 397:714–719 13. Dard A, Reboulet J, Jia Y et al (2018) Human HOX proteins use diverse and contextdependent motifs to interact with TALE class cofactors. Cell Rep 22:3058–3071 14. LaRonde-LeBlanc NA, Wolberger C (2003) Structure of HoxA9 and Pbx1 bound to DNA: Hox hexapeptide and DNA recognition anterior to posterior. Genes Dev 17:2060–2072 15. Shyu YJ, Liu H, Deng X et al (2006) Identification of new fluorescent protein fragments for bimolecular fluorescence complementation analysis under physiological conditions. BioTechniques 40:61–66 16. Shaner NC, Campbell RE, Steinbach PA et al (2004) Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol 22:1567–1572 17. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682 18. Blogger G. When is a monomer not a monomer? The top three ways your favorite fluorescent protein oligomerizes in cells. https:// blog.addgene.org/when-is-a-monomer-not-amonomer-the-top-three-ways-your-favoritefluorescent-protein-oligomerizes-in-cells 19. Zacharias DA, Violin JD, Newton AC et al (2002) Partitioning of lipid-modified monomeric GFPs into membrane microdomains of live cells. Science 296:913–916 20. Kudla J, Bock R (2016) Lighting the way to protein-protein interactions: recommendations

on best practices for bimolecular fluorescence complementation analyses. Plant Cell 28:1002–1008 21. Horstman A, Tonaco IAN, Boutilier K et al (2014) A cautionary note on the use of splitYFP/BiFC in plant protein-protein interaction studies. Int J Mol Sci 15:9628–9643 22. Xia J, Kong L, Zhou L-J et al (2018) Genomewide bimolecular fluorescence complementation-based proteomic analysis of toxoplasma gondii ROP18’s human Interactome shows its key role in regulation of cell immunity and apoptosis. Front Immunol 9:61 23. Yue L, Li L, Li D et al (2017) Highthroughput screening for Survivin and Borealin interaction inhibitors in hepatocellular carcinoma. Biochem Biophys Res Commun 484:642–647 24. Lepur A, Kovacˇevic´ L, Beluzˇic´ R et al (2016) Combining unique multiplex gateway cloning and bimolecular fluorescence complementation (BiFC) for high-throughput screening of protein–protein interactions. J Biomol Screen 21:1100–1111 25. Vidi P-A, Przybyla JA, Hu C-D et al (2010) Visualization of G protein-coupled receptor (GPCR) interactions in living cells using bimolecular fluorescence complementation (BiFC). Curr Protoc Neurosci Chapter 5:Unit-5.29 26. PB helping cells and sections to stick: cleaning, sterilising and coating slides and coverslips | Agar Scientific. http://www.agarscientific. net/helping-cells-and-sections-to-stickcleaning-sterilising-and-coating-slides-andcoverslips/ 27. Ando K, Parsons MJ, Shah RB et al (2017) NPM1 directs PIDDosome-dependent caspase-2 activation in the nucleolus. J Cell Biol 216:1795–1810 28. Szymczak AL, Vignali DAA (2005) Development of 2A peptide-based strategies in the design of multicistronic vectors. Expert Opin Biol Ther 5:627–638 29. Liu Z, Chen O, Wall JBJ et al (2017) Systematic comparison of 2A peptides for cloning multi-genes in a polycistronic vector. Sci Rep 7:2193

Chapter 13 Out-of-Phase Imaging after Optical Modulation (OPIOM) for Multiplexed Fluorescence Imaging Under Adverse Optical Conditions Raja Chouket, Ruikang Zhang, Agne`s Pellissier-Tanon, Annie Lemarchand, Agathe Espagne, Thomas Le Saux, and Ludovic Jullien Abstract Fluorescence imaging has become a powerful tool for observations in biology. Yet it has also encountered limitations to overcome optical interferences of ambient light, autofluorescence, and spectrally interfering fluorophores. In this account, we first examine the current approaches which address these limitations. Then we more specifically report on Out-of-Phase Imaging after Optical Modulation (OPIOM), which has proved attractive for highly selective multiplexed fluorescence imaging even under adverse optical conditions. After exposing the OPIOM principle, we detail the protocols for successful OPIOM implementation. Key words Fluorescence imaging, Fluorescence microscopy, Macroscale imaging, Fluorescence endomicroscopy, Dynamic contrast, Reversibly photoswitchable fluorophores

1

Introduction Fluorescence exhibits favorable features for optical observations in biology: [1] (i) Sensitivity. With respect to the excitation wavelength, the red-shifted fluorescence signal facilitates its collection by spectral filtering. Hence fluorescence-based imaging is highly sensitive; (ii) Labeling specificity. Since most endogenous biological molecules are non-fluorescent, labeling with exogenous fluorophores is commonly performed by genetic engineering, which secures exclusive selectivity; (iii) Discriminative power. To selectively distinguish a fluorophore from an interfering background, one can rely on both spectral and temporal domains by exploiting

The original version of this chapter was revised. The correction to this chapter is available at https://doi.org/ 10.1007/978-1-0716-1593-5_22 Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_13, © Springer Science+Business Media, LLC, part of Springer Nature 2021, Corrected Publication 2021

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distinct absorption or emission spectra or different lifetimes of excited or photoactivated states; (iv) Relevance for imaging. The fluorescence imaging devices provide spatial and intensity information respectively dealing with the localization and the concentration of the fluorophore of interest. Film recordings further provide temporal information such as the movement of a fluorophore or the evolution of its fluorescence intensity. Despite such favorable features, fluorescence suffers from limitations for sensitive and quantitative imaging: (i) Optical interferences. Reflection or light scattering of ambient light, as well as autofluorescence originating from endogenous fluorophores can interfere with the emission from an exogenous fluorophore. This background may result in uncertain target quantification [2, 3] and further give rise to artifacts through the image, which are detrimental to the contrast and the localization of a fluorophore of interest [4]. It also contributes to the shot noise, which decreases the measurement precision. This last point is significant since the shot noise—in contrast to the intensity added by the background— cannot be eliminated and degrades the signal-to-noise ratio (SNR) [5]; (ii) Lack of multiplexing. The absorption and emission bands of the bright fluorophores overlap, which limits the number of distinguishable fluorophores in multiplexed imaging; (iii) Photobleaching. Multiple on-off cycles fatigue fluorophores, which ultimately photobleach upon exposure to continuous light excitation. This account concerns the limitations (i) and (ii). We first examine the current approaches which address these limitations. Then we more specifically report on Out-of-Phase Imaging after Optical Modulation (OPIOM), which has proved attractive for highly selective multiplexed fluorescence imaging even under adverse optical conditions. After exposing the OPIOM principle, we detail the protocols for successful OPIOM implementation. 1.1 Fighting Against Ambient Light

To avoid its detrimental interference, the simplest way is to eliminate ambient light from the environment. Hence fluorescence imaging is usually performed in the dark. For clinical applications, the lighting in the operating room can be turned off and the excitation light switched on during the acquisition [6, 7], which is not practical for real time operation. Considering that fluorescence imaging under daylight is also increasingly used in agronomy (e.g. monitoring of photosynthetic performance and environmental stress of the plants) [8–11], alternative strategies have been sought for.

1.1.1 Spectral Analysis

In spectral analysis, optical filters are used to selectively extract the fluorescence emission from a targeted label. Since the spectrum of most light sources spans a broad wavelength range from UV to IR which covers the emission spectrum of the most commonly used fluorescence probes, spectral filtering has found limited use in elimination of ambient light. In agronomy, it is worth to mention the Fraunhofer Line Depth (FLD) method in order to retrieve the

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sunlight-induced red fluorescence of Chlorophyll from the reflected sunlight [12–16]. FLD relies on light intensity measurements inside and outside of the Fraunhofer lines of the solar spectrum resulting from the absorption of Sun and Earth atmospheres, and deduces the fluorescence signal by comparison of the two measurements. 1.1.2 Dynamic Excitation

The fluorescence signal can be distinguished from ambient light by tailoring the excitation light. One can subtract the images recorded under ambient lighting with and without light exciting fluorescence. It has been implemented to image the Green Fluorescent Protein (GFP) expressed in a brain slice under indoor background light [17]. Subtraction has also been applied to detect fluorescence in plants [18]. As simple as it is, subtraction has limited interest in fluorescence imaging under a temporally varying lighting environment. It cannot be used as well if ambient light is too strong as a consequence of the shot noise (vide supra). Pulsed excitation light with gated detection is more commonly used for fluorescence imaging under ambient light from room light to sunlight conditions. The pulsed excitation light is usually generated with a flash lamp or Light Emitting Diode (LED) source (duration of millisecond down to microsecond) and the camera is synchronized with the flash at a fast shutter speed. Such a system not only enhances the instantaneous fluorescence response but it also reduces the exposure time to diminish the input of ambient light intensity [19–25]. Lock-in imaging is also used to distinguish a fluorescence signal from ambient light. It relies on periodic modulation of the intensity of the excitation light with an angular frequency at which any modulation of the ambient light can be neglected. After timedomain Fourier transform, the modulated component of the fluorescence is extracted, whereas the background is filtered. For instance, lock-in imaging has been applied for imaging a living animal [26] or detect a bacterial sensor in the soil under daylight condition [27]. Compared to the preceding approach, lock-in imaging benefits from a higher Signal-to-Noise-Ratio (SNR) due to frequency-domain noise filtering. In addition, it is insensitive to irregular fluctuation of ambient light, which is advantageous with respect to the approaches reported above.

1.2 Fighting Against Autofluorescence

Autofluorescence originates from endogenous fluorescent molecules. In animals, they are mainly NAD(P)H, flavin and lipopigments, and porphyrins [28–34]. Many other fluorophores are found in plants, such as alkaloids, flavonoids and phenolics, substances of the cell wall (mainly ferulic acid) [35–41], lipofuscins [42–44], and chlorophyll [45]. In general, the absorption and emission spectra of endogenous fluorophores respectively span the UV and the visible range [46–50]. Specific fluorophores such as melanin even emit IR fluorescence upon NIR excitation [51]. To

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eliminate autofluorescence has been a major concern of fluorescence bioimaging and several strategies have been correspondingly introduced to fight its interference. 1.2.1 The Growth Medium

Most endogenous fluorophores are important cellular metabolites. As such, they are often components of growth and culture media, where they generate an intense and variable (e.g. due to cell secretion [52]) autofluorescence [53]. The fluorescent culture medium can be exchanged for a synthetic buffer right before recording an image. However, medium removal influences the longevity of cells and hampers long-term observation. Low autofluorescence culture media have been reported [54–56] but they are not always available for specific uses.

1.2.2 The Optical Filter Set

In a fluorescence imaging microscope, a fluorescence filter set consists of an excitation filter, a dichroic beam-splitter, and an emission filter. It is used to separate the optical pathways of excitation and emission, as well as to confine the band pass of both channels in order to avoid the interference of excitation light and enhance the fluorescence intensity from the specific fluorescent reporter. Since autofluorescence arises from endogenous molecules absorbing and emitting light in different wavelength ranges, excitation filters with narrow band passes are favorable to limit the autofluorescence to the one originating from the endogenous fluorophores sharing the wavelength range used to excite the exogenous label [57–60]. Similarly, narrow band pass filters centered around the emission band of the reporter fluorophores should be adopted to augment the contribution of the desired signals against the non-specific broad band autofluorescence [58, 60].

1.2.3 The Wavelength Range

Near infrared (NIR; 650–950 nm) fluorescence imaging has been another strategy to get rid of autofluorescence [61, 62]. Indeed very few endogenous fluorophores emit fluorescence beyond 650 nm [63]. The second NIR region (1200–1800 nm) is even more favorable since autofluorescence is now almost negligible [51, 64]. A variety of near infrared dyes (e.g. Indocyanine Green) are currently available [65, 66]. Hence fluorescence imaging in the NIR region has been widely adopted to investigate biological systems [65–69].

1.2.4 Chemical Treatments

Autofluorescence can be diminished by specific chemical treatments but in the limit of causing also signal reduction of exogenous fluorescent labels. Sodium borohydride quenches autofluorescence induced from aldehyde or formalin fixatives [70–72]. Riboflavin can be reduced to a non-fluorescent state by sodium dithionite [73]. Copper sulfate can reduce lipofuscin-like autofluorescence [74] and hemosiderin-laden macrophages autofluorescence [75]. Diazo dyes (e.g. trypan blue [72, 76, 77] or Sudan black B

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[78–81]) are frequently adopted to mask or absorb visible autofluorescence in immunofluorescence applications [82, 83]. Such treatments are often combined for better diminishing of autofluorescence [72, 84–86]. 1.2.5 Photobleaching

Photobleaching of endogenous fluorophores [87] mediated by UV irradiation has been useful for diminishing autofluorescence of animal tissues such as brain, liver, and lung [88–91]. The sample is often photobleached prior to immunolabeling so as to avoid to affect the fluorescent labels.

1.3 Multiplexed Fluorescence Imaging

Two strategies have been implemented for multiplexed fluorescence imaging.

1.3.1 Spectral Discrimination

The common approach to image several fluorescent labels exploits discrimination in the spectral domain (see Table 1). Fluorescent Labels: Myriads of organic fluorophores are used in biological applications [92]. They benefit from a small size— enabling one to label biomolecules without disturbing their biological function—and versatility making possible to fit specific conditions required for biological studies. Common fluorophores such as fluoresceins, rhodamines, 4,4-difluoro-4-bora-3a,4a-diazas-indacene (BODIPY dyes), and cyanines exhibit favorable features: high molar absorption coefficients (25,000–250,000 M
1cm
1),

Table 1 Representative examples of multiplexed fluorescence imaging relying on spectral analysis

Probe type

Degree of Spectral multiplexing technology

Spectral resolution Applications

References

FPs

4

LCTF

5 nm

Multiplexed imaging of living cells

[205]

Q-dots

6

AOTF

1 nm

Simultaneous imaging of insulin [206] resistance signaling

5 FP Fluorescent dyes

SPIM

1 nm

Multiplexed imaging and autofluorescence removal in zebrafish embryo

[207]

Fluorescent dyes

3

CTIS

5 nm

Multiplexed imaging of living cells

[208]

FPs

3

3 nm IMS (snapshot technique)

Multiplexed imaging of pancreatic β cell dynamics

[209]

Point10 nm scanning microscope

Multiplexed imaging of organelle interactome

[130]

FPs 6 Fluorescent dyes

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moderate-to-high fluorescence quantum yields over a large wavelength range, and often limited photobleaching [93, 94]. Despite these favorable features, organic fluorophores generally suffer from poor specificity in direct labeling. Hence numerous strategies have been introduced to target these fluorophores to biomolecules of interest [1]. In immunolabeling, the fluorophore is conjugated to an antibody recognizing the biomolecular target. Although simple, this strategy requires specific protocols of fixation and permeabilization of cells [95]. Alternative strategies rely on fluorophorederivatizable tags (e.g. SNAP-tag, [96] CLIP-tag, [97] Halo-tag [98]) the fusion of which with a biomolecule of interest secures exclusive labeling selectivity. Quantum dots (QDs) are another class of attractive fluorophores for biological applications. These fluorescent nanocrystals with 1–10 nm diameter are generally semiconductors composed of elements from the periodic groups II–VI or III–V [99]. They exhibit broad excitation spectra and rather narrow emission bands (typically 50 nm at half-width), which can be tuned by the QD size [100, 101]. QDs are much less sensitive to photobleaching than organic fluorophores and as a consequence, they exhibit good fluorescence stability [99]. To overcome concerns about the toxicity of the semiconducting QD upon illumination or oxidation [102, 103], carbon quantum dots (C-Dots) with a diameter of less than 10 nm have been introduced in the past decade [104]. Antibody-derivatized QDs have been used for labeling. This approach was limited to molecules exposed to the extracellular medium in view of the large size and poor transmembrane permeability of the QDs [105–108]. The discovery of GFP has revolutionized the field of fluorophores for bioimaging. Thirty years after its discovery in the jellyfish Aequorea Victoria [109], the first cloning of the GFP gene [110] and its successful expression in both prokaryotic and eukaryotic cells [111] have opened unprecedented opportunities for selectively labeling proteins and visualizing their role in dynamic cellular processes (e.g. protein expression, localization, translocations, interactions, and degradation) in living systems in real time [112]. A major effort has been subsequently undertaken to engineer brighter FPs and to extend their chromatic palette [113]. It has resulted in a wide rainbow palette of FPs absorbing from the UV to the near infrared [114–122] with absorption and emission features, which fairly compare with the ones of the organic fluorophores in terms of bandwidths. Image Processing: The rather broad emission bands (at least 50 nm for the half-width) of the preceding fluorophores make spectral discrimination to exhibit limitations for highly multiplexed observations. Even with a rich hardware of light sources, optics corrected for chromatic aberration, dichroic mirrors, optical

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filters,. . .spectral analysis of overlapping absorption and emission bands can routinely discriminate a maximum of 3–4 labels. Beyond these numbers, it is necessary to process the fluorescence signals at each pixel, in order to build the final image. The two main algorithms of image processing are linear unmixing and spectral phasor analysis. Linear Unmixing: In linear unmixing, the signal detected in each spectral channel is considered as a linear combination of the contributing fluorophores [123]. After collecting their reference spectra, the concentrations of the fluorophore components at each image pixel are computed from least-square fitting that minimizes the square difference between the calculated and measured levels of fluorescence. Several criteria must be fulfilled for efficient linear unmixing: (i) The number of spectral channels for detection should be at least equal to the number of fluorophores contained in the sample. Otherwise, no unique solution results from spectral analysis [124]; (ii) All fluorophores present in the sample must be involved for the unmixing calculation. Spectral Phasor Analysis: The phasor approach has been originally implemented to facilitate the extraction of fluorescence lifetimes from fluorescence decays [125–127]. The spectral phasor analysis exploits the first harmonic of the Fourier transform of the fluorescence spectra [128]. At each pixel of the image, the spectral information is reduced to a “phasor,” which is composed of the real and imaginary first order amplitudes of the Fourier transform. Both amplitudes (or similarly a modulus and a phase) are used as coordinates in the phasor plot [129]. In the case of a mixture of fluorophores, spectral unmixing results from simple and rapid vectorbased mathematical operations in the phasor plot, without necessitating accurate reference spectra or elaborate fitting procedures [128]. Achievements and Limitations: Table 1 provides representative examples of multiplexed fluorescence imaging relying on spectral analysis. The state-of-the-art spectral unmixing in living biological samples currently makes possible to distinguish six fluorophores (of which five are genetically encoded) [130]. However, this number is obtained at significant cost in terms of photon budget and computation time [124, 131–136]. Autofluorescence removal is another limitation of spectral analysis for multiplexed imaging, which can lead to significant artifact in the processed image [124]. When the background is spectrally homogeneous, it can be handled as a further fluorophore. However, this approach is limited in biological samples, where autofluorescence arises from diverse substances at specific locations [54, 137]. Eventually linear unmixing may be highly compromised by noise arising from the detector or from the fluorescence signal itself, an issue which may be critical

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at the low illumination required to preserve living biological samples. 1.3.2 Temporal Discrimination

The optimization of fluorophores (cross section for light absorption, quantum yield of luminescence, half-width of absorption/ emission bands) has essentially reached its physical limits. Considering that fluorescence should remain a much favored observable for imaging live cells [138], it is attractive to complement the spectral dimension by another one for further discriminating fluorophores. In fact, fluorescence emission reports on a photocycle of reactions including light absorption and relaxation pathways of an excited state. As such, fluorescence contains a wealth of dynamic information, which can be used for discrimination in the time domain beyond the sole wavelength of its emission. Fluorescent Labels: To be distinguished with temporal discrimination, fluorescent labels have to exhibit different relaxation times of the reaction network in which they are engaged under illumination. The photocycle of simple fluorophores involving absorption followed by fluorescence emission has been used early on for the discrimination of fluorophores when they spectrally overlap by exploiting their distinct rate constants for radiative deexcitation [139, 140]. Depending on a variety of factors (e.g. temperature, viscosity, pH, solvent polarity, and the presence of fluorescence quenchers), the fluorescence lifetime of common fluorophores ranges from hundreds of picoseconds (e.g. for cyanines) to hundreds of nanoseconds (e.g. for pyrenes) and is generally anticorrelated with the quantum yield of fluorescence [141]. Exploiting an organic chromophore, fluorescent proteins exhibit similar fluorescence lifetimes ranging from 1 to 4 ns [142]. In the purpose of expanding the range of fluorescence lifetimes, long-lived luminophores (e.g. lanthanide-based luminescent probes [143], azadioxatriangulenium (ADOTA) fluorophores [144, 145], Ag clusters [146]) have been developed. Eventually QDs often exhibit longer fluorescence lifetimes than organic fluorophores (e. g. 10–100 ns for CdSe nanocrystals protected with a ZnS shell). Recently popularized by super-resolution microscopies [147– 150], reversibly photoswitchable fluorophores (RSFs) which exhibit light-driven switch between states of different brightness upon illumination at one or two different wavelengths [151–153] provide more opportunities than the simple fluorophores for discrimination with dynamic contrast. In RSFs, illumination drives photocycles including photochemical and thermal steps, which intervene over a wide palette of timescales (μs to s) so as to facilitate the discrimination of RSFs by analyzing the temporal response of their fluorescence to appropriate light variations. Among RSFs,

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Reversibly Photoswitchable Fluorescent Proteins (RSFPs) are FPs, which can reversibly photoswitch between fluorescent and non-fluorescent states [153, 154]. Dronpa has been the first discovered RSFP in 2004 [155]. The 28 kDa Dronpa is made of an 11-stranded beta-barrel surrounding the p-hydroxybenzylidene imidazolinone chromophore [156]. At neutral pH, the absorption spectrum of Dronpa exhibits a major peak at 503 nm, which is associated with the anionic deprotonated state of the chromophore [155, 157]. At lower pH, this peak is converted into a peak at 388 nm, which is associated with the protonated state of the chromophore [155, 157]. The emission spectrum of Dronpa displays a predominant peak at 515 nm [157]. Upon illumination with blue light, Dronpa is photoswiched from its bright ON state to a dark OFF state, which makes it a negative photoswitcher. The recovery of its ON state can be either spontaneous (thermally driven) in the dark with a half time of 14 h at room temperature or light-driven upon illumination at 388 nm [155]. Dronpa exhibits a low ON-to-OFF photoswitching quantum yield (3.2  10
4) and a relatively high OFF-to-ON photoswitching quantum yield (3.7  10
1) [155]. Several fast-photoswitching mutants of Dronpa have been reported such as Dronpa-2 [158], Dronpa-3 [159], and rsFastLime [160]. Moreover, positive photoswitchers (photoswitching from a non-fluorescent to a fluorescent state upon illumination in the absorbance band of the fluorescent state) such as Padron have been introduced [160]. In view of their significance for the protocols of fluorescence imaging discussed below, Table 2 gathers the key features of the main RSFPs used for imaging. Strategies of Dynamic Contrast: In the following subsection, we report on several optical imaging techniques exploiting different types of dynamic contrast of fluorophores [138]. They rely on light as a perturbation parameter and further signal processing to selectively retrieve the signal from a targeted fluorophore upon eliminating spectral interferences from ambient light, autofluorescence, or other fluorescent labels. Discrimination by the Lifetimes of the Excited States: Fluorescence lifetime imaging microscopy (FLIM) discriminates fluorophores by the differences of the lifetimes of their first singlet excited state [139, 161]. Being an intrinsic property of a fluorophore, lifetime does not depend on its local concentration nor on its brightness, but it may vary with its molecular environment [162]. Three different protocols have been implemented to extract lifetime information. In time-correlated single-photon counting (TCSPC), individual emitted photons and the associated delay time of their arrival are detected to build a histogram of the number of photons at different time points, which generally provides the lifetime(s) after (multi)exponential fitting [163]. In a second protocol, the fluorophores are excited with a pulsed laser and the

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Table 2 List of representative RSFPs and of their absorption and emission features. λmax, ε(λmax), and ϕF respectively designate the wavelength of maximal absorption, the molar absorption coefficient at λmax, and the quantum yield of fluorescence RSFPs

Parent protein

λmax(nm) (exc/em)

ε(λmax)(M
1.cm
1)

Dronpa

22G

503/517

94,000

0.67

[155]

Dronpa-2

Dronpa

489/515

56,000

0.28

[159]

Dronpa-3

Dronpa

489/515

58,000

0.33

[159]

rsFastlime

Dronpa

496/518

39,000

0.77

[158]

bsDronpa

Dronpa

460/504

45,000

0.5

[160]

ffDronpa

Dronpa

503/517

105,000

0.75

[210]

Padron

rsFastlime

503/522

43,000

0.64

[160]

Kohinoor

Padron

495/514

63,000

0.71

[211]

Padron0.9

Padron

505/522

36,000

0.61

[212]

rsEGFP

mEGFP

493/510

47,000

0.36

[213]

rsEGFP2

rsEGFP

478/503

61,000

0.30

[214]

rsFolder

SuperFolder GFP

477/503

52,000

0.25

[215]

rsFolder-2

rsFolder

478/503

44,000

0.23

[215]

Gamillus

dfGFP

504/519

83,000

0.90

[216]

mGeos-M

mEos

503/514

52,000

0.85

[217]

Skylan

mEos3.1

499/511

152,000

0.60

[218]

Skylan-NS

mEos3.1

499/511

133,000

0.59

[219]

IrisFP-M159A

IrisFP

484/513

63,000

0.18

[220]

rsTagRFP

TagRFP

567/585

37,000

0.11

[221]

rsFusionRed1

FusionRed

577/605

82,000

0.10

[222]

PAmCherry1

mCherry

564/595

18,000

0.46

[223]

rsCherry

mCherry

572/610

80,000

0.02

[224]

rsCherryRev

mCherry

572/608

84,000

0.005

[225]

ϕF

Refs

detection is triggered after a series of time delays in order to reconstruct the decay lifetime(s) after analysis of the recorded intensities [164]. In contrast to both former protocols exploiting pulsed excitation, FLIM can also be implemented with a frequencydomain detection: the excitation light is modulated and the fluorescence lifetime is retrieved from analyzing the depth and phase delay of the modulated fluorescence intensity [139, 165]. FLIM has been used to discriminate fluorescent emitters from time-independent ambient light in homodyne detection [26] as

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well as from autofluorescence by exploiting differences of lifetimes. Hence time-gated detection has been used to selectively image GFP and YFP in plant cells by eliminating autofluorescence from the chloroplast [166]. Despite complex multiexponential fluorescence decay [167, 168], the long fluorescence lifetime of QDs has facilitated their straightforward temporal discrimination against cellular autofluorescence and scattered excitation light by time-gated measurements and considerably enhanced the imaging sensitivity [169, 170]. However, autofluorescence has an average lifetime around 4 ns (autofluorescence exhibits lifetimes ranging from ps to several ns [171–174]), which overlaps the one of the most common organic fluorophores. Hence long-lived luminophores (e.g. lanthanide-based luminescent probes, [143]; azadioxatriangulenium (ADOTA) fluorophores, [144, 145]; Ag clusters [146]) have been designed to detect their emission after complete decay of the autofluorescence. FLIM has also been used in multiplexed fluorescence imaging analysis for histopathological identification of different stained tissues [175]. Yet FLIM-based multiplexed imaging has remained limited by the narrow range of lifetimes of the common bright fluorophores currently used in fluorescence imaging (a same few nanoseconds) and it has necessitated deconvolutions [176] or the adoption of subtractive schemes [139]. Discrimination by the Lifetimes of Photoswitched States: Several protocols (e.g. transient state imaging microscopy—TRAST [177– 179], optical lock-in detection—OLID [180], synchronously amplified fluorescence image recovery—SAFIRe [181, 182], and out-of-phase imaging after optical modulation—OPIOM [183– 187]) have exploited the time domain for imaging spectrally similar RSFs, this neither by relying on deconvolution nor on subtraction schemes. Transient state imaging microscopy (TRAST) selectively discriminates fluorophores by monitoring long-lived, photo-induced transient dark states of organic dyes and their dynamics [177– 179]. For imaging, the transient state information was obtained by recording the time-averaged fluorescence response to a timemodulated excitation. This imaging protocol combines the detection sensitivity of the fluorescence signal with the environmental sensitivity of the long-lived transient states. TRAST has been successfully used to achieve multiplexed imaging by exploiting the triplet state, which is sensitive to the presence of molecular dioxygen and heavy ions such as iodide. More specifically, fluorophorefilled liposomes containing or not triplet state quenchers have been distinguished thanks to the different lifetimes of their triplet state [178]. Optical Lock-In Detection (OLID) analyzes the temporal response of the RSF fluorescence to light variations. A periodic light perturbation drives the RSF throughout several cycles of

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photoswitching. The resulting RSF sawtooth-like modulated fluorescence signal is selectively retrieved after cross-correlation with a reference waveform picked from a small region where the RSF only is present. The background (e.g. ambient light, autofluorescence,. . .not supposed to exhibit any photoswitching feature) does not correlate with the reference and thus yields a crosscorrelation close to zero. In contrast, the targeted RSF generating a modulated fluorescence response similar to the reference wave leads to a non-vanishing cross-correlation. First validated with a small RSF (nitrospirobenzopyran) and with a RSFP (Dronpa) [180], OLID was subsequently implemented with a cyanine, which could be photoswitched at high light intensity [188]. In its initial version [180], the OLID observable (the cross-correlation coefficient) was not linearly related to the RSF concentration. In order to further enhance the contrast as well as the signal-to-noise ratio, OLID has subsequently used another observable (“scope”) [189, 190]. OLID has been more recently revisited to make it quantitative [191]. Synchronously amplified fluorescence imaging recovery (SAFIRe) exploits RSFs possessing long-lived dark states, which can be depopulated upon illumination at a second wavelength, which is red-shifted with respect to the one of fluorescence emission [192, 193]. While maintaining the primary excitation light constant, optical modulation of the secondary light dynamically controls the population of the emissive state, which results in modulating fluorescence intensity. Since the collected fluorescence background is free of the crosstalk from the secondary light, it remains unmodulated. The SAFIRe image of the targeted RSF is extracted after time-domain Fourier transformation of the signal from each pixel, which leads to eliminate autofluorescence and the signals from spectrally interfering fluorophores [182]. SAFIRe has been first validated with Ag nanodots [192], and then with organic fluorophores like xanthene [193] and Cy5 [194]. It was later implemented with RSFPs [195–197]. The combination of SAFIRe and FRET has been introduced to overcome the limitation of relying on a red-shifted illumination for dark state depopulation [198]. SAFIRe has been also extended to simple fluorophores by depleting their fluorescent state from stimulating emission induced by a modulated secondary laser at a longer wavelength than the fluorescence emission [199]. Eventually SAFIRe has been successfully used in fluorescence correlation spectroscopy (FCS) [200]. SAFIRe can overcome the limitations of autofluorescence and ambient light in fluorescence bioimaging. In a couple of RSFs, it makes also possible to filter out the signal from the RSF, which respond at the slowest to the optical modulation of the secondary light. In contrast, as a high-pass filter, SAFIRe cannot directly extract the respective contributions in a RSF mixture containing more than two components. Out-of-Phase Imaging after Optical

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Modulation (OPIOM) has added the phase lag of the modulated signal to the frequency-dependent modulation depth exploited by SAFIRe as a further control parameter. The OPIOM exhaustive theoretical frame has led to extract analytic conditions for generating optimal band pass filters, which enable to selectively retrieve the signal from a targeted RSF in a mixture involving more than two components. In the initial version of OPIOM, illumination at one wavelength (intensity I) drives RSF photoswitching between two states 1 and 2 of distinct brightness (see Fig. 1a) [183, 184]. The thermodynamically stable state 1 is photochemically depopulated to the thermodynamically unstable state 2 with a rate constant k12(t) ¼ σ 12I(t), whereas it is populated back either by photochemical induction or thermal recovery at a rate constant k21 ðtÞ ¼ σ 21 I ðtÞ þ kΔ 21 , where σ 12 and σ 21 are the photoswitching action cross-sections of the RSF, and kΔ 21 is the rate constant for thermal recovery of 1 from 2. Since the photoswitching rates are proportional to the light intensity I(t), its sinusoidal modulation at I0 offset intensity and ω angular frequency harmonically modulates the concentrations of the two RSF states at the same ω angular frequency but with a phase delay introduced by the relaxation time of the RSF switch. Interestingly the in-phase and out-of-phase components of the concentrations at the modulation frequency depend on I0 and on ω. In particular, the amplitude of the outof-phase term exhibits a resonant behavior with a single optimum when the parameters fulfill two conditions: I0

¼

kΔ 21 σ 12 þ σ 21

ð1Þ

ω ¼

2kΔ 21

ð2Þ

The fluorescence emission is directly related to the population of the bright state and it exhibits a similar resonant behavior with identical resonance conditions. Hence the out-of-phase component of the modulated fluorescence has been adopted as the OPIOM signal (see Fig. 1c). It can be simply extracted by temporal Fourier transformation and used for quantification of the RSF concentration upon additionally benefiting from lock-in amplification to improve the signal-to-noise ratio. Since the resonance conditions (1,2) exclusively involve the kinetic RSF photoswitching features, OPIOM enables to discriminate a targeted RSF against another spectrally similar fluorophore or RSF endowed with distinct photoswitching kinetics. OPIOM has been experimentally validated by selective imaging of green fluorescent RSFPs in microsystems, mammalian cells, and zebrafish with a wide field epifluorescence microscope or a single plane illumination microscope (vide infra) [183].

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a 2

1

OPIOM

b

1

2 SpeedOPIOM

c

d 0.125

0.25

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

-7

-6

-5

-4

-3

-2

-5

-4

-3

-2

-1

0

1

0

0 -4

0.25

-3

-2

-1

0

1

2 0

1

2

3

4

5

6

0

Fig. 1 Principle of (Speed-) Out-of-phase imaging after optical modulation (OPIOM). (a, c) In OPIOM, a periodically modulated light at angular frequency ω and average intensity I 01 generates modulation of the fluorescence emission from a RSF exchanging between two states (1 and 2), each having a different brightness (a). The OPIOM signal SOPIOM is the amplitude of the out-of-phase component of the fluorescence emission, which exhibits a resonance in the space of the illumination parameters {σ 12/ σ 21,ω=ðσ 12 þ σ 21 ÞI 01 þ k Δ 21} (c). The OPIOM image of a targeted RSF is selectively and quantitatively retrieved at resonance upon matching I 01 and ω to its dynamic parameters σ 12, σ 21, and k Δ 21; (b, d) In Speed-OPIOM, the periodically modulated illumination now involves two light sources modulated in antiphase at angular frequency ω with respective average light intensities I 01 and I 02 (b). The Speed-OPIOM signal SSpeedOPIOM is again the amplitude of the out-of-phase component of the fluorescence emission. It now exhibits a resonance in the space of the illumination parameters {I 02 =I 01,ω=I 01} (d, which is used to record the Speed-OPIOM image of a targeted RSF after matching I 02 =I 01 and ω=I 01 to its dynamic parameters σ 12,1, σ 21,1, σ 12,2, and σ 21,2). The theoretical plots of SOPIOM and SSpeedOPIOM have been computed by using σ 12 ¼ σ 12,1 ¼ 196 m2 mol
1, σ 21,1 ¼
2 0 m2 mol
1, σ 12,2 ¼ 0 m2 mol
1, σ 21 ¼ σ 21,2 ¼ 413 m2 mol
1, k Δ s
1 with I 01 ¼ 21 ¼ 1:5  10 kΔ

21 100 σ 12,1 þσ in (d) 21,1

OPIOM for Multiplexed Fluorescence Imaging

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In its original implementation, OPIOM has suffered from too low values of the rate constant kΔ 21 for thermal recovery. Even with the fastest recovering RSFP (Dronpa-3), OPIOM image acquisition took more than 2 min [183]. Since most photoswitched green fluorescent RSFPs can be photoswitched back to their stable state upon illumination at 405 nm, it has been proposed to overcome the preceding limitation by introducing a secondary light at 405 nm in order to accelerate the recovery process [185, 201]. In the SpeedOPIOM advanced protocol exploiting periodic two-color illumination, the light sources at 480 and 405 nm are modulated in antiphase at angular frequency ω with respective average light intensities I 01 and I 02 (see Fig. 1b) [185]. Following the theoretical analysis, the RSFP fluorescence signal exhibits a tunable out-ofphase response with a single resonance in the space of the illumination parameters {I 02 =I 01,ω=I 01 }. The resonant values are determined by the RSFP photoswitching cross-sections σ 12,i (respectively σ 21,i) associated with photoswitching from 1 to 2 (respectively from 2 to 1) driven at 480 (i ¼ 1) and 405 (i ¼ 2) nm

 ð3Þ σ 12,1 þ σ 21,1 I 01 ¼ σ 12,2 þ σ 21,2 I 02
 0 ð4Þ ω ¼ 2 σ 12,1 þ σ 21,1 I 1 The Speed-OPIOM signal is twice higher than the one obtained in the original one-color OPIOM (see Fig. 1d) [185]. Moreover, the resonance conditions (3,4) show that the light sources can be modulated at much higher angular frequency than in one-color OPIOM. Increasing both light intensities I 01 and I 02 upon keeping constant their ratio allows one to shorten the imaging time down to the millisecond timescale. Experimental validations have evidenced that Speed-OPIOM is a powerful protocol for quantitative imaging of RSFs, which can overcome a background of autofluorescence or ambient light [185]. It is as well efficient for multiplexed fluorescence imaging. Hence by using Speed-OPIOM we could independently image at the Hz frequency of image acquisition three spectrally similar RSFPs having different responses to light modulation [185]. Bleaching-Assisted Multichannel Microscopy (BAMM): Bleaching-Assisted Multichannel Microscopy (BAMM) exploits the specific kinetic signature of photobleaching, which has made possible to discriminate up to three spectrally similar fluorophores with an unmixing algorithm [202]. BAMM was successfully applied to organic dyes, autofluorescent biomolecules and fluorescent proteins [202].

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Materials Practical implementation of the (Speed-)OPIOM protocols requires an optical setup equipped for light modulation. A major advantage of the (Speed-)OPIOM protocols is that they can be easily adapted onto commercially available microscopes. Indeed, they essentially require to replace light sources delivering temporally constant light for light sources, which can be modulated so as to fulfill the (Speed-)OPIOM resonance conditions. Alternatively, specific optical setups can be built to implement OPIOM and Speed-OPIOM.

2.1 Modulatable Light Sources

1. High-power LEDs. Choose colored high-power LEDs, which can deliver 0.1–1 W of light power within a discrete excitation peak of 50 nm width in wavelength. Their emitted light has usually to be filtered before entering the optical path of the imaging instruments. Light intensity delivered by LEDs can be controlled by the amount of current driving the LEDs. Commercially available instruments to drive high-power LEDs enable direct light modulation in the DC—200 kHz frequency range while custom made electronics can provide modulation above the MHz frequency [203, 204]. 2. Laser modules. Lasers can deliver few hundreds of mW of light intensity with low divergence in a much sharper emission peak than LEDs (less than 1 nm of spectral linewidth). Laser modules using laser diodes as light sources provide the fastest modulation rates: analog modulation where the light intensity is varied according to an analog input signal can commonly reach a few MHz modulation frequencies while digital modulation (i.e. on/off) can be commonly made up to 150 MHz with commercially available modules. In contrast, DPSS laser does not allow direct modulation of the emitted light intensity but the light intensity can be controlled using an acousto-optic modulator (AOM) which commonly allows modulations up to a few MHz.

2.2 Home-Built Setup for OPIOM Implementation in Epifluorescence Microscopy

OPIOM has been implemented in epifluorescence microscopy [183]. The original optical setup is described in Note 1.

2.3 Home-Built Setup for OPIOM Implementation in Single Plane Illumination Microscopy (SPIM)

OPIOM has been implemented in single plane illumination microscopy (SPIM) [183]. The original optical setup is described in Note 2.

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2.4 Home-Built Setup for SpeedOPIOM Implementation in Epifluorescence Microscopy

Speed-OPIOM has been implemented in epifluorescence microscopy [185]. The original optical setup is described in Note 3.

2.5 Home-Built Setup for SpeedOPIOM Implementation in Macroscale Fluorescence Imaging

Speed-OPIOM has been implemented in macroscale fluorescence imaging [185, 186]. The original optical setup is described in Note 4.

2.6 Home-Built Setup for SpeedOPIOM Implementation in Fluorescence Endomicroscopy

Speed-OPIOM has been implemented in fluorescence endomicroscopy [187]. The original optical setup is described in Note 5.

3

Methods

3.1 Measurement of Light Intensities

3.2

Video Acquisition

Measure light intensities at the sample in order to fulfill the resonance conditions given in Eqs. 1, 2 and 3, 4 either by using a power-meter (see Note 6) or an actinometry protocol (see Note 7). 1. Record films for m periods of light modulation (m is an integer) upon setting the acquisition frequency of the camera so as to obtain 2N (N is an integer) frames per period of modulation (then the acquisition frequency is fs ¼ 2Nfm, where fm is the modulation frequency of the excitation light).

2. Correct the time lag ϕacq in the recorded fluorescence emission acquired at pixel(x,y) of the kth frame given in Eq. 5    N X πn f s k sin ℑn, ðx, yÞ sin þ ϕ I F ðx, y, kÞ ¼ T s ℑ0F ðx, yÞ þ acq F fs N n¼1    πn f s k cos þℑn, ðx, yÞ cos þ ϕ acq F fs N

ð

f

gÞ

ð5Þ by recording the fluorescence emission from a sample containing an instantaneously responding fluorophore (such as EGFP or Fluorescein). In Eq. 5, T s ¼ 1f refers to the exposure time scos sin ðx, yÞ and ℑn, ðx, yÞ are respectively the of one frame, ℑn, F F sines and cosines components of the fluorescence signal at harmonic n around the average value ℑ0F ðx, yÞ . ϕacq may

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originate from distinct starting times for the light modulation and the acquisition of the camera. 3. By assuming the photobleaching to exhibit a linear decay, compensate for possible photobleaching of the fluorophores by evaluating the compensation factor K(x, y) from the average of two successive periods: K ðx, yÞ ¼


1 2N
1 hI F ðx, y, kÞi4N k¼2N
hI F ðx, y, kÞik¼0 2N

ð6Þ

4. Correct all the frames over the whole video for photobleaching by applying the same algorithm for all the successive pairs of two periods as in Eq. 7 I corr F ðx, y, kÞ ¼ I F ðx, y, kÞ
K ðx, yÞ  k

ð7Þ

ℑ0F ðx, yÞ

by averaging the 5. Calculate the Pre-OPIOM image frames over the whole film and normalizing to unit time X f s 2mN
1 I corr ð8Þ F ðx, y, kÞ 2mN k¼0 h  i πn f s k 6. Multiply the kth frame I corr þ ϕ acq F ðx, y, kÞ with cos fs N and average over the whole film to get the first order cosines component, namely the Speed-OPIOM image:     X

f s 2mN
1 πn f s k cos corr ℑ1, ¼ I ðx, y, kÞ cos þ ϕ acq F F mN N fs ℑ0F ðx, yÞ ¼

k¼0

ð9Þ 3.3 Extraction of the (Speed-)OPIOM Images

1. Use the following Matlab code to compute the (Speed-) OPIOM image. function [IF0,IF1out,IF1outmed,Phase]=opiom_(filename,period, phi_acq,n_per,skip_per) % Computes pre-OPIOM (IF0), OPIOM (IF1out), OPIOM % median-filtered (IF1outmed) and phase (Phase) images % We assume movie acquired is already loaded in Matlab % (as camera manufacturer usually provide code to import files) % ’filename’ is the matrix to be treated % ’period’ is the period of the light excitation defined as frame number % ’phi_acq’ is the phase delay between the dates of camera recording % and light excitation previously calibrated with fluorescein or EGFP % ’n_per’ is the total number of periods used for calculation

OPIOM for Multiplexed Fluorescence Imaging

209

% ’skip_per’ is the number of periods to skip before calculation % npts = period∗n_per; first = skip_per∗period; [X,Y,Z]=size(filename); Holder = zeros(X,Y,npts); IF1out = zeros(X,Y); IF1in

= zeros(X,Y);

for i=1:npts Holder(:,:,i)=filename(:,:,i+first); end %%%%%% Photobleaching correction assuming a linear decay%%%%%% % Calculate the average signal over the first half of periods linePoint1=mean(Holder(:,:,1:((npts)/2)),3); %Calculate the average signal over the last half of periods linePoint2=mean(Holder(:,:,((npts)/2+1):end),3); % Calculate the slope B(x,y) (See Eq. XXX) slope=(linePoint2-linePoint1)/(npts/2); %Correction for photobleaching for i=1:npts Holder(:,:,i)=(Holder(:,:,i)-slope.∗i); end %%%%%% Data processing %%%%%% % Compute pre-OPIOM image IF0 = mean(Holder,3); % Compute OPIOM image for i=1:npts IF1out(:,:) = (IF1out(:,:) + (Holder(:,:,i)).∗cos(pi∗(2∗i1)/period + phi_acq)); IF1in (:,:) = (IF1in(:,:) + (Holder(:,:,i)).∗sin(pi∗(2∗i1)/period + phi_acq)); end IF1out = 2.∗IF1out ./ npts; IF1in = 2.∗IF1in ./ npts; IF1outmed=medfilt2(IF1out,[3,3]); Phase = atan(IF1out./IF1in); \frac{o}{o} %%% Display Pre-Opiom image figure;imagesc(IF0);colormap(gray(4096));colorbar;

210

Raja Chouket et al. %%% Display Opiom image figure;imagesc(IF1outmed); colormap(gray(4096)); colorbar;

3.4

1. Fluorescence imaging has recently benefited from significant advances for biological applications. To overcome the diffraction limit with super-resolution microscopies has been a first illustration of this progress.

Conclusion

2. More recently, imaging protocols exploiting dynamic contrast (e.g. TRAST, OLID, SAFIRe, OPIOM) have enabled to address further challenges, which had been considered limiting for fluorescence imaging. Hence their achievements allow to anticipate that conjugated efforts aiming at the development of tailored RSFs and optimized protocols will soon permit to selectively observe many more fluorescent labels than the few, which are presently accessible. 3. Similarly, as illustrated by OPIOM, it has become simple and cheap to overcome the background of ambient light and autofluorescence, so as to provide new fields of applications for fluorescence imaging.

4

Notes

4.1 OPIOM Implementation in Epifluorescence Microscopy

Follow the scheme displayed in Fig. 2a to build an epifluorescence microscopy setup appropriate for OPIOM implementation: l

l

Use a LED (LXML-PB01) as light source filtered at 480  20 nm (F480-40; Semrock, Rochester, NY) supplied by a LED driver (LEDD1B, Thorlabs Inc, Newton, NJ) and modulated by a waveform generator (33220A, Agilent Technologies); Place a first lens (FCN10804-LR1-RS; f ¼ 3 mm, Ledil, Salo, Finland) just after the LED and a second one (AC254-050-A; f ¼ 50 mm, Thorlabs Inc, Newton, NJ) to focus the light in the back focal plane of the objective after going through the dichroic filter;

l

Mount a 10 fluar (NA 0.5, Carl Zeiss AG, Feldbach, Switzerland), a 20 Plan Apo (NA 0.75, Nikon Instruments, Amsterdam, Netherlands), or a 63 PL fluotar L (NA 0.7, Leica Microsystems, Germany) objective on the microscope equipped with a Luca-R CCD camera (Andor Technology, Belfast, UK) to acquire fluorescence images at 525  15 nm (F525-30; Semrock);

l

Place the bottom glass surface of the imaged sample on a 0.4 mm thick copper disk in which a hole of 8 mm in diameter had been opened for observation with the objective. Mount this metal holder itself on an aluminum block thermostated at

OPIOM for Multiplexed Fluorescence Imaging

211

37  0.2∘C with two thermoelectric Peltier devices (CP 1.0-6305 L-RTV; Melcor, Trenton, NJ), monitor the stage temperature with a TCS610 thermistor (Wavelength Electronics, Bozeman, MT) and drive the feedback loop by a MPT10000 temperature controller (Wavelength Electronics);

4.2 OPIOM Implementation in Single Plane Illumination Microscopy (SPIM)

l

Synchronize triggering of the camera acquisition with the onset of the periodic excitation light (e.g. using the option “External start” in the Solis software, Andor Technology);

l

Start acquisition with modulated illumination (e.g. under illumination conditions matching the resonance conditions of Dronpa-2 (resp. Dronpa-3), 512 images were recorded at 0.32 Hz (resp. 3.2 Hz) with 3 s exposure time (resp. 0.3116 s) corresponding to 8 periods of 200 s (resp. 20 s) (64 images per period in both cases)).

Follow the scheme displayed in Fig. 2b to convert an upright Olympus microscope (BX51WI) to enable SPIM experiments with OPIOM: l

Create the light sheet by a cylindrical lens (LJ1558RM-A, f ¼ 300 mm, Thorlabs) shaping the illumination laser beam from a PhoxX 488–100 laser diode (Omicron-Laserage Laserprodukte, Rodgau-Dudenhofen, Germany) into a rectangle. Pass this rectangle through a 20 objective (Olympus LMPLANFL, NA 0.40) which focuses it only in one plane generating a thin light sheet in the focal plane of the detection objective (the geometry of the illuminating beam can be approximated by Gaussian beam optics, with a central thickness of 3.4 μm of full width at half maximum);

b

a

Camera

Sample

Camera

LED 480nm

LASER 488nm

Fig. 2 Optical setups for OPIOM implementation. (a) Epifluorescence setup; (b) Single plane illumination microscopy (SPIM) setup

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To match the OPIOM resonance conditions, vary the laser power between 0.1 mW to 5 mW using analog modulation input of the laser head and a square waveform generator (33220A; Agilent Technologies). Attenuate the laser beam by a NE10A neutral density filter (Thorlabs) to deliver a light sheet intensity of 1.5 μW and 80 μW, respectively;

l

Collect fluorescence light with a 40 water dipping objective (Olympus LUMPLFLN, NA 0.80), and filter it with a beamsplitter (Di01-R405/488/532/635 BrightLine quad-edge laser-flat dichroic beam-splitter, Semrock) and an emission filter (BA510-550, Olympus);

l

Synchronize triggering of the camera acquisition as explained above with the onset of the square wave excitation light;

l

Start acquisition with modulated illumination (e.g. under illumination conditions matching the resonance conditions of Dronpa-3 at 20∘C, 512 images were recorded at 0.46 Hz with 2.187 s exposure time corresponding to 8 periods of 140 s).

Follow the scheme displayed in Fig. 3a to build an epifluorescence microscopy setup for Speed-OPIOM:

4.3 Speed-OPIOM Implementation in Epifluorescence Microscopy

a

b Camera

Sample

arduino

LED 405nm LED 550nm

Camera

LED 480nm LED 405nm

LED 480nm

c

LED 405nm

LED 480nm

Camera

Fiber bundle

GRIN lens

Sample

Sample

Fig. 3 Optical setups for Speed-OPIOM implementation. (a) Epifluorescence setup; (b) Macroscale fluorescence imaging; (c) Fluorescence endomicroscopy

OPIOM for Multiplexed Fluorescence Imaging l

Illuminate the sample using a LXZ1-PB01 LED (Philips Lumileds) filtered at 480  20 nm (F480-40; Semrock, Rochester, NY) and a LHUV-0405 (Philips Lumileds) LED filtered at 405  20 nm (F405-40; Semrock, Rochester, NY);

l

Supply each LED with a LED driver (LEDD1B, Thorlabs Inc, Newton, NJ) and modulate both LEDs synchronously to each other by a waveform generator (33612A, Keysight Technologies);

l

4.4 Speed-OPIOM Implementation in Macroscale Fluorescence Imaging

213

Place a lens (ACL2520U; Thorlabs, f ¼ 20 mm) just after each diode to collimate the light sources. Next combine the two light beams thanks to a dichroic mirror (T425LPXR, Chroma, Bellows Falls, VT) and a second pair of lenses to focus the light at the back focal plane of the objective after being reflected by the dichroic filter (Di-FF506, Semrock, Rochester, NY);

l

Mount a 10 fluar (NA 0.5, Carl Zeiss AG, Feldbach, Switzerland; for microdevice imaging) and a 60 UPlanApo (NA 1.2, Olympus Corporation, Tokyo, Japan; for cell imaging) objective on a home-built microscope equipped with a Luca-R CCD camera (Andor Technology, Belfast, UK) and acquire the fluorescence images at 525  15 nm (F525-30; Semrock);

l

Place the bottom surface of the imaged sample on a 0.4 mm thick copper disk in which a hole of 8 mm in diameter, which has been opened for observation with the objective. Mount this metal holder itself on an aluminum block thermostated at 37  0.2∘C with two thermoelectric Peltier devices (CP 1.063-05 L-RTV; Melcor, Trenton, NJ) and monitor the stage temperature with a TCS610 thermistor (Wavelength Electronics, Bozeman, MT), the feedback loop of which is driven by a MPT10000 temperature controller (Wavelength Electronics);

l

Synchronize triggering of the camera acquisition with the onset of the periodic excitation light (e.g. using the option “External start” in the Solis software, Andor Technology). The fixed phase delay ϕacq between the dates of camera recording and light excitation (due to integration of the signal over the exposure time and the triggering of the camera) can be calibrated as described in Subheading 3.2;

l

Start acquisition with modulated illumination.

Follow the scheme displayed in Fig. 3b to implement (Speed)OPIOM in macroscale fluorescence imaging: l

Use three high-power color LED chips (LXZ1-PB01, LHUV0400, LXZ1-PX01; Lumileds, NL) as excitation lights for the green (such as the RSFPs used in this work) and red (such as DsRed) fluorescent emitters;

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4.5 Speed-OPIOM Implementation in Fluorescence Endomicroscopy

l

Collimate the light sources by high NA condensers (ACL25416U-A, f ¼ 16 mm, Thorlabs, NJ, US) and filter them by band pass filters (ET470/40X, ET402/15X, ET550/ 15x; Chroma, VT, US) to avoid spectral overlaps;

l

Combine the three quasi-parallel beams by three dichroic mirrors (T425LPXR, T505LPXR, 59004bs; Chroma, VT, US);

l

Use an optimized beam expander system integrating one divergent lens (ACN254-040-A, f ¼
40 mm, Thorlabs, NJ, US) and two convergent lenses (AC508-100-A, f ¼ 100 mm, Thorlabs, NJ, US) to clearly refocus the lights at a distance of 120 mm away onto the sample;

l

To obtain the fluorescence image of the illuminated area, collect the fluorescent light emitted from the sample by the expander system and imaged to infinity. Use band pass filter (ET525/36m or ET585/20m, Chroma, VT, US) for green emission or red emission;

l

Focus light onto the camera (iDS 3060cp) to get the image of the fluorescent sample;

l

Combine three spherical singlet lenses (LC2679-A, LB1757-A, LA1422-A, Thorlabs, NJ, US) and optimize the air spaces to get an achromatic system with an effective focal length of f ¼ 30 mm, enabling for macroimaging of small size objects;

l

Start acquisition with modulated illumination.

Follow the scheme displayed in Fig. 3c to implement (Speed-) OPIOM in fluorescence endomicroscopy: l

Use two high-power color LED chips (LXZ1-PB01, LHUV0400, LXZ1-PX01; Lumileds, Amsterdam, Netherlands) as excitation lights;

l

Collimate their lights by optical condensers L1 (ACL2520U-A, f ¼ 20 mm, Thorlabs, Newton, NJ) and filter them by band pass filters (ET470/40x, ET405/20x; Chroma Technology, Bellows Falls, VT);

l

Combine the UV and blue beams by a dichroic mirror (T425LPXR, Chroma Technology) and send them to an afocal system, which consists of two convergent lenses (L2: AC254100-A, f ¼ 100 mm, L3: AC254-040-A, f ¼ 40 mm; Thorlabs);

l

Position an iris (SM1D12C, Φmax ¼ 12 mm, Thorlabs) at the exit pupil of the afocal system through which lights with different field angles pass. The iris is also at the focal plan of the lens L4 (AC254-100-A, f ¼ 100 mm, Thorlabs), which is used to conjugate the iris stop to the focal plan of the objective (10, NA ¼ 0.5, Zeiss, Jena, Germany). The effective NA of the

OPIOM for Multiplexed Fluorescence Imaging

215

objective matches the NA of the fiber to secure a maximal light delivery;

4.6 Absolute Measurement of Light Intensities

l

Cement a GRIN lens (NEM-100-06-08-520-S, GrinTech, Jena, Germany) with a magnification of 1:1 onto the distal tip of an imaging fiber (FIGH-30-850N, Fujikura, Tokyo, Japan) as a fused silica-based coherent fiber bundle of 30 cm long, which consists of 30000 cores of about 5 μm in diameter with a core to core distance of around 4.3 μm;

l

Embed the distal tip of the fiber and the GRIN lens in a 2.5 cm long tube of stainless steel and glue them together to ensure mechanical stability of the ensemble during experiments;

l

Filter the fluorescence image transferred from the fiber and magnified by the objective by an emission filter (ET525/36m, Chroma Tech.) and focus it onto the camera (3080cp, iDS, Obersulm, Germany) by a tube lens of f ¼ 50 mm (AC254050-A, Thorlabs). The effective magnification goes to 3X.

l

Start acquisition with modulated illumination.

Measure light intensity by locating the sensitive element of the power-meter (e.g. Nova II power-meter from Laser Measurement Instruments) at the position of the biological sample (e.g. at the front of a microscope objective). With one-photon excitation, knowing the frequency ν (expressed in Hz) of the light source (or its wavelength: λ ¼ hc/ν) and the surface S (expressed in m2) of the illuminated zone, transform the power P (in Watt or Joule. s
1) provided by the power-meter into a photon flux I0 (in mol. m
2.s
1) with Eq. 10 I0 ¼

P N A hνS

ð10Þ

where the denominator is the energy of one mole of photon which involves the Avogadro number NA ¼ 6  1023 photons/mole), the Planck constant h, and the light frequency ν. 4.7 Calibration of Light Intensities by Actinometry

l

l

l

l

Use fixed cells expressing Dronpa-2 in the nucleus as a calibrating sample, which is photochemically reliable beyond the month timescale; Apply a light jump at 480 nm with constant light intensity I 01 and record the image as a function of time; Plot the fluorescence signal (usually obtained by averaging over the Dronpa-2 containing zone of the collected image) as a function of time (Fig. 4a); Extract the relaxation time τλ1 associated with the conversion between the on and off Dronpa-2 states (respectively denoted 1 and 2 below) by fitting with Eq. 11

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a

b Fluorescence intensity (a.u.)

Fluorescence intensity (a.u.)

75 80

60

40

20 0.0

70 65 60 55 50 45

0.1

0.2

0.3 t (s)

0.4

0.5

0.6

0.0

0.1

0.2

0.3 t (s)

0.4

0.5

0.6

Fig. 4 Calibration of light intensities by analyzing the photoisomerization kinetics of Dronpa-2. (a) Evolution of the fluorescence emission of Dronpa-2 upon illumination at 480 nm (I 01 ¼ 4:0  10
2 Ein.m
2s
1); (b) Evolution of the fluorescence emission of Dronpa-2 upon illumination at both 480 and 405 nm (I 01 ¼ 4:0  10
2 Ein.m
2s
1 and I 02 ¼ 1:9  10
2 Ein.m
2s
1). Markers: Experimental points; line: Fit with Eqs. 11 (in a) and (13) (in b). τλ1 and τλ1 λ2 were respectively equal to 0.127 and 0.066 s    t ð11Þ I F ðtÞ ¼ I F ð0, λ1 Þ þ A λ1 1
exp
τλ1

l

where A λ1 is a pre-exponential term, which accounts for the molecular brightnesses of the ON and OFF states as well as their relative proportions (see [185]); Extract I 01 from Eq. 12: 1
kΔ 21 τ λ 1 I 01 ¼ σ 12,1 þ σ 21,1

l

l

ð12Þ


1 and σ 12,1 + σ 21,1 ¼ 196 m2mol
1 (σ 12,1 where kΔ 21 ¼ 0:014 s and σ 21,1 are respectively the molecular action cross-sections for photoisomerization at wavelength λ1, which converts 1 to 2 and 2 to 1; see [185]); While maintaining I 01 at its original value, apply a light jump at 405 nm at constant light intensity I 02 and record the temporal evolution of the fluorescence emission (see Fig. 4b);

Extract the relaxation time τλ1 λ2 from fitting the temporal evolution of the fluorescence emission with Eq. 13:    t ð13Þ I F ðtÞ ¼ I F ð0, λ1 , λ2 Þ þ A λ1 λ2 1
exp
τλ1 λ2

OPIOM for Multiplexed Fluorescence Imaging

l

217

where A λ1 λ2 is a pre-exponential term, which accounts for the molecular brightnesses of the ON and OFF states as well as their relative proportions (see [185]); Retrieve I 02 from using Eq. 14 I 02

1 0
kΔ 21
ðσ 12,1 þ σ 21,1 ÞI 1 τ λ1 λ2 ¼ σ 12,2 þ σ 21,2

ð14Þ

s
1, σ 12,1 + σ 21,1 ¼ 196 m2mol
1, and where kΔ 21 ¼ 0:014 2 σ 12,2 + σ 21,2 ¼ 413 m mol
1 (σ 12,2 and σ 21,2 are respectively the molecular action cross-sections for photoisomerization at wavelength λ2, which converts 1 to 2 and 2 to 1; see [185]).

Acknowledgements This work was supported by the ANR (France BioImaging - ANR10-INBS-04, Morphoscope2 - ANR-11-EQPX-0029, IPGG ANR-10-IDEX-0001-02 PSL, ANR-10-LABX-31, ANR-19CE11-0005, and ANR-19-CE29-0003-01), the SATT Lutech (OPIOM), the Fondation de la Recherche Me´dicale (FRM DEI201512440), the Mission Interdisciplinarite´ du CNRS, and the Domaine d’Inte´reˆt Majeur Analytics de la Re´gion Ile de France (DREAM). References 1. Giepmans BNG, Adams SR, Ellisman MH, Tsien RY (2006) The fluorescent toolbox for assessing protein location and function. Science 312:217–224 2. Waters JC (2009) Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185:1135–48 3. Swedlow JR, Hu K, Andrews PD, Roos DS, Murray JM (2002) Measuring tubulin content in toxoplasma gondii: a comparison of laser-scanning confocal and wide-field fluorescence microscopy. Proc Nat Acad Sci 99:2014–2019 4. Murray JM, Appleton PL, Swedlow JR, Waters JC (2007) Evaluating performance in three-dimensional fluorescence microscopy. J Microsc 228:390–405 5. Manley S, Gillette J, H Patterson G, Shroff H, F Hess H, Betzig E, Lippincott-Schwartz J (2008) High-density mapping of singlemolecule trajectories with photoactivated localization microscopy. Nat Methods 5:155–157 6. George T, Jung Sun Y, Kwang-Sup S, Ralf S, Vasilis N (2009) Real-time intraoperative fluorescence imaging system using light-

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Chapter 14 Multicolor Bioluminescence Imaging of Subcellular Structures and Multicolor Calcium Imaging in Single Living Cells Kazushi Suzuki, Md Nadim Hossain, Tomoki Matsuda, and Takeharu Nagai Abstract The recent development of the bright luciferase NanoLuc (Nluc) has greatly improved the sensitivity of bioluminescence imaging, enabling real-time cellular imaging with high spatial resolution. However, the limited color variants of Nluc have restricted its wider application to multicolor imaging of biological phenomena. To address this issue, we developed five new spectral variants of the bright bioluminescent protein with emissions across the visible spectrum. In this chapter, we describe the following two protocols for single-cell bioluminescence imaging: (a) multicolor bioluminescence imaging of subcellular structures and (b) multicolor calcium imaging in single living cells. Key words Bioluminescence, Multicolor, NanoLuc, Calcium dynamics, Bioluminescence resonance energy transfer (BRET)

1

Introduction To investigate biocomplexity at a cellular level, it is imperative to observe multiple biomolecular dynamics and activities at high spatial and temporal resolution. An ever-increasing color palette of fluorescent proteins (FPs) has met this demand by acting as genetically encoded fluorescent tags as well as functional indicators. However, the excitation light required for detection of fluorescence can cause serious problems such as phototoxicity, perturbation of photo-dependent biological phenomena, and autofluorescence from the specimen. In contrast, bioluminescence imaging is totally independent of external light sources and has the potential to circumvent the problems that fluorescence imaging presents. However, the number of photons generated by bioluminescent proteins (BPs) is over three orders of magnitude less than those generated

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by FPs [1]. Consequently, detecting a sufficient signal from BPs typically requires long exposure times or massive numbers of cells. A luciferase called NanoLuc (Nluc), developed through the genetic engineering of Oplophorus luciferase (Oluc) along with its optimal substrate, furimazine, solved this problem [2]. Three-color variants of bright bioluminescent proteins—yellow, cyan, and orange Nano-lantern [1, 3]—were developed through the fusion of an enhanced version of Renilla luciferase (Rluc) and various FPs. In the resulting proteins, efficient FRET from the Rluc donor to various FP acceptors contributes to an increase in both the brightness and color hue of Rluc, shifting it toward yellow and orange. This strategy was applied to expand Nluc color hues, and subsequently five spectral variants called enhanced Nano-lanterns (eNLs) were developed [4]. Owing to their brightness and emission profiles, which are distinct from Nluc, eNLs have enabled fivecolor live-cell imaging, as well as the detection of single-protein complexes and even single molecules [4]. Multicolor eNL-based Ca2+ indicators with various Ca2+ affinities were also developed [5], enabling observation of the Ca2+ dynamics in three distinct organelles (nuclei, mitochondria, and ER) simultaneously. In the present chapter, we describe the protocol for (a) multicolor bioluminescence imaging of subcellular structures using Nluc and eNLs and (b) multicolor calcium imaging in different cellular compartments using CeNL(Ca2+)_110μ, GeNL(Ca2+) _520, and OeNL(Ca2+)_18μ. In addition to detailed protocols, we try to explain the principles underlying their operation in the hope that users will gain a mechanistic understanding of multicolor bioluminescence imaging that will be useful for interpreting the literature and designing new experiments.

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Materials

2.1 Imaging of Subcellular Structures

1. Culture medium for HeLa cells: Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum (FBS). 2. Observation medium: DMEM/F12 (1:1) (1). 3. Furimazine (Promega, Nano-Glo® Luciferase Assay System). 4. HeLa cells expressing mito-Nluc (mitochondria), CeNL-ER (endoplasmic reticulum), GeNL-fibrillarin (nucleoli), Lyn-OeNL (plasma membrane), and ReNL-H2B (nucleus) attached on a 35 mm glass bottom dish. 5. HeLa cells expressing only mito-Nluc (mitochondria), CeNLER (Endoplasmic Reticulum), GeNL-fibrillarin (nucleoli), Lyn-OeNL (plasma membrane), or ReNL-H2B (nucleus), attached on a 35 mm glass bottom dish (reference samples for linear unmixing).

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1. Histamine dihydrochloride. 2. HeLa cells expressing CeNL(Ca2+)_110μ-ER (in the ER), GeNL(Ca2+)_520-H2B (in the nucleus), and mito-OeNL (Ca2+)_18μ (in the mitochondria) attached on a 35 mm glass bottom dish. 3. HeLa cells expressing only CeNL(Ca2+)_110μ-ER (in the ER), GeNL(Ca2+)_520-H2B (in the nucleus), or mito-OeNL(Ca2+) _18μ (in the mitochondria) attached on a 35 mm glass bottom dish (reference sample for linear unmixing).

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Equipment

1. Micropipettes (2, 20, 200, and 1000 μL). 2. Pipette tips. 3. Microcentrifuge tubes (1.5 mL). 4. Inverted fluorescence microscope. 5. 100 objective lens with high numerical aperture. 6. 0.5 relay lens. 7. Emission filter set for observing the bioluminescence of Nluc and eNLs (a) Semrock FF01-447/60 filter for Nluc. (b) Olympus BA460-510CFP filter for CeNL. (c) Semrock FF01-525/35 filter for GeNL. (d) Semrock FF01-562/40 filter for OeNL. (e) Semrock FF01-593/40 filter for ReNL. 8. Imaging software. (a) Personal computer installed with MetaMorph software (Molecular Devices). (b) Personal computer installed with PrizMage software (Molecular Devices).

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3.1 Multicolor Bioluminescence Imaging of Subcellular Structures in Single Living Cells

Since detection of bioluminescence does not require illumination light, bioluminescent signals from multiple color probes should be separated based only on differences in bioluminescence spectra using emission filters. Although eNLs and Nluc have distinct bioluminescence spectra (Fig. 1a), their emission profiles are highly overlapping, and signals from each probe considerably bleed through individual imaging channels. To solve this issue, a linear unmixing technique can be applied to separate the contributions of individual probes in the image [6]. Figure 1b illustrates how to determine the cross talk in a two-color experiment. It is crucial to take reference images from a sample containing only a single probe to determine the relative

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Fig. 1 (a) Emission spectra of multicolor eNLs. (b) Conceptual diagram of multichannel imaging with reference samples. The yellow bioluminescent probe contributes more to channel 1 than to channel 2, whereas the red bioluminescent probe contributes more to channel 2 than to channel 1

contributions of the signal to the individual channels of the image. Next, a sample expressing all probes is imaged with the same settings. Subsequently, the bioluminescent image of the sample is digitally processed using the linear unmixing algorithm. 3.1.1 Microscope Setup

Most wide-field fluorescence microscopes equipped with a highsensitivity camera are amenable for bioluminescence imaging. To minimize the contamination of external light during measurement, the imaging equipment should be placed in a dark box, and all the light from the equipment within the dark box should be turned off or masked. Furthermore inserting an IR-cut filter just before camera might reduce the background from stray light inside the microscope body, although it depends on microscopes.

3.1.2 Selection of Objective Lens

Generally, the degree of brightness of an image is directly proportional to the square of the numerical aperture (NA) of the objective lens and inversely proportional to the square of the magnification of the image [7, 8]. Thus, the aperture and magnification are the most important parameters to consider when selecting an objective lens. On this basis, we often combine a high-NA objective lens with 0.5 relay lens, increasing the brightness of an image. To obtain images of subcellular structures in single cells, at least 50 magnification of the image (100 objective lens) is required.

3.1.3 Selection of Emission Filters

For linear unmixing, the number of emission filters has to equal the number of probes used in the experimental setup. A sufficient number of filters must be installed in a filter wheel, which allows for rapid filter-changing. Filters suitable for efficient linear unmixing vary depending on the bioluminescent probe used. We typically use emission filters with a wide bandwidth that includes the maximum peak wavelength of the bioluminescent probe.

3.1.4 Checking the Expression Levels of eNLs in Cells

1. Exchange culture medium for observation medium in the glass bottom dish (see Note 1). We usually use 2 mL of observation medium in one dish.

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2. Place the glass bottom dish on the microscope and focus the objective lens. 3. Find cells exhibiting similar expression levels of CeNL-ER, GeNL-fibrillarin, Lyn-OeNL, and ReNL-H2B by checking the fluorescence of the corresponding fluorescent protein moieties. 3.1.5 Taking Bioluminescence Images of eNLs and Nluc

1. Add 4 μL of furimazine microcentrifuge tube.

solution

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a

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2. Take 500 μL of the observation medium from the glass bottom dish, and mix it with the furimazine solution. 3. Add 500 μL of the resulting solution from step 2 to the glass bottom dish, and mix well by pipetting. 4. Focus the objective lens again by checking fluorescence. 5. Adjust the exposure time of the EM-CCD camera without applying the EM gain (see Note 2). 6. Acquire a series of bioluminescence images using the five different emission filters. 7. Repeat steps 1–6 for HeLa cells expressing either Nluc or eNLs under identical experimental conditions. As shown in Fig. 2, 5  5 bioluminescent images of five probes in five different channels can be obtained. 8. The signals from Nluc and eNLs are separated by linear unmixing (Fig. 3).

3.2 Multicolor Calcium Imaging in Single Living Cells 3.2.1 Checking Expression Levels of eNL(Ca2+)S in Cells

3.2.2 Taking Bioluminescence Images of eNL(Ca2+)S

1. Exchange culture medium with observation medium in the glass bottom dish. We usually use 2 mL of observation medium in one dish. 2. Place the glass bottom dish on the microscope and focus the objective lens. 3. Find cells exhibiting similar expression levels of CeNL(Ca2+) _110μ-ER, GeNL(Ca2+)_520-H2B, and mito-OeNL(Ca2+) _18μ by checking the fluorescence of the corresponding fluorescent protein moieties. 1. Add 4 μL of furimazine microcentrifuge tube.

solution

into

a

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2. Take 500 μL of the observation medium from the glass bottom dish, and mix it with the furimazine solution. 3. Add 500 μL of the resulting solution from step 2 to the glass bottom dish, and mix well by pipetting. 4. Focus the objective lens again by checking fluorescence. 5. Adjust the exposure time of the EM-CCD camera without applying the EM gain.

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Fig. 2 Reference images showing linear unmixing. HeLa cells expressing mito-Nluc, CeNL-ER, GeNL-fibrillarin, Lyn-OeNL, or ReNL-H2B were imaged using five filters. The images were obtained with Olympus LV200. (This figure is adapted with permission from Ref. [4], CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/))

6. Acquire a series of images continuously. 7. After a few minutes, add histamine to the dish (final concentration of histamine, 10 μM), and continue acquiring images. 8. Use linear unmixing to separate the signals from CeNL(Ca2+) _110μ-ER, GeNL(Ca2+)_520-H2B, and mito-OeNL(Ca2+) _18μ (Fig. 4).

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Fig. 3 Multicolor bioluminescent images of subcellular structures. Bioluminescent images of each emission channel for mito-Nluc, CeNL-ER, GeNL-fibrillarin, Lyn-OeNL, and ReNL-H2B in HeLa cells, before and after linear unmixing. Scale bar, 10 μm. The images were obtained with Olympus LV200. (This figure is adapted with permission from Ref. [9])

4

Notes 1. Bioluminescence intensity decays rapidly upon adding fetal bovine serum (FBS). Therefore, we used the observation medium without FBS. With serum-free medium, the bioluminescence is easily detectable 1 h after furimazine addition. 2. Image acquisition using EM gain results in a considerable noise level, making the fine subcellular structures blurred. To mitigate this negative effect, we do not apply the EM-gain feature of the EM-CCD camera and instead use it as a cooled CCD camera. Under these conditions, the Nluc and eNLs are bright enough to obtain high-quality bioluminescence images using several seconds of exposure time.

Acknowledgments This work was supported by a Grant-in-Aid for Scientific Research (A) of MEXT (No. 26251018), the JST-SENTAN program, The Uehara Memorial Foundation, and the Naito Foundation to T.N. K.S. was supported by Grant-in-Aid 18J01772 for the JSPS Research Fellow.

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Fig. 4 Multicolor calcium images of different subcellular compartments. (a) Bioluminescent images of HeLa cells expressing ER_CeNL(Ca2+)_110μ, H2B_GeNL(Ca2+)_520, and CoxVIIIx2_OeNL(Ca2+)_18μ, before and after unmixing. (b) Merged image (ER, cyan; nucleus, magenta; mitochondria, yellow) before and after histamine stimulation. Scale bar, 10 μm. The images were obtained with Olympus LV200

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References 1. Saito K, Chang YF, Horikawa K et al (2012) Luminescent proteins for high-speed single-cell and whole-body imaging. Nat Commun 3:1262 2. Hall MP, Unch J, Binkowski BF et al (2012) Engineered luciferase reporter from a deep sea shrimp utilizing a novel imidazopyrazinone substrate. ACS Chem Biol 7(11):1848–1857 3. Takai A, Nakano M, Saito K et al (2015) Expanded palette of Nano-lanterns for realtime multicolor luminescence imaging. Proc Natl Acad Sci U S A 112(14):4352–4356 4. Suzuki K, Kimura T, Shinoda H et al (2016) Five colour variants of bright luminescent protein for real-time multicolour bioimaging. Nat Commun 7:13718 5. Hossain MN, Suzuki K, Iwano M et al (2018) Bioluminescent low-affinity Ca. ACS Chem Biol 13(7):1862–1871

6. Zimmermann T, Rietdorf J, Girod A, Georget V, Pepperkok R (2002) Spectral imaging and linear un-mixing enables improved FRET efficiency with a novel GFP2–YFP FRET pair. FEBS Lett 531(2):245–249 7. Murphy DB, Davidson MW (2013) Fundamentals of light microscopy and electronic imaging, 2nd edn. Wiley-Blackwell, Hoboken, NJ, p xiii, 538 p 8. Ogoh K, Akiyoshi R, May-Maw-Thet et al (2014) Bioluminescence microscopy using a short focal-length imaging lens. J Microsc 253 (3):191–197 9. Suzuki K, Nagai T (2017) Super-duper chemiluminescent proteins applicable to wide range of bioimaging. In: Optogenetics and optical manipulation. pp 1005202

Chapter 15 Nanoparticles for In Vivo Lifetime Multiplexed Imaging Erving Ximendes, Emma Martı´n Rodrı´guez, Dirk H. Ortgies, Meiling Tan, Guanying Chen, and Blanca del Rosal Abstract Lifetime multiplexed imaging refers to the simultaneous labeling of different structures with fluorescent probes that present identical photoluminescence spectra and distinct fluorescence lifetimes. This technique allows extracting quantitative information from multichannel in vivo fluorescence imaging. In vivo lifetime multiplexed imaging requires fluorophores with excitation and emission bands in the near-infrared (NIR) and tunable fluorescence lifetimes, plus an imaging system capable of time-resolved image acquisition and analysis. Key words Bioimaging, Near-infrared imaging, Rare-earth-doped nanoparticles

1

Introduction Multiplexed imaging allows labeling several structures simultaneously by means of different fluorescent markers [1, 2]. This technique has widespread application in cell imaging, with fluorescent dyes showing different excitation/emission profiles used to label multiple cellular structures [3, 4]. Direct application of this technique (spectral multiplexing) in vivo is not straightforward due to the complexity of biological tissues and their interaction with light. First, there is a very limited library of fluorescent materials suitable for in vivo deep-tissue imaging [5]. The large attenuation of visible light by biological tissues renders visible-emitting fluorophores incapable of providing contrast for in vivo fluorescence imaging at tissue depths larger than a few hundreds of micrometers. Only fluorophores whose excitation and emission bands fall in the near-infrared (NIR, 700–2000 nm) enable deep-tissue fluorescence imaging due to the reduced light-tissue interaction in this spectral range [6, 7]. Fluorescent probes emitting in the second NIR window (NIR-II, 1000–1700 nm) are preferred to those emitting in the first window (NIR-I, 700–950 nm) as they result in higher-

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contrast fluorescence images [8]. This is due to the decrease in autofluorescence and scattering with increasing wavelength: 1. NIR autofluorescence under NIR optical excitation becomes negligible for emission wavelengths longer than 1200 nm [9, 10]. 2. Different biological tissues present different scattering coefficients, but all of them follow a decreasing trend with increasing wavelengths [11]. Besides the limited availability of NIR-II-fluorescent probes, there are currently no commercial optical filter sets designed to work with specific NIR-II excitation and absorption bands. This further complicates the practical implementation of in vivo fluorescence imaging with spectral multiplexing. On top of these practical issues, spectral multiplexing fails at providing accurate quantitative information in vivo. The intensity registered by a detector when conducting in vivo fluorescence imaging does not only depend on the concentration of the fluorophore used as contrast agent. Biological tissues will absorb and scatter a fraction of the emitted light, determining how much of the emission reaches the detector [12]. As shown in Fig. 1, this interaction between light and tissues is wavelength-dependent, making it impossible to quantify the relative concentration of fluorophores emitting at different wavelengths from a simple image analysis. A multiplexed imaging approach based on fluorescence lifetimes (lifetime multiplexed imaging) instead of emission wavelengths can overcome the limitations of spectral multiplexing for in vivo imaging, as the wavelength dependence of the light-tissue interaction is no longer a problem [13, 14]. Lifetime multiplexing

Fig. 1 (a) Procedure for in vivo lifetime multiplexed imaging. A delay mechanism enables imaging at a tunable delay time after the end of each pulse of the laser used for optical excitation, as shown in (b). Images obtained for different delay times are then computationally analyzed to generate a lifetime image. (Adapted from reference [13] with permission. Copyright © 2018, American Chemical Society)

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requires three essential components, as schematically shown in Fig. 1: 1. NIR-II-fluorescent contrast agents with identical photoluminescence spectra and different fluorescence lifetimes. 2. A NIR-II imaging system capable of time-resolved image acquisition. This is achieved by imaging at different delay times between the end of an optical excitation pulse and image acquisition (see Fig. 1b), as is done for fluorescence lifetime imaging microscopy (FLIM) [15]. 3. A computational analysis method that generates a lifetime map from the images collected at different time points. This lifetime map is the equivalent of a color map in a spectral multiplexing approach, displaying the intensity generated by each of the fluorophores at each point in the image. In the following sections, we will describe the specifics of each of these elements.

2 2.1

Materials Nanoparticles

In vivo multiplexed lifetime imaging requires biocompatible luminescent nanoparticles (NPs) with tunable emission lifetimes longer than the lifetime of the molecules that create the autofluorescence (typically in the range of nanoseconds) [1]. Lanthanide-doped NPs with tailored long emission lifetimes (from several tens to hundreds of microseconds) are especially suited for this purpose (see Note 1 and Note 2). To synthesize the poly(acrylic acid)-coated core/shell NPs NaY0.9
xYb0.1NdxF4@CaF2 (with x ¼ 0.1, 0.2, 0.3, 0.5, 0.8, and 0.9) that we developed for in vivo lifetime multiplexed imaging, the following reagents are required: 1. Rare-earth oxides (Y2O3, Yb2O3, Nd2O3). 2. Sodium trifluoroacetate (NaTFA). 3. Poly(acrylic acid) (PAA, MW ¼ 18,000). 4. Trifluoroacetic acid (TFA). 5. Calcium oxide (CaO). 6. Hexane. 7. Ethanol. 8. Oleic acid. 9. Oleylamine (OM, >70%). 10. 1-Octadecene (ODE, >90%). 11. N,N-dimethylformamide (DMF). 12. Nitrosonium tetrafluoroborate (NOBF4). Additionally, to allow their use in vivo, a 1x phosphate buffer saline (PBS) solution is needed.

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Imaging System

The imaging system required for in vivo lifetime multiplexing is a modified conventional NIR-II imaging setup (see Fig. 2a). To the essential elements for in vivo NIR-II imaging (imaging chamber, excitation laser, optical filters, NIR-II-sensitive camera), an external electronic device that enables delaying the image acquisition from each laser pulse (trigger delay system) is added (see Note 3 for alternative approaches). 1. Imaging chamber: to maximize the signal-to-noise ratio (SNR), preventing the ambient light from reaching the camera is essential. The easier way to achieve this is by placing the animal under study, the excitation light, and the camera within a black box (see Note 4 for further detail), as shown in Fig. 2a. The imaging chamber must have holes drilled at the top (for

Fig. 2 (a) Schematic representation of the imaging setup for in vivo lifetime multiplexed imaging. (b) Diagram of the trigger delay circuit

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the NIR-II camera), top or sides (for the optical excitation fiber), and one of the sides (for the anesthesia tube). It is also essential to have a door at one of the sides so that it is easy to move the animal into and out of the imaging chamber. 2. Excitation laser: whole-body small animal fluorescence imaging requires an optical excitation source that can provide sufficient power density over a wide field. Lifetime multiplexed imaging requires, additionally, that the laser operates in pulsed mode and that it can generate a synchronized triggering signal if no external triggering source is used. For optical excitation of the Nd/Yb co-doped NPs described in the previous section, we used a fiber-coupled 808 nm laser providing a maximum output power of 10 W. 3. Optical filters: an 850 nm longpass optical filter placed in the image collection path reduces the background noise to a minimum. 4. NIR-II camera: an InGaAs CCD camera with sensitivity in the 900–1700 range is essential for in vivo fluorescence imaging (see Note 5). For lifetime multiplexing, the camera must admit external triggering. The camera must be fitted with a NIR-IIspecific telescope that allows wide-field imaging and focusing at a distance of about several tens of cm. 5. Trigger delay circuit: this electronic circuit is composed of inverter logic gates and 2 RC phases (see Fig. 2b and Note 6). The trigger output generated by the laser is fed to the trigger delay circuit, which generates a pulse of width w delayed a certain time tdelay from the end of the laser pulse. The first RC phase is responsible for introducing the time delay, which can be tuned by adjusting the potentiometer Pdelay. The potentiometer in the second RC phase allows tuning the width of the output pulse. The output of the trigger delay circuit acts as trigger for the NIR-II camera. 6. Oscilloscope: although it is not a part of the imaging system, it is essential to monitor and adjust tdelay. Two channels in the oscilloscope must be connected to the laser trigger output and to the output of the trigger delay circuit, respectively. 7. BNC cables and connector: two BNC female T connectors are required to connect a) the laser trigger output to the oscilloscope and the trigger delay circuit and b) the trigger delay circuit output to the oscilloscope and the camera. All cables are regular BNC cables except for the one that connects the trigger delay circuit output to the NIR-II camera trigger input, which has a BNC end and a camera-specific end (see Note 7).

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Methods Nanoparticles

The procedure to synthesize PAA-coated NaY0.9
xYb0.1NdxF4@CaF2 NPs is split into three separate parts, plus a further fourth step required to disperse them in PBS for their injection in vivo. 1. Synthesis of the NaY0.9
xYb0.1NdxF4 core NPs via thermal decomposition of trifluoroacetates: (a) Load 0.05 mmol of Yb2O3, x mmol of Nd2O3 (x ¼ 0.05, 0.1, 0.15, 0.4), and (0.45 – x) mmol of Y2O3 into a 250 mL flask containing 5 mL of deionized water and 5 mL of TFA. (b) Heat it to 90  C for 1 hour to yield a clear solution. (c) Evaporate it under argon atmosphere to obtain muddylooking powdered RE(TFA)3. (d) Add 8 mL of OA, 8 mL of OM, 12 mL of ODE, and 2 mmol of NaTFA into the flask. (e) Heat the solution to 120  C, and keep it at that temperature for 30 minutes to remove water and oxygen. (f) Heat the flask to 300  C for 30 minutes, and let it naturally cool to room temperature under an inert argon atmosphere. (g) Add 10 mL of ethanol to the cooled reaction flask to precipitate the NPs. (h) Wash with ethanol and centrifuge at 8000 rpm three times. (i) Collect the resulting white powder and disperse it in 10 mL of hexane. 2. Preparation of the NaY0.9
xYb0.1NdxF4@CaF2 core/shell NPs via epitaxial growth of the shell. Carry out steps a–e under argon atmosphere and magnetic stirring at 250 rpm: (a) Add 2 mmol of CaO, 5 mL of deionized water, and 5 mL of TFA to a 250 mL flask, and heat it to 90  C for 1 h. (b) Evaporate the resulting clear solution to yield the shell precursor: Ca(TFA)2. (c) Add 0.5 mmol of core NPs, 7 mL of OA, and 7 mL of ODE to the flask. (d) Heat the solution to 120  C for 30 minutes to remove water and oxygen. (e) Heat to 300  C for 30 minutes and let it cool down to room temperature. (f) Precipitate the resulting core/shell NPs by adding 20 mL of ethanol to the cooled reaction flask.

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(g) Wash with ethanol and centrifuge three times. (h) Collect the resulting core/shell NPs, and disperse them in 10 mL of hexane. 3. PAA coating: (a) Mix 5 mL of hexane-dispersed core/shell NPs with 5 mL of a 0.1 M solution of N,N-dimethylformamide (DMF) solution of nitrosonium tetrafluoroborate (NOBF4) at room temperature. (b) Gently shake the mixture until precipitation is observed. (c) Add toluene and hexane (1:1, volume) into the solution. (d) Centrifuge the solution for 10 min at 10,000 rpm, collect the precipitate, and disperse it in 5 mL of DMF. (e) Add 250 mg of PAA to the dispersion of NPs in DMF and heat it to 80  C. Keep it at that temperature, under vigorous stirring, for 30 minutes. (f) Add acetone to precipitate the NPs, wash them with ethanol, and disperse them in water. 4. Dispersion of NPs in PBS for in vivo applications: (a) Centrifuge the water-dispersed PAA-coated core/shell NPs at 6000 rpm for 15 minutes to recover the NPs. (b) Redisperse the NPs in PBS to the desired concentration. 3.2

Imaging

1. Animal manipulation must follow the requirements of the specific ethics committee that regulates the experiments. A variety of protocols for anesthesia induction and maintenance can be utilized [16]. For our experiments, we typically employ 12-week-old CD1 female mice, typically weighing around 35 g, and use 4% isoflurane gas to induce the anesthesia. After injecting the NPs via the desired routes, place the animal in the imaging chamber, and keep it there under anesthesia (with a lower concentration of isoflurane, typically 1%) while time-resolved images are recorded. 2. Before starting the imaging process, set the laser pulse parameters and laser intensity to the preferred values (see Notes 8 and 9) and the NIR-II camera to operate in triggered mode (see Note 10). Set the exposure time of the camera to ensure each image acquisition has finished well before the next laser pulse has started. 3. After placing the animal in the imaging chamber, record multiple images at different delay times. Adjust the time delay by manipulating the potentiometer in the first RC phase of the trigger delay circuit, and monitor it using an oscilloscope (see item 6 in Subheading 2.2).

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3.3 Computational Analysis

1. After acquiring a series of time-gated fluorescence intensity images at a range of time delays after excitation, fit the decay profile of every pixel on the field of view (see Fig. 3a, b) to the stretched exponential function (StrEF), also known as the Kohlrausch–Williams–Watts function (see Note 11) [15, 17– 19]:
β ! t ð1Þ I ðt Þ ¼ I 0 exp
τk where I0 is the initial intensity at t ¼ 0, τk the characteristic time constant, and β the heterogeneity parameter (0 < β < 1).

Fig. 3 (a) Series of time-gated fluorescence intensity images. (b) Acquisition process. For each fluorescence image obtained at Δtx, a new point of the decay profile of every pixel is added to the analysis. (c) Schematic representation of the computational fit process to the stretched exponential function. (d) Multiplication between the amplitude image (black and white) and lifetime map (colorful) in order to produce the final fluorescence lifetime image. (The images were reproduced from reference [13] with authorization. Copyright © 2018 American Chemical Society)

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2. To correctly describe the pixel decay, it is necessary to interpret the lifetimes in a statistical manner. For each and every pixel on the field of view, evaluate its corresponding characteristic lifetime, defined as [20, 21]:
 1 1 ð2Þ hτi ¼ τk Γ β β where Γ is the gamma function. 3. Once the values of and I0 are obtained for every single pixel on the field of view, create two different images: the first with determining the value of pixels and the second with I0 (for complementary comments, see Note 12). 4. Put the first image in a pseudo color scale in such a way that there is contrast between lifetimes. 5. Keep the second image in gray scale. 6. Having those two images, multiply them (see Note 13) to produce the final fluorescence lifetime image (Fig. 3d). The resolution and quality of these images will depend on how fast the laser pulse and the acquisition rate of the camera are and on the finesse of the fitting protocol followed in the computer.

4

Notes 1. Although in theory NPs with a lifetime longer than nanoseconds would suffice to filter the autofluorescence contribution to the images, in practice, the characteristics of the experimental setup impose more restrictions to the lifetimes needed for the NPs. Two elements of the equipment are particularly critical: (a) The method of pulsing the excitation laser: many commercially available diode lasers can operate in pulsed mode, but the pulses are generated by a periodic electronic signal that will not be necessarily a perfect square wave. This means that the decay of the laser can last a few microseconds. The actual time when the contribution of the laser is not negligible can be directly measured by using a photodiode detector and an oscilloscope. (b) The response time of the detection camera to the triggering signal, which can be also in the order of a few microseconds. The first images acquired after the end of the laser pulse can thus have a non-negligible contribution by the setup, which could distort the lifetime values estimated from the images. To avoid this problem, it is advisable to use NPs with an emission

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lifetime larger than 100 μs. This can be achieved with lanthanide-doped NPs, characterized by long luminescence lifetimes due to the forbidden nature of f–f transitions. In addition, the electronic structure of rare-earth ions “protects” the lifetime against the environment, so the luminescence lifetime will not suffer a great variation once the nanoparticles are administered in vivo. 2. Other synthesis methods and ion combinations have been reported to obtain lifetime-tunable NPs for in vivo lifetime multiplexed imaging [14]. 3. As an alternative to the electronic-circuit-based approach here to delay the image acquisition, an all-optical approach based on a high-speed chopper can be used. This approach was initially devised for autofluorescence-free in vivo imaging and recently adapted to in vivo multiplexed imaging [14, 22]. 4. It is important to design the imaging chamber so that all elements fit—the space for the NIR-II camera (the largest of all the holes to be drilled) needs to be designed so that the camera fits snugly and there is no chance of it moving during image acquisition. For the rest of the elements, a snug fit is not essential, but the holes need to be covered with black cloth or equivalent during the imaging process so that no stray light makes it into the imaging chamber. Black boxes made of methacrylate can be easily custom-made at any machining workshop. Besides the essentials described in Subheading 2.2, it is desirable to design the imaging chamber so that a heating pad can be fitted at the bottom of the box. The purpose of the heating pad is to maintain the body temperature of the animals, as it tends to fall when they are kept under anesthesia for relatively long periods. 5. There is a large variety of NIR-II cameras in the market. The signal-to-noise ratio will be highest for high-end cameras that allow for deep cooling (up to
80  C). However, more budget-friendly NIR-II cameras (that typically do not allow cooling beyond 0  C) are also suitable for in vivo lifetime multiplexed imaging. 6. The second RC phase of our trigger delay circuit was designed to determine the duration of the image acquisition window. However, most NIR-II cameras allow regulating the acquisition time via software and only use the rising edge of the trigger signal. If this is the case for a particular NIR-II camera, it is not necessary to introduce a potentiometer to adjust the time constant of the second RC phase of the circuit. 7. Trigger cables have a coaxial connector end and pin connector with a camera-specific pin configuration on the other end. They are usually provided with the camera, but a custom-made

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trigger cable can also be fabricated according to the pin diagram provided with the manual of each specific NIR-II camera if needed. 8. There is almost complete flexibility when choosing the pulse parameters of the excitation laser, which will be largely determined by the features of the laser itself. One thing needs to be kept in mind: the pulse period must be long enough to avoid overlap between the image acquisition and the next laser pulse even for the longest time delay. A relative long period between pulses also ensures that there is no substantive laser-induced heating that can cause damage to the animal. 9. It is important to trial the imaging system with nanoparticle dispersions at relatively low concentrations (such as those expected to have in vivo) to have a clear idea of what laser intensity will be required for the in vivo multiplexed imaging experiments. 10. Most cameras give several options for the triggered mode (use the rising or falling edge as trigger for the image acquisition, for instance). It is essential to characterize those for your particular NIR-II camera before the in vivo imaging experiments to make sure you are using the rising edge of the triggering signal as trigger. 11. Depending on the fluorescent probes selected, some models could be more appropriate than others. A simplistic and direct model would consider the decay of a pixel to be equivalent to the one of an excited singlet electron in a fluorophore molecule in a noninteracting medium. If one, however, starts to consider regions where different types of fluorescent probes coexist (and a pixel signal could be mixed) as well as the existence of sinks that capture excitation (as is the case of fluorescence resonance energy transfer), this model does not correspond to reality [15, 23, 24]. A different option would be fitting the decay profile to a multi-exponential model. Though this model seems to be successful in some cases, there are many situations in which one does not expect a limited number of discrete lifetimes [25–27]. Furthermore, as a computational drawback, the use of double, triple, or higher exponential decay models can only improve the quality of the fit by adding an extra parameter. From what experiments seem to provide, the most reasonable model is the one which considers the decay of a pixel to be described by the stretched exponential function (StrEF), also known as the Kohlrausch–Williams–Watts function. The reason for this is the fact that not only the stretched exponential has the property of being easily evaluated by a computer but also it can be understood as a continuous sum of exponential decays. Thus, one could avoid the problems of arbitrarity in the

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Fig. 4 Linear plots of the stretched exponential distribution function for values of the heterogeneity parameter β between 0.65 and 0.95

multi-exponential models. Figure 4 contains a graphical representation of the contribution given by an exponential term with lifetime τ ¼ τk/s. As one can see, the smaller the value of β, the broader the distribution of fluorescence lifetimes contributing to the signal, i.e., the higher its heterogeneity. In the special case of β ¼ 1 (minimal heterogeneity), the StrEF becomes a single exponential (this is also noted in the behavior of P(s, β) in Fig. 4). Since Eq. 1 can describe complicated signals in terms of only three parameters (Io, τk, and β), it is not so hard to see why the StrEF is much more appropriate for FLIM than the models previously mentioned. 12. As a matter of completeness, some FLIM analysis would also consider constructing an image where β determines the pixel value [20]. The applications for this particular image would, of course, depend on the luminescent samples being observed. In the case where various luminescent samples with completely different lifetimes are distributed over a common region, this image could, for instance, be used to distinguish the areas where there is only one type of sample from the areas where there is an overlap of signals coming from different samples. 13. This multiplication has a twofold purpose: (a) to ensure that the brightest points on the FLIM are the ones that presented higher emission during the measurement and (b) to completely eliminate the points where no fluorescence was detected (and whose lifetime, would, of course, be interpreted as infinite). References 1. Mansfield JR, Gossage KW, Hoyt CC, Levenson RM (2005) Autofluorescence removal, multiplexing, and automated analysis methods for in-vivo fluorescence imaging. J Biomed Opt 10(4):041207 2. Chan WCW, Maxwell DJ, Gao X, Bailey RE, Han M, Nie S (2002) Luminescent

quantum dots for multiplexed biological detection and imaging. Curr Opin Biotechnol 13(1):40–46 3. Zhu H, Fan J, Du J, Peng X (2016) Fluorescent probes for sensing and imaging within specific cellular organelles. Acc Chem Res 49 (10):2115–2126

In Vivo Lifetime Multiplexed Imaging 4. Ettinger A, Wittmann T (2014) Fluorescence live cell imaging. In: Waters JC, Wittmann T (eds) Quantitative imaging in cell biology, Methods in cell biology, vol 123. Elsevier Academic Press Inc, San Diego, pp 77–94 5. Hong G, Antaris AL, Dai H (2017) Nearinfrared fluorophores for biomedical imaging. Nat Biomed Eng 1:0010 6. Smith AM, Mancini MC, Nie S (2009) Bioimaging: second window for in vivo imaging. Nat Nanotechnol 4(11):710–711 7. Weissleder R (2001) A clearer vision for in vivo imaging. Nat Biotechnol 19(4):316–317 8. Hong G, Diao S, Chang J, Antaris AL, Chen C, Zhang B, Zhao S, Atochin DN, Huang PL, Andreasson KI, Kuo CJ, Dai H (2014) Through-skull fluorescence imaging of the brain in a new near-infrared window. Nat Photonics 8:723 9. del Rosal B, Villa I, Jaque D, Sanz-Rodrı´guez F (2016) In vivo autofluorescence in the biological windows: the role of pigmentation. J Biophotonics 9(10):1059–1067 10. Villa I, Vedda A, Cantarelli I, Pedroni M, Piccinelli F, Bettinelli M, Speghini A, Quintanilla M, Vetrone F, Rocha U, Jacinto C, Carrasco E, Rodrı´guez F, Juarranz ´ , del Rosal B, Ortgies D, Gonzalez P, Sole´ J, A Garcı´a D (2015) 1.3 μm emitting SrF2:Nd3+ nanoparticles for high contrast in vivo imaging in the second biological window. Nano Res 8 (2):649–665 11. Jacques SL (2013) Optical properties of biological tissues: a review. Phys Med Biol 58 (11):R37–R61 12. Bashkatov A, Genina E, Kochubey V, Tuchin V (2005) Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm. J Phys D Appl Phys 38(15):2543 13. Ortgies DH, Tan M, Ximendes EC, del Rosal B, Hu J, Xu L, Wang X, Martı´n Rodrı´guez E, Jacinto C, Fernandez N, Chen G, Jaque D (2018) Lifetime-encoded infrared-emitting nanoparticles for in vivo multiplexed imaging. ACS Nano 12(5):4362–4368 14. Fan Y, Wang P, Lu Y, Wang R, Zhou L, Zheng X, Li X, Piper JA, Zhang F (2018) Lifetime-engineered NIR-II nanoparticles unlock multiplexed in vivo imaging. Nat Nanotechnol 13:941–946 15. Siegel J, Elson DS, Webb SE, Lee KB, Vlandas A, Gambaruto GL, Leveˆque-Fort S, Lever MJ, Tadrous PJ, Stamp GW (2003) Studying biological tissue with fluorescence

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lifetime imaging: microscopy, endoscopy, and complex decay profiles. Appl Opt 42 (16):2995–3004 16. Hildebrandt IJ, Su H, Weber WA (2008) Anesthesia and other considerations for in vivo imaging of small animals. ILAR J 49(1):17–26 17. Lee KB, Siegel J, Webb S, Leveque-Fort S, Cole M, Jones R, Dowling K, Lever M, French P (2001) Application of the stretched exponential function to fluorescence lifetime imaging. Biophys J 81(3):1265–1274 18. Jang C, Lee JH, Sahu A, Tae G (2015) The synergistic effect of folate and RGD dual ligand of nanographene oxide on tumor targeting and photothermal therapy in vivo. Nanoscale 7 (44):18584–18594 19. Bodunov EN, Danilov VV, Panfutova AS, Simoes Gamboa AL (2016) Roomtemperature luminescence decay of colloidal semiconductor quantum dots: nonexponentiality revisited. Ann Phys 528(3–4):272–277 20. Laherrere J, Sornette D (1998) Stretched exponential distributions in nature and economy:“fat tails” with characteristic scales. Eur Phys J B 2(4):525–539 21. Lindsey C, Patterson G (1980) Detailed comparison of the Williams–Watts and Cole–Davidson functions. J Chem Phys 73 (7):3348–3357 22. Zheng X, Zhu X, Lu Y, Zhao J, Feng W, Jia G, Wang F, Li F, Jin D (2016) High-contrast visualization of upconversion luminescence in mice using time-gating approach. Anal Chem 88(7):3449–3454 23. Dowling K, Dayel M, Lever M, French P, Hares J, Dymoke-Bradshaw A (1998) Fluorescence lifetime imaging with picosecond resolution for biomedical applications. Opt Lett 23 (10):810–812 24. Salihoglu O, Kakenov N, Balci O, Balci S, Kocabas C (2016) Graphene as a reversible and spectrally selective fluorescence quencher. Sci Rep 6:33911 25. Lakowicz JR, Szmacinski H, Nowaczyk K, Berndt KW, Johnson M (1992) Fluorescence lifetime imaging. Anal Biochem 202 (2):316–330 26. Alcala JR, Gratton E, Prendergast F (1987) Fluorescence lifetime distributions in proteins. Biophys J 51(4):597–604 27. Alcala JR (1994) The effect of harmonic conformational trajectories on protein fluorescence and lifetime distributions. J Chem Phys 101(6):4578–4584

Chapter 16 Versatile On-Demand Fluorescent Labeling of Fusion Proteins Using Fluorescence-Activating and Absorption-Shifting Tag (FAST) Arnaud Gautier, Ludovic Jullien, Chenge Li, Marie-Aude Plamont, Alison G. Tebo, Marion Thauvin, Michel Volovitch, and Sophie Vriz Abstract Observing the localization, the concentration, and the distribution of proteins in cells or organisms is essential to understand theirs functions. General and versatile methods allowing multiplexed imaging of proteins under a large variety of experimental conditions are thus essential for deciphering the inner workings of cells and organisms. Here, we present a general method based on the non-covalent labeling of a small protein tag, named FAST (fluorescence-activating and absorption-shifting tag), with various fluorogenic ligands that light up upon labeling, which makes the simple, robust, and versatile on-demand labeling of fusion proteins in a wide range of experimental systems possible. Key words Fluorescence labeling, Protein tag, Fluorogenic chromophore, Non-covalent labeling

1

Introduction Cells and organisms are complex machines driven by a set of dynamic biological events tightly orchestrated in space and time. Our understanding of their inner workings is intricately related to our ability to observe how their constituents organize and interact. A common strategy for imaging proteins is to fuse them to peptide or protein sequences that provide fluorescence. Investigators can thus use the fluorescence signal to obtain information on the distribution, quantity, and localization of the protein of interest. Various labeling techniques have been developed in the last decades. Cell biologists use mainly fluorescent proteins as labels. Since the discovery of the green fluorescent protein (GFP) in Aequorea victoria, a large toolbox containing color variants spanning the entire visible spectrum has been developed for various applications in multicolor imaging and biosensing [1]. Recently, selective genetic tagging with peptide and protein tags able to react

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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or interact specifically with synthetic probes has been proposed as an alternative labeling strategy [2]. The use of synthetic molecules allows one to exploit the latest developments in chemistry to tune, optimize, and diversify the label properties. In addition, the need for adding a small molecule provides investigators with new means and opportunities to control protein labeling. The most mature technologies (e.g., SNAP-tag [3], HaloTag [4]) use self-labeling tags that react covalently (and thus irreversibly) with substrates bearing various chemical functionalities including fluorophores. Once the labeling reaction is complete, the unreacted substrate is washed away to enable imaging with high contrast. Here we describe a method for the rapid labeling of fusion proteins that relies on the use of a small protein tag of 125 residues (14 kDa), termed FAST (fluorescence-activating and absorptionshifting tag) [5] (Fig. 1a). FAST can be genetically fused at the Nor C-termini of any protein of interest or inserted within a protein of interest using a circularly permuted variant [6]. FAST has been engineered to form a non-covalent and reversible assembly with its ligands, which include chromophores from the hydroxybenzylidene rhodanine (HBR) family [5, 7, 8] (Fig. 1b). The FAST ligands have the property to be fluorescent only when bound to FAST, allowing imaging of FAST-tagged proteins with high contrast in the presence of unbound ligands. This selective fluorescence activation is due to the fact that FAST ligands are fluorogenic chromophores. When unbound, these so-called fluorogens dissipate light energy in a non-radiative manner by internal rotation or cis-trans isomerization. Their immobilization within FAST slows down these non-radiative processes, increasing their quantum yield of fluorescence. A chromogenic effect further enhances this fluorescence activation: binding to FAST induces a large red shift in absorption (due to a change in ionization state), which allows the distinction of bound fluorogens from unbound ones via the difference in their absorption properties. FAST has several advantages over fluorescent proteins and selflabeling tags. FAST is a fully monomeric protein of only 14 kDa, half the size of GFP-like fluorescent proteins, reducing the risk of dysfunctional fusions. As, moreover, the association with its fluorogens is very fast, in the presence of fluorogens, FAST fluoresces as soon as it is folded, allowing fast processes, such as protein synthesis, to be followed in real time [5]. Finally, since FAST does not require oxygen to operate, FAST works regardless of the oxygen level. This property was shown to allow imaging under anaerobic conditions [9, 10], unlike fluorescent proteins. FAST presents also unique features [5]. As fluorogens bind FAST non-covalently, labeling is not permanent and can be easily reversed by washing the fluorogen away. Nonpermanent labeling opens great opportunities for on-demand applications, in which the fluorescent label is needed only transitorily. Moreover, as

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Fig. 1 Protein labeling using the FAST technology. (a) Principle of fluorescent protein labeling via FAST. (b) Chemical structures of fluorogens available for FAST-tagged protein labeling

investigator can control fluorogen concentrations at will, it is also possible to label only a subset of proteins independently of their expression level. This property was shown to be useful to image proteins below the diffraction limit using super-resolution imaging by radial fluctuations (SRRF) [11], which is sensitive to fluorophore density, or by single-molecule localization microscopy (SMLM) [12], which necessitates the stochastic generation of sparse subsets of emitters over time. FAST and its fusions were shown to be functional in different systems from microorganisms such as bacteria and yeast to mammalian cells, including neurons, and even multicellular organisms such as zebrafish embryos [5]. FAST fluorogens have been shown to have no deleterious effects on cultured cells. Cells can be exposed to fluorogens for extended periods of time without effect on cell viability. Moreover, zebrafish embryos have been shown to

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develop normally in the continuous presence of FAST fluorogens, further demonstrating their biological inertness. Various fluorogens are available enabling labeling FAST-tagged proteins with different fluorescence colors [7, 8] (Fig. 1b and Table 1), providing an experimental versatility not encountered with fluorescent proteins. The availability of various spectrally different fluorogens allows furthermore to combine FAST with fluorescent proteins and self-labeling tags such as SNAP-tag and Halo-tag (which both rely on orthogonal labeling chemistries) for multiplexed imaging. FAST fluorogens have been originally designed for efficient crossing of the plasma membrane: complete labeling of intracellular proteins in living cells occurs within few tens of seconds upon addition of the fluorogen solution. Negatively charged fluorogens with reduced cell uptake are also available for the selective labeling of cell surface proteins [13] (Fig. 1b). These membraneimpermeant fluorogens allowed the development of an effective and simple protocol for the quantification of cell surface protein levels by flow cytometry [13]. The original FAST (initially named Y-FAST in ref. [5]) is a variant of the apo photoactive yellow protein (PYP) with the mutations C69G, Y94W, T95M, F96I, D97P, Y98T, Q99S, M100R, and T101G. Other variants have been described and characterized (see ref. [8]), in particular an improved variant iFAST containing the additional V107I mutations. We report the properties of optimal tag/fluorogen pairs in Table 1. Note that tandem versions of FAST and iFAST, named td-FAST and td-iFAST, are also available (see ref. [8]) and can be used to increase the fluorescence signal for detection of low-abundant proteins. The protocols below are suitable for all FAST variants and tandems. This chapter presents protocols for the rapid and reversible labeling of proteins using FAST, a new tag that opens great prospects for multiplexed imaging through combination with other fluorescent proteins and labeling tags. The chapter contains protocols for labeling FAST-tagged proteins in live cells for imaging by microscopy (Subheading 3.1) or analysis/sorting by flow cytometry (Subheading 3.2), for the labeling of FAST-tagged proteins in fixed cells (Subheading 3.3), for the quantification of FAST-tagged protein in solutions (e.g., cell lysates, cell supernatants, or cell-free expression systems) (Subheading 3.4), and for the labeling of FAST-tagged proteins in live zebrafish embryos (Subheading 3.6). Subheading 3.5 presents a protocol for the expression and purification of recombinant FAST protein that can be used as reference for quantification in Subheading 3.4.

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Table 1 Properties of some optimal fluorogen/tag pairs. Abbreviations are as follows: λabs, wavelength of maximal absorption; λem, wavelength of maximal emission; ε, molar absorption coefficient at λabs; ϕ, fluorescence quantum yield; KD, thermodynamic dissociation constant Tag

Fluorogen

λabs (nm)

λem (nm)

ε (mM

iFAST

HMBR

480

541

iFAST

HBR-3,5DM

499

iFAST

HBR-3OM

iFAST FAST

2 2.1

ϕ

KD (μM)

Ref

41

0.23

0.07

[8]

558

46

0.57

0.06

[8]

495

560

39

0.49

0.2

[8]

HBR-3,5-DOM

516

600

38

0.40

0.41

[8]

HBRAA-3E

505

559

61

0.08

1.3

[13]

1

cm 1)

Materials General

1. FAST fluorogens (see Note 1). The membrane-permeant fluorogens HMBR, HBR-3,5DM, HBR-3OM, and HBR-3,5DOM for intracellular protein labeling and the membrane-impermeant HBRAA-3E for selective cell-surface protein labeling are commercially available from The Twinkle Factory (thetwinklefactory.com) under the names TFLime, TF Amber, TFCitrus, TFCoral, and TFAmber NP, respectively. 2. Molecular biology grade dimethyl sulfoxide (DMSO).

2.2 Protein Labeling in Live and Fixed Cells

1. Mammalian cells expressing transiently or constitutively FASTtagged proteins. 2. Dulbecco’s phosphate-buffered saline (D-PBS) buffer. 3. Hanks’ balanced salt solution (HBSS). 4. Culture medium suitable for your cell line without serum and without phenol red. 5. Cell fixation buffer: 4% paraformaldehyde, 1 D-PBS (pH 7.4). Prepare freshly for each fixation experiment.

2.3 Expression and Purification of Recombinant FAST Protein

1. Bacterial expression vector with T7Lac promoter allowing the expression of His-tagged FAST protein (available from A. Gautier upon request). 2. Rosetta™(DE3)pLysS E. coli. 3. Lysogeny broth (LB) medium. 4. Phosphate-buffered saline (PBS) buffer: 50 mM sodium phosphate, 150 mM NaCl, pH 7.4. 5. Lysis buffer: 50 mM sodium phosphate, 150 mM NaCl, 2.5 mM MgCl2, protease inhibitors, DNase, pH 7.4. 6. Kanamycin.

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7. Chloramphenicol. 8. Isopropyl β-D-1-thiogalactopyranoside (IPTG). 9. Imidazole (BioUltra grade > 99.5%). 10. Ni-NTA agarose beads. 11. PBS complemented with 10 mM imidazole: 50 mM sodium phosphate, 150 mM NaCl, 10 mM imidazole, pH 7.4. 12. PBS complemented with 20 mM imidazole: 50 mM sodium phosphate, 150 mM NaCl, 20 mM imidazole, pH 7.4. 13. PBS complemented with 40 mM imidazole: 50 mM sodium phosphate, 150 mM NaCl, 40 mM imidazole, pH 7.4. 14. PBS complemented with 0.5 M imidazole: 50 mM sodium phosphate, 150 mM NaCl, 0.5 M imidazole, pH 7.4. 2.4 Protein Labeling in Zebrafish Embryo

1. Zebrafish embryos expressing transiently (e.g., mRNA-injected embryos) or stably (e.g., transgenic fish line) FAST-tagged proteins. 2. Mineral water suitable for zebrafish (e.g., Volvic water). 3. 15 mM tricaine mesylate solution (25). 4. Forceps to remove embryo chorions. 5. Glass material for FAST fluorogen treatments (petri dish). 6. Low-melting agarose. 7. Appropriate polymer or glass bottom dish for microscope imaging.

3

Methods

3.1 Rapid and Reversible Labeling of FAST-Tagged Proteins in Live Mammalian Cells for Fluorescence Microscopy Experiments

This protocol allows the labeling of FAST-tagged proteins expressed transiently or constitutively in live mammalian cells for imaging by fluorescence microscopy (see Note 1). 1. Dissolve the FAST fluorogen in DMSO to yield a 5 mM (1000) stock solution. Mix by vortexing for a few seconds until all fluorogen is dissolved (see Notes 2 and 3). 2. Dilute the stock solution 1:1000 in culture medium without phenol red (see Note 4) (or alternatively in D-PBS or HBSS buffer) to yield a 5 μM (1) labeling solution (see Notes 5 and 6). Mix thoroughly by vortexing. For best performance, use serum-free medium/buffer (see Note 7), and do not keep/ store the labeling solution. 3. For labeling cells expressing FAST-tagged proteins, remove cell culture medium, wash cells gently with D-PBS, and replace the buffer with the 1 labeling solution. For microscope imaging,

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cells must be grown in imaging dishes with polymer or glass coverslip bottom. 4. Incubate for 15–30 s (see Note 8), and directly image cells using microscope settings appropriate for the spectral properties of the FAST fluorogen used. Image under temperature and CO2-level conditions suitable for your cells. As FAST fluorogens have been shown to have no deleterious effects on cells, long-term imaging on several hours or days is possible. 5. To unlabel cells, remove the labeling solution, wash with D-PBS or fresh culture medium, and replace ultimately with fresh culture medium. This step can be performed either by gentle manual pipetting or by using an automated perfusion system. 3.2 Rapid Labeling of FAST-Tagged Proteins in Live Cells for Flow Cytometry Experiments

This protocol allows the labeling of FAST-tagged proteins in live cells for analysis or sorting by flow cytometry. This protocol is general and can be used with any suspension of cells (bacteria, yeast, or mammalian cells) expressing FAST-tagged proteins (see Note 1). 1. Dissolve the FAST fluorogen in DMSO to yield a 5 mM (1000) stock solution. Mix by vortexing for a few seconds until all fluorogen is dissolved (see Notes 2 and 3). 2. Dilute the stock solution 1:200 in your standard cytometry buffer (see Note 9) to yield a 25 μM (5) labeling solution (see Notes 5 and 6). Mix thoroughly by vortexing. For best performance, do not keep/store the labeling solution. 3. Prepare cell suspension in your standard cytometry buffer (see Note 9). For labeling cells expressing FAST-tagged proteins, add the appropriate volume of the 5 labeling solution to obtain the final 1 labeling concentration of 5 μM (see Note 10). Mix gently. Note that antibody-based staining using standard immunolabeling protocols can be performed if required before adding the labeling solution. 4. After incubation for 15–30 s, labeling is complete, and cells are ready to be analyzed or sorted by flow cytometry using settings appropriate for the spectral properties of the FAST fluorogen used. Note that as flow cytometry is based on hydrodynamic flow focusing, the concentration of FAST fluorogen in the cell suspension remains constant during the entire analysis/ sorting.

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3.3 Labeling of FAST-Tagged Proteins in Fixed Mammalian Cells for Fluorescence Microscopy Experiments

This protocol allows the labeling of FAST-tagged proteins in fixed mammalian cells for imaging by fluorescence microscopy (see Note 1). 1. Dissolve the FAST fluorogen in DMSO to yield a 5 mM (1000) stock solution. Mix by vortexing for few seconds until all fluorogen is dissolved (see Notes 1 and 2). 2. Dilute the stock solution 1:1000 in D-PBS buffer to yield a 5 μM (1) labeling solution (see Notes 4 and 5). Mix thoroughly by vortexing. 3. Prepare cells for fixation, remove culture medium, wash cells twice with D-PBS, and add the cell fixation buffer. Incubate for 20 min. Note that any fixation protocols can be used a priori. Use the one that is the best suited for your cell line and experiment. In particular antibody-based staining using standard immunolabeling protocols can be performed before adding the labeling solution (step 4). 4. Remove the fixation buffer, wash three times with D-PBS, and add the labeling solution. 5. Incubate for 15–30 s at room temperature (see Note 8), and directly image cells using microscope settings appropriate for the spectral properties of the FAST fluorogen used. 6. Samples can be kept in the labeling solution at 4  C for several days for further analysis. For longer storage, it is recommended to remove the labeling solution and wash cells two to three times with D-PBS. For new analysis, just add 1 labeling solution.

3.4 Quantification of FAST-Tagged Proteins in Solutions

This protocol allows the labeling of FAST-tagged proteins in solutions (e.g., cell lysate, cell supernatant, or cell-free expression systems) for further analysis by fluorometric techniques (see Note 1). The presented protocol is adaptable to any solution containing FAST-tagged proteins at concentrations below 500 nM. For quantitative analysis, a solution of same composition but that does not contain FAST-tagged proteins is required to prepare (a) a blank solution and (b) standard solutions containing known concentrations of recombinant FAST protein for quantification (see Note 10). Expression and purification of recombinant FAST proteins is described in Subheading 3.5. 1. Dissolve the FAST fluorogen in DMSO to yield a 5 mM (1000) stock solution. Mix by vortexing for few seconds until all fluorogen is dissolved (see Notes 2 and 3). 2. Dilute the stock solution 1:1000 in the solution containing FAST-tagged proteins to yield a 5 μM (1) solution. Do the same with the blank solution and the standard solutions.

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3. Incubate for few seconds at room temperature, before analyzing the different samples by fluorimetry. We recommend the use of a fluorescence microplate reader and black well plates for measuring the fluorescence intensities of the different solutions. Use the same volumes for each solution samples, and measure fluorescence on triplicates. Choose the excitation and emission settings appropriate for the FAST fluorogen used. 4. For quantification, use for each sample the triplicate measurements to determine the mean fluorescence  standard deviation. Subtract the fluorescence value of the blank solution to all samples. Use the fluorescence of the standard solutions to draw a calibration curve. Use this calibration curve to determine the unknown concentration of the FAST-tagged protein in your sample. 3.5 Expression and Purification of FAST Protein

This protocol allows the expression and purification of recombinant His-tagged FAST proteins. Recombinant His-tagged FAST proteins can be used for preparing standard solutions for determining the concentration of FAST-tagged proteins in unknown solutions. 1. Transform expression vector in Rosetta™(DE3)pLysS E. coli. 2. Grow cells at 37  C in LB medium complemented with 50 μg/ ml kanamycin and 34 μg/ml chloramphenicol to OD600nm 0.6. 3. Induce protein expression by adding IPTG to a final concentration of 1 mM. 4. Incubate for 4 h at 37  C (see Note 11). 5. Harvest cells by centrifugation (4000  g, 20 min, 4  C). 6. Freeze cell pellet. 7. Resuspend cell pellet in lysis buffer and sonicate (5 min at 20% of amplitude, 3 s on, 1 s off). 8. Incubate lysate for 2 h at 4  C to allow DNA digestion by DNase. 9. Centrifuge the solution (9200  g, 1 h, 4  C) to remove cellular fragments. 10. Incubate the supernatant overnight at 4  C under gentle agitation with Ni-NTA agarose beads in PBS complemented with 10 mM imidazole. 11. Wash beads with 20 volumes of PBS containing 20 mM imidazole and with 5 volumes of PBS complemented with 40 mM imidazole. Note that 1 volume correspond to the volume of beads used. 12. Elute His-tagged proteins with 5 volumes of PBS complemented with 0.5 M imidazole. Make fractions.

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13. Quantify the protein content of each fractions, and pull together the fractions with the highest protein content. 14. Exchange buffer with PBS using desalting columns or standard dialysis protocols. 15. Determine protein concentration by standard quantification techniques. 3.6 Labeling FAST-Tagged Proteins in Live Zebrafish Embryo

This protocol allows the labeling of FAST-tagged proteins expressed transiently or stably in live zebrafish embryos for microscope imaging (see Note 1). 1. Dissolve the FAST fluorogen in DMSO to yield a 5 mM (1000) stock solution. Mix by vortexing for few seconds until all fluorogen is dissolved (see Notes 2 and 3). 2. Manually remove the chorion of zebrafish embryos (in glass petri dish for early embryos, e.g., before bud stage, because of the vitellus stickiness) with forceps, and put them in the appropriate volume of mineral water (e.g., see Subheading 2.4). 3. For labeling, first pre-treat the dechorionated embryos by adding FAST fluorogen to a final concentration of 5 μM. To obtain homogenous labeling, this is generally best achieved by diluting the stock solution 1:1000 in mineral water (e.g., see Subheading 2.4) and rapidly replacing the mineral water bathing the embryos with the fluorogen-diluted solution. This is not possible with early embryos up to bud stage, because contact with air will cause vitellus collapse and embryo death. Pre-treat these early embryos by adding 1:1000 volume of 5 mM FAST fluorogen stock solution, and mix by stirring the glass dish. 4. Incubate in the dark for 15–20 min at 28  C. 5. During the incubation, melt low-melting agarose (0.8% in Volvic water) at 70  C. 6. Anesthetize the embryos by adding 25 tricaine solution to the preincubation solution. 7. Just before mounting the embryos in agarose, add 1:1000 volume of 5 mM FAST fluorogen stock solution directly on lukewarm agarose, and then proceed to embryo mounting. 8. Let the agarose jellify, and then add the remaining solution from step 5 on top of the agarose, to avoid preparation drying and to keep the embryos anesthetized, as illustrated in Fig. 2.

4

Notes 1. The protocol can be adapted to multiplexed analysis by co-expressing proteins fused to spectrally orthogonal fluorescent proteins or fused to self-labeling tags labeled with spectrally orthogonal fluorophores.

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Fig. 2 Schematic showing standard protocols for labeling FAST-tagged proteins in in zebrafish embryo

2. Dry FAST fluorogens should be stored at—20  C in the dark. Once dissolved in DMSO, the solution should be aliquoted to avoid repeated freeze/thaw cycles and stored at—20  C in the dark. With proper storage, FAST fluorogens should be stable at least 2 years dry or 6 months dissolved in DMSO. 3. Different stock concentrations can be made depending on your requirements. FAST fluorogens are soluble in DMSO up to at least 50 mM. 4. Phenol red should be avoided as it generates background in fluorescence microscopy experiments. 5. Different concentrations of fluorogen can be made depending on your requirements and your cell lines. The efficiency of fluorogen uptake can depend on the cell line. Optimal final fluorogen concentrations range from 1 to 10 μM. Titration experiments adding increasing concentration of fluorogen can be performed in cells to determine the optimal labeling concentration. Ideally, keep DMSO content at 0.1% v/v. 6. Filtration of the labeling solution through membrane syringe filters should be avoided as FAST fluorogen may adsorb on the membrane, reducing thus its concentration.

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7. Serum should be avoided as it may bind FAST fluorogens and deplete the labeling solution. Serum-free medium such as Opti-MEM is recommended. 8. Once the labeling solution is added, the labeling is over in few tens of seconds. After addition of the labeling solution, you can directly install your dish on the microscope stage. The cells are ready to be imaged. 9. D-PBS is advised for mammalian cells. Depending on your cells of interest, other buffers might be used. Serum-free buffers are recommended as serum may bind FAST fluorogens and deplete the labeling solution. 10. For quantification of low concentration of FAST proteins, avoid solutions in buffers or media containing fluorescent components as they may increase the fluorescence background and reduce the sensitivity. 11. Incubation at 16  C overnight is also possible. References 1. Rodriguez EA, Campbell RE, Lin JY, Lin MZ, Miyawaki A, Palmer AE, Shu X, Zhang J, Tsien RY (2017) The growing and glowing toolbox of fluorescent and photoactive proteins. Trends Biochem Sci 42:111–129. https://doi.org/10. 1016/j.tibs.2016.09.010 2. Xue L, Karpenko IA, Hiblot J, Johnsson K (2015) Imaging and manipulating proteins in live cells through covalent labeling. Nat Chem Biol 11:917–923. https://doi.org/10.1038/ nchembio.1959 3. Keppler A, Gendreizig S, Gronemeyer T, Pick H, Vogel H, Johnsson K (2003) A general method for the covalent labeling of fusion proteins with small molecules in vivo. Nat Biotechnol 21:86–89 4. Los G, Encell L, McDougall M, Hartzell D, Karassina N, Zimprich C, Wood M, Learish R, Ohana R, Urh M, Simpson D, Mendez J, Zimmerman K, Otto P, Vidugiris G, Zhu J, Darzins A, Klaubert D, Bulleit R, Wood K (2008) HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem Biol 3:373–382. https://doi.org/ 10.1021/cb800025k 5. Plamont M-A, Billon-Denis E, Maurin S, Gauron C, Pimenta FM, Specht CG, Shi J, Querard J, Pan B, Rossignol J, Moncoq K, Morellet N, Volovitch M, Lescop E, Chen Y, Triller A, Vriz S, Le Saux T, Jullien L, Gautier A (2016) Small fluorescence-activating and absorption-shifting tag for tunable protein imaging in vivo. Proc Natl Acad Sci U S A

113:497–502. https://doi.org/10.1073/ pnas.1513094113 6. Tebo AG, Pimenta FM, Zoumpoulaki M, Kikuti C, Sirkia H, Plamont M-A, Houdusse A, Gautier A (2018) Circularly permuted fluorogenic proteins for the design of modular biosensors. ACS Chem Biol 13:2392–2397. https://doi.org/10.1021/ acschembio.8b00417 7. Li C, Plamont M-A, Sladitschek HL, Rodrigues V, Aujard I, Neveu P, Le Saux T, Jullien L, Gautier A (2017) Dynamic multicolor protein labeling in living cells. Chem Sci 8:5598–5605. https://doi.org/10.1039/ C7SC01364G 8. Tebo AG, Pimenta FM, Zhang Y, Gautier A (2018) Improved chemical-genetic fluorescent markers for live cell microscopy. Biochemistry 57:5648–5653. https://doi.org/10.1021/ acs.biochem.8b00649 9. Monmeyran A, Thomen P, Jonquie`re H, Sureau F, Li C, Plamont M-A, Douarche C, Casella J-F, Gautier A, Henry N (2018) The inducible chemical-genetic fluorescent marker FAST outperforms classical fluorescent proteins in the quantitative reporting of bacterial biofilm dynamics. Sci Rep 8:10336. https:// doi.org/10.1038/s41598-018-28643-z 10. Streett HE, Kalis KM, Papoutsakis ET (2019) A strongly fluorescing anaerobic reporter and protein-tagging system for Clostridium organisms based on the fluorescence-activating and absorption-shifting tag protein (FAST). Appl

On-Demand Fluorescent Protein Labeling with FAST Environ Microbiol 85:714. https://doi.org/ 10.1128/AEM.00622-19 11. Venkatachalapathy M, Belapurkar V, Jose M, Gautier A, Nair D (2019) Live cell super resolution imaging by radial fluctuations using fluorogen binding tags. Nanoscale 124:1607. https://doi.org/10.1039/c8nr07809b 12. Smith EM, Gautier A, Puchner EM (2019) Single-molecule localization microscopy with the fluorescence- activating and absorption-

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shifting tag (FAST) system. ACS Chem Biol 14:1115–1120. https://doi.org/10.1021/ acschembio.9b00149 13. Li C, Mourton A, Plamont M-A, Rodrigues V, Aujard I, Volovitch M, Le Saux T, Perez F, Vriz S, Jullien L, Joliot A, Gautier A (2018) Fluorogenic probing of membrane protein trafficking. Bioconjug Chem 29:1823–1828. https://doi.org/10.1021/acs.bioconjchem. 8b00180

Chapter 17 UltraPlex Hapten-Based Multiplexed Fluorescent Immunohistochemistry Matt Levin, Amy C. Flor, Helen Snyder, Stephen J. Kron, and David Schwartz Abstract The UltraPlex method for multiplexed two-dimensional fluorescent immunohistochemistry is described, in which hapten tags conjugated to primary antibodies facilitate multiplexed imaging of four or more antigens per tissue section at once. Anti-hapten secondary antibodies labeled with fluorophores provide amplified signal for detection, which is accomplished using a standard fluorescent microscope or digital slide scanner. The protocol is rapid and straightforward and utilizes conventionally prepared tissue samples. The resulting staining is highly sensitive and specific, enabling high-resolution imaging of multiple cellular subtypes within tissue samples. Tumor cells and tumor-infiltrating lymphocytes are presented as examples. Multiple 4-plex-stained tissue samples can be digitally overlaid to create 8-plex (or more) high-content images, enabling visualization of distribution of complex cellular subtypes across tissues. Key words Immunohistochemistry, Immunofluorescence

1

Antibody,

Hapten,

Microscopy,

Multiplex

imaging,

Introduction In recent decades, there has been tremendous progress in understanding the role of inflammation in disease and treating illness by targeting the immune system rather than the disease process itself. While blood biomarkers can give a window into inflammatory disease, it is often important to examine the affected tissue directly. This has led to increasing demand for tools to characterize the tissue immune microenvironment, particularly in oncology, where novel antibody and cellular immunotherapies that drive an antitumor immune response are making dramatic impacts. The inflammatory infiltrate in tumors is traditionally examined by microscopy analysis of tumor-infiltrating lymphocytes (TILs) in thin sections cut from formalin-fixed, paraffin-embedded (FFPE) tissue blocks and stained with hematoxylin and eosin (H&E). While TILs can be generally recognized by their characteristic H&E

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_17, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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staining pattern, this basic method fails to differentiate among lymphocyte subsets, leaving the nature of the immune infiltrate a mystery. Chromogenic immunohistochemistry (IHC) improves upon H&E staining by using a primary antibody (1
Ab) to detect a specific protein marker, allowing straightforward identification of, e.g., the CD8+ cytotoxic lymphocytes among the TILs. However, the practical limit for chromogenic IHC is two antigens on a single sample, which is insufficient to assess many immune cell phenotypes. Given this technology gap, there has been growing interest in multiplexed detection as a strategy to examine tissue to better define the cell types and their activation states within the immune microenvironment (reviewed by [1, 2]). Rather than relying on chromogens, for multiplexed immunofluorescence (MxIF), each target is labeled with a unique fluorophore which can be detected at a distinct wavelength using a standard fluorescent microscope. Much like the multiplexed fluorescent detection used for flow cytometry, a simple solution is to use simultaneous staining with fluorescently labeled 1
Abs for direct MxIF. However, in order to detect many relevant antigens, the amplification provided by fluorescent secondary antibodies (2
Abs) is required. A long-standing problem in this indirect immunofluorescence approach is that 2
Abs detect their cognate 1
Abs based on unique features of the constant region linked to species of origin (mouse, rabbit, etc.) and/or isotype. Typically, the different 1
Abs in a MxIF panel must come from different species. However, many of the best antibodies to detect immune markers are rabbit monoclonals, obviating simultaneous MxIF detection with 2
Abs. Simultaneous MxIF is also limited by the number of channels available for detection. Most widefield and confocal fluorescence research microscopes offer four to seven fluorescent channels, which can potentially be extended by spectral unmixing [3, 4] or other strategies, but imaging whole slides is often challenging. However, whole slide scanners are available from various companies which support fluorescent scanning of four or more fluorescence channels. To solve the various problems commonly encountered with conventional approaches, UltraPlex MxIF technology (Cell IDx) provides signal amplification by indirect immunofluorescence detection while remaining compatible with 1
Abs derived from any species and a wide range of existing imaging platforms. To assemble an UltraPlex MxIF panel, a set of four 1
Abs are covalently coupled to peptide-like hapten tags. These barcoded tags are then recognized by four fluorescently labeled, high-affinity (KD < 1010 M) anti-hapten monoclonal 2
Abs. The haptencoding strategy permits both the 1
Abs and 2
Abs to be applied as “cocktails” without risk of cross-reactivity (Fig. 1a). Along with high specificity, because each 1
Ab is conjugated to multiple haptens and each anti-hapten 2
Ab is conjugated to multiple

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Fig. 1 UltraPlex MxIF technology. (a) Workflow schematic in which a cocktail of hapten-labeled primaries is incubated on the tissue followed by a cocktail of fluorophore-labeled anti-hapten antibodies. (b) Images of luminal B “triple-positive” breast cancer tissue stained with (left) a cocktail of directly labeled fluorescent 1
Abs detecting HER2, ER, and PR; (center) rabbit anti-HER2 1
Ab followed by fluorophore-labeled anti-rabbit 2
Ab; (right) an UltraPlex MxIF staining panel consisting of hapten-labeled HER2, ER, and PR 1
Abs followed by fluorophore-labeled anti-hapten 2
Abs. In the left panel, use of directly labeled antibodies renders the ER and PR signals too dim to be seen. In the center panel, only a single antigen (Her2) can be probed as all 1
Abs are of rabbit origin. Only the UltraPlex approach can be used to multiplex all three antigens, while clearly revealing each antigen on the tissue

fluorophore molecules, UltraPlex offers high sensitivity, with detection equivalent to that of conventional anti-species 2
Abs and superior to that of directly conjugated fluorescent 1
Abs (Fig. 1b). In contrast to the limited use of anti-species 2
Abs in MxIF applications, UltraPlex MxIF is limited only by the number of available hapten/anti-hapten pairs. However, MxIF of four antigens with four fluorescent colors on each slide (plus a nuclear counterstain) is well matched to the capabilities of commercial fluorescent microscopes or digital slide scanners. Thus, we have constructed panels of four 1
Abs that recognize related antigens such as TIL subsets or tumor cell markers. The UltraPlex MxIF experiment starts with cutting and processing tissue sections exactly

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as for conventional IHC. Either frozen or FFPE tissues are acceptable. Then, a “cocktail” of four hapten-barcoded 1
Abs is applied to each section, incubated for 1 h, and washed. This is followed by applying a cocktail of four fluorescent anti-hapten 2
Abs, incubation for 1 h, and washing. Each slide is then coverslipped and imaged by fluorescence microscopy or digital slide scanning. Optionally, UltraPlex hapten-modified microspheres can be used for instrument calibration and image quantitation. Images can be analyzed using standard biological image processing freeware (ImageJ-Fiji, [5–7]). Alternatively, commercially available digital pathology software packages can be used to facilitate quantitative and spatial analysis of features of the immune microenvironment revealed by UltraPlex MxIF staining. UltraPlex panels are able to simultaneously assess a number of parameters of potential prognostic and therapeutic significance in disease (Fig. 2). UltraPlex staining of normal tonsil tissue (Fig. 2a) with CD4, CD8, Ki-67, and CD31 antibodies shows T cells residing primarily in the interfollicular areas (CD4 and CD8), proliferating cells in the germinal centers (Ki-67) and well-organized endothelial cells lining the blood vessels (CD31) within this immune tissue. The use of this same panel to stain an aggressive form of breast cancer, triple-negative breast cancer (TNBC, Fig. 2b), shows infiltration of T cells (CD4 and CD8), significant cellular proliferation (Ki-67), and a high degree of angiogenesis (CD31). The latter is shown by the numerous small spheroid budding structures stained by CD31 within the tumor tissue. The identification of cells co-expressing two or three markers further demonstrates the capabilities of the UltraPlex MxIF technology (Fig. 3). Here, melanoma tissue was stained with a 4-plex

Fig. 2 UltraPlex 4-plex staining panel utilized on two types of tissue. CD4 (green), CD8 (yellow), Ki-67 (purple), and CD31 (red) 4-plex images of (a) tonsil tissue and (b) triple-negative breast cancer tissue (TNBC). CD4- and CD8+ T-cell subtypes are visible in both images, as are proliferating Ki-67+ cells, either in the germinal centers of tonsil tissues or throughout the tumor tissue. Note CD31 staining of fully formed vesicles in the tonsil, compared to the punctate CD31 staining on TNBC indicating widespread angiogenesis

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Fig. 3 UltraPlex single-cell phenotyping. 4-Plex staining of immune cell markers CD4 (purple), CD8 (green), FOXP3 (yellow), and T-bet (magenta) on melanoma tissue (left) allows identification of CD4+ FOXP3+, CD4+ T-bet+, CD8+ T-bet+, and CD4+ FOXP3+ T-bet+ co-expressing cells (right). Each of these phenotypes represents a unique immune cell function

panel detecting CD4, CD8, FOXP3, and T-bet leading to identification of eight phenotypes including CD4+ FOXP3+, CD4+ T-bet+, CD8+ T-bet+, and CD4+ FOXP3+ T-bet+ co-expressors. Each of these phenotypes represents an immune cell type with a unique biological function. As a simple approach to allow rapid high multiplexing, multiple 4-plex panels can be applied to serial FFPE tissue sections. The images from two or more sections are digitally aligned and the data combined into a composite image, yielding virtual images with 8 or more antigens. As an example (Fig. 4), a digital 8-plex image of tumor tissue with immune infiltrate was produced by overlaying serial sections stained with two 4-plex MxIF panels: a tumor cell panel including Her2, ER, PR, and Ki-67 and a TIL panel including CD3, CD4, CD8, and CD20. The 4-plex images are used to identify cellular subsets, and the 8-plex image allows visualization of cell subsets across tissue.

2

Materials The UltraPlex staining procedure requires standard molecular biology laboratory materials, including adjustable volume pipettes, volumetric glassware, balance, pH meter, mixing apparatus, purified water, and a ventilated chemical fume hood. Utilize

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Fig. 4 Digital 8-plex image of tumor tissue obtained by overlaying images of 4-plex-stained tissue sections. 4-Plex staining of serially cut tissue sections reveals tumor cell antigens (left, 4-plex A) including HER2 (red), ER (blue), PR (green), and Ki-67 (magenta) or TIL antigens (right, 4-plex B) including CD3 (cyan), CD4 (light green), CD8 (orange), and CD20 (light magenta). The 4-plex images are then digitally overlaid to create a digital 8-plex (center), allowing visualization of both tumor cells and TIL subsets across the tumor tissue

appropriate personal protective equipment as needed during staining procedure. Dispose of all used materials in accordance with laboratory regulations. 2.1 UltraPlex Fluorescent Immunohistochemistry

1. Tissue slides: tissue may be prepared as either formalin-fixed paraffin-embedded (FFPE) sections (see Note 1) or frozen sections (see Note 2). Prepared tissue slides can be mounted with either 1 or 2 tissue sections of up to 1 cm diameter each. Alternatively, tissue microarrays may be used. Tissue sections should be cut to 3–5 μm thickness and evenly spaced across slide surface. Preferably, tissue will be selected from serially cut sections, enabling digital image overlay. All tissue should be mounted on positively charged slides for enhanced adherence, preventing sample loss during staining process. Glass slides with “frosted” labeling ends are preferred for sample identification, marked by the researcher using a solvent-resistant pencil. 2. Glass slide staining containers: with slide holder inserts, solvent-resistant, volume capacity 200 mL.

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3. Deparaffinization reagent: xylene, ACS grade, 98%. Xylene is flammable and hazardous by skin contact or vapor inhalation; see Note 3 for safety considerations. Alternatively, if using autostainer, use deparaffinization reagent provided by autostainer manufacturer. 4. Ethanol: histology grade, 95%. Ethanol is flammable; use appropriate storage and working conditions. 5. Purified water: distilled or deionized water (dH2O). For some steps, tap water may be acceptable (see Note 4). 6. Heat-induced epitope retrieval (HIER) buffer: select UltraPlex kits (Cell IDx) will include HIER buffer 10 concentrate. For most applications, 1 HIER buffer consists of 10 mM sodium citrate, pH 6.0. Add 2.94 g sodium citrate tribasic dihydrate and 0.5 mL Tween 20–800 mL of dH2O, and use a magnetic stirrer to mix until dissolved. Adjust pH to 6.0. Add dH2O to bring volume to 1000 mL. HIER buffer can be stored for up to 3 months at 4
C, but pH should be checked prior to each experiment if stored solution is used. 10 HIER buffer concentrate may also be prepared if desired and then diluted to 1 and pH checked just prior to experiment. If using autostainer, use HIER buffer provided by autostainer manufacturer. If researchers provide 1
Abs, appropriate HIER buffer should be empirically determined for optimal epitope detection of the provided 1
Abs. 7. Heat-resistant plastic “Coplin”-style slide jars, buffer volume capacity 50 mL. 8. Pressure cooker or histology steamer capable of maintaining a constant temperature of 120
C with interior capacity sufficient to hold at least two of the Coplin jars described above. 9. 10% neutral-buffered formalin: necessary only if using frozen unfixed tissue. Formalin is toxic; use appropriate storage and working conditions. 10. Phosphate-buffered saline (PBS) buffer 1: 137 mM sodium chloride (NaCl), 2.7 mM potassium chloride (KCl), 10 mM sodium phosphate dibasic (Na2HPO4), 1.8 mM potassium phosphate monobasic (KH2PO4), pH 7.4. Many premixed commercially available PBS formulations are suitable. If 10 PBS concentrate is obtained, it should be diluted to 1 using dH2O prior to use. 11. Wash buffer: select UltraPlex kits will include 10 wash buffer concentrate. Generally, a suitable 1 wash buffer can be prepared as 0.2% Tween 20 in PBS pH 7.4. For 1 L of 1 wash buffer, add 2 mL Tween 20 to 998 mL PBS. Wash buffer should be prepared fresh and not stored. Alternatively, if using autostainer, use wash buffer provided by autostainer manufacturer.

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12. Hydrophobic “PAP” slide pen. 13. Blocking buffer: provided with UltraPlex kit at working concentration. Consists of 3% normal rabbit serum and 0.1% Triton X-100 in PBS. 14. Primary antibodies (1
Abs): UltraTag-conjugated 1
Abs, provided with UltraPlex kit. Each of the UltraTag 1
Abs is provided as a 100 solution. Dilute 100-fold into antibody diluent buffer just prior to use in a volume suitable for applying 150 μL per tissue section. Alternatively, researcher-provided 1
Abs may be used instead of Cell IDx 1
Abs, but these must be first conjugated to UltraTag barcodes; for more information, see Note 5. 15. Secondary antibodies (2
Abs): anti-UltraTag rabbit monoclonal antibodies conjugated to spectrally distinct “CL” series fluorophores, provided with UltraPlex kit. Each 2
Ab staining solution is provided as a 100 concentrate. Dilute 100-fold into antibody diluent buffer just prior to use in a volume suitable for applying 150 μL per tissue section. User-provided 2
Abs are not compatible with the UltraPlex system. 16. Calibration microspheres: UltraTag-barcoded, uniformly sized 6 μm particles suitable for microscopy imaging, provided in solution for labeling with anti-UltraTag fluorescent 2
Abs alongside tissue staining experiment. Calibration microspheres are an optional UltraPlex kit component and may also be purchased separately as needed. 17. Antibody diluent buffer: provided with UltraPlex kits at working concentration. Consists of 1% bovine serum albumin and 0.2% Tween 20 in PBS. 18. Slide staining tray with water reservoir for humidification and light-blocking lid. 19. Mounting medium: select UltraPlex kits will include mounting medium. In general, an aqueous antifade mounting media with a nuclear counterstain, such as Fluoroshield with DAPI, is suitable. It is critical to use an aqueous mounting medium without solvents or phenylenediamine, which can quench UltraPlex fluorophores. For example, VECTASHIELD and PolyMount mounting media are incompatible with UltraPlex assay fluorophores. 20. Cover glass: 24  50 mm. Other lengths of cover glass may be used; however, the cover glass must be able to completely cover the tissue specimen. A cover glass thickness of 0.13–0.19 mm (designated #1 or #1.5) is recommended for use with many standard microscope objective lenses. 21. Clear nail polish for permanently sealing edges of coverslip, if desired. 22. Slide storage boxes.

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UltraPlex-stained tissue slides may be imaged using a conventional widefield fluorescent microscope or a digital fluorescent slide scanner. For comments on use of a confocal fluorescence microscope, see Note 6. For imaging UltraPlex slides using a widefield fluorescent microscope, the following system components are recommended: 1. Inverted widefield fluorescence microscope with mechanical X-Y stage, ocular lenses for specimen viewing, multiple objective turret, bright-field illumination, port for external fluorescence excitation lamp, and port for digital camera mounting. 2. Light source capable of fluorophore excitation within a range of approximately 300–800 nm. Mercury, metal halide, or LED light sources may provide acceptable excitation. 3. Fluorescence filter sets for 4-plex assay (Table 1): Additional filter sets and fluorophores will be required if performing 5- or 6-channel custom UltraPlex assays or if any filter set listed above is not available to the researcher (see Note 7). 4. Objective lens(es) with 4–100 magnification as desired. If available, “apochromat” objectives are preferred for high compatibility with multiple-channel fluorescence and excellent optical flatness. 5. High-resolution monochrome digital camera. Cooled CCD or CMOS cameras have both been used with success. 6. Computer workstation running image acquisition software. For imaging UltraPlex slides using a digital slide scanner, components should consist minimally of the materials below. For component specifications of a commercial digital slide scanner validated in the laboratory by Cell IDx for scanning UltraPlex slides, see Note 8.

Table 1 Commonly used fluorescence filter sets compatible with the CL fluorophores used in the UltraPlex MxIF assay. Wavelength units shown are in nanometers (nm). These and comparable filter sets are commercially available from several companies Fluor Fluorophore excitation

Fluor emission

Common filter set name

Excitation filter

Bandpass filter

Emission filter

CL490

491

515

EGFP/FITC

470/40

495

525/50

CL550

550

565

Cy3/TRITC

545/25

565

605/70

CL650

655

676

Cy5

620/60

660

700/75

CL750

759

780

Cy7

710/75

760

810/90

DAPI

358

461

DAPI

350/50

400

460/50

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7. Automated slide loading compartment with 8 slide capacity. 8. Objective lenses of 1.25–40. 9. Light source capable of fluorophore excitation within a range of approximately 300–800 nm. 10. Emission filter sets compatible with CL fluorophore excitation/emission spectra. 11. High-resolution camera with multi-megapixel sensors for a large field of view and a wide dynamic range. 12. Computer workstation running image acquisition software capable of opening and viewing microscopy images generated by digital slide scanner. Software provided by scanner manufacturer is generally preferable. The following components are required for digital image overlay and analysis: 13. Technical software for image processing and analysis. ImageJFiji (NIH) freeware is recommended, as described in Subheading 3.5. 14. Large capacity (1 terabyte) external storage drive for storing and transporting digital image files (optional, but recommended).

3

Methods

3.1 UltraPlex Fluorescent Immunohistochemistry Manual Staining Procedure

This procedure begins with several steps for preparation of FFPE tissue for staining. If frozen tissue is preferred, omit steps 1–5 below and instead perform the brief steps described in Note 9. Then, complete the procedure below, starting at step 6. This procedure is written for staining of 10 slides with 1 tissue section per slide. Each tissue section is treated with 150 μL per reagent unless otherwise indicated. Researchers may scale volumes and quantities accordingly depending on size of tissue sections and number of slides per assay. Control tissue slides should be included with all experiments (see Note 10). 1. Prepare 100 mL of HIER buffer and verify pH ¼ 6.0. Add 50 mL of prepared HIER buffer to each of 2 heat-resistant plastic Coplin jars. Loosely affix the jar lids. Place containers into pressure cooker or histology steamer apparatus. Preheat apparatus to  120
C. This step serves to preheat the HIER buffer, improving heat-induced epitope retrieval. 2. In a chemical fume hood, fill a glass slide staining container with 200 mL xylene. Deparaffinize tissue by immersing slides 2  5 min in xylene, using fresh xylene for the second immersion step. Transfer used xylene into a labeled and sealed solvent waste container for disposal.

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3. Fill glass slide staining container with 200 mL of 95% ethanol. Immerse slides for 2 min. Perform subsequent 2-min immersions in 200 mL each of descending ethanol concentrations at 90% (2 min), 70% (2 min), and 50% ethanol (2 min). Use dH2O to dilute 100% ethanol to desired final percentage. 4. Fill a clean glass slide staining container with dH2O. Immerse slides for 2  5 min, using fresh dH2O for the second immersion step. 5. Temporarily remove plastic Coplin jars from heated antigen retrieval apparatus using heat-resistant gloves. Evenly distribute slides into preheated plastic Coplin jars containing HIER buffer, and return containers to heated apparatus. Steam for 15 min. Power off the apparatus and cool slides within for 10 min. Release pressure from apparatus, if using a pressure cooker. Using heat-resistant gloves, remove slide containers and place on benchtop. 6. Transfer slides to a staining container filled with 200 mL wash buffer, and rest for 20 min on benchtop. 7. Fill humidified staining tray with dH2O to level recommended by tray manufacturer. 8. Remove slides from wash buffer. Remove excess slide moisture by blotting the edge of glass with absorbant lens paper (e.g., “Kimwipes”) or lint-free paper towels. Use caution to avoid contact with tissues. 9. Transfer slides to elevated supports within humified staining tray, at least 1 cm above the level of the water in tray basin. 10. Allow slides to briefly air dry (1 min). Excessive moisture on slide glass may interfere with application of hydrophobic pen liquid. 11. Encircle each tissue section with hydrophobic pen. Allow to dry for 1 min. 12. Pipet 150 μL of blocking buffer per tissue section. Cover humidified tray. Block slides for 20 min. 13. Prepare the 1
Ab staining solution. Into 1.5 mL antibody diluent buffer, pipet 15 μL of each 100 1
Ab concentrate. Mix by pipetting and place tube on ice. 14. Remove blocking buffer by tilting each slide to a 90
angle on a paper towel. Place slides flat on elevated supports in humidity tray. 15. Pipet 150 μL of the diluted 1
Ab staining solution onto each tissue section. Place lid on tray. Incubate for 60 min. 16. Fill a clean glass slide staining container with 200 mL of wash buffer. Load slides into container insert, slowly immerse in container, and incubate for 3 min. Repeat three times, adding fresh wash buffer each time.

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17. Prepare the 2
Ab staining solution. Into 1.5 mL antibody diluent buffer, pipet 15 μL of each 100 2
Ab concentrate. Mix by pipetting and place tube on ice. 18. After step 16 is concluded, tip off excess wash buffer onto paper towels. Place slides flat in staining tray. 19. Pipet 150 μL of the diluted 2
Ab staining solution onto each tissue section. Place lid on tray. Incubate for 60 min. 20. Wash slides as described in step 16. 21. Add 200 mL of dH2O to a clean glass slide staining container. Incubate slides for 3 min. 22. Align slides in staining tray, and apply 2 drops of aqueous mounting medium (e.g. Fluoroshield plus DAPI) to each slide, ensuring tissue is covered by media. Incubate for 5 min in the staining tray with the light-blocking lid in place. 23. Apply cover glass to each slide, avoiding the introduction of air bubbles to the specimen under the glass. 24. Allow mounting media to harden by letting slides sit for at least 1 h at room temperature before imaging. Use light-blocking tray lid or foil to protect from ambient light. Additionally, clear nail polish may also be applied to the borders of the cover glass for immobilization. 25. When slides are dry, load into slide box for transport to imaging and long-term storage. 26. Proceed to imaging. 3.2 UltraPlex Fluorescent Immunohistochemistry-Automated Staining Procedure

This section of the protocol summarizes the use of UltraPlex reagents within an established autostaining method. This method employs FFPE tissue and a Leica Bond Rx-automated research stainer. Other autostainers have been reported to work with UltraPlex reagents, such as the Ventana DISCOVERY. Manufacturer protocol dewaxing and epitope retrieval functions are run on the Bond Rx instrument, followed by a custom program for blocking and antibody incubation optimized for UltraPlex staining. For more detailed information on routine use of the Leica Bond Rx, please see manufacturer user manual. 1. Load required premixed solutions into appropriate reservoirs in Bond Rx instrument, including Leica Bond Dewax Solution (s), Bond Epitope Retrieval Solution 1/2, and Bond Wash Solution. If these solutions already exist in the instrument reservoirs, verify solutions are fresh and that volume levels are sufficient to run desired number of UltraPlex slides. 2. Add dH2O to appropriate reservoir. 3. Add blocking buffer to appropriate reservoir.

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Table 2 Custom program for automated UltraPlex MxIF slide staining using Leica Bond Rx autostainer Step

Reagent

Step type

Time (min:s)

Temp

Dispense Vol (μL)

1

dH2O

Reagent incubation

0:00

Ambient

150

2

Bond wash

Wash step

0:10

Ambient

150

3

Bond wash

Wash step

0:10

Ambient

150

4

Blocking Soln

Reagent incubation

20:00

Ambient

150

0

5

1 Ab

Reagent incubation

60:00

Ambient

150

6

Bond wash

Wash incubation

3:00

Ambient

150

7

Bond wash

Wash step

3:00

Ambient

150

8

Bond wash

Wash step

3:00

Ambient

150

0

9

2 Ab

Reagent incubation

60:00

Ambient

150

10

Bond wash

Wash step

3:00

Ambient

150

11

Bond wash

Wash step

3:00

Ambient

150

12

Bond wash

Wash step

3:00

Ambient

150

4. Prepare 1
Ab and 2
Ab UltraPlex antibody solutions diluted to 1 in desired amount of antibody diluent buffer. Add 300 μL to total preparation volume to account for dead volume in solution reservoirs of autostainer. 5. Transfer 1
Ab and 2
Ab solutions to appropriate instrument reservoirs. 6. Load FFPE slides into Bond Rx instrument. 7. Run standard “Dewax” function using program [*D]. 8. Run standard “HIER” function using program [ER2*H2 (20)]. 9. Run UltraPlex IF function using custom program as shown in Table 2 below: 10. At the conclusion of the Bond Rx autostaining protocol, remove slides from instrument. Proceed to tissue mounting procedure at Subheading 3.1, step 21. 3.3 Preparation of UltraTag Calibration Microspheres

It is most efficient to perform this brief procedure concurrently with 2
Ab labeling of tissues as described in Subheading 3.1, step 19. 1. Prepare 100 μL of each anti-UltraTag 2
Ab by adding 1 μL of 100 concentrate to 99 μL of antibody dilution buffer. 2. Briefly vortex each tube of UltraTag microspheres.

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3. Add 10 μL of each UltraTag microsphere suspension to corresponding anti-UltraTag 2
Ab solution. Mix by pipetting. 4. Incubate for 60 min at ambient temperature with gentle mixing. 5. Centrifuge tubes for 5 min at 2000  g at ambient temperature. 6. Carefully remove supernatant. 7. Wash microspheres by adding 1 mL of PBS and pipetting to mix. 8. Centrifuge as described in step 5 above. 9. Resuspend in 100 μL PBS. 10. Pipet 100 μL of microsphere suspension onto standard glass slides. Use a separate slide for each microsphere suspension. 11. Apply cover glass. 12. Remove excess moisture from slide surface. 13. Seal cover glass edges with clear nail polish. 14. Allow slides to dry in the dark alongside UltraPlex-stained tissue slides until imaging session. 3.4 UltraPlex Slide Imaging Procedure

Whether utilizing a conventional widefield fluorescent microscope or digital slide scanner, the workflow is similar for imaging UltraPlex-stained slides. Order the slides such that the first in the group are the calibration microspheres, followed by the positive tissue antigen controls, the unstained and the 2
Ab-only tissue controls, and lastly the experimental tissue slides. 1. Power on fluorescence excitation lamp, microscope components, digital camera, and computer workstation. 2. Open image acquisition software. 3. Clean residual mounting media from slides, if necessary, using lens paper and a small amount of water or mild glass cleaner. 4. Place slides into autoloader of digital slide scanner, if applicable, according to manufacturer’s instructions. 5. Position first slide on microscope stage with cover glass facing objective lens. 6. Rotate desired magnification objective into place under slide (typically 10–40). 7. Move the DAPI filter set into place. 8. Using the camera software, focus the calibration microspheres. 9. Adjust the DAPI signal exposure time such that the majority (> 90%) of microspheres are visible, but not exhibiting signal overexposure. Record the optimal signal exposure time in milliseconds.

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Fig. 5 UltraTag calibration microspheres visualized at 20 magnification using a widefield fluorescent microscope, here shown labeled with anti-UltraTag CL490 2
Ab. Microspheres allow calibration of instrument settings and subsequent evaluation of tissue antigen expression levels across fluorescent channels

10. Move the CL490 filter set into place. 11. Adjust the CL490 signal exposure time such that the majority (>90%) of microspheres are visible, but not exhibiting signal overexposure (Fig. 5). Record the optimal signal exposure time in milliseconds. 12. Proceed to the next calibration slides for CL550, CL650, and CL750, repeating steps 10 and 11 for each fluorescent channel. This will calibrate the microscope for tissue imaging. Then, proceed to tissue slides. 13. Move the first antigen-positive tissue slide into place on the microscope stage. 14. Using the camera software, observe the DAPI signal at low magnification to focus the tissue. 15. Proceed to image all slides using the calibrated exposure times determined in steps 9–12. 16. If using a standard microscope, capture at least three regions of interest (ROI) per tissue section. If using digital scanner, capture at least 80% of tissue area. Images taken in each fluorescent channel must be recorded for each ROI or tissue section. If performing serial section alignment as described in Subheading 3.4, imaged tissue ROIs must be aligned using distinct features of tissue morphology known to be present across serially cut sections, such as ducts, crypts, or other macrostructures.

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17. Save each acquired image as a separate, 16-bit gray scale TIFF file, unless otherwise mandated by the acquisition software. JPEG or otherwise compressed files are not recommended due to loss of important pixel data by compression algorithms. 18. Keep a record of the image file names while saving the TIFF files. It is crucial to be able to properly match image files with their corresponding tissues after the imaging session is complete. The fluorescent channel of each image should also be noted. 19. After saving the TIFF files to a hard drive location, copying the TIFF files to an external drive or server is recommended, as the file set may be quite large (gigabytes) and use a significant amount of hard drive space. The external storage location also allows data portability. 3.5 UltraPlex Digital Image Overlay Procedure

The following procedure describes how to prepare a digital 8-plex image using two 4-plex images as input. The 4-plex images should be derived from serially cut tissue sections to ensure biological relevance of signal colocalization. The digital image overlay procedure can be scaled up as needed, i.e., to 12-plex (or higher). The workflow described below is for ImageJ-Fiji 2019 and should be updated by the user as needed for compliance with future versions of the software. 1. Open ImageJ-Fiji software on computer workstation. 2. Perform digital alignment of tissue images: (a) Install .jar files for plugins “StackReg” and “TurboReg” [8, 9] in the ImageJ-Fiji plugins folder. (b) Open TIFF files in ImageJ-Fiji. (c) Combine images into a stack using the menu commands Image ! Stacks ! Images To Stack. (d) Convert stack to an 8-bit file (Image ! Type ! 8-bit). (e) Run plugin “StackReg.” Follow plugin menu prompts to perform alignment. Adjust parameters to achieve optimal alignment across series of images. (f) Save image stack as a new TIFF file for record-keeping purposes. The TIFF stack is then used in several applications in the following steps. 3. Utilize UltraPlex 4-plex images to identify cell types of interest: (a) Convert the TIFF stack prepared above to individual images using Image ! Stacks ! Stack to Images. (b) Select 4 images from one ROI with staining of cell marker panel of interest (e.g., tumor cell markers or lymphocyte subset markers).

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(c) Create a pseudocolored 4-plex composite image using the selected images using Image ! Color ! Merge Channels and completing the menu prompts. The resulting image will enable identification and quantitation of cell types displayed in the 4-plex image (Fig. 4, left and right panels). (d) Repeat steps a–c for each ROI and 4-plex panel desired. (e) Save each composite image as a new TIFF for analysis and record keeping. (f) Perform quantitation on 4-plex images. For example, total counts of cell subsets, distance between types of immune cells and tumor cells, or percent of cells exhibiting a specific marker. Many detailed procedures for biological image quantitation using ImageJ-Fiji can be found in [5–7] and elsewhere. 4. Overlay UltraPlex 4-plex images to create 8- or 12-plex (or more) images: (a) Open 4-plex composite TIFFs desired for overlaying purposes. (b) Select the first 4-plex composite window. (c) Overlay the second 4-plex composite window using Image ! Overlay ! Add Image. . . and completing the menu prompts to create the overlaid image. The resulting image should appear comparable to Fig. 4, center panel. (d) Repeat step c to overlay additional 4-plex composites as desired. (e) Flatten finalized composite image using Image ! Overlay ! Flatten. (f) Save each flattened image as a TIFF for further analysis (if necessary) and record-keeping purposes.

4

Notes 1. FFPE tissue slides should be prepared from FFPE tissue blocks according to conventional procedure [10]. Tissue sections should be cut to a thickness of 5 μm and affixed to positively charged slides with a “frosted” labeling area (e.g., Fisherbrand “Superfrost Plus”). Following standard FFPE slide preparation, FFPE tissue slides should be oven-baked at 60
C for up to 2 h to ensure adherence of tissue sections to slides. FFPE slides may be stored at ambient temperature. Prior to staining, inspect slides with bright-field microscopy to assess tissue embedding integrity. Tissue should appear uniformly embedded within paraffin and affixed to the slide surface. Tissue

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without this appearance may be subject to partial or complete dissociation of the tissue from the slides and, therefore, an abrogation of the staining process. If tissue slides pass visual inspection, proceed to staining. 2. Frozen tissue sections are generally cut from a frozen tissue block embedded in OCT cryoprotectant [10]. It is necessary for sections and tissue blocks to be stored at 80
C to prevent ice crystal formation. Once tissue is thawed, it must be used immediately to prevent cellular autolysis. If tissue has been previously fixed, either by immersion or perfusion, HIER may be desired, as determined by the researcher. If frozen tissue is unfixed, a short fixation step is recommended (e.g., in 10% neutral-buffered formalin as described in Note 9). A fixation step helps to ensure epitope recognition by the 1
Abs used in the UltraPlex assay, which are typically optimized for detection of fixed epitopes in FFPE tissue. 3. Xylene is hazardous by inhalation or skin contact. Always work with xylene in a ventilated chemical fume hood wearing protective gloves and eyewear. Xylene is also highly flammable; keep away from heat sources. Store in flammable-safe cabinet. For disposal, place in a labeled and sealed hazardous waste container. Do not mix with other waste chemicals. Never dispose of xylene in laboratory sinks. 4. While purified dH2O is generally preferred for all molecular biology procedures, tap water is often used for routine histology wash steps in research and clinical labs. If necessary, a cold tap water bath can be created in a clean laboratory sink for slides immobilized in an immersion rack. The tap water stream should not run directly over the slides, so that it does not disturb the tissue adhering to the slides. If desired, a tap water bath can be used after the ethanol rehydration step (Subheading 3.1, step 3) and/or just prior to mounting slides (Subheading 3.1, step 21), in place of the noted dH2O steps. Tap water may also be used to fill the humidified staining tray. However, for buffer preparation, tap water is not recommended and dH2O should be used. 5. An UltraTag barcode conjugation kit is available from Cell IDx that enables rapid (< 3 h) conjugation of UltraTags to userprovided 1
Abs. The kit is resin spin column based and requires only basic laboratory equipment (pipets, microcentrifuge). In brief, the materials for this procedure include: (a) Desalting and buffer exchange spin columns. (b) Conjugation buffers. (c) UltraTag barcodes in solution.

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(d) User-provided, protein carrier-free 1
Abs. Protein carriers, such as bovine serum albumin (BSA), may interfere with conjugation, 50 μg of each 1
Ab at 1.0  0.1 mg/ mL. The conjugation procedure briefly involves: (a) 1
Ab preparation for conjugation (1 h) (b) Conjugation reaction (1 h). (c) Conjugate purification (10 min). A detailed protocol and kit purchasing information are available at http://www.cellidx.com. 6. Confocal microscopes have not yet been extensively validated with the UltraTag multiplex technology. Widefield microscopy has been initially used as it enables more sensitive detection of fluorescent signals (many confocal microscopes compromise signal brightness in order to resolve a single focal plane). However, a confocal microscope with similar system components to the widefield microscope described in Subheading 2.2 would likely be effective. Because signal intensity is an issue, confocal microscopes are equipped with high-powered excitation lasers rather than a typical broad-spectrum excitation lamp used for widefield microscopy. If using excitation lasers for UltraPlex imaging, the researcher should identify the optimal excitation laser for each fluor used in the UltraPlex assay. For example, for CL490, a 488 nm excitation laser would be appropriate, while for CL650, a 633 or 640 nm laser would likely be effective. 7. The procedure described herein utilizes a 4-plex fluorescent panel consisting of CL490, CL550, CL650, and CL750. Using these CL fluors with the appropriate filter sets shown in Table 1 results in excellent fluorescent channel specificity, with extremely low (if any) spectral cross talk across fluorescent channel. This feature obviates the need for complex spectral unmixing procedures to correct spectral cross talk. If a higher multiplexed assay is desired, a 6-plex panel has been validated by Cell IDx using narrow bandwidth excitation and emission filters (see Table 3). If narrow bandwidth filter sets are not available to the researcher, to enable the 6-plex, a spectral unmixing procedure must be conducted to correct for spectral cross talk, for example, as described in [3]. 8. Component specifications for the Leica Aperio VERSA digital slide scanner: (a) Objectives: 1.25 HC PL Fluotar 0.04 NA; 5 HC PL Fluotar 0.15 NA; 10 HC PL Fluotar 0.32 NA; 20 HC PL APO 0.80 NA; 40 – HC PL APO 0.95 NA. (b) Light source: X-Cite series 120PCQ high-pressure metal halide arc lamp. (c) Fluorescence filter sets (Table 3):

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Table 3 Filter sets used for a 6-plex UltraPlex MxIF assay using a commercially available fluorescent digital slide scanner (Leica Aperio VERSA). Wavelength units shown are in nanometers (nm). These and comparable filter sets are commercially available from several companies Fluorophore Fluor excitation Fluor emission Excitation filter Bandpass filter Emission filter CL490

491

515

485/25

505

525/30

CL550

550

565

546/22

565

590/33

CL650

655

676

635/30

660

680/30

CL700

707

730

695/30

720

740/35

CL750

759

780

750/20

770

800/50

DAPI

358

461

350/50

400

460/50

(d) Camera: Zyla 5.5 sCMOS (Andor Technology). (e) Imaging software: Aperio ImageScope (Leica). 9. This note briefly summarizes steps specific to the preparation of unfixed frozen tissue sections for the UltraPlex assay. Perform the steps below instead of steps 1–5 in Subheading 3.1, and then continue the Subheading 3.1 protocol at step 6. (a) Remove slides from freezer. Thaw for 10 min on benchtop. Transfer to a staining container filled with 200 mL wash buffer for 5 min. (b) In a chemical fume hood, fill a staining container with 200 mL of 10% neutral-buffered formalin. Immerse slides for 10 min. Discard used formalin in a labeled waste container. (c) Proceed to step 6 of Subheading 3.1. If previously fixed frozen tissue is to be used, the researcher should determine if an HIER procedure is necessary to render fixed epitopes accessible to 1
Abs. If HIER is desired, follow the HIER procedure in Subheading 3.1 (steps 1 and 5), and then continue through the protocol from step 6 onward. If HIER is not desired, simply start the Subheading 3.1 staining procedure at step 6. 10. It is recommended to include in each experiment the following control tissue sections: (a) Tissue antigen-positive controls for each antigen, one for each fluorescent channel. (b) Unstained control to evaluate tissue autofluorescence. (c) 2
Ab-only control to evaluate possible nonspecific binding of 2
Ab (nonspecific binding of anti-UltraTag 2
Abs is usually minimal on most tissues).

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References 1. Parra ER, Francisco-Cruz A, Wistuba II (2019) State-of-the-art of profiling immune contexture in the era of multiplexed staining and digital analysis to study paraffin tumor tissues. Cancers 11(2):E247 2. Mansfield JR (2017) Phenotyping multiple subsets of immune cells in situ in FFPE tissue sections: an overview of methodologies. Methods Mol Biol 1546:75–99 3. Walter J (2004) Spectral unmixing for ImageJ v1.2 - documentation. https://imagej.nih. gov/ij/plugins/spectral-unmixing.html 4. Feng Z et al (2015) Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. J Immunother Cancer 3(1):1–11 5. Schindelin J et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682

6. Schindelin J et al (2015) The ImageJ ecosystem: an open platform for biomedical image analysis. Mol Reprod Dev 82(7–8):518–529 7. Jensen EC (2013) Quantitative analysis of histological staining and fluorescence using ImageJ. Anat Rec 296(3):378–381 8. The´venaz P, Ruttimann UE, Unser M (2019) An ImageJ plugin for the recursive alignment of a stack of images. http://bigwww.epfl.ch/ thevenaz/stackreg; Available from: http:// bigwww.epfl.ch/thevenaz/stackreg 9. The´venaz P, Ruttimann UE, Unser M (1998) A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process 7(1):27–41 10. Kalyuzhny AE (2016) Preparing tissues for immunohistochemistry, in immunohistochemistry: essential elements and beyond. Springer, Cham, pp 37–47

Chapter 18 Multimodal Approach for Cancer Cell Investigation Alexandre Berquand and Jeroˆme Devy Abstract Atomic force microscopy (AFM) enables the characterization of a wide range of samples including live cells. It is generally admitted that cancer cells are significantly softer than their normal counterparts, but imaging live cells by AFM using traditional modes can be at the cost of time or resolution. We describe how this tool can be used to estimate the motility of cancer versus normal cells, based on topographical and mechanical approaches, and coupled to optical imaging. Key words Atomic force microscopy, Force measurements, PeakForce QNM, Video microscopy, Fluorescence imaging, Cancer

1

Introduction The low-density lipoprotein receptor-related protein-1 (LRP-1) is a 600 kDa large multifunctional endocytotic receptor involved in the clearance of various molecules from the extracellular matrix. It consisted in a 515 kDa α-chain protruding from the plasma membrane non-covalently linked to an 85 kDa transmembrane receptor. The α-chain is made of four interaction domains able to bind more than 40 ligands as lipoproteins, extracellular matrix macromolecules, and enzymes [1, 2]. LRP-1 is involved in numerous physiological and pathological processes like maintaining the vascular integrity [3] or the blood-brain barrier permeability [4] and regenerating the peripheral nerves [5]. It is also widely involved in the cell migration and proliferation [6]. LRP-1 was found to exhibit antitumor properties in cells derived from prostate [7], kidney [8], colon [9, 10], or endometrium [11] cancers and pro-tumor properties in cell lines derived from brain cancer [12]. Surprisingly, LRP-1 was also proven to have both anti- and pro-tumor functions in the same cell lines. This was shown, for instance, for those derived from the thyroid [13, 14] and breast [15, 16]. Therefore it is of high interest for the cancer community to get better insight on how

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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LRP-1 drives or inhibits the tumor progression at a cellular or tissue level. Since its development in the 1980s, AFM [17] has proven itself to be a suitable tool to image biological samples, more in particular with the emergence of tapping mode [18] and force spectroscopy [19] in near-physiological conditions. AFM is often used to correlate cell migration or division [20–23]. Tapping mode is generally used to get topographical information and force spectroscopy to extract mechanical information. Nevertheless tapping mode is not a quantitative technique, and force spectroscopy suffers from a low acquisition speed. More recently developed, PeakForce Tapping mode (PFT) was developed to overcome those two bottlenecks. It can be briefly described as follows: the piezo is excited at a low frequency, and a force curve is generated each time the tip interacts with the surface, several signals like the height, the maximum loading force, the induced deformation, Young’s modulus, and the energy dissipated between the tip and the sample. If the tip is calibrated prior to the experiment, the technique is referred to as PeakForce QNM (PFQNM) since it allows extracting quantitative nanomechanical information. As a consequence, Young’s modulus will be directly displayed in kPa. This technique has already been successfully used in marine biology [24] or on mammal cells [25] and was recently utilized to put in evidence the topographical, mechanical, and dynamic differences between MDA-MB-231, supposed to be highly invasive, and their counterparts where the expression of LRP-1 has been silenced [26]. The present work details the experimental routine process to enable such comparison. The parallel to results obtained using more classical optical techniques is also discussed.

2 2.1

Materials Cell Culture

1. MDA-MB-231 cell line from the American Type Culture Collection (ATCC). 2. Dulbecco’s Modified Eagle Medium (DMEM) supplemented with GlutaMAX™-I (PAN-Biotech) and 10% fetal bovine serum at 37  C in a 5% CO2 atmosphere. 3. LacSwitch II mammalian expression system (Agilent Technologies, Stratagene Products Division) and shRNA sequences for LRP-1 silencing. 4. Fibronectin from bovine plasma at 1 mg/l diluted in sterile PBS to a final concentration of 7 μg/ml. 5. Type I collagen diluted to a final concentration of 35 μg/ml and used at 5 μg/cm2.

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291

1. 4% (v/v) paraformaldehyde for fixation. 2. 3% (w/v) BSA for saturation step. 3. 1% (v/v) anti-talin antibody (clone 1676, Merck Millipore). 4. Alexa Fluor 488™-conjugated phalloidin and anti-mouse Alexa Fluor 568™. 5. Mounting medium containing DAPI (Antifade Mountant with DAPI). 6. Inverted confocal laser scanning microscope with a 63 oil-immersion objective operating system (see Note 1). 7. Data processing: projection and isosurface representations using AMIRA v6.2 software (Thermo Fisher Scientific). For colocalization studies, a multichannel field module was used, followed by a correlation plot treatment (subrange values 15/255, gamma 0.5).

2.3 Video Microscopy

1. Inverted video microscope with a 20 objective. 2. Environmental chamber conditions.

to work in near-physiological

3. Data acquisition software (here MetaMorph software, Molecular Devices, is implemented). 4. ImageJ software (National Institutes of Health, Bethesda). 2.4 Atomic Force Microscopy

1. AFM (here a BioScope Catalyst™, Bruker, is implemented coupled to a Nikon Ti Eclipse, Nikon) used in bright field with a 10 or 20 magnification. 2. The AFM should be operated in PeakForce Tapping mode with PFQNM-LC-A-CAL probes having a nominal spring constant of 0.1 N/m and a resonance frequency of ~45 KHz. The tip geometry (“ace of spade” shape, with a tip height of 17 μm and an apex diameter of 130 nm) should be designed to minimize the background effect and apply a low force (see Note 2). With respect to the tip calibration, see Note 3. 3. NanoScope Analysis software (Bruker). 4. Gwyddion software (GNU General Public License).

3 3.1

Methods Cell Culture

1. Plate the control and LRP-1-transfected cells in 50 mm glass bottom dishes. 2. Always check the confluence before AFM imaging. If the cells are too confluent, they should be discarded. 3. The dishes should always be kept at 37  C in a 5% CO2 atmosphere.

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3.2 Immunofluorescence

1. Plate the cells (25  104) on fibronectin- or type I collagencoated glass slides. 2. Incubate the samples for 24 h at 37  C. 3. Fix the cells using 4% (v/v) paraformaldehyde. 4. Wash the slides with PBS with 3% BSA (w/v) for 30 min, and incubate them overnight with the anti-talin antibody (final concentration 1% v/v). 5. Wash the slides with PBS and incubate them for 45 min with the Alexa Fluor 488™-conjugated phalloidin and Alexa Fluor 568™ (final concentration 1/1000 v/v). 6. Eventually, incubate the samples overnight with the mounting medium containing DAPI.

3.3 Video Microscopy

1. Seed the cells on type I collagen-coated glass bottom chamber slides, and allow them to attach to the substrate overnight. 2. Observe the cells over 24 h with a 20 objective while maintained at 37  C under a 5% CO2 atmosphere. 3. Calculate the cell speed and directional persistence using ImageJ. 4. For statistics, 50 cells per condition should be tracked.

3.4 Atomic Force Microscopy

1. Calibration and pre-imaging: approximately 2 h before imaging, adjust the set point to 37  C using the lowest possible ramp size. Align the laser in fluid (for instance, PBS buffer) on a PFQNM-LC-CAL probe; wait a minimum of 30 min for thermal equilibration and engage on a poorly compliant sample (like a glass slide). Capture a minimum of three force curves to extract a reproducible value of the deflection sensitivity. Either enter the spring constant value of the manufacturer or calculate it by withdrawing the probe (of at least 20 μm) and running a thermal tune. 2. Put the dish on the base plate, as well as the AFM head on the top of it. Wait another 15–20 min, checking the position of the laser spot in z, which should be idle. Make sure the scan size is set to zero. Engage. 3. When reaching the surface, minimize the loading force and optimize the gains to obtain a decent tracking. Image the cells as long as possible. Always check their status on the optical image to estimate their viability. As soon as 10% of the cells start dying off, stop the experiment and retract the probe. Unlike in video microscopy, AFM is a high-resolution technique so that not only the core of the cells can be tracked over time but also some more specific parts like the migration front or the retraction tails, providing additional details on the migration capabilities.

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Fig. 1 PeakForce QNM images of live MDA-MB-231 cells captured at 128  128 resolution. (a) PeakForce error image showing topographical information. This resolution is good enough to estimate the cell height, volume, or surface, outnumber the protrusions or filopodia, or calculate the migration speed at the retraction tail or the migration front; (b) Young’s modulus image obtained using a Sneddon fit from the raw force curves; (c) deformation image, which contrast is inverted to those of the previous channel; (d) 3D representation of the cell topography in addition to Young’s modulus “skin.” This type of display comes handy to compare height and mechanical information at the same spot; E: typical force curves detailing how the various information can be extracted. For instance, Young’s modulus is obtained by extrapolating the linear part of the extension curve. The deformation is given by the horizontal distance between the contact point and the turn-away point. When the tip is calibrated prior to the experiment, all those parameters should be displayed in a quantitative manner

4. Since the probes have a large tip radius, they may be rinsed with ethanol and cleaned by UV/ozone plasma treatment for further use. 5. Process the data using NanoScope Analysis or Gwyddion. ImageJ can also be used to track cells over time. Various parameters can be extracted like the maximum cell height, the cell projected surface, the cell volume, Young’s modulus, and the speed at the migration front and the retraction tail. Figure 1 displays a typical image which can be obtained in about 6 min at 128  128 pixel/line resolution, as well as a random force curve captured while imaging (see Note 4). The interest of combining confocal and AFM technique is discussed in Note 5.

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Notes 1. Confocal microscopy imaging is used to observe actin cytoskeleton and talin distribution. Actin cytoskeleton labeling reveals stress fibers and protrusions. This information can be directly correlated to the cell polarization and migrating state. In order to get more insight about migration, talin was immunolabeled. Talin is a high-molecular-weight cytoskeletal protein concentrated at regions of cell-substratum contact. It is a ubiquitous protein that is found at high concentration in focal adhesions. Talin is capable of linking integrins to the actin cytoskeleton either directly or indirectly by interacting with vinculin and α-actinin. Therefore, by using Amira™ or ImageJ, it is possible to spot colocalization between actin and talin in order to highlight focal adhesion complexes. A typical example of confocal image is shown in Fig. 2. 2. With respect to AFM imaging, other probes having nominal spring constants below 100 mN/m can also be used. Nevertheless the QNM-LC-CAL probes offer the best compromise since the curvature radius is large and the “ace of spade” shape minimizes the damping effects in fluid. As a consequence, the nominal forces are better spread over the sample. In addition to this although carrying cell debris and membrane tethers over time is inevitable, the tip radius varies definitely less than with a standard probe. At last, recycling such probes after use is much easier. 3. Regarding the tip calibration, the deflection sensitivity and the spring constant must always be calculated prior to each experiment. In theory, for the reasons mentioned above, the tip radius does not have to be estimated for each new experiment. Practically, it is strongly advised to recapture force curves at the end of each imaging session to make sure the deflection sensitivity remains the same; if not, it must be adjusted for Young’s modulus calculation and batch-processed for all the curves. 4. For this type of investigation, we mainly focused our attention on the height, Young’s modulus, and deformation channels. Note that the adhesion and the dissipation can also be extracted from the force curves as follows: the adhesion force is given by the vertical distance between the baseline and the most negative point of the retraction curve. The energy dissipated between the tip and the sample is obtained by integrating the area between the extension and the retraction curves. Nevertheless, the adhesion is not of any relevance for this type of study since the tip is not functionalized. The dissipation is also

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Fig. 2 Immunofluorescence confocal microscopy of MDA-MB-231. MDA-MB231 cells were plated onto collagen for 24 h before being fixed (4% paraformaldehyde). Cells were incubated with phalloidin-Alexa 488™ for actin filaments staining (green) and anti-talin antibody and labeled with secondary antibody linked to Alexa 568™ (red). Nuclei were counterstained with DAPI (blue). Fifteen to 20 images were captured with a 0.25 μm Z step. The isosurface representation was realized with Amira™ software. The actin cytoskeleton reflects the cell migration capabilities and how polarized they are, whereas the focal adhesions reflect the anchoring spots of the cells

quite hard to interpret, given that the dissipated energy may have several contributing factors including the cell viscosity. 5. The interest of such approach consists in combining the information given by fluorescence imaging and video microscopy and those given by AFM [26]. Fluorescence imaging reveals the density of the cell cytoskeleton and the number and localization of adhesion points on the substrate; video microscopy allows determining the average migration speed over a long period of time (typically 24 h); AFM allows determining the migration speed on a shorter period of time (typically a few hours) with much more accuracy (from the movements of the retraction tail or the migration front). The cell’s mechanical properties can also be extracted. Finally, it is mainly a surface investigation technique that returns topographical information like the cell surface, volume, height, or the number of protrusions. All together, the data provided by the three combined techniques gives valuable information of the cells migratory potential.

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References 1. Lillis AP, Greenlee MC, Mikhailenko I, Pizzo SV, Tenner AJ, Strickland DK et al (2008) Murine low-density lipoprotein receptorrelated protein 1 (LRP) is required for phagocytosis of targets bearing LRP ligands but is not required for C1q-triggered enhancement of phagocytosis. J Immunol 181:364–373 2. Gonias SL, Campana WM (2014) LDL receptor-related protein-1: a regulator of inflammation in atherosclerosis, cancer and injury to the nervous system. Am J Pathol 184:18–27 3. Strickland DK, Au DT, Cunfer P, Muratoglu SC (2014) Low-density lipoprotein receptorrelated protein-1: role in the regulation of vascular integrity. Arterioscler Thromb Vasc Biol 34:487–498 4. Zhao Y, Li D, Zhao J, Song J, Zhao Y (2016) The role of the low-density lipoprotein receptor-related protein 1 (LRP-1) in regulating blood-brain barrier integrity. Rev Neurosci 27:623–634 5. Landowski LM, Pavez M, Brown LS, Gasperini R, Taylor BV, West AK et al (2016) Low-density lipoprotein receptor-related proteins in a novel mechanism of axon guidance and peripheral nerve regeneration. J Biol Chem 291:1092–1102 6. Llorente-Cortes V, Barbarigo V, Badimon L (2012) Low density lipoprotein receptorrelated protein 1 modulates the proliferation and migration of human hepatic stellate cells. J Cell Physiol 227:3528–3533 7. Kancha RK, Stearns ME, Hussain MM (1994) Decreased expression of the low density lipoprotein receptor-related protein/alpha 2-macroglobulin receptor in invasive cell clones derived from human prostate and breast tumor cells. Oncol Res 6:365–372 8. Desrosiers RR, Rivard ME, Grundy PE, Annabi B (2006) Decrease in LDL receptorrelated protein expression and function correlates with advanced stages of Wilms tumors. Pediatr Blood Cancer 46:40–49 9. Obermeyer K, Krueger S, Peters B, Falkenberg B, Roessner A, Rocken C (2007) The expression of low density lipoprotein receptor-related protein in colorectal carcinoma. Oncol Rep 17:361–367 10. Boulagnon-Rombi C, Schneider C, Leandri C, Jeanne A, Grybek V, Bressenot AM et al (2018) LRP1 expression in colon cancer predicts clinical outcome. Oncotarget 9:8849–8869 11. Foca C, Moses EK, Quinn MA, Rice GE (2000) Differential expression of the alpha

(2)-macroglobulin receptor and the receptor associated protein in normal human endometrium and endometrial carcinoma. Mol Hum Reprod 6:921–927 12. Song X, Wang JB, Yin DL, Yang HY, Liu LX, Jiang HC (2009) Downregulation of lung resistance related protein by RNA interference targeting survivin induces the reversal of chemoresistances in hepatocellular carcinoma. Chin Med J (Engl) 122:2636–2642 13. Sid B, Dedieu S, Delorme N, Sartelet H, Rath GM, Bellon G et al (2006) Human thyroid carcinoma cell invasion is controlled by the low density lipoprotein receptor-related protein-mediated clearance of urokinase plasminogen activator. Biochem Cell Biol 38:1729–1740 14. Dedieu S, Dedieu S, Langlois B, Devy J, Sid B, Henriet P, Sartelet H et al (2008) LRP-1 silencing prevents malignant cell invasion despite increased pericellular proteolytic activities. Mol Cell Biol 28:2980–2995 15. Li Y, Wood N, Grimsley P, Yellowlees D, Donnelly PK (1998) In vitro invasiveness of human breast cancer cells is promoted by low density lipoprotein receptor-related protein. Invasion Metastasis 18:240–251 16. Fayard B, Bianchi F, Dey J, Moreno E, Djaffer S, Hynes NE et al (2009) The serine protease inhibitor protease nexin-1 controls mammary cancer metastasis through LRP-1mediated MMP-9 expression. Cancer Res 69:5690–5698 17. Binnig G, Quate CF, Gerber C (1986) Atomic force microscope. Phys Rev Lett 56:930–933 18. Zhong Q, Innis D, Kjoller K, Ellings VB (1993) Fractured polymer/silica fiber surface studied by tapping mode atomic force microscopy. Surf Sci 290:688–692 19. Tao NJ, Lindsay SM, Lees S (1992) Measuring the microelastic properties of biological samples. Biophys J 3:1165–1169 20. Lee J, Hishihara A, Theriot JA, Jacobson K (1993) The principles of locomotion for simple-shaped cells. Nature 362:167–171 21. Stossel TP (1993) On the crawling of animal cells. Science 26:1086–1094 22. Dorak JA, Nagao E (1998) Kinetic analysis of the mitotic cycle of living vertebrate cells by atomic force microscopy. Exp Cell Res 242:69–74 23. Rotsch C, Jacobson K, Radmacher M (1999) Dimensional and mechanical dynamics of active and stable edges in mobile fibroblasts

Multimodal Cell Migration Analysis investigated by atomic force microscopy. Proc Natl Acad Sci U S A 96:921–926 24. Pletikapic G, Berquand A, Misic T, Svetlicic V (2012) Quantitative nanomechanical mapping of marine diatom in seawater using peak force tapping AFM. J Phycol 48:174–185 25. Berquand A, Kuhn HM, Holloschi A, Mollenhauer J, Kioschis P (2013) Expression of tumor suppressors PTEN and TP53 in isogenic glioblastoma U-251MG cells affects

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Chapter 19 Multiplexed Fourier Transform Infrared and Raman Imaging Guillermo Quinta´s, Bayden R. Wood, Hugh J. Byrne, and David Perez-Guaita Abstract Infrared (IR) and Raman spectroscopies are being increasingly employed for the label-free analysis of biochemical samples. Both are vibrational imaging techniques, but they provide complementary information about the chemical composition of the sample, and thus the integration of Raman and IR images leads to a comprehensive understanding of the samples. Here, we summarize the steps needed for performing multiplexed infrared and Raman imaging, identifying and overcoming the two main challenges: first, the technical difficulties caused by the incompatibilities of the techniques and, second, the necessity to extract the information from the large number of vibrational variables found in both IR and Raman spectra. Key words Infrared, Raman, Data fusion, Multimodal imaging, Label-free imaging

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Introduction The comprehensive discovery of biomarkers in cells and tissues is challenging, considering the complexity of biological processes. Several imaging techniques, including Raman spectroscopy (RS), Fourier transform infrared (FTIR), X-ray fluorescence (XRF), and mass spectrometry (MS), are needed to analyse specific fractions of cells and tissues [1]. The information obtained from these techniques in terms of elemental composition, protein, lipid, and metabolic composition is complementary, but the analysis of the data is normally performed individually for each technique. Data fusion (i.e. the integrated analysis of data obtained from different sources) is an emerging approach, which is used in biomedical research to obtain a more complete overview of the biochemical status of a biological sample than that obtained by the separate analysis of each source [2]. The ultimate goal is to establish whether the whole is greater than the sum of its parts, i.e. does the combination of the variables from different sources give more information than the analysis of each individual source?

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_19, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The development of statistical tools for data fusion is an active field of research, and a number of advanced multivariate approaches including statistical heterospectroscopy, orthogonal partial least squares (O-PLS and O2-PLS) [3], and joint and individual variation explained (JIVE) [4] have been recently developed and tested on a wide range of biological systems and data sources. IR and RS are emerging as techniques poised to revolutionize clinical research and diagnosis [5–7]. Both are vibrational spectroscopic techniques (i.e. they provide information about the vibrational modes of molecules), but they are complementary in terms of providing molecular information. IR active bands are generally from molecules/functional groups with strong dipole moments that occur during nuclei oscillation, whereas in RS the dipole moment has to be induced by the lasers electric field, and thus symmetric vibrational modes tend to scatter stronger especially those in conjugated molecular structures [8, 9]. Thus, molecules or functional groups that tend to be subject to strong Raman scattering are usually weak IR absorbers and vice versa. The complementarity of the information acquired makes the integration of both modalities very appealing because in theory it should maximize the insights from data. Furthermore, in both IR and Raman spectra, the bands from fundamental transitions are often overlapped when compared with MS or XRF, and thus the assignment of bands to the corresponding biomolecule can be troublesome [10]. Multiplexed imaging can potentially help in this task, as the correlation of IR and Raman bands can elucidate band assignments [11–13]. However, both techniques are based on very distinct quantum events. While IR generally involves the absorption of a mid-IR photon (2.5–25μm), RS relies on a form of inelastic scattering of light and is typically studied using laser sources ranging from the ultraviolet (UV) to the near-infrared (near-IR). These differences provoke several technical difficulties in the integration of the techniques: firstly, RS outperforms FTIR in terms of the achievable spatial resolution, typically in the 0.2–1μm and >5μm range, respectively (see Note 1). Secondly, both techniques require different light sources and detectors (see Note 2). Finally, there are problems in terms of substrate compatibility when performing Raman and IR on the same material (see Note 3). Here, we describe methodologies to overcome these challenges for performing multiplexed hyperspectral images integrating IR and RS of cells and tissues. This involves the combination of IR and Raman hyperspectral images to create an extended data matrix in which each pixel is represented by a unique IR and Raman spectrum. We then discuss basic data fusion procedures that can be performed on the multiplexed images and how to extract the information relative to spatial structures and correlation of variables from them.

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Materials All solutions should be prepared in ultrapure and sterile water. Preparation and storage of the reagents and samples should be done at room temperature. Store organic solvents in suitable cabinets according the appropriate regulations. All fixation processes with solvents should be performed in a suitable fume hood. Tissues and cells must be manipulated following the protocols assigned to their respective biosafety class. Dispose reagents and samples according to the safety rules in place.

2.1 Preparation of Cells for Multiplex Analysis

1. Cell resuspension solution (phosphate-buffered saline (PBS)): To an 800 mL water, add subsequently 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO2, and 0.24 g KH2PO4. Using HCl, adjust the pH to 7.4 and then add ultrapure water to a final volume of 1 L. 2. Cell washing solution: Dissolve 0.9 g NaCl in 100 mL water. 3. Cleaning solution for the CaF2 slides: Isopropanol, analytical grade (>99%). 4. CaF2 slides: CaF2 slides should be Raman grade to avoid interferences on the Raman spectra. 5. Fixation reagent (formalin): Formalin solution, neutral buffered (10%). 6. Fixation reagent (methanol): Methanol anhydrous (>99.8%).

2.2 Preparation of Tissue for Multiplex Analysis

1. 10% neutral-buffered formalin (NBF). Adjust pH to 6.8–7.2 using HCl and NaOH. 2. Methylated spirit (IDA 99), 99% (v/v) (industrial methylated spirit (IMS) T100). 3. Xylenes, histological grade. As an alternative, xylene can be replaced by Histo-Clear or hexane (histological grade). 4. Embedding wax. 5. Ethanol absolute (histological grade).

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3.1 Preparation of Cells for Multiplex Analysis

Sample preparation parameters such as concentration of cells (i.e. number of cells per unit of volume), initial volume of solution, centrifugation time, and speed may require an initial optimization. Further methodological information can be found here [14]. 1. Prepare a 103–106 cells suspension, depending on whether the slide needs to be highly sparse with isolated cells or a highly

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packed monolayer of cells. If cells are not in PBS, resuspend the cells in PBS. 2. Clean the suspension of cells using a washing solution. 3. CaF2 Raman-grade windows can be attached to a microscope slide and mounted into a metal holder using a cytocentrifuge funnel (see Note 4). It is imperative that the CaF2 slide covers the hole of the cytocentrifuge funnel. Metal-coated reflective slides can be mounted directly in the metal holder. 4. Insert 100–400μL of suspension into the cytocentrifuge funnel. 5. Centrifuge for 15 min, adjusting the speed to the cell size. As a rule of thumb, 1000 rpm and 200 rpm (9 and 220 g) are recommended for small (e.g. erythrocytes) or big cells (e.g. leukocytes), respectively [15]. 6. Carefully remove the slide from the metal holder and allow to dry. 7. If required, fix the cells by introducing the slide on the fixative solution (e.g. formalin, methanol). 3.2 Preparation of Tissue for Multiplex Analysis

Although using fresh or fresh frozen tissue are often preferable methods, tissue is more frequently obtained from formalin-fixed, paraffin-preserved tissue blocks. For spectroscopic analysis, the tissue blocks are microtomed to produce tissue sections (5–10μm thick depending on whether a transmission or transflection measurement is performed). For parallel sectioning, the tissue slice is “dewaxed” with multiple washings in clean xylene [16, 17]. For purely spectroscopic analysis, it has been demonstrated that either FTIR or Raman analysis can be performed on the formalin-fixed paraffin-preserved (FFPP) tissue slices and that the strong paraffin peaks can be digitally removed from the spectra through protocols of varying sophistication [18, 19]. 1. FFPP of Tissue. (a) Vacuum fixation in 10% buffered formal saline histological grade pH 6.8–7.2 and heating to 30  C. (b) Vacuum dehydration in industrial methylated spirit IMS T100. (c) Vacuum clearing in xylene and heating to 35  C. (d) Vacuum impregnation with tissue embedding, wax with polymer added, and heating to 59  C. 2. FFPP Tissue Cutting and Mounting Tissue blocks are microtomed to a thickness of 5–10μm, mounted on the appropriate substrate, typically spectroscopicgrade CaF2, and dried. As an alternative, xylene can be replaced by Histo-Clear or hexane.

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3. FFPP Dewaxing The samples are immersed in a series of baths consisting of two baths of xylene for 5 min and 4 min, respectively, two baths of ethanol absolute for 3 min and 2 min, respectively, and a final bath of 95% industrial methylated spirits for 1 min. 3.3 Identification of the Region of Interest

1. Fix the slide to a microscope holder ensuring that it will be stable during the measurement process. Acquiring images with slightly different angles would considerably confound the registration process. 2. Using a visible microscope, locate the feature of interest (e.g. a specific cell or tissue region) to image. 3. Use a marker or a scalpel to create marks around the region of interest (ROI), which will be used as reference points/flags for performing the infrared and Raman images on the exactly same regions. 4. Using soft tissue impregnated with isopropanol, carefully clean a region of the slide close to the ROI (see Note 5). 5. Obtain a visible image of the surroundings of the ROI and reference marks using, e.g. a 4 or 10 objective. Use a highmagnification objective to acquire a high-resolution image of the ROI. Never use an oil objective as it will contaminate the sample. 6. Depending on the spectral acquisition conditions, the use of Raman lasers could result in sample heating and even damage, and so, to be cautious, FTIR images should be performed before Raman.

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Infrared Imaging

1. Put the sample holder on the microscope stage using the same orientation as the visible image. Ensure that the holder sits tight and will not move. 2. Select the appropriate parameters for FTIR imaging. These should be selected on a case by case basis, considering the size and thickness of the sample, the signal-to-noise required, and the analysis time available (see Note 6). Here we provide some basic guidelines: Pixel size. Although the lateral resolution of the IR is limited, high-magnification objectives and elements allow the acquisition of images with pixel sizes smaller than the IR resolution. It has been demonstrated that oversampling could improve the quality of the images, but it also reduces the signal-to-noise ratio and increases the measurement time and the amount of data to process [20]. As a rule of thumb, the lowest pixel size, which provides an acceptable signal-to-noise ratio in a reasonable time, should be

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prioritized. The selection of matching IR and RS pixel sizes facilitates the registration process. Spectral region and resolution. In general, to increase the amount of biochemical information provided by the analysis, it is recommended to acquire the whole spectral region allowed by the optics of the microscope. Typical spectral resolution values used are 4, 8, or 16 cm 1. In general, a lower value improves the assignment of vibrational bands but increases the analysis time and makes water vapour contamination more visible. Number of scans. In general, the noise is inversely proportional to the square root of the number of scans. Thus, a high number of scans will improve the results but increases the acquisition time. For large data sets in which several images of a mosaic should be collected, a high number of scans could be impractical. 3. Perform the calibration and alignment procedures recommended by the manufacturer (e.g. find the zero-path difference). 4. Acquire a background spectrum from a clean area of the slide (see step 4 of Subheading 3.3). 5. Go to the ROI defined, and measure either a single image or a mosaic of sequentially collected FTIR images. 3.5

Raman Imaging

1. Perform the calibrations indicated by the manufacturer. 2. Place the holder on the stage of the microscope ensuring that it is in the same orientation as in the visible and IR images. 3. Select the appropriate parameters for RS imaging (see Note 7). These should be selected on a case by case basis, and preliminary experiments are normally required for optimizing the measurement conditions. (a) Pixel size/step size: In general, the minimum pixel size used in Raman is half the wave number of the laser used (see Note 1). Nevertheless, if the area to be covered is large, undersampling can be used to facilitate the data analysis and facilitate the registration with the IR images. (b) Laser power: Higher laser powers will improve the signalto-noise ratio but could damage the cells or tissue. Heat dissipation systems can be employed to allow the use of higher laser power. For example, the slide can be immersed in a petri dish filled with water/saline/media. In the case of multiplexed imaging, this should be done considering the orientation of the sample, ensuring that the petri dish and slides are properly fixed on the stage.

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(c) Integration time: High-integration times improve the signal-to-noise ratio but increase the risk of sample damaging as well as the measurement time. 4. Move the stage to the ROI and acquire the hyperspectral image. If the measurement is performed under water, sample stability during the measurement should be checked. 3.6 Image Registration

The registration of the images is an essential step that entails resizing and reorienting the IR and Raman hypercube to align the spatial features and obtain a unique IR and Raman spectrum for each pixel. This can be done with different software available (see Note 8). 1. Import the data from the Raman and IR instruments. 2. Perform independent preprocessing for IR and Raman spectra. This kind of preprocessing is specific for each technique and in general aims to (a) eliminate other sources of variation such as water vapour removal for the IR image (see Note 9) and spike removal for the Raman spectra or (b) to enhance spectral features of interest, such as by derivatization [21]. 3. Scale the spectra to make them compatible. IR absorbance and Raman intensity are very different parameters, and their values can differ by several orders of magnitude. Prior to analysis, the images should be scaled to constrain their values to a similar range. This can be done, for example, by normalizing the spectra using the standard deviation or the average. 4. Crop the 3D images to make sure that only the ROI is used. This will help the registration and will save computation time. 5. If required, resize the images to compensate for differences of the pixel size using one of the images as reference image and scaling the second to match the reference one. 6. Register the images by selecting again a reference image. Reshape and rotate the other image to register them. 7. Create an extended hypercube by combining both images.

3.7 Basic Data Analysis 3.7.1 Principal Component Analysis (PCA)

PCA is one of the most frequently used chemometric techniques for the analysis of IR and RS data sets. PCA aims at the determination of linearly independent components (i.e. principal components, PCs), while retaining most of the variation in the multivariate data, allowing a rapid data visualization on a much lower dimensional scale and the inspection of the most relevant variables. 1. Select the spectral variables of interest in both IR and Raman spectra, excluding regions without spectral information.

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Fig. 1 Example of a PCA of a multiplexed vibrational image of a cell, indicating the relationship between spatial and spectral regions. Pseudocolour images of the first and second PC scores (a and c). These plots indicate the distribution of the score values across the sample. Score 1 is highly intense in the right half of the body of the algae and also in the edge, where the cellular walls are located (red Arrows). Score 2 shows high intensity in the left half of the body and between the 2 right arms (black arrows). This shows similarities on the composition of these cell segments. Loading values for the infrared and Raman variables for the first and second PC (b and d). They provide information about the relationship between the old variables (wave numbers) and new variables (PCs). For PC1, loadings are particularly strong on the C-O-H stretching region for IR (900–1100 cm 1). This band can be assigned either to lipids or to carbohydrates, but considering the negative bands at the same region in the Raman spectra, where they should be very active, they are more likely associated to the presence of carbohydrates. This could indicate that the spatial regions with high score value of PC1 contain large amounts of carbohydrates, which is explained with the presence of cellular wall and cellulose (red arrows). For PC2, IR band at 1750 cm 1 and Raman band at 1640 cm 1 are associated to the presence of lipids, and band at 1640 cm 1 in IR is assigned to the amide I band from proteins. This indicates that regions inside the cell (with high values of PC2 score) contain high amount of lipids and proteins (chloroplasts)

2. Apply a preprocessing over the extended image. Any manipulation at this point should be done considering that the variables come from different modalities. For example, a standard derivative will not be advisable as it will generate spectral artefacts in the points where the Raman and IR are joined. Variable-

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wise preprocessing such as mean centring are normally applied at this point. 3. Perform a PCA indicating a maximum number of PCs required for the analysis. 4. Plot the score images. These show the values of every pixel according to the new variables (i.e. PCs) created. This could lead to highlighting spatial features and to establishing new interrelationships of the pixels. For each score, a pseudocolour image can be plotted, as suggested by Fig. 1a, c, which represents the first and the second scores of a PCA performed over a multiplexed image of an alga. 5. Plot the loadings. This shows the relationship between the original wave numbers and the PCs. Loading plots can provide insight into the chemical composition of observed clusters and pixel associations observed in the scores plot (e.g. see Fig. 1b, d). 3.7.2 Statistical Heterospectroscopy (SHY)

SHY allows the study of correlations between variables from different sources. It involves the computation of the intrinsic covariance between features obtained from different techniques/modalities across different samples. In the case of imaging, each pixel can be considered a different entity, and thus, a correlation coefficient (e.g. Pearson coefficient) can be calculated for each combination of IR wave number and Raman shift values. 1. Select the spectral regions of interest, and exclude the variables with no information or low signal-to-noise ratio. 2. For each combination of IR and Raman variables, compute the correlation coefficients (R), and estimate the probability (p) of getting the observed correlation coefficient by chance. 3. If necessary, exclude the non-significant correlations by eliminating all the correlations with a probability p-value higher than a user-defined threshold (e.g. 0.05). 4. Plot the resulting correlation map as a pseudocolour image. The average IR and Raman spectra could be placed on the axes for a better interpretation of the output.

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Notes 1. Spectral resolution: The main limitation of lateral resolution in both techniques is caused by the diffraction limit, lambda/NA (NA, numerical aperture) being the maximum achievable resolution. FTIR photons are of much longer wavelength than those of the NIR/VIS/UV lasers employed in RS, and therefore the technique has substantially more limited spatial resolution. Nevertheless, in recent years, new techniques which can

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obtain IR spectra with lateral resolution comparable to that of Raman have been developed. Atomic force microscopyinfrared (AFM-IR) uses an AFM tip to detect the thermal expansion of a sample produced by the absorption of infrared photons. The spatial resolution of this technique is limited by the thickness of the sample, but it can go below 100 nm, and studies have shown that it can be used to image cells with resolution comparable to RS [22]. On the other hand, macro-attenuated total reflection (ATR) can also perform IR imaging with submicron resolution using crystals with a high reflective index [23]. 2. Multiplex FTIR and Raman imaging will gain popularity if instrumentation enabled IR and RS imaging on a single microscope. However, Raman scattering is based on very different phenomena, involving different sections of the electromagnetic spectrum, and, consequently, to develop a microscope which integrates both modalities is extremely complicated. Recently, however, the development of optical photo-thermal infrared has opened a door to building microscopes which can record IR and Raman spectra simultaneously. 3. Infrared requires IR reflective substrates for transflection and IR transparent substrates for transmission measurements. Raman generally uses glass slides with a reflective coating or Raman-grade substrates with low Raman signal. CaF2 windows are the most advisable substrate to perform multiplex transmission IR and RS. They are considerably transparent in the mid-infrared range (4000–600 cm 1) and do not show any Raman signal as long as they are Raman grade (i.e. free from impurities). For transflection IR measurements, metals like silicon chips or glass coated with aluminium can be used. 4. Cytocentrifuges employ centrifuge force for deposition of cells on a slide and for separating them from the rest of the components of the cell suspension. They use plastic funnels with circular overtures which are generally prepared to operate with a classical microscope glass slide. Not all the substrates compatible with Raman and IR can be straightforwardly placed on the sample holders. In this case, supports should be placed mechanically between the plastic funnel and the holder. If a classical small CaF2 substrate (e.g. a circular window with 1–2 cm in diameter and 0.5–1 mm in thickness) is used, the fixation of the substrate to the funnel can be challenging. The window should be attached between the plastic and the metal part. The window can be moved or break during the centrifugation. We recommend to tape it to the funnel using thin pieces of tape on the external part of the window. If some glue contaminates the bottom of the window, it can be cleaned with some tissue with isopropanol.

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5. Sometimes it can be difficult to find a clean position on the slide to acquire a background reference spectrum. The best approach is to clean an area of the slide at a specific position which does not have tissue or cells. This step should be done carefully to not destroy or contaminate the sample. It is recommended to use a metal needle wrapped on cellulose paper to clean a small area of the slide. 6. In recent years, FTIR microscopy has become the most common technique used. FTIR sources generally consists of an inert solid material which emits strong IR light when heated (>1500 K). A Michelson interferometer and an FT of the recorded signal are used to mathematically discriminate the different wave numbers. In modern FTIR microscopes employed on tissue and cell imaging, the detector is generally a focal plane array (FPA), which is generally composed by arrays of 64 by 64, 128 by 128, or 256 by 256 pixels. Alternatively, linear arrays can also be employed. Microscopes are equipped with objectives ranging from 4 to 25, and highmagnification accessories can also be employed, to provide (projected) pixel sizes as low as 0.6μm. It is necessary to choose the most suitable magnification, considering the sample and analytical problem faced. Measurements are generally performed either in transmission or transflection, which require different substrates (see Note 3). For selecting the optical mode, the following should be considered: (a) in transfection mode, the light passes through the sample twice, so the path length is twice the thickness of the sample, and therefore it provides twice the absorbance as transmission. (b) Transmission spectra are less prone to be perturbed by artefacts such as electric field standing wave (EFSW) [24, 25] and reflection. More recently, a new family of infrared microscopes based on quantum cascade laser (QCL) sources is gaining popularity [26]. These lasers enable the selection of discrete wavelengths, enabling the acquisition of the full spectrum in a few minutes. QCL microscopes also use FPA detectors, but they are more compact, as they do not require an interferometer for the wave number discrimination. Finally, recent efforts have been focused on the development of photo-thermal IR, allowing to achieve images with lateral resolution below the micron level (see Note 1). 7. A Raman spectrometer typically consists of three major components: an excitation source, a sampling apparatus, and a detector. While these three components have evolved in varying forms over the years, modern Raman instrumentation has developed around using a laser as an excitation source, a spectrometer for the detector, and either a microscope or a fibre

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optic probe for the sampling apparatus. A wide range of lasers can be used as the light source, although, for biological applications, longer wavelength, near-infrared sources are commonly employed, to minimize photodamage, scattering, and/or fluorescence. Dispersive microscope instruments make use of a notch or edge filter, which directs the laser along the axis of the microscope objective lens, which both delivers the incident light and collects the scattered light, in the commonly employed backscattering geometry, after which the notch/edge filter directs the light to a high-quality grating, which disperses the light onto a charge-coupled detector array. The spatial resolution is determined by the laser wavelength and the numerical aperture of the objective lens, while the spectral dispersion is determined by the grating. In confocal instruments, the scattered light is refocussed through a confocal hole to improve the spatial definition. The spectrally dispersed, detected Raman scattered light is displayed as a Raman shift from the source wavelength, which is converted to units of wave number (cm 1), such that Raman spectra can easily be compared and contrasted with equivalent FTIR spectra. 8. Software Nowadays, there is a wide range of dedicated software for modelling spectral data, such as Unscrambler (CAMO Analytics, Norway) or PLS-Toolbox (Eigenvector Research Inc., USA). Analysis of hyperspectral images can be performed using dedicated software such as CytoSpec (CytoSpec, Germany) and MIA_Toolbox (Eigenvector Research Inc.), which allows advanced preprocessing and modelling. Nevertheless, for the fusion and the registration of images, high-level programming languages such as MATLAB (Mathworks Inc., USA) offer interactive environments and a number of built-in functions and toolboxes for numerical computation and data visualization, such as the imresize and imregistre functions available at the imaging processing MATLAB toolbox, among many others. 9. For eliminating water vapour contribution, we recommend [27]. For correcting for spectral artefacts, such as the EFWS and Mie scattering, other algorithms are also available [28].

Acknowledgements This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 796287. D.P.-G. acknowledges the financial support of the 2019 Ramo´n y Cajal (RYC) Contracts Aids (RYC2019- 026556-I).

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References 1. Bock C, Farlik M, Sheffield NC (2016) Multiomics of single cells: strategies and applications. Trends Biotechnol 34:605–608. https://doi.org/10.1016/j.tibtech.2016.04. 004 2. Bro R, Nielsen HJ, Savorani F et al (2012) Data fusion in metabolomic cancer diagnostics. Metabolomics 9:3–8. https://doi.org/10. 1007/s11306-012-0446-0 ˜ averas JC 3. Quintás G, Portillo N, Garcı´a-Can et al (2011) Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool. Metabolomics 8:86–98. https://doi.org/10.1007/ s11306-011-0292-5 4. Kuligowski J, Pe´rez-Guaita D, Sánchez-Illana ´ et al (2015) Analysis of multi-source metaA bolomic data using joint and individual variation explained (JIVE). Analyst 140:4521–4529. https://doi.org/10.1039/ C5AN00706B 5. Perez-Guaita D, Marzec KM, Hudson A et al (2018) Parasites under the spotlight: applications of vibrational spectroscopy to malaria research. Chem Rev 118:5330–5358. https:// doi.org/10.1021/acs.chemrev.7b00661 6. Lasch P, Kneipp J (2008) Biomedical vibrational spectroscopy. Wiley-Interscience, Hoboken, NJ 7. Byrne HJ, Baranska M, Puppels GJ et al (2015) Spectropathology for the next generation: quo vadis? Analyst 140(7):2066–2073. https:// doi.org/10.1039/C4AN02036G 8. Esmonde-White FWL, Morris MD (2010) Raman Imaging and Raman Mapping. In: Emerging Raman applications and techniques in biomedical and pharmaceutical fields. Springer, Berlin, pp 97–110 9. Malek K, Wood BR, Bambery KR (2014) FTIR imaging of tissues: techniques and methods of analysis. In: Baranska M (ed) Optical spectroscopy and computational methods in biology and medicine. Springer Netherlands, Dordrecht, pp 419–473 10. Kohler A, Kristian Afseth N, Martens H (2006) Chemometrics in biospectroscopy. In: Handbook of vibrational spectroscopy. Wiley 11. Perez-Guaita D, Kochan K, Martin M et al (2017) Multimodal vibrational imaging of cells. Vib Spectrosc 91:46–58 12. Lasch P, Noda I (2017) Two-dimensional correlation spectroscopy for multimodal analysis of FT-IR, Raman, and MALDI-TOF MS hyperspectral images with hamster brain tissue.

Anal Chem 89:5008–5016. https://doi.org/ 10.1021/acs.analchem.7b00332 13. Lasch P, Noda I (2019) Two-dimensional correlation spectroscopy (2D-COS) for analysis of spatially resolved vibrational spectra. Appl Spectrosc 73:359–379. https://doi.org/10. 1177/0003702818819880 14. Koh CM (2013) Chapter sixteen - Preparation of cells for microscopy using cytospin. In: Lorsch J (ed) Methods in enzymology. Academic Press, pp 235–240 15. Stokes BO (2004) Principles of cytocentrifugation. Lab Med 35:434–437. https://doi.org/ 10.1309/FTT59GWKDWH69FB0 16. Hughes C, Gaunt L, Brown M et al (2014) Assessment of paraffin removal from prostate FFPE sections using transmission mode FTIRFPA imaging. Anal Methods 6:1028–1035. https://doi.org/10.1039/C3AY41308J 17. Lyng F, Gazi E, Gardner P (2010) Chapter 5: Preparation of tissues and cells for infrared and Raman spectroscopy and imaging. In: Biomedical applications of synchrotron infrared microspectroscopy, pp 145–191 18. Ibrahim O, Maguire A, Meade AD et al (2017) Improved protocols for pre-processing Raman spectra of formalin fixed paraffin preserved tissue sections. Anal Methods 9:4709–4717. https://doi.org/10.1039/C6AY03308C 19. Tfayli A, Gobinet C, Vrabie V et al (2009) Digital dewaxing of Raman signals: discrimination between nevi and melanoma spectra obtained from paraffin-embedded skin biopsies. Appl Spectrosc 63:564–570 20. Perez-Guaita D, Andrew D, Heraud P et al (2016) High resolution FTIR imaging provides automated discrimination and detection of single malaria parasite infected erythrocytes on glass. Faraday Discuss 187:341–352. https://doi.org/10.1039/C5FD00181A 21. Lasch P (2012) Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemom Intell Lab Syst 117:100–114. https://doi.org/10. 1016/j.chemolab.2012.03.011 22. Perez-Guaita D, Kochan K, Batty M et al (2018) Multispectral atomic force microscopy-infrared Nano-imaging of malaria infected red blood cells. Anal Chem 90:3140–3148. https://doi.org/10.1021/ acs.analchem.7b04318 23. Vongsvivut J, Pe´rez-Guaita D, Wood BR et al (2019) Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells. Analyst

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144:3226–3238. https://doi.org/10.1039/ C8AN01543K 24. Wrobel TP, Wajnchold B, Byrne HJ, Baranska M (2013) Electric field standing wave effects in FT-IR transflection spectra of biological tissue sections: simulated models of experimental variability. Vib Spectrosc 69:84–92. https:// doi.org/10.1016/j.vibspec.2013.09.008 25. Perez-Guaita D, Heraud P, Marzec KM et al (2015) Comparison of transflection and transmission FTIR imaging measurements performed on differentially fixed tissue sections. Analyst 140:2376–2382. https://doi.org/10. 1039/C4AN02034K 26. Schwaighofer A, Montemurro M, Freitag S et al (2018) Beyond Fourier transform infrared

spectroscopy: external cavity quantum cascade laser-based mid-infrared transmission spectroscopy of proteins in the amide I and amide II region. Anal Chem 90:7072–7079. https:// doi.org/10.1021/acs.analchem.8b01632 27. Perez-Guaita D, Kuligowski J, Quintás G et al (2013) Atmospheric compensation in Fourier transform infrared (FT-IR) spectra of clinical samples. Appl Spectrosc 67:1339–1342. https://doi.org/10.1366/13-07159 28. Konevskikh T, Lukacs R, Blu¨mel R et al (2016) Mie scatter corrections in single cell infrared microspectroscopy. Faraday Discuss 187:235–257. https://doi.org/10.1039/ c5fd00171d

Chapter 20 Multiplexed Imaging Mass Spectrometry of Histological Staining, N-Glycan and Extracellular Matrix from One Tissue Section: A Tool for Fibrosis Research Cassandra L. Clift, Anand Mehta, Richard R. Drake, and Peggi M. Angel Abstract We describe a multiplexed imaging mass spectrometry approach especially suitable for fibrosis research. Fibrosis is a process characterized by excessive extracellular matrix (ECM) secretion. Buildup of ECM impairs tissue and organ function to promote further progression of disease. It is an ongoing analytical challenge to access ECM for diagnosis and therapeutic treatment of fibrosis. Recently, we reported the use of the enzyme collagenase type III to target the ECM proteome in thin histological tissue sections of fibrotic diseases including hepatocellular carcinoma, breast cancer, prostate cancer, lung cancer and aortic valve stenosis. Detection of collagenase type III peptides by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) allows localization of ECM peptide sequences to specific regions of fibrosis. We have further identified that the ECM proteome accessed by collagenase type III has on average 3.7 sites per protein that may be differentially N-glycosylated. N-glycosylation is a major posttranslational modification of the ECM proteome, influencing protein folding, secretion, and organization. Understanding both N-glycosylation signaling and regulation of ECM expression significantly informs on ECM signaling in fibrosis. Key words Imaging mass spectrometry, Extracellular matrix, N-Glycan, N-Glycosylation, Fibrosis, Proteomics, Tissue imaging, Collagen peptide imaging, Collagen, Histology

1

Introduction Fibrosis is a process characterized by excessive and disorganized extracellular matrix (ECM) involved in many organ diseases but especially heart, kidney, liver, and lung diseases. Fibrotic accumulation of ECM diminishes normal tissue function leading to obstruction of organ function [1–3]. This promotes disease progression and eventually leads to organ failure. A main challenge in fibrosis research is accessing the ECM to understand how ECM proteins yield feedback information promoting disease progression. We describe a multiplexed imaging strategy exceptionally useful for fibrosis research that reports N-glycan and ECM information

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_20, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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from tissue sections after histological staining. After histology staining, the tissue is analyzed sequentially for N-glycans and ECM peptides. N-glycans and ECM peptides are detected as 2D maps across the tissue using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). MALDI-IMS is a robust technique that uses mass spectrometry to map molecules such as lipids, peptides, proteins, and N-glycans from histological tissue samples [4–8]. MALDI-IMS pinpoints molecular expression to groups of cells or regions of pathology in a tissue section. Thus, for fibrosis research, comparisons may be made from differences in pathological regions, e.g., changes in stroma output and organization related to specific fibroblast populations compared to normal adjacent cells. MALDI-IMS has been used by our group and others to spatially probe thin formalin-fixed, paraffin-embedded (FFPE) samples for their N-glycan content by treating the tissue with the enzyme endopeptidase F (PNGase F) [9–12]. Both the N-glycome and tryptic peptides can be accessed within the same tissue section [9]. Recently, we developed proteomic workflows using collagenase type III to specifically target and report on ECM proteins from FFPE tissues [13, 14]. ECM protein targeting by collagenase type III reproducibly reports on 60–75 ECM proteins, including collagen types [13, 14]. Sequence analysis of identified ECM proteins revealed that each protein had on average 3.7 sites for N-glycosylation as depicted by the consensus sequence N-X-S/ T 6¼ P. A challenge with transferring a sequential approach to serial analysis of N-glycans and ECM from the same tissue section is that collagenase type III treatment can disrupt tissue histology. Here, we describe a strategy that starts by capturing the tissue histology using a standard histology stain, hematoxylin and eosin (H&E). Hematoxylin dyes the nuclear content dark purple; eosin stains cytoplasm and stroma pink. After digital high-resolution capture of the stain, the same tissue section is processed for N-glycan and ECM peptide imaging mass spectrometry through sequential application of PNGase F and collagenase type III. Specific washing steps are required to remove matrix, enzymes, and the N-glycans between enzyme treatments. This multiplexed imaging approach is particularly useful for deep mining of ECM information from fibrotic tissue, as one 5-μm-thick FFPE tissue section allows for pathological evaluation of ECM organization by staining as well as N-glycan signaling and ECM peptide data.

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Materials All solutions should be prepared using double-distilled or HPLC grade water, following all necessary safety and waste disposal regulations.

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1. Xylenes: Pour xylenes to the fill line of a Coplin jar. 2. 100% 200 proof ethanol: Pour 200 proof ethanol to the fill line of a Coplin jar. 3. 95% ethanol solution: Add 950 mL of 200 proof ethanol to a clean bottle. Add 50 mL of water and mix. 4. 70% ethanol solution: Add 700 mL of 200 proof ethanol to a clean bottle. Add 300 mL of water and mix. 5. HPLC grade or triple-distilled water.

2.2 Hematoxylin and Eosin Staining Solutions (See Note 1)

1. Gill’s hematoxylin 2 [Cancer Diagnostics CM5951]

2.3 MALDI-IMS Solutions

1. PNGase F antigen retrieval solution: 10 mM citraconic buffer, pH 3. Pour 40 mL water into a clean 50 mL centrifuge tube. Add 25 μL of citraconic acid anhydride. Add 2 μL of 12 M HCl and mix thoroughly. Add water for a total volume of 50 mL. Check that the solution has a pH of 3. Solution should be prepared same day of use.

2. Eosin Y solution [Fisherbrand 314-631]

2. Collagenase type III antigen retrieval solution: 10 mM Tris pH 9. To prepare a 1 L stock solution, pour 900 mL of water into a clean 1 L bottle. Add 1.21 g of Trizma base. Dissolve completely, bringing volume of water to 975 mL. Adjust to pH 9 with 1 M NaOH or 1 M HCl (see Note 2). 3. 25% trifluoroacetic acid (TFA): Add 3 mL of water to a clean glass bottle. Under a laboratory hood, carefully add 1 mL of trifluoroacetic acid to the water, and mix. 4. Matrix solvent A: 50% ACN, 0.1% TFA. Add 25 mL water to a clean 100 mL glass bottle. Add 400 μL of 25% TFA (solution #3) to the water and mix. Add 50 mL of acetonitrile (ACN) and mix. Add water to a final volume of 100 mL and mix (see Note 3). 5. MALDI matrix for N-glycan imaging: 7 mg/mL CHCA in 50% ACN/0.1% TFA. Weigh out 42 mg alpha-cyano-4-hydroxycinnamic acid (CHCA). Add the solid CHCA to a clean 50 mL conical tube. Add 6 mL of matrix solvent A (50% ACN/0.1% TFA). Vortex and sonicate for 5 min with a benchtop sonicator. Filter matrix solution using specified 13 mm 0.2 μm PTFE hydrophilic HPLC grade syringe filter (see Note 4). 6. Glu-Fib internal standard stock solution for collagen peptide imaging: 0.1 mg/mL Glu1-fibrinopeptide B (Glu-Fib) in 50% ACN/0.1% TFA. Add 1 mL of matrix solvent A (50% ACN/0.1% TFA) directly to 1 mg bottle of Glu1fibrinopeptide B (see Note 5).

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7. Matrix solvent B: 50% ACN, 1.0% TFA. Add 25 mL water to a clean 100 mL bottle. Add 1 mL of trifluoroacetic acid to the water and mix. Add 50 mL of ACN and mix. Add water to a final volume of 100 mL and mix (see Note 6). 8. MALDI matrix for collagen peptide imaging: 7 mg/mL CHCA, 200 fmole/μL Glu-Fib, in 50% ACN/1.0% TFA. Weigh out 42 mg alpha-cyano-4-hydroxycinnamic acid (CHCA, 99.9% purity). Add the solid CHCA to a clean 50 mL centrifuge tube. Add 6 mL of matrix solvent B (50% ACN/1.0% TFA). Vortex and sonicate for 5 min with a benchtop sonicator. Filter matrix solution using specified 13 mm 0.2 μm PTFE hydrophilic HPLC grade syringe filter (see Note 4). After filtering, add 18.8 μL of Glu-Fib internal standard stock solution (solution #6); mix well. 9. Ammonium phosphate stock (500 mM): Weigh out 2.88 g of ammonium phosphate monobasic. Add solid ammonium phosphate to a clean 50 mL centrifuge tube. Bring up to 50 mL volume with water. Mix well before use. 10. Ammonium phosphate working solution: 5 mM ammonium phosphate, monobasic. Add 49.5 mL of cold water into a clean 50 mL conical tube. Add 500 μL of 500 mM ammonium phosphate stock (solution #9), and mix well (see Note 7). 2.4 Tissue Clearing Solutions

1. High pH tissue clearing solution: 10 mM Tris pH 9. Preparation is the same as collagenase type III antigen retrieval solution, and solution #2 (see Subheading 2.3) may be used. 2. Low pH tissue clearing solution: 10 mM citraconic buffer, pH 3. Preparation is the same as PNGase F antigen retrieval solution, and solution #1 (see Subheading 2.3) may be used.

2.5 Enzyme Solutions

1. PNGase F solution (0.1 μg/μL): To 100 μg of PNGase F, add 1 mL of HPLC grade water and mix (see Notes 8 and 9). This volume is enough to cover four microscope slides. 2. Buffer for collagenase type III: 25 mM AB, 1 mM CaCl2, pH 7.35. Add 50 mL water to a clean 100 mL bottle. Add 197.6 mg of ammonium bicarbonate and 11.10 mg of calcium chloride to water and mix. Bring up to 100 mL volume with water and mix. Adjust pH to 7.25. 3. Collagenase type III solution (0.1 μg/μL): Immediately before use, prepare a stock collagenase type III solution by measuring 1 mg of collagenase type III into a 1.5 mL centrifuge tube and adding 1 mL of buffer for collagenase type III (solution #2, Subheading 2.5). To prepare the 0.1 μg/μL working solution, add 300 μL of the high-concentration collagenase type III stock to 2700 μL of collagenase type III buffer (solution #2,

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Subheading 2.5) (see Note 9). This volume is enough to cover four microscope slides. 2.6 Instrumentation and Tools

1. Syringe pump (here, NE-1000, Pump Systems Inc., was used). 2. Isocratic pump (here, BlueShadow Pump 20P, Knauer, was used). 3. M3 TM-Sprayer™ (HTX Imaging). 4. 7.0 Tesla solariX™ Legacy FT-ICR (Bruker Daltonics). 5. Vegetable steamer (here, Rival Model CKRVSTLM21 was used). 6. 5-slide mailer.

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Methods

3.1 Tissue Heating and Dewaxing

The full workflow is outlined in Fig. 1. 1. Incubate the slides for 1 h at 60  C. Lay slides flat on heated surface with tissue facing up. 2. Preparing a series of eight Coplin jars with dewaxing solutions in the following order: xylenes, xylenes, 100% ethanol, 100% ethanol, 95% ethanol, 70% ethanol, HPLC grade water, HPLC grade water (see Note 10). 3. Dewax the slides as follows: xylenes for 3 min each; 100% ethanol for 1 min each; 95% ethanol for 1 min; 70% ethanol for 1 min; HPLC grade water for 3 min each (see Note 11). 4. Slides can be stored in a vacuum desiccator overnight before proceeding with hematoxylin and eosin staining.

3.2 Hematoxylin and Eosin Staining

1. Prepare a series of 9 Coplin jars with fresh solutions in the following order: 95% ethanol, 70% ethanol, HPLC grade water, hematoxylin, HPLC grade water, eosin, 100% ethanol, 100% ethanol, xylenes. 2. Stain slides as follows: (a) 95% ethanol, 30 s. (b) 70% ethanol, 20 s. (c) HPLC grade water, 30 s. (d) Hematoxylin, 2 min. (e) HPLC grade water (new jar), 30 s. (f) HPLC grade water (step c jar), 30 s. (g) 70% ethanol (step b jar), 30 s. (h) 95% ethanol (step a jar), 30 s. (i) Eosin, 30 seconds to 1 min.

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Heat & Dewax

H&E Staining

High Resolution Digital Capture

Coverslip Removal

Antigen Retrieval

PNGaseF Application & Incubation

MALDI Matrix Application

MALDI IMS

Tissue Clearing & Antigen Retrieval

COLase3 Application & Incubation

MALDI Matrix Application

MALDI IMS

Fig. 1 Workflow of tissue preparation for N-glycan and collagen peptide imaging by MALDI imaging mass spectrometry. H&E, hematoxylin and eosin stain; Colase3, collagenase type III

(j) 95% ethanol (step a jar), 30 s. (k) 100% ethanol, 1 min (l) 100% ethanol, 1 min (m) Xylenes, 1 min. 3. Mount slides in a xylene-based mounting medium and coverslip. 4. Allow coverslips to completely dry before scanning. 5. Take high-resolution image of stained slide with a histology slide scanner or stitched microscopy image. It is important to capture a high-resolution H&E image before on-tissue digestion with enzymes as post-MALDI imaging tissue histology will be compromised by enzyme activity. 6. After capturing H&E image digitally (Fig. 2), remove coverslip by incubating slide in Xylene for 30 min and carefully sliding, not lifting, the coverslip away from tissue (see Note 12). 7. Clear and rehydrate slide by following the dewaxing protocol (see Subheading 3.1) before continuing with antigen retrieval (see Note 13). 3.3 Antigen Retrieval for PNGase F Application

1. Fill vegetable steamer to the marked water level and preheat for 5 min prior to antigen retrieval. 2. Using a plastic 5-slide mailer with side opening, prefill the mailer with ~10 mL of PNGase F antigen retrieval solution (solution #1, Subheading 2.3). 3. Place a maximum of 3 slides into each 5-slide mailer with side opening. Slides in position 1 and 5 are placed with tissue facing

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Fig. 2 High-resolution digital capture of hematoxylin and eosin stain of the tissue before proceeding with N-glycan and collagen peptide imaging. Nuclei is stained in dark blue/purple, while cytoplasm is pink

inward toward solution, away from walls. Position 3 may face either way. This allows good solvent access to the tissue. 4. Fill the rest of the slide mailer with PNGase F antigen retrieval solution (solution #1, Subheading 2.3) so that all tissue is completely covered. Any tissue not covered by solution will show variable signal. 5. Allow for release of steam from slide mailer during antigen retrieval by closing only one corner of the mailer. 6. Place mailer in the center of the vegetable steamer and heat for 30 min. Temperature should reach 95  C for a minimum of 20 min (see Note 14).

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7. Place the hot mailer in a container of cool water (see Note 15). Cooling water should come midway up the slide of the mailer. Cool for 5 min in the water bath. 8. Remove half the buffer from the mailer and replace with HPLC grade water. Cool on the countertop for 5 min. 9. Repeat step 8 a total of three times. 10. Complete by filling mailer with 100% distilled water and incubate for 5 min. 11. Remove the slides from the mailer. Dry the slides in a desiccator until completely dry, generally 5 min. 3.4

Slide Scanning

This step produces an optimal image needed for imaging mass spectrometry analysis. This allows referencing of the tissue location on the slide for data acquisition. 1. Use a reflective metallic marker to create a reference circle at each corner of the microscope slide. Using a black permanent marker, draw a hash mark on top of each circle. The reflective marker allows clear visualization of the black hash mark to be used as a reference point in setting up image acquisition. 2. For image data that will be acquired at 100 μm step size, the slide should be scanned at a minimum 1200 ppi resolution. For image data that will be acquired at 100 μm step size, scan the slide at a minimum of 2400 ppi resolution. Save the images as JPEG. 3. After scanning, slides may be stored overnight in a desiccator. For storage over 2 days, store the slides at 20  C to 80  C. It is preferable to proceed with the next step immediately.

3.5 PNGase F Application by the M3 TM-Sprayer™

1. Turn on TM-Sprayer™ and then the controlling computer and software. 2. Open the nitrogen gas tank valve and set the regulator to 10 psi. 3. Set the TM-Sprayer™ temperature to 45  C. 4. Program the x-y coordinates of the TM-Sprayer™ to cover the appropriate area for the number of slides being run, with an additional 5 mm edge to account for sprayer head turnaround. 5. Clean TM-Sprayer™ line used for enzymes by loading 3 mL of the following solvents sequentially and running for 5 min each at 65 μL/min: 5% ammonium hydroxide, 0.1% TFA, HPLC grade water. 6. After loading samples on TM-Sprayer™ stage and fastening them with laboratory tape, fill a clean 1 mL Luer lock syringe with prepared PNGase F solution (solution #1, Subheading 2.5) (see Note 16 and 17).

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7. Change the pump flow rate to 25 μL/min. Do not start pumping at this time. 8. Program the TM-Sprayer™ method for PNGase F to use 15 passes, flow rate of 25 uL/min, crisscross pattern, velocity of 1200 mm/min, 3.0 mm track spacing, and a dry time of zero. The tip of the spray head should be 50 mm from the slide’s surface. 9. Start the syringe pump, waiting 1–3 min for PNGase F to start spraying. 10. After 1–3 min, press “Start” in the TM-Sprayer™ software. 11. While waiting for TM-Sprayer™ to complete, continue with incubation preparation for on-tissue digestion (step 1, Subheading 3.6). 3.6 PNGase F Incubation for on-Tissue Digestion

1. Prepare one incubation chamber per slide for on-tissue digestion using a plastic 100  15 mm cell culture dish. Place a single layer of a cloth wipe (Wypall 60) in the bottom of the dish. Fold two 4  6 in. Kimwipes into short-stacked rectangles, and place on either side of the dish. Saturate the Kimwipes and bottom of the dish with water (approximately 5 mL of water for a 4  6 in. Kimwipe). 2. Preheat the incubation chambers at 37.5  C for 15–30 min (approximately the duration of the spraying protocol). A thin layer of condensation should be visible on the lid of the culture dish prior to loading in the sample. If humidity is not high enough to detect condensate, samples will not digest consistently. 3. Once the TM-Sprayer™ protocol is complete, remove slides and place them tissue-side up in their own incubation chamber. Ensure the slide is pushed down enough so that the lid of the incubation chamber does not come into contact with the tissue. 4. For PNGase F digestions, incubate tissues for 2 h. 5. After incubation, carefully remove slide and wipe excess condensation off the slide backing. Ensure no excess moisture drops onto tissue; this will delocalize analytes produced by digestion. 6. Proceed to MALDI matrix application (see Note 18).

3.7 MALDI Matrix Application by the TM-Sprayer™

1. MALDI matrix application is performed after each enzyme digestion. 2. Turn on TM-Sprayer™ and then the controlling computer and software. 3. Open the nitrogen gas tank valve, setting the regulator to 10 psi.

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4. Set the TM-Sprayer™ temperature to 79  C. 5. Program the x-y coordinates of the TM-Sprayer™ to cover the appropriate area for the number of slides being run, with an additional 5 mm edge to account for sprayer head turnaround. 6. After loading samples on TM-Sprayer™ stage and fastening them with laboratory tape, fill the glass syringe (attached to line going to the 6-port valve) with prepared MALDI matrix solution. (a) For PNGase F, use MALDI matrix for N-glycan imaging (solution #5, Subheading 2.3). 7. With the valve switch in the “Load” position, inject the MALDI matrix solution into the 5 mL loop. 8. Change the pump flow rate to 70 μL/min. Move the valve switch to the “Spray” position. 9. Program the TM-Sprayer™ method for CHCA to use 14 passes, 70 uL/min, crisscross pattern, velocity of 1300 mm/min, 2.5 mm track spacing, and a dry time of zero. The tip of the spray head should be 50 mm from the surface of the slide. 10. Start the syringe pump, waiting 1–3 min for matrix to start spraying. 11. After 1–3 min, press “Start” in the TM-Sprayer™ software. 12. MALDI matrix solution will spray in a thin layer onto the slides. 13. When finished, matrix-coated slides may be imaged immediately by MALDI-IMS (Fig. 3) or stored in a vacuum desiccator until ready to image. 3.8 Tissue Clearing of Matrix between Enzymes

1. N-Glycans and the enzyme PNGase F must be removed prior to collagenase type III digestion as N-glycan signal will limit detection of collagenase type III peptides. The enzyme PNGase F must be removed to limit ion suppression. 2. Remove matrix by immersing the slide in 100% ethanol for 1 min. 3. Immerse the slide in 95% ethanol solution for 1 min. 4. Immerse the slide in 70% ethanol solution for 1 min. 5. Remove cleaved N-glycans by incubating the slide in water for 1 min, high pH tissue clearing solution (solution #1, Subheading 2.4) for 1 min, water for 1 min, and low pH tissue clearing solution (solution #2, Subheading 2.4) for 1 min. At the end of each step, gently agitate the slides 3–5 times. 6. Dry for 5 min in the desiccator.

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Fig. 3 Comparison of N-glycan imaging data due to PNGase F application either alone or applied after H&E staining. (a) Overall average mass spectrum of PNGase F-treated tissue (top, black) and PNGase F-treated tissue after undergoing H&E staining (bottom, red). N-Glycan structures corresponding to m/z are shown. (b) Examples of N-glycan images created as heat maps of the corresponding m/z peak intensity. N-Glycan structure and m/z are shown left of each image, created using GlycoWorkbench 2.0 [15]. Peak 1570.6768 m/z corresponds to the Glu1-fibrinopeptide B (Glu-Fib) internal standard spikes in at 200 femtomole/microliter 3.9 Antigen Retrieval for Collagenase Type III Application

1. Fill vegetable steamer to the marked water level, and preheat for 5 min prior to antigen retrieval. 2. Add around 10 mL of collagenase antigen retrieval solution (solution #2, Subheading 2.3) to a plastic 5-slide mailer with side opening. 3. Place a maximum of three slides into each 5-slide mailer with side opening. Slides in position 1 and 5 are placed with tissue facing inward toward solution, away from walls. Position 3 may face either way. This allows good solvent access to the tissue. 4. Fill the rest of the slide mailer with collagenase antigen retrieval solution (solution #2, Subheading 2.3) so that all tissue is completely covered. 5. In order to allow for release of steam from slide mailer during antigen retrieval, you can (1) punch a hole into the mailer lid using an 18-gauge needle or (2) snap closed only one corner of the mailer. 6. Place mailer in the center of the vegetable steam and heat for 30 min. Temperature should reach 95  C for a minimum of 20 min (see Note 14). 7. Place the hot mailer in a container of cool water (see Note 15). Cooling water should come midway up the slide of the mailer. Cool for 5 min in the water bath. 8. Remove half the buffer from the mailer and replace with distilled water. Cool on the countertop for 5 min.

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9. Repeat step 8 a total of three times. 10. Complete by filling mailer with distilled water and incubate for 5 min. 11. Dry the slides for 5 min in a desiccator. 3.10 Collagenase Type III Application by the M3 TM-Sprayer™

1. Turn on TM-Sprayer™ and then the controlling computer and software. 2. Open the nitrogen gas tank valve and set the regulator to 10 psi. 3. Set the TM-Sprayer™ temperature to 45  C. 4. Program the x-y coordinates of the TM-Sprayer™ to cover the appropriate area for the number of slides being run, with an additional 5 mm edge to account for sprayer head turnaround. 5. Without running a program via the software, clean TM-Sprayer™ line used for enzymes by loading 3 mL of the following solvents sequentially and running for 5 min each at 65 μL/min: 5% ammonium hydroxide, 0.1% TFA, water. 6. After loading samples on TM-Sprayer™ stage and fastening them with laboratory tape, fill a new 1 mL Luer lock syringe with freshly prepared collagenase type III solution (solution #3, Subheading 2.5) (see Notes 9, 16, and 17). 7. Change the pump flow rate to 25 μL/min. Do not start pumping at this time. 8. Program the TM-Sprayer™ method for PNGase F to use 15 passes, 25 μL/min, crisscross pattern, velocity of 1200 mm/min, 3.0 mm track spacing, and a dry time of zero. The tip of the spray head should be 50 mm from the slide’s surface. 9. Start the syringe pump, waiting 1–3 min for collagenase type III to start spraying. 10. After 1–3 min, press “Start” in the TM-Sprayer™ software. 11. While waiting for protocol to complete, continue with incubation preparation for on-tissue digestion (step 1, Subheading 3.11).

3.11 Collagenase Type III Incubation for on-Tissue Digestion

1. Prepare one incubation chamber per slide for on-tissue digestion using a plastic 100  15 mm cell culture dish. Place a single layer cloth wipe (Wypall 60) in the bottom of the dish. Fold two 4  6 in. Kimwipes into short-stacked rectangles and place on either side of the dish. Saturate the Kimwipes and bottom of the dish with water (approximately 5 mL of water for a 4  6 in. Kimwipe). 2. Preheat the incubation chambers at 37.5  C for 15–30 min (approximately the duration of the spraying protocol). A thin

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layer of condensation should be visible on the lid of the culture dish prior to loading in the sample. If humidity is not high enough to detect condensate, samples will not digest uniformly. 3. Once the TM-Sprayer™ protocol is complete, remove slides, and place them tissue-side up in their own incubation chamber. Ensure the slide is pushed down enough that the lid of the incubation chamber does not come into contact with the tissue. 4. For collagenase type III, incubate tissues for 5 h. 5. After incubation, carefully remove slide, and wipe excess condensation off the slide backing. Ensure no excess moisture drops onto tissue; this will delocalize the analytes cleaved during digestion. 6. Proceed to MALDI matrix application (see Note 18). 3.12 MALDI Matrix Application by the TM-Sprayer™

1. Turn on TM-Sprayer™ and then the controlling computer and software. 2. Open the nitrogen gas tank valve, setting the regulator to 10 psi. 3. Set the TM-Sprayer™ temperature to 79  C. 4. Program the x-y coordinates of the TM-Sprayer™ to cover the appropriate area for the number of slides being run, with an additional 5 mm edge to account for sprayer head turnaround. 5. After loading samples on TM-Sprayer™ stage and fastening them with laboratory tape, fill the glass syringe (attached to line going to the 6-port valve) with prepared MALDI matrix solution. (a) For collagenase type III use MALDI matrix for collagen peptide imaging (solution #8, Subheading 2.3) (see Note 16). 6. With the valve switch in the “Load” position, inject the MALDI matrix solution into the 5 mL loop. 7. Change the pump flow rate to 70 μL/min. Move the valve switch to the “Spray” position. 8. Program the TM-Sprayer™ method for CHCA to use 14 passes, 70 μL/min, crisscross pattern, velocity of 1300 mm/min, 2.5 mm track spacing, and a dry time of zero. The tip of the spray head should be 50 mm from the slide’s surface. 9. Start the syringe pump, waiting 1–3 min for matrix to start spraying. 10. After 1–3 min, press “Start” in the TM-Sprayer™ software.

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A) 60

C3 alone PNGaseF followed by C3

60 r. int. (%)

30 r. int. (%)

PNGaseF followed by C3 H&E, PNGaseF followed by C3

90

0

30 0 30

30

60 Glu-Fib Internal Standard (1570.6768 m/z)

Glu-Fib Internal Standard (1570.6768 m/z)

90

60 1000

2000

1500

C)

2500

m/z

# m/z

ppm

1000

D)

Protein

Peptide GPVGPVGAR

1

809.4628

4.46

COL3A1

2

869.4730

2.86

COL1A2 (HYP) GAPGLP(Ox)GFP(Ox)

3

1082.6317

0.30

COL3A1

4

1098.5902

0.18

COL1A2

GPVGRTGEVGAV

5

1242.5822

0.36

COL1A1

GPAGARGNDGATGAA

6

1283.6339

4.64

COL1A2

1500 C3 alone

2000

2500

m/z PNGaseF, C3

H&E, PNGaseF, C3

COL6a2 VALLQFG GPGEQQ m/z 1343.6954

GPLGIAGITGAR

GPAGARGSDGSVGPV

COL3a1 GLAGTAGEp GRDGNpGS DGLP(Ox) m/z 1942.8737

Fig. 4 Comparison of ECM peptides detected with collagenase type III (C3) application after different sequential treatments. C3 was applied alone, after deglycosylation of the tissue with PNGase F or after both H&E staining and deglycosylation. (a) Overall average mass spectrum of collagenase type II- treated tissue (top, black) and collagenase type III-treated tissue after undergoing PNGase F digest and deglycosylation (bottom, blue). Annotated collagenase peptides and corresponding proteins are shown in (c). (b) Overall average mass spectrum of collagenase type III-treated tissue after PNGase F and deglycosylation (top, blue) and collagenase type III-treated tissue after undergoing H&E staining, PNGase F digest, and deglycosylation (bottom, red). Annotated collagenase peptides and corresponding proteins are shown in (c). (d) Examples of collagen peptide images created as heat maps of the corresponding m/z peak intensity. Collagen peptide sequence and m/z are shown left of each image. Peak 1570.6768 m/z corresponds to the Glu1-fibrinopeptide B (Glu-Fib) internal standard. Peptide m/z have been annotated via previously generated databases [4, 5]

11. MALDI matrix solution will spray in a thin layer onto the target tissues. 12. When finished, briefly dip slides (1 s) in cold ammonium phosphate monobasic working solution (solution #10, subheading 2.3), then stand slides vertically in desiccator to dry completely. Matrix-coated slides may be imaged immediately after drying by MALDI-IMS (Figs. 4 and 5) or stored in a vacuum desiccator until ready to image.

4

Notes 1. There are many variations of hematoxylin and eosin (H&E) available; the staining solutions noted here have been verified in our laboratory as compatible with mass spectrometry.

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Fig. 5 Example of image segmentation analysis used to identify shared tissue regions between N-glycan and collagen peptides detected by MALDI-IMS on the same tissue section. (a) Output of image segmentation analysis performed with a combined list of identified N-glycan and collagen peptide peaks from the same tissue section. The blue region from N-glycan imaging and orange region from collagen peptide imaging display similar regions of interest. (b) Segmentation analysis showing hierarchal clustering. N-glycan or image data type is annotated by color. Image segmentation was done with SCiLS (Bruker Scientific, LLC)

Additional H&E solutions have been reported as compatible from laboratories doing imaging mass spectrometry but are not listed here. 2. The 10 mM Tris should be made the day before and adjusted for pH after overnight mixing. This allows the solution to completely dissolve before adjusting pH. 3. Matrix solvent A (50% acetonitrile/0.1% TFA) can be stored at room temperature for a maximum of 2 months in a glass container. 4. Certain vendor-sourced PTFE hydrophilic filters can leech polymers that override mass spectrometry signal. We use Millex-LG 0.020 from MilliporeSigma. 5. Glu-Fib internal standard stock solution for collagen peptide imaging may be stored at 4  C for up to 4 months. 6. Matrix solvent B (50% acetonitrile/1.0% TFA) can be stored at room temperature for a maximum of 2 months in a glass container. 7. Ammonium phosphate monobasic working solution should be made using cold HPLC grade water immediately before performing the ammonium phosphate monobasic dip. Alternatively, chill the prepared solution before use. 8. We use PNGase F Prime(TM) from Bulldog Bio; this enzyme comes with mass spectrometry compatible buffers to optimize enzyme activity on solid substrates. Only HPLC grade water needs to be added to the solution. 9. Prepare enzyme solutions immediately before being loaded into the TM-Sprayer™. Collagenase type III activity decreases

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rapidly in solution. Preparing immediately prior to application ensures optimal enzyme activity during incubation. 10. Fill Coplin jars such that tissues on the slide are completely immersed in solution. 11. It is possible to proceed from HPLC grade water (step 3, Subheading 3.1) used in dewaxing directly to the hematoxylin staining step 2d, Subheading 3.2. 12. Xylene incubation time may need to be significantly increased up to 48 h to dissolve mounting media enough to remove coverslip. 13. Slides can be stored in vacuum desiccator for up to 2 days before antigen retrieval. For long-term storage, store at 20  C or 80  C. 14. Certain tissue with high-fat content or those with open areas such as the normal skin, breast, and lung may detach during lengthy antigen retrieval. To limit loss of tissue from the slide, reduce antigen retrieval time to 20 min. 15. Use hot pads or heat-resistant gloves to remove the mailer. It is full of near-boiling solution and could be a burn hazard to the skin and eyes. 16. Ensure that there are no air bubbles in the syringe before loading solution into pump. 17. Ensure that the same syringe is not being used for PNGase F and collagenase type III loading as to prevent crosscontamination of enzymes. 18. If unable to spray MALDI matrix immediately after enzyme incubation, slides may be stored in a slide mailer in a desiccator (6–16 h) or at 20  C (2–3 days). If storing at 20  C, immediately upon removal, dry slides in a vacuum desiccator to prevent condensate from delocalizing analytes.

Acknowledgments PMA and CLC are supported by P20GM103542 (NIH/NIGMS), HL007260 (NHLBI), and in part by pilot research funding, Hollings Cancer Center’s Cancer Center Support Grant P30 CA138313 at the Medical University of South Carolina. Additional support was provided by the South Carolina Centers of Economic Excellence SmartState program to RRD and ASM. The MUSC Mass Spectrometry Facility is supported by the Office of the Provost and the South Carolina COBRE in Oxidants, Redox Balance and Stress Signaling (P20GM 103542).

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References 1. Cox TR, Erler JT (2011) Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer. Dis Model Mech 4:165–178. https://doi.org/10. 1242/dmm.004077 2. Fan D, Takawale A, Lee J, Kassiri Z (2012) Cardiac fibroblasts, fibrosis and extracellular matrix remodeling in heart disease. Fibrogenes Tissue Repair 5(1):15. https://doi.org/10. 1186/1755-1536-5-15 3. Walraven M, Hinz B (2018) Therapeutic approaches to control tissue repair and fibrosis: extracellular matrix as a game changer. Matrix Biol 71-72:205–224. https://doi.org/10. 1016/j.matbio.2018.02.020 4. Schwamborn K, Kriegsmann M, Weichert W (2017) MALDI imaging mass spectrometry — from bench to bedside. Biochim Biophys Acta Proteins Proteom 1865(7):776–783. https://doi.org/10.1016/j.bbapap.2016.10. 014 5. West CA, Wang M, Herrera H et al (2018) N-linked glycan branching and fucosylation are increased directly in Hcc tissue as determined through in situ glycan imaging. J Proteome Res 17:3454–3462. https://doi.org/10. 1021/acs.jproteome.8b00323 6. Drake RR, West CA, Mehta AS, Angel PM (2018) MALDI mass spectrometry imaging of N-linked glycans in tissues. Springer, Singapore, pp 59–76 7. Powers T, Holst S, Wuhrer M et al (2015) Two-dimensional N-glycan distribution mapping of hepatocellular carcinoma tissues by MALDI-imaging mass spectrometry. Biomol Ther 5:2554–2572. https://doi.org/10. 3390/biom5042554 8. Powers TW, Jones EE, Betesh LR et al (2013) Matrix assisted laser desorption ionization imaging mass spectrometry workflow for spatial profiling analysis of N-linked glycan expression in tissues. Anal Chem 85:9799–9806. https://doi.org/10.1021/ac402108x

9. Angel PM, Mehta A, Norris-Caneda K, Drake RR (2018) MALDI imaging mass spectrometry of N-glycans and tryptic peptides from the same formalin-fixed, paraffin-embedded tissue section. Methods Mol Biol 1788:225–241. https://doi.org/10.1007/7651_2017_81 10. Angel PM, Bayoumi AS, Hinton RB et al (2016) MALDI-IMS as a lipidomic approach to heart valve research. J Heart Valve Dis 25:240–252 11. Scott DA, Casadonte R, Cardinali B et al (2019) Increases in tumor N-glycan polylactosamines associated with advanced HER2positive and triple-negative breast Cancer tissues. Proteomics Clin Appl 13(1):e1800014. https://doi.org/10.1002/prca.201800014 12. Powers TW, Neely BA, Shao Y et al (2014) MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays. PLoS One 9(9):e106255. https://doi. org/10.1371/journal.pone.0106255 13. Angel, PM (Corresponding author), ComteWalters, Ball, LE, Talbot, K, Mehta AS, Drake RR (2018) Mapping extracellular matrix proteins in formalin-Fixed, paraffin-embedded tissues by MALDI imaging mass spectrometry. J Proteome Res 17:635–646. https://doi. org/10.1021/acs.jproteome.7b00713 14. Angel, PM (Corresponding author), Bruner E, Bethard JR, Clift CL, Ball LE, Drake RR, Feghali-Bostwick C (2019) Extracellular matrix alterations in low grade lung adenocarcinoma compared to normal lung tissue by imaging mass spectrometry. J Mass Spect 55 (4):e4450. https://doi.org/10.1002/jms. 4450 15. Ceroni A, Maass K, Geyer H et al (2008) GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res 7:1650–1659. https://doi.org/10. 1021/pr7008252

Chapter 21 Multiplexed Raman Imaging in Tissues and Living Organisms Travis M. Shaffer and Sanjiv S. Gambhir Abstract Surface-enhanced Raman scattering (SERS) nanoparticles (NPs) are ideal multiplexing probes for in vivo imaging and tissue staining. Their remarkable sensitivity and unique Raman molecular fingerprint results in minimal background compared to other optical modalities. These characteristics also allow multiplexing down to the attomolar concentration. Here we describe the synthesis and in vivo multiplexing application of a SERS NP library. Key words Raman, SERS, Nanoparticles, Multiplexing, Resonance

1

Introduction Raman spectroscopy is an invaluable tool for chemists to characterize small molecules based on their unique molecular vibrations. However, the Raman mechanism of inelastic light scattering has a low probability (1 in 107) of occurring [1]. To increase the probability of Raman scattering, Raman-active molecules can be attached to plasmonic metals that act as antennas and amplify Raman scattering by up to 14 orders of magnitude [2]. This phenomenon is called surface-enhanced Raman scattering (SERS) and allows sensitivities down to the attomolar range [3]. For in vivo use, this process involves a plasmonic nanoparticle (NP) with Raman reporter molecules on the NP surface surrounded by a biocompatible coating. Gold NPs are most often used due to their biocompatibility and the historical use of gold in humans [4]. The sensitivity of SERS, combined with the unique Raman fingerprint of molecules absorbed to the nanoparticle surface, makes SERS NPs an ideal multiplexing system. Multiplexing with SERS NP typically includes a nontargeted “control color” that measures nonspecific uptake as well as SERS NPs with ligands that bind a specific target. Control SERS NPs are

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_21, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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necessary due to the enhanced permeability and retention (EPR) effect seen with both injected and topically applied NPs, which may preclude quantitative multiplexing [5]. There are a number of decisions a researcher must make when designing an in vivo Raman multiplexing experiment. SERS NPs may be synthesized [6] as described below or purchased if commercially available. Another important decision is determining the number of molecular targets and the corresponding targeting ligands. While SERS libraries of 8–10 colors are possible, 5 or fewer targets are preferable for obtaining optimal SERS sensitivity and specificity [7, 8]. Lastly, the in vivo model needs to be compatible with NPs reaching the site of interest with sufficient uptake. For example, if SERS NPs are administered intravenously, high nonspecific liver and spleen uptake could preclude targeted multiplex imaging in these organs. Here, we discuss methods for synthesizing a three-color SERS NP library, NP characterization, and in vivo multiplex imaging. CD47 and carbonic anhydrase 9 (CA9) are shown as examples, based on a recent publication that uses SERS NP multiplexing for delineating tumor margins in human bladder cancer samples [9]. The overall workflow of the SERS NP synthesis is shown in Fig. 1.

2

Materials Prepare all solutions using deionized water (18 MΩ-cm at 25
C). All reagents may be stored at room temperature unless otherwise noted. Follow all waste disposal regulations and observe appropriate precautions.

2.1 SERS NP Synthesis

1. Sodium citrate tribasic dihydrate (M.W. ¼ 294.10): Prepare a 1.7 mM citrate solution adding 7.5 mg of sodium citrate to 15 mL of water and mixing until dissolved. 2. Gold chloride trihydrate (M.W. ¼ 393.83): Prepare a 25 mM gold chloride solution by dissolving 491 mg of gold chloride trihydrate in 50 mL of water. 3. Tetraethyl orthosilicate (TEOS) (M.W. ¼ 208.33, density ¼ 0.933 g/mL at 20
C). 4. Pure ethyl alcohol, ACS grade (EtOH) (see Note 1). 5. Pure isopropanol, ACS grade (IPA) (see Note 2). 6. Dimethyl sulfoxide (DMSO). 7. 28% ammonium hydroxide solution (NH4OH). 8. Raman control, trans-1,2-bis (4-pyridyl)-ethylene (BPE) (M.W. ¼ 182.22): Prepare a 25 mM solution by dissolving 4.56 mg in 1 mL DMSO.

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Fig. 1 The workflow of generating SERS NP for multiplex imaging. Gold cores are synthesized and the same batch used for all “colors” of SERS NP. Three different Raman reporters are added to the NP surface, and silica is used as the coating, followed by thiol functionalization and linker conjugation. Lastly, targeting moieties are attached along with polyethylene glycol (PEGylation). Figure created with Biorender.com.

9. Raman reporter 1, IR-780 perchlorate (M.W. ¼ 609.15): Prepare a 25 mM solution by dissolving 15.23 mg in 1 mL DMSO. 10. Raman reporter 2, IR-792 perchlorate (M.W. ¼ 713.37): Prepare a 25 mM solution by dissolving 17.83 mg in 1 mL DMSO. 11. 2 L Erlenmeyer flask. 12. 96-well plate (black or clear wall). 13. Hot plate. 14. Magnetic stirrer. 15. Bucket centrifuge. 16. Sonicator. 17. Raman system with a 785 nm excitation laser and couplecharged device (CCD) camera. 2.2 Functionalization and Ligand Conjugation

1. (3-Mercaptopropyl)trimethoxysilane (MPTMS) (M.W. ¼ 196.34, density ¼ 1.507 g/mL at 20
C). 2. Pure ethyl alcohol, ACS grade (EtOH). 3. 28% ammonium hydroxide solution (NH4OH).

2.3 Conjugating Targeting Ligands

1. MES buffer: Prepare a 10 mM MES buffer by dissolving 195 mg of MES in 100 mL of water. Adjust the pH to 7.1 using 0.1 M NaOH.

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2. NHS-PEG12-maleimide (M.W. ¼ 865.92): Prepare a 1.15 mM solution by dissolving 1 mg of NHS-PEG12-maleimide in 1 mL water. 3. CD47-unconjugated antibody (M.W. ¼ ~150,000), 1 mg/ mL. 4. Carbonic anhydrase 9 (CA9)-unconjugated antibody (M.W. ¼ ~150,000), 1 mg/mL. 5. Methyl-PEG12-maleimide (M.W. ¼ 710.82): Prepare a 24 mM solution by dissolving 17.06 mg of NHS-PEG12-maleimide in 1 mL water. 2.4 In Vitro Targeting Validation

1. Cell staining buffer. 2. CD47-positive cells. 3. Carbonic anhydrase 9 (CA9)-positive cells. 4. Negative cells (i.e., cells that do not appreciably express CD47 or CA 9).

2.5 In Vivo SERS NP Multiplexing

1. Injection syringes. 2. 5% dextrose in water (D5W). 3. Raman measurement system: This can be a fiber-based system or a microscope, depending on the application. 4. Mice or tissue(s) that contain targets of interest.

2.6

3

Data Analysis

1. Computer and software to analyze numerical data from Raman system (e.g., MATLAB, Microsoft Excel, Prism).

Methods

3.1 SERS NP Synthesis

1. Prepare a 0.25 mM gold chloride solution in a 2 L Erlenmeyer flask by adding 10 mL of the 25 mM gold chloride solution to 975 mL of water. Add a magnetic stirrer and place on a hot plate set to 100
C. Bring the solution to boiling. Rapidly add 15 mL of 1.7 mM sodium citrate, and allow to continue heating for 10 min. Remove the Erlenmeyer flask from the hot plate, and allow the gold NP colloid system to cool to room temperature (see Note 3). 2. Purify the gold NPs by pouring into twenty 50 mL tubes. Spin down at 7000  g for 5 min at 25
C. Dispose of supernatant (~48–49 mL) and keep gold NP pellet (~1–2 mL). Sonicate the pellets in the 50 mL tubes and combine together for a final volume of approximately 20–40 mL. 3. Determine the NP concentration and size using a NanoSight system. The concentration should be approximately 1–2 nM (assuming 25 mL volume), and the size should be between

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50 and 65 nm (see Note 4). Transmission electron microscopy (TEM) can also be performed at this step to assess the morphology of the gold NPs. Other optional tests include UV-Vis absorbance, dynamic light scattering, and zeta potential measurements. 4. Prepare a 1 nM gold NP solution, diluting with water as needed. The total volume to prepare is dependent on the amount of SERS NPs being synthesized in the following steps. 5. BPE SERS NP synthesis. In one 50 mL tube, add 9 mL of IPA and 600μL of ammonium hydroxide. In another 50 mL tube, add 1.2 mL of TEOS, 4 mL of 1 nM gold NPs, 30 mL of IPA, and 100μL of the 25 mM BPE Raman reporter for 30 s. After the 30 s incubation, combine the tubes together, cap, and proceed to step 8 (see Note 5). 6. IR-780 perchlorate SERS NP synthesis. Add 4 mL of 1 nM gold NPs, 600μL of ammonium hydroxide, and 30 mL of IPA to a 50 mL tube. In another 50 mL tube, add 1.2 mL of TEOS, 9 mL of IPA, and 15μL of the 25 mM IR-780 perchlorate Raman reporter molecule. Rapidly add the two tubes together, cap, and proceed to step 8 (see Note 6). 7. IR-792 perchlorate SERS NP synthesis. Add 4 mL of 1 nM gold NPs, 600μL of ammonium hydroxide, and 30 mL of IPA to a 50 mL tube. In another 50 mL tube, add 1.2 mL of TEOS, 9 mL of IPA, and 15μL of the 25 mM IR-792 perchlorate Raman reporter molecule. Rapidly add the two tubes together, cap, and proceed to step 8 (see Note 7). 8. Silica shell growth step for all SERS NP flavors (see Note 8). Shake the SERS NPs at RT for 15 min on a thermomixer at 600 rpm followed by centrifugation at 7000  g for 10 min at 4
C. Remove the supernatant, resuspend in 10 mL pure EtOH, centrifuge, and remove the supernatant as before. Finally, resuspend in 2 mL of EtOH for a final concentration of approximately 2 nM. 9. Determine the NP concentration and size using a NanoSight as before. If a NanoSight is unavailable, dynamic light scattering (DLS) can be used to determine the NP size by diluting the SERS NPs 1:100 in ethanol or water. The final diameter is dependent on the desired application; the above synthesis should yield 130–150 nm diameter NPs (see Note 9). 10. Complete a serial dilution of the SERS NP in ethanol or water in a 96-well plate. Start with the 2 nM SERS NP solutions, and complete a series of log10 dilutions to the 1015 M range. Place 100μL of these solutions in a 96-well plate along with a gold NP core plan (i.e., no Raman molecule). Complete Raman scans on each well, subtract the gold NP core Raman signal, and plot. Exact parameters will depend on the laser power,

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objective, and other parameters specific per setup. It is recommended to start with 100% laser power, 5 objective, and a 5-s scan with the most concentrated SERS NP solution and decrease the scan time or laser power if saturation occurs. The IR-780 and IR-792 SERS NP should have sensitivities down to 1014 M, while the BPE SERS NP should have the sensitivity down to 1013 M (see Note 10). 3.2 Thiol Functionalization

1. MPTMS is used to introduce thiols to the NP surface. Add 100μL of MPTMS and 20μL of ammonium hydroxide to 2 mL of 2 nM gold NPs, and incubate for 70
C for 2 h (see Note 11). 2. Centrifuge the NPs at 7000  g for 10 min at 25
C and resuspend in ethanol. Repeat twice for washing. Finally, resuspend the NPs in EtOH if storing for an extended period of time. If functionalizing further, wash twice in water after the ethanol washes are complete. The NPs should now be in 1 mL of water.

3.3 Conjugating Targeting Ligands

1. The exact targeting moieties are highly dependent on the application. Here we describe targeting the CD47 and CA9 proteins with antibodies. First, a short PEG linker with an amine-reactive end is conjugated to the thiolated SERS NP using maleimide chemistry. Following this, the NHS esters on the PEG are conjugated to the lysine residues on the antibodies. Lastly, free thiols are blocked with maleimide PEG. 2. Generally, the order of sensitivity for the SERS NPs will be (lowest to highest) BPE, IR-792, and IR-780 (assuming a 785 nm excitation laser). Therefore, a recommended strategy is to have BPE as the control color, IR-792 for the most abundant protein, and IR-782 for the least abundant protein. 3. Centrifuge the SERS NP colloids at 7000  g for 5 min at 25
C, remove the supernatant, and resuspend in a volume of 10 mM MES that results in a 0.4 nM solution. For a 2 mL dispersion of 2 nM SERS NPs, this would be 5 mL. To this, add 5.2μL of 1.15 mM NHS-PEG12-maleimide along with 60μL of 1 mg/mL antibody. In this example, IR-780 NPs will target CA9 and IR-792 NPs will target CD47. Place the SERS NP reactions on a thermomixer for 3 h at 25
C. Next, add 50μL of 17.06 mM methyl-PEG12-maleimide and incubate at RT for 3 h. The control SERS NPs (BPE) will only have the addition of 50μL of methyl-PEG12-maleimide. 4. Purify the SERS NPs by centrifuging at 7000  g for 10 min at 25
C and resuspending in MES buffer. Wash the SERS NPs three times and finally resuspend them in 1 mL of MES for a concentration of approximately 4 nM.

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Table 1 In vitro Raman binding assay SERS NP added

Cell line (3 samples per condition)

CD47 SERS NP

Negative line

CD47 SERS NP

CD47-positive line

CA9 SERS NP

Negative line

CA9 SERS NP

CA9-positive line

Control SERS NP

Negative line

Control SERS NP

CD47-positive line

Control SERS NP

CA9-positive line

3.4 In Vitro Targeting Validation

1. Test the binding efficacy of the targeted SERS NPs in cells that express the target as well as a negative control. Add 4μL of 1 nM of SERS NPs to 200,000 positive or negative cells in 100μL cell staining buffer (see Table 1). Incubate at 4
C for 30 min (see Note 12). 2. Remove the free SERS NPs from cells by adding 2 mL of MES buffer to each tube and centrifuging at 300  g for 5 min at 25
C, completing this step twice. Finally, resuspend the cell pellets in 200μL of MES. 3. Measure the SERS signal of each cell pellet, and normalize for the SERS signal of each flavor using the calibrated intensities determined previously. Positive cells with the corresponding SERS NP flavor should result in the highest normalized Raman signals.

3.5 In Vivo SERS NP Multiplexing

1. The following protocol can be used for multiplexing Raman imaging. Topical and intravesical applications of SERS NP have all been developed [9–12]. Here we present a methodology for intravenous administration of SERS NP. However, the basic ratio metric principle of this method applies to each system (Fig. 2). 2. First prepare a SERS NPs solution containing equal molar amounts of each SERS NP flavor. Prepare a 150μL volume containing 50μL each of 4 nM SERS NP flavors in a D5W solution. Inject via the tail vein of an appropriate mouse model (see Note 13). 3. Twenty-four hours after injection, complete SERS imaging of the disease site of interest. For a Raman microscope system, the mouse is anesthetized, and the Raman laser is focused on the site of interest. Either a series of points or an entire map of

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Fig. 2 SERS NP multiplexing in bladder cancer via intraurethral injection (a). A number of SERS NP “flavors” are administered (injected or topically applied) to a site of interest (b). After incubation and allowing the non-bound particles to clear, a Raman measurement can be taken (c). There is often a control (i.e., nontargeted) SERS flavor administered to account for the EPR effect (blue), and by calculating the ratio of the control and targeted SERS NPs, the target expression can be determined (d and e) (reproduced from ref. 9 with permission from the American Chemical Society (ACS, https://pubs.acs.org/doi/full/10.1021/acsnano. 8b03217). Further permissions related to this figure should be directed to the ACS)

the site can be imaged, although mapping an area can take appreciable time (i.e., 4 h). 4. Further validation is possible by euthanizing mice and completing Raman scans on excised tissue of interest. 3.6

Data Analysis

1. For a Renishaw system that has Wire 3.4 Raman imaging software, different SERS NP colors can be assigned and unmixed using a direct classical least squares (DCLS) algorithm. By collecting SERS NP spectra of each flavor, the data collected from tissue can be deconvoluted and a quantitative map can be made of the positive areas in the tissue [13]. 2. Alternatively, quantitative images of SERS NPs can be generated by using either a principle component analysis (PCA) or a direct classical least squares (DCLS) method [10]. For DCLS, reference spectra of each color (BPE, IR-780, and IR-792) are collected. For the tissue or organ SERS data, the linear combination of these spectra that most closely matches the collected spectrum is calculated.

Multiplexed Raman Imaging in Tissues and Living Organisms

4

339

Notes 1. It is important to use pure ethyl alcohol without any additives or water. 2. It is recommended to use IPA with 1 month of opening for reproducible syntheses. 3. The color of the solution should be burgundy red. If the color is gold red, the particles are likely to be in the 75–95 nm range. If the color is black, aggregation has occurred. 4. The gold nanospheres can be stored for at least 6 months after synthesis in water. 5. If aggregation occurs during this step, add the two reaction tubes together after 15 s, rather than 30 s. Alternatively, the concentration of BPE can be decreased. If the BPE Raman signal is lower than desired, the two reaction tubes can be added together after a longer period of time (60–120 s). 6. If aggregation occurs during this step, lower the concentration of IR-780 until aggregation no longer occurs. 7. If aggregation occurs during this step, lower the concentration of IR-792 until aggregation no longer occurs. 8. While there are a number of coating techniques available, silica coatings offer a stable, biocompatible layer that can be functionalized. Additionally, the silica coating can quench fluorescence and increase the SERS intensity, sensitivity, and specificity [14]. 9. If the silica shell is too small, increase the synthesis time from 15 to 20 min (or longer), or add 500μL of H2O at the start of the synthesis. If the silica shell is too large, decrease the synthesis time to 10 min, or decrease the amount of TEOS used. 10. While molarity sensitivities are often reported, this value is highly dependent on the instrument, laser power (calibrated), and scan time. Therefore, it is important to report this information to aid in experimental replication. 11. MPTMS has a pungent odor and should be handled in a fume hood. 12. Details of cell staining will depend on the exact cell lines. Fragment crystallizable region (FC) block is recommended for immune cell staining. If high nonspecific signal is seen, decrease the SERS NP concentration added to the cells. 13. >90% of injected SERS NPs of this size are sequestered in the liver and spleen when intravenously injected. Other routes of administration address this limitation and are recommended when applicable.

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Travis M. Shaffer and Sanjiv S. Gambhir

Acknowledgments This work was supported by an NIH Cancer-Translational Nanotechnology Training grant (T32 CA196585 to T.M.S.) and 1R01CA222836-01A1 (S.S.G.). Note: Prof. Sanjiv Sam Gambhir was the corresponding author of this chapter but passed away during publication. We would like the dedicate this work to Dr. Gambhir (1962–2020) for his excellent mentoring and tremendous support through the years. References 1. Andreou C, Kishore SA, Kircher MF (2015) Surface-enhanced Raman spectroscopy: a new modality for cancer imaging. J Nucl Med 56 (9):1295–1299. https://doi.org/10.2967/ jnumed.115.158196 2. Zhang Y, Chu W, Foroushani AD, Wang H, Li D, Liu J, Barrow CJ, Wang X, Yang W (2014) New gold nanostructures for sensor applications: a review. Materials (Basel) 7 (7):5169–5201. https://doi.org/10.3390/ ma7075169 3. Harmsen S, Bedics MA, Wall MA, Huang R, Detty MR, Kircher MF (2015) Rational design of a chalcogenopyrylium-based surfaceenhanced resonance Raman scattering nanoprobe with attomolar sensitivity. Nat Commun 6:6570. https://doi.org/10.1038/ ncomms7570 4. Faa G, Gerosa C, Fanni D, Lachowicz JI, Nurchi VM (2018) Gold - old drug with new potentials. Curr Med Chem 25(1):75–84. https://doi.org/10.2174/ 0929867324666170330091438 5. Wang YW, Khan A, Som M, Wang D, Chen Y, Leigh SY, Meza D, McVeigh PZ, Wilson BC, Liu JT (2014) Rapid ratiometric biomarker detection with topically applied SERS nanoparticles. Technology 2(2):118–132. https://doi. org/10.1142/s2339547814500125 6. Harmsen S, Wall MA, Huang R, Kircher MF (2017) Cancer imaging using surfaceenhanced resonance Raman scattering nanoparticles. Nat Protoc 12(7):1400–1414. https://doi.org/10.1038/nprot.2017.031 7. Zhang F, Fan Y, Wang S (2019) Optical multiplexed bioassays improve biomedical diagnostics. Angew Chem Int Ed Engl 58 (38):13208–13219. https://doi.org/10. 1002/anie.201901964 8. Zavaleta CL, Garai E, Liu JT, Sensarn S, Mandella MJ, Van de Sompel D, Friedland S, Van Dam J, Contag CH, Gambhir SS (2013) A Raman-based endoscopic strategy for

multiplexed molecular imaging. Proc Natl Acad Sci U S A 110(25):E2288–E2297. https://doi.org/10.1073/pnas.1211309110 9. Davis RM, Kiss B, Trivedi DR, Metzner TJ, Liao JC, Gambhir SS (2018) Surface-enhanced Raman scattering nanoparticles for multiplexed imaging of bladder cancer tissue permeability and molecular phenotype. ACS Nano 12 (10):9669–9679. https://doi.org/10.1021/ acsnano.8b03217 10. Davis RM, Campbell JL, Burkitt S, Qiu Z, Kang S, Mehraein M, Miyasato D, Salinas H, Liu JTC, Zavaleta C (2018) A Raman imaging approach using CD47 antibody-labeled SERS nanoparticles for identifying breast cancer and its potential to guide surgical resection. Nanomaterials (Basel) 8(11):953. https://doi.org/ 10.3390/nano8110953 11. Kang S, Wang Y, Reder NP, Liu JT (2016) Multiplexed molecular imaging of biomarkertargeted SERS nanoparticles on fresh tissue specimens with channel-compressed spectrometry. PLoS One 11(9):e0163473. https://doi. org/10.1371/journal.pone.0163473 12. Wang YW, Kang S, Khan A, Bao PQ, Liu JT (2015) In vivo multiplexed molecular imaging of esophageal cancer via spectral endoscopy of topically applied SERS nanoparticles. Biomed Opt Express 6(10):3714–3723. https://doi. org/10.1364/boe.6.003714 13. Huang R, Harmsen S, Samii JM, Karabeber H, Pitter KL, Holland EC, Kircher MF (2016) High precision imaging of microscopic spread of glioblastoma with a targeted ultrasensitive SERRS molecular imaging probe. Theranostics 6(8):1075–1084. https://doi.org/10.7150/ thno.13842 14. Walters CM, Pao C, Gagnon BP, Zamecnik CR, Walker GC (2018) Bright surfaceenhanced Raman scattering with fluorescence quenching from silica encapsulated J-aggregate coated gold nanoparticles. Adv Mater 30(5). https://doi.org/10.1002/adma.201705381

Correction to: Out-of-Phase Imaging after Optical Modulation (OPIOM) for Multiplexed Fluorescence Imaging Under Adverse Optical Conditions Raja Chouket, Ruikang Zhang, Agne`s Pellissier-Tanon, Annie Lemarchand, Agathe Espagne, Thomas Le Saux, and Ludovic Jullien

Correction to: Chapter 13 in: Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5 The chapter was inadvertently published with a typo error in the Table 13.2 for the line “ffDronpa”, molar extinction coefficient as “11 000 M-1cm-1” instead of “105 000 M-1cm-1”. The correction has been incorporated by changing the molar extinction coefficient from “11 000 M-1cm-1”to “105 000 M-1cm-1”.

The updated online version of this chapter can be found at https://doi.org/10.1007/978-1-0716-1593-5_13 Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5_22, © Springer Science+Business Media, LLC, part of Springer Nature 2021

C1

INDEX A Antibody ...............................................25, 26, 28, 32, 51, 69, 78, 84–91, 96, 106, 107, 109, 110, 112–113, 115, 116, 122, 127, 129, 130, 142, 143, 186, 196, 259, 260, 267, 268, 274, 277–279, 291, 292, 295, 334, 336 Atomic force microscopy (AFM) ................290–295, 308 Autofluorescence.............................................57, 96, 101, 107, 130, 131, 134–136, 146, 149, 150, 152, 153, 192–195, 197, 199, 201, 202, 205, 229, 240, 241, 247, 248, 286

B Bimolecular fluorescence complementation (BiFC) ....................................................... 173–189 multicolor BiFC .................................... 174, 178, 185 Bioimaging .................................................. 194, 196, 202 Bioluminescence.............................................16, 229–235 bioluminescence imaging .............................. 229–235 bioluminescence resonance energy transfer (BRET) ..........................................................16, 17 multicolor bioluminescence .......................... 229–235 Biosensor ..........................................1–17, 43, 44, 46–52, 54, 56–59, 61–66, 96 Blocking..............................................23, 25, 58, 96–101, 109, 128, 129, 274, 277–280 Bone marrow....................................................95, 97, 103 Bone tissue ............................................95, 100, 102, 103

C Calcium calcium imaging ............................................. 229–235 Camera.................................................. 46, 52–59, 63, 64, 66, 69, 72, 74, 110, 158, 166, 168, 171, 187, 193, 207, 208, 210–215, 232, 233, 235, 242, 243, 245, 247–249, 275, 276, 280, 286, 333 Cancer.................................................1, 79, 83, 105–122, 125, 138, 143, 269, 270, 289–295, 315 Cell migration ............................................. 289, 290, 295 Collagen................................................................. 98, 149, 152–155, 187, 290, 292, 295, 314–316, 318, 319, 322–328 Collagen peptide imaging................................... 316, 318, 319, 327

Confocal microscopy ................................. 36, 38, 39, 85, 88, 89, 97–99, 116, 117, 119, 161, 167, 168, 171, 179, 186, 187, 285, 294, 295

D Deep tissue imaging..................... 77, 105–122, 146, 239 Deformation ................................................ 290, 293, 294 Dendritic cells (DC) .............................65, 154, 155, 166 Dimerization .........................................5, 6, 49, 176, 185 Disseminated tumour cells/Disseminated tumor cells ...............................106, 107, 117–120 Dynamic contrast ........................................ 198, 199, 210

E Electron-multiplying charge-coupled device (EMCCD/EM-CCD) ................ 34, 72, 233, 235 Extracellular matrix (ECM).........................289, 313–328

F Fast Fourier transform (FFT).............................. 372–373 Fibrosis.................................................................. 313–328 Fixation ....................................................... 75, 78, 81, 82, 84, 87, 102, 128, 196, 256, 257, 260, 284, 291, 301, 302, 308 Fluorescence endomicroscopy ............................ 207, 212, 214–215 imaging ............................................................ 96, 158, 191–217, 229, 243, 295 labeling .................................................................... 116 microscopy.........................................................21–29, 80, 187, 206, 207, 210–212, 258–260, 263, 270 Fluorescence-activating and absorption-shifting tag (FAST) ................................................ 253–264 Fluorescence lifetime imaging microscopy (FLIM) fourier multiplexed FLIM (FmFLIM)......... 159–164, 168, 170, 171 Fluorescent protein (FP) .................................... 2, 4, 6–8, 10–14, 16, 32, 37–38, 44, 49, 50, 174–177, 181, 185, 186, 195, 233, 253, 255 Fluorochrome..................................................... 23, 28, 29 Fluorogen ............................................................. 254–263 Fluorogenic chromophore............................................ 254

Eli Zamir (ed.), Multiplexed Imaging: Methods and Protocols, Methods in Molecular Biology, vol. 2350, https://doi.org/10.1007/978-1-0716-1593-5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

341

MULTIPLEXED IMAGING: METHODS

342 Index

AND

PROTOCOLS

Formalin fixed paraffin embedded (FFPE)............ 79, 80, 125–144, 267, 270–272, 276, 278, 279, 283, 284, 314 Formalin fixed paraffin preserved (FFPP) .......... 302, 303 Fo¨rster resonance energy transfer (FRET) FRET biosensor ............................................ 8–11, 43, 44, 46–48, 50, 52, 58, 65 Homo-FRET.................................................. 8, 11, 12 Fourier transform....................... 193, 197, 202, 299–310

G GTPase....................................................... 6, 7, 13, 43–67

H Hapten .................................................................. 267–286 Histology ............................. 98, 100, 273, 276, 314, 318 Histone histone modifications ................................................ 32

I Imaging buffer ..........................................................71, 73 Imaging mass spectrometry (IMS) .................... 195, 302, 313–328 Immunofluorescence ........................................ 21–29, 89, 130, 195, 268, 291, 292, 295 Immunohistochemistry ................................82, 109–110, 115, 125, 267–286 Immunoprofiling............................................................. 32 Infrared (IR) infrared imaging ............................................. 303–304 In situ.................................................................... 126, 153 Intravital imaging .......................................................... 151

L Label-free imaging ........................................................ 299 Laser............................................... 10, 21, 34, 46, 72, 77, 98, 107, 146, 158, 180, 199, 240, 285, 291, 299, 314, 333 Linear unmixing.................. 13, 197, 230–232, 234, 235 Live-cell imaging ........................45, 50, 52, 64, 168, 230 Living cells .......................................................2, 4, 32, 34, 36, 39, 43–67, 173–189, 195, 231–233, 256 Luciferase................................................................ 16, 230

Microscopy ................................................ v, 9, 10, 21–30, 34–35, 39, 69–75, 80, 107, 115–117, 119, 120, 122, 145, 147, 157, 160, 186, 187, 199, 201, 205–207, 210–215, 241, 255, 256, 258–260, 263, 267, 270, 274, 276, 283, 285, 291–295, 308, 309, 318, 335 Modification-specific intracellular antibody .................. 32 Mounting..............................................24, 27, 29, 66, 96, 97, 99–102, 110, 127, 130, 165–166, 180, 262, 274, 275, 278–280, 284, 291, 292, 302, 318, 328 Multicolor imaging ....................................................... 253 Multicolor/Multicolour ..........................................69–75, 95–103, 138, 174, 178, 185, 229–236, 253 Multi-modal imaging/Multimodal imaging ............... 299 Multiphoton imaging ..................................107, 145–155 Multi-photon microscopy............................................. 107 Multiplex imaging/Multiplexed imaging....................v, 2, 3, 8–11, 13–17, 31–40, 43–67, 107, 125–145, 173–189, 192, 195, 197, 201, 239–250, 256, 300, 304, 313–328, 332, 333 Multiplex immunohistochemistry....................... 267–286 Multiplexing ....................................................3, 8–16, 49, 96, 158, 159, 192, 195, 239–243, 271, 331, 332, 334, 337–338 Multispectral imaging ................................................... 135 mVenus ....................................... 174–178, 180, 181, 185

N NanoLuc (Nluc)............................................................ 230 Nanoparticle (NP)/Nanoparticles (NPs) .......... 239–250, 257, 331–339 Near-infrared (NIR) fluorescent dyes ....................................................... 107 fluorescent protein .................................................... 43 imaging ........................................................ 44, 46, 48, 52, 58, 59, 107, 146, 194, 239, 241–243, 245, 248, 249 N-glycan ............................................................... 313–328 N-glycosylation ............................................................. 314 Non-covalent labeling................................................... 254

O Optical parametric oscillator......................................... 147 OxEA imaging buffer ...............................................71, 73

M

P

Macroscale imaging......................................207, 212–214 Matrix-assisted laser desorption/ionization imaging (MALDI) MALDI-IMS ............... 314–316, 318, 322, 326, 327 mCerulean ...........................................174–176, 178, 185 Methylation ........................................................ 32, 34–37 Micrometastases ................................................... 105–122

PeakForce QNM (PFQNM) ............................... 290–293 Permeabilization.............................................99, 100, 102 Phasor analysis ...................................................... 163, 197 Photobleaching ..................................................... 63, 106, 145, 153, 195, 196, 205, 208, 209 Photomultiplier tube (PMT) ............................. 146, 148, 149, 152, 161, 164–166, 168, 169, 171

MULTIPLEXED IMAGING: METHODS

AND

PROTOCOLS Index 343

Quantitative microscopy ............................................... 270 Quantum dots (QD) ...................................105–122, 196

115–118, 121, 125, 126, 128–131, 136–140, 144, 145, 147, 149, 259, 260, 267–272, 274, 276–279, 282–284, 286, 295, 313–328, 334, 337, 339 Super resolution super resolution microscopy............. 69–75, 198, 210 Surface-enhanced Raman scattering (SERS) ...................................................... 331–339

R

T

Posttranslational modifications (PTMs) ........... 31–40, 43 Protein-protein interaction............................43, 173–189 Protein tag ............................................................ 253, 254 Proteomics..................................................................... 314

Q

Raman imaging .......................................... 299–310, 331–339 spectroscopy (RS) ......................................... 210, 299, 300, 304, 305, 307, 308, 331 Rare-earth-doped nanoparticles ................................... 248 Resonance..................................................... 4, 16, 32, 46, 173, 203–206, 211, 212, 249, 291 Reversibly photoswitchable fluorophores .................... 198 Rho GTPases ................................................................... 66

S Signalling/Signaling ...........................1–17, 63, 195, 314 Signal transduction ................................. 1, 2, 4, 7, 13–17 Similarity unmixing ..................................... 146, 151, 155 Single cell...........................................................16, 17, 32, 36, 88, 96, 232, 271 Single-domain antibodies .................................... 105–122 Single molecule localization microscopy (SMLM)......................................... 69, 70, 72, 255 Single plane illumination microscopy (SPIM) ............................ 195, 203, 206, 211–212 Spectrally unmixing/Spectral unmixing/ Spectral Analysis ...................................... 146, 149, 151, 154, 192–193, 195, 197, 268 Spectra/spectrum.................................................. 2, 3, 11, 12, 50, 89, 106, 112, 130, 132, 133, 136, 149, 151–153, 175, 177, 191, 193, 196, 197, 231, 232, 241, 276, 300–302, 305–308, 310, 338 Staining ................................................. 22–29, 36, 69, 73, 81, 82, 85, 87–89, 91, 96, 98, 99, 101–103,

3D imaging.................................................................... 116 Time-correlated single photon counting (TCSPC) ................................................... 158, 199 Tissue clearing......................................................... 77, 85, 88, 91, 316, 322–323 imaging ...............................................................71, 77, 105–122, 146, 239, 281, 282 Tomography tomography imaging/tomographic imaging ....................................165, 167, 170, 171 Transfection.......................................................32, 34, 35, 37, 38, 45, 47, 59, 178–181, 184–187, 189, 309 Tumour/Tumor................................................ 77, 79–84, 86–88, 90, 91, 106, 107, 114, 115, 117–122, 125, 135, 136, 143, 267, 269–272, 282, 289, 290, 332 Two-color-two-photon excitation ............................... 146 Two-photon ....................................................79, 80, 107, 110, 116, 117, 119, 145–150, 153, 158

V Videomicroscopy/Video-microscopy ........ 291, 292, 295

Y Young’s modulus ........................................ 290, 293, 294

Z Zebrafish ................... 195, 203, 255, 256, 258, 262, 263