Extracellular Vesicles in Diagnosis and Therapy (Methods in Molecular Biology, 2504) 1071623400, 9781071623404

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Methods in Molecular Biology 2504

Maurizio Federico Barbara Ridolfi Editors

Extracellular Vesicles in Diagnosis and Therapy

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-by step 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.

Extracellular Vesicles in Diagnosis and Therapy Edited by

Maurizio Federico and Barbara Ridolfi National Center for Global Health, Istituto Superiore di Sanità, Rome, Italy

Editors Maurizio Federico National Center for Global Health Istituto Superiore di Sanita` Rome, Italy

Barbara Ridolfi National Center for Global Health Istituto Superiore di Sanita` Rome, Italy

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2340-4 ISBN 978-1-0716-2341-1 (eBook) https://doi.org/10.1007/978-1-0716-2341-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of 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. 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.

Dedication In memory of Elisabetta Federico, Lisa, too young a victim. For more on Lisa’s story, Dr. Maurizio Federico has written an essay, “Dedicated to Lisa. An Italian Story that Should Never Repeat” available online.

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Preface Transport of macromolecules among cytoplasmic structures is mediated by vesicles moving in a densely populated microenvironment. Part of these vesicles can be released into the extracellular milieu, thereby being identified as extracellular vesicles (EVs). They are part of the mechanism of intercellular communication, also at long distance. The discovery of nano/micro-vesicular structures released into the extracellular space, containing a multitude of factors including signaling molecules, proteins, and nucleic acids, has added a new layer of complexity to our understanding of cell-to-cell communication. All EV subtypes are limited by a lipid bilayer membrane surrounding a specific cargo of molecules, and having different sizes and buoyant densities. Current research mainly considers two types of EVs according to their biogenesis, i.e., ectosomes/microvesicles and exosomes. Ectosomes/microvesicles are of 150–1000 nm in diameter and bud directly from plasma membrane. Exosomes refer to vesicles of 30–150 nm in diameter generated intracellularly by inward invagination of endosome membranes leading to the formation of intraluminal vesicles. They became part of multivesicular bodies which are released in the extracellular space upon fusion with plasma membrane Body fluids (e.g., blood, urine, saliva, amniotic fluid, bronchoalveolar lavage fluid, synovial fluid, breast milk) contain various types of membrane-enclosed vesicles recognizing diverse pathways of biogenesis. These vesicles possess different biophysical features and functions in health, e.g., protein clearance, immune regulation, cell signaling, as well as in disease, such as in infections and cancer. The presence in many biological fluids of EVs prompted many research groups to investigate their possible use as disease biomarkers as well as tools for the development of new therapies. The first part of the present volume is opened by an introductive chapter by Tan and colleagues overviewing current methods to detect cancer exosomes through aptasensors, i.e., biosensors using aptamers (single-stranded DNA or RNA molecules) as recognition element. Al-Jamal et al. then provide a detailed application of the aptasensor-based technique. This section also comprises a couple of papers from Takov and colleagues and McDonnell and colleagues, describing methods to isolate EVs from both human and mouse plasma. In the last chapter of the section, van der Pol et al. report a detailed protocol for evaluating concentrations of EVs in human plasma by flow cytometry. The second part of this book is dedicated to the description of different methods to isolate and quantify EVs from specialized tissues/organs and body fluids, i.e., from placenta by Salomon and colleagues, cardiovascular stem cells by Yang and colleagues, saliva by Punyadeera and colleagues, and urine by Reithmair and colleagues. Together, these chapters offer to readers the ability to deal with effective analysis of EVs isolated from a wide range of districts. In the third section, methods devoted to analyzing EV components are presented. In particular, Jin and colleagues furnish a protocol for the miRNA detection in EVs, whereas Iliuk presents a method for the detection and analysis of EV phosphoproteins. This section is then completed by the contribution of Saito et al., who describe a protocol for the analysis of EV lipids. The last frontier in terms of macromolecule delivery by natural nanovesicles is represented by engineered EVs. In the last section of this book, a number of methods to engineer

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EVs are provided. This section is opened by a review from Chiozzini and colleagues offering a comparative analysis of EV-based versus other nanovesicle-based methods in terms of antigen delivery to induce immunity. Dieterich and colleagues then describe a protocol for fluorescently labeling the membrane of tumor EVs, whereas Manfredi and colleagues propose a way to incorporate fluorescent proteins into the EV lumen. Finally, two different methods to upload miRNAs and other nucleic acids are provided by Pomatto and colleagues and Jeyaram and colleagues, respectively. Overall, this book provides an exhaustive picture of current methods to detect, isolate, and analyze EVs from diverse sources. In addition, the methods of EV labeling and macromolecule uploading would be of outmost utility for the study of still incompletely known EV processes (i.e., cell attachment, cell entry, fate of EV cargo) as well as for the use of EVs as immunogens/therapeutics. Clearly, EVs have been proven to have great potentialities as disease biomarkers as well as delivery tools of therapeutic/immunogenic molecules. Recent advances pave the way for a wide use of EVs in both diagnosis and therapy. Protocols included in the present volume are expected to be of great utility to further enlarge the number of scientists interested in EV research. Rome, Italy

Maurizio Federico Barbara Ridolfi

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

PART I

EXTRACELLULAR VESICLES DETECTION AND ISOLATION

1 Aptasensors for Cancerous Exosome Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Li, Sitao Xie, Fengli Qu, and Weihong Tan 2 Detection of Cancer-Derived Exosomes Using a Sensitive Colorimetric Aptasensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lizhou Xu and Khuloud T. Al-Jamal 3 Isolation of Circulating Extracellular Vesicles by High-Performance Size-Exclusion Chromatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaloyan Takov, I-Jou Teng, and Manuel Mayr 4 Isolation and Proteomic Analysis of Mouse Serum Small Extracellular Vesicles for Individual Subject Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Federica Anastasi, Marialaura Dilillo, Davide Pellegrini, and Liam A. McDonnell 5 Protocol for Measuring Concentrations of Extracellular Vesicles in Human Blood Plasma with Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Najat Hajji, Chi M. Hau, Rienk Nieuwland, and Edwin van der Pol

PART II

v vii xi

3

21

31

41

55

ISOLATION AND CHARACTERIZATION OF TISSUE AND BIOFLUID-SPECIFIC EVS

6 Targeted Mass Spectrometry-Based Proteomics Method to Quantify Placental Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Andrew Lai, Carlos Palma, Alexis Salas, Flavio Carrion, and Carlos Salomon 7 Isolation and Characterization of Extracellular Vesicles Secreted from Human Pluripotent Stem Cell-Derived Cardiovascular Progenitor Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Qiang Wu, Min-Xia Ke, and Huang-Tian Yang 8 Isolation and Characterization of Salivary Exosomes for Cancer Biomarker Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 ¨ ller Bark, Lucas Trevisan Franc¸a de Lima, Juliana Mu Mohammad Rasheduzzaman, Chameera Ekanayake Weeramange, and Chamindie Punyadeera

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9 Isolation and Characterization of Urinary Extracellular Vesicles for MicroRNA Biomarker Signature Development with Reference to MISEV Compliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Marlene Reithmair, Anja Lindemann, Veronika Mussack, and Michael W. Pfaffl

PART III 10

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12

Direct Detection of Extracellular Vesicle miRNAs Using a Single-Step RT-qPCR Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Ayyanar Sivanantham, Heedoo Lee, and Yang Jin Purification and Phosphoproteomic Analysis of Plasma-Derived Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Anton B. Iliuk Lipidomic Analysis of Extracellular Vesicles Isolated from Human Plasma and Serum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Yuchen Sun, Kosuke Saito, and Yoshiro Saito

PART IV 13 14

15

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ANALYSIS OF EV COMPONENTS

EV ENGINEERING

Extracellular Vesicles and Their Use as Vehicles of Immunogens . . . . . . . . . . . . . . Chiara Chiozzini, Barbara Ridolfi, and Maurizio Federico Isolation and Fluorescent Labeling of Extracellular Vesicles from Cultured Tumor Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Noelle Leary, Sarina Walser, and Lothar C. Dieterich Generation, Characterization, and Count of Fluorescent Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flavia Ferrantelli, Valentina Tirelli, Valeria Barreca, and Francesco Manfredi Optimized Protocol for Plasma-Derived Extracellular Vesicles Loading with Synthetic miRNA Mimic Using Electroporation . . . . . . . . . . . . . . . . . . . . . . . Margherita A. C. Pomatto, Federica Negro, and Giovanni Camussi Extracellular Vesicle Loading Via pH-Gradient Modification . . . . . . . . . . . . . . . . . Stephanie M. Kronstadt, Steven M. Jay, and Anjana Jeyaram

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors KHULOUD T. AL-JAMAL • Institute for Pharmaceutical Science, King’s College London, London, UK FEDERICA ANASTASI • NEST Laboratories, Scuola Normale Superiore, Pisa, Italy; Fondazione Pisana per la Scienza ONLUS, Pisa, Italy ` , Rome, VALERIA BARRECA • National Center for Global Health, Istituto Superiore di Sanita Italy GIOVANNI CAMUSSI • Department of Medical Sciences, University of Turin, Turin, Italy FLAVIO CARRION • Departamento de Investigacion, Postgrado y Educacion Continua (DIPEC), Facultad de Ciencias de la Salud, Universidad del Alba, Santiago, Chile ` (ISS), CHIARA CHIOZZINI • National Center for Global Health, Istituto Superiore di Sanita Rome, Italy LOTHAR C. DIETERICH • Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland MARIALAURA DILILLO • Fondazione Pisana per la Scienza ONLUS, Pisa, Italy ` (ISS), MAURIZIO FEDERICO • National Center for Global Health, Istituto Superiore di Sanita Rome, Italy `, FLAVIA FERRANTELLI • National Center for Global Health, Istituto Superiore di Sanita Rome, Italy NAJAT HAJJI • Laboratory Experimental Clinical Chemistry, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands; Vesicle Observation Center, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands CHI M. HAU • Laboratory Experimental Clinical Chemistry, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands; Vesicle Observation Center, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands ANTON B. ILIUK • Tymora Analytical Operations, West Lafayette, IN, USA STEVEN M. JAY • Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA; Program in Molecular and Cell Biology, University of Maryland, College Park, MD, USA ANJANA JEYARAM • Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA YANG JIN • Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, USA MIN-XIA KE • CAS Key Laboratory of Tissue Microenvironment and Tumor, Laboratory of Molecular Cardiology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences (CAS), Shanghai, China STEPHANIE M. KRONSTADT • Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA ANDREW LAI • Exosome Biology Laboratory, University of Queensland Centre for Clinical Research, Royal Brisbane and Women’s Hospital, The University of Queensland, Brisbane, QLD, Australia NOELLE LEARY • Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland

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HEEDOO LEE • Department of Biology and Chemistry, Changwon National University, Changwon, Korea JIN LI • The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China ANJA LINDEMANN • Institute of Human Genetics, University Hospital Munich, LudwigMaximilians-University Munich, Munich, Germany `, FRANCESCO MANFREDI • National Center for Global Health, Istituto Superiore di Sanita Rome, Italy MANUEL MAYR • King’s College London British Heart Foundation Centre, School of Cardiovascular Medicine and Sciences, London, UK LIAM A. MCDONNELL • Fondazione Pisana per la Scienza ONLUS, Pisa, Italy JULIANA MU¨LLER BARK • Saliva and Liquid Biopsy Translational Laboratory, The School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD, Australia VERONIKA MUSSACK • Department of Animal Physiology and Immunology, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany FEDERICA NEGRO • Department of Medical Sciences, University of Turin, Turin, Italy RIENK NIEUWLAND • Laboratory Experimental Clinical Chemistry, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands; Vesicle Observation Center, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands CARLOS PALMA • Exosome Biology Laboratory, University of Queensland Centre for Clinical Research, Royal Brisbane and Women’s Hospital, The University of Queensland, Brisbane, QLD, Australia DAVIDE PELLEGRINI • NEST Laboratories, Scuola Normale Superiore, Pisa, Italy; Fondazione Pisana per la Scienza ONLUS, Pisa, Italy MICHAEL W. PFAFFL • Department of Animal Physiology and Immunology, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany MARGHERITA A. C. POMATTO • Department of Medical Sciences, University of Turin, Turin, Italy CHAMINDIE PUNYADEERA • Saliva and Liquid Biopsy Translational Laboratory, The School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD, Australia; Saliva and Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery (GRIDD) and Menzies Health Institute Queensland (MIHQ), Griffith University, Brisbane, QLD, Australia FENGLI QU • The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China MOHAMMAD RASHEDUZZAMAN • Saliva and Liquid Biopsy Translational Laboratory, The School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD, Australia MARLENE REITHMAIR • Institute of Human Genetics, University Hospital Munich, LudwigMaximilians-University Munich, Munich, Germany ` (ISS), BARBARA RIDOLFI • National Center for Global Health, Istituto Superiore di Sanita Rome, Italy KOSUKE SAITO • Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan YOSHIRO SAITO • Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan

Contributors

xiii

ALEXIS SALAS • Department of Pharmacology, Faculty of Biological Sciences, University of Concepcion, Concepcion, Chile CARLOS SALOMON • Exosome Biology Laboratory, University of Queensland Centre for Clinical Research, Royal Brisbane and Women’s Hospital, The University of Queensland, Brisbane, QLD, Australia; Department of Pharmacology, Faculty of Biological Sciences, University of Concepcion, Concepcion, Chile; Departamento de Investigacion, Postgrado y Educacion Continua (DIPEC), Facultad de Ciencias de la Salud, Universidad del Alba, Santiago, Chile AYYANAR SIVANANTHAM • Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, USA YUCHEN SUN • Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan KALOYAN TAKOV • King’s College London British Heart Foundation Centre, School of Cardiovascular Medicine and Sciences, London, UK WEIHONG TAN • The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Collaborative Innovation Center for Chemistry and Molecular Medicine, Hunan University, Changsha, China; Institute of Molecular Medicine (IMM), Renji Hospital, State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, China I-JOU TENG • King’s College London British Heart Foundation Centre, School of Cardiovascular Medicine and Sciences, London, UK ` , Rome, Italy VALENTINA TIRELLI • Core Facilities, Istituto Superiore di Sanita LUCAS TREVISAN FRANC¸A DE LIMA • Saliva and Liquid Biopsy Translational Laboratory, The School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD, Australia EDWIN VAN DER POL • Laboratory Experimental Clinical Chemistry, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands; Vesicle Observation Center, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands; Biomedical Engineering and Physics, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands SARINA WALSER • Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland CHAMEERA EKANAYAKE WEERAMANGE • Saliva and Liquid Biopsy Translational Laboratory, The School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD, Australia QIANG WU • CAS Key Laboratory of Tissue Microenvironment and Tumor, Laboratory of Molecular Cardiology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences (CAS), CAS, Shanghai, China SITAO XIE • The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China

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LIZHOU XU • Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China; Institute for Pharmaceutical Science, King’s College London, London, UK HUANG-TIAN YANG • CAS Key Laboratory of Tissue Microenvironment and Tumor, Laboratory of Molecular Cardiology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences (CAS), CAS, Shanghai, China; Translational Medical Center for Stem Cell Therapy and Institute for Heart Failure and Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine and Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China; Institute for Stem Cell and Regeneration, CAS, Beijing, China

Part I Extracellular Vesicles Detection and Isolation

Chapter 1 Aptasensors for Cancerous Exosome Detection Jin Li, Sitao Xie, Fengli Qu, and Weihong Tan Abstract Cancerous exosomes that carry multiple biomarkers are attractive targets for the early diagnosis and therapy of cancer. As one of the powerful molecular recognition tools, aptamers with excellent binding affinity and specificity toward biomarkers have been exploited to construct various aptamer-based biosensors (aptasensors) for exosome detection. Here, we review recent advances in aptasensors for the detection of cancerous exosomes. We first discuss the importance and potential of cancerous exosomes in cancer diagnosis and then summarize some conventional aptasensors from the perspective of biomarker recognition and signal collection strategies. Finally, we comment on the outlook for aptasensor research and new directions for cancerous exosome detection. Key words Cancerous exosome, Aptamer, Early diagnosis, Aptasensor, Exosome detection

1

Cancerous Exosome Detection for Diagnosis and Therapy The increasingly high incidence of cancers is marked by equally high worldwide mortality [1]. Clinical and basic scientists have agreed that the survival rate of patients depends on early diagnosis and therapeutic intervention and that this goal can be improved by the detection of cancer biomarkers. Different types of detection tools have been developed to accurately identify various types of cancer [2–4]. In particular, single-factor biomarker identification has achieved some success, but the simultaneous detection and classification of multiple biomarkers is more likely to lead to improved prognoses in the future [5–9]. One such ideal biomarker is the exosome which is a class of extracellular vesicle-like structures released by cells. Ranging in size from 30 to 120 nm, the exosome contains DNAs, RNAs, proteins, and lipids. It participates in the regulation of normal physiological processes, but it is also suspected of regulating many cancerous processes [10]. Specifically, the formation and production of exosomes in source cells, whether normal or cancerous, involves the process of the membrane invagination of late endosomes and

Maurizio Federico and Barbara Ridolfi (eds.), Extracellular Vesicles in Diagnosis and Therapy, Methods in Molecular Biology, vol. 2504, https://doi.org/10.1007/978-1-0716-2341-1_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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multivesicular body (MVB) fusion with the plasma membrane. Thus, exosomes share some features in common, such as nucleic acids (mRNA, miRNA, and long non-coding RNAs), lipids (cholesterol and ceramide), and proteins (tetraspanins, heat shock proteins, membrane transport and fusion proteins) [11–14]. Owing to the presence and stability of exosomes in most bodily fluids, cancerderived exosomes can carry cargoes reflective of changes in genetic material or signals in cancer cells, thus acting as an early warning system for the presence of cancer and allowing the collection and analysis of exosomes in bodily fluids for diagnostic purposes. As the role of exosomes begins to emerge in cancer diagnosis, remarkable progress has been achieved by researchers in advancing the development of exosome detection methods using nanomaterials [15], optics [16], electrochemistry [17, 18], and so on [11].

2

Aptamers for Cancerous Exosome Recognition Since exosomes can carry multiple biomarkers from sourced cancer cells, they may allow clinicians to determine the type and progress of cancers. However, in order to simultaneously and accurately detect and analyze the species and abundance of biomarkers carried by exosomes, powerful molecular recognition tools are required. Aptamers, known as “chemical antibodies,” are short singlestranded nucleic acids evolved by the process termed systematic evolution of ligands by exponential enrichment (SELEX) [19, 20]. The essence of aptamers is the three-dimensional structure produced by the folded nucleic acid. Aptamers can fold into specific secondary or tertiary structures and bind to their targets by forming stable complexes with their targets. With high binding affinity and specificity to targets, such as metal ions [21], proteins [22], whole cells [23], and even tissues [24], and the advantages of easy chemical modification and programmability, aptamers are attractive alternatives to antibodies for molecular recognition, making them a powerful tool for the accurate recognition and identification of multiple biomarkers on the exosome plasma membrane [25]. Table 1 summarizes some cancerous protein targets commonly used for exosome detection. Related cancer types and some corresponding aptamers are listed. In the next section, we review recent advances in aptasensor development for the detection of cancerous exosomes. In particular, fluorescent-, electrochemical-, and colorimetric-based methods are discussed. Our concluding remarks include some perspectives on the research directions and opportunities in developing aptasensors for cancerous exosome detection.

Aptasensors for Cancerous Exosome Detection

5

Table 1 Summary of cancerous protein target and corresponding aptamers Protein target

Aptamer

Cell type

References

CD24

CD24A_2

HT29

[26]

CD63

LL4A, CD63-1

Melanoma cells, MDA-MB-231, HT29, MCF-7, HepG2

[27–30]

PD-L1

aptPD-L1, XQ-P3

A549, MDA-MB-231

[31]

EGFR

CL4, GR200, TuTu22

A549, A431, U87, U251

[32, 33]

HER2

Aptamers 2-2, Heraptamer

N87, SKOV3

[34, 35]

EpCAM

19-nt RNA aptamer, Ep1 aptamer

Breast, colorectal, gastric cancer cells

[36]

PSMA

xPSM aptamer

PSMA-expressing LNCaP cells

[37]

PTK7

sgc8

CCRF-CEM cells

[23]

CEA

YJ-1

CEA-positive cell lines

[38]

MUC1

S1.3, 5TR1, GalNAc3 aptamers

MUC1-positive cell lines

[39]

AFP

AP273

HepG2

[40]

HSP70

A8

Breast, lung, ovarian cancer cells

[41]

3

Aptasensors for Exosome Detection Aptamers are oligonucleotides that can be easily synthesized and modified with desired functional groups [42, 43]. Based on these characteristics, aptamer-based sensors (aptasensors) have been developed to detect exosomes through different signal transduction mechanisms, e.g., fluorescent [44, 45], electrochemical [46, 47], and colorimetric [48, 49]. In this section, aptasensorbased exosome detection strategies are introduced.

3.1 Fluorescent Aptasensor-Based Methods

Fluorescence imaging has the advantages of high sensitivity and reliability, noninvasiveness, and short-term analysis and is, therefore, the most commonly used method in exosome detection [50, 51]. Fluorescent aptasensors can be easily constructed by integrating aptamers with fluorescent dyes, quantum dots, upconversion nanoparticles, or other fluorescent materials. For example, Zhang et al. designed an “on-off”-type aptasensor for detecting cancer exosomes (Fig. 1a) [52]. A TAMRA fluorophore and a Dabcyl quencher were labeled at the two ends of the hairpin-like aptamer, respectively. In the absence of tumor exosomes (TEX), the fluorescence generated by TAMRA was quenched by the

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Fig. 1 Aptasensor-based fluorescent platform for exosome detection. (a) Fluorescence resonance energy transfer (FRET)-based aptasensor for exosome detection, reproduced from Zhang 2018 with permission from Elsevier [52]. (b) HCR-based signal amplification and detection of exosomes [53]. (c) Exosome detection based on nuclease-assisted signal amplification, reproduced from Wang 2018 with permission from Elsevier [54]. (d) Identifying exosomes with a FP-based aptasensor, reproduced from Zhang 2019 with permission from Royal Society of Chemistry [55]

neighboring Dabcyl, and the aptasensor was in the “off” state. However, upon aptamer binding to its cognate target, Mucin 1 (MUC1), on the exosome plasma membrane, TAMRA and Dabcyl separated, turning on the aptasensor for exosome detection by measuring the fluorescence intensity of TAMRA. This simple fluorescent aptasensor achieved the detection of MCF-7 cell-derived exosomes with a low detection limit (3
105 particles μL1). In another work, Zhang et al. constructed another turn-on fluorescent aptasensor for exosome detection [56]. This time, Cy3-modified CD63 aptamer was first adsorbed onto Ti3C2 MXene nanosheets, and the fluorescence was quenched by the nanosheets. When exosomes were added to the nanocomposites, CD63 aptamers were released from the nanosheets owing to specific binding between CD63 aptamers and the CD63 proteins present on exosomes. The fluorescence of aptasensors was then recovered and the exosomes were identified according to the recovered fluorescence. The detection limit of this fluorescent aptasensor was reported to be 1.4
103 particles mL1. Meanwhile, by integrating other specific aptamers with Ti3C2 MXene nanosheets,

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multiple proteins on different exosomes were identified. Thus, this Ti3C2 MXene nanosheet-based fluorescent aptasensor could profile multiple biomarkers on exosomes. Aptamers can also be easily combined with multiple cyclic amplification reactions, including hybridization chain reaction (HCR), nuclease-assisted signal amplification (NSA), and rollingcircle amplification (RCA), to construct aptasensors for accurate detection of exosomes. For example, Shi et al. developed an HCR-based fluorometric assay for highly sensitive detection of HepG2 cell-secreted exosomes (Fig. 1b) [53]. In this design, exosomes could be captured by anti-CD63 antibody-modified magnetic nanoparticles (MNPs), and epithelial cell adhesion molecule (EpCAM) aptamers could specifically bind to the captured exosomes. The bound EpCAM aptamers then initiated a hybridization chain reaction between two FAM-labeled probes to obtain a strong fluorescence signal. Finally, exosomes were detected by measuring the fluorescence intensity of FAM, and the detection limit of this HCR-based aptasensor was reported to be 100 particles·mL1. As oligonucleotides, aptamers are easily degraded by nucleases. Several NSA-based aptasensors have been constructed for exosome detection. By constructing an NSA platform, Wang et al. achieved detection of colorectal cancer exosomes with a limit of detection (LOD) of 2.1
104 particles μL1 (Fig. 1c) [54]. In this work, fluorophore-labeled aptamers were adsorbed onto graphene oxide (GO), and the fluorescence was quenched by GO. Once exosomes were added, aptamers could specifically bind to the exosomes, and the fluorescence was recovered. Subsequently, the aptamer-bound exosomes were degraded by DNase I, and more fluorophorelabeled aptamers that had adsorbed to GO could bind to the “free” exosomes, resulting in cyclic digestion of aptamers and amplification of the fluorescent signal. Importantly, this NSA-based aptasensor has been used to distinguish healthy and cancer patients by detecting their blood serum samples, showing its potential in cancer diagnosis. Using a similar strategy, Jin et al. identified cancer cell-secreted exosomes with a LOD of 1.6
102 particles μL1 and profiled seven biomarkers on exosomes that derived from five types of cells [57]. Rolling-circle amplification-based aptasensors are also widely used to detect exosomes by generating a large number of repeating units to achieve satisfactory signal amplification. Huang et al., for instance, constructed a branched RCA (BRCA)-based aptasensor for highly sensitive detection of exosomes derived from gastric cancer cells [50]. In this design, the MUC1-binding aptamer was sequentially used to identify exosome, trigger circularization of a padlock probe, and initiate the BRCA reaction. SYBR Green I bound to BRCA products and was used as a fluorescent dye to identify exosomes. The LOD of this aptasensor was reported to be 4.27
104 particles·mL1. This aptasensor has also been used to

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test plasma samples, and it showed promising clinical potential. In another work, Huang et al. constructed a dual-signal amplificationbased aptasensor to detect leukemia cell-secreted exosomes [51]. RCA and NSA reaction were used for primary signal amplification and secondary signal amplification, respectively. Using this dual-signal amplification strategy, the detection of exosomes was down to 102 particles μL1. The fluorescence polarization (FP) assay, which does not rely on quenchers or donor-acceptor pairs, is another good choice for exosome detection. Zhang et al. developed an FP-based assay that does not require amplification of fluorescence or separation of exosomes from human plasma for detection (Fig. 1d) [55]. When low molecular weight dye-labeled aptamers bound to the high molecular weight exosomes, the FP value increased, and exosomes could be identified according to the change in FP value. The LOD of this FP-based aptasensor was reported to be 5
102 particles μL1. Importantly, this simple (one step) and fast (within 30 min) assay has been used to directly quantify exosomes in clinical samples of healthy and lung cancer patients, showing their potential in cancer diagnosis and therapy monitoring. 3.2 Electrochemical Aptasensor-Based Methods

Electrochemical assays have attracted widespread attention in bioanalysis for their high sensitivity, low cost, and fast response. In recent years, by using aptamers as a recognition element, electrochemical aptasensors have been developed for exosome detection [58, 59]. Zhou et al. developed an electrochemical aptasensorbased assay to detect exosomes by directly modifying aptamers and redox reporters onto gold electrodes [60]. In the presence of exosomes, CD63 aptamers could specifically bind to exosomes, resulting in the release of their complementary sequences modified by redox moieties. A decreased electrochemical signal was detected by the release of the redox moieties, and exosomes could then be identified according to the changed redox signal. The LOD of this electrochemical-based aptasensor was reported to be 1
103 particles μL1. Single-stranded DNA probes immobilized on electrodes tend to become entangled, thwarting highly sensitive detection of exosomes. To solve this problem, Wang et al. constructed a nanotetrahedron (NTH)-assisted aptasensor to improve the detection performance of aptamers on gold electrodes (Fig. 2a) [61]. In this design, DNA tetrahedrons contain an aptamer at the top vertex, while three thiols at bottom vertices were constructed and immobilized onto gold electrodes through the thiols. Compared with single-stranded aptasensors, this NTH-assisted aptasensor achieved exosome detection with 100-fold higher sensitivity. To improve the sensitivity of exosome detection, amplification strategies, such as HCR, RCA, NSA, and DNA walker, were used in developing electrochemical-based aptasensors. An et al. constructed an HCR-based electrochemical aptasensor to detect

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Fig. 2 Aptasensor-based electrochemical platform for exosome detection. (a) NTH-assisted aptasensor for exosome detection, reproduced from Wang 2017 with permission from American Chemical Society [61]. (b) HCR-based signal amplification and detection of exosomes, reproduced from An 2019 with permission from Elsevier [62]. (c) RCA-based signal amplification and detection of exosomes [63]. (d) Dual-amplified-based aptasensor for exosome detection, reproduced from Zhao 2019 with permission from American Chemical Society [64]

exosomes derived from MCF-7 cells (Fig. 2b) [62]. In this design, CD63 aptamers were immobilized onto glassy carbon electrodes to capture exosomes. Then, a DNA initiator was anchored to the captured exosomes to initiate HCR between two biotin-labeled DNA monomers. Finally, horseradish peroxidase (HRP) was combined with the HCR products through interactions between biotin and streptavidin. HRP could catalyze the oxidation of o-phenylenediamine (OPD) to obtain detectable electrical signals, thus identifying exosomes. By using this HCR-based amplification strategy, exosomes could be detected with a LOD of 9.6
101 particles μL1. The ability of this HCR-based electrochemical aptasensor to detect exosomes in serum samples has also been proved. In another work, Huang et al. constructed an RCA-based electrochemical aptasensor for detecting gastric cancer exosomes (Fig. 2c) [63]. In this work, anti-CD63 antibodies were immobilized onto gold electrodes to capture exosomes. MUC1 aptamers bound to the captured exosomes and subsequently triggered RCA to generate a range of G-quadruplex-hemin complexes. The Gquadruplex-hemin complexes catalyzed the reduction of H2O2 to

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obtain electrical signals for exosome detection. The LOD of this RCA-based electrochemical aptasensor was reported to be 9.54
102 particles mL1. This economical and simple aptasensor was also used to analyze plasma samples, revealing its potential application in the diagnosis of gastric cancer. The DNA walker has also been used to construct a highly sensitive electrochemical aptasensor for exosome detection (Fig. 2d) [64]. Zhao et al. developed a dual-amplified system to detect exosomes derived from MCF-7 cells. The system contains two parts. In the first part, CD63 aptamers and single-stranded DNAs that contain a ribonucleobase (rA) cleavage point were labeled to magnetic beads (MB). When exosomes were added, exosomes could be captured by the CD63 aptamer and another aptamer (EpCAM) linked to a DNAzyme could then bind to the captured exosomes. The DNAzyme acted as a DNA walker to induce the cleavage of the multiple single-stranded DNAs on the MB, leading to the first signal amplification of exosomes and releasing an oligonucleotide fragment (namely, P1) to the second part. In the second part, methylene blue-labeled, hairpin-like DNAs (MB-DNA) were immobilized on the gold electrode to capture P1. MB-DNA hybridized with P1 to form a double-stranded DNA, followed by Exo III digestion of MB-DNA in the double-stranded DNA. This resulted in the release of P1 from the ds-DNA for hybridization with another MB-DNA, leading to cyclic digestion of MB-DNA and the second signal amplification of exosomes. Finally, a large number of short DNAs were left on the electrode to capture ferrocene-modified DNAs to generate detectable electrical signals for exosome detection. Based on this dual-amplified strategy, exosomes could be detected with a LOD of 1.3
104 particles mL1. This dual-amplified aptasensor was also used to analyze complex biological samples and differentiate plasma samples between healthy and breast cancer patients, indicating that it is a promising tool for exosome-based cancer diagnosis. 3.3 Colorimetric Aptasensor-Based Methods

Colorimetric sensors have been widely used in bioanalysis for their simplicity and ability to convert sample data into a signal readable by the naked eye. Thus far, several colorimetric aptasensors have been constructed for exosome detection [49, 65]. Xia et al. constructed a single-walled carbon nanotubes (s-SWCNTs)-based colorimetric aptasensor to detect exosomes derived from MCF-7 cells (Fig. 3a) [66]. In this design, CD63 aptamers able to improve the peroxidase activity of s-SWCNTs were first absorbed onto s-SWCNTs. The absorbed s-SWCNTs could then efficiently catalyze the oxidation of 3,30 ,5,50 -tetramethylbenzidine (TMB) to generate a large number of products with blue color. In the presence of exosomes, CD63 aptamers specifically bound to exosomes, and their release from s-SWCNTs led to a decrease in peroxidase activity of s-SWCNTs. As a consequence, fewer products with blue

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Fig. 3 Aptasensor-based colorimetric platform for exosome detection. (a) s-SWCNTs-based aptasensor for exosome detection, reproduced from Xia 2017 with permission from Elsevier [66]. (b) Multicolor detection platform for visual detection of exosomes, reproduced from Zhang 2019 with permission from American Chemical Society [67]. (c) Profiling the membrane proteins of exosomes with Aptamer/AuNP sensing platform, reproduced from Jiang 2017 with permission from Wiley [68]

color were generated, and exosomes could be determined by the solution’s color change. Using this colorimetric aptasensor, exosomes were detected with a LOD of 5.2
105 particles μL1. This s-SWCNTs-based aptasensor has the advantages of fast response, low cost, freedom from labeling, and visual analysis, holding potential in the construction of point-of-care testing devices for the detection of exosomes. In another work, Zhang et al. developed a multicolor detection platform for visual detection of exosomes (Fig. 3b) [67]. Exosomes were first captured by CD63 aptamer-modified magnetic beads (MB). Cholesterol-labeled DNAs containing a DNA initiator was anchored to the captured exosomes through hydrophobic interaction between cholesterol and lipid membrane of exosomes. Then,

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the DNAs initiated HCR to load alkaline phosphatases (ALP), thereby promoting the generation of ascorbic acid. Subsequently, silver ions were reduced by ascorbic acid, and the obtained silver was deposited on Au nanorods to generate multicolor Au@Ag nanorods. Thus, exosomes could be identified according to variable colors in the solution with LOD down to 9
103 particles μL1 as detectable by the naked eye. Compared with single-color-based visual analysis, this multicolor detection platform is novel and useful as multicolor variation is easier to discern by the naked eye. In addition to detecting exosomes, aptasensors have also been used to profile the membrane proteins of exosomes. By developing a colorimetric aptasensor platform (namely, Aptamer/AuNP), Jiang et al. successfully profiled five proteins on four different exosomes (Fig. 3c) [68]. In this work, aptamers were first absorbed to gold nanoparticles (AuNP) to prevent their aggregation. Once exosomes were added, aptamers could bind to exosomes through the specific binding between aptamers and their target proteins on exosomes, resulting in AuNP aggregation and changing of the solution’s color. In this way, the proteins present on exosomes could be identified by observing the color change of the solution by the naked eye and monitored by UV-vis spectrometry. Importantly, the developed Aptamer/AuNP sensor was able to profile membrane proteins of exosomes within a few minutes, making it suitable for high-throughput analysis of clinical samples. 3.4 Other Aptasensor-Based Methods

Surface plasmon resonance (SPR)-based [69], surface-enhanced Raman scattering (SERS)-based [70], or electrochemiluminescence (ECL)-based [71] aptasensors have also been developed for exosome detection. SPR has been widely used in bioanalysis because it is a label-free and real-time detection assay. Wang et al. developed an SPR-based, dual-amplification aptasensor for detecting cancerous exosomes (Fig. 4a) [27]. In this work, exosomes were first captured by CD63 aptamers immobilized on the Au film and detected by measuring the SPR signal. Then, CD63 aptamer-modified A30-gold nanoparticles (A30-AuNP) could specifically bind to the captured exosomes via interactions between CD63 aptamers and CD63 proteins on exosomes, resulting in the first amplification of an SPR signal. Subsequently, T30-AuNPs bound to the A30-AuNPs through hybridization between the A30 sequence and the T30 sequence to achieve the dual-signal amplification. This dual-amplification aptasensor could be used to detect exosomes with a LOD of 5 particles μL1 and to distinguish exosomes derived from MCF-7 breast cancer cells from MCF-10A normal breast cells. SERS-based aptasensors are also powerful tools for exosome detection because they can be used to screen different exosomes with single-molecule sensitivity. Wang et al. constructed a SERSbased aptasensor for simultaneous detection of multiple exosomes

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Fig. 4 (a) SPR-based, dual-amplification aptasensor for exosome detection, reproduced from Wang 2019 with permission from Elsevier [27]. (b) Identifying exosomes with a SERS-based aptasensor, reproduced from Wang 2018 with permission from Royal Society of Chemistry [72]. (c) Exosome detection based on AuNPsMXenes-Apt nanoprobes, reproduced from Zhang 2020 with permission from American Chemical Society [73]

(Fig. 4b) [72]. In this design, magnetic nanobeads were modified by CD63 aptamers to capture and separate exosomes. Raman reporter and a specific aptamer (HER2 aptamer, PSMA aptamer or CEA aptamer) were labeled to gold nanoparticles for specific recognition of target exosomes and provide a SERS signal. Once exosomes were added, the CD63 aptamer-modified magnetic nanobeads and the specific aptamer-labeled gold nanoparticles bound to the target exosomes to form a ternary complex, leading to a decreased intensity of SERS signal in the supernatant. Thus, exosomes could be determined by the changed SERS signal. Using

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this SERS-based aptasensor, three kinds of exosomes (derived from SKBR3, T84, and LNCaP cells) could be detected with a LOD of 32, 73, and 203 particles μL1, respectively. Furthermore, this SERS-based aptasensor could be used to determine exosomes in blood samples, revealing its potential applications in exosomebased cancer screening. Owing to its low background signal and high sensitivity, the ECL assay has also been used for exosome detection. Zhang et al. constructed an ECL-based aptasensor for highly sensitive detection of tumor exosomes (Fig. 4c) [73]. In this design, CD63 aptamers were immobilized on glassy carbon electrodes to effectively capture exosomes. Then, the CD63 aptamer-modified Ti3C2 MXenes nanosheets bound to the captured exosomes through the specific binding between CD63 aptamers and their target proteins present on exosomes. The Ti3C2 MXenes nanosheets reduced HAuCl4 to generate gold nanoparticles in situ, thereby forming AuNPsMXenes-Apt nanoprobes for exosome detection. This ECL-based aptasensor could be used to detect exosomes (and their surface proteins) secreted by various cancer cells in serum, indicating that it is a promising tool for clinical exosomes detection and cancer diagnosis. 3.5 AptasensorBased Exosome Detection for Cancer Diagnosis

Cancerous exosomes carrying abundant cancer biomarkers, e.g., cancer-related proteins, are attractive targets for early diagnosis of cancer. Due to their high specificity and affinity, flexible design and the ability to simultaneously detect multi-parameter targets, aptamers have become promising tools for exosome-based cancer diagnosis [54, 55, 57, 62]. Huang et al. developed a thermophoresisbased aptasensor to detect programmed death-ligand 1 (PD-L1) on circulating exosomes for cancer diagnosis [74]. In this study, fluorescent dye-labeled anti-PD-L1 aptamers were first mixed with isolated circulating exosomes to achieve the specific binding between PD-L1 and the aptamers (Fig. 5a). Then, the mixture was exposed to an infrared laser to generate a temperature gradient that could induce the accumulation of aptamer-bound exosomes, and thus result in an amplified signal for the quantification of exosomes. By using this thermophoresis-based aptasensor, exosomal PD-L1 could be detected with a LOD of 17.6 pg mL1. Importantly, the authors found that the level of exosomal PD-L1 can not only distinguish cancer patients from healthy controls, but also has a positive correlation with tumor metastasis. These results indicate that this thermophoretic-based aptasensor has great potential in the early diagnosis of cancer. In another work, Li et al. developed an aptasensor-based nanoplatform for diagnosing prostate cancer [75]. PSMA aptamer which can specifically bind to the prostate specific membrane antigen was first modified to Fe3O4 nanoparticles and then hybridized with two single-stranded DNAs (Fig. 5b). In the presence of

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Fig. 5 (a) Thermophoresis-based aptasensor for cancer diagnosis [74]. (b) Aptasensor-based nanoplatform for cancer diagnosis, reproduced from Li 2019 with permission from American Chemical Society [75]. (c) Thermophoretic aptasensor for cancer screening and classification, reproduced from Liu 2019 with permission from Springer Nature [76]

PSMA-positive exosomes, PSMA aptamers could bind to the exosomes and cause the release of the ssDNAs. The released ssDNAs could then cyclically initiate the hybridization of two hairpin monomers to obtain fluorescence signal for exosome detection. By using this strategy, 100 exosomes could be detected in 1 μL urine sample. The level of PSMA-positive exosomes in the urine of prostate cancer patients is higher than that in healthy persons. This aptasensor-based nanoplatform has the ability to distinguish prostate cancer patients from healthy controls revealing its potential applications in diagnosing prostate cancer. Compared with the detection of single protein biomarker on exosomes for cancer detection, multi-protein biomarkers profiling on exosomes is more attractive because it can be used to screen and classify cancer. In a recent work, by developing a thermophoretic aptasensor (Fig. 5c), Liu et al. could profile the surface proteins of extracellular vesicles that derived from 6 types of cancers with 7 aptamers [76]. Fluorescent aptamers were first incubated with

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the diluted serum sample and the mixture was then locally heated with an infrared laser to accumulate extracellular vesicles and obtained a detectable fluorescence signal. Then, the surface proteins of extracellular vesicles could be profiled by detecting the fluorescence signal. Importantly, this isolation-free, small-volume (99% ethanol stock 1/5 with ultrapure water to obtain 20% ethanol solution for column cleaning and storage. 5. Amicon Ultra-0.5 Centrifugal Filter Unit, 3 kDa cut-off (regenerated cellulose membrane, 0.5 mL volume). 6. Benchtop centrifuge (e.g., Eppendorf 5430 R, 5920 R). 7. Ultracentrifuge (e.g., Beckman Coulter Optima-MAX, Optima-MAX-XP, Optima-TLX) and rotor (e.g., MLA-80, MLA-55). 8. Ultracentrifuge tubes (e.g., Beckman Coulter polycarbonate ultracentrifuge tubes). 9. HPLC system (e.g., Ultimate 3000) with appropriate software (e.g., Chromeleon™ Chromatography Data System Software) (see Note 4). 10. SEC columns: TSKgel® PWXL Type Guard Column (stationary phase: hydroxylated methacrylate, 12 μm particle size, length: 40 mm, diameter: 6 mm, mixed pore size), TSKgel® G5000PWXL HPLC Column, and TSKgel® G4000PWXL HPLC Column (stationary phase: hydroxylated methacrylate, 10 μm particle size, mobile phase: PBS, length: 300 mm; diameter: 7.8 mm, mean pore size: 100 nm for G5000PWXL, 50 nm for G4000PWXL); mobile phase: PBS (see Notes 4 and 5). 11. Sample plates (e.g., Nunc™ 96-Well V-shape Polypropylene Storage Microplates). 12. Collection plates (e.g., Abgene™ 96 Well 1.2 mL Polypropylene Deepwell Storage Plate).

3

Methods

3.1 Plasma Collection and Storage

1. Collect blood using standard phlebotomy techniques and preferably citrated tubes (see Note 3). Standardized protocols using the same anticoagulants are essential for consistency, and guidelines by the International Society on Thrombosis and Haemostasis should be followed [15].

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2. Keep blood at room temperature to avoid platelet activation and centrifuge shortly after collection for 15 min at 1500
g to separate plasma from blood cells (see Note 6). Avoid application of centrifuge brake. 3. Transfer plasma to a new tube and perform an additional centrifugation for 15 min at 2500
g to remove residual platelets and obtain platelet-poor plasma (PPP) (see Notes 6 and 7). 4. If processed immediately, keep PPP on ice or at 4  C prior to use. For long-term storage, aliquot and freeze at 80  C. 3.2 SEC Column Equilibration

1. Columns are provided by the manufacturer with water as solvent. Perform installation in the following order: Pump ! TSKgel Guard Column ! TSKgel G5000PWXL (100 nm mean pore size) ! TSKgel G4000PWXL (50 nm mean pore size) ! UV chamber ! collection plate. Use polyether ether ketone (PEEK) tubing with outer diameter 1/16 inch to connect the columns to the system and to each other (see Note 8). 2. Flush the columns with PBS (at room temperature) at a flow rate of 0.6 mL/min. At least six column volumes are required to equilibrate the column to the mobile phase at the correct temperature (note: system volume is ~30 mL in this setup). If the column mobile phase is being changed to/from 20% ethanol (i.e., storage solvent) from/to PBS, the flow rate should not exceed 0.2 mL/min (see Note 9).

3.3 Extracellular Vesicle Isolation 3.3.1 Ultracentrifugation (UC) (Fig. 1) (see Note 10)

1. Cool the ultracentrifuge to 4  C in a pre-run. 2. Dilute 1 mL of PPP with 5 mL of PBS in an ultracentrifuge tube and centrifuge at 100,000
g for 70 min at 4  C (see Note 11). 3. Decant the supernatant (see Note 12). Optionally, supernatant can be kept for determining the final EV recovery (see Note 13). 4. Resuspend the pellet in 6 mL of PBS (wash step) and perform a second centrifugation at 100,000
g for 70 min at 4  C. 5. Discard the supernatant by decanting and resuspend the pellet in a desired volume (e.g., 100 μL) (see Notes 12 and 13). 6. Ultracentrifuged samples can be stored at 80  C (see Notes 14–16).

3.3.2 High-Performance Size-Exclusion Chromatography (SEC) (Fig. 2) (see Notes 5, 10, and 17)

1. Turn on the UV lamp and allow sufficient time for warming up. 2. Dilute 20 μL of PPP 1/5 with PBS in a sample plate. Spare volume should be prepared to account for inaccuracies in pipetting (see Note 11).

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Fig. 1 Plasma EV isolation by ultracentrifugation (UC). EVs were isolated from human plasma by UC as described in this chapter. Samples were characterized by dot blots immobilizing equal volumes (a) or equal protein amount (b) of the UC supernatant and UC pellet. Panel a represents the recovery of EVs in each fraction (i.e., yield). Panel b shows enrichment of EV markers (tetraspanins CD9 and CD63) relative to total protein in the harvests (i.e., purity). APOB and APOA1 are shown to estimate the contamination by non-HDL and HDL, respectively. Note the poor recovery of EVs in the pellets: most of the tetraspanin signal remains in the supernatant after UC (panel a), despite the enrichment of EV markers relative to total protein and lipoproteins in the collected pellet (panel b)

3. Set the method to inject 100 μL of each sample with slow draw speed (i.e., 2 μL/s) due to the high viscosity of plasma (see Note 11). 4. Set the mobile phase to run at a flow rate of 0.6 mL/min after sample injection. Each run lasts 75 min to ensure complete sample elution and allow for washing and equilibration of the columns prior to injecting the next sample (~1.5 mL column volumes in total) (see Note 11). 5. Set the UV unit to acquire absorbance at 280 nm continuously to monitor protein elution (see Note 18). 6. Set collection to start at 18.5 min (11.1 mL elution volume) with fraction period of 78 s (780 μL). Collect 18 fractions for each PPP sample. For collection of early fractions only, start collection at 23.5 min (14.1 mL elution volume) with fraction period of 25 s (250 μL), and collect 12 fractions (see Notes 11 and 13). 7. Fractionated PPP samples can be stored at 80  C (see Notes 14–16).

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Fig. 2 Plasma EV isolation by high-performance size-exclusion chromatography (SEC). EVs were isolated from human plasma by SEC as described in this chapter. Selected fraction pools were characterized by A280 (a) and dot blots for EV markers (CD9, GP1BA a platelet membrane glycoprotein) and apolipoproteins (APOB, APOA1) (b). Coomassie staining was used to evaluate total protein content (b). Note the recovery of EVs in the early SEC fractions (F2-F3) and separation from late-eluting lipoproteins and abundant plasma proteins (F4-F7). An

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3.4 Sample Concentration (Optional for SEC) (Fig. 2)

Fractions can be pooled as per individual requirements and concentrated on Amicon Ultra-0.5 Centrifugal Filter Units with 3 kDa cut-off or a similar concentration device. If total volumes are higher than the capacity of the filters, topping up can be performed until all the sample has been filtered (see Note 13).

3.5 SEC Column Cleaning

In case additional samples are going to be processed, columns can be kept in place for several days with very low flow rates of the mobile phase. For long-term storage, the solvent must be replaced with 8–10 column volumes of 20% ethanol (see Note 9).

4

Notes 1. Plasma is the preferred source for blood EV isolation. Serum can be used if necessary or in circumstances where this matrix is of particular interest. Serum contains higher number of EVs that are predominantly platelet-derived [16]. Moreover, during coagulation, fibrinogen, but also various protein complexes, including EVs, are depleted due to non-specific or specific clot entrapment. 2. Murine blood plasma can also be used but characterization is necessary as human and murine lipoprotein profiles differ considerably. For example, HDL is the major carrier of cholesterol in mice but not in human. 3. Alternative anticoagulants can be used for blood collection, for example, EDTA, provided they do not interfere with downstream processing. 4. The protocol can be adapted for alternative SEC columns or HPLC systems. Gravity-driven SEC columns can also be used but their resolution is usually lower. 5. This SEC platform is also performing well for fractionating conditioned cell culture medium and isolating EVs with high purity [17]. 6. Centrifugation speed for removal of cells from blood may vary. However, two centrifugation steps are required for a more efficient platelet depletion [18]. Optionally, 1 μM prostacyclin can be added to the plasma prior to the second centrifugation

ä Fig. 2 (continued) example for separation at higher resolution is shown on panels (c) and (d): only early fractions are collected and characterized by immunoblotting techniques. Note that early, EV-rich fractions are not devoid of lipoproteins but the APOB and APOA1 signal is minute compared to the original plasma sample. Nonetheless, EV-rich fractions may still contain other abundant plasma proteins. Thus, a comprehensive characterization by mass spectrometry is advisable

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step to prevent platelet activation. A single centrifugation step at a higher speed may also be used [18], but this approach may induce platelet activation. 7. PPP is sometimes referred to as platelet-free plasma (PFP) [15]. We recommend the use of the term PPP since complete removal of platelets is difficult to achieve and rarely investigated. 8. The length of the tubing is not critical in this SEC setup, but it should generally be minimized to reduce sample dilution. 9. Ethanol viscosity is considerably higher than PBS resulting in increased system pressures. A speed of 0.2 mL/min is recommended when 20% ethanol is flushed through the columns or when PBS is used to replace the ethanol in the columns. 10. The protocols for UC and SEC presented here do not intend to isolate subpopulations of EVs or discriminate between large EVs (lEVs) or small EVs (sEVs). As an additional pre-clearing step, PPP can be centrifuged at 10,000
g for 30 min prior to UC/SEC to remove lEVs as per The´ry et al. [19]. Then, the isolation will yield mostly sEVs. 11. Injection volume, starting PPP amount, flow rate, fraction collection start, collection period, and fraction number can be modified by the operator. Check column requirements to avoid overloading the resin and titrate plasma volumes during the initial optimization experiments. 12. Decanting of the supernatant after UC tends to provide better consistency than pipetting. The resulting pellet is too small to be visible. 13. To study the recovery of the EVs in the UC pellet or SEC fractions by particle enumeration or blotting for EV markers, the operator should dilute the starting sample (prior to isolation), UC supernatants/pellets, and SEC fractions to the same volume. 14. It is advisable to check for contamination with APOB, APOA1, albumin, and other abundant plasma proteins. Since plasma proteins can still be abundant in the EV-rich fractions, users are advised to perform an in-depth characterization, preferably by mass spectrometry-based proteomics. 15. Particle enumeration techniques can be used for characterization (e.g., nanoparticle tracking analysis, NTA), but care should be taken as they detect protein and lipoprotein aggregates as well as other particulate matter. Thus, results may not accurately reflect the EV numbers in plasma fractions. 16. Electron microscopy (EM, e.g., transmission-EM, cryo-EM) can be employed as an additional characterization experiment to identify fractions containing EVs and determine their purity.

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17. It is advisable to optimize and fully characterize the SEC fractionation prior to use to ensure high resolution and to meet individual requirements for subsequent experiments. 18. Other organics may also absorb light of 280 nm wavelength.

Acknowledgments M. M. is a British Heart Foundation (BHF) Chair Holder (CH/16/3/32406) with BHF programme grant support (RG/16/14/32397). This work was supported by ERA-CVD Transnational Grant “MacroERA: Noncoding RNAs in cardiac macrophages and their role in heart failure,” and the Leducq Foundation (18CVD02). References 1. Mathieu M, Martin-Jaular L, Lavieu G, The´ry C (2019) Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication. Nat Cell Biol 21: 9–17. https://doi.org/10.1038/s41556-0180250-9 2. Shah R, Patel T, Freedman JE (2018) Circulating extracellular vesicles in human disease. N Engl J Med 379:958–966. https://doi.org/ 10.1056/NEJMra1704286 3. Yoshioka Y, Kosaka N, Konishi Y et al (2014) Ultra-sensitive liquid biopsy of circulating extracellular vesicles using ExoScreen. Nat Commun 5:3591. https://doi.org/10.1038/ ncomms4591 4. Simonsen JB (2017) What are we looking at? Extracellular vesicles, lipoproteins, or both? Circ Res 121:920–922. https://doi.org/10. 1161/CIRCRESAHA.117.311767 5. Johnsen KB, Gudbergsson JM, Andresen TL, Simonsen JB (2019) What is the blood concentration of extracellular vesicles? Implications for the use of extracellular vesicles as bloodborne biomarkers of cancer. Biochim Biophys Acta Rev Cancer 1871:109–116. https://doi. org/10.1016/j.bbcan.2018.11.006 6. Gardiner C, Di Vizio D, Sahoo S et al (2016) Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell Vesicles 5:32945. https://doi.org/10.3402/jev.v5.32945 7. Baranyai T, Herczeg K, Ono´di Z et al (2015) Isolation of exosomes from blood plasma: qualitative and quantitative comparison of ultracentrifugation and size exclusion chromatography methods. PLoS One 10:e0145686.

https://doi.org/10.1371/journal.pone. 0145686 ´ , Pa´lo´czi K et al (2016) 8. So´dar BW, Kittel A Low-density lipoprotein mimics blood plasma-derived exosomes and microvesicles during isolation and detection. Sci Rep 6: 24316. https://doi.org/10.1038/srep24316 9. Takov K, Yellon DM, Davidson SM (2019) Comparison of small extracellular vesicles isolated from plasma by ultracentrifugation or size-exclusion chromatography: yield, purity and functional potential. J Extracell Vesicles 8: 1 5 6 0 8 0 9 . h t t p s : // d o i . o r g / 1 0 . 1 0 8 0 / 20013078.2018.1560809 10. Dong L, Zieren RC, Horie K et al (2020) Comprehensive evaluation of methods for small extracellular vesicles separation from human plasma, urine and cell culture medium. J Extracell Vesicles 10:e12044. https://doi. org/10.1002/jev2.12044 11. Karimi N, Cvjetkovic A, Jang SC et al (2018) Detailed analysis of the plasma extracellular vesicle proteome after separation from lipoproteins. Cell Mol Life Sci 75:2873–2886. https://doi.org/10.1007/s00018-0182773-4 12. Zhang X, Borg EGF, Liaci AM et al (2020) A novel three step protocol to isolate extracellular vesicles from plasma or cell culture medium with both high yield and purity. J Extracell Vesicles 9:1791450. https://doi.org/10. 1080/20013078.2020.1791450 13. Van Deun J, Jo A, Li H et al (2020) Integrated dual-mode chromatography to enrich extracellular vesicles from plasma. Adv Biosyst 4:

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1900310. https://doi.org/10.1002/adbi. 201900310 14. Gutmann C, Takov K, Burnap S et al (2021) SARS-CoV-2 RNAemia and proteomic biomarker trajectory inform prognostication in COVID-19 patients admitted to intensive care. Nat Commun 12(1):3406 15. Lacroix R, Judicone C, Mooberry M et al (2013) Standardization of pre-analytical variables in plasma microparticle determination: results of the International Society on Thrombosis and Haemostasis SSC Collaborative Workshop. J Thromb Haemost 11: 1190–1193. https://doi.org/10.1111/jth. 12207 16. Palviainen M, Saraswat M, Varga Z et al (2020) Extracellular vesicles from human plasma and serum are carriers of extravesicular cargo—

implications for biomarker discovery. PLoS One 15:e0236439. https://doi.org/10. 1371/journal.pone.0236439 17. Lai RC, Arslan F, Lee MM et al (2010) Exosome secreted by MSC reduces myocardial ischemia/reperfusion injury. Stem Cell Res 4: 214–222. https://doi.org/10.1016/j.scr. 2009.12.003 18. Rikkert LG, Coumans FAW, Hau CM et al (2021) Platelet removal by single-step centrifugation. Platelets 32:440–443. https://doi. org/10.1080/09537104.2020.1779924 19. The´ry C, Amigorena S, Raposo G, Clayton A (2006) Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Protoc cell Biol Chapter 3:Unit 3.22. https://doi.org/10. 1002/0471143030.cb0322s30

Chapter 4 Isolation and Proteomic Analysis of Mouse Serum Small Extracellular Vesicles for Individual Subject Analysis Federica Anastasi, Marialaura Dilillo, Davide Pellegrini, and Liam A. McDonnell Abstract Proteomics characterization of blood and circulating material has been extensively explored for the study of pathological states. In particular, circulating small extracellular vesicles (sEV, diameter: 30–150 nm) are known to play an important role in intercellular communication processes, and proteomics profiling has been explored to develop minimally invasive assays for disease monitoring and diagnosis. Due to the genetic and physiological similarities between the two species, and also on account of their shorter life span and rapid disease progression, rodent models are the most commonly used animal model for many human diseases. Such models have provided invaluable insight into the molecular mechanisms of disease progression, candidate drug efficacy, therapy monitoring, and biomarkers research. Longitudinal investigations, in which individuals are monitored over periods of time, are more able to resolve molecular changes during disease progression because they circumvent the inter-individual variation. Longitudinal investigations of rodent models are challenging because of the limited amount of blood that can be withdrawn at each time; the American Association of Veterinary Science stipulates that fortnightly sampling should be limited to a maximum of 10% of the total blood volume. For adult mice this corresponds to approximately 75 μL of serum. We developed an approach for the isolation and characterization of serum sEV proteins from just 50 μL of serum, for longitudinal studies of disease mouse models. This chapter describes in detail the steps and considerations involved in the sEV isolation, morphological characterization, and proteome profiling by mass spectrometry. Key words Small extracellular vesicles, Exosomes, Longitudinal study, Proteomics, Mass spectrometry, Size exclusion chromatography, Early biomarker, Mouse serum

1

Introduction Blood is an almost ideal source of clinical information because it can be routinely obtained from patients. Blood perfuses all body compartments and organs, and so contains the proteins and vesicles secreted from diverse cell and tissue types [1]. Small extracellular vesicles (sEV, diameter 30–150 nm) are actively secreted by all cell types and have gained much interest because of their high stability

Maurizio Federico and Barbara Ridolfi (eds.), Extracellular Vesicles in Diagnosis and Therapy, Methods in Molecular Biology, vol. 2504, https://doi.org/10.1007/978-1-0716-2341-1_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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and their informative molecular cargo, which reflect their cell-oforigin (e.g., proteins, lipids, DNA, RNA, and miRNAs) [2, 3]. The enrichment of circulating sEV from serum or plasma is a widely used approach to gain insight into pathological states and for resolving molecular changes due to disease progression, or therapeutic intervention [4]. The use of mouse models to study human disease is invaluable on account of their genetic and physiological similarity and the short life span with consequent rapid disease progression [5, 6]. Therapeutic advances rely on model-based investigations, particularly for rare disease for which only small numbers of patients may be available for patient-based studies at any given time [7], or for investigating the early stages of pathologies that are most commonly diagnosed at an advanced stage [8]. Longitudinal investigations have a number of significant advantages over cross-sectional studies; rather than comparing different population of animals, the repeated measurements over time on individual animals better resolve molecular changes due to disease progression and circumvent inter-subject variability. Moreover, this type of analysis satisfies the 3Rs animal welfare requirements (replace, reduce, and refine) by ensuring maximum information from the minimum number of involved subjects [9]. However, longitudinal research based on serum/plasma in mouse models has the disadvantage of the limited blood amount that can be withdrawn over time; for instance, about 75 μL of serum can be collected from a mouse every 14 days to preserve animal welfare [10]. Effective methods for the enrichment and proteome profiling of sEV from low serum/plasma volumes require high-sensitivity and robust workflows, adapted for low amounts of total protein. Our group recently performed a longitudinal proteomics analysis of serum sEV on a glioblastoma multiforme (GBM) mouse model for early biomarker discovery [11]. We identified size exclusion chromatography as an efficient and effective sEV isolation approach, and coupled it with a high-sensitivity micro proteomics protocol, namely single-pot solid-phase-enhanced sample preparation (SP3) [12, 13]. Our group previously optimized the SP3 procedure to enable the processing of microgram amounts of proteins [14, 15]; when processing sEV proteins an additional digestion step was included to further increase digestion efficiency and thereby the precision of protein quantitation. Overall, these strategies enabled the isolation of serum sEV from the longitudinal serum samples obtained from individual mice, and led to the identification of 274 protein groups allowing a statistical analysis to be applied for the identification of early stage GBM biomarkers [11]. In this chapter we describe the method to isolate sEV from 50 μL of mouse serum and to process the sEV proteins for LC-MS based quantitative proteomics (Fig. 1).

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Fig. 1 Experimental workflow for the proteomics analysis of mouse serum sEV from individual animals. Sample pretreatment is needed to remove cell debris and microvesicles (>150 nm). SEC is then used to separate proteins from sEV. Purified sEV are concentrated and lysed on protein concentrator spin filters; extracted proteins are then quantified with an optimized Micro BCA assay and digested using a modified SP3 protocol. Peptide analysis is performed by LC-MS, and the raw data processed with MaxQuant software for protein identification

2

Materials

2.1 Mouse Serum Pretreatment

1. Refrigerated (4
C) benchtop centrifuge with 20,000  g centrifugation capability. 2. 0.5 mL Safe-Lock tubes (Eppendorf). 3. 0.5 mL centrifugal filters 0.22 μm (Ultrafree-MC, Merck).

2.2 sEV Isolation by Size Exclusion Chromatography

1. Phosphate-Buffered Saline solution (1  PBS, pH 7.3–7.5): dissolve 1 VWR PBS tablet in 100 mL Milli-Q water. Store the solution at 4
C for up to 1 month. Before using, filter the PBS solution with a 0.2 μm syringe filter (PTFE membrane, Whatman) and sonicate with an ultrasonic bath to remove air bubbles that could impair the resolution of the SEC elution profile. 2. Size exclusion chromatography (SEC) columns (qEVsingle/ 70 nm, IZON, New Zealand). 3. SEC columns holder (e.g., qEV rack from IZON or a homemade column holder).

2.3 sEV Concentration and OnFilter Lysis

1. 0.5 mL 3 kDa protein concentrators (Pierce Protein Concentrators PES, 3K MWCO, 0.5 mL). 2. SP3 Lysis Buffer (LB): prepare 10 mL of LB in a glass vial by adding 4.5 mL of HPLC grade water, 100 μL of 1 M HEPES, 2 mL of 25 mM ethylenediaminetetraacetic acid (EDTA, 14.7 mg in 2 mL of 50 mM NaOH), 2 mL of 25 mM ethylene glycol-bis( -amino-ethyl ether)-N,N,N0 ,N0 -tetraacetic acid

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(EGTA, 19 mg in 2 mL of 50 mM NaOH), 1 mL of sodium dodecyl sulfate 10% (SDS), 1 Protease inhibitor pill. Adjust the pH to 8.5 with NaOH (about 120 μL of 1 M NaOH). Store at 4
C for up to 1 month. SDS precipitates at 4
C, leave at room temperature before use until the solution is clear. 3. Sonicator: Bioruptor Pico (Diagenode). 4. 0.5 mL protein LoBind tubes (Eppendorf). 2.4 SP3 Proteomics Sample Preparation

1. 100 mg/mL bead stock solution: equilibrate two commercial paramagnetic bead solutions (Sera-Mag Speedbeads GE65152105050250; Sera-Mag Speedbeads, GE45152105050250, Sigma) at room temperature by mixing vigorously. Combine 100 μL of each suspension in a 0.5 mL tube, add 160 μL of MilliQ water, and mix well. Let the beads settle on the magnet and discard the supernatant containing sodium azide. Rinse the beads with 200 μL of MilliQ and mix well. Let the beads settle on the magnet and discard supernatant. Repeat the washing step three times. Store the bead stock in 100 μL of MilliQ water at 4
C. The beads stock solution can be used for up to 1 month. 2. Magnetic rack: any magnetic rack capable of holding 0.2 mL PCR tubes is compatible. We used a homemade magnetic rack. 3. Trifluoroethanol (TFE). 4. 50 mM HEPES pH 8: add 500 μL of 1 M HEPES to 9.0 mL of HPLC grade water, adjust the pH to 8 with 1 M NaOH and fill with HPLC grade water up to 10 mL. Store at 4
C. The solution can be stored for 1 month. 5. 50 mM HEPES pH 8.5: add 500 μL of 1 M HEPES to 9.0 mL of HPLC grade water, adjust to pH 8.5 with 1 M NaOH, and fill with HPLC grade water up to 10 mL. Store at 4
C. The solution can be stored for 1 month. 6. 200 mM Dithiothreitol (DTT) solution: dissolve 30.85 mg in 1 mL of 50 mM HEPES pH 8.5. Prepare the solution directly before use because DTT is oxygen sensitive. Place in ice. 7. 400 mM Iodoacetamide (IAA) solution: dissolve 36 mg in 500 μL of 50 mM HEPES pH 8.5. Prepare the solution directly before use because IAA is light sensitive. Place in ice and in the dark. 8. ThermoMixer capable of holding 0.2 mL PCR tubes (Eppendorf). 9. LC/MS grade formic acid. 10. LC/MS grade acetonitrile. 11. Washing solution: prepare 70% ethanol in HPLC grade water (see Note 1).

Proteomics of Mouse Serum Small Extracellular Vesicles

12. Protein digestion (Promega).

enzymes:

Trypsin/Lys-C

45

mixture

13. 0.2 mL PCR tubes. 14. Micro BCA Protein Assay kit (Thermo Scientific). 15. BSA stock solution (concentration: 2 μg/μL) for BSA standard preparation for Micro BCA assay calibration curve. 16. Measurement tool for the quantification of small-volume samples (2 μL) in absorbance mode (e.g., Tecan NanoQuant Plate or Eppendorf μCuvette). 17. Spectrophotometer compatible with the 2 μL measurement tool and capable of reading 562 nm. 18. Thermostat oven capable of reaching 60
C. 19. Protein elution buffer: 2% DMSO in LC/MS grade water. 2.5 Morphological Characterization of sEV

1. Disposable Micro Cuvettes (ZEN0040, Malvern Instruments Ltd., UK). 2. Zetasizer Dynamic Light Scattering (Malvern Instruments Ltd., UK). 3. Malvern Zetasizer Software (Malvern Instruments Ltd., UK).

2.6 Proteomics Sample Preparation and Protein Identification

1. Easy-nLC 1000 Liquid Chromatography system (Thermo Fisher Scientific, Bremen, Germany). 2. Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). 3. EASY-Spray PepMapTM analytical column (50 cm  75 μm, C18, 2 μm, 100 A˚; Thermo Fisher Scientific). 4. Acclaim PepMapTM trap-column (2 cm  75, C18, 3 μm, ˚ ; Thermo Fisher Scientific). 100 A 5. MaxQuant software (Max-Planck-Institute of Biochemistry).

3

Methods

3.1 sEV Purification by Size Exclusion Chromatography

1. Filter the PBS using a 0.22 μm syringe filters and sonicate prior to use to remove the air bubbles, then leave it at room temperature before use. 2. Leave the SEC columns at room temperature before use (see Note 2). 3. Thaw the mouse serum on ice and centrifuge it at 4000  g for 30 min at 4
C (see Note 3). 4. Transfer the supernatant directly on a 0.22 μm centrifugal filter, centrifuge at 16,000  g for 1 min at 4
C (see Note 4). Place the sample on ice after the filtration.

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5. Remove the column top cap and replace the buffer on top of the column filter with 1 mL of filtered PBS. Then remove the bottom cup and wash the qEV column with a total of 5 mL of filtered PBS. Before the PBS ends, place the bottom cap and remove the buffer remaining on the top of the column filter. 6. Mix 50 μL of filtered mouse serum with 50 μL of PBS and load the sample on top of the column filter (see Note 5). 7. Gently remove the bottom cup and elute the sample with PBS (average flow rate of 0.25 mL/min). Start collecting 200 μL fractions (see Note 6); the first five fractions (1 mL) correspond to the column void volume, small extracellular vesicles elute in the next three fractions (fractions: 6–7–8; total of 600 μL). Use LoBind tubes for collecting the fractions of interest. 8. sEV enriched fractions can be directly processed for further analysis or can be stored at 80
C (see Note 7). 3.2 sEV Sample Preparation for Morphological Characterization 3.2.1 Transmission Electron Microscopy (TEM)

1. Prepare a 3 kDa protein concentrator for each sample, and wash it three times with 500 μL of HPLC grade water at 15,000  g for 15 min at 4
C. 2. Concentrate the sEV containing SEC fractions using the washed 3 kDa protein concentrator at 14,000  g for 40 min at 4
C and loading the sample (600 μL in total) in two steps. 3. When the sEV final volume is about 100 μL, add 200 μL of Milli-Q water and concentrate (14,000  g at 4
C for 20 min, see Note 8). 4. The supernatant can be then analyzed by TEM using a standard negative staining protocol [16].

3.2.2 Dynamic Light Scattering (DLS)

1. sEV containing SEC fractions can be analyzed by DLS without any further sample preparation; mix well (e.g., by vortexing) and load a minimum of 40 μL of the SEC fraction into a disposable Micro Cuvette. 2. Perform at least three measurements at 25
C using a scattering angle detection of 173
. 3. Use the DLS intensity signal [intensity (%)] to plot and extract the sEV size distribution assuming a spherical shape of sEV, using the Malvern Zetasizer software.

3.3 sEV Concentration and Buffer Exchange

1. Wash the 3 kDa protein concentrator three times with 500 μL of HPLC grade water at 15,000  g for 15 min at 4
C. 2. Concentrate the sEV containing SEC fractions using the 3 kDa protein concentrator at 14,000  g for 40 min at 4
C, load the sample (600 μL in total) in two steps.

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47

3. When the sEV suspension volume is about 80 μL wash by adding 200 μL of PBS and concentrate at 14,000  g at 4
C for 20 min. 4. Switch the centrifuge temperature to 15
C (see Note 9). 5. Exchange the PBS buffer with lysis buffer by three additions of LB. Required volumes for buffer exchange: 200 μL + 200 μL + 100 μL at 14,000  g for 40 min at 15
C. Increase the time of the last centrifugation step until the sample reaches a volume of 50 μL or lower (see Note 10). 6. Sonicate the filters using the Bioruptor (30 s on/30 s off) for 10 min at 15
C (see Note 11). 7. Use a pipette to collect the sample and place into a 0.5 mL LoBind tube. 8. Centrifuge the empty filter for 1 min at 15,000  g and collect any retained sample volume. 9. sEV lysate can be processed immediately, stored at 20
C for next day analysis, or stored at 80
C for longer periods. 3.4 sEV Proteins Reduction and Alkylation

1. If previously frozen, thaw the samples on ice, then check sample volumes and add the same amount of TFE (dilution 1:1). 2. Mix the beads stock solution vigorously (e.g., by vortexing), then add 2 μL of the beads stock solution to the sample. 3. Sonicate using the Bioruptor (30 s on/30 s off) for 10 min at 4
C. 4. Protein reduction: add 1 μL of the freshly prepared 200 mM DTT to each 20 μL of LB:TFE. Heat the samples at 95
C for 5 min using a ThermoMixer (see Note 12). 5. Move the sample on ice for 1 min, spin down to collect the condensate and then incubate at 45
C for 30 min using a ThermoMixer. 6. Protein alkylation: add 1 μL of the freshly prepared 400 mM IAA to each 20 μL of LB:TFE (same volume of the previous DTT solution addition). Incubate the sample in the dark at room temperature for 30 min. 7. Quench the reaction with 200 mM DTT using the same volume of the previous addition (1 μL for each 20 μL of LB:TFE) (see Note 13).

3.5

Protein Clean-Up

1. Add formic acid to the samples to reach pH 2 (add 1 μL of 100% formic acid to each 20 μL of LB:TFE) and right after add acetonitrile to reach a final acetonitrile concentration of 50%. 2. Incubate the solution at room temperature until bead aggregation is visible (about 8 min).

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3. Place the sample on the magnetic holder for 2 min, then remove the supernatant taking care not to interfere with the beads aggregate and rinse the beads two times with 200 μL of washing buffer (70% EtOH) keeping the tube on the magnetic rack, incubating and removing the supernatant as described above. 4. Add 180 μL of 100% acetonitrile, then incubate for 2 min and remove supernatant. Air dry the beads. 5. Add 12 μL of 50 mM HEPES pH 8 to the dried beads, vortex to suspend the beads, and then sonicate using the Diagenode Bioruptor for 5 min at room temperature to facilitate protein elution. 3.6 sEV Protein Quantification

1. Spin down the sample and incubate the tube on the magnetic holder. The protein suspension is ready to be used for quantification using an optimized Micro BCA protein assay (see Note 14). 2. Prepare the BSA protein standards by diluting the BSA stock in 50 mM HEPES pH 8; 18 μL of BSA stock solution (2 μg/μL) are sufficient to prepare a set of 7 BSA standards with a concentration from 0 ng/μL to 360 ng/μL (see Note 15). 3. Prepare Micro BCA working reagent (WR) by mixing 25 parts of Micro BCA reagent A, 24 parts of Micro BCA reagent B, and 4 parts of Micro BCA reagent C. Prepare a sufficient volume of WR considering that each reaction involves 1 μL of sample + 2 μL of WR. Once prepared, place the solution in ice. 4. Pipette 1 μL of each standard and 1 μL of each sample into 0.2 mL labeled PCR tubes, place the tubes in ice. Sample dilution or duplicate can be also prepared (see Note 16). 5. Add 2 μL of WR solution to each sample tube and pipette to mix well, work in ice during WR addition. 6. Check that the tubes are closed and incubate at 60
C for 1 h. When the incubation is finished, spin down the samples and place the tubes on ice to stop the colorimetric reaction. 7. Using a suitable 2 μL cuvette, measure the absorbance with a spectrophotometer set a 562 nm. Use water to measure the blank absorbance and plot a calibration curve with the BSA standard absorbance vs. the concentration in ng/μL. Consider a dilution factor of 1:3 due to the addition of 2 μL of WR to the 1 μL of standards and samples. Use this curve to determine the protein concentration of the serum sEV protein samples.

3.7 Two-Step Protein Digestion

1. Remove any protein excess to perform the digestion using the same protein amount for all samples (i.e., the highest common amount that may be obtained from all samples, see Note 17).

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49

Then add Try/Lys-C mixture in a 1:25 (wt/wt) ratio of enzymes to protein. Add the enzymes in 2 μL of HEPES pH 8 (dilute the 1 μg/μL Try/Lys-C stock with 50 mM HEPES pH 8). 2. Seal the tubes with Parafilm and incubate at 37
C for 16 h. 3. Add acetonitrile to reach a 60% acetonitrile concentration. 4. Add the Try/Lys-C mixture 1:75 (wt/wt) ratio of enzymes to protein. Add the enzymes in 2 μL of HEPES pH 8 (dilute the 0.1 μg/μL stock with 50 mM HEPES pH 8). 5. Seal the tubes with Parafilm and incubate again at 37
C for 2 h. 3.8

Peptide Clean-up

1. Vortex the samples to suspend the beads and add acetonitrile to reach a final 95% acetonitrile concentration. Incubate the solution at room temperature (about 8 min). 2. Incubate the tubes on the magnetic rack and remove the supernatant. 3. Rinse the beads with 180 μL acetonitrile, incubate the solution on the magnetic rack, and then remove the supernatant. 4. Air dry the beads. 5. Add 10 μL of elution buffer (2% DMSO). 6. Sonicate using the Diagenode Bioruptor for 5 min at 4
C to elute the peptides from the beads. 7. Incubate on the magnetic rack and collect supernatant. 8. Centrifuge the supernatant at 20,000  g for 5 min to remove any beads carryover. Repeat the centrifugation if needed. 9. Dilute 1:1 with 10% formic acid (10 μL) for immediate LC-MS analysis or store at 80
C. Do not store the samples if the beads are still present.

3.9 LC-MS Analysis (see Note 18)

1. After acidification, centrifuge the tryptic peptide solution at 20,000  g for 15 min; leave 2 μL at the bottom, the supernatant (18 μL) is ready to be analyzed by LC-MS. 2. Perform the LC-MS analysis by using nanoflow liquid chromatography to separate the peptide mixture (e.g., Easy-nLC1000, Thermo Scientific) coupled to a high-resolution mass spectrometer (e.g., Orbitrap Fusion, Thermo Scientific) equipped with a nano-electrospray ion source (EASY-spray sources, Thermo Scientific). 3. LC method: Load the samples on a C18 pre-column and separate the peptide mixture using a C18-reversed phase column with a 75-min nonlinear gradient from 6 to 90% (v/v) phase B (0.1% (v/v) formic acid in acetonitrile) at a constant flow rate of 300 nL/min.

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6% B

23% B

33% B

90% B

End

0 min

53 min

60 min

66 min

75 min

4. MS method: acquire mass spectrometry data using a datadependent acquisition method that relies on a MS1 survey scan (Orbitrap mass analyzer) with a resolution of 120,000 FWHM, a mass range of 375–1500 m/z, a maximum injection time of 100 ms, and an AGC target of 5  105. Select the precursor ions (intensity greater than 5  103) with a Top speed method with a 3 s cycle time from the MS1 survey scan. Perform peptide selection with a quadrupole isolation window of 1.6 m/z, and fragmentation of ions with charge states from 2+ to 7+ using HCD, with 32% normalized collision energy. MS2 spectra are acquired in the linear ion trap using an AGC target of 1  104 and a maximum injection time of 35 ms. Enable dynamic exclusion with exclusion duration of 60 s and a mass tolerance of 10 ppm. 5. Database search and protein identification: process raw data using MaxQuant software. Perform the database search using the Andromeda search tool and an updated database of Mus musculus (UniProt), with the following parameters: trypsin digestion; up to two missed cleavages; carbamidomethylation of cysteine as fixed modifications; and oxidation of methionine and protein N-terminal acetylation as variable modifications; mass tolerance of 4.5 and 20 ppm for the MS1 and MS2, respectively. Enable match between runs using a 0.7-min retention-time alignment. Select peptides with a minimum length of 7 amino acids and 1% FDR. 6. Data preprocessing: from the protein groups matrix delete all entries marked by MaxQuant as “Potential contaminant,” “Reverse,” and “Only identified by site” (see Note 19). Transform raw intensities by Log2 and normalize the intensities by median subtraction. Use the preprocessed matrix for statistical comparison among different conditions.

4

Notes 1. The use of 80% ethanol can be considered for washing as recently suggested by the group of Hughes [17]. 2. IZON qEV single 70 nm are disposable SEC columns. Prepare one column for each sample. 3. Multiple freezing-thawing cycles should be avoided because have been shown to cause sEV aggregation or loss.

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51

4. The 0.22 μm centrifugal filter can become clogged without the previous centrifugation step; if this happens after centrifugation, try moving supernatant to a new filter or increase the centrifugation time. 5. Load the sample on the column and then remove the bottom cap. Wait for the sample to fall below the filter surface before filling the column top with PBS. Pay attention so as not to dilute the sample. The total volume loaded is 100 μL (50 μL of serum +50 μL of PBS) because we previously optimized the procedure using 100 μL of serum. 6. Fraction collection may be performed manually by counting the number of droplets (average volume/drop ¼ 45 μL) and/or by placing a calibration line on the collection tubes. IZON recently released an automated fraction collector for increased ease of use. 7. We usually processed the SEC fractions on the same day as they were collected. If stored prior to analysis, it is recommended to check for sEV aggregation or loss. 8. This step reduces the PBS concentration, as high salt concentration may interfere with the TEM analysis of the sEV suspension. 9. This step is important since the SDS present in the lysis buffer precipitates at lower temperatures. The SDS precipitation on the filter membrane would clog the filter and impair vesicle recovery. 10. The exchange of the PBS buffer with the LB is required to lower the total volume containing the sEV prior to processing the sample for proteomics analysis. A final volume of 50 μL or lower allows 0.2 mL PCR tubes to be used for the SP3 protocol. 11. During this step an on-filter sEV lysis is performed. The sonication of the sample detaches the material from the filter membrane increasing sample recovery. 12. Do not agitate the samples during the incubation at 95
C; the tubes may open due to the high internal pressure causing material losses. If the initial sEV lysate volume (prepared in Subheading 3.3) is greater than 50 μL, we recommend to use the 0.5 LoBind tubes instead of the 0.2 mL PCR tubes. If so, magnetic and ThermoMixer holders compatible with the larger tube size will be needed. 13. After quenching with DTT the procedure can be stopped by placing the samples at 4
C for a few hours. 14. The Thermo Fisher Micro BCA protein assay protocol was modified to increase the sensitivity and allow the quantification of low sample amounts; only 1 μL of the sample solution is needed for the quantification [14].

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15. BSA calibration standard for Micro BCA protein quantification. Work in ice to avoid sample evaporation. Different BSA standard dilutions can be used. An example of stock BSA (2 μg/μL) dilution for Micro BCA standard preparation is reported in the table below: BSA concentration

Volume of HEPES pH 8

360 ng/µL

82 µL

240 ng/µL

4 µL

180 ng/µL

6 µL

120 ng/µL

8 µL

60 ng/µL

10 µL

30 ng/µL

11 µL

0 ng/µL

12 µL

Volume of BSA 18 µL of BSA (2 µg/µL) 8 µL of BSA (360 ng/µL) 6 µL of BSA (360 ng/µL) 4 µL of BSA (360 ng/µL) 2 µL of BSA (360 ng/µL) 1 µL of BSA (360 ng/µL) -

16. In order to use the least amount of sample as possible for the Micro BCA protein quantification we propose the following: add 2 μL of 50 mM HEPES to 1 μL of undiluted sample. Perform the quantification assay in technical triplicate using 1 μL of the diluted sample for each measurement. 17. If the protein amount is highly variable, perform the protein digestion using the total protein amount. Then use a peptide quantification assay to quantify the digested peptides after the protein digestion. This will allow the LC-MS/MS analysis to be performed on the same peptide amount for all samples. 18. Due to the low amount of proteins that may be obtained from sEV samples, high-sensitivity instrumentation is needed for their characterization. Different instruments, technical setups, and MS methods can be used for the LC-MS/MS analysis; in this chapter we describe a label-free analysis for serum sEV proteome profiling of individual mice [11]. The experiments

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used a data-dependent acquisition (DDA) mass spectrometry approach, in which the most intense peptide ions detected in MS1 were selected, fragmented, and analyzed in MS2. It is this combination of peptide ion mass (MS1) and structurally informative fragments (MS2) that is used to identify the peptides via the database search. Data-independent acquisition (DIA) has been developed as a competitive alternative to DDA [18, 19]. In a DIA experiment all precursor ions within a specified m/z range are simultaneously subject to MS fragmentation. The complex MS2 spectra are then matched with spectral libraries that contain the list of fragment ion spectra, precursor ion exact mass and its normalized retention time. The DIA approach combines the deep proteome coverage capabilities of typical shotgun proteomics with the accurate quantification of targeted proteomics and thus could be used to circumvent the variability of precursor selection in a DDA experiment; however, we recommend to first assess the availability of sufficient material for the preparation of the reference spectral libraries [20]. 19. Highly variable serum proteins (serum albumin and immunoglobulins) can be filtered out from the dataset prior to the data normalization, since their intensity is likely to depend on the reproducibility of the serum sample preparation and sEV isolation steps.

Acknowledgments This work was performed with the support of Regione Toscana under Grant PAR FAS 2007-2013—“Gliomics: proteomics/genomics/metabolomics for the identification of biomarkers and the development of an ultrasensitive sensing platform for peripheral body fluids: applied to glioblastoma multiforme,” and Fondazione Pisa grant RST 148/16—“Nanobiomarker: nanotechnology for tumour molecular fingerprinting and early diagnosis.” The authors acknowledge Eleonora Vannini and Matteo Caleo for providing the mouse serum samples. References 1. Thadikkaran L, Siegenthaler MA, Crettaz D et al (2005) Recent advances in blood-related proteomics. Proteomics 5:3019–3034. https://doi.org/10.1002/pmic.200402053 2. Bebelman MP, Smit MJ, Pegtel DM, Baglio SR (2018) Biogenesis and function of extracellular vesicles in cancer. Pharmacol Ther 188:1–11. https://doi.org/10.1016/j.pharmthera.2018. 02.013

3. Johnsen KB, Gudbergsson JM, Andresen TL, Simonsen JB (2019) What is the blood concentration of extracellular vesicles? Implications for the use of extracellular vesicles as bloodborne biomarkers of cancer. Biochim Biophys Acta Rev Cancer 1871:109–116. https://doi. org/10.1016/j.bbcan.2018.11.006 4. Simpson RJ, Lim JW, Moritz RL, Mathivanan S (2009) Exosomes: proteomic insights and

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diagnostic potential. Expert Rev Proteomics 6: 267–283. https://doi.org/10.1586/epr. 09.17 5. Reza Khorramizadeh M, Saadat F (2020) Animal models for human disease. In: Animal biotechnology. Elsevier, pp 153–171. https://doi. org/10.1016/B978-0-12-811710-1. 00008-2 6. Perlman RL (2016) Mouse models of human disease: an evolutionary perspective. Evol Med Public Health 2016:170–176. https://doi. org/10.1093/emph/eow014 7. Vaquer G, Rivie`re F, Mavris M et al (2013) Animal models for metabolic, neuromuscular and ophthalmological rare diseases. Nat Rev Drug Discov 12:287–305. https://doi.org/ 10.1038/nrd3831 8. Samaey C, Schreurs A, Stroobants S, Balschun D (2019) Early cognitive and behavioral deficits in mouse models for tauopathy and Alzheimer’s disease. Front Aging Neurosci 11:335. https://doi.org/10.3389/fnagi.2019.00335 9. Prescott MJ, Lidster K (2017) Improving quality of science through better animal welfare: the NC3Rs strategy. Lab Anim (NY) 46:152–156. https://doi.org/10.1038/laban.1217 10. Animal Research Advisory Committee—Office of Animal Care Guidelines for blood collection in mice and rats. https://oacu.oir.nih.gov/ sites/default/files/uploads/arac-guidelines/ b2_blood_collection_in_mice_and_rats.pdf. Accessed 15 Jun 2020 11. Anastasi F, Greco F, Dilillo M et al (2020) Proteomics analysis of serum small extracellular vesicles for the longitudinal study of a glioblastoma multiforme mouse model. Sci Rep 10: 20498. https://doi.org/10.1038/s41598020-77535-8 12. Hughes CS, Foehr S, Garfield DA et al (2014) Ultrasensitive proteome analysis using paramagnetic bead technology. Mol Syst Biol 10: 757. https://doi.org/10.15252/msb. 20145625 13. Hughes CS, Moggridge S, Mu¨ller T et al (2019) Single-pot, solid-phase-enhanced sample preparation for proteomics experiments.

Nat Protoc 14:68–85. https://doi.org/10. 1038/s41596-018-0082-x 14. de Graaf EL, Pellegrini D, McDonnell LA (2016) Set of novel automated quantitative microproteomics protocols for small sample amounts and its application to kidney tissue substructures. J Proteome Res 15:4722– 4 7 3 0 . h t t p s : // d o i . o r g / 1 0 . 1 0 2 1 / a c s . jproteome.6b00889 15. Pellegrini D, del Grosso A, Angella L et al (2019) Quantitative microproteomics based characterization of the central and peripheral nervous system of a mouse model of Krabbe disease. Mol Cell Proteomics 18:1227–1241. https://doi.org/10.1074/mcp.RA118. 001267 16. Moscardini A, Di Pietro S, Signore G et al (2020) Uranium-free X solution: a new generation contrast agent for biological samples ultrastructure. Sci Rep 10:11540. https://doi. org/10.1038/s41598-020-68405-4 17. Moggridge S, Sorensen PH, Morin GB, Hughes CS (2018) Extending the compatibility of the SP3 paramagnetic bead processing approach for proteomics. J Proteome Res 17: 1730–1740. https://doi.org/10.1021/acs. jproteome.7b00913 18. Braga-Lagache S, Buchs N, Iacovache MI et al (2016) Robust label-free, quantitative profiling of circulating plasma microparticle (MP) associated proteins. Mol Cell Proteomics 15:3640–3652. https://doi.org/10.1074/ mcp.M116.060491 19. Koopmans F, Ho JTC, Smit AB, Li KW (2018) Comparative analyses of data independent acquisition mass spectrometric approaches: DIA, WiSIM-DIA, and untargeted DIA. Proteomics 18:1700304. https://doi.org/10. 1002/pmic.201700304 20. Finamore F, Cecchettini A, Ceccherini E et al (2021) Characterization of extracellular vesicle cargo in Sjo¨gren’s syndrome through a SWATH-MS proteomics approach. Int J Mol Sci 22:4864. https://doi.org/10.3390/ ijms22094864

Chapter 5 Protocol for Measuring Concentrations of Extracellular Vesicles in Human Blood Plasma with Flow Cytometry Najat Hajji, Chi M. Hau, Rienk Nieuwland, and Edwin van der Pol Abstract Extracellular vesicles (EVs) are lipid membrane enclosed particles that are released from cells into body fluids, such as blood. EVs offer potential new biomarkers of diseases, because the cellular origin, composition, concentration, and function of EVs change in health and disease. The concentration of EVs from specific cell types in blood can be determined with flow cytometry. A flow cytometer measures fluorescence and light scattering signals from single EVs, but only if these signals are sufficiently bright to be detected. Measured concentrations of EVs are therefore only reproducible and comparable if the detection ranges are known and reported in standard units, such as molecules of equivalent soluble fluorophore (MESF) for fluorescence signals and the diameter in nm for scatter signals. The goal of this protocol is to discuss all steps needed to derive the concentration of cell-type specific EVs within a known diameter range and fluorescence range. More specifically, this protocol describes how to determine the concentration of CD61+ (Integrin beta-3, platelet marker), CD235a+ (Glycophorin A, erythrocyte marker), and CD45+ (leukocyte common antigen) EVs in human blood plasma with an Apogee A60-Micro flow cytometer using scatterbased triggering. The principles behind this protocol could lay a firm basis for the design of a protocol suitable for other flow cytometers and body fluids. Key words Calibration, Extracellular vesicles, Flow cytometry, Fluorescent antibody labeling, Human blood plasma, Number concentration, Standardization

1

Introduction Extracellular vesicles (EVs) are lipid membrane enclosed particles that are released from cells into body fluids, such as blood [1]. Blood contains different types of EVs originating from a variety of cells, including erythrocytes, leukocytes, and platelets [2]. EVs offer potential new biomarkers of diseases, because the cellular origin, composition, concentration, and function of EVs change in health and disease [2]. The concentration of EVs in blood can be determined with flow cytometry [3]. A flow cytometer measures fluorescence and light scattering signals from single EVs, but only if the signals

Maurizio Federico and Barbara Ridolfi (eds.), Extracellular Vesicles in Diagnosis and Therapy, Methods in Molecular Biology, vol. 2504, https://doi.org/10.1007/978-1-0716-2341-1_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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originating from an EV are sufficiently bright to be detected [4]. Measured concentrations of EVs are therefore only reproducible and comparable if the detection ranges are known and reported in standard units [5]. Fluorescence signals are used to detect the presence of labeled transmembrane proteins at the surface of EVs, thereby revealing the cellular origin of EVs. The standard units of choice for fluorescence are molecules of equivalent soluble fluorophore (MESF). Light scattering signals can be used to determine the diameter of EVs in units of nm [6]. The goal of this protocol is to guide the reader through all steps needed to derive the concentration of cell-type specific EVs within a known diameter range and fluorescence range, e.g.: “We measured a concentration of 8.3
104 CD45+ EVs
μL1 with a diameter between 160 and 1000 nm and an allophycocyanin (APC) fluorescence intensity exceeding 90 MESF.” Figure 1 shows a schematic of the protocol. In short, the protocol describes how to collect blood, prepare plasma, store plasma, and stain plasma specifically for flow cytometry analysis. To enable the generation of reproducible data, the protocol describes how to use reference materials to calibrate the fluorescence and light scattering signals. Here, calibration means to relate the arbitrary units of a flow cytometry measurement to standard units. In addition, to verify whether the flow cytometer is stable during a study, the protocol describes how to run and analyze daily quality controls. The last sections of the protocol describe how to verify that signals are originating from EVs and how to determine the concentration of cell-type specific EVs within known detection ranges. More specifically, this protocol describes how to determine the concentration of CD61+ (Integrin beta-3, platelet marker), CD235a+ (Glycophorin A, erythrocyte marker), and CD45+ (leukocyte common antigen) EVs in human blood plasma with an Apogee A60-Micro flow cytometer. The protocol is designed for scatter-based triggering. The principles behind this protocol could lay a firm basis for the design of a protocol suitable for other flow cytometers and body fluids.

2

Materials All reagents are prepared and stored at room temperature unless indicated otherwise.

2.1 Blood Collection and Storage

1. 21-gauge Needle. 2. 6-mL Plastic blood collection tubes (at atmospheric pressure) containing the anticoagulant ethylenediaminetetraacetic acid (EDTA; see Note 1). 3. Storage vials with a screw lid and rubber ring (see Note 2).

Protocol for Measuring EV Concentrations by Flow Cytometry

57

Starting up flow cytometer Clean §3.4

Buffer only §3.4, §3.12

Run calibrators §3.5

Sample work flow EV sample §3.1-§3.4

Serial dilutions §3.7

Run quality controls §3.6

Controls to validate that reagents stain EVs

Pre-staining sample dilution §3.7

Buffer with reagents §3.12

Sample staining §3.8-§3.10

Isotype controls §3.12

Post-staining sample dilution §3.10

Detergent treatment §3.12

Data acquisition §3.11

Data analysis Apply calibrations §3.14

EV characterization §3.14

Data reporting §3.14

Fig. 1 Diagram of the protocol for measuring concentrations of extracellular vesicles (EVs) from human blood plasma with flow cytometry. § Subheading 2.2 Reference Materials for Calibrating Fluorescence and Light Scattering Detectors

1. APC calibrant: 2 μm APC Quantitative (Q-APC) Beads (custom-order, BD, USA). Reconstitute the dried pellet by adding 500 μL of Dulbecco’s phosphate-buffered saline (DPBS) and vortex for 10 s. Use the same day. 2. Brilliant Violet 421 (BV421) calibrant: Dried 3-micron BV421 Quantitative Beads (custom-order, BD, USA). Reconstitute the dried pellet by adding 500 μL of DPBS and vortex for 10 s. Use the same day. 3. Phycoerythrin (PE) calibrant: PE Easy Calibration Kit, 5 peaks (Spherotech, USA). Manually tilt PE Easy Calibration Kit

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ten-fold before use. Add one droplet from each dropper bottle (5 bottles in total) to 300 μL of DPBS and vortex for 10 s. Use the same day (see Note 3). 4. Light scattering detector calibrant: Rosetta Calibration beads (Exometry, The Netherlands). Manually tilt Rosetta Calibration beads ten-fold before use. Add one droplet of Rosetta Calibration beads to 160 μL of Milli-Q water and vortex for 10 s. Use the same day. 2.3

Quality Controls

1. QC sample for fluorescence detectors: SPHERO™ Rainbow Calibration particles, 8 peaks (Spherotech, USA). Manually tilt SPHERO™ Rainbow Calibration particles ten-fold before use. Add 2 droplets to 1000 μL of Milli-Q water, vortex 10 s, and label the tube with the date. Add 200 μL from the prepared solution to a new tube to be measured. Store the remaining solution at 4  C for a maximum period of 5 days. 2. QC sample for light scattering detectors and sample volume: ApogeeMix (Apogee Flow Systems, UK). Manually tilt ApogeeMix ten-fold before use. Add 100 μL of ApogeeMix to 100 μL of Milli-Q water and vortex for 10 s. Use the same day.

2.4 Reagents for Staining and Lysing Extracellular Vesicles

1. CD45-APC: APC mouse anti-human CD45 antibody (BioLegend, USA), clone HI30, isotype mouse IgG1. 2. CD61-BV421: BV421 mouse anti-human CD61 antibody (BD, USA), clone VI-PL2, isotype mouse IgG1. 3. CD235a-PE: PE mouse anti-human CD235a antibody (Agilent Dako, USA), clone JC159, isotype mouse IgG1. 4. APC mouse IgG1 (BD, USA). 5. BV421 mouse IgG1 (BD, USA). 6. PE mouse IgG1 (BD, USA). 7. 96-Well microplate (flat well bottom). 8. X-Pierce™ film (Sigma-Aldrich, USA). 9. Detergent treatment reagent: prepare a solution of 10% Nonidet™ P 40 Substitute (Sigma-Aldrich, USA) by adding 200 μL of Nonidet™ P 40 Substitute to 1800 μL of DPBS. Filter the solution with a 50-nm Nuclepore Hydrophilic Membrane filter (Whatman plc, UK).

2.5 Solutions for Cleaning the Flow Cytometer Fluidics

1. BD FACSRinse Solution (BD, USA). 2. 1.5% CITRANOX® acid detergent: dilute CITRANOX® acid detergent (Sigma-Aldrich, USA) stock solution 66.7-fold in Milli-Q water and vortex for 10 s. 3. COULTER CLENZ® Coulter, USA).

Cleaning

4. ProClin™ 300 (Sigma-Aldrich, USA).

Agent

(Beckman

Protocol for Measuring EV Concentrations by Flow Cytometry

2.6

Equipment

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1. Table centrifuge with a minimum acceleration force of 2500  g and an adjustable break setting. 2. Microcentrifuge with a minimum acceleration force of 18,890  g and an adjustable break setting. 3. Flow cytometer: A60-Micro (Apogee Flow Systems, UK) equipped with an autosampler and a 405-nm, 488-nm, and 638-nm laser (see Note 4). 4. Vortex mixer with a minimum number of 2500 rotations
min1. 5. Water bath.

3

Methods All procedures are performed at room temperature unless specified otherwise.

3.1

Blood Collection

1. Collect blood from overnight fasting individuals using a 21-gauge needle [7–9]. 2. Avoid prolonged use of a tourniquet [10]. 3. Discard the first 2 mL of collected blood [11, 12]. 4. Collect blood in plastic collection tubes containing the anticoagulant EDTA (see Note 1). 5. Fill the tubes to get the appropriate EDTA to blood ratio and mix gently [13]. 6. Transport the blood collection tubes vertically. 7. Minimize, measure, and report the time interval between blood collection and plasma preparation [14–17]. 8. Report all pre-analytical details [1, 7].

3.2 Plasma Preparation

1. Remove cells by centrifugation at 2500  g for 15 min [18]. Turn off the break or set the lowest deceleration on the centrifuge. 2. Collect the plasma to 10 mm above the buffy coat and pipette the plasma into a clean plastic tube. 3. Remove residual cells by centrifugation at 2500  g for 15 min [18]. Turn off the break or set the lowest deceleration on the centrifuge. 4. Collect the plasma to 10 mm above the bottom of the tube and pipette the plasma into a clean plastic tube. 5. Confirm the absence of residual platelets and hemolysis in plasma using clinical routine laboratory tests (see Note 5).

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6. Report all pre-analytical details [1, 7], including the centrifugation settings, the type of centrifuge and tubes used, the volume of fluid in the tubes during centrifugation, and the volume of collected supernatant [19]. 3.3

Plasma Storage

1. Pipette aliquots of 100 μL into the storage vials. 2. Snap-freeze aliquots in liquid nitrogen [20]. 3. Store aliquots at or below 80  C until further use [21, 22]. 4. Report all pre-analytical details [1, 7], including the type of storage vials used.

3.4 Starting Up the Flow Cytometer

1. Turn on the flow cytometer and start the data acquisition software. 2. Verify whether there is sufficient sheath fluid to run all samples (see Note 6). When required, fill the sheath tank(s) with MilliQ water (see Note 7). 3. Apply the function “flow cell soaking” once every 2 weeks (see Note 7). 4. Apply the function “flow cell clean” daily (see Note 7). 5. Apply the function “remove air from syringe” daily (see Note 7). 6. Load the data acquisition settings optimized for EV characterization (Table 1). 7. Run Milli-Q water to verify whether the flow cytometer is clean and whether the trigger threshold is set above the background noise level. The flow cytometer is clean when the count rate is 50 events
s1 (see Note 7). 8. Repeat step 7 until the flow cytometer is clean. If the flow cytometer does not become clean, apply steps 1 and 2 of Subheading 3.13 and repeat steps 3–5, and/or replace the sheath fluid filter, and/or increase the trigger threshold, and/or request maintenance.

3.5 Run Reference Materials to Calibrate Fluorescence and Light Scattering Detectors

1. Run the APC calibrant, the BV421 calibrant, and the PE calibrant with settings optimized for fluorescent beads (Table 1). 2. Verify whether 3 distinct bead populations exceed the background fluorescence level of the corresponding fluorescence detector and whether each population contains 500 events (Fig. 2a, see Note 8). 3. For each bead population, plot the logarithm of the specified MESF value versus the logarithm of the measured median fluorescence intensity in arbitrary units. Fit the data points with a linear regression (Fig. 2b). Verify whether the coefficient

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Table 1 Data acquisition settings of the A60-Micro to measure EVs and fluorescent beads Setting

EV characterization

Fluorescent beads

Aspirated sample volume (μL)

140

140

Data storage buffer (events)

5,000,000

(default) 500,000

Flow rate (μL
min1)

3.0

4.5

Measurement time (min)

2

5

Number of flush cycles

2

2

PMT voltages fluorescence detectors

Optimal

Optimal

PMT voltages FSC + SSC

Optimal

Beads should not saturate detector

Sheath pressure (mbar)

130

130

Trigger threshold detector

SSC

SSC

PMT photomultiplier tube, FSC forward light scattering, also named small angle light scattering (SALS), SSC side light scattering, also named large angle light scattering (LALS)

of determination (R2) exceeds 0.99. Save the intersect and slope of the linear regression for step 1 in Subheading 3.14 (see Note 9). 4. Run the light scattering detector calibrant with settings optimized for EV characterization (Table 1). 5. Verify whether 3 distinct populations exceed the background level of the side scattered light (SSC) detector, whether at least 1 population exceeds the background level of the fluorescein (FITC) detector, and whether each population contains 1000 events (Fig. 2c, see Note 8). 3.6 Daily Quality Controls

1. Run the QC sample for fluorescence detectors with settings optimized for fluorescent beads (Table 1). 2. Verify whether 3 distinct peaks exceed the background fluorescence level of the APC, BV421, and PE detectors, and whether each population contains 1000 events (Fig. 3a). 3. Verify whether the fluorescence detectors are stable during all measurement days. Therefore, plot the median APC, BV421, and PE fluorescence intensities of a dim and a bright bead versus time (Fig. 3b). The fluorescence detectors are considered stable if the coefficient of variation (CV) of the median fluorescence intensities is