Melanoma: Methods and Protocols [1 ed.] 9781071612040, 9781071612057, 1071612042

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Methods in Molecular Biology 2265

Kristian M. Hargadon Editor

Melanoma Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

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

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

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

Melanoma Methods and Protocols

Edited by

Kristian M. Hargadon Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA, USA

Editor Kristian M. Hargadon Department of Biology Hampden-Sydney College Hampden-Sydney, VA, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1204-0 ISBN 978-1-0716-1205-7 (eBook) https://doi.org/10.1007/978-1-0716-1205-7 © Springer Science+Business Media, LLC, part of Springer Nature 2021 Chapter 14 is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Illustration Caption: Immunofluorescent microscopy image of a human melanoma specimen with peritumoral tertiary lymphoid structures (see Chapter 40, Fig. 1). Formalin-fixed paraffin-embedded tumor specimens were subjected to multiplex immunofluorescence histology analysis of immune cell infiltrates as described in Chapter 40. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface To quote Siddhartha Mukherjee, the Columbia University oncologist and Pulitzer Prize Award-winning author of The Emperor of All Maladies: A Biography of Cancer, “If the cancer cell is evolving, then so are we.” No truer words could be spoken about the evolution of our understanding and treatment of melanoma, a highly malignant form of cancer derived from pigment-producing melanocytes and the leading cause of skin cancer–related deaths worldwide. With incidence rates that have risen steadily over the last 40 years, recent estimates of annual incidence approaching 300,000 cases, and a projected increase in incidence over at least the next decade, melanoma has emerged as a global public health concern, and significant efforts have been made to improve the diagnosis and treatment of this cancer. Having entered this field in the year 2000 with the desire to make my own contribution to the War on Cancer, I have been witness to the progress born of these great efforts, the dividends of which were made possible only by major advances in our understanding of melanoma biology. Indeed, it is our improved understanding of melanoma and the factors that drive its progression that have brought the prognosis for advanced stages of this disease from a place where, just over two decades ago, 1-year survival rates rarely exceeded 25% to a place where, in 2020, 5-year survival rates have surpassed 50% in patients treated with combination immunotherapy. Despite these successes, however, many challenges in this field remain to be addressed, including the need to detect metastatic or recurrent disease earlier, the need to identify biomarkers predictive of disease progression and therapeutic efficacy, and the need to develop alternative strategies for treating melanomas that exhibit innate or acquired resistance to currently available therapeutics. It is advances in these areas that will make the next 20 years of melanoma research as fruitful as the previous 20 years. It is ongoing cooperation between basic research scientists and clinicians that will bring questions from the clinic into the lab and that will translate observations in the laboratory into improved patient care. And it is insight into the multifactorial nature of melanoma development and progression that will continue to transform the clinical management of this disease in the years ahead. This volume of the Methods in Molecular Biology series, entitled Melanoma: Methods and Protocols, brings together leading melanoma researchers from across the world and highlights many of the cutting-edge protocols and experimental systems currently being used to investigate questions surrounding this disease. Following an introductory chapter describing the various model systems used to study malignant melanoma, the volume is divided into eight parts, each with extensive step-by-step protocols and review chapters that will be of interest to anyone who studies cancer, from basic research scientists to veterinary and clinical oncologists. Parts I and II cover traditional 2D and 3D cell culture systems for studying melanoma in both in vitro and ex vivo settings and include protocols for investigating melanoma migration, adhesion, metabolism, drug susceptibility, and interactions with tumor microenvironmental cell populations. Also included in this section are techniques for enriching melanoma stem cells and for generating gene knockout melanoma cell lines by CRISPR-Cas9 gene editing, both of which are powerful tools for studying the contribution of cancer-initiating cells and specific gene products, respectively, to melanoma progression. Parts III and IV describe various approaches for detecting, isolating, and characterizing circulating melanoma cells, circulating tumor DNA, and exosomes, all of which are

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emerging as important biomarkers of disease progression and therapeutic efficacy as liquid biopsies become more attractive as a noninvasive diagnostic procedure in the clinic. Part V focuses on experimental procedures for studying and detecting melanoma metastasis in both preclinical and clinical settings. A variety of bioinformatics-based approaches for identifying diagnostic and prognostic biomarkers for melanoma are described in Part VI. These protocols include strategies for assessing how the gut metabolome and microbiome contribute to melanoma progression; procedures for identifying serum and salivary biomarkers of uveal melanoma and canine oral melanoma, respectively; and methods for studying the role of microRNAs and their regulation of target genes as drivers of melanoma progression. Part VII begins with a review of the most widely used methodologies for studying tumorinfiltrating lymphocytes, and this review is followed by detailed protocols for quantifying and characterizing immune cell infiltrates in both melanoma tumors and tertiary lymphoid structures. Finally, Part VIII culminates with several chapters dedicated to therapeutic strategies for treating melanoma and includes protocols related to nucleic acid– and adoptive cell transfer–based therapies. Editing this volume has been both a pure joy and a great learning experience for me, and there are several individuals to whom I am greatly indebted. First, I must thank my contributing authors. You have all been a pleasure to work with, and I thank you for your collective efforts to bring this project to completion, despite the significant disruptions caused by the COVID-19 pandemic, which struck just months ahead of the chapter submission deadline. Many of you have clinical responsibilities, and others were kept away from the lab, yet you all worked diligently and allowed me to bring the volume to the publisher ahead of schedule. I have been fortunate to work directly with a handful of you during my career, and I am blessed to call you colleagues and friends. To those I have only more recently collaborated with through this project, know that I have admired your work from afar for many years, and I am grateful that you accepted my invitation to contribute your expertise to this volume. Your dedication and passion for your work show in its quality and impact. I am inspired by you all and confident that we will continue to make great progress against melanoma. I would also like to thank John Walker, Series Editor for Methods in Molecular Biology, for the invitation to serve as editor of this volume. Your guidance throughout this project, particularly in its early stages, was instrumental to this volume’s success, and it is clear to me that your leadership is a large reason why MiMB has become the gold standard for protocol publishing. Likewise, I would like to thank the staff at Springer for their assistance throughout this process, from the initial stages of the volume’s inception to its ultimate publication. Patrick Marton, David C. Casey, and Anna Rakovsky in particular, I appreciate your rapid and thorough responses to any questions I have had—you have been nothing but kind and professional, and I hope we have a chance to work together again in the future. Finally, I would like to thank Patricia Hargadon and Michael Hargadon (aka Mom and Dad), who might just be the only parents out there who take the time to read all of their son’s work (and who may have caught one or two typos in my own chapters—THANK YOU!). I can’t thank you enough for your support and enthusiasm, not only for this project but for all I do. Your love and encouragement mean the world to me—you know just when I need a “pick-me-up” after a frustrating day in the lab, you know when to celebrate a breakthrough (be it with an experiment or a student!), and you know when to tell me to just relax a bit. You’ve supported my journey from Day 1, and I am beyond grateful for all you do for me. I dedicate this book to you! Hampden-Sydney, VA, USA

Kristian M. Hargadon

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

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1 Model Systems for the Study of Malignant Melanoma . . . . . . . . . . . . . . . . . . . . . . . Randal K. Gregg

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PART I

2D CELL CULTURE-BASED APPROACHES FOR STUDYING MELANOMA BIOLOGY

2 Generation of Functional Gene Knockout Melanoma Cell Lines by CRISPR-Cas9 Gene Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kristian M. Hargadon, David Z. Bushhouse, Coleman E. Johnson, and Corey J. Williams 3 A Fluorescent Gelatin Degradation Assay to Study Melanoma Breakdown of Extracellular Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ewa Mazurkiewicz, Ewa Mr
o wczyn´ska, Aleksandra Simiczyjew Dorota Nowak, and Antonina J. Mazur 4 Wound Healing Assay for Melanoma Cell Migration . . . . . . . . . . . . . . . . . . . . . . . . Juliano T. Freitas, Ivan Jozic, and Barbara Bedogni 5 A Fluorescence-Based Assay for Measuring Glucose Uptake in Living Melanoma Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jelena Grahovac, Marijana Pavlovic´, and Marija Ostojic´

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6 Analyzing Melanoma Cell Oxygen Consumption and Extracellular Acidification Rates Using Seahorse Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Ashley V. Menk and Greg M. Delgoffe 7 Analysis of Melanoma Cell Glutamine Metabolism by Stable Isotope Tracing and Gas Chromatography-Mass Spectrometry. . . . . . . . . . . . . . . . . . . . . . . 91 David A. Scott 8 Determination of Cytotoxic Activities Against Melanoma Cells Using Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Guilan Shi and Richard Heller 9 In Vitro Differentiation of Tumor-Associated Macrophages from Monocyte Precursors with Modified Melanoma-Conditioned Medium . . . . . . . . 119 Tao Wang and Russel E. Kaufman 10 A Flow Cytometric Assay for Investigating Melanoma Cell Adhesion to Lymphatic Endothelial Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Kristian M. Hargadon and Coleman E. Johnson

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PART II 11

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An Approach to Study Melanoma Invasion and Crosstalk with Lymphatic Endothelial Cell Spheroids in 3D Using Immunofluorescence . . . . . Sanni Alve, Silvia Gramolelli, and P€ a ivi M. Ojala Evaluating Melanoma Viability and Proliferation in 3D Microenvironments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasanth Siruvallur Murali, Murat Can Cobanoglu, and Erik S. Welf Preparation, Drug Treatment, and Immunohistological Analysis of Tri-Culture Spheroid 3D Melanoma-Like Models . . . . . . . . . . . . . . . . . . . . . . . . Maximilian E. A. Sch€ a fer, Julia Klicks, Mathias Hafner, ¨ diger Rudolf and Ru Enrichment of Melanoma Stem-Like Cells via Sphere Assays . . . . . . . . . . . . . . . . . Nabanita Mukherjee, Karoline A. Lambert, David A. Norris, and Yiqun G. Shellman

PART III 15

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3D CELL CULTURE SYSTEMS FOR STUDYING MELANOMA 141

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TECHNIQUES FOR ISOLATING AND STUDYING CIRCULATING MELANOMA CELLS

Capture and Isolation of Circulating Melanoma Cells Using Photoacoustic Flowmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robert H. Edgar, Justin Cook, Madeline Douglas, Anie-Pier Samson, and John A. Viator Multi-Marker Immunomagnetic Enrichment of Circulating Melanoma Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aaron B. Beasley, Emmanuel Acheampong, Weitao Lin, and Elin S. Gray PD-L1 Detection on Circulating Melanoma Cells . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph W. Po, Yafeng Ma, Bavanthi Balakrishnar, Daniel Brungs, Farhad Azimi, Adam Cooper, Erin Saricilar, Vinay Murthy, Paul de Souza, and Therese M. Becker Transcript-Based Detection of Circulating Melanoma Cells . . . . . . . . . . . . . . . . . . Michael Morici, Weitao Lin, and Elin S. Gray Isolation and Quantification of Plasma Circulating Tumor DNA from Melanoma Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gabriela Marsavela, Anna Reid, Elin S. Gray, and Leslie Calapre Simultaneous BRAFV600E Protein and DNA Aberration Detection in Circulating Melanoma Cells Using an Integrated Multimolecular Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alain Wuethrich, Shuvashis Dey, Kevin M. Koo, Abu A. I. Sina, and Matt Trau

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Single-Cell Analysis of BRAFV600E and NRASQ61R Mutation Status in Melanoma Cell Lines as Method Generation for Circulating Melanoma Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Joseph W. Po, Yafeng Ma, Alison W. S. Luk, David Lynch, Bavanthi Balakrishnar, Daniel Brungs, Farhad Azimi, Adam Cooper, Erin Saricilar, Vinay Murthy, Paul de Souza, and Therese M. Becker

PART IV 22

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METHODS TO STUDY MELANOMA-DERIVED AND MELANOMA-ASSOCIATED EXOSOMES

A Rapid Exosome Isolation Using Ultrafiltration and Size Exclusion Chromatography (REIUS) Method for Exosome Isolation from Melanoma Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shin La Shu, Cheryl L. Allen, Shawna Benjamin-Davalos, Marina Koroleva, Don MacFarland, Hans Minderman, and Marc S. Ernstoff Immunoaffinity-Based Isolation of Melanoma Cell-Derived and T Cell-Derived Exosomes from Plasma of Melanoma Patients . . . . . . . . . . . . . . . . Sujan Kumar Mondal and Theresa L. Whiteside An Immunocapture-Based Assay for Detecting Multiple Antigens in Melanoma-Derived Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carmen Campos-Silva, Yaiza Ca´ceres-Martell, Sheila L
opez-Cobo, ˜ ez-M
o , Marı´a Josefa Rodriguez, Ricardo Jara, Marı´a Ya´n and Mar Vale´s-G
o mez Postlymphadenectomy Analysis of Exosomes from Lymphatic Exudate/Exudative Seroma of Melanoma Patients . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ oz, Susana Garcı´a-Silva, Pilar Xime´nez-Embu´n, Javier Mun and He´ctor Peinado

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PART V ASSESSING MELANOMA METASTASIS IN PRE-CLINICAL AND CLINICAL SETTINGS 26

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Fate Mapping of Cancer Cells in Metastatic Lymph Nodes Using Photoconvertible Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethel R. Pereira, Dmitriy Kedrin, and Timothy P. Padera Detection of Melanoma Cells in Lymphatic Drainage (LD) After Lymph Nodes Dissection Via Nested RT-PCR Analysis of Molecular Melanocytic Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aleksandra Gos, Piotr Rutkowski, and Janusz A. Siedlecki A Clonogenic Assay to Quantify Melanoma Micrometastases in Pulmonary Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fabrizio Mattei, Sara Andreone, and Giovanna Schiavoni PET Imaging of Melanoma Using Melanin-Targeted Probe. . . . . . . . . . . . . . . . . . Xiaowei Ma and Zhen Cheng Imaging and Isolation of Extravasation-Participating Endothelial and Melanoma Cells During Angiopellosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tyler A. Allen and Ke Cheng

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PART VI

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Salivary Proteomic Analysis of Canine Oral Melanoma by MALDI-TOF Mass Spectrometry and LC-Mass Spectrometry/Mass Spectrometry . . . . . . . . . . Sekkarin Ploypetch, Sittiruk Roytrakul, and Gunnaporn Suriyaphol Detection of Uveal Melanoma by Multiplex Immunoassays of Serum Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Song, Zhen Zhang, and Daniel W. Chan Unbiased Microbiome and Metabolomic Profiling of Fecal Samples from Patients with Melanoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashley Bui, Yongbin Choi, Arthur E. Frankel, and Andrew Y. Koh Assessment of Cell-Free microRNA by NGS Whole-Transcriptome Analysis in Cutaneous Melanoma Patients’ Blood . . . . . . . . . . . . . . . . . . . . . . . . . . . Kevin D. Tran, Rebecca Gross, Negin Rahimzadeh, Shanthy Chenathukattil, Dave S. B. Hoon, and Matias A. Bustos High-Throughput Identification of miRNA–Target Interactions in Melanoma Using miR-CATCHv2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Marranci, Romina D’Aurizio, Milena Rizzo, Catherine M. Greene, and Laura Poliseno

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STRATEGIES FOR DIAGNOSTIC AND PROGNOSTIC BIOMARKER IDENTIFICATION FOR MELANOMA AND COMPUTATIONAL/ BIOINFORMATIC APPROACHES TO OMICS ANALYSIS OF MELANOMA 429

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CHARACTERIZATION OF MELANOMA-ASSOCIATED TUMORINFILTRATING LYMPHOCYTES AND TERTIARY LYMPHOID STRUCTURES

Immunotyping and Quantification of Melanoma Tumor–Infiltrating Lymphocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Max O. Meneveau, Zeyad T. Sahli, Kevin T. Lynch, Ileana S. Mauldin, and Craig L. Slingluff Jr. Single-Cell Gene Expression, Clonality, and Feature Barcoding of Melanoma Tumor-Infiltrating Lymphocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angela Pizzolla, Simon P. Keam, Criselle D’Souza, Timothy Semple, and Paul J. Neeson Using Mass Cytometry to Analyze the Tumor-Infiltrating Lymphocytes in Human Melanoma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniela Tantalo, Thu Nguyen, Han Xian Aw Yeang, Joe Zhu, Sean Macdonald, Minyu Wang, Harini de Silva, Criselle D’Souza, Angela Pizzolla, and Paul J. Neeson Multiplex Immunohistochemistry Analysis of Melanoma Tumor-Infiltrating Lymphocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thu Nguyen, Nikolce Kocovski, Sean Macdonald, Han Xian Aw Yeang, Minyu Wang, and Paul J. Neeson Multiplex Immunofluorescence Histology for Immune Cell Infiltrates in Melanoma-Associated Tertiary Lymphoid Structures . . . . . . . . . . . . . . . . . . . . . Ileana S. Mauldin, Adela Mahmutovic, Samuel J. Young, and Craig L. Slingluff Jr.

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PART VIII 41

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DEVELOPMENT AND EVALUATION OF THERAPEUTIC STRATEGIES FOR MELANOMA TREATMENT

Use of Nanoparticles in Delivery of Nucleic Acids for Melanoma Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad A. Obeid, Alaa A. A. Aljabali, Meriem Rezigue, Haneen Amawi, Hanin Alyamani, Shatha N. Abdeljaber, and Valerie A. Ferro siRNA Delivery to Melanoma Cells with Cationic Niosomes . . . . . . . . . . . . . . . . . Mohammad A. Obeid, Hanin Alyamani, Haneen Amawi, Alaa A. A. Aljabali, Meriem Rezigue, Shatha N. Abdeljaber, and Valerie A. Ferro Controlled Delivery of Plasmid DNA to Melanoma Tumors by Gene Electrotransfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richard Heller and Guilan Shi Generation of Murine Chimeric Antigen Receptor T Cells for Adoptive T Cell Therapy for Melanoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amorette Barber Generation of Phosphopeptide-Specific T Cell Lines as Tools for Melanoma Immunotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rebecca C. Obeng and Angela L. Ambakhutwala

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

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Contributors SHATHA N. ABDELJABER • Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Yarmouk University, Irbid, Jordan EMMANUEL ACHEAMPONG • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia ALAA A. A. ALJABALI • Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Yarmouk University, Irbid, Jordan CHERYL L. ALLEN • Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA TYLER A. ALLEN • Division of Medical Oncology, Department of Medicine, Duke University Medical Center, Durham, NC, USA; Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA SANNI ALVE • Translational Cancer Medicine Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland HANIN ALYAMANI • Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK HANEEN AMAWI • Department of Pharmacy Practice, Faculty of Pharmacy, Yarmouk University, Irbid, Jordan ANGELA L. AMBAKHUTWALA • Smithers PDS LLC, Gaithersburg, MD, USA SARA ANDREONE • Department of Oncology and Molecular Medicine, Istituto Superiore di ` , Rome, Italy Sanita FARHAD AZIMI • Liverpool Hospital, Liverpool, NSW, Australia BAVANTHI BALAKRISHNAR • Liverpool Hospital, Liverpool, NSW, Australia AMORETTE BARBER • Department of Biological and Environmental Sciences, Longwood University, Farmville, VA, USA AARON B. BEASLEY • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia THERESE M. BECKER • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; Western Sydney University, School of Medicine, Campbelltown, NSW, Australia; University of New South Wales, School of Medicine, Kensington, NSW, Australia BARBARA BEDOGNI • Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA SHAWNA BENJAMIN-DAVALOS • Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA DANIEL BRUNGS • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; University of Wollongong, School of Medicine, Wollongong, NSW, Australia; Illawarra Cancer Centre, Wollongong Hospital, Wollongong, NSW, Australia ASHLEY BUI • Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA DAVID Z. BUSHHOUSE • Hargadon Laboratory, Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA, USA

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Contributors

MATIAS A. BUSTOS • Department of Translational Molecular Medicine, JWCI at Providence Saint John’s Health Center, Santa Monica, CA, USA YAIZA CA´CERES-MARTELL • Department of Immunology and Oncology, Spanish National Centre for Biotechnology, CNB-CSIC, Madrid, Spain LESLIE CALAPRE • School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia CARMEN CAMPOS-SILVA • Department of Immunology and Oncology, Spanish National Centre for Biotechnology, CNB-CSIC, Madrid, Spain DANIEL W. CHAN • Center for Biomarker Discovery and Translation, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA SHANTHY CHENATHUKATTIL • Department of Translational Molecular Medicine, JWCI at Providence Saint John’s Health Center, Santa Monica, CA, USA KE CHENG • Department of Molecular Biomedical Sciences and Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA; Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA ZHEN CHENG • Molecular Imaging Program at Stanford (MIPS), Bio-X Program, and Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA, USA YONGBIN CHOI • Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA MURAT CAN COBANOGLU • Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA JUSTIN COOK • Department of Engineering, Duquesne University, Pittsburgh, PA, USA ADAM COOPER • Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; Western Sydney University, School of Medicine, Campbelltown, NSW, Australia; Liverpool Hospital, Liverpool, NSW, Australia ROMINA D’AURIZIO • Institute of Informatics and Telematics, CNR, Pisa, Italy CRISELLE D’SOUZA • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia HARINI DE SILVA • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia PAUL DE SOUZA • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Western Sydney University, School of Medicine, Campbelltown, NSW, Australia; University of New South Wales, School of Medicine, Kensington, NSW, Australia; Liverpool Hospital, Liverpool, NSW, Australia; University of Wollongong, School of Medicine, Wollongong, NSW, Australia GREG M. DELGOFFE • Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA SHUVASHIS DEY • Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St. Lucia, QLD, Australia MADELINE DOUGLAS • Department of Engineering, Duquesne University, Pittsburgh, PA, USA ROBERT H. EDGAR • Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

Contributors

xv

MARC S. ERNSTOFF • Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA VALERIE A. FERRO • Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK ARTHUR E. FRANKEL • Department of Internal Medicine, West Palm Beach Veterans Administration Medical Center, West Palm Beach, FL, USA JULIANO T. FREITAS • Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA SUSANA GARCI´A-SILVA • Microenvironment and Metastasis Laboratory, Molecular Oncology Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain ALEKSANDRA GOS • Department of Molecular and Translational Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland JELENA GRAHOVAC • Experimental Oncology Department, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia SILVIA GRAMOLELLI • Translational Cancer Medicine Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland ELIN S. GRAY • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia CATHERINE M. GREENE • Department of Clinical Microbiology, Royal College of Surgeons in Ireland, Dublin, Ireland RANDAL K. GREGG • Department of Basic Medical Sciences, DeBusk College of Osteopathic Medicine at Lincoln Memorial University-Knoxville, Knoxville, TN, USA REBECCA GROSS • Department of Translational Molecular Medicine, JWCI at Providence Saint John’s Health Center, Santa Monica, CA, USA MATHIAS HAFNER • Institute of Molecular and Cell Biology, Hochschule Mannheim, Mannheim, Germany KRISTIAN M. HARGADON • Hargadon Laboratory, Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA, USA RICHARD HELLER • Department of Medical Engineering, Colleges of Medicine and Engineering, University of South Florida, Tampa, FL, USA DAVE S. B. HOON • Department of Genomic Sequencing Center, John Wayne Cancer Institute (JWCI) at Providence Saint John’s Health Center, Santa Monica, CA, USA; Department of Translational Molecular Medicine, JWCI at Providence Saint John’s Health Center, Santa Monica, CA, USA RICARDO JARA • Immunostep, S.L., Centro Investigaci
on del Ca´ncer (CIC), Salamanca, Spain COLEMAN E. JOHNSON • Hargadon Laboratory, Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA, USA IVAN JOZIC • Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA RUSSEL E. KAUFMAN • The Wistar Institute, Philadelphia, PA, USA SIMON P. KEAM • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia DMITRIY KEDRIN • Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital (MGH) Research Institute, MGH and Harvard Medical School (HMS), Boston, MA, USA; Elliot Hospital System, Manchester, NH, USA JULIA KLICKS • Institute of Molecular and Cell Biology, Hochschule Mannheim, Mannheim, Germany

xvi

Contributors

NIKOLCE KOCOVSKI • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia ANDREW Y. KOH • Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA; Department of Microbiology, University of Texas Southwestern, Dallas, TX, USA; Harold C. Simmons Cancer Center, University of Texas Southwestern, Dallas, TX, USA KEVIN M. KOO • Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St. Lucia, QLD, Australia MARINA KOROLEVA • Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA KAROLINE A. LAMBERT • Department of Dermatology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA WEITAO LIN • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia SHEILA LO´PEZ-COBO • Department of Immunology and Oncology, Spanish National Centre for Biotechnology, CNB-CSIC, Madrid, Spain; INSERM U932, Institut Curie, PSL Research University, Paris, France ALISON W. S. LUK • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia DAVID LYNCH • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; School of Medicine, Western Sydney University, Campbelltown, NSW, Australia KEVIN T. LYNCH • Department of Surgery, The University of Virginia Health System, Charlottesville, VA, USA XIAOWEI MA • Department of Nuclear Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China YAFENG MA • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; University of New South Wales, School of Medicine, Kensington, NSW, Australia SEAN MACDONALD • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia DON MACFARLAND • Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA ADELA MAHMUTOVIC • Department of Surgery, University of Virginia, Charlottesville, VA, USA ANDREA MARRANCI • Oncogenomics Unit, CRL-ISPRO, Pisa, Italy; Institute of Clinical Physiology, CNR, Pisa, Italy GABRIELA MARSAVELA • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia FABRIZIO MATTEI • Department of Oncology and Molecular Medicine, Istituto Superiore di ` , Rome, Italy Sanita ILEANA S. MAULDIN • Department of Surgery, The University of Virginia Health System, Charlottesville, VA, USA ANTONINA J. MAZUR • Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wrocław, Poland

Contributors

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EWA MAZURKIEWICZ • Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wrocław, Poland MAX O. MENEVEAU • Department of Surgery, The University of Virginia Health System, Charlottesville, VA, USA ASHLEY V. MENK • Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA HANS MINDERMAN • Flow and Image Cytometry Shared Resource, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA SUJAN KUMAR MONDAL • Department of Pathology, University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center, Pittsburgh, PA, USA MICHAEL MORICI • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia EWA MRO´WCZYN´SKA • Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wrocław, Poland NABANITA MUKHERJEE • Department of Dermatology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA JAVIER MUN˜OZ • Proteomics Unit—ProteoRed-ISCIII, Spanish National Cancer Research Centre (CNIO), Madrid, Spain VASANTH SIRUVALLUR MURALI • Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA VINAY MURTHY • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; Western Sydney University, School of Medicine, Campbelltown, NSW, Australia; Liverpool Hospital, Liverpool, NSW, Australia PAUL J. NEESON • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia THU NGUYEN • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia DAVID A. NORRIS • Department of Dermatology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Dermatology Section, Department of Veterans Affairs Medical Center, Denver, CO, USA DOROTA NOWAK • Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wrocław, Poland MOHAMMAD A. OBEID • Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Yarmouk University, Irbid, Jordan REBECCA C. OBENG • Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA PA€ IVI M. OJALA • Translational Cancer Medicine Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Section of Virology, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK MARIJA OSTOJIC´ • Experimental Oncology Department, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia TIMOTHY P. PADERA • Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital (MGH) Research Institute, MGH and Harvard Medical School (HMS), Boston, MA, USA

xviii

Contributors

MARIJANA PAVLOVIC´ • Experimental Oncology Department, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia HE´CTOR PEINADO • Microenvironment and Metastasis Laboratory, Molecular Oncology Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain ETHEL R. PEREIRA • Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital (MGH) Research Institute, MGH and Harvard Medical School (HMS), Boston, MA, USA; Bristol Myers Squibb, Oncology Discovery, Cambridge, MA, USA ANGELA PIZZOLLA • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia SEKKARIN PLOYPETCH • Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand; Companion Animal Cancer Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand JOSEPH W. PO • Centre for Circulating Tumour Cell Diagnostics & Research at the Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Centre for Oncology Education and Research Translation (CONCERT), Liverpool, NSW, Australia; Western Sydney University, School of Medicine, Campbelltown, NSW, Australia LAURA POLISENO • Oncogenomics Unit, CRL-ISPRO, Pisa, Italy; Institute of Clinical Physiology, CNR, Pisa, Italy NEGIN RAHIMZADEH • Department of Translational Molecular Medicine, JWCI at Providence Saint John’s Health Center, Santa Monica, CA, USA ANNA REID • School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia MERIEM REZIGUE • Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Yarmouk University, Irbid, Jordan MILENA RIZZO • Institute of Clinical Physiology, CNR, Pisa, Italy MARI´A JOSEFA RODRIGUEZ • Department of Molecular and Cell Biology, Spanish National Centre for Biotechnology, CNB-CSIC, Madrid, Spain SITTIRUK ROYTRAKUL • Functional Ingredients and Food Innovation, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand RU¨DIGER RUDOLF • Institute of Molecular and Cell Biology, Hochschule Mannheim, Mannheim, Germany PIOTR RUTKOWSKI • Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland ZEYAD T. SAHLI • Department of Surgery, The University of Virginia Health System, Charlottesville, VA, USA ANIE-PIER SAMSON • Department of Engineering, Duquesne University, Pittsburgh, PA, USA ERIN SARICILAR • Western Sydney University, School of Medicine, Campbelltown, NSW, Australia; University of New South Wales, School of Medicine, Kensington, NSW, Australia; Liverpool Hospital, Liverpool, NSW, Australia; University of Sydney, Camperdown, NSW, Australia MAXIMILIAN E. A. SCHA€ FER • Institute of Molecular and Cell Biology, Hochschule Mannheim, Mannheim, Germany GIOVANNA SCHIAVONI • Department of Oncology and Molecular Medicine, Istituto Superiore ` , Rome, Italy di Sanita

Contributors

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DAVID A. SCOTT • Cancer Metabolism Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA TIMOTHY SEMPLE • Molecular Genomics Core, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia YIQUN G. SHELLMAN • Department of Dermatology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Gates Center for Regenerative Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA GUILAN SHI • Department of Medical Engineering, Colleges of Medicine and Engineering, University of South Florida, Tampa, FL, USA SHIN LA SHU • Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA JANUSZ A. SIEDLECKI • Department of Molecular and Translational Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland ALEKSANDRA SIMICZYJEW • Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wrocław, Poland ABU A. I. SINA • Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St. Lucia, QLD, Australia CRAIG L. SLINGLUFF JR. • Department of Surgery, The University of Virginia Health System, Charlottesville, VA, USA JIN SONG • Center for Biomarker Discovery and Translation, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA GUNNAPORN SURIYAPHOL • Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand; Companion Animal Cancer Research Unit, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, Thailand DANIELA TANTALO • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia KEVIN D. TRAN • Department of Genomic Sequencing Center, John Wayne Cancer Institute (JWCI) at Providence Saint John’s Health Center, Santa Monica, CA, USA MATT TRAU • Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St. Lucia, QLD, Australia; School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, QLD,, Australia MAR VALE´S-GO´MEZ • Department of Immunology and Oncology, Spanish National Centre for Biotechnology, CNB-CSIC, Madrid, Spain JOHN A. VIATOR • Department of Engineering, Duquesne University, Pittsburgh, PA, USA MINYU WANG • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, Parkville, VIC, Australia TAO WANG • Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA ERIK S. WELF • Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA THERESA L. WHITESIDE • Department of Pathology, University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center, Pittsburgh, PA, USA; Departments of Immunology and Otolaryngology, University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center, Pittsburgh, PA, USA

xx

Contributors

COREY J. WILLIAMS • Hargadon Laboratory, Department of Biology, Hampden-Sydney College, Hampden-Sydney, VA, USA ALAIN WUETHRICH • Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St. Lucia, QLD, Australia PILAR XIME´NEZ-EMBU´N • Proteomics Unit—ProteoRed-ISCIII, Spanish National Cancer Research Centre (CNIO), Madrid, Spain MARI´A YA´N˜EZ-MO´ • Department of Molecular Biology, Universidad Aut
onoma de Madrid and Centro de Biologı´a Molecular Severo Ochoa, IIS-IP, Madrid, Spain HAN XIAN AW YEANG • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, Parkville, VIC, Australia SAMUEL J. YOUNG • Department of Surgery, University of Virginia, Charlottesville, VA, USA ZHEN ZHANG • Center for Biomarker Discovery and Translation, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA JOE ZHU • Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia

Chapter 1 Model Systems for the Study of Malignant Melanoma Randal K. Gregg Abstract Since the first resection of melanoma by Hunter in 1787, efforts to treat patients with this deadly malignancy have been ongoing. Initial work to understand melanoma biology for therapeutics development began with the employment of isolated cancer cells grown in cell cultures. However, these models lack in vivo interactions with the tumor microenvironment. Melanoma cell line transplantation into suitable animals such as mice has been informative and useful for testing therapeutics as a preclinical model. Injection of freshly isolated patient melanomas into immunodeficient animals has shown the capacity to retain the genetic heterogeneity of the tumors, which is lost during the long-term culture of melanoma cells. Upon advancement of technology, genetically engineered animals have been generated to study the spontaneous development of melanomas in light of newly discovered genetic aberrations associated with melanoma formation. Culturing melanoma cells in a matrix generate tumor spheroids, providing an in vitro environment that promotes the heterogeneity commonplace with human melanoma and displaces the need for animal care facilities. Advanced 3D cultures have been created simulating the structure and cellularity of human skin to permit in vitro testing of therapeutics on melanomas expressing the same phenotype as demonstrated in vivo. This review will discuss these models and their relevance to the study of melanomagenesis, growth, metastasis, and therapy. Key words B16, BRAF, NRAS, Genetically engineered mice, Malignant melanoma, Tumor spheroids, Ultraviolet light, Xenograft transplantation, YUMM, Zebrafish

1

Introduction Aberrant changes at the molecular and biochemical levels underlie the development of a malignant tumor of melanocytes termed melanoma. The pigment-producing melanocytes of humans are primarily found in the skin, eyes, inner ear, and meninges so primary malignancies develop at these sites. While basal and squamous cell carcinomas account for most cases of skin cancer, melanoma is responsible for the vast majority of skin cancer–related deaths [1]. The greatest risk factor is excessive exposure to the sun or ultraviolet (UV) light which initially promotes protective melanin production by melanocytes to prevent cellular damage. Overexposure to UV light can cause mutations of genes associated with cell

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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cycle regulation in melanocytes, leading to transformation and cancer development. However, sunlight is not the only risk factor for melanoma; although not as common, inherited gene mutations can also contribute to cases, especially for patients with related diseases such as xeroderma pigmentosum [2]. In this condition, DNA repair systems are inefficient and sun exposure can trigger damage that is not repaired properly, thereby potentially triggering unregulated cell cycle progression. Melanoma is the most serious type of skin cancer due to its fast rate of growth and tendency to metastasize throughout the body resulting in a 5-year survival rate of 5–19% [3]. The aggressiveness of these tumors is related to the genetic instability or heterogeneity within the tumor mass. Heterogeneity arises in the tumor given a mutation rate that surpasses all other cancers, fast growth rate, and environmental stresses such as immune reactivity and the differing oxygenation and nutrition availability to cancer cells within the tumor. Such heterogeneity greatly contributes to the lower rate of tumor regression, higher resistance to multiple therapeutics, and decreased survival rate seen in patients [4]. The extensive variety of mutations found in melanoma makes it difficult to determine which particular genetic aberration serves as the driver rather than passenger mutation. Some of the more common gene mutations associated with melanoma involve pathways of cell cycle progression and differentiation such as CDKN2A (cyclin-dependent kinase N2A), BRAF (B-raf proto-oncogene serine/threonine protein kinase), NRAS (N-rat sarcoma protein), and TP53 (tumor protein 53) [5–7]. The CDKN2A gene encodes two tumor suppressor proteins by alternate reading frames, p16INK4A and p14ARF (p19ARF in mice). G1 to S phase cell cycle progression is blocked when retinoblastoma (RB) protein remains unphosphorylated due to the action of p16INK4A that functions to inhibit cyclin D-cyclindependent kinase 4/6 from phosphorylating RB [8]. The action of p14ARF is to degrade MDM2 (E3 ubiquitin-protein ligase) which normally operates to promote TP53-mediated cell cycle arrest [9]. In melanoma patients, mutations of the CDKN2A gene diminish the expression of both p16INK4A and p14ARF, thereby leading to cellular progression through G1 to S because of inactivation of RB and TP53, respectively [10]. RAS-mediated signaling is regulated by GDP-GTP modulation where bound GTP to RAS recruits and activates RAF. In turn, RAF phosphorylates MEK (mitogen-activated protein kinase kinase), leading to phosphorylation of ERK (extracellular receptor-stimulated kinase) for translocation of ERK to the nucleus to mediate transcription factor activation, resulting in mitotic stimulation [11]. Activating mutations of BRAF and NRAS together account for up to 80% of human melanomas [5, 12]. Thus, constitutively active BRAF and NRAS proteins in melanomas drive ERK activation and nuclear localization for a strong proliferation stimulus. Interestingly,

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HRAS and KRAS mutations are rare in human melanomas comparatively but when present can stimulate growth. The most common mutation identified in human melanomas is BRAF V600E (BRAF V600E) [13]. Identification of the genetic association with human melanoma has been exploited in the development of various animal models of melanoma. PTEN (phosphatase and tensin homolog) is a tumor suppressor protein that operates to remove phosphates from phosphatidylinositol (PI)-3,4,5-triphosphate (PIP3) to PI-4,5-biphosphate, thereby preventing the action of PI3K. This action blocks the activation of the serine/threonine kinase AKT or protein kinase B. Active AKT promotes cell growth, cell cycle progression, migration, and survival [14]. Mutant PTEN gene permits the unregulated activities of AKT. There are certainly other genetic aberrations associated with melanoma development and progression, but these are the most commonly observed in human melanomas and the focus of model development for the disease. In addition, the number of melanoma cases has increased annually in the last 20 years [15], with 350,000 new cases expected to be diagnosed worldwide in 2020. Given the growing number of cases each year and the high case fatality rate, a number of animal and human model systems continue to be developed to offer insight into the molecular and cellular characteristics of melanomas for the generation and testing of new therapeutics and vaccines. Much progress has been made in understanding the biology and genetics of melanomas using model systems. However, despite decades of research, malignant melanoma remains a common cause of morbidity and mortality in the world with limited treatment success. This review offers a survey of the past and present model systems for investigation of melanoma epidemiology, pathogenesis, genetics, and pharmaceutical discovery and testing. These models have provided extensive information about the disease and are being modified or used to develop new models of melanoma to create innovative therapeutic approaches for successful tumor regression and possibly prevention.

2

Mouse Models of Melanoma Although many advances have been made in the treatment of melanoma, patient outcomes remain poor, and the 5-year survival rate for metastatic melanoma patients is at best 19% [3]. For the development of new therapeutics such as ipilimumab and pembrolizumab, which restore some function to anti-melanoma T cells, a clearer picture of melanoma biology and the microenvironment is needed. This can be accomplished through the employment of in vitro and in vivo model systems that simulate the host–tumor interactions and growth characteristics of tumors in human

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patients. Indeed, model systems must recreate at least aspects of the microenvironment of the tumor including the cellular constituents, blood network, and extracellular matrix. This is often accomplished by the use of preclinical models and the most widely utilized is the murine model. Several murine-based models have been generated including transplanted (or injected) tumors into syngeneic animals, xenografts, genetically engineered mice, and UV-induced melanomas. 2.1 Transplantation of Murine Melanoma Cell Lines in Syngeneic Mice

Transplantation of murine melanoma cell lines into syngeneic animals is one of the oldest models used to study tumor biology, dating back to the mid-twentieth century. The most common and useful murine cell line is B16 and its sublines [16, 17]. Melaninproducing B16 melanoma cells (B16-F0) were originally harvested, growing spontaneously from the posterior ears of C57Bl/6 mice in the 1950s. Once appropriate inoculation protocols were established in the 1970s, the B16 melanoma model provided a better understanding of tumor growth in vivo and served as a system for testing therapeutics. B16-F1 was established from 1 cycle of in vivo growth in the subcutaneous space of mice. Subsequently, B16-F0 cells were inoculated into mice and the B16-F10 line derived by selection for the capacity to form lung colonies in vivo following intravenous delivery and in vitro culture after 10 cycles of lung colonization [16]. Typically, B16-F1 cells are inoculated by subcutaneous injection into the nape of the neck or flank for the establishment of a solid tumor [17]. This tumor subline is minimally metastatic. It is used as a solid tumor model for testing melanoma progression and therapeutic efficacy. B16-F10 melanoma cells are injected intravenously and lodge in the lung to establish colonies at that site, thereby serving as a simulated model of metastasis [17]. A drawback of using this approach for metastatic modeling is that the events pertaining to metastasis are largely bypassed. Another metastatic model of melanoma involves the cell line, B16-4A5, which was harvested from a skin melanoma of C57Bl/6 mice [18]. When these cells are injected intraperitoneally into syngeneic mice, local colonies of the tumor are established in both the spleen and lung within 30 days of inoculation. These approaches are useful for tumor biology studies and drug effectiveness against melanoma at primary and metastatic sites. To better assess the T- and B-cell responses to B16 melanoma, B16-F0 cells were transfected with a plasmid encoding full-length ovalbumin (OVA) creating the B16-OVA cell line [19, 20]. B16-F0 is weakly immunogenic, so any significant responses would be due to lymphocyte reactivity towards the liberated OVA peptides expressed by the cancer cells. This model system allows for a clearer understanding of the host–tumor interaction as the OVA-specific T cells can be assessed ex vivo for surface markers and secreted products. To improve OVA-specific T-cell recovery, CD8+ and CD4+ T

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cells specific for OVA, derived from OT-I and OT-II TCR transgenic mice, respectively, can be transferred into C57Bl/6 mice before B16-OVA inoculation [21, 22]. Moreover, T cells from OT-I and/or OT-II mice can be transferred into mice with growing or established B16-OVA tumors to assess the potential efficacy of adoptive cell therapy [23]. B16 cancer cells do express self-antigens such as tyrosinase and gp100 for targeting T-cell responses. However, similar to human melanomas, B16 variants express low levels of major histocompatibility complex I to display the antigens and thus are poorly immunogenic in terms of being recognized by activated CD8+ cytotoxic T lymphocytes [24]. Additionally, B16 melanomas express B7 molecules and programmed cell death (PD-1) ligands for induction of T-cell exhaustion through ligation of T-cell surface CTLA-4 (cytotoxic T lymphocyte antigen-4) and PD-1, respectively [25]. Efficacy of the clinically approved checkpoint inhibitors, anti-CTLA-4 and anti-PD-1 antagonistic antibodies, in the regression of melanoma was initially tested using transplanted B16 melanoma in mice [26]. A disadvantage of this model system is the rapid growth of the tumors in vivo which limits the therapeutic window. This rapid proliferation can also impair any long-term investigations of melanoma behavior, given the mice most often die within 3–4 weeks after transplantation with cancer cells. More importantly, B16 cancer cells, like other cultured melanoma lines, are different from their human counterparts in that human melanomas have much greater genomic heterogeneity [4]. B16 cancer cells possess inactivating mutations of CDKN2A but few modifications of the commonly implicated protein in human melanomas, BRAF [27]. Human melanomas are also known to have a deficiency of PTEN protein while B16 and other melanoma lines express this protein [27]. Despite these genetic discrepancies, B16 variants have been used as a preclinical model for melanoma, providing valuable information which has aided the design and testing of therapeutics for humans. 2.2 Xenograft Transplantation

To develop more clinically relevant models for human melanoma, xenografts of human cancer cells into immunocompromised mice were first implemented in the late 1960s and early 1970s [28]. In the years after and even today, cultured human cancer cells are injected either subcutaneously or intradermally into nude athymic or severe combined immunodeficient (SCID) mice. Nude athymic mice lack mature T cells while SCID animals are deficient in both T and B cells [29, 30]. This model is useful for studying melanoma cell interactions with subcutaneous tissues, lymphatics, and blood vessels with an emphasis on microenvironments, angiogenesis, and metastasis. Many of the subcutaneously delivered human melanoma lines metastasize to other anatomical sites, most often the lungs. The melanoma cells are derived from metastatic sites in the

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immunocompromised mice after several in vivo passages, indicating that these cells are most likely subpopulations of cancer cells within the tumor population with the propensity to metastasize. A number of human melanoma cell lines are available for use as xenografts including A2058 (lymph node), A375 (skin), IGR-1 (lymph node), MEL290 (uvea), SK-MEL (skin), WM-115 (metastatic skin), and UACC (skin). The relative importance of melanoma xenograft transplantation is that it allows investigations of tumor growth and progression in the presence of test drugs in vivo. An advantage of this system, like that of the B16 transplants, is the control of tumor induction and drug administration. Given that there are no T cells in either mouse, adaptive responses cannot contribute to this process. Thus, the data reflects more of the direct effects of the therapeutic on the tumor cells than can be gleaned from treatment of patients, where contributions by a more complex microenvironment play a role in treatment outcome. In particular, melanomaspecific helper and cytotoxic T cells, natural killer T cells, and T regulatory cells are absent. These cells all play vital roles in tumor growth and survival. Furthermore, often the experiments are of short duration due to the ulceration and necrosis that occurs at the tumor site, especially in animals inoculated with tumors via the intradermal route. For this reason, most xenografts are injected through the subcutaneous route; however, like the B16 model, the tumor that develops is comparable to a skin metastasis rather than the true primary tumor. More recently, this model has been modified from cultured human cancer cells to freshly isolated tumor cells from patients. This approach is advantageous as the patient-derived xenografts retain their characteristics in the mice such as histology, transcriptome, DNA polymorphisms, gene expression, and chromosomal architecture [31]. Given that the freshly derived human melanomas preserve the heterogeneity missing in other models, patient-derived xenografts are more appropriate for studying the mechanisms of metastasis and the cytotoxic potential of new drugs as well as any tumor resistance mechanisms. Some patient-derived melanomas have been transplanted into immunodeficient mice and the animals treated with anti-cancer drugs for 8 weeks or more. Portions of surviving tumor cells are harvested, reimplanted, and again exposed to the same drug. Molecular studies can follow to discern the mechanism of tumor resistance to currently used or experimental therapeutics. Together, these studies have led to the development of clinical trials of patient tumors in mice just ahead of the treatment of the same patient with the same chemotherapy. While there is much promise in patient-derived xenografts, clinical studies in the mice can take a long time, and melanoma colonization may not occur in all of the animals. Additionally, as noted earlier, these host animals are immunodeficient so only direct effects of the drugs can be ascertained, and assessments of immune

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modulatory treatments are not possible. The costs of purchasing, care, and breeding of the mice can be high since the facilities must be germ-free and technicians highly skilled. Furthermore, since the patient-derived xenograft model is established usually from tumor biopsy fragments, methods of transfection to label or mark the cells for detection in vivo are inefficient [32]. Finally, the growth rate of the fresh tumor fragments determines the success of the model in that slow-growing cancer cells tend to be difficult to establish a model for study, whereas fast-growing tumor cells are much easier to develop a usable model [33]. 2.3 Genetically Engineered Mice (GEM)

Thus far, the model systems for melanoma have relied upon established tumor lines or freshly isolated patient tumor fragments injected into wild-type and immunocompromised mice. There is no point in these systems where the transformation of normal cells occurs and host–tumor interactions influence the outcome of melanomagenesis. Instead, the cultured or freshly isolated cancer cells, carrying preexisting mutations and epigenetics, are injected into a naı¨ve and unfamiliar environment within the subcutaneous region, intradermal space, or blood circulation. These models are very useful for therapeutic efficacy studies, given that treatments are usually administered to cancer patients with growing tumors. Transgenic and knockout mice (i.e., genetically engineered mice or GEM) have been generated to allow for investigation of the series of genetic and epigenetic changes that occur at the outset of spontaneous tumor formation [34–36]. Also, cell culture and passage through mice can generate subpopulations within the tumor that become resistant to apoptosis in cell lines. That is avoided in GEM models. Another advantage of GEM is that tumors develop in an immunocompetent environment. So initial host–tumor interactions can be explored especially in the tissues in which they would occur in humans. Moreover, cell line- and xenograft-based models have more variability between the animals transplanted as the populations are often heterogeneous for mutations, whereas GEM tumors possess the same basal mutations in all of the animals in the study. This permits an examination of subsequent signaling pathway alterations associated with tumor formation and the contribution of the immune response to melanoma biology and the drug resistance of the cancer cells as they accumulate mutations. However, similar to xenograft models, GEM are costly and require skilled technicians for handling and care as well as germ-free environments for housing the animals. One of the first melanoma GEM models developed was generated with a deletion of the CDKN2A gene, thereby eliminating both p16INK4A and p19ARF proteins to alleviate cell cycle regulation. In addition, the HRAS gene is overexpressed under the control of the melanocyte differentiation and pigmentation pathway tyrosinase promoter as a strong inducer of mitosis, and

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designated as the HRAS CDKN2A / mice [37]. In order to minimize the development of nonmelanoma tumors, melanocytespecific promoters are used to promote expression of oncogenes like HRAS such as paired box 3 (PAX3), dopachrome tautomerase (DCT), microphthalmia-associated transcription factor (MITF ), and tyrosinase (TYR) [38–41]. HRAS activation drives proliferation through the MAPK pathway that in human melanomas are often due to NRAS or BRAF mutations. Despite the use of melanocyte-specific promoters, one disadvantage with this model is that melanomas are still accompanied by the generation of B-cell lymphomas and soft tissue sarcomas and the melanomas that form remain primary tumors without metastasis. However, the melanomas develop before the other tumors form in most of the animals examined. The BRAF mutation V600E (BRAFV600E) is notable in human melanomas and the GEM developed with this mutation produced spontaneous tumors but only after long periods, so additional mutations have been crossed into these animals to promote earlier melanomagenesis [42]. GEM models have been developed to harbor a heterozygous germline BRAFV600E mutation which is only expressed upon induction of Cre recombinase (Cre) in melanocytes, termed BRAFCA mice [43]. Cre is expressed upon topical treatment of BRAF CA mice with 4-hydroxytamoxifen (4-HT) to facilitate recombination of conditional alleles (BRAF V600E), and the animals develop hyperpigmented lesions at the application site within 3–4 weeks. However, melanocyte-specific expression of the BRAF V600E mutation alone did not produce spontaneous melanoma formation. Homozygous expression of BRAF CA generated highly pigmented hyperplasias upon topical 4-HT (at the application site and others), suggesting a dosage effect of BRAF V600E expression upon melanocyte proliferation. Given the association of PTEN deficiency and human melanoma, alleles of the PTEN gene were flanked with loxP sequences such that Cre expression would delete contributions by this gene in melanocytes upon 4-HT application. Within 10 days after 4-HT, highly pigmented neoplasias develop that progress to malignant melanomas that metastasize. The BRAF CA PTEN lox/lox mice permit studies of the mechanisms of melanoma metastasis and application of therapeutics aimed at suppressing this process [43]. Most melanoma lines discussed above do not have defined mutations correlating with human melanomas so that consistency among in vitro cultures and animal recipients of tumor transplants remained stringent. To address these issues, cell lines were developed from C57Bl/6 backcrossed GEM expressing melanocytespecific driver mutations involving BRAF activation (BRAF CA) as well as PTEN and CDKN2A. About 1–6 months after application of the 4-HT to the GEM-C57Bl/6 congenic mice, melanomas were formed and were excised upon reaching 100 mm3 in diameter and cultured to liberate cell lines termed Yale University Mouse

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Melanoma (YUMM) lines [44]. Different genetic aberrations have been incorporated into the GEM from which the lines were derived and two numbers applied for a particular genotype to differentiate among the >37 congenic lines generated to date. For instance, YUMM1 lines are categorized by the gene profile BRAF V600E PTEN / CDKN2A / while YUMM4 lines are deficient in the expression of both PTEN and CDKN2A. Given YUMM lines are derived from C57Bl/6 mice, transplant into these immunocompetent animals permits an examination of the processes of tumor growth in the presence of unaltered immunity. That is, the mutations driving tumor growth are restricted to the cancer cells themselves and not expressed by all cells of the mice as is the case for many GEM animals. YUMM lines are also advantageous to other melanoma lines, such as B16, which do not have the same genotype as most human melanoma cells. However, the transplantation of the YUMM lines again localizes them within the subcutaneous space rather than the native site of melanomas. Together, these different GEM have permitted an examination of the molecular events critical to melanomagenesis, establishing those that are driver and others which serve as bystander mutations. Furthermore, these model systems are developed in immunocompetent mice which permit investigations of the interactions of host immunity and proliferating cancer cells. 2.4 Carcinogenand UV-Induced Melanoma

Spontaneous melanoma models involving the transformation of melanocytes through exposure to UV light are the most relevant for studying melanoma development in humans, given the link between sun exposure and melanoma incidence [45]. However, the distribution of melanocytes in rodents differs from humans in that melanocytes in mice are localized to the hair follicles rather than epidermis and epidermal–dermal junction of the skin [31, 46]. Accordingly, the hair follicle residence of melanocytes diminishes the invasiveness of developing melanomas, characteristic in human cases, into the skin in mouse models. One approach to circumvent this is to develop systems where melanocyte localization can be redirected to the epidermis. Induction of stem cell factor in keratinocytes of mouse skin has been shown to promote melanocyte localization to the epidermis [47]. An important murine melanoma model where melanocytes are localized to the epidermis is the HGF/SF (hepatocyte growth factor/scatter factor) transgenic mouse expressing the HGF receptor (or MET gene) under the control of the metallothionein (MT-1) promoter [48]. First identified as a protein tyrosine kinase receptor in immature hematopoietic progenitor cells (HPC), the HGF receptor is triggered by bone marrow stromal cell ligands and promotes the proliferation, adhesion, and survival of HPC [49]. Ligation of the HGF receptor leads to MAPK and PI3K pathway signaling and in the HGF/SF transgenic mouse, these pathways are then constitutively active.

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Interestingly, low-dose single exposure of neonatal (day 3.5) HGF/ SF transgenic animals to UV radiation induces the formation of melanoma within 25 weeks in up to 30% of the mice exposed [50]. An increase in the percentage of animals developing melanoma is possible through use of UVB radiation with 56% of treated mice expressing melanoma at an average of 18 weeks of age after exposure [51]. Crossing the HGF/SF transgenic mice with P16INK4A/P19ARF / mice eventually generated HGF/SF P16INK4A/P19ARF / animals that developed melanomas after UV radiation in 75% of the mice treated by 7 weeks of age [52]. While UV radiation–mediated melanomagenesis is the most relevant clinical model of spontaneous human melanoma, carcinogens have also been used to drive mutations for melanocyte transformation, albeit these are often employed together with UV radiation. Transgenic overexpression of HGF has been coupled with the CDK4 mutation R24C, HGF CDK4R24C, to generate a model inducible by the carcinogen, 7,12-dimethylbenz(a)anthracene (DMBA) in addition to repeated exposures to UV radiation [53]. Primary cutaneous melanomas form and spread to the lungs and lymph nodes. The DMBA-induced tumors can be harvested and transplanted into C57Bl/6 mice and used as a metastatic lung model. Another model utilizes overexpression of a HRAS gene with the mutation given as G12V [54]. HRASG12V is under the control of the tyrosinase promoter which produces hyperpigmentation and hyperproliferation of melanocytes. However, this overexpressed mutant RAS does not generate spontaneous melanomas. Only when DMBA is applied, mice will develop metastatic melanoma at 45 weeks in 80–100% of the mice treated [55]. In this same model, 12-O-tetradecanoylphorbol-13-acetate (TPA) or UVB radiation can also be used to generate melanomas with a penetrance of up to 40% and 57%, respectively. Additionally, repeated UV irradiation of RFP-RET transgenic mice resulted in the development of cutaneous melanoma and some of the animals also demonstrated metastases in the lungs [56]. The RET gene encodes a receptor tyrosine kinase protooncogene which is placed under the control of the MT-1 promoter in this transgenic model, resulting in a slow course of metastatic melanoma without UV radiation. Following exposure to UV light, the RET gene is activated and underlies the rapid growth and metastasis of tumors by 7 weeks compared to 12 months without irradiation. Inclusion of carcinogen- and UV radiation-mediated extensions of GEM has created model systems better able to serve as a preclinical system for human melanoma, especially those like HGF/ SF transgenic mice where the melanocytes are localized to the epidermis and epidermis–dermis junction.

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Other Animal Models of Melanoma Because of the cellular and physiological similarity with humans, extensive information about genetics, cost-effectiveness of care, and availability of reagents, mice are commonly used in melanoma research. Recently, however, it was reported that preclinical murine model translation into successful human treatments is about 8%, strongly suggesting better model systems are needed [57]. Melanoma has also been known to spontaneously occur in animals such as dogs and horses, making these animals potentially informative models for human melanoma.

3.1

Dog Model

Domesticated animals such as dogs, cats, horses, and swine are known to develop spontaneous cases of melanoma, though it is more common in dogs. Most cases of melanoma in dogs have been shown to develop in the oral mucosa with frequent metastases to the lungs and lymph nodes, similar to the progression of melanoma in humans [58]. Dogs may serve as a better model than mice in that canine DNA and proteins are more similar to humans than mice. This has eased the cost of reagents since many of the antibodies targeting human proteins also cross-react to bind well to dog antigens. However, oral mucosal melanomas of dogs differ from human, and some mouse model melanomas in that melanomas in dogs are not strongly associated with UV radiation [59]. When evaluating the genetics of dog melanoma cells, BRAFV600E, NRAS, and PTEN mutations are similar to those in humans [60, 61]. Interestingly, many of the cancer drugs used in dogs were derived from testing in humans. Thus, the current role of dog oral mucosal melanoma models is to either serve as a preclinical trial or co-clinical trial of cancer therapeutics. The model could be informative given the shorter lifespan of a dog and the more rapid progression of tumors in the animals. Dogs currently serve as a model system for melanoma phase I safety clinical trials [62]. Thus far, most therapeutics tested in dogs for human use have failed to regress tumors, yielding preclinical results similar to those observed in human clinical trials, indicating that dogs might serve as a more clinically relevant model for human melanoma.

3.2

Horse Model

Like dogs, melanomas are common in horses but develop as histopathologically similar lesions of two types: dermal melanomas and dermal melanomatosis. Some 80% of gray horses (>15 years of age) will develop melanomas in the chambers of the ear, salivary glands, lymph nodes, and neck region. However, not all are malignant as some remain benign. Transformations of benign melanomas are more common in non-gray horses with organ and lymphatic metastases [63]. Similar to dogs, UV radiation does not seem to be associated with horse melanomas but there is a strong

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demonstration of constitutive activation of the MAPK pathway [64]. Cell lines derived from primary and metastatic horse melanomas serve as model systems for human studies being transplanted as xenografts in immunocompromised mice. 3.3

Swine Model

Spontaneous melanoma development is not as common in pigs as it is in dogs and horses. However, three pig models that develop melanoma lesions have arisen through breeding, and these animals include the Sinclair miniature swine, Munich miniature swine Troll, and the Libechov minipig [65]. Black offspring of the Sinclair miniature swine develop superficial and disseminating melanoma (lungs, liver, and lymph nodes) during the first month after birth in up to 60% of piglets [66]. However, all or nearly all of the affected pigs will regress the melanomas as T cells infiltrate the tumors by the fourth month of life. Thus, this model is a good system for assessing events associated with spontaneous regression and how that might be accentuated. The Munich miniature swine Troll, developed in Munich, Germany, were generated by breeding from founder animals possessing spontaneous melanomas [67]. Tumors are present at birth or develop within the first 2 months of life. As with the Sinclair, spontaneous regression occurs on a large scale along with skin depigmentation. Originally bred for blood group antigen studies in the 1960s, the melanoma bearing Libechov minipigs were developed from crossbreeding Minnesota miniature pigs with several commercial breeds to create the first black piglets of the line with cutaneous lesions. Selective breeding increased melanoma incidences to 50%. The newly established line of melanoma pigs was termed melanoma-bearing Libechov minipig (MeLiM) [68]. This breed is mostly black pigmented, but there are varieties of colors with the most frequent rusty-red, brown, or white. The black piglets of the MeLiM animals demonstrate the highest frequency of melanoma. Similar to the two previous swine models, MeLiM animals are born with melanomas or develop the lesions within the first 2 months of life. These animals also develop metastases to areas like the stomach, lung, liver, intestines, pancreas, kidneys, heart, lymph nodes, and thymus. Up to 30% of the MeLiM animals maintain tumor progression while others in the population undergo spontaneous regression. Animals with progressive tumors often die within the first 3 months of life due to lung metastases. All three models permit the monitoring of spontaneous regression and the assessment of the involvement of immune function in these processes to apply to human therapy for highly resistant melanomas. However, only MeLiM animals have a portion of the population that has cancer progression and death due to metastasis. Studies in these animals could help in the understanding of mechanisms of cancer progression and metastasismediated death of patients and could therefore be useful for the development of therapeutics. Furthermore, these animals permit

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long-term studies given their long lifespan of up to 18 years, and the size of the animals allows for repeated tissue and blood sampling during the process of regression and/or tumor progression. Most important is the resemblance and distribution of pig melanocytes to human melanocytes. 3.4

Zebrafish Model

A recent addition to cancer models is the zebrafish, a small tropical fish, due to its 70% genetic similarity with humans, suitability for tumor induction (chemical means, genetic deletion, gene overexpression, xenotransplantation), and adaptation for different cancers (skin, pancreatic, breast, leukemia, glioma, lung, and melanoma) [69]. Additional advantages of using zebrafish are low costs of use, the rapid development of embryos (major organs developed by 5 days post-fertilization), a large number of offspring, 3–5 months lifespan and generation time, ease of drug testing for efficacy and toxicity (delivery of therapeutics into water) and optical transparency for superior in vivo imaging studies. In particular, zebrafish share similarities with humans in terms of the immune response, allowing for translatable pharmacologic studies [70]. As indicated above, human cancer cells can also be transplanted into zebrafish to establish xenograft models to study cancer growth, angiogenesis, metastasis, and drug responses. This is possible because the immune system of the zebrafish is only functional at 28 days of development. Thus, upon xenograft transplantation, the development of a tumor at the early stages can be followed visually. In addition to xenograft models using zebrafish, genetically engineered zebrafish similar to GEM have been developed to study melanoma.

3.4.1 Genetically Engineered Zebrafish (GEZ)

Genetically engineered zebrafish have been developed in the last 15 years. One of the first involves BRAFV600E under control of the melanocyte-specific promoter mitfa [71]. These animals develop spontaneous melanomas, and when crossed on a P53-deficient background, melanomas form at an increased rate. Subsequently, mutant NRAS expression was driven by the mitfa promoter in NRAS Q61K transgenic zebrafish [72]. The HRAS G12V mutant under the melanocyte-specific promoter kita gives rise to hyperpigmentation within 3-day-old embryos and melanomas by 1 month of age [73]. Tumors derived from this transgenic zebrafish are transplantable. These models are very useful in the examination of early events during the development of melanoma.

3.4.2 Xenograft Transplantation in Zebrafish

Human melanoma cell lines have been transplanted into zebrafish embryos, most often the yolk sac, allowing for the study of tumor establishment, angiogenesis, and metastasis [74]. The cell lines can be fluorescently labeled before transplant to visualize the interaction of the tumor cells with endothelia and the process of metastasis given that the embryos are transparent [75]. An advantage of this

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system is that the numbers of embryos can be large to provide adequate statistical power, and the cell lines can be injected within 48 h of fertilization. Since the embryos are transparent, blackpigmented melanomas can easily be seen in the zebrafish. Again, the immune system of zebrafish is not established until 1 month of age, so the processes are assessed in the absence of a host response [76]. Transplants of cell lines into adult zebrafish must be performed after irradiation or treatment with dexamethasone of the zebrafish to avoid immune rejection of the xenografts. One disadvantage of using zebrafish for xenotransplantation is that the optimal growth of the zebrafish occurs at 28  C rather than 37  C for human cells so temperature differences and its impact upon tumor growth and development must be considered during data analysis. That is, transplanted embryos are usually incubated at the higher temperature given the cells are more sensitive to the temperature differential. However, xenograft transplantation of human melanoma cells into zebrafish has been informative in terms of the early events of tumor formation. Indeed, a vascular endothelial growth factor 2 inhibitor was shown to inhibit the growth and vascularization of a xenograft [77]. One of the advantages of the zebrafish is that the whole animal is treated since the drugs are added directly to the water. For instance, uveal melanoma cell lines, primary and metastatic, were transplanted into embryos and treated with anticancer drugs [78]. In these animals, a drug that blocked the antiapoptotic factor BCL mediated decreased growth of melanoma of the primary human cancer line, whereas treatment with a cell cycle blocking agent and pro-apoptotic factor suppressed the growth of both primary and metastatic cell lines in the zebrafish. Thus, the zebrafish models of melanoma hold great promise for more translatable studies given the genetic similarity with humans, ease and low cost of care, statistical power in greater numbers of embryos, a short period of development, and growing interests in the generation of reagents for research.

4

Tissue-Engineered 3D Models of Melanoma Two-dimensional (2D) cell cultures involve the growth of cancer cells in a monolayer on the surface of a plastic culture plate or flask. This method ensures optimal nutrition and oxygen for the cells and drugs can be easily applied for testing. Moreover, this is the preferred method for culturing human melanoma cell lines before injection into live animals. The cultured melanoma cells can be assessed for gene and protein expression, cytotoxicity, and migration/adhesion properties in response to reagents or drugs. However, 2D cultures do not account for the oxygen and nutrient gradients observed within in vivo tumor settings. A more accurate representation of the in vivo environment is the use of 3D culture

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systems [79]. Tumor spheroids can be generated by implanting melanoma cells into a gel matrix such as collagen and then growing the cells in culture [80]. Given that the matrix permits 3D growth, this culture approach simulates tumor heterogeneity so commonly observed in melanomas resected from patients [81]. The levels of nutrition and oxygenation impact gene expression as cancer cells in the center of a 3D spheroid are likely not actively replicating in comparison to the proliferating cells at the tumor periphery. Thus, drugs targeting certain proliferative pathways may be effective against 2D growing monolayer cancer cells but have low to moderate impact on cancer cells of a 3D tumor spheroid [82]. This has been demonstrated for MAPK inhibitors [83]. What is interesting is that 3D spheroid models maintain the growth characteristics of different tumor types, including such behaviors as invasion, and they are likely more representative of the true response of a tumor to drugs in vivo. Indeed, BRAF inhibitors have been shown to translate from inhibition of 3D melanoma spheroid growth to successful inhibition of melanoma progression in clinical trials [84]. The gel matrix of the spheroids can also be altered to drive adherence-dependent cell signaling in the melanoma cells that impact migration for studies on environmental influences upon melanoma growth, invasiveness, and metastasis [85]. Additional cell types can also be co-cultured with tumor spheroids to determine how microenvironment constituents influence the growth of a particular tumor. This system was used in much of the work identifying the presence of cancer-associated fibroblasts [86]. A more advanced model for in vitro preclinical melanoma studies has been the creation of skin reconstructs [87]. These are generated by seeding culture systems with keratinocytes, melanocytes, and fibroblasts all embedded in collagen. These collaborations operate to produce a basement membrane, extracellular matrix, and cellular composition similar to that found in human skin [88]. The introduced melanoma cells in this culture system retain the characteristics of the tumor itself in terms of growth, spread, invasiveness, metastasis, and drug resistance/susceptibility. Therefore, this system is useful for studying the inhibitory effects and specificity of anti-melanoma drugs in targeting abnormal vs. normal melanocytes. While the skin reconstructs are informative, they are costly to maintain and require a high level of technical skill as well as continuous care.

5

Conclusion Overall, the object of developing a model system for disease is to provide a platform for discerning information about the growth and characteristics of a set of cancer cells and their responses to therapeutics before inclusion in the treatment of patients. Most

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likely the best means of studying melanoma and testing melanoma therapeutics or vaccines is to use a combination of the models discussed above and to incorporate new approaches that are sure to follow this review as they emerge. Certainly, 2D and 3D culture systems provide insight and strong indicators of drug responses, but in vivo testing using syngeneic cell line transplants or xenografts is an important measure of drug efficacy where melanomas are growing in the natural surroundings of the tumor microenvironment. The invention of genetically engineered animal models has permitted the assessment of tumor growth and drug responses of spontaneously developing or environmentally/chemically induced melanomas. The additional models to follow this review should advance our knowledge and understanding of melanoma dynamics in vivo and aid in the design and generation of the next phase of therapeutics and vaccines for melanoma treatment. References 1. Force USPST, Grossman DC, Curry SJ, Owens DK, Barry MJ, Caughey AB, Davidson KW, Doubeni CA, Epling JW Jr, Kemper AR, Krist AH, Kubik M, Landefeld S, Mangione CM, Silverstein M, Simon MA, Tseng CW (2018) Behavioral counseling to prevent skin cancer: US preventive services task force recommendation statement. JAMA 319 (11):1134–1142. https://doi.org/10.1001/ jama.2018.1623 2. Paszkowska-Szczur K, Scott RJ, SerranoFernandez P, Mirecka A, Gapska P, Gorski B, Cybulski C, Maleszka R, Sulikowski M, Nagay L, Lubinski J, Debniak T (2013) Xeroderma pigmentosum genes and melanoma risk. Int J Cancer 133(5):1094–1100. https://doi. org/10.1002/ijc.28123 3. Sandru A, Voinea S, Panaitescu E, Blidaru A (2014) Survival rates of patients with metastatic malignant melanoma. J Med Life 7 (4):572–576 4. Grzywa TM, Paskal W, Wlodarski PK (2017) Intratumor and intertumor heterogeneity in melanoma. Transl Oncol 10(6):956–975. https://doi.org/10.1016/j.tranon.2017.09. 007 5. Leonardi GC, Falzone L, Salemi R, Zanghi A, Spandidos DA, McCubrey JA, Candido S, Libra M (2018) Cutaneous melanoma: from pathogenesis to therapy (review). Int J Oncol 52(4):1071–1080. https://doi.org/10.3892/ ijo.2018.4287 6. Rossi A, Roberto M, Panebianco M, Botticelli A, Mazzuca F, Marchetti P (2019) Drug resistance of BRAF-mutant melanoma: review of up-to-date mechanisms of action

and promising targeted agents. Eur J Pharmacol 862:172621. https://doi.org/10.1016/j. ejphar.2019.172621 7. Amann VC, Ramelyte E, Thurneysen S, Pitocco R, Bentele-Jaberg N, Goldinger SM, Dummer R, Mangana J (2017) Developments in targeted therapy in melanoma. Eur J Surg Oncol 43(3):581–593. https://doi.org/10. 1016/j.ejso.2016.10.014 8. Serra S, Chetty R (2018) p16. J Clin Pathol 71 (10):853–858. https://doi.org/10.1136/ jclinpath-2018-205216 9. Wu CE, Koay TS, Esfandiari A, Ho YH, Lovat P, Lunec J (2018) ATM dependent DUSP6 modulation of p53 involved in synergistic targeting of MAPK and p53 pathways with trametinib and MDM2 inhibitors in cutaneous melanoma. Cancers (Basel) 11(1):3. https://doi.org/10.3390/cancers11010003 10. Nelson AA, Tsao H (2009) Melanoma and genetics. Clin Dermatol 27(1):46–52. https://doi.org/10.1016/j.clindermatol. 2008.09.005 11. Yajima I, Kumasaka MY, Thang ND, Goto Y, Takeda K, Yamanoshita O, Iida M, Ohgami N, Tamura H, Kawamoto Y, Kato M (2012) RAS/RAF/MEK/ERK and PI3K/PTEN/ AKT signaling in malignant melanoma progression and therapy. Dermatol Res Pract 2012:354191. https://doi.org/10.1155/ 2012/354191 12. Erlich TH, Fisher DE (2018) Pathways in melanoma development. G Ital Dermatol Venereol 153(1):68–76. https://doi.org/10.23736/ S0392-0488.17.05795-9

Melanoma Model Systems 13. Ascierto PA, Kirkwood JM, Grob JJ, Simeone E, Grimaldi AM, Maio M, Palmieri G, Testori A, Marincola FM, Mozzillo N (2012) The role of BRAF V600 mutation in melanoma. J Transl Med 10:85. https://doi. org/10.1186/1479-5876-10-85 14. Schadendorf D, Fisher DE, Garbe C, Gershenwald JE, Grob JJ, Halpern A, Herlyn M, Marchetti MA, McArthur G, Ribas A, Roesch A, Hauschild A (2015) Melanoma. Nat Rev Dis Primers 1:15003. https://doi. org/10.1038/nrdp.2015.3 15. Rastrelli M, Tropea S, Rossi CR, Alaibac M (2014) Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification. In Vivo 28(6):1005–1011 16. Giavazzi R, Decio A (2014) Syngeneic Murine Metastasis Models: B16 Melanoma. In: Dwek M, Schumacher U, Brooks S (eds) Metastasis research protocols. Methods in molecular biology (methods and protocols), vol 1070. Humana Press, New York, NY 17. Overwijk WW, Restifo NP (2001) B16 as a mouse model for human melanoma. Curr Protoc Immunol. Chapter 20:Unit 20 21. https:// doi.org/10.1002/0471142735.im2001s39 18. Danciu C, Falamas A, Dehelean C, Soica C, Radeke H, Barbu-Tudoran L, Bojin F, Pinzaru SC, Munteanu MF (2013) A characterization of four B16 murine melanoma cell sublines molecular fingerprint and proliferation behavior. Cancer Cell Int 13:75. https://doi.org/ 10.1186/1475-2867-13-75 19. Brown DM, Fisher TL, Wei C, Frelinger JG, Lord EM (2001) Tumours can act as adjuvants for humoral immunity. Immunology 102 (4):486–497. https://doi.org/10.1046/j. 1365-2567.2001.01213.x 20. Dobrzanski MJ, Reome JB, Dutton RW (1999) Therapeutic effects of tumor-reactive type 1 and type 2 CD8+ T cell subpopulations in established pulmonary metastases. J Immunol 162(11):6671–6680 21. Clarke SR, Barnden M, Kurts C, Carbone FR, Miller JF, Heath WR (2000) Characterization of the ovalbumin-specific TCR transgenic line OT-I: MHC elements for positive and negative selection. Immunol Cell Biol 78(2):110–117. https://doi.org/10.1046/j.1440-1711.2000. 00889.x 22. Robertson JM, Jensen PE, Evavold BD (2000) DO11.10 and OT-II T cells recognize a C-terminal ovalbumin 323-339 epitope. J Immunol 164(9):4706–4712. https://doi. org/10.4049/jimmunol.164.9.4706 23. Menager J, Gorin JB, Maurel C, Drujont L, Gouard S, Louvet C, Cherel M, Faivre-

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Chauvet A, Morgenstern A, Bruchertseifer F, Davodeau F, Gaschet J, Guilloux Y (2015) Combining alpha-radioimmunotherapy and adoptive T cell therapy to potentiate tumor destruction. PLoS One 10(6):e0130249. https://doi.org/10.1371/journal.pone. 0130249 24. Ouyang GF, Saio M, Suwa T, Imai H, Nakagawa J, Nonaka K, Umemura N, Kijima M, Takami T (2006) Interleukin-2augmented activation of tumor associated macrophage plays the main role in MHC class I in vivo induction in tumor cells that are MHC negative in vitro. Int J Oncol 28 (5):1201–1208 25. Newton JM, Hanoteau A, Liu HC, Gaspero A, Parikh F, Gartrell-Corrado RD, Hart TD, Laoui D, Van Ginderachter JA, Dharmaraj N, Spanos WC, Saenger Y, Young S, Sikora AG (2019) Immune microenvironment modulation unmasks therapeutic benefit of radiotherapy and checkpoint inhibition. J Immunother Cancer 7(1):216. https://doi.org/10.1186/ s40425-019-0698-6 26. Zhang Y, Fang C, Wang RE, Wang Y, Guo H, Guo C, Zhao L, Li S, Li X, Schultz PG, Cao YJ, Wang F (2019) A tumor-targeted immune checkpoint blocker. Proc Natl Acad Sci U S A 116(32):15889–15894. https://doi.org/10. 1073/pnas.1905646116 27. Melnikova VO, Bolshakov SV, Walker C, Ananthaswamy HN (2004) Genomic alterations in spontaneous and carcinogen-induced murine melanoma cell lines. Oncogene 23 (13):2347–2356. https://doi.org/10.1038/ sj.onc.1207405 28. Giovanella BC, Yim SO, Stehlin JS, Williams LJ Jr (1972) Development of invasive tumors in the "nude" mouse after injection of cultured human melanoma cells. J Natl Cancer Inst 48 (5):1531–1533 29. Flanagan SP (1966) ’Nude’, a new hairless gene with pleiotropic effects in the mouse. Genet Res 8(3):295–309. https://doi.org/ 10.1017/s0016672300010168 30. Manning J, Pellegrini M, Davidson N (1977) A method for gene enrichment based on the avidin-biotin interaction. Application to the drosophila ribosomal RNA genes. Biochemistry 16(7):1364–1370. https://doi.org/10. 1021/bi00626a020 31. Becker JC, Houben R, Schrama D, Voigt H, Ugurel S, Reisfeld RA (2010) Mouse models for melanoma: a personal perspective. Exp Dermatol 19(2):157–164. https://doi.org/10. 1111/j.1600-0625.2009.00986.x

18

Randal K. Gregg

32. Ilie M, Nunes M, Blot L, Hofman V, LongMira E, Butori C, Selva E, Merino-Trigo A, Venissac N, Mouroux J, Vrignaud P, Hofman P (2015) Setting up a wide panel of patientderived tumor xenografts of non-small cell lung cancer by improving the preanalytical steps. Cancer Med 4(2):201–211. https:// doi.org/10.1002/cam4.357 33. Xiao M, Rebecca VW, Herlyn M (2019) A melanoma patient-derived xenograft (PDX) model. J Vis Exp (147). https://doi.org/10. 3791/59508 34. Adams JM, Cory S (1991) Transgenic models of tumor development. Science 254 (5035):1161–1167. https://doi.org/10. 1126/science.1957168 35. Frese KK, Tuveson DA (2007) Maximizing mouse cancer models. Nat Rev Cancer 7 (9):645–658. https://doi.org/10.1038/ nrc2192 36. Perez-Guijarro E, Day CP, Merlino G, Zaidi MR (2017) Genetically engineered mouse models of melanoma. Cancer 123 (S11):2089–2103. https://doi.org/10.1002/ cncr.30684 37. Chin L, Pomerantz J, Polsky D, Jacobson M, Cohen C, Cordon-Cardo C, Horner JW 2nd, DePinho RA (1997) Cooperative effects of INK4a and ras in melanoma susceptibility in vivo. Genes Dev 11(21):2822–2834. https://doi.org/10.1101/gad.11.21.2822 38. Galibert MD, Yavuzer U, Dexter TJ, Goding CR (1999) Pax3 and regulation of the melanocyte-specific tyrosinase-related protein1 promoter. J Biol Chem 274 (38):26894–26900. https://doi.org/10. 1074/jbc.274.38.26894 39. Ludwig A, Rehberg S, Wegner M (2004) Melanocyte-specific expression of dopachrome tautomerase is dependent on synergistic gene activation by the Sox10 and Mitf transcription factors. FEBS Lett 556(1–3):236–244. https://doi.org/10.1016/s0014-5793(03) 01446-7 40. Yasumoto K, Yokoyama K, Shibata K, Tomita Y, Shibahara S (1994) Microphthalmia-associated transcription factor as a regulator for melanocyte-specific transcription of the human tyrosinase gene. Mol Cell Biol 14(12):8058–8070. https://doi.org/10. 1128/mcb.14.12.8058 41. Bentley NJ, Eisen T, Goding CR (1994) Melanocyte-specific expression of the human tyrosinase promoter: activation by the microphthalmia gene product and role of the initiator. Mol Cell Biol 14(12):7996–8006. https:// doi.org/10.1128/mcb.14.12.7996

42. Dhomen N, Reis-Filho JS, da Rocha Dias S, Hayward R, Savage K, Delmas V, Larue L, Pritchard C, Marais R (2009) Oncogenic Braf induces melanocyte senescence and melanoma in mice. Cancer Cell 15(4):294–303. https:// doi.org/10.1016/j.ccr.2009.02.022 43. Dankort D, Curley DP, Cartlidge RA, Nelson B, Karnezis AN, Damsky WE Jr, You MJ, DePinho RA, McMahon M, Bosenberg M (2009) Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat Genet 41 (5):544–552. https://doi.org/10.1038/ng. 356 44. Meeth K, Wang JX, Micevic G, Damsky W, Bosenberg MW (2016) The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res 29(5):590–597. https://doi. org/10.1111/pcmr.12498 45. Zaidi MR, Day CP, Merlino G (2008) From UVs to metastases: modeling melanoma initiation and progression in the mouse. J Invest Dermatol 128(10):2381–2391. https://doi. org/10.1038/jid.2008.177 46. Ha L, Noonan FP, De Fabo EC, Merlino G (2005) Animal models of melanoma. J Investig Dermatol Symp Proc 10(2):86–88. https:// doi.org/10.1111/j.1087-0024.2005. 200409.x 47. Vanover JC, Spry ML, Hamilton L, Wakamatsu K, Ito S, D’Orazio JA (2009) Stem cell factor rescues tyrosinase expression and pigmentation in discreet anatomic locations in albino mice. Pigment Cell Melanoma Res 22(6):827–838. https://doi.org/10. 1111/j.1755-148X.2009.00617.x 48. Takayama H, LaRochelle WJ, Sharp R, Otsuka T, Kriebel P, Anver M, Aaronson SA, Merlino G (1997) Diverse tumorigenesis associated with aberrant development in mice overexpressing hepatocyte growth factor/scatter factor. Proc Natl Acad Sci U S A 94 (2):701–706. https://doi.org/10.1073/pnas. 94.2.701 49. Weimar IS, Miranda N, Muller EJ, Hekman A, Kerst JM, de Gast GC, Gerritsen WR (1998) Hepatocyte growth factor/scatter factor (HGF/SF) is produced by human bone marrow stromal cells and promotes proliferation, adhesion and survival of human hematopoietic progenitor cells (CD34+). Exp Hematol 26 (9):885–894 50. Noonan FP, Recio JA, Takayama H, Duray P, Anver MR, Rush WL, De Fabo EC, Merlino G (2001) Neonatal sunburn and melanoma in mice. Nature 413(6853):271–272. https:// doi.org/10.1038/35095108

Melanoma Model Systems 51. De Fabo EC, Noonan FP, Fears T, Merlino G (2004) Ultraviolet B but not ultraviolet A radiation initiates melanoma. Cancer Res 64 (18):6372–6376. https://doi.org/10.1158/ 0008-5472.CAN-04-1454 52. Recio JA, Noonan FP, Takayama H, Anver MR, Duray P, Rush WL, Lindner G, De Fabo EC, DePinho RA, Merlino G (2002) Ink4a/arf deficiency promotes ultraviolet radiationinduced melanomagenesis. Cancer Res 62 (22):6724–6730 53. Tormo D, Ferrer A, Gaffal E, Wenzel J, BasnerTschakarjan E, Steitz J, Heukamp LC, Gutgemann I, Buettner R, Malumbres M, Barbacid M, Merlino G, Tuting T (2006) Rapid growth of invasive metastatic melanoma in carcinogen-treated hepatocyte growth factor/scatter factor-transgenic mice carrying an oncogenic CDK4 mutation. Am J Pathol 169 (2):665–672. https://doi.org/10.2353/ ajpath.2006.060017 54. Powell MB, Hyman P, Bell OD, Balmain A, Brown K, Alberts D, Bowden GT (1995) Hyperpigmentation and melanocytic hyperplasia in transgenic mice expressing the human T24 Ha-ras gene regulated by a mouse tyrosinase promoter. Mol Carcinog 12(2):82–90. https://doi.org/10.1002/mc.2940120205 55. Broome Powell M, Gause PR, Hyman P, Gregus J, Lluria-Prevatt M, Nagle R, Bowden GT (1999) Induction of melanoma in TPras transgenic mice. Carcinogenesis 20 (9):1747–1753. https://doi.org/10.1093/ carcin/20.9.1747 56. Kato M, Takahashi M, Akhand AA, Liu W, Dai Y, Shimizu S, Iwamoto T, Suzuki H, Nakashima I (1998) Transgenic mouse model for skin malignant melanoma. Oncogene 17 (14):1885–1888. https://doi.org/10.1038/ sj.onc.1202077 57. Mak IW, Evaniew N, Ghert M (2014) Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res 6 (2):114–118 58. Prouteau A, Andre C (2019) Canine melanomas as models for human melanomas: clinical, histological, and genetic comparison. Genes (Basel) 10(7):501. https://doi.org/10.3390/ genes10070501 59. Nishiya AT, Massoco CO, Felizzola CR, Perlmann E, Batschinski K, Tedardi MV, Garcia JS, Mendonca PP, Teixeira TF, Zaidan Dagli ML (2016) Comparative aspects of canine melanoma. Vet Sci 3(1):7. https://doi.org/10. 3390/vetsci3010007 60. Gillard M, Cadieu E, De Brito C, Abadie J, Vergier B, Devauchelle P, Degorce F, Dreano S, Primot A, Dorso L, Lagadic M,

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Galibert F, Hedan B, Galibert MD, Andre C (2014) Naturally occurring melanomas in dogs as models for non-UV pathways of human melanomas. Pigment Cell Melanoma Res 27 (1):90–102. https://doi.org/10.1111/pcmr. 12170 61. Mochizuki H, Kennedy K, Shapiro SG, Breen M (2015) BRAF mutations in canine cancers. PLoS One 10(6):e0129534. https://doi.org/ 10.1371/journal.pone.0129534 62. Gardner HL, Fenger JM, London CA (2016) Dogs as a model for cancer. Annu Rev Anim Biosci 4:199–222. https://doi.org/10.1146/ annurev-animal-022114-110911 63. Valentine BA (1995) Equine melanocytic tumors: a retrospective study of 53 horses (1988 to 1991). J Vet Intern Med 9 (5):291–297. https://doi.org/10.1111/j. 1939-1676.1995.tb01087.x 64. Jiang L, Campagne C, Sundstrom E, Sousa P, Imran S, Seltenhammer M, Pielberg G, Olsson MJ, Egidy G, Andersson L, Golovko A (2014) Constitutive activation of the ERK pathway in melanoma and skin melanocytes in Grey horses. BMC Cancer 14:857. https://doi. org/10.1186/1471-2407-14-857 65. Horak V, Palanova A, Cizkova J, Miltrova V, Vodicka P, Kupcova Skalnikova H (2019) Melanoma-bearing Libechov minipig (MeLiM): the unique swine model of hereditary metastatic melanoma. Genes (Basel) 10 (11):915. https://doi.org/10.3390/ genes10110915 66. Hook RR Jr, Aultman MD, Adelstein EH, Oxenhandler RW, Millikan LE, Middleton CC (1979) Influence of selective breeding on the incidence of melanomas in Sinclair miniature swine. Int J Cancer 24(5):668–672. https://doi.org/10.1002/ijc.2910240522 67. Muller S, Wanke R, Distl O (1995) Segregation of pigment cell anomalies in Munich miniature swine (MMS) troll crossed with German landrace. Dtsch Tierarztl Wochenschr 102 (10):391–394 68. Bourneuf E (2017) The MeLiM minipig: an original spontaneous model to explore cutaneous melanoma genetic basis. Front Genet 8:146. https://doi.org/10.3389/fgene.2017. 00146 69. Howe K, Clark MD, Torroja CF, Torrance J, Berthelot C, Muffato M, Collins JE, Humphray S, McLaren K, Matthews L, McLaren S, Sealy I, Caccamo M, Churcher C, Scott C, Barrett JC, Koch R, Rauch GJ, White S, Chow W, Kilian B, Quintais LT, Guerra-Assuncao JA, Zhou Y, Gu Y, Yen J, Vogel JH, Eyre T, Redmond S, Banerjee R, Chi J, Fu B, Langley E, Maguire SF, Laird

20

Randal K. Gregg

GK, Lloyd D, Kenyon E, Donaldson S, Sehra H, Almeida-King J, Loveland J, Trevanion S, Jones M, Quail M, Willey D, Hunt A, Burton J, Sims S, McLay K, Plumb B, Davis J, Clee C, Oliver K, Clark R, Riddle C, Elliot D, Threadgold G, Harden G, Ware D, Begum S, Mortimore B, Kerry G, Heath P, Phillimore B, Tracey A, Corby N, Dunn M, Johnson C, Wood J, Clark S, Pelan S, Griffiths G, Smith M, Glithero R, Howden P, Barker N, Lloyd C, Stevens C, Harley J, Holt K, Panagiotidis G, Lovell J, Beasley H, Henderson C, Gordon D, Auger K, Wright D, Collins J, Raisen C, Dyer L, Leung K, Robertson L, Ambridge K, Leongamornlert D, McGuire S, Gilderthorp R, Griffiths C, Manthravadi D, Nichol S, Barker G, Whitehead S, Kay M, Brown J, Murnane C, Gray E, Humphries M, Sycamore N, Barker D, Saunders D, Wallis J, Babbage A, Hammond S, MashreghiMohammadi M, Barr L, Martin S, Wray P, Ellington A, Matthews N, Ellwood M, Woodmansey R, Clark G, Cooper J, Tromans A, Grafham D, Skuce C, Pandian R, Andrews R, Harrison E, Kimberley A, Garnett J, Fosker N, Hall R, Garner P, Kelly D, Bird C, Palmer S, Gehring I, Berger A, Dooley CM, Ersan-Urun Z, Eser C, Geiger H, Geisler M, Karotki L, Kirn A, Konantz J, Konantz M, Oberlander M, Rudolph-Geiger S, Teucke M, Lanz C, Raddatz G, Osoegawa K, Zhu B, Rapp A, Widaa S, Langford C, Yang F, Schuster SC, Carter NP, Harrow J, Ning Z, Herrero J, Searle SM, Enright A, Geisler R, Plasterk RH, Lee C, Westerfield M, de Jong PJ, Zon LI, Postlethwait JH, Nusslein-Volhard C, Hubbard TJ, Roest Crollius H, Rogers J, Stemple DL (2013) The zebrafish reference genome sequence and its relationship to the human genome. Nature 496(7446):498–503. https://doi.org/10.1038/nature12111 70. Zhao S, Huang J, Ye J (2015) A fresh look at zebrafish from the perspective of cancer research. J Exp Clin Cancer Res 34:80. https://doi.org/10.1186/s13046-015-01968 71. Patton EE, Widlund HR, Kutok JL, Kopani KR, Amatruda JF, Murphey RD, Berghmans S, Mayhall EA, Traver D, Fletcher CD, Aster JC, Granter SR, Look AT, Lee C, Fisher DE, Zon LI (2005) BRAF mutations are sufficient to promote nevi formation and cooperate with p53 in the genesis of melanoma. Curr Biol 15(3):249–254. https://doi.org/ 10.1016/j.cub.2005.01.031 72. Dovey M, White RM, Zon LI (2009) Oncogenic NRAS cooperates with p53 loss to

generate melanoma in zebrafish. Zebrafish 6 (4):397–404. https://doi.org/10.1089/zeb. 2009.0606 73. Santoriello C, Gennaro E, Anelli V, Distel M, Kelly A, Koster RW, Hurlstone A, Mione M (2010) Kita driven expression of oncogenic HRAS leads to early onset and highly penetrant melanoma in zebrafish. PLoS One 5(12): e15170. https://doi.org/10.1371/journal. pone.0015170 74. Hill D, Chen L, Snaar-Jagalska E, Chaudhry B (2018) Embryonic zebrafish xenograft assay of human cancer metastasis. F1000Res 7:1682. https://doi.org/10.12688/f1000research. 16659.2 75. Hoffman SJ, Psaltis PJ, Clark KJ, Spoon DB, Chue CD, Ekker SC, Simari RD (2012) An in vivo method to quantify lymphangiogenesis in zebrafish. PLoS One 7(9):e45240. https:// doi.org/10.1371/journal.pone.0045240 76. Taylor AM, Zon LI (2009) Zebrafish tumor assays: the state of transplantation. Zebrafish 6 (4):339–346. https://doi.org/10.1089/zeb. 2009.0607 77. Zhao C, Wang X, Zhao Y, Li Z, Lin S, Wei Y, Yang H (2011) A novel xenograft model in zebrafish for high-resolution investigating dynamics of neovascularization in tumors. PLoS One 6(7):e21768. https://doi.org/10. 1371/journal.pone.0021768 78. van der Ent W, Burrello C, Teunisse AF, Ksander BR, van der Velden PA, Jager MJ, Jochemsen AG, Snaar-Jagalska BE (2014) Modeling of human uveal melanoma in zebrafish xenograft embryos. Invest Ophthalmol Vis Sci 55 (10):6612–6622. https://doi.org/10.1167/ iovs.14-15202 79. Smalley KS, Lioni M, Noma K, Haass NK, Herlyn M (2008) In vitro three-dimensional tumor microenvironment models for anticancer drug discovery. Expert Opin Drug Discov 3 (1):1–10. https://doi.org/10.1517/ 17460441.3.1.1 80. Ishiguro T, Ohata H, Sato A, Yamawaki K, Enomoto T, Okamoto K (2017) Tumorderived spheroids: relevance to cancer stem cells and clinical applications. Cancer Sci 108 (3):283–289. https://doi.org/10.1111/cas. 13155 81. Beaumont KA, Mohana-Kumaran N, Haass NK (2013) Modeling melanoma in vitro and in vivo. Healthcare (Basel) 2(1):27–46. https://doi.org/10.3390/healthcare2010027 82. Zhuang L, Lee CS, Scolyer RA, McCarthy SW, Palmer AA, Zhang XD, Thompson JF, Bron LP, Hersey P (2005) Activation of the extracellular signal regulated kinase (ERK) pathway in

Melanoma Model Systems human melanoma. J Clin Pathol 58 (11):1163–1169. https://doi.org/10.1136/ jcp.2005.025957 83. Haass NK, Sproesser K, Nguyen TK, Contractor R, Medina CA, Nathanson KL, Herlyn M, Smalley KS (2008) The mitogenactivated protein/extracellular signal-regulated kinase kinase inhibitor AZD6244 (ARRY142886) induces growth arrest in melanoma cells and tumor regression when combined with docetaxel. Clin Cancer Res 14 (1):230–239. https://doi.org/10.1158/ 1078-0432.CCR-07-1440 84. Bollag G, Hirth P, Tsai J, Zhang J, Ibrahim PN, Cho H, Spevak W, Zhang C, Zhang Y, Habets G, Burton EA, Wong B, Tsang G, West BL, Powell B, Shellooe R, Marimuthu A, Nguyen H, Zhang KY, Artis DR, Schlessinger J, Su F, Higgins B, Iyer R, D’Andrea K, Koehler A, Stumm M, Lin PS, Lee RJ, Grippo J, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, Chapman PB, Flaherty KT, Xu X, Nathanson KL, Nolop K (2010) Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467(7315):596–599. https://doi.org/10.1038/nature09454

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85. Tevis KM, Colson YL, Grinstaff MW (2017) Embedded spheroids as models of the cancer microenvironment. Adv Biosyst 1(10). https://doi.org/10.1002/adbi.201700083 86. Jeong SY, Lee JH, Shin Y, Chung S, Kuh HJ (2016) Co-culture of tumor spheroids and fibroblasts in a collagen matrix-incorporated microfluidic chip mimics reciprocal activation in solid tumor microenvironment. PLoS One 11(7):e0159013. https://doi.org/10.1371/ journal.pone.0159013 87. Meier F, Nesbit M, Hsu MY, Martin B, Van Belle P, Elder DE, Schaumburg-Lever G, Garbe C, Walz TM, Donatien P, Crombleholme TM, Herlyn M (2000) Human melanoma progression in skin reconstructs: biological significance of bFGF. Am J Pathol 156(1):193–200. https://doi.org/10.1016/ S0002-9440(10)64719-0 88. Berking C, Herlyn M (2001) Human skin reconstruct models: a new application for studies of melanocyte and melanoma biology. Histol Histopathol 16(2):669–674. https://doi. org/10.14670/HH-16.669

Part I 2D Cell Culture-Based Approaches for Studying Melanoma Biology

Chapter 2 Generation of Functional Gene Knockout Melanoma Cell Lines by CRISPR-Cas9 Gene Editing Kristian M. Hargadon, David Z. Bushhouse, Coleman E. Johnson, and Corey J. Williams Abstract Recent advances in the treatment of metastatic melanoma have emerged only from advances in our understanding of melanoma development and progression at the cellular and molecular levels. Despite the impact that such advances have made on the clinical management of this cancer over the last decade, additional insights into factors that promote melanoma progression and therapeutic resistance are needed to combat this disease. CRISPR-Cas9 gene editing technology is a powerful tool for studying gene function in a timely and cost-effective manner, enabling the manipulation of specific DNA sequences via a targeted approach. Herein, we describe a protocol for generating functional gene knockouts in melanoma cell lines by CRISPR-Cas9 gene editing, and we present an example application of this protocol for the successful knockout of the Foxc2 transcription factor-encoding gene in the B16-F1 murine melanoma cell line. Key words CRISPR-Cas9, Gene editing, Melanoma, Gene knockout, B16, FOXC2, Transcription factor, Limiting dilution cloning

1

Introduction Melanoma is the leading cause of skin cancer–related mortality, accounting for over 60,000 annual deaths worldwide [1]. The significance of this cancer as a global public health concern is underscored by the fact that its incidence has increased steadily over the last 40 years [2], a trend that is expected to continue over at least the next decade [3]. Although recent advances in our understanding of melanoma cell biology have led to the development of targeted and immunotherapeutic strategies that have improved the prognosis and clinical outcome for melanoma patients, there are still many patients who do not benefit from these regimens, and of those who do respond, a substantial number ultimately experience disease relapse as a result of tumor-acquired resistance [4–7]. Therefore, it is critical that we gain additional insights into factors that promote melanoma progression and

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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therapeutic resistance so that we may improve current, and develop new, strategies for the treatment of this cancer. A number of technological advances have contributed to our understanding of cancer progression over the last two decades, and they continue to be useful tools for investigating tumor biology. High-throughput DNA and RNA sequencing efforts, such as those that have led to the large datasets publicly available in The Cancer Genome Atlas, have revealed specific driver mutations in various cancers and have highlighted distinct gene expression profiles in normal versus tumor tissue that demonstrate epigenetic dysregulation as a key contributor to tumor progression as well [8]. Gene overexpression and silencing studies in various animal and preclinical models have in turn enabled direct assessment of the role of specific gene products in the development, progression, and therapeutic resistance of cancer. More recently, gene editing technologies have in many ways improved on these methods to understand gene function in cancer cells, allowing the introduction of specific nucleotide sequences at targeted sites within a cancer cell genome or complete knockout of a specific gene’s function (as compared to only the partial and less stable knockdown of gene function often achieved with gene silencing approaches). A number of genome editing technologies have now been developed to study the function of specific genes and gene sequences within various cell types. One of the most widely used genome editing tools is the CRISPR-Cas9 system, which utilizes a specific guide RNA (gRNA) to direct the Cas9 endonuclease to a complementary DNA target sequence for cleavage. Specifically, Cas9 cleaves DNA at a defined site upstream of a protospacer adjacent motif (PAM) sequence found immediately 30 to the gRNA (on the strand opposite to that complemented by the gRNA). Following cleavage, DNA is repaired by double-strand break repair machinery, enabling genome editing to be achieved in one of two ways: (1) homology-directed repair (HDR) that introduces desirable nucleotide sequences (knock-ins) from a homologous repair template (“donor” DNA sequence) or (2) nonhomologous end joining (NHEJ) that rejoins the cleaved DNA sequences while often introducing mutations as a result of random nucleotide insertions/deletions (indels) during the repair process. In cases where indels are introduced in a way that disrupts the reading frame in all alleles of the target gene, a functional gene knockout is effectively introduced [9]. In this chapter, we describe a protocol for the generation of functional gene knockout melanoma cell lines using CRISPR-Cas9 gene editing technology, and we demonstrate in the B16-F1 melanoma model the successful application of this protocol to knock out Foxc2, a forkhead box transcription factor-encoding gene that has been linked to multiple oncogenic pathways in melanoma [10] and whose expression is a poor prognostic factor for survival of melanoma patients treated with dacarbazine or ipilimumab [11].

CRISPR-Cas9 Gene Editing of Melanoma Cell Lines

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Materials Store all media at 4  C in the dark. Store fetal bovine serum (FBS) at 20  C in the dark. Store all other supplies at room temperature. Use only sterile reagents for all procedures.

2.1 Cell Culture Media and Supplies

1. B16-F1 murine melanoma cell line. 2. Complete growth medium: Roswell Park Memorial Institute (RPMI)-1640 medium with L-glutamine supplemented with 10% FBS. 3. Selection medium: Complete growth medium supplemented with 2 μg/ml of puromycin. 4. Freezing medium: Complete growth medium supplemented with 5% dimethyl sulfoxide (DMSO). 5. Trypsin-EDTA Solution (1): 0.25% Trypsin/0.53 mM EDTA in Hanks balanced salt solution without calcium or magnesium. 6. Tissue culture-treated 96-well plates. 7. Tissue culture-treated 24-well plates. 8. Tissue culture-treated 6-well plates. 9. Tissue culture-treated T25 flasks. 10. Nuclease-free, non-pyrogenic serological pipets and pipet tips. 11. Nuclease-free, non-pyrogenic 15 ml conical centrifuge tubes. 12. Nuclease-free, non-pyrogenic microcentrifuge tubes. 13. Tabletop centrifuge. 14. Hemocytometer. 15. Cryovials. 16. Light microscope. 17. Inverted light microscope.

2.2 Transfection Reagents

1. Attractene Transfection Reagent. 2. pSpCas9 BB-2A-Puro (PX459) V2.0 All-In-One plasmid vector (GenScript) encoding Cas9 and appropriately designed gRNA (see Notes 1 and 2) for target gene of interest (Fig. 1). 3. Opti-MEM I Reduced Serum Medium.

2.3

DNA Analysis

1. DNeasy Blood & Tissue Kit (Qiagen): This kit contains all proprietary buffers and the proteinase K reagent necessary for DNA extraction. 2. Endotoxin-free 1 phosphate-buffered saline (PBS), pH 7.4. 3. 100 mg/ml RNase A solution.

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Fig. 1 Strategy for editing the Foxc2 gene in the B16-F1 melanoma cell line using a CRISPR All-In-One plasmid-based genome editing approach. (a) Map of the pSpCas9 BB-2A-Puro (PX459) V2.0 All-In-One plasmid vector encoding Cas9 and a gRNA designed to target the murine Foxc2 gene. In addition to these features, the plasmid carries an ampicillin resistance gene for the selection of transformed bacterial cells and a puromycin resistance gene for the selection of eukaryotic transfectants. (b) Schematic of the functional domains of the murine Foxc2 gene and how the gRNA designed for the example gene editing approach described in this protocol targets a 50 region of the gene encoding the N-terminal forkhead DNA-binding domain (FHD). AD activation domain, ID inhibitory domain. (Portions of this figure are reproduced from Fig. 1 in our previously published article in Cancer Genomics and Proteomics [11] with permission from the International Institute of Anticancer Research)

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4. 100% ethanol. 5. Isopropanol. 6. Heat block. 7. Microcentrifuge. 8. Spectrophotometer. 9. Q5 Hot Start High Fidelity 2 Master Mix. 10. PCR primers (see Note 3). 11. 0.2 ml PCR tubes. 12. Thermal cycler. 13. Nuclease-free water. 14. Agarose. 15. Ethidium bromide (EtBr). 16. Gel Loading Dye, Purple (6). 17. Quick-Load® 100 bp DNA Ladder. 18. TAE Buffer (50): Prepare 1 L of 50 TAE Buffer by dissolving 242 g of Tris-base in ~700 ml of deionized water and then adding 57.1 ml of 100% glacial acetic acid and 100 ml of 0.5 M EDTA (pH 8.0). Adjust the solution to a final volume of 1 L with deionized water. Store at room temperature. Make 1 TAE Running Buffer by diluting 50 stock 1:50 in deionized water. 19. UV light box. 20. Razor blades. 21. Laboratory analytical balance. 22. QIAquick Gel Extraction Kit (Qiagen): This kit contains all proprietary buffers necessary for DNA purification from agarose gel. 23. Parafilm. 24. DNA sequencer for Sanger sequencing. 25. CRISP-ID online tool [12].

3

Methods Carry out all cell culture procedures in a biosafety cabinet to maintain sterility throughout the experiment. All steps following cell lysis may be performed under nonsterile conditions, though it is important to maintain a nuclease-free environment throughout the entire protocol. A summary of the experimental workflow described below is shown in Fig. 2.

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Fig. 2 Summary of the experimental workflow for CRISPR All-In-One plasmid-based genome editing of melanoma cell lines. Melanoma cells are transfected with a CRISPR All-In-One plasmid encoding Cas9 and a gRNA of interest. Polyclonal transfectants undergo puromycin selection, and clonal populations are generated by limiting dilution cloning. DNA is isolated from individual clones and the gene of interest is PCR amplified. PCR amplicons are gel purified and sequenced to screen clones for genome editing at the target site. Protein-based analysis is then performed to validate functional knockout in clones presenting with out-of-frame gene edits in all alleles 3.1 Designing a gRNA for CRISPR-Cas9 Gene Editing

For the protocol described herein, we utilize the pSpCas9 BB-2APuro (PX459) V2.0 All-In-One plasmid vector (GenScript) for delivery of both the Cas9 enzyme and the gRNA targeting the gene of interest. The GenScript sgRNA Design Tool (developed by the Broad Institute of Harvard and MIT and freely available at

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https://www.genscript.com/gRNA-design-tool.html) can be used to design an appropriate gRNA, whose coding sequence can then be engineered by GenScript into the Broad Institute-validated pSpCas9 BB-2A-Puro (PX459) V2.0 All-In-One plasmid. 1. Open the GenScript sgRNA Design Tool. 2. Input a sequence name, and select the desired Target Genome (in this case, Mouse [mm9]). 3. Search the National Center for Biotechnology Information (NCBI) for the gene sequence of interest. 4. Copy and paste into the Sequence box of the sgRNA Design Tool a region of up to 250 nucleotides from a portion of the gene’s protein-coding sequence in which you wish to introduce an edit (see Note 4). 5. Click “Submit.” 6. Retrieve the link that is emailed to the address used to register with GenScript, and click on the link to be taken to a list of potential gRNA sequences targeting the gene region of interest. 7. Choose an appropriate gRNA based on information in the gRNA sequence analysis, which is provided for all gRNAs generated when clicking on any one sequence from the list (see Note 5). 8. Input the desired gRNA sequence into GenScript’s Custom CRISPR Plasmid Ordering form, along with the appropriate vector and species information, to order the All-In-One vector necessary for gene editing experiments (see Note 6). 3.2 Transfection of Melanoma Cells with CRISPR All-In-One Plasmid Vector

1. Harvest B16-F1 cells from culture and resuspend at a concentration of 1.6  105 cells/ml in complete growth medium. 2. Add 500 μl (8.0  104 cells) to separate wells of a 24-well tissue culture plate. Set up three wells: one that will serve as an untransfected control, one that will serve as a mock transfection control, and one that will be transfected with the CRISPR AllIn-One plasmid. 3. Incubate cells overnight at 37  C in a humidified incubator with 5% CO2 (see Note 7). 4. The following morning, add 0.4 μg (4 μl of plasmid at a concentration of 0.1 μg/ml) of pSpCas9 BB-2A-Puro (PX459) V2.0 All-In-One plasmid encoding the gRNA of interest into 56 μl of Opti-MEM I Reduced Serum Medium in a microcentrifuge tube. Mix gently by flicking the tube several times. Set up a separate microcentrifuge tube for the mock transfection group, but simply add 60 μl of Opti-MEM I Reduced Serum Medium to this tube (do not add plasmid).

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5. To the above tubes, add 1.5 μl of Attractene Transfection Reagent, mix gently by flicking the tubes several times, and incubate at room temperature for 15 min to allow formation of transfection complexes. 6. During the 15-min incubation period, remove medium from B16-F1 cultures in the 24-well plate, and wash once with fresh complete growth medium. 7. Discard the wash medium and replace with a fresh 500 μl of complete growth medium. 8. Add the transfection reagent mix with or without plasmid dropwise onto the cells in the appropriate wells, gently swirling the culture plate after each drop to ensure uniform distribution of the transfection complexes. 9. Incubate cells overnight at 37  C in a humidified incubator with 5% CO2. 3.3 Antibiotic Selection of Transfected Cells

1. The following morning, aspirate medium from cultures and add 250 μl of 1 trypsin-EDTA solution. 2. Return culture plate to the 37  C incubator long enough for cells to detach (see Note 8). 3. Following cell detachment (this can be evaluated by observation under an inverted light microscope), neutralize trypsin by adding 1 ml of complete growth medium (see Note 9). 4. Transfer cells to a nuclease-free, non-pyrogenic 15-ml conical tube and centrifuge at 450  g for 5 min. 5. Remove supernatant, resuspend cells in complete growth medium, and count cells using a hemocytometer. 6. Centrifuge cells at 450  g for 5 min, discard supernatant, and resuspend at a concentration of 5.0  104 cells/ml in Selection Medium containing 2 μg/ml of puromycin (see Note 10). 7. Plate 1 ml of cell suspension (5.0  104 cells) into separate wells of a six-well tissue culture plate in order to seed cells at a low density (10% confluence) and culture at 37  C in a humidified incubator with 5% CO2. 8. Replace Selection Medium in cultures daily to remove dead cells, and monitor cells for growth. 9. Once all untransfected and mock-transfected control cells have been killed (see Note 11), remove Selection Medium from plasmid transfectants, replace it with complete growth medium without puromycin (see Note 12), and continue to culture cells at 37  C in a humidified incubator with 5% CO2.

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Fig. 3 Serial dilution scheme for limiting dilution cloning 3.4 Limiting Dilution Cloning of Transfected Melanoma Cells

1. One day after removing transfectants from puromycin selection, remove medium and collect polyclonal cells by trypsinization for limiting dilution cloning (see Note 13). 2. Count cells on a hemocytometer and resuspend in complete growth medium to a concentration of 1.0  106 cells/ml. 3. Perform serial dilutions to achieve a concentration of 1 cell/ 400 μl of complete growth medium (Fig. 3). 4. Plate 200 μl of diluted cells into each well of a 96-well tissue culture plate such that a single cell is deposited into approximately every other well (see Note 14). Incubate cultures at 37  C in a humidified incubator with 5% CO2. 5. Monitor cultures daily under an inverted microscope, taking care to note which wells contain single cells (see Note 15). 6. As clones from single cells expand over time, collect clonal populations and scale up growth by transferring cells to 24-well tissue culture plates. 7. Once clones approach confluence in 24-well plates, collect cells and continue to scale up growth by transferring to T25 flasks. 8. When T25 flasks reach confluence, collect cells and divide into two equal aliquots. Centrifuge cells at 450  g for 5 min and discard supernatant. Resuspend one aliquot in Freezing Medium and store at 80  C for future use. Use the second aliquot for isolation of DNA.

3.5 Isolation of DNA from Melanoma Clones

In addition to isolating DNA from the potentially gene-edited clones, it is important to also isolate DNA from the unedited parental cell line for comparison during DNA sequence analysis. For DNA extraction, the DNeasy Blood & Tissue Kit should be

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used according to the manufacturer’s protocol, with modifications as described below: 1. Resuspend pelleted cells in 200 μl of 1 PBS. 2. Add 20 μl of proteinase K and 4 μl of RNase A, mix by vortexing, and incubate at room temperature for 2 min. 3. Add 200 μl of Buffer AL, mix thoroughly by vortexing, and incubate at 56  C on a heat block for 10 min (see Note 16). 4. Add 200 μl of 100% ethanol and mix thoroughly by vortexing. 5. Pipet the mixture into a DNeasy Mini spin column in a 2-ml collection tube, and centrifuge at 6000  g for 1 min. Discard the flow-through and collection tube (see Note 17). 6. Place the DNeasy Mini spin column in a new 2-ml collection tube, add 500 μl of Buffer AW1, and centrifuge at 6000  g for 1 min. Discard the flow-through and collection tube. 7. Place the DNeasy Mini spin column in a new 2-ml collection tube, add 500 μl of Buffer AW2, and centrifuge at 20,000  g for 3 min. Discard the flow-through and collection tube. 8. Place the DNeasy Mini spin column in a new 1.5-ml microcentrifuge tube with a cap. Pipet 100–200 μl of nuclease-free water (volume will depend on the expected DNA yield) into the spin column, incubate at room temperature for 1 min, and centrifuge at 6000  g for 1 min to elute the DNA (see Note 18). 9. Measure DNA concentration on a spectrophotometer (see Note 19). 3.6 PCR Amplification of DNA of Interest and Purification by Gel Extraction

1. Amplify DNA from the gene of interest by PCR using primers that flank the intended Cas9 cut site where gene editing is expected to occur (see Note 3). (a) For DNA from each clone and parental sample to be analyzed, set up a PCR reaction tube as indicated in Table 1. (b) Load thermal cycler and program the following conditions for the PCR: l

Initial denaturation: 98  C for 30 s.

l

Denature at 98  C for 30 s. Anneal at 68  C for 30 s (see Note 20). Extend at 72  C for 30 s (see Note 21). Repeat for 35 cycles.

l

Final extension: 72  C for 2 min.

l

Hold at 4  C for 1.

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Table 1 PCR setup for amplification of DNA from clonal melanoma cell populations Reagent

Volume

Final concentration

Q5 hot start high Fidelity 2 master mix

25 μl

1

10 μM forward primer

2.5 μl

0.5 μM

10 μM reverse primer

2.5 μl

0.5 μM

Template DNA

x μl

500 ng

Nuclease-free water

x μl Total volume ¼ 50 μl

2. During the PCR, prepare a 1.5% agarose gel containing 0.5 μg/ml of EtBr. 3. Submerge solidified gel in an electrophoresis chamber containing 1 TAE Buffer with 0.5 μg/ml of EtBr. 4. After the PCR, remove 40 μl of PCR product and mix with 8 μl of Gel Loading Dye, Purple (6). 5. Load samples into separate gel wells. Also load one well with 10 μl of 100 bp DNA ladder (or other appropriate ladder based on the expected size of the PCR products). 6. Run the gel at an appropriate voltage for as long as necessary to separate PCR products (see Note 22). 7. Excise PCR products of interest from the gel using a clean razor blade (see Note 23). 8. Zero out an analytical balance with a microcentrifuge tube, and place the slice of agarose containing the DNA band of interest into the tube in order to weigh the agarose slice. The weight of the agarose is necessary to proceed with gel extraction using the QIAquick Gel Extraction Kit, which should be used according to the manufacturer’s protocol with modifications as described here. 9. Add three volumes of Buffer QG to one volume of agarose gel (i.e., 300 μl of Buffer QG for every 100 mg of agarose gel). 10. Completely dissolve the agarose by incubating on a heat block at 50  C for 10 min (see Note 16). 11. Add 1 gel volume of isopropanol to the sample and mix by vortexing. 12. Apply the sample mixture to a QIAquick column that has been placed into a 2-ml collection tube, and centrifuge at 17,900  g for 1 min. Discard the flow-through (see Note 24).

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13. Place the QIAquick column back into the same 2-ml collection tube, add 500 μl of Buffer QG, and centrifuge at 17,900  g for 1 min to remove all traces of agarose. Discard the flowthrough and return column to the same 2-ml collection tube. 14. Add 750 μl of Buffer PE to the QIAquick column, incubate at room temperature for 5 min, and centrifuge at 17,900  g for 1 min to wash the sample. Discard the flow-through and return the column to the same 2-ml collection tube. 15. Centrifuge again at 17,900  g for 1 min to remove residual ethanol that is present in Buffer PE. 16. Transfer the QIAquick column to a new nuclease-free 1.5-ml microcentrifuge tube with a cap. 17. Add 25 μl of nuclease-free water directly to the QIAquick membrane and incubate at room temperature for 5 min. 18. Centrifuge tubes at 17,900  g for 1 min to elute DNA. 19. Quantify DNA concentration on a spectrophotometer. 3.7 DNA Sequencing and Analysis

Sanger sequencing can be performed in-house, or samples can be submitted to a number of companies that provide DNA sequencing services. We use GenScript’s DNA Sequencing Service and provide guidelines below for sample submission, along with sample data from Foxc2 gene sequencing from wild-type B16-F1 and a series of B16-F1-derived gene-edited clones (Fig. 4). 1. Submit purified PCR product at a concentration ranging from 30 to 50 ng/μl in a nuclease-free microcentrifuge tube sealed with parafilm. Include 10 μl of template DNA per sequencing reaction (see Note 25). 2. Submit the forward primer used for PCR amplification at a concentration of 5 μM in a separate microcentrifuge tube sealed with parafilm, and include 10 μl of primer per sequencing reaction (see Note 25). 3. After Sanger sequencing is performed, review the sequencing chromatogram for the unedited wild-type sample and confirm that it matches the NCBI reference sequence for the gene of interest (Fig. 4a). 4. Review the sequencing chromatograms of the potentially geneedited clones, and determine how any indels introduced impact the reading frame of the gene (see Note 26). 5. If sequence chromatograms for any samples reveal overlapping spectra (Fig. 4b–d), deconvolute the overlapping spectra using CRISP-ID [12], a freely available web application that distinguishes from a single sample the sequence of up to three alleles that are too similar in size to be separated by gel electrophoresis (see Note 27).

Fig. 4 Example DNA sequencing chromatograms and CRISP-ID deconvolution/alignment of Foxc2 alleles from wild-type B16-F1 melanoma and gene-edited clones of B16-F1 carrying heterozygous alleles. (a) Sanger sequencing chromatogram of wild-type, parental B16-F1 melanoma cells, showing the region of the Foxc2 gene that was targeted for gene editing in our study. CRISP-ID analysis shows perfect alignment of this sequenced DNA with the NCBI’s reference sequence for the murine Foxc2 gene. (b) An example of a clone in which both Foxc2 alleles were edited at the target site, though with only one allele being edited out-of-frame. (c) An example of a clone in which CRISP-ID deconvolution yields three distinct alleles of the Foxc2 gene (see Note 27). (d) An example of a clone (Clone 43) in which edits in both Foxc2 alleles resulted in the desirable outcome of indels that produce out-of-frame mutations. (Portions of this figure are reproduced from Fig. 1 in our previously published article in Cancer Genomics and Proteomics [11] with permission from the International Institute of Anticancer Research)

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(a) Access the CRISP-ID application at http://crispid. gbiomed.kuleuven.be/. (b) Upload the sequence trace file in either ABI or SCF format. (c) Input the NCBI reference sequence for the gene of interest, using only the sequence of the PCR-amplified region of the gene. (d) Set the “Start position” and “End position” as necessary to exclude terminal ends of the sequence read where there may be less confidence in base calling. (e) Adjust the “Background cutoff (%)” to improve the accuracy of base calling as necessary (i.e., if there is high background signal in the sequencing chromatogram). (f) Input the “Max insert size,” which can be estimated for PCR products from gene-edited clones by comparing their electrophoretic mobility with that of a DNA ladder and the PCR product obtained from the wild-type sample. (g) Click “Analyze” and retrieve the data, which are reported as an alignment of resolved sequences with the reference sequence and which reveal the nature of individual indels from heterozygous alleles that had produced the originally overlapped sequencing chromatogram. Examples of several possible gene editing outcomes as identified by CRISP-ID are shown in Fig. 4. 6. Upon identifying a clone with out-of-frame edits in all alleles for the gene of interest, recover frozen cells of that clone and perform protein analysis by Western blot (Fig. 5), flow cytometry, or ELISA to verify functional knockout of the gene (see Note 28). 7. Use validated knockout as intended in functional studies (Fig. 6).

Fig. 5 Western blot validation of functional Foxc2 gene knockout in a clone of B16-F1 melanoma cells edited as per the protocol described herein. (This figure is reproduced from a portion of Fig. 1 in our previously published article in Cancer Genomics and Proteomics [11] with permission from the International Institute of Anticancer Research)

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Fig. 6 Tumor challenge studies with wild-type versus CRISPR-Cas9 gene-edited, FOXC2-deficient B16-F1 melanoma. C57Bl/6 mice were challenged subcutaneously with 4  105 B16-F1 melanoma cells or B16-F1ΔFOXC2 melanoma cells (generated as described herein) and monitored for tumor growth and progression. Images of representative mice at day 12 postchallenge are shown in (a), and pooled data from three separate experiments, each with 2–4 mice per group, are shown in (b) and (c). The rate of tumor outgrowth was calculated as the size of tumor at the time of death divided by the number of days from the appearance of a measurable tumor to death. Data are graphed as the average of all replicates with error bars that represent standard deviation of the mean. ***p < 0.001. (This figure is reproduced from Fig. 2 in our previously published article in Cancer Genomics and Proteomics [11] with permission from the International Institute of Anticancer Research)

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Notes 1. When designing a gRNA to target a gene of interest for knockout, it is best to identify a targetable region near the 50 end of the gene’s coding sequence, so that any translatable sequence yields only minimal C-terminal portions of the protein. This strategy will reduce the likelihood of translating protein with partial functionality or the ability to still participate in protein– protein or protein–DNA/RNA interactions. In the example highlighted herein, we targeted a region near the 50 end of the murine Foxc2 gene encoding the N-terminal forkhead DNA-binding domain of the FOXC2 transcription factor— the targeted Cas9 cut site was located after the 257th nucleotide of the gene’s 1485-nucleotide-long coding sequence (Fig. 1b). 2. When designing a gRNA to target a gene of interest for knockout, care should also be taken as described in Subheading 3.1 to avoid/minimize off-target effects of the gRNA, so that unintended gene edits are not introduced. 3. PCR primers should be designed to amplify a region of the target gene’s DNA sequence that includes the intended site for gene editing, allowing for the possibility of the introduction of potentially large indels. In the example highlighted herein, we used forward primer 50 -CATGCAGGCGCGTTACTC-30 and reverse primer 50 - ATAGCCCGCATACTGCACTGGTAG -30 to amplify a 1005 bp region spanning nucleotide 1 to nucleotide +1004 of the Foxc2 gene’s coding sequence, in which gene editing was expected to occur near nucleotide +257 of the coding sequence. 4. In the example we describe here, nucleotides 187–396 of the murine Foxc2 gene’s coding sequence (beginning prior to and spanning the 50 region that encodes the N-terminus of FOXC2’s forkhead DNA-binding domain) were used to identify potential Cas9 target sites that would disrupt this key functional domain of the Foxc2 gene. 5. Potential gRNA sequences are color coded for quality, with high-quality gRNAs indicated by green, mid-quality gRNAs indicated by yellow, and low-quality gRNAs indicated by red. Additionally, for all gRNA sequences generated, a quality score that represents the inverse likelihood of off-target binding is provided. Upon selecting a particular gRNA, the number of potential off-target sites is also reported, and this list can be filtered down to include only those sites that are found within exonic regions of genes. It is best to choose a gRNA with a high quality score and as few off-target sites as possible, particularly those within other exons. Should a desirable gRNA contain

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off-target sites within exonic regions, the NCBI reference sequences for such genes are provided, along with their chromosomal locations, so that investigators may perform quality control checks following gene editing to assess whether or not off-target mutations were introduced at these sites. 6. GenScript All-In-One vectors may be ordered as Research Grade, which require bacterial transformation to generate sufficient plasmid for multiple transfections, or as HT Transfection Grade or Industrial Grade, both of which are supplied in larger quantities and may not require scaling up of the plasmid before use. 7. Cells should be ~75% confluent at the time of transfection. Seeding at this density will achieve this level of confluence when working with B16-F1 melanoma cells. 8. The duration of trypsinization will vary by cell type and is dependent on the strength of cell adhesion to the culture plate. It is important not to trypsinize cells longer than necessary, as this enzymatic digestion will eventually damage the integrity of the cell membrane. B16-F1 melanoma cells typically detach within 2–3 min, and those that have not lifted from the plate after this time can be detached easily by gentle pipetting at this time. 9. FBS proteins in the complete growth medium serve as targets for trypsin and prevent over reactivity against cell membrane proteins. 10. The pSpCas9 BB-2A-Puro (PX459) V2.0 All-In-One plasmid carries a puromycin resistance gene (Fig. 1) to allow for selection of transfected cells. The 2 μg/ml dose of puromycin described here is specific for selection against untransfected B16-F1 melanoma cells. The dose of puromycin to use for selection must be empirically determined for different cell types by generating a dose–response curve following cell exposure to various dilutions of the antibiotic. The lowest dose that effectively kills 100% of untransfected cells should be used for selection of transfectants. 11. For B16-F1 melanoma cells, all control cells are typically killed within 3 days of puromycin selection. 12. Once all control cells have been killed by puromycin, antibiotic selection is no longer needed in the plasmid transfectants, which should be growing at this stage as tiny patches of cells. Maintenance of puromycin selection at this stage runs the risk of promoting plasmid integration into the host cell genome. Not only would such integration have unintended consequences on the host cell genome, it might also result in longterm expression of the puromycin resistance gene as well as the S. pyogenes Cas9 enzyme, which could in turn inadvertently

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result in the presentation of foreign antigen on the melanoma cell surface and lead to altered immune reactivity against the melanoma in in vivo settings. 13. It is best to collect the patches of plasmid transfectants before they become too overgrown. Each patch of cells typically represents a successful transfectant that grew into a small colony of cells, meaning that gene editing is likely identical in all cells in a given patch. Because the introduction of indels is random, many edits will not result in a functional knockout (i.e., small in-frame edits may yield a slightly truncated or elongated, but possibly still functional, protein), and if the patches of cells become too overgrown, there may be many cells arising from the same transfectant that all carry the same undesirable edit. Therefore, collecting cells after colonies have grown too large will most likely increase the number of clones that must be screened after limiting dilution cloning in order to identify one with the desirable knockout-inducing edit, making this process unnecessarily laborious. On the other hand, collecting the transfectants early means that more diverse clones are likely to be plated, thereby increasing the likelihood of identifying one with an edit that will yield a functional knockout. 14. Plating cells in this way will necessarily result in some wells receiving no cells but will also reduce the likelihood of depositing more than one cell into a given well. The number of plates loaded will likely depend on the melanoma cell line being used, as some cell lines are more easily cloned by limiting dilution than others. B16-F1 grows relatively well from single cells, and we typically generate 25–30 clones per 96-well plate. The growth and survival of other melanoma cell lines may depend more heavily on cell–cell contact, meaning that clones are less easily generated from single cells. For these cell lines, several plates must be used to generate a sufficient number of clones for analysis. 15. In many cases, it will be possible to visualize single cells that have adhered to the tissue culture plate. In some instances, though, cells may not be detected until a small colony forms, but it is important to identify these wells as soon as possible after plating. Effort should be made to avoid collecting cells from wells where two or more cells were originally deposited, as these wells will not yield clonal populations. Therefore, identifying wells with individual cells or single, small colonies early will reduce the unwanted outcome later of finding a well with a large patch of cells that could represent two or more colonies that have grown together.

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16. It is recommended to add some water to the wells of the heat block in order to provide a more uniform temperature when heating the samples. 17. The DNA is trapped in the DNeasy Mini spin column at this stage and is subsequently washed to remove impurities prior to elution. 18. Though the kit comes with an elution buffer (Buffer AE), we prefer to elute DNA with nuclease-free water in order to avoid any potential impacts that Buffer AE components may have on downstream reactions in the workflow. 19. A260:280 values should ideally be 1.8, as this value indicates high DNA purity (minimal protein contamination) in the prep. We typically observe A260:280 values in the 1.9–2.0 range using this protocol on B16-F1 melanoma cells. 20. This annealing temperature is optimal for the primers used in our example for Foxc2 DNA amplification described herein but will likely vary for other primer sets. We recommend utilizing the New England BioLabs Tm Calculator v1.12.0, a freely accessible web-based tool (https://tmcalculator.neb.com/#!/ main) that calculates an optimal annealing temperature for the PCR based on user-generated information on the PCR Master Mix, primer concentration, and primer sequences being used. 21. The duration of the extension stage may need to be altered depending on the length of the PCR amplicon. A 20- to 30-s extension period is recommended per kb of amplicon. 22. The gel should be removed from the electrophoresis chamber and monitored periodically to evaluate the size of bands. Though there is an expected product size in wild-type parental cells, the product sizes of the potentially edited clones may vary significantly based on the size of the indels introduced, which we have observed ranging from a single nucleotide to several hundred nucleotides. 23. While only a single PCR product should be generated in the wild-type parental cell line, clones generated from CRISPR AllIn-One plasmid transfectants may yield one or multiple PCR amplicons. In some cases, even though cells are transfected and survive puromycin selection, no gene editing will occur, resulting in a PCR amplicon identical to that in wild-type cells. If only small indels are introduced in a gene-edited clone, the PCR product may run at the same speed as that from unedited parental cells. In these cases, one visible indication that an edit (s) has been introduced is the appearance of multiple faint bands that exhibit shifts in electrophoretic mobility as a result of heteroduplex formation between PCR products that are complementary in portions of their sequence but mismatched at a region where differential gene editing has occurred in

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Fig. 7 Example of heteroduplex formation and the resulting shift in electrophoretic mobility that occurs when heterozygous alleles with partial complementarity anneal with one another after gene editing and PCR amplification. Note for Clone 12 the presence of multiple bands, three of which were purified for Sanger sequencing following PCR amplification of the region of interest from the Foxc2 gene. The top band represents an unedited allele and aligns perfectly with the sequence obtained from wild-type, parental B16-F1 melanoma cells. The bottom band represents a PCR-amplified fragment of the Foxc2 gene with a 628 bp deletion. The middle band that was sequenced produced an overlapping chromatogram. Deconvolution of this chromatogram by CRISP-ID revealed this band to be two distinct alleles, one equivalent to the wild-type allele and one equivalent to the allele carrying the 628 bp deletion

different alleles. If editing results in two alleles of significantly different size, these heteroduplexes are readily apparent (Fig. 7). 24. The DNA is trapped in the QIAquick column at this stage and is subsequently washed to remove impurities prior to elution. 25. It is best to submit enough DNA and forward primer for two sequencing reactions per sample in case any technical issues arise and a repeat of the sequencing is required. 26. Indels that do not occur in multiples of 3 are desirable, as these will completely alter translation of any transcribed RNA from the point of editing. Indels that occur in multiples of 3, on the other hand, will ultimately lead to restoration of the reading frame, meaning that proper translation will be restored following addition/deletion of some number of amino acids. In such a case, the resulting protein may retain some level of functionality.

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27. Though counterintuitive, there are two ways by which one might obtain three different alleles from a population of cells that is diploid. The first way involves a technical issue with limiting dilution cloning, such that two cells are plated unknowingly into a single well, with one having two uniquely edited alleles and the other being unedited or with each cell having single alleles edited in different ways (either outcome yields two uniquely edited alleles and one unedited allele in the resulting population, which is not clonal). Alternatively, this outcome might also arise if gene editing is not complete at the time of limiting dilution cloning but instead continues after division of a single plated cell. In this scenario, a single allele is edited in one cell at the time of cloning, and upon cell division, one daughter cell is edited in a different way at the second allele while the other daughter cell either remains unedited at its second allele or undergoes unique editing at its second allele as well (Fig. 4c). 28. Be sure to use an antibody specific for a C-terminal region of the protein that should no longer be encoded by the edited gene. Antibodies that recognize linear or conformational epitopes of N-terminal regions of a protein encoded by DNA sequences prior to the site of gene editing will still bind to any partially translated amino acid sequences.

Acknowledgments This work was supported by funding from Virginia’s Commonwealth Health Research Board, a Jeffress Trust Awards Program in Interdisciplinary Research Grant from the Thomas F. and Kate Miller Jeffress Memorial Trust (Bank of America, N.A., Trustee), a Virginia Foundation for Independent Colleges (VFIC) Maurice L. Mednick Memorial Fellowship, and a Hampden-Sydney College Research Grant from the Arthur Vining Davis endowment (to KMH). This work was also supported by a VFIC Undergraduate Science Research Fellowship to CJW and a Virginia Academy of Science Undergraduate Research Grant to DZB. We also thank Mr. Michael Hargadon and Mrs. Patricia Hargadon for generous donations to support the involvement of Hampden-Sydney College undergraduate students in this research. References 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424

2. Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotta A, Lewis DR, Chen HS, CK FEJ (2020) SEER cancer statistics review, 1975–2017. National Cancer Institute,

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Bethesda, MD, https://seer.cancer.gove/csr/ 1975_2017/, based on November 2019 Seer data submission, posted to the SEER web site, April 2020 3. Whiteman DC, Green AC, Olsen CM (2016) The growing burden of invasive melanoma: projections of incidence rates and numbers of new cases in six susceptible populations through 2031. J Invest Dermatol 136:1161–1171 4. Amann VC, Ramelyte E, Thurneysen S, Pitocco R, Bentele-Jaberg N, Goldiner SM, Dummer R, Mangana J (2017) Developments in targeted therapy in melanoma. Eur J Surg Oncol 43:581–593 5. Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, Sileni VC, Schachter J, Garbe C, Bondarenko I, Gogas H, Mandala´ M, Haanen JBAG, Lebbe´ C, Mackiewicz A, Rutkowski P, Nathan PD, Ribas A, Davies MA, Flaherty KT, Burgess P, Tan M, Gasal E, Voi M, Schadendorf D, Long GV (2019) Five-year outcomes with dabrafenib plus trametinib in metastatic melanoma. N Engl J Med 381:626–636 6. Hargadon KM, Johnson CE, Williams CJ (2018) Immune checkpoint blockade therapy for cancer: an overview of FDA-approved immune checkpoint inhibitors. Int Immunopharmacol 62:29–39 7. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Lao CD, Cowey CL, Schadendorf D, Wagstaff J, Dummer R,

Ferrucci PF, Smylie M, Hogg D, Hill A, Ma´rquez-Rodas I, Haanen J, Guidoboni M, Maio M, Scho¨ffski P, Carlino MS, Lebbe´ C, McArthur G, Ascierto PA, Daniels GA, Long GV, Bastholt L, Rizzo JI, Balogh A, Moshyk A, Hodi FS, Wolchok JD (2019) Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med 381:1535–1546 8. Lee JS (2016) Exploring cancer genomic data from the cancer genome atlas project. BMB Rep 49:607–611 9. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339:819–823 10. Hargadon KM, Williams CJ (2020) RNA-seq analysis of wild-type vs. FOXC2-deficient melanoma cells reveals a role for the FOXC2 transcription factor in the regulation of multiple oncogenic pathways. Front Oncol 10:267 11. Hargadon KM, Gyo¨rffy B, Strong EW, Tarnai BD, Thompson JC, Bushhouse DZ, Johnson CO, Williams CJ (2019) The FOXC2 transcription factor promotes melanoma outgrowth and regulates expression of genes associated with drug resistance and interferon responsiveness. Cancer Genomics Proteomics 16:491–503 12. Dehairs J, Talebi A, Cherifi Y, Swinnen JV (2016) CRISP-ID: decoding CRISPR mediated indels by sanger sequencing. Sci Rep 6:28973

Chapter 3 A Fluorescent Gelatin Degradation Assay to Study Melanoma Breakdown of Extracellular Matrix Ewa Mazurkiewicz, Ewa Mro´wczyn´ska, Aleksandra Simiczyjew, Dorota Nowak, and Antonina J. Mazur Abstract In order to protrude within a dense tissue, tumor cells have to develop the ability to digest the extracellular matrix (ECM). Melanoma cells, similarly to other types of tumor cells, form invadopodia, membranous invaginations rich in filamentous actin and several other proteins including matrix metalloproteinases (MMPs). MMPs degrade ECM structural proteins such as collagens, fibronectin, or laminin. Here we describe an assay that allows the detection of gelatinase activity exhibited by tumor cells under 2D conditions and methods to present obtained data in both a quantitative and a qualitative manner. Key words Melanoma, Invadopodia, Invasion, Gelatin-fluorescein degradation assay, Extracellular matrix (ECM) degradation, Matrix metalloproteinases (MMPs)

1

Introduction According to Clark’s model, transformed melanocytes during tumor progression undergo transition from radial growth phase (RPG), characterized by proliferation and radial spreading over the basement membrane, to vertical growth phase (VPG), when the cells gain the potential to invade the dermis [1]. During this process, melanoma cells have to acquire the ability to digest extracellular matrix (ECM) in order to transverse the basement membrane. ECM degradation is further needed to protrude through skin tissue toward other skin areas and blood vessels to form metastases. The protrusions in tumor cells participating in ECM breakdown are invadopodia [2]. These structures are membranous invaginations, for which actin polymerization, orchestrated by a few actin binding proteins, is crucial. Within the invadopodium there are present several signaling, scaffolding, and adhesion proteins as well as matrix metalloproteinases (MMPs). MMPs are divided into five main groups according to their substrate activity

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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and localization, i.e., collagenases (e.g., MMP-1), gelatinases (MMP-2 and MMP-9), stromelysins (e.g., MMP-3), matrilysins (MMP-7), and membrane-bound enzymes (e.g., MT1-MMP/ MMP-14) [3]. In melanoma cells, several MMPs are expressed, and among them are MMP-2 and MMP-9, both gelatinases that cleave gelatin, a few types of collagen (including type IV), and fibronectin [3, 4]. The basement membrane of skin is structurally composed mainly of collagen type IV, laminin, nidogen/enactin, perlecan, and heparin sulfate proteoglycan [5]. As far as we know, Galis and her colleagues described for the first time an assay employing fluorescein-labeled gelatin coating of glass slides to detect the MMP activity of vascular tissue sections [6]. The assay described here was developed by Artym and her colleagues and was applied to study the dynamics of invadopodia formation in the breast cancer MDA-MB-231 cell line [7]. On a thin layer of fluorescently labeled gelatin (denatured collagen [8, 9]), cells are seeded and in a given time one can observe dark spots/areas by fluorescence microscopy, which are positions of active invadopodia digesting fluorescently labeled gelatin. We routinely use this gelatin-fluorescein digestion assay in our research, as is reflected in several papers published by our group. The majority of these studies focused on melanoma cell biology [10–14], though some were done on breast cancer cells [15, 16] and colon adenocarcinoma cells [17]. A good method to confirm the presence of gelatinase activity in studied cells is also gelatin zymography [11, 12, 14].

2

Materials

2.1 Preparation of Fluorescein-Labeled Gelatin-Coated Coverslips

1. Glass coverslips of 12 mm diameter coated with poly-L-lysine. 2. 0.2% (w/v) gelatin from pig skin, fluorescein conjugate lyophilized from PBS (see Note 1). 3. Sterile phosphate-buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4. Store at 4
C. 4. 0.5% glutaraldehyde solution: Dilute a 25% stock solution to 0.5% in PBS and sterilize using a syringe filter. Store at 4
C. 5. Sodium borohydride solution: Freshly prepare a 5 mg/ml solution in PBS and sterilize using a syringe filter (see Note 2). 6. Parafilm (see Note 3). 7. Lignin (see Note 3). 8. A needle and tweezers (see Note 3). 9. 24-well plate for cell culture. 10. Syringe filter.

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Cell Seeding

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1. Appropriate cell culture medium (here we used DMEM with high glucose (4.5 g/l) and low NaHCO3 (1.5 g/l) supplemented with 10% (v/v) fetal bovine serum (FBS), 1% (v/v) glutamine, 1% (v/v) antibiotic antimycotic). 2. Cells of interest (here we used human melanoma cell line A375). 3. 0.25% (w/v) trypsin/0.05% (w/v) EDTA solution. 4. Automated cell counter, e.g., the EVE™ Automated Cell Counter (NanoEnTek), or the Bu¨rker chamber. 5. T25 Flasks. 6. Water bath. 7. Inverted microscope. 8. Centrifuge suitable for cell culture.

2.3

Cell Staining

1. Non-sterile PBS. Store at 4
C (see Note 4). 2. 4% formaldehyde in PBS: Prepare the solution in the chemical hood. Add 500 ml of deionized water to a glass beaker and heat to 60
C with simultaneous stirring. Weigh 24 g of paraformaldehyde powder and add to the heated water. Raise the pH by adding a drop of 5 M NaOH to facilitate dissolving of paraformaldehyde. After dissolving and cooling the solution, add 60 ml of 10 concentrated PBS and adjust the pH to 7.0. Adjust the final volume to 600 ml with deionized water. Aliquot the solution into 50 ml tubes and store at 20
C. 3. Hoechst 33342. 4. Alexa Fluor™ 568-labeled phalloidin. 5. Dako Mounting Medium. 6. Glass microscope slides. 7. Staining chamber (Fig. 1a).

3

Methods

3.1 Preparation of Fluorescein-Labeled Gelatin-Coated Coverslips (See Note 5)

1. Using tweezers, place sterile coverslips coated with poly-Llysine with the coating on top in the wells of a 24-well plate (Fig. 1b). 2. Carefully wash the coverslips with 0.5 ml of cold PBS per well (Fig. 1c). 3. Remove cold PBS. To fix poly-L-lysine, add 0.5% glutaraldehyde solution in PBS and incubate the coverslips for 15 min at room temperature (Fig. 1c) (see Note 6). 4. Following incubation, carefully wash the coverslips with 0.5 ml of PBS (Fig. 1c).

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Fig. 1 Photographical demonstration of the individual steps of preparing gelatin-fluorescein-coated coverslips (b–k) and the cell staining procedure (a, l). (a) The staining chamber for six 12 mm coverslips. (b) Transfer of a coverslip to a well of a 24-well plate. (c) Washing of the coverslip with PBS. (d) Drops of gelatin-fluorescein on a piece of parafilm. (e) Transfer of the coverslip with a needle and tweezers from the 24-well plate onto the parafilm piece. (f) Placing of the coverslip with its edge on a piece of lignin to remove solution from it. (g) Lying of the coverslip on a drop of gelatin-fluorescein. (h) Inversion of the coverslip on the parafilm. (i) Dropping of sodium borohydride solution on the coverslip. (j) Transfer of the coverslip with the needle and tweezers from the parafilm to a well of the 24-well plate containing PBS. (k) Washing of the coverslip with the cell culture medium. (l) The way the coverslips are mounted on a microscope glass. All four coverslips should be placed in the middle of the glass slide. Otherwise, there could be difficulties in microscopic imaging due to the layout of the slide holder.

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5. Cut two pieces of parafilm in a size corresponding to the number of coverslips and place them on the bench top. Drop 30 μl/per coverslip of 0.2% (w/v) gelatin-fluorescein solution in PBS on one piece of the parafilm (Fig. 1d) (see Note 1). 6. Remove the solution by placing the coverslips with the edge on a piece of lignin using a needle and tweezers (Fig. 1e, f). Be careful not to touch the coated layer. 7. Place the coverslips immediately on a drop of gelatinfluorescein with the poly-L-lysine-coated and glutaraldehydetreated side down (Fig. 1g). Incubate the coverslips for 10 min at room temperature (see Notes 7 and 8). 8. Invert the coverslips and place them onto the second parafilm sheet using the needle and tweezers (Fig. 1h) and drop 150 μl of sodium borohydride solution onto the side coated with gelatin-fluorescein (Fig. 1i). This reagent quenches residual glutaraldehyde. Incubate the coverslips at room temperature for 1 min (see Note 6). 9. Remove the solution by placing the coverslips with the edge on a piece of lignin and transfer them next into 0.5 ml of fresh PBS in the wells of a 24-well plate (Fig. 1j). Be careful not to touch the coated layer of the coverslip (see Note 9). 3.2 Cell Seeding (See Note 5)

1. Warm cell culture medium and trypsin/EDTA solution to 37
C using a water bath. 2. Detach the cells from a T25 flask using trypsin solution: (a) Carefully aspirate and discard the cell culture medium. (b) Remove any remaining cell culture medium by washing the flask with attached cells with 1 ml of trypsin solution. Aspirate and discard the liquid. (c) Again add 1 ml of trypsin solution to the flask and incubate the cells at 37
C until they detach. Carefully monitor cell detachment using an inverted microscope. (d) Following cell detachment, stop the trypsin activity by adding 3 ml of cell culture medium and rinse the surface of the culture flask with it (see Note 10). (e) Transfer the cell suspension into a 15-ml centrifuge tube. 3. Centrifuge the cells at 100  g for 5 min at room temperature. Following centrifugation, discard the supernatant and resuspend the pellet in 1 ml of warmed fresh cell culture medium. 4. Count the cells with a suitable method, e.g., using an automated cell counter or Bu¨rker’s chamber. 5. Carefully wash the coverslips with 0.5 ml of warmed cell culture medium (Fig. 1k).

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6. Seed, e.g., 30,000 cells per well onto the side of the coverslip coated with gelatin-fluorescein. The final volume of the cell culture medium containing cells should be 0.5 ml. The cell number depends on the cell line being used and needs to be empirically determined. 7. Culture the cells at 37
C in 5% CO2 for 6–24 h (see Note 11). 3.3 Cell Staining (See Notes 12 and 13) and Fluorescence Imaging

1. Gently aspirate and discard the cell culture medium. 2. Carefully wash the coverslips three times with PBS (see Note 4). 3. Fix the cells by adding 0.5 ml of 4% formaldehyde solution to each well and incubate them for 20 min. Perform this step in the chemical hood (see Note 6). 4. Carefully wash the coverslips three times with PBS (see Note 14). 5. Prepare staining solution composed of 5 ng/ml of Hoechst 33342 and 2 U/ml of Alexa Fluor™ 568-labeled phalloidin in PBS. Protect the mixture from light to avoid photobleaching (see Note 15). 6. Place the coverslips in the staining chamber (Fig. 1a) with the cells facing upwards using the needle and tweezers. Then quickly overlay every coverslip with 30 μl of staining solution and incubate it for 1 h in the dark (see Note 16). 7. 7. Transfer the coverslips to the 24-well plate filled with PBS. 8. Carefully wash the coverslips with PBS for 5 min, three times. This step is required to remove nonspecifically attached fluorescent dye particles. 8. Wash the coverslips once with deionized water. 9. Following washings, transfer the coverslips with help of the needle and tweezers and mount them on glass slides using Dako Mounting Medium with the cells facing downward. On one glass slide, four coverslips can be centrally mounted (Fig. 1l). 10. Allow the Dako Mounting Medium to dry. (a) If acquisition of photos is planned for the same day, leave the glass slides with mounted coverslips at room temperature in darkness for about 2 h. (b) Otherwise, place the microscope slides on a tray at 4
C. Glass slides can be stored for several days at 4
C in the dark. However, leaving the coverslips for longer than 1 week may result in fluorescent dyes diffusing out from the cells.

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11. Image the microscope slides with mounted coverslips with confocal microscopy (see Notes 17–20). 12. Using 60/63 or 100 oil objective, capture photos of at least 30 individual cells per analyzed group. It is important to take photos of the cells randomly, from different areas of the coverslip to avoid bias (see Note 21). 3.4 Quantification of Invadopodia Formation

Analysis of invadopodia formation includes (1) the percentage of cells forming invadopodia as well as (2) the mean number of invadopodia per cell.

3.4.1 Calculating the Percentage of Cells Able to Form Invadopodia

Based on images showing phalloidin staining, one can calculate the percentage of cells forming invadopodia. It is a simple analysis to perform but may be very informative for the research. 1. Open the images one by one in any Image Viewer and verify visually if the cells formed invadopodia (small F-actin foci usually near the cell nucleus, Figs. 2c and 3a). 2. Transfer this observation to Microsoft Excel spreadsheet (if using Microsoft Office) as “1” if the cell formed at least one invadopodium, and “0” if the cell did not form any invadopodium. 3. Using the formula, sum up the number of cells which formed invadopodia and report the results as the percentage of the cells forming invadopodia (see Note 22).

3.4.2 Calculating the Number of Invadopodia per Cell

Two kinds of invadopodia can be observed in the cells, and their final number may differ depending on which ones are used for analysis. One may count only active invadopodia associated with gelatin-fluorescein degradation (colocalizing with dark areas) or only non-active invadopodia. Results may also be reported as the total number of invadopodia per cell [18, 19]. Due to the staining with fluorescently labeled phalloidin which detects all F-actin structures like stress fibers, it is better to quantify manually the number of invadopodia (small F-actin foci usually near the cell nucleus, Figs. 2c and 3a). Using automatic methods would result in difficulties and lack of credibility. 1. Download and install Fiji (Fiji Is Just ImageJ) application [20] from the website https://imagej.net/Fiji (we use version 1.52p of the software). 2. Open Fiji and then load the image of phalloidin-stained cell. 3. Using the “Multi-point” tool , mark each invadopodium (degrading/non-degrading or both). Each selected point will be numbered (see Note 23). 4. Calculate the mean number of invadopodia per cell.

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Fig. 2 Representative examples of A375 melanoma cells’ gelatinase activity detected with the gelatin degradation assay. (a) Photos of fixed cells after 6, 12, and 24 h of incubation on the gelatin-fluorescein coated coverslips. The cells were stained with phalloidin-Alexa Fluor™ 568 and Hoechst 33342 to detect filamentous (F) actin and cell nuclei. Images were obtained using a Leica TCS SP8 Confocal Laser Scanning Microscope equipped with 63 oil objective. The microphotographs (apart from merged images) are shown as negatives to present better details. The red arrow points at a cell without proteolytic activity, pink arrows point at cells exhibiting proteolytic activity, and blue arrows highlight proteolytically degraded gelatin-fluorescein. (b) 3D stack of a cell incubated for 12 h on a gelatin-fluorescein layer and fixed/stained as described above. Enlargement of the cell area containing invadopodia (marked with the white rectangle in panel b) is shown in panel c. The yellow arrow indicates an invadopodium presented in three axes.

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Fig. 3 A scheme showing the main steps of quantifying invadopodia and cell areas using the Fiji software. (a) Original image presenting fluorescently labeled gelatin layer. Degraded areas of gelatin layer are seen as dark spots. (a0 ) Alternative method for measurement of particle size using the “Freehand selection” tool. (a00 )

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5. Alternatively, count the number of invadopodia in relation to gelatin-fluorescein degradation and the total number of invadopodia separately, and then calculate active invadopodia as a percentage of total invadopodia. 3.5 Quantification of Invadopodia Activity

Invadopodia activity is measured based on the degraded area in the gelatin-fluorescein layer and can be reported in different ways, e.g., (1) percentage of the cells associated with gelatin digestion, (2) mean digested area per cell, and (3) gelatin-digested area per area of a single cell.

3.5.1 Calculating the Percentage of Cells Associated with Gelatin Digestion

Following the protocol described in Subheading 3.4.1, one may also quantify the cells associated with gelatin digestion. 1. Open the images and verify visually if there are any gelatinfluorescein degraded areas within the cell. 2. Transfer this information to Excel spreadsheet as “1” if the cell digested gelatin and “0” if the cell did not exhibit any gelatinfree areas. 3. Use the formula to sum up the number of cells which are positive for gelatin-fluorescein degradation (“1”) and report the results as the percentage of the cells capable of degrading the gelatin layer (see Note 22).

3.5.2 Calculating the Mean Gelatin-Digested Area per Cell

1. Open Fiji software and load an image showing digestion of gelatin-fluorescein (Fig. 3a). 2. Set the scale to show the results in micrometers rather than pixels (see Note 24). 3. Click /Analyze ! Set scale/ in Fiji menu, fill the gaps in the “Set scale” window (distance in pixels, known distance, unit of length—μm), select “Global” and confirm settings with OK (see Note 25). You can also set the scale based on the scale bar present on the analyzed image. Using the “Straight line” tool in Fiji, draw a line between the start and end points of the scale bar and then go to /Analyze ! Set scale/. Fill the “Known distance” (read from the scale bar) and “Unit of length,” select “Global” and click OK. “Distance in pixels” should then be filled automatically by Fiji (see Note 26).

ä Fig. 3 (continued) Original image presenting a cell stained with fluorescently labeled phalloidin growing on the gelatin-fluorescein layer. (b, b0 )—(a and a00 ) images converted to the 8-bit format. (c, c00 ) Threshold of both degraded area of gelatin layer and cell area, respectively. (c0 ) Threshold window showing exemplary settings for analyzing gelatin-fluorescein-free areas. (d, d00 ) Outcomes of the “Analyze Particles” command. Images showing areas of interests (red overlay) measured automatically by Fiji. Colorful rectangles presenting the results of the automatic measurements: area of gelatin-fluorescein degradation by the cell and the single cell area. (d0 ) “Analyze Particles” window settings.

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4. Go to /Analyze ! Set measurements/ and select “Area” and “Limit to threshold” and next click OK. 5. Change the format of the image to 8-bit by going to the / Image ! Type ! 8-bit/ command (Fig. 3b). 6. Open the threshold setting window: /Image ! Adjust ! Threshold/ and adjust the settings of threshold matching to the gelatin-digested areas presented at the original fluorescein-stained image, select “B&W” and “Dark background.” Click “Apply” to confirm settings (Fig. 3c, c0 , c00 ). 7. Measure the area of threshold by going to /Analyze ! Analyze Particles/ and selecting “Display results,” “Clear results,” “Summarize,” “Include holes,” and “In situ Show.” Click OK (Fig. 3d, d0 ) (see Notes 27–29). 8. Copy “Total Area” data from the “Summary” table (Fig. 3d) to the Excel spreadsheet. 9. Measure gelatin-degraded area for all analyzed cells. 10. Calculate mean gelatin-digested area per cell by dividing the sum of digested areas by the number of cells analyzed. 3.5.3 Calculating the Gelatin-Digested Area per Area of a Single Cell

In some cases, i.e., comparison of different cell lines or treatment of cells with drugs, etc., there might be some differences in spreading level and thus the area size of the cells. For this reason, one can calculate and show the results as gelatin-digested area per area of a single cell. The procedure for this analysis is identical to that described in steps 1–9 of Subheading 3.5.2, followed by modifications. 1. Using the same protocol for the measurement of digested area, follow steps 1–9 of Subheading 3.5.2 to calculate the cell surface area. In this case, though, use images with stained F-actin detected, and adjust the threshold to the border of the cell (Fig. 3a00 , b0 , c00 , d00 ). 2. Be sure to select “Include holes” in the “Analyze Particles” window for correct measurement of the cell area (red stars in Fig. 3d0 , d00 ) (see Note 28). 3. Take the “gelatin-digested area” data for each cell and divide it per area of a single cell. 4. Display results as the percentage or fraction of gelatin-digested area per area of a single cell.

3.5.4 Gelatin Degradation in Third Dimension

The depth of invadopodia embedded in the gelatin-fluorescein layer or the depth of fluorescein-labeled gelatin degradation is also noteworthy parameters to analyze. It may happen that degraded areas are not visible as black puncta but only seem to be less intense than the rest of the gelatin-fluorescein layer, although

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the gelatin-degraded area can be similar. The reason for this is an incomplete degradation of gelatin in the third dimension (z-axis). Analysis of the gelatin-degraded area in the third dimension is possible by using a Z-stack scanning (Fig. 2b, c) and measuring the depth of the degraded gelatin-fluorescein layer or the “height” of invadopodium by referring to the scale bar.

4

Notes 1. 5 mg of lyophilized gelatin-fluorescein in PBS should be dissolved in 2.5 ml of sterile deionized water (0.2% w/v) and suspended very well by vortexing or sonication. Upon dissolving, solution should be aliquoted and stored at 20
C. After thawing gelatin-fluorescein solution, it should again be wellsuspended by pipetting or vortexing in order to avoid the presence of clumps, which could give an uneven coating. 2. Leave the tube with the sodium borohydride solution open to allow evaporation of forming hydrogen gas. Upon preparation of solution, it should be immediately used while the solution forms bubbles. 3. Parafilm, lignin, a needle, and tweezers need to be sterilized before starting the procedure by using a UV lamp for 30 min in the laminar flow cabinet. Alternatively, a needle and tweezers can be sterilized in an oven at 180
C for 4 h. 4. For immunocytochemical staining, PBS does not have to be sterile. However, before use, make sure that PBS is fresh and does not become cloudy (which otherwise implicates contamination). 5. All steps of the experiment (Subheadings 3.1 and 3.2) must be performed under sterile conditions using a laminar flow cabinet to avoid any contamination. Also, whenever a solution is poured into a well containing the coated coverslip with and without attached cells, it should be added to the side of the well in order not to damage the coating and the cells. 6. Handle and dispose of any solutions hazardous to health and the environment (e.g., solutions containing glutaraldehyde, sodium borohydride, or formaldehyde) according to the Institutional Environmental Hazards Safety regulations. 7. To prevent potential photobleaching, the coverslips should be protected from light at every step of the procedure by switching off the light in the laminar flow cabinet during the incubation. 8. The gelatin-fluorescein solution can be reused many times. Collect the liquid from the parafilm and immediately store at 20
C.

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9. Our experience shows that gelatin-coated coverslips can be stored at 4
C for 24 h. However, others have shown that gelatin-coated coverslips can be stored for up to 2 weeks in a solution containing antibiotic [21]. 10. FBS contains α1-antitrypsin and α2-macroglobulin, which act as an inhibitor of trypsin activity. 11. The incubation time should be empirically determined, because it is dependent on the ability of cells to digest the extracellular matrix as well as the migration capacity of the cell line. If the incubation time is too short, cells may not have enough time to secrete matrix-degrading enzymes (Fig. 2a, red arrow), while incubation time that is too long results in cells moving away from the gelatin-fluorescein degraded area (Fig. 2a, blue arrows). Therefore, if the cell is absent, we cannot be sure that the degradation area is caused by one cell, and it is impossible to assign invadopodia. For A375 cells in our experiments, the optimal incubation time on gelatin-fluorescein-coated coverslips is 12 h (Fig. 2a). 12. At each step the coverslips should be protected from light using a cardboard cover box during the staining procedure. 13. All steps of the procedure should be performed at room temperature. 14. Fixed coverslips can be stored at 4
C for up to 1 week before staining. However, the quality of the staining may be affected by the storage of the samples. Therefore, it is best to take photos of stained cells within a few days upon mounting the coverslips on the microscope slides. 15. Instead of phalloidin staining as a means to detect invadopodia, one can also use antibodies recognizing other constituents of invadopodia, e.g., cortactin [7], which is present in invadopodia of melanoma cells [11–14]. However, if one would like to detect cortactin or other protein, the staining protocol should be appropriately adjusted and solutions to permeabilize the cells and block the unspecific interactions of antibodies should be used [13]. 16. The staining chamber is a plastic Petri dish of 10 cm diameter, which has 1.5 ml microcentrifuge tube caps glued inside (Fig. 1a). Alternatively, one can stain the cells by adding 200 μl of staining solution to each well of a 24-well plate. However, the staining chamber enables the use of much less of the fluorescent dyes. 17. We use a Leica TCS SP8 Confocal Laser Scanning Microscope and Leica Application Suite X (LAS X). Because we use phalloidin conjugated with Alexa Fluor™ 568 dye, F-actin is detected with fluorescent diode of 568 excitation wavelength.

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Invadopodia are detected as rather concentrated small dots within the cell. It is critical not to use Alexa Fluor™ 488 dye for phalloidin staining, because the excitation wavelength of this dye is already reserved for imaging of the second parameter—the invadopodia proteolytic activity . Areas free of gelatin, which were degraded by the cells, are visible as dark spots within the fluorescent green–stained gelatin (conjugated with fluorescein, excitation at 499 nm and emission at 519 nm). Phalloidin-positive puncta (invadopodia) should mostly colocalize with dark areas of digested gelatin. 18. The presence of areas degraded by gelatinases which do not colocalize with F-actin-rich puncta within the cell (Fig. 2a, blue arrows) indicates that the cell moved away during the duration of the experiment, and it should not be considered for further analysis. 19. Do not take photos of the cells that are undergoing division. This will require an additional normalization due to the presence of two connected or separated cell nuclei into one bigger area of the cell/cells. 20. Usually, we observe in melanoma cell lines studied by us a typical presentation of invadopodia, i.e., several spots rich in F-actin and cortactin located in the vicinity of the nucleus [22]. However, some experimental conditions or modifications of the cells might result in changed manifestation of invadopodia [12]. 21. We recommend to take the photos at higher magnification (objective 60/63 or 100) and zoom in for better visualization of invadopodia, which sometimes are very close to each other or form aggregates. We also recommend the following rule: One cell per one photo. It is easier to analyze and will not require additional normalization. Alternatively, one can take photos at smaller magnification (40 objective) or zoom out while using the 60/63 objective to have several cells on one photo in order to increase the number of cells for the analysis (Fig. 2a). 22. If using software other than Microsoft Windows, apply the command corresponding to , which was used by us in the case of Microsoft Excel. 23. This tool provides the invadopodia number and by marking invadopodia as they are counted it prevents mistakes such as counting the same invadopodium many times or missing any invadopodia in calculations. A corresponding tool to easily follow invadopodia number is also available in LasX (/annotation ! /) if that application is preferred.

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24. If using the LasX application, read the pixel size/voxel size (find it in the properties of the image or its MetaData), which gives information on how many micrometers correspond to 1 pixel. 25. When selecting “global,” make sure to use the same magnification for all analyzed images. 26. This method is less precise than reading the scale details from the original image’s properties or Metadata because it is dependent on a manual drawing. 27. You can set “Overlay” in “Show” options in the “Analyze Particles” window to control which areas were counted (Fig. 3d, d0 , d00 ). 28. If it is not possible to set a threshold which truly corresponds to the cell surface area or to the gelatin-digested area, this parameter can be measured by using the “Freehand selection” tool (Fig. 3a0 ). Outline analyzed areas and next go to /Analyze ! Measure/ command. “Result” window will show the size of the manually selected area (Fig. 3a0 ). It is a less precise method compared to the workflow exploiting the threshold function, but it is acceptable when the automatic measurement fails. 29. If the threshold is imperfect and some small extra areas are still included in the threshold, some settings can be changed in order not to take these areas for calculations. First, the size of the particles intended to be calculated can be changed by going to the “Analyze Particles” window and adjusting “Size (μm2).” Click OK and verify the changes on the “outline” image. A second method to improve analysis is evaluation of each threshold-positive area. Go to /Image ! Overlay ! Overlay options/ and select “Show labels” and “Apply.” Next, click OK. Go to the “Result” window of “Analyze Particles” which presents areas of individual measured particles separately. Comparison of a single measurement and its label (number) presented on the image allows one to remove individual measurements which, in the opinion of the user, turned out not to be invadopodia.

Acknowledgments This research was funded by National Science Centre, Poland, grant numbers 2015/17/B/NZ3/03604 (Opus 9, granted to A. J.M.) and 2016/22/E/NZ3/00654 (Sonata Bis 6, granted to A.J. M.).

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References 1. Miller AJ, Mihm MC (2006) Melanoma. N Engl J Med 355:51–65. https://doi.org/10. 1056/NEJMra052166 2. Sibony-Benyamini H, Gil-Henn H (2012) Invadopodia: the leading force. Eur J Cell Biol 91:896–901. https://doi.org/10.1016/ j.ejcb.2012.04.001 3. Quintero-Fabia´n S, Arreola R, BecerrilVillanueva E et al (2019) Role of matrix metalloproteinases in angiogenesis and cancer. Front Oncol 9:1370. https://doi.org/10.3389/ fonc.2019.01370 4. Bastian A, Nichita L, Zurac S (2017) Matrix metalloproteinases in melanoma with and without regression. In: Travascio F (ed) The role of matrix metalloproteinase in human body pathologies. IntechOpen, London 5. Breitkreutz D, Koxholt I, Thiemann K, Nischt R (2013) Skin basement membrane: the foundation of epidermal integrity-BM functions and diverse roles of bridging molecules nidogen and perlecan. Biomed Res Int 2013:179784. https://doi.org/10.1155/ 2013/179784 6. Galis ZS, Sukhova GK, Libby P (1995) Microscopic localization of active proteases by in situ zymography: detection of matrix metalloproteinase activity in vascular tissue. FASEB J 9:974–980. https://doi.org/10.1096/fasebj. 9.10.7615167 7. Artym VV, Zhang Y, Seillier-Moiseiwitsch F et al (2006) Dynamic interactions of cortactin and membrane type 1 matrix metalloproteinase at invadopodia: defining the stages of invadopodia formation and function. Cancer Res 66:3034–3043. https://doi.org/10.1158/ 0008-5472.CAN-05-2177 8. Forastieri H, Ingham KC (1983) Fluid-phase interaction between human plasma fibronectin and gelatin determined by fluorescence polarization assay. Arch Biochem Biophys 227:358–366. https://doi.org/10.1016/ 0003-9861(83)90464-2 9. Lu ML, McCarron RJ, Jacobson BS (1992) Initiation of HeLa cell adhesion to collagen is dependent upon collagen receptor upregulation, segregation to the basal plasma membrane, clustering and binding to the cytoskeleton. J Cell Sci 101:873–883 10. Makowiecka A, Simiczyjew A, Nowak D, Mazur AJ (2016) Varying effects of EGF, HGF and TGFβ on formation of invadopodia and invasiveness of melanoma cell lines of different origin. Eur J Histochem 60:2728. https://doi.org/10.4081/ejh.2016.2728

11. Pietraszek-Gremplewicz K, Simiczyjew A, Dratkiewicz E et al (2019) Expression level of EGFR and MET receptors regulates invasiveness of melanoma cells. J Cell Mol Med 23:8453–8463. https://doi.org/10.1111/ jcmm.14730 12. Dratkiewicz E, Simiczyjew A, PietraszekGremplewicz K et al (2019) Characterization of melanoma cell lines resistant to vemurafenib and evaluation of their responsiveness to EGFR- and MET-inhibitor treatment. Int J Mol Sci 21:113. https://doi.org/10.3390/ ijms21010113 13. Malek N, Mro´wczyn´ska E, Michrowska A et al (2020) Knockout of ACTB and ACTG1 with CRISPR/Cas9(D10A) technique shows that non-muscle β and γ actin are not equal in relation to human melanoma cells’ motility and focal adhesion formation. Int J Mol Sci 21:2746. https://doi.org/10.3390/ ijms21082746 14. Simiczyjew A, Pietraszek-Gremplewicz K, Dratkiewicz E et al (2019) Combination of selected MET and EGFR inhibitors decreases melanoma cells’ invasive abilities. Front Pharmacol 10:1116. https://doi.org/10.3389/ fphar.2019.01116 15. Simiczyjew A, Mazur AJ, Ampe C et al (2015) Active invadopodia of mesenchymally migrating cancer cells contain both β and γ cytoplasmic actin isoforms. Exp Cell Res 339:206–219. https://doi.org/10.1016/j.yexcr.2015.11. 003 16. Simiczyjew A, Dratkiewicz E, Van Troys M et al (2018) Combination of EGFR inhibitor lapatinib and MET inhibitor foretinib inhibits migration of triple negative breast cancer cell lines. Cancers 10:335. https://doi.org/10. 3390/cancers10090335 17. Podgo´rska M, Pietraszek-Gremplewicz K, Nowak D (2018) Apelin effects migration and invasion abilities of colon cancer cells. Cells 7:113. https://doi.org/10.3390/ cells7080113 18. Branch KM, Hoshino D, Weaver AM (2012) Adhesion rings surround invadopodia and promote maturation. Biol Open 1:711–722. https://doi.org/10.1242/bio.20121867 19. Enderling H, Alexander NR, Clark ES et al (2008) Dependence of invadopodia function on collagen fiber spacing and cross-linking: computational modeling and experimental evidence. Biophys J 95:2203–2218. https://doi. org/10.1529/biophysj.108.133199

Fluorescein-Gelatin Degradation Assay for Active Invadopodia 20. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682. https://doi.org/10.1038/ nmeth.2019 21. Dı´az B (2013) Invadopodia detection and gelatin degradation assay. Bio Protoc 3:e997. https://doi.org/10.21769/BioProtoc.997

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22. Schoumacher M, Louvard D, Vignjevic D (2011) Cytoskeleton networks in basement membrane transmigration. Eur J Cell Biol 90:93–99. https://doi.org/10.1016/j.ejcb. 2010.05.010

Chapter 4 Wound Healing Assay for Melanoma Cell Migration Juliano T. Freitas, Ivan Jozic, and Barbara Bedogni Abstract Cell migration is a critical process involved in morphogenesis, inflammation, and cancer metastasis. Wound healing assay is a simple, non-expensive, and highly reproducible method to study cancer cell migration in vitro. It is based on the observation that cells growing in a monolayer migrate to re-establish cell contacts after the development of an artificial wound. The assay involves creation of a wound in a monolayer, image acquisition during wound closure, and comparison of migrated area at initial and final time points. Key words Melanoma, Metastasis, Cell migration, Wound healing, Wound scratch

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Introduction Cell migration is a pivotal process involved in physiologic and pathologic events including morphogenesis, immune cell trafficking, inflammation, and cancer metastasis [1, 2]. In the context of cancer, metastasis is the ultimate cause of death in 90% of patients, and there are still many remaining gaps in our understanding of metastasis formation [3]. The establishment of metastases involves a series of intricate events. Cancer cells have to detach from the primary tumor and migrate through the extracellular matrix. Once invasive cells access the blood or lymphatic vessels, they can travel around the body and establish new tumor colonies at distant sites [4]. Melanoma is a highly metastatic cancer that is responsible for 75% of all deaths related to cutaneous tumors. Once melanoma becomes metastatic, the patient has a very poor prognosis with the median survival of 6–12 months [5]. Therapeutic strategies aiming to disrupt the metastatic process are imperatively required for patients with melanoma. Along these lines, a better understanding of melanoma cell migration becomes essential given that cell migration is an event that takes places early during melanoma metastasis formation.

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The wound healing assay is an easy, non-expensive, and highly reproducible method to study melanoma cell migration in vitro [6]. Wound healing assays can replicate some features of cell migration that happen in vivo. The foundation of this assay is the fact that the creation of an artificial wound in cells growing in a monolayer initiates cell migration. Cells migrate perpendicularly to the wound edge until cellular contacts are re-established. The wound healing assay is appropriate to study cell–cell and cell–extracellular matrix interactions during cell migration [7]. The conventional wound healing assay requires the formation of a cell monolayer that is scratched in order to create a wound. Cell migration is monitored as images are captured immediately after wound creation as well as at given time points during wound closure. Images are then compared in order to calculate cell migration [8].

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Materials 1. Growth medium: DMEM supplemented with 10% fetal bovine serum (FBS), 1
non-essential amino acids, 2.5 μg/ml of plasmocin, and 50 μg/ml of gentamicin. 2. Serum-free medium: DMEM. 3. Migration medium: Serum-free medium supplemented with 1% FBS. 4. Proliferation inhibitor: Mitomycin C. 5. Migration inhibitor (negative control): Dexamethasone. 6. Migration stimulator (positive control): Epidermal growth factor (EGF). 7. Phosphate-buffered saline (PBS). 8. Incubator (5% CO2, 37  C). 9. Six-well plate (or other size). 10. 200 μl micropipette tip (sterile). 11. Ultrafine tip marker. 12. Inverted microscope with camera. 13. ImageJ software (https://imagej.nih.gov/ij/download.html).

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Methods 1. Seed 5
105 cells per well of a six-well dish and grow to 90–95% confluence (1–2 days) (see Note 1). 2. When cells reach desirable confluency, serum-starve overnight using serum-free medium (see Note 2).

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Fig. 1 Layout of wounds. Three wounds per well are created in the cell monolayer. The first wound is close to the left end of the well, the second is central to the well, and the third is close to the right end of the well. Two images are acquired for each wound: one below and the other above the reference line

3. The following morning treat all cells with mitomycin C (5–8 μg/ml) for 1 h to remove any effects of proliferation on wound closure [9] (see Note 3). 4. Use an ultrafine tip marker to draw a reference line on the exterior plastic surface of each well (see Note 4). 5. Make three separate wounds per well by scratching the monolayer culture using a sterile 200-μl micropipette tip. To create a wound, move the micropipette tip through the cells. Each wound should be perpendicular to the line drawn in step 4. The layout of the wounds is illustrated in Fig. 1 (see Note 5). 6. Carefully rinse the cells 3
with PBS to remove cell debris as well as to eliminate any remaining mitomycin C. 7. Using the reference line drawn in step 4 as a guide, acquire six images of each well (as shown in Fig. 1) on an inverted microscope at 40
(see Note 4). 8. Incubate cells with negative (10 μM dexamethasone) and positive (10 ng/ml EGF) controls for cell migration, as well as test compounds (dissolved in migration medium) at 37  C for 12–36 h (see Notes 6 and 7). 9. After the desirable incubation time, use the reference line drawn in step 4 to monitor the same region (as described in step 7), and acquire a second image of the same wound field (see Note 4).

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Fig. 2 Cell migration of melanoma cells. (a) Representative images acquired at 0 and 24 h post-wounding of A375 human melanoma cells treated with migration inhibitor (10 μM dexamethasone) or migration stimulator (10 ng/ml EGF). The initial wound area is represented in red while the migrated area is shown in black. (b) Percentage invaded area was determined by calculating the invaded area at 24 h to that at time 0 h. Six wound fields per treatment were examined. Data are presented as mean  SEM. *p < 0.05

10. Quantify the migrated area in each image using ImageJ software. (a) Select the edge of a cohesive group of cells using the freehand selection tool. (b) Under the Analyze tab, select the option Measure (or Press M key) and the software will automatically calculate the selected area (see Note 8). (c) Calculate migrated ratio as the migrated area of each image normalized to its respective time 0 [10, 11] (see Note 9). (d) Alternatively, the migration effect of each treatment can be calculated as: 1— migrated ratio (see Note 9) and is presented as % migrated. The migration effect of a migration inhibitor (dexamethasone) and a migration stimulator (EGF) on human A375 melanoma cells is shown in Fig. 2.

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Notes 1. Experiments should be performed to empirically determine the cell density and time required for each cell line tested to reach 90–95% confluence [2]. 2. In order to form a monolayer, cells are cultivated in growth medium containing regular amount of serum (around 10% FBS). Before scratching the monolayer, cells should be

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serum-starved since serum contains growth factors that might stimulate cell proliferation. Additionally, to reduce the effect of proliferation, the migration medium should have a lower percentage of serum than growth medium. The amount of serum in the migration medium should be just enough to avoid cell death [7]. 3. It is very important to prevent the confounding effect of cell proliferation during the wound healing assay. It has been suggested that mitogenic stimuli are released when the cell monolayer is scratched [6]. Consequently, wound closure could be interpreted as a result of cell proliferation rather than cell migration. Treatment with proliferation inhibitors (i.e., mitomycin C) minimizes the impact of cell proliferation during the wound healing assay. Experiments should be performed to establish the ideal concentration at which the proliferation inhibitor prevents cell proliferation, and this can be easily achieved by counting the number of cells treated with the proliferation inhibitor. Previous experiments using cell counting are also helpful to validate that test conditions do not affect cell proliferation. 4. For quantitative analysis, it is crucial to acquire images of the same wound field during wound closure. As cells migrate, it might become difficult to match the initial and final wound field. Reference points on the bottom of the dish made using a razor blade can be used to monitor the same wound field. Alternatively, a reference line drawn using an ultrafine tip marker helps to match the same wound field for images acquired at time 0 and the next time point [7, 8, 12]. The reference line is also useful to increase the sample size of wound fields as from the same scratch different images could be acquired above and below the reference line. An alternative approach that reduces the chances to acquire images of different wound fields is the employment of a time-lapse microscope. When using an automated time-lapse microscope, the plates are placed under the microscope in the same position at all times. Hence, the same wound field will be inevitably monitored during time-lapse acquisition [8]. 5. To avoid contamination, scratching should be performed in sterile conditions [10]. Wounds should be as wide as possible since narrow wounds usually close faster [12]. Wounds must have similar width to reduce the variation that is not a consequence of different treatments [7]. To obtain wounds with similar sizes, move the micropipette tip slowly and continuously when scratching the cell monolayer [1]. It is possible that some wounds have slightly different sizes. This may lead to misleading migratory rates. In order to minimize this bias, during the quantification process, the final area of each wound is normalized to its respective initial area.

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6. In addition to the tested conditions, proper controls for cell migration should be included in the experimental design. For example, dexamethasone inhibits cell migration [13] and can be used as a negative control for cell migration. On the other hand, EGF [14] stimulates cell motility and can be considered a positive control for cell migration. 7. The amount of time cells migrate under different conditions should be determined based on previous trials by monitoring cell migration at different time points during wound closure. The ideal incubation time is determined by the time it takes cells moving under the fastest migration condition to reach imminent wound closure [7, 8]. 8. During the quantification process of migrated area, it is critical to standardize the cut-off point for what is included in the area of migration. When measuring collective cell migration, the migratory area is determined as the edge of a cohesive group of cells that can be visually identified, which excludes cells that migrated individually. 9. After incubation, experimental conditions that stimulate cell migratory behavior will result in smaller migrated area and therefore a reduced migrated ratio when normalized to time 0. Conversely, experimental conditions that inhibit cell migration will generate a larger migrated area resulting in an increased migration ratio. In order to intuitively visualize the results, the migration effect is calculated as 1—migration ratio and is presented as % migrated, so that treatments that induce cell migration will result in higher values, while treatments that inhibit cell migration will present lower values. References 1. Pijuan J, Barcelo´ C, Moreno DF et al (2019) In vitro cell migration, invasion, and adhesion assays: from cell imaging to data analysis. Front Cell Dev Biol 7:1–16 2. Justus CR, Leffler N, Ruiz-Echevarria M et al (2014) In vitro cell migration and invasion assays. J Vis Exp 88:51046 3. Chaffer CL, Weinberg RA (2011) A perspective on cancer cell metastasis. Science 331:1559–1564 4. Langley RR, Fidler IJ (2011) The seed and soil hypothesis revisited--the role of tumor-stroma interactions in metastasis to different organs. Int J Cancer 128:2527–2535 5. Schadendorf D, Fisher DE, Garbe C et al (2015) Melanoma. Nat Rev Dis Prim 1:15003 6. Marshall J (2011) Transwell invasion assays. In: Wells C, Parsons M (eds) Cell migration.

Methods Mol Biol, vol 769. Humana Press, New York 7. Liang C-C, Park AY, Guan J-L (2007) In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro. Nat Protoc 2:329–333 8. Rodriguez LG, Wu X, Guan J-L (2005) Wound-healing assay. In: Guan J-L (ed) Cell migration. Methods Mol Biol, vol 294. Humana Press, New Jersey 9. Jozic I, Sawaya AP, Pastar I et al (2019) Pharmacological and genetic inhibition of caveolin1 promotes epithelialization and wound closure. Mol Ther 27:1992–2004 10. Moreno-Bueno G, Peinado H, Molina P et al (2009) The morphological and molecular features of the epithelial-to-mesenchymal transition. Nat Protoc 4:1591–1613

Wound Healing Migration Assay 11. Zhang P, Huang C, Fu C et al (2015) Cordycepin (30 -deoxyadenosine) suppressed HMGA2, Twist1 and ZEB1-dependent melanoma invasion and metastasis by targeting miR-33b. Oncotarget 6:9834–9853 12. Valster A, Tran NL, Nakada M et al (2005) Cell migration and invasion assays. Methods 37:208–215

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13. Han S, Bui NT, Ho MT et al (2016) Dexamethasone inhibits TGF-β1–induced cell migration by regulating the ERK and AKT pathways in human colon cancer cells via CYR61. Cancer Res Treat 48:1141–1153 14. Wickert LE, Pomerenke S, Mitchell I et al (2016) Hierarchy of cellular decisions in collective behavior: implications for wound healing. Sci Rep 6:20139

Chapter 5 A Fluorescence-Based Assay for Measuring Glucose Uptake in Living Melanoma Cells Jelena Grahovac, Marijana Pavlovic´, and Marija Ostojic´ Abstract Melanoma cells have high glycolytic capacity. Glucose uptake is a key rate-limiting step in glucose utilization. Here we describe a simple protocol for measuring direct glucose uptake in living melanoma cells by flow cytometry. Key words Melanoma, 2-NBDG, Glucose uptake, Flow cytometry

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Introduction Melanoma cells have high metabolic plasticity that sustains tumor growth and metastasis. About 50% of melanomas harbor BRAFV600E mutation that enhances glucose metabolism and represses oxidative phosphorylation [1, 2]. Increased expression of glucose transporter isoform 1 (GLUT1) enhances melanoma metastatic potential [3], and melanoma cells can metabolize glucose into lactose even in the presence of oxygen in the process of aerobic glycolysis [4]. Therefore, studying glucose metabolism and targeting metabolic flexibility in melanoma are of great interest and are the areas of extensive research [5–7]. Here we describe a simple protocol for measuring glucose uptake in melanoma cells by flow cytometry. 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2deoxyglucose (2-NBDG) is a fluorescent glucose analog [8] that is taken up by the glucose transporters but is not metabolized and accumulates in living cells [9]. It can be used for the direct measurement of glucose uptake in single living melanoma cells.

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Materials 1. Melanoma cell lines: (a) A375 (ATCC CRL-1619). (b) HTB-140 (ATCC HTB-140). (c) Cell line 518a2 was procured though inter-laboratory exchange. 2. 25-cm2 cell culture flasks: Polystyrene-treated, with filter caps, for adherent cells. 3. 15-mL conical centrifuge tubes. 4. Six-well cell culture plates. 5. Complete nutrient medium: Roswell Park Memorial Institute (RPMI)-1640 containing 2 g/L of D-glucose, pH 7.2. Add fetal calf serum (FCS) to a final concentration of 10% and penicillin–streptomycin solution to a final concentration of 100 U/mL of penicillin and 100 μg/mL of streptomycin. Store at 4  C. 6. Phosphate-buffered saline (PBS), sterile filtered. Store at 4  C. 7. Dissociation buffer: 0.25% Trypsin with 0.5 mM EDTA. Store at 4  C. 8. 10 mM 2-NBDG: Dissolve 1 mg of 2-NBDG (Mw 342.26) in 292.17 μL DMSO to get 10 mM stock solution and filter through 0.2-μm filter. The solution should be clear bright yellow-orange. We aliquot and store the stock solution at 20  C for up to a year (see Note 1). 9. Flow cytometer with FL-1/FITC (green fluorescence) channel (Ex/Em 485/535 nm). We used a BD Calibur flow cytometer with Cell Quest computer software. 10. Flow cytometry 5-mL round bottom tubes.

3

Methods

3.1 Cell Culture and Treatment

1. Culture melanoma cells in complete nutrient medium in 25-cm2 cell culture flasks, at 37  C in an incubator with a humidified atmosphere of 5% CO2. Pay attention to the level of glucose in the medium (we use 2 g/L), and if comparing glucose uptake between the cell lines culture under the same conditions. 2. After the cells have reached 80% confluence, subculture cells for experimentation.

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3. Seed 200,000 cells per well into six-well culture plates in 2 mL of RPMI per well. Make sure that cells are equally distributed across the well surface as single cells. 4. Allow cells to adhere overnight. 5. The next day, prepare working solution of 20 μM 2-NBDG in RPMI (see Notes 2 and 3). 6. (a) For comparing glucose uptake between the cell lines, set up unstained sample (negative control; with only RPMI) and stained sample (treated with 2-NBDG) for every cell line. (b) For examining the effect of drugs on direct glucose uptake in a given cell line, add appropriate concentration of the drug into the 2-NBDG working solution. In example given here, we treated A375 cells with 50 μM pioglitazone hydrochloride which stimulates glucose uptake [10]. If the viability of the cells in the presence of the drug tested is a concern, see Note 4. 7. Aspirate the medium from the six-well plate and add 1 mL of working 2-NBDG solution per well. 8. Incubate cells for 1 h in a 37  C incubator with a humidified atmosphere of 5% CO2 (see Note 3). 9. After incubation, remove the 2-NBDG solution and wash the cells with 2 mL of PBS. 10. Add 0.5 mL of 0.25% Trypsin–0.5 mM EDTA per well and incubate at 37  C until the cells round up and detach. (Depending on the cell line, start with 5 min and observe under the microscope.) 11. Neutralize Trypsin–EDTA by adding 2 mL of RPMI. Gently disperse by pipetting up and down to obtain a single cell suspension. 12. Transfer the cell suspension to a 15-mL centrifuge tube and centrifuge at 400  g, for 5 min, at room temperature. 13. Carefully discard supernatant without disturbing the cell pellet. 14. To wash the pellet, add 1 mL of PBS and resuspend by gently pipetting up and down. 15. Centrifuge at 400  g, for 5 min, at room temperature. 16. Resuspend the cells in 1 mL PBS, transfer to a flow tube, and keep on ice, until measurement on the flow cytometer (Note: for the highest fluorescence signal, analyze cells immediately, as 2-NBDG decomposes into non-fluorescent derivative over time [11, 12], see Note 5).

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3.2 Flow Cytometric Analysis of Glucose Uptake 3.2.1 Flow Cytometry Setup

1. Prepare the flow cytometer for analysis. 2. Fluorescence of 2-NBDG can be detected in the FL-1/FITC (green fluorescence) channel (Ex/Em 485/535 nm) (see Note 6). 3. Set the acquisition to 20,000 events for each sample. 4. By adjusting FSC and SSC parameters, position cell population at the center of FSC/SSC plot and gate out debris (Fig. 1). 5. Set the laser so that the unstained sample positions on the left side of the histogram plot. The stained sample should have the highest peak around the center of the x-axis (around 102) (Fig. 2). 6. After the parameters are set for the first recorded cell line, proceed to other samples (unstained and stained or untreated and treated with the drug of interest), and collect them without any further adjustments.

3.2.2 Comparison of Glucose Uptake Between the Cell Lines

1. Overlay histograms of the unstained and stained samples for each cell line and set the gate (M1) to collect the signal from the stained sample (Fig. 2). Export statistics. For quantification, we use the mean fluorescence intensity of an event in the gate M1 (Table 1, see Note 7). 2. For creating the image for illustration, overlay all the stained samples for each cell line (Fig. 3a). 3. Perform three biological replicates of the experiment and plot the average of the mean (we use PRISM 7 from GraphPad for plotting and statistical analysis) (Fig. 3b). When comparing multiple cell lines, we use one-way ANOVA and Tukey’s multiple comparisons test (see Note 8).

Fig. 1 Scatterplot and gating of A375 cells incubated with 2-NBDG

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Fig. 2 Overlay of histograms for unstained and 2-NBDG-stained A375 line and example of the M1 gate

Table 1 Example of the exported statistics for the graph in Fig. 3a Cell line

Events

% Gated

Mean

CV

Median

Peak Ch

A375

19,669

99.51

105.83

46.58

98.22

98

518a2

19,362

98.97

109.08

55.57

101.82

110

HTB-140

19,718

99.68

63.61

57.78

56.23

59

Fig. 3 (a) Overlay of 2-NBDG histograms for the three cell lines and example of the M1 gate. (b) Quantification of the average fluorescent intensity per cell from biological replicates of the experiment. ** p < 0.005, one-way ANOVA with Tukey’s multiple comparison test

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Fig. 4 (a) Overlay of 2-NBDG histograms of untreated (ctrl) and pioglitazone-treated A375 cells. (b) Quantification of the average fluorescent intensity per cell from the biological replicates of the experiment. ** p < 0.0005, unpaired t-test Table 2 Example of the exported statistics for the graph in Fig. 4a A375

Events

% Gated

Mean

CV

Median

Peak Ch

Control

19,092

97.49

12.58

38.15

11.76

11

Pioglitazone

17,213

88.38

28.62

34.26

28.13

33

3.2.3 Measurement of the Effects of Drugs on Glucose Uptake

1. Overlay histograms of the untreated (control) and treated (pioglitazone) samples to observe the shift and set the gate (M1) to collect the signal from both (Fig. 4a). 2. Export statistics for each treatment separately. For quantification, we use the mean fluorescence intensity of an event in the gate M1 (Table 2, see Notes 7, 9 and 10). 3. Perform three biological replicates of the experiment and plot the average of the mean (we use PRISM 7 from GraphPad for plotting and statistical analysis) (Fig. 4b). When comparing control versus treated, we use unpaired t-test. When comparing multiple treatments, we use one-way ANOVA and Tukey’s multiple comparison test.

4

Notes 1. Although breakdown of 2-NBDG upon freezing has been reported [9], in our hands, 10 mM 2-NBDG stock solution stored at 20  C could be thawed and frozen several times without losing potency and was stabile for a year.

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2. If the cells used in experimentation grow in high glucose medium (4.5 g/L) replace the medium to low or no glucose prior to cell labeling, as glucose in the medium and 2-NBDG compete for glucose transporters. 3. For increasing fluorescence signal, cells can be incubated in glucose-free medium, or 2-NBDG concentrations can be increased up to 100 μM. Concentrations higher than 250 μM have been reported to cause self-quenching [13]. For further enhancement of the fluorescence signal, incubation time can be increased up to 3 h without affecting the cell viability. 4. When the viability of cells is a concern, after labeling with 2-NBDG and washing in step 16 of Subheading 3.1, resuspend the cells in PBS with 1 μg/mL propidium iodide (PI), keep the cells on ice and measure within 30 min. 5. Keep the cells on ice until the flow cytometry to slow down 2-NBDG loss of signal. 6. If including PI stain for cell viability, collect the signal in FL3 channel and gate out PI-positive (dead) cells. 7. We export (see Tables 1 and 2) mean (average fluorescence intensity of an event within the gate M1 which is a measure of the average glucose uptake per cell), CV (coefficient of variation that is equal to standard deviation divided by the mean expressed as percentage and is a measure of variation in glucose uptake in the cell population), median (intensity value that divides a histogram into two equal parts with equal number of events), and peak channel intensity (the peak within the gate M1, the highest fluorescence detected in the population). We usually graph and compare mean fluorescence intensity. 8. In the example in Fig. 3, one can observe higher glucose uptake in BRAFV600E mutated cell lines A375 and 518a2 compared to the BRAF WT cell line HTB-140. 9. If the population of the cells investigated is heterogeneous and has several subpopulations with different GLUT expression, one might observe two peaks and analyze the shift between populations upon treatment with the tested dug. 10. One also has to bear in mind to set up appropriate unlabeled controls for drugs that exhibit autofluorescence, and compounds with emission wavelength close to 2-NBDG cannot be tested by this protocol.

Acknowledgments This work was supported by the grant 451-03-68/2020-14/ 200043 from the Serbian Ministry for Education, Science and Technology Development.

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References 1. Abildgaard C, Guldberg P (2015) Molecular drivers of cellular metabolic reprogramming in melanoma. Trends Mol Med 21(3):164–171. https://doi.org/10.1016/j.molmed.2014.12. 007 2. Haq R, Fisher DE, Widlund HR (2014) Molecular pathways: BRAF induces bioenergetic adaptation by attenuating oxidative phosphorylation. Clin Cancer Res 20 (9):2257–2263. https://doi.org/10.1158/ 1078-0432.CCR-13-0898 3. Koch A, Lang SA, Wild PJ, Gantner S, Mahli A, Spanier G, Berneburg M, Mu¨ller M, Bosserhoff AK, Hellerbrand C (2015) Glucose transporter isoform 1 expression enhances metastasis of malignant melanoma cells. Oncotarget 6(32). https://doi.org/10.18632/ oncotarget.4977 4. Fischer GM, Vashisht Gopal YN, McQuade JL, Peng W, DeBerardinis RJ, Davies MA (2018) Metabolic strategies of melanoma cells: mechanisms, interactions with the tumor microenvironment, and therapeutic implications. Pigment Cell Melanoma Res 31 (1):11–30. https://doi.org/10.1111/pcmr. 12661 5. Brummer C, Faerber S, Bruss C, Blank C, Lacroix R, Haferkamp S, Herr W, Kreutz M, Renner K (2019) Metabolic targeting synergizes with MAPK inhibition and delays drug resistance in melanoma. Cancer Lett 442:453–463. https://doi.org/10.1016/j. canlet.2018.11.018 6. Grahovac J, Srdic-Rajic T, Francisco Santibanez J, Pavlovic M, Cavic M, Radulovic S (2019) Telmisartan induces melanoma cell apoptosis and synergizes with vemurafenib in vitro by altering cell bioenergetics. Cancer Biol Med 16(2):247–263. https://doi.org/ 10.20892/j.issn.2095-3941.2018.0375

7. Chaube B, Malvi P, Singh SV, Mohammad N, Meena AS, Bhat MK (2015) Targeting metabolic flexibility by simultaneously inhibiting respiratory complex I and lactate generation retards melanoma progression. Oncotarget 6 (35):37281–37299. https://doi.org/10. 18632/oncotarget.6134 8. Yoshioka K, Takahashi H, Homma T, Saito M, Oh KB, Nemoto Y, Matsuoka H (1996) A novel fluorescent derivative of glucose applicable to the assessment of glucose uptake activity of Escherichia coli. Biochim Biophys Acta 1289 (1):5–9. https://doi.org/10.1016/03044165(95)00153-0 9. Lloyd PG, Hardin CD, Sturek M (1999) Examining glucose transport in single vascular smooth muscle cells with a fluorescent glucose analog. Physiol Res 48(6):401–410 10. el-Kebbi IM, Roser S, Pollet RJ (1994) Regulation of glucose transport by pioglitazone in cultured muscle cells. Metabolism 43 (8):953–958. https://doi.org/10.1016/ 0026-0495(94)90173-2 11. Yoshioka K, Saito M, Oh KB, Nemoto Y, Matsuoka H, Natsume M, Abe H (1996) Intracellular fate of 2-NBDG, a fluorescent probe for glucose uptake activity, in Escherichia coli cells. Biosci Biotechnol Biochem 60 (11):1899–1901. https://doi.org/10.1271/ bbb.60.1899 12. Zou C, Wang Y, Shen Z (2005) 2-NBDG as a fluorescent indicator for direct glucose uptake measurement. J Biochem Biophys Methods 64 (3):207–215. https://doi.org/10.1016/j. jbbm.2005.08.001 13. Ball SW, Bailey JR, Stewart JM, Vogels CM, Westcott SA (2002) A fluorescent compound for glucose uptake measurements in isolated rat cardiomyocytes. Can J Physiol Pharmacol 80 (3):205–209. https://doi.org/10.1139/y02043

Chapter 6 Analyzing Melanoma Cell Oxygen Consumption and Extracellular Acidification Rates Using Seahorse Technology Ashley V. Menk and Greg M. Delgoffe Abstract Cancer cells have deregulated metabolism that can contribute to the unique metabolic makeup of the tumor microenvironment. This can be variable between patients, and it is important to understand these differences since they potentially can affect therapy response. Here we discuss a method of processing and assaying metabolism from direct ex vivo murine and human tumor samples using seahorse extracellular flux analysis. This provides real-time profiling of oxidative versus glycolytic metabolism and can help infer the metabolic status of the tumor microenvironment. Key words Extracellular flux, Seahorse, Metabolism, Glycolysis, Oxidative phosphorylation

1

Introduction It is widely appreciated that cancer cells undergo metabolic reprogramming in order to enhance cell proliferation and survival. These cells increase both aerobic glycolysis and oxidative phosphorylation, resulting in a distinct metabolic landscape that has low glucose and oxygen, as well as a decreased pH due to an abundance of lactic acid [1–4]. This metabolic landscape can affect anti-tumor immunity and may have an impact on treatment selection and response to therapy [5, 6]. Understanding the metabolic variabilities between patients is important, but previous techniques including radiolabeling, mass spectrometry, and NMR analysis can be laborious and require large amounts of tissue [7]. Here we developed a method to use seahorse technology to measure tumor cell metabolism directly ex vivo using a relatively small number of cells. Seahorse extracellular flux analysis has been used previously to quantify cell metabolism in real time with a variety of direct ex vivo cells such as lymphocytes [8–11]. The XFe96 analyzer is a 96-well instrument that can measure the uptake and secretion of metabolic

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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products involved in glycolysis and oxidative metabolism. It detects changes in oxygen and pH in the media surrounding the cells being analyzed with fluorescent biosensors in each well [12]. Each sensor cartridge also contains four chambers to allow for the delivery of various reagents into all 96 wells during the assay. In order to quantify oxidative and glycolytic capabilities in cells, the assay we developed uses the reagents oligomycin, carbonyl cyanide-p-trifluoromethoxyphenyl-hydrazon (FCCP), 2-deoxy-D-glucose, rotenone, and antimycin A, which are loaded into the four chambers. Oligomycin is an inhibitor of ATP synthase and can be used to determine the level of ATP-linked respiration in a cell [13]. FCCP uncouples the mitochondrial membrane and allows protons to flow freely across the membrane. This results in maximal respiration in the cell after the addition of FCCP and can be used to calculate the spare respiratory capacity, or the ability of the cells to meet energy demands during times of stress [13]. 2-Deoxy-D-glucose acts as an inhibitor of glycolysis by blocking hexokinase and preventing the production of glucose-6-phosphate [12]. This causes glycolysis to decrease and can be used to determine non-glycolytic acidification of the medium. Rotenone and antimycin A together inhibit complex I and III of the electron transport chain. The combination of these two inhibitors stops oxidative metabolism through the mitochondria of the cells and can be used to calculate nonmitochondrial respiration [12]. Using this technology, we have developed a way to metabolically profile melanoma cell lines, murine tumors harvested directly ex vivo from genetically engineered mouse models of melanoma and tumors biopsied from melanoma patients so that we may better understand heterogeneity between samples.

2

Materials

2.1 Tumor Processing and Culture

1. RPMI-1640-based medium for tumor cell culture: Remove 50 mL of RPMI-1640 from a 1-L bottle of base medium. Add 100 mL of heat-inactivated fetal bovine serum, 10 mL of sodium pyruvate (100 mM), 10 mL of MEM nonessential amino acids (100), 5 mL of HEPES (1 M), 10 mL of Lglutamine (200 mM), and 10 mL of penicillin (10,000 IU)/ streptomycin (10,000 μg/mL). Filter through a 0.22-μm filter. 2. Murine tumor digestion solution: Add 100 μL of liberase (2.5 mg/mL), 100 μL of DNase I (2000 Kunitz), and 400 μL of dispase (5 U/mL) to 400 μL of serum-free RPMI1640 medium. 3. Human tumor digestion solution: Add 50 μL of collagenase II (100 mg/mL) and 50 μL of DNase I (10 mg/mL) to 5 mL of RPMI-1640-based medium for tumor cell culture.

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4. 1 red blood cell lysis buffer: Dilute 5 mL of proprietary 10 red blood cell lysis buffer in 45 mL of deionized water. 5. Sterile PBS (1): Dilute 50 mL of 10 PBS with 450 mL of deionized water and autoclave. 6. Trypsin–EDTA: 0.05% Trypsin and 0.53 mM EDTA in Hank’s balanced salt solution. 7. Six-well tissue culture plate. 8. Scalpel. 9. 5-mL syringe. 10. 70-μm strainer. 11. Hemocytometer. 12. 60-mm petri dish. 13. 15-mL conical centrifuge tube. 14. Rotating platform. 2.2 Seahorse Mito Stress Test

1. Seahorse XFe96 Analyzer. 2. Seahorse XFe96 FluxPak: XFe96 sensor cartridges, XF96 cell culture microplates, and 500 mL of XF Calibrant Solution. 3. Standard seahorse mito stress test assay medium: Prepare medium fresh prior to the assay by mixing 98 mL of Seahorse XF Base Medium with 1 mL of L-glutamine (200 mM), 1 mL of sodium pyruvate (100 mM), and 10 mM glucose (180 mg), pH 7.4. 4. 5 mM oligomycin: Dissolve 10 mg of oligomycin in 2.53 mL of DMSO. 5. 5 mM FCCP: Dissolve 10 mg of FCCP in 7.87 mL of DMSO. 6. 600 mM 2-deoxy-D-glucose: Dissolve 490 mg of 2-deoxy-Dglucose in 5 mL of DMSO. 7. 3 mM antimycin A: Dissolve 50 mg of antimycin A in 30.38 mL of DMSO. 8. 2.5 mM rotenone: Dissolve 1 g of rotenone in 10.14 mL of DMSO to make a stock solution of 250 mM. Further dilute stock by adding 1 μL of 250 mM rotenone to 99 μL of DMSO to reach a 2.5 mM working concentration. 9. Seahorse Wave Desktop Software (Agilent). 10. Microsoft Excel or GraphPad Prism.

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Methods

3.1 Tumor Processing and Cell Preparation 3.1.1 Murine Tumors

1. Place resected tumor into a six-well tissue culture plate and use a scalpel to cut tumor into small pieces. 2. Add 2 mL of murine tumor digestion solution to the well and incubate at 37  C for 15–20 min. 3. Grind the tumor using the back of a 5-mL syringe and filter using a 70-μm strainer. 4. Spin cells at 520  g for 5 min, and discard supernatant. 5. Resuspend cell pellet in 1 mL of 1 red blood cell lysis buffer, and incubate at room temperature for 1 min. 6. Add 4 mL of RPMI-1640-based medium for tumor cell culture. 7. Spin cells at 520  g for 5 min and resuspend cell pellet in RPMI-based medium. Count cells using a hemocytometer.

3.1.2 Human Tumors

1. Place tissue in 60-mm petri dish and mince with scalpel until pieces are around 1 mm3. 2. Add 5 mL of RPMI-based medium for tumor cell culture and transfer tissue and medium into a 15-mL conical tube. 3. Add 50 μL of collagenase II and 50 μL of DNase I to the conical tube, and incubate tissue in this human tumor digestion solution at 37  C for to 10–30 min on a rotating platform (see Note 1). 4. Remove the tube from 37  C and carefully pipette the cell suspension up and down. 5. Filter the cell suspension using a 70-μm strainer. 6. Wash the strainer using RPMI-based medium for tumor cell culture, and centrifuge at 500  g for 5 min. 7. Resuspend cell pellet in RPMI-based medium, and count cells using a hemocytometer.

3.2 Preparation of Cells from Culture

1. For melanoma cell lines, aspirate medium in cell culture plate, rinse plate with 5 mL of 1 PBS and aspirate, and add 5 mL of Trypsin–EDTA. 2. Place plate in 37  C incubator for 2–5 min (or until cells have detached from the cell culture plate). 3. Add 5 mL of RPMI-based medium for tumor cell culture and harvest the cells. Spin cells at 520  g for 5 min and resuspend cell pellet in RPMI-based medium. 4. Count cells using a hemocytometer.

Metabolic Profiling of Melanoma Cells Using Seahorse

3.3 Seahorse Mito Stress Test

85

1. Hydrate a sensor cartridge the night before running the assay. 2. Place the sensor cartridge upside down next to the utility plate. 3. Fill each well of the utility plate with 200 μL of Seahorse XF Calibrant Solution. 4. Lower the sensor cartridge onto the utility plate, submerging the sensors in Seahorse XF Calibrant Solution. 5. Place the sensor cartridge and utility plate in a non-CO2 37  C incubator overnight (see Notes 2 and 3). 6. Plate tumor cells in XF96 cell culture microplate in 100 μL of RPMI-based tumor cell culture medium. (a) For melanoma cell lines, plate cells at a seeding density of 10,000–20,000 cells per well. (b) For ex vivo-derived murine or human melanoma cells, plate cells at a seeding density of 40,000–50,000 cells per well (see Note 4). (c) Place the XFe96 cell culture microplate in a CO2 37  C incubator overnight. 7. On the day of the assay, prepare the standard seahorse mito stress test medium as described in Subheading 2, and pH the solution to 7.4  0.05. Warm to 37  C. 8. Prepare seahorse mito stress test inhibitors in standard mito stress test medium as follows: (a) Dilute 12 μL of 5 mM oligomycin stock into 3 mL of assay medium to reach the concentration of 20 μM. (b) Dilute 12 μL of 5 mM FCCP stock into 3 mL of assay medium to reach the concentration of 20 μM. (c) Dilute 500 μL of 600 mM 2-Deoxy-D-glucose stock into 2.5 mL of assay medium to reach the concentration of 100 mM. (d) Dilute 5 μL of 3 mM antimycin A stock and 6 μL of 2.5 mM rotenone stock into 3 mL of assay medium to reach the concentration of 5 μM (see Note 5). (e) pH all solutions again to 7.4  0.05. Warm to 37  C. 9. Remove XF96 cell culture microplate with cells from the CO2 37  C incubator. 10. Examine cells with a microscope to check that cells are adhering to the bottom of the plate. 11. Carefully aspirate RPMI-based tumor cell culture medium using a multichannel pipette, and wash one time with 100 μL of standard seahorse mito stress test medium (see Note 6). 12. Aspirate the medium using a multichannel pipette, and add 180 μL of standard seahorse mito stress test medium.

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Table 1 Loading schematic and volumes of mito stress test inhibitors that are to be loaded in the 96-well sensor cartridge Injection port

Inhibitor

Volume

A

Oligomycin

20 μL

B

FCCP

22 μL

C

2-Deoxy-D-glucose

25 μL

D

Rotenone + Antimycin A

27 μL

Fig. 1 Representative image of loading ports for injections on the 96-well sensor cartridge

13. Examine cells again under a microscope for adherence. 14. Place the XF96 cell culture microplate in a non-CO2 37  C incubator for 1 h. 15. Using the provided Seahorse loading guides (see Notes 7–11), load the mito stress test inhibitors as described in Table 1 into the appropriate injection ports in the sensor cartridge (Fig. 1) using a multichannel pipette. 16. Using Seahorse Wave Desktop Software, create an experimental template for the standard seahorse mito stress test. Open up a new template in Wave, and add injection strategies, cell types and seeding density, cell plate layout, and measurement cycles for each injection (see Note 12). 17. Calibrate the Seahorse XFe96 Analyzer by starting the run and adding the sensor cartridge and utility plate containing Seahorse XF Calibrant Solution to the machine. 18. After calibration is complete (or after the XFe96 cell culture microplate has been in the non-CO2 37  C incubator for 1 h), begin the assay by removing the utility plate containing Seahorse XF Calibrant Solution and replacing it with the XFe96 cell culture microplate containing the tumor cells.

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3.4

Data Analysis

87

1. After the assay is complete, remove the XFe96 cell culture microplate from the Analyzer, and view the results by clicking on the add view, overview icon. 2. Examine oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) measurements for each well for all experimental groups for any outliers, and remove wells by clicking on that well of the plate so that it is highlighted gray. 3. Export data into a Prism or Excel file by clicking on the export icon (see Note 13). 4. Determine baseline OCR and ECAR by calculating the overall average between all the wells of the groups for the last reading measured before the oligomycin injection. 5. Calculate spare respiratory capacity by subtracting the average maximum OCR measurement and the average baseline OCR measurement from step 4 of this section (see Note 14). 6. Calculate glycolytic reserve by subtracting the average maximum ECAR measurement and the average baseline ECAR measurement from step 4 of this section.

4

Notes 1. Digestion time will depend on the size of the biopsy or the number of cores received. Digest tissue from 2 to 4 cores for 10 min. Larger pieces of tissue from surgery should be digested for 20–30 min. 2. To prevent evaporation of the Seahorse XF Calibrant Solution overnight, verify that the non-CO2 incubator is properly humidified. 3. The sensor cartridge should be hydrated at least 3 h before the assay and can be used up to 72 h after hydration as long as the calibrant solution has not evaporated. 4. Cell numbers per well may vary between cell lines and should be titrated in the seahorse before experimentation. 5. Seahorse mito stress test inhibitors are made at 10 concentrations so that they are 1 upon injection into the cellcontaining wells. 6. For direct ex vivo tumors, cells may need to be washed more than once if there is debris left in the well after the first wash. Carefully wash until most of the debris is out of the well. 7. Before loading the sensor cartridge, lift sensors up and down from utility plate containing the Seahorse XF Calibrant Solution a few times until all bubbles in the plate are gone.

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8. Check that the appropriate loading guides are used for the appropriate injection ports. 9. Check that the blue triangle on the sensor cartridge and utility plate is in the bottom left-hand corner before loading any ports. This ensures that the injections are loaded in the correct orientation. 10. Be sure to change the pipette volume each time you switch inhibitors, and monitor the volume in the multichannel pipette tips to ensure everything is loading evenly in all of the wells. 11. Before loading the next inhibitor with the loading guides, check for leftover residue liquids on the loading guide from the previous inhibitor loaded. You do not want to transfer one inhibitor into another port by accident. 12. We usually do 3–5 measurement cycles for any seahorse run, but this can be modified according to user preference. 13. Older versions of the Wave Desktop Software will not have this export option. Newer versions of the software will export the data for all wells, excluding those that were selected as outliers. 14. Maximum respiration usually occurs after FCCP is injected, but for some experiments, there can be an increase in OCR after 2-deoxy-D-glucose. Always use the highest OCR measurement to calculate spare respiratory capacity.

Acknowledgments This work was supported by the UPMC Hillman Cancer Center Melanoma and Skin Cancer SPOREs (P50CA121973-09 to YGN, JMK, and GMD); Stand Up To Cancer—American Association for Cancer Research (SU2C-AACR-IRG-04-16 to GMD); an NIH Director’s New Innovator Award (DP2AI136598 to GMD); Young Investigator Award from Alliance for Cancer Gene Therapy/Swim Across America (to GMD); the Hillman Fellows for Innovative Cancer Research Program funded by the Henry L. Hillman Foundation; Cancer Center Support Grant P30CA047904 and the UPMC Hillman Cancer Center. References 1. Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314 2. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324(5930):1029–1033 3. Gogvadze V, Orrenius S, Zhivotovsky B (2008) Mitochondria in cancer cells: what is

so special about them? Trends Cell Biol 18 (4):165–173 4. Scharping NE, Delgoffe GM (2016) Tumor microenvironment metabolism: a new checkpoint for anti-tumor immunity. Vaccines (Basel) 4(4):46 5. Wegiel B, Vuerich M, Daneshmandi S, Seth P (2018) Metabolic switch in the tumor

Metabolic Profiling of Melanoma Cells Using Seahorse microenvironment determines immune responses to anti-cancer therapy. Front Oncol 8:284 6. Najjar YG, Menk AV, Sander C et al (2019) Tumor cell oxidative metabolism as a barrier to PD-1 blockade immunotherapy in melanoma. JCI Insight 4(5):e124989 7. Pike Winer LS, Wu M (2014) Rapid analysis of glycolytic and oxidative substrate flux of cancer cells in a microplate. PLoS One 9(10):e109916 8. Scharping NE, Menk AV, Moreci RS et al (2016) The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction. Immunity 45 (2):374–388 9. Scharping NE, Menk AV, Whetstone RD et al (2017) Efficacy of PD-1 blockade is potentiated by metformin-induced reduction of tumor hypoxia. Cancer Immunol Res 5 (1):9–16

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10. Menk AV, Scharping NE, Rivadeneira DB et al (2018) 4-1BB costimulation induces T cell mitochondrial function and biogenesis enabling cancer immunotherapeutic responses. J Exp Med 215(4):1091–1100 11. Menk AV, Scharping NE, Moreci R et al (2018) Early TCR signaling induces rapid aerobic glycolysis enabling distinct acute T cell effector functions. Cell Rep 22(6):1509–1521 12. Wu M, Neilson A, Swift AL et al (2007) Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells. Am J Physiol Cell Physiol 292(1):C125–C136 13. Reily C, Mitchell T, Chacko BK et al (2013) Mitochondrially targeted compounds and their impact on cellular bioenergetics. Redox Biol 1 (1):86–93

Chapter 7 Analysis of Melanoma Cell Glutamine Metabolism by Stable Isotope Tracing and Gas Chromatography-Mass Spectrometry David A. Scott Abstract Glutamine is a major substrate for biosynthesis. It contributes to multiple pathways required for cell proliferation, supports antioxidant defense via glutathione synthesis, and sustains the tricarboxylic acid (TCA) cycle through anaplerosis. Glutamine-fueled anaplerosis and related biosynthesis can be studied in detail in melanoma using stable isotope (13C) labeling followed by gas chromatography-mass spectrometry (GC-MS) analysis of metabolite amounts and labeling. Detailed protocols for the assay of polar metabolites (including amino acids, TCA cycle, and glycolysis metabolites) and fatty acids by these methods following cell treatment with 13C-glutamine or 13C-glucose are presented. Key words Glutamine, Melanoma, Stable-isotope tracing, GC-MS, Metabolite quantification, Amino acids, Tricarboxylic acid cycle, Glycolysis, Fatty acids

1

Introduction Glutamine is the most abundant free amino acid in muscle or blood [1]. In adult human blood, its concentration is in the range of 500–700 μM, in contrast to other amino acids which range from ~400 μM for alanine down to ~20 μM for aspartate [2]. Glutamine is used by normal or tumor cells as an amide donor in the synthesis of asparagine, NAD, amino-sugars, and at various steps in nucleotide synthesis [3]. Glutamate, produced from glutamine by these transamidation reactions or by glutaminase, is a component of the antioxidant glutathione and serves as a counter-substrate in the uptake of cystine, another glutathione precursor [1]. Glutamate can also be converted to alpha-ketoglutarate by glutamate dehydrogenase or transaminases. As alpha-ketoglutarate is a component of the tricarboxylic acid (TCA) cycle, glutamine effectively serves as an anaplerosis substrate, balancing the outflow from the TCA cycle, particularly that required for the synthesis of aspartate, asparagine,

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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and nucleotides [4–6]. Via its control of the cellular pool of alphaketoglutarate, glutamine can also affect histone methylation and cell dedifferentiation, as alpha-ketoglutarate is a cofactor for JmJCdomain-containing histone demethylases [7]. Thus, glutamine is potentially important as a substrate for tumor proliferation, antioxidant defense, and cell fate. Doubts have been raised about the usefulness of intervention in glutamine utilization in melanoma, as metabolic reprogramming may reduce glutamine dependence, at least for brain metastases [8]. However, resistance to the clinically important mutant-BRAF inhibitors is associated with increased dependence on glutamine [9–11], and increased glutamine demand is also induced by treatment with arginase [12] or lactate dehydrogenase inhibition [13]. Therefore, at least for combination therapies, there is still potential for the inhibition of glutamine metabolism in melanoma and, consequently, a need for further studies in this area. We have used stable-isotope tracing and metabolite quantification to study glutamine-related metabolism in melanoma cells [6, 14–16]. This work identified glutamine as an important precursor for TCA cycle metabolism [14]. Notably, under hypoxia, reductive carboxylation catalyzed by isocitrate dehydrogenase operating in the reverse of the conventional cycle direction generates carbon flux from glutamine to fatty acids [14, 16]. Stable isotope (13C) labeling and analysis of polar metabolites and fatty acids by gas chromatography-mass spectrometry (GC-MS) yields a detailed profile of central carbon metabolism. The combination of 13 C-tracing and metabolite quantification provides complementary results which can more clearly indicate changes in metabolic pathway activity than either technique on its own. The methods detailed here are based on those originally described in our papers [6, 14], with the inclusion of more recent refinements. While the focus here is on glutamine metabolism, it is useful to do comparative labeling with 13C-glucose as well as 13C-glutamine, so methods including both stable isotope tracers are included. A metabolic chart of the metabolites in glycolysis, the TCA cycle, and related amino acids where labeling can be measured by these protocols is provided in Fig. 1.

2 2.1

Materials Cell Culture

1. Six-well culture plates. 2. Standard culture medium, e.g., DMEM or RPMI-1640 supplemented with 10% fetal calf serum (depending on cell line). 3. Culture medium for stable isotope labeling. Two identical media for 13C labeling are prepared, differing only in the 13C substrate.

13

C-Tracing and GC-MS for Melanoma Metabolism Analysis

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Fig. 1 Metabolic chart of metabolites labeled from 13C-glutamine or 13C-glucose. The GC-MS polar metabolite protocol described here can be used to quantify and determine the labeling of the underlined metabolites. In melanoma cells, glycolysis metabolites are typically not labeled from 13C-glutamine, but are labeled from 13C-glucose. DHAP dihydroxyacetone phosphate, P-glycerol glycerol-3-phosphate, 3PG 3-phosphoglycerate, PEP phosphoenolpyruvate, GABA γ-aminobutyrate

(a) For 13C-glutamine: To 100 ml of MEM (containing 1 g/l of glucose and no glutamine), add a further 1 g/l of glucose (1 ml
100 g/l), 1 mM unlabeled glutamine (0.5 ml
200 mM), 1 mM 13C5-glutamine (0.5 ml
200 mM), non-essential amino acids supplement (NEAA, 1 ml
100
, final concentration of each NEAA 0.1 mM), and 10% fetal bovine serum (FBS). (b) For 13C-glucose: To 100 ml of MEM containing 1 g/l of glucose and no glutamine, add 1 g/l of 13C6-glucose (1 ml
100 g/l), 2 mM unlabeled glutamine (1 ml
200 mM), NEAA (1 ml
100
), and 10% FBS (see Note 1). 2.2 Sample Collection

1. Phosphate-buffered saline, ice-cold. 2. Cell scrapers. 3. Extraction solution: Prepare a water/methanol mixture containing 20 μM L-norvaline by mixing equal volumes of water and methanol and adding 20 μl of L-norvaline (100 mM solution in water) per 100 ml water/methanol mixture. Keep this solution at 20  C (this storage is designed to keep the solution cold for cell extraction rather than as a necessity for the stability of the mixture).

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4. Dry ice and wet ice. 5. Chloroform containing 4 μg/ml of heptadecanoic acid: Prepare a stock of heptadecanoic acid at 1 mg/ml in ethyl acetate, dilute into chloroform, and store stocks and dilutions in 80  C freezer to reduce evaporation. 6. Bench-top refrigerated centrifuge. 7. Microcentrifuge tubes tested for low extractable fatty acid content (see Subheading 3.2, step 4). 8. Solvent-resistant centrifugal evaporator (Speedvac). 2.3

1. Polar metabolites standards: A stock (1
) solution in water, containing 42 metabolites, is prepared according to Table 1. This solution is diluted in water to give 0.1
and 0.01
solutions, and amounts ranging from 50 μl of 0.1
solution down to 10 μl of 0.01
solution are dried by centrifugal evaporation to provide 5- to 7-point calibration curves. Thereby, the range of standards is 1–50 nmol for the (typically) most abundant metabolites and 0.025–1.25 nmol for the least abundant metabolites (see Note 2).

Standards

Table 1 Standards for polar metabolites analysis: concentration in stock standard solution, IBOX-TBDMS derivatives detected, GC elution time, and m/z of fragments Derivatized withc

Metabolitea

Stock 1
concentration, mM

Adjustment factorb

IBO TBDM X S

Pyruvate

1

0.2

1

Lactate

10

Alanine

Elutes at (min)d

Fragment, m/ze

1

4.0

160, 216

2

2

4.8

233, 261

5

1

2

5.7

232, 260

Glycine

5

1

2

6.1

218, 246

β-Alanine

3

0.6

2

7.6

218, 260

Urea

1

0.2

2

7.8

231

Valine

2

0.4

2

7.9

186, 260, 288

Norvaline (internal standard)

5

1

2

8.2

186, 260, 288

Leucine

2

0.4

2

8.7

200, 302

Isoleucine

2

0.4

2

9.3

200, 274, 302

γ-Aminobutyrate

0.5

0.1

2

9.8

274 (continued)

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Table 1 (continued) Derivatized withc

Metabolitea

Stock 1
concentration, mM

Adjustment factorb

Putrescine

1

0.2

2

9.8

170, 259

Succinate

0.5

0.1

2

9.9

289

Proline

4

0.8

2

9.9

184, 258, 286

Fumarate

0.5

0.1

2

10.4

287

Methionine

1

0.2

2

13.3

218, 292, 320

Serine

2.5

0.5

3

13.8

288, 302, 362, 390

Threonine

10

2

3

14.2

302, 404

Phenylalanine

1

0.2

2

15.2

234, 302, 308, 336

N-Acetyl aspartate

2

0.4

2

15.5

346

Malate

1.5

0.3

3

15.7

391, 419

Aspartate

3

0.6

3

16.3

302, 390, 418

α-Ketoglutarate

1

0.2

2

16.6

388

Hydroxyproline

0.5

0.1

3

16.7

314, 416

Cysteine

0.5

0.1

3

17.0

304, 378

2-Hydroxyglutarate

0.5

0.1

3

17.2

433

Phosphoenolpyruvate 0.5

0.1

3

17.4

259, 453

N-Acetyl glutamate

0.5

0.1

2

17.5

360

Glutamate

10

2

3

18.0

330, 404, 432

Ornithine

0.5

0.1

3

18.1

184, 286

Asparagine

0.5

0.1

3

18.4

302, 417

Lysine

1

0.2

3

19.5

300, 431

cis-Aconitate

0.5

0.1

3

19.6

459

Glutamine

10

2

3

20.1

329, 431

Dihydroxyacetone phosphate

5

1

3

21.3

526

IBO TBDM X S

1

1

Elutes at (min)d

Fragment, m/ze

Glycerol 3-phosphate 1.5

0.3

4

21.6

571

Histidine

0.1

3

22.1

338, 440

0.5

(continued)

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Table 1 (continued) Derivatized withc

Metabolitea

Stock 1
concentration, mM

Adjustment factorb

Citrate

1

0.2

3-Phosphoglycerate

1

Tyrosine

IBO TBDM X S

Elutes at (min)d

Fragment, m/ze

4

22.6

489, 591

0.2

4

22.7

585

1

0.2

3

22.7

302, 364, 466

Isocitrate

0.25

0.05

4

22.7

591

Tryptophan

0.5

0.1

2

23.1

302, 375

a

Enantiomers exactly co-elute in this system, so D-, L- or D/L-isomers can be used as standards See step 11 in Subheading 3.8 c Number of keto groups, or other functional groups, that are derivatized by isobutylhydroxylamine (IBOX) or MTBSTFA (TBDMS), respectively d Approximate: exact elution times depend on exact length and age of column e Mass (m/z) fragments used for quantification. Fragments in bold are also used in labeling analysis b

2. Fatty acid standards: A mixture of 37 fatty acid methyl esters at 0.2–0.6 mg/ml in dichloromethane (Sigma Supelco-37) is diluted tenfold in this solvent, and 10–100 μl volumes of the dilution are dried in a centrifugal evaporator to provide a range of 5 standards. 2.4

Derivatization

1. 20 mg/ml of O-isobutylhydroxylamine hydrochloride, dissolved in dry pyridine. 2. N-tert-Butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA). 3. 0.5 N hydrochloric acid in methanol. 4. 60–80  C oven.

2.5

GC-MS

1. 2-ml autosampler vials and caps with 0.15-ml inserts. 2. GC-MS with autosampler, 15 m
0.25 mm
0.25 μm 5-ms-type column, electron impact ionization.

2.6

Data Analysis

1. Program for 13C data correction (e.g., DExSI [17]). 2. Programs for quantification of metabolites (e.g., Metaquant [18], with further data processing in Microsoft Excel).

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Methods

3.1 Cell Culture and Labeling

1. Seed the cells into six-well plates such that by the end of the labeling period, they are at the desired density/confluency. For cancer cells, 3–10
105 cells (0.3 mg or more of cell protein) should give good GC-MS signals. Do 2–3 (preferably 3) separate wells for each condition for GC-MS analysis; do at least one extra well per condition for protein analysis, cell counts, etc., as the wells used for GC-MS analysis are extracted in situ, which precludes cell counting and makes it difficult to analyze protein in the extracted material (see Note 3). At this stage, cells are grown in standard tissue culture media at 37  C with 5% CO2. 2. At the start of the labeling period, culture medium is aspirated and replaced with the labeling medium (2 ml per well in a six-well plate), containing either 13C-glutamine or 13C-glucose. Cells are replaced in the culture incubator for 24 h (see Note 4).

3.2 Sample Collection and Extraction

1. Before extraction, save 1 ml of medium from labeled cells (see Note 5). Centrifuge to clarify the medium if there are a lot of floating cells. Also, recover cells (e.g., by trypsinization) from extra well(s) for protein determination, blotting, etc. as necessary. 2. Aspirate the remaining medium from the labeled cells. Wash the cells quickly three times with cold PBS (see Note 6). Thorough but rapid washing is necessary to remove traces of medium from cells. Add 0.45 ml cold water/methanol extraction solution with L-norvaline (internal standard for polar metabolites) to each well. Place plates on dry ice for 30 min, then thaw on ice for 10 min. 3. Use a cell scraper or end of P1000 pipet tip to detach any cells stuck to the plate (see Note 7). 4. Transfer cell suspension/extract to micro-centrifuge tubes. If analyzing fatty acids, use tubes which have been checked for low fatty acid contamination, and add 0.45 ml of extraction solution to empty tubes of the same type to act as controls (see Note 8). 5. Add 0.225 ml of chloroform containing 4 μg/ml (3.3 nmol) heptadecanoic acid (as internal standard for fatty acids; see Note 9). 6. Mix samples briefly by vortexing and then centrifuge for 5 min at 20,000
g at 4  C.

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7. Transfer the top layer (methanol/water extract; polar phase) and bottom layer (chloroform extract; non-polar phase) to separate tubes by pipette. As far as possible, avoid collecting any solids from the boundary between the phases. It is better to sacrifice a little of the extracts than to end up with a lot of particulate matter in the samples. Recovery is controlled for by the use of internal standards. Extracts may be stored frozen at this stage (80  C). 8. Dry extracts using a centrifugal evaporator. Standards for polar metabolites and fatty acids (see Subheading 2.3) may be conveniently dried at the same time. 3.3 Derivatization (Polar Samples and Standards)

1. Add 30 μl of isobutylhydroxylamine (20 mg/ml solution in pyridine; see Note 10) to dried polar extracts or standards, and incubate at 80  C for 20 min. 2. Cool, centrifuge briefly to remove condensation from lids of tubes, and add 30 μl MTBSTFA. 3. Incubate for 60 min at 80  C. 4. Centrifuge briefly and transfer to autosampler vial with insert. Samples are best prepared the day prior to running (see Note 11). 5. Leave samples at 4  C overnight.

3.4 GC-MS (Polar Samples and Standards)

The method described here is specific for a Shimadzu QP2010 Plus GC-MS, but can be adapted as necessary for other GC-MS models. 1. Program the GC with an injection temperature of 250  C, split ratio 1/10, and sample injection volume up to 2 μl. 2. Set the GC oven temperature initially to 130  C for 4 min, followed by a rise to 250  C at 6  C/min and then to 280  C at 60  C/min, with a final hold at this temperature for 2 min. 3. Set the GC flow rate with helium carrier gas to 50 cm/s. 4. Set the GC-MS interface temperature to 300  C and (electron impact) ion source temperature to 200  C, with 70 eV ionization voltage. 5. Set the mass spectrometer to scan m/z range 160–600 (starting at 3.5 min; see Note 12). Detector sensitivity is varied through the run as necessary for optimal detection of peaks without saturating the detector. 6. Run the batches of samples and standards in the following order: standards, samples, then standards again. Replicate samples of the same treatment are spaced evenly throughout the sample batch to compensate for any drift in signal.

13

3.5 Derivatization (Nonpolar Samples, for Fatty Acids)

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1. Add 50 μl methanol/ 0.5 N HCl to dried samples. 2. Heat for 30 min at 60  C. 3. Cool, dry using a centrifugal evaporator, and resuspend in 50 μl pyridine (heat 10 min, 60  C). 4. Transfer to autosampler vial with insert. FAMES standards are fatty acids already modified suitably for GC-MS, so they just need to be similarly resuspended in pyridine.

3.6 GC-MS (Fatty Acid Samples and Standards)

Run samples and standards on the GC-MS as described above for polar metabolites, with the following changes: 1. Set the GC oven temperature initially to 80  C for 3.5 min, followed by a rise to 280  C at 10  C/min, with a final hold at this temperature for 2 min. 2. Set the mass spectrometer to scan m/z range 100–500.

3.7 13C Data Analysis (Polar Metabolites)

An important first step in 13C data analysis is the correction of the naturally occurring stable isotope content of the metabolite ion fragments. Carbon is naturally approximately 1% 13C, and silicon (from the MTBSTFA derivatization reagent) is 4.7% 29Si (+1) and 3.1% 30Si (+2). Oxygen, nitrogen, hydrogen, and (if present) sulfur also, to a lesser extent, have heavy stable isotopes. Without correction for the natural isotopic content of these elements, any added 13 C labeling can be completely obscured. Natural isotope correction can be done using programs such as DexSi [17] or, as described here, using Excel spreadsheets based on the framework set out in Nanchen et al. [14, 19]. 1. Identify sample peaks in GC-MS chromatograms in the GC-MS program (Shimadzu GCMS solution Postrun Analysis) by matching characteristic ion fragments (fragments containing all or part of the metabolite and residues of the derivatization reagents) and elution times to that of standards (Tables 1 and 2; a partial chromatogram with the aspartate peak highlighted is shown in Fig. 2a). 2. In the GC-MS program, select metabolite peaks for labeling analysis and export mass ion count (intensity) data (averaged across each peak) to the clipboard and paste into an Excel file. Repeat for all labeled metabolite peaks of interest in all sample files. 3. The fragment m/z values listed in Table 2 represent the lowest masses of sequences of metabolite ions that are used for labeling analysis. These lowest masses are designated m0, representing ions that do not contain any heavy isotopes. Copy ion counts for m0, plus counts for ions with additional mass (m/z) of 1, 2, 3, 4, 5, or 6 units, designated m1, m2, m3,

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Table 2 Chemical formulae for fragment ions used for

13

C labeling calculations

Metabolite

Fragment, m/z Formula (minus metabolite C) Metabolite carbons

Pyruvate

216

C6H18N1O3Si1

3

Lactate

233 261

C8H25O2Si2 C8H25O3Si2

2 3

Alanine

232 260

C8H26N1O1Si2 C8H26N1O2Si2

2 3

Glycine

218 246

C8H24N1O1Si2 C8H24N1O2Si2

1 2

γ-Aminobutyrate

274

C8H28N1O2Si2

4

Succinate

289

C8H25O4Si2

4

Proline

184 258

C6H22N1Si1 C8H28N1O1Si2

4 4

Fumarate

287

C8H23O4Si2

4

Serine

302 390

C12H32N1O2Si2 C14H40N1O3Si3

2 3

N-Acetyl aspartate

346

C8H28N1O5Si2

6

Malate

419

C14H39O5Si3

4

Aspartate

302 390 418

C12H32N1O2Si2 C14H40N1O3Si3 C14H40N1O4Si3

2 3 4

α-Ketoglutarate

388

C12H34N1O5Si2

5

Phosphoenolpyruvate

453

C14H38O6P1Si3

3

Glutamate

330 432

C12H36N1O2Si2 C14H42N1O4Si3

4 5

Asparagine

417

C14H41N2O3Si3

4

Dihydroxyacetone phosphate

526

C18H49N1O6P1Si3

3

Glycerol 3-phosphate

571

C20H56O6P1Si4

3

Citrate

591

C20H55O7Si4

6

3-Phosphoglycerate

585

C20H54O7P1Si4

3

etc., into another Excel sheet set up to perform calculations per the following steps. Repeat for all the designated metabolite fragments (see Note 13). 4. Normalize the data for each fragment to a sum of 1 by dividing m0, m1, m2, m3, etc. by the sum of m0 through m6. This represents the uncorrected mass isotopomer distribution vector (MDV).

Fig. 2 Analysis of polar metabolites from IGR37 melanoma cells following CB-839 treatment and 13C labeling. (a) Partial GC-MS chromatogram showing total ion count versus time (13–20 min). Metabolite peaks shown: (1) methionine, (2) serine, (3) threonine, (4) phenylalanine, (5) malate, (6) aspartate, (7) α-ketoglutarate, (8) cysteine, (9) phosphoenol-pyruvate, (10) glutamate, (11) lysine, (12) glutamine. Detector sensitivity varies during the run. (b) Screenshot of quantification of aspartate in MetaQuant. (c) Changes in cellular aspartate, glutamate, and glutamine over 24 h treatment with 10 nM CB-839, relative to controls. Calculated amounts of metabolites were corrected for 13C labeling and normalized to mg protein per sample. (d) Partial mass spectrum of aspartate; m/z 418 fragment includes all four aspartate carbons and is prominently +4 labeled (m/z 422) from 13C5-glutamine. (e) Fraction of aspartate derived from glutamine and effects of CB-839. Calculation based on relative fractional labeling. The opposite effect (proportionally increased labeling with CB-839) was seen with 13C-glucose labeling (not shown)

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5. For each fragment ion and each element within that fragment with natural heavy isotopes (C, H, N, O, S, Si), set up a 7
7 correction matrix, which defines the expected fractional abundance of m0 through m6 based on the number of atoms for that element within the fragment (Table 2) and the natural abundance of its stable isotopes [19]. For carbon atoms, only those arising from the derivatization reagents are counted (potentially labeled metabolite carbons are excluded). 6. Multiply together the correction matrices for the individual elements to yield an overall correction matrix C for a particular fragment. 7. Solve the equation ClMID ¼ MDV, where MID is the corrected mass isotopomer distribution, by multiplying both sides of the equation by the inverse of C (see Note 14). 8. Calculate fractional labeling (the probability that any carbon within a metabolite fragment containing n carbons is P 13 C-labeled, FL) as: FL ¼ ni¼1 mni :i where m1 through mn are now from MID. By dividing the fractional labeling of a metabolite with the fractional labeling of a 13C substrate (glucose, glutamine), the fraction of the metabolite derived from the substrate can be determined. 3.8 Metabolite Quantification (Polar Metabolites)

Quantification is performed using standard curves constructed in MetaQuant [18]. 1. From the GC-MS software, export data files for samples and standards in netCDF format, which can be read by MetaQuant. 2. Under the “Input” tab in Metaquant, enter the metabolite names, retention times, and fragment ion masses as listed in Table 1. 3. In MetaQuant, prepare “mix” files, which repeatedly list metabolites (with names matching those entered at step 2 above), with different “concentrations” (or amounts), corresponding to the varied amounts of the standard mixture run in a particular experiment. The highest standard (50 μl of 0.1
solution, see Subheading 2.3, item 1) is given a value of 25, the lowest standard (10 μl of 0.01
solution) is given a value of 0.5, and so on, proportionally for intermediate standards. The “mix” files are simple text documents and may be more conveniently edited in a text editor once set up within MetaQuant. 4. Under the “Autocalibration” tab in MetaQuant, load “mix” files and the corresponding netCDF files for each amount of the standard mixture. Set other parameters per the MetaQuant program [18], and “Start Autocalibration” for each standard file. Inspect the calibration curve output under the “Calibration” tab, and choose the best curve-fitting option. If a peak

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has been missed, re-adjust retention times as necessary under “Input” and repeat the process. 5. Load sample netCDF files under the “Batch-Analysis” tab, and press “Analyse.” 6. Inspect the results, adjust the calibration by deleting unnecessary high standards under “Calibration” and redo the batch analysis. 7. Export the calibrated metabolite data to Excel. 8. In Excel, manually inspect the data. Remove missing values, check values for different fragments for possible interference (e.g., for putrescine, the m/z 259 fragment is liable to interference from the adjacent m/z 258 proline fragment, so it is often better to only use m/z 170 for putrescine calibration), delete the metabolite average values output from MetaQuant, and recalculate these averages using the cleaned-up data. 9. Adjust metabolite amounts per the recovery of the internal standard, L-norvaline. The value for norvaline should be 9 (corresponding to 9 nmol), so metabolite amounts are multiplied
9 and divided by the norvaline amount registered for each sample. 10. Where quantification is done in conjunction with 13C labeling, correction for labeling is needed because the calibration is done using unlabeled standards and the unlabeled (m0) ions. Multiply the norvaline-corrected metabolite amounts by m0nat/ m0expt, where m0nat ¼ fraction of m0 (in the mass isotopomer distribution) is expected if only natural labeling is present, and m0expt ¼ experimentally determined fraction of m0 (see Note 15). 11. Adjust the metabolite amounts to nmol by multiplying them by a factor based on their relative amounts in the standard mixture. For metabolites such as norvaline that were added to the initial 1
standard mixture to 5 mM, the factor is 1; for others the range is between 0.05 and 2 (Table 1). 3.9 13C Data Analysis (Fatty Acids)

For the purpose of measuring the contribution of glutamine or glucose to fatty acid synthesis via 13C labeling, it is usually sufficient to only analyze labeling of palmitic acid (C16:0), which after derivatization is separated by GC as methyl palmitate. Labeling analysis is done as for polar metabolites, with the following modifications and additional information: 1. The methyl palmitate molecular ion m/z 270 elutes from the GC at 15.1 min (Table 3). This ion has the molecular formula C17H34O2, and correction is done including all the carbons in the ion (otherwise, m1 and m2 in the corrected mass isotopomer distribution are dominated by natural 13C labeling).

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Table 3 Quantification of major fatty acid species: amounts of fatty acid methyl esters in FAMES-37 standard, GC elution time, and m/z of fragments used for quantification

Fatty acid

Amount (nmol) per 5 μl undiluted FAMES-37 standard

Elutes at (min)

Fragment, m/z

3.7

14.9

194, 268

11.1

15.1

227, 270

Heptadecanoic (C17:0)

3.5

16.0

241, 284

Linoleic (C18:2 n-6)

3.4

16.6

263, 294

Oleic (C18:1 n-9)

6.8

16.7

264, 296

cis-Vaccenic (C18:1 n-7)

3.4

16.8

264, 296

Stearic (C18:0)

6.7

17.0

255, 298

Arachidonic (C20:4 n-6)

3.1

18.1

203

Palmitoleic (C16:1 n-7) Palmitic (C16:0)

2. The raw mass spectral data for the palmitate peak should be examined, and if labeling (which characteristically increases in steps of two mass units) extends beyond m6, extend the MDV (see Subheading 3.7, step 4) for the m/z 270 ion beyond m6, and correspondingly enlarge the size of the correction matrix (Subheading 3.7, step 5) (see Note 16). 3.10 Metabolite Quantification (Fatty Acids)

Parameters for quantification in MetaQuant of the principal fatty acids in nonpolar cell extracts, plus the C17:0 internal standard, are listed in Table 3. The analysis follows the same procedure as for polar metabolites (Subheading 3.8), noting the following specific points: 1. In step 9, adjust for sample recovery based on the recovery of added C17:0 (3.3 nmol). 2. In step 11, convert the calibrated amounts for fatty acids (FAMES) to nmol based on the varying nmol/μl of these FAMES in the FAMES-37 standard mixture (Table 3). The data analysis process and examples of obtainable results are illustrated in Fig. 2. These data are from an experiment in which IGR37 melanoma cells were treated with the glutaminase inhibitor CB-839 [20] and labeled with 13C-glutamine or 13C-glucose. For clarity, the figure mainly focuses on aspartate, but similar effects were seen for TCA cycle metabolites and related amino acids. Shown are: part of the GC-MS chromatogram (total ion count measured by MS versus time, Fig. 2a), with the aspartate peak highlighted; a screenshot of aspartate quantification in MetaQuant (Fig. 2b); the effects over time of CB-839 on cellular amounts of

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aspartate, glutamate, and glutamine (Fig. 2c); the raw mass (m/z) distribution of an aspartate-derived ion from a 13C-glutaminelabeled culture, showing a prominent +4 mass labeling (Fig. 2d); effects of CB-839 on the proportion of asparate that is derived from glutamine, as inferred from labeling data (Fig. 2e).

4

Notes 1. The ideal medium would be one that best matches physiological conditions. Customized media based on blood (serum) metabolite concentrations have been reported [21, 22]. However, it is also important that nutrients are not depleted during labeling procedures that may take several hours or days, or else 13 C-labeling patterns will be distorted. Glutamine, if added at a physiological concentration of 0.5 mM, may be rapidly consumed leading to metabolic changes, including induction of glutamine synthesis and a broader amino acid starvation response. A useful quality-control check is to measure the usage of glucose and glutamine in culture media to ensure they are not depleted. For this purpose, we use a YSI 2950 Biochemical Analyzer. 2. The design of the standard mixture is based on the experience of what is measurable in cultured cancer cells. Not all metabolites are detectable in all cells, and other metabolites not listed here can be measured by this protocol in specific cell types. The standard solution is stable at 20  C for 6–12 months (glutamine is the most unstable component). 3. It may also be critical to measure the number of cells at the start of the labeling period, if substrate or metabolite measurements are to be made on the medium (see Note 5, below). Initial and final cell numbers can be used to estimate per-cell rates of substrate uptake or metabolite secretion using “area-underthe-curve” measurements [23]. This is most important when comparing cells growing at different rates. 4. For labeling with a single time point, 24 h is a good compromise. Labeling from glucose into the TCA cycle is relatively slow, and it may take 16–24 h to reach a stable maximum. In contrast, labeling from glutamine into the TCA cycle is quicker, reaching a maximum in a few hours. If feasible, the use of a range of labeling periods will allow for the estimation of labeling rates and flux through different metabolic pathways [24]. 5. It is worth saving the medium so substrate depletion can be checked (see Note 1; lactate secretion can also be measured with the YSI instrument), and medium may also be analyzed by

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GC-MS to measure consumption of amino acids (e.g., serine and branched-chain amino acids) and secretion of other amino acids (e.g., glycine, alanine and glutamate), and thereby provide complementary results to those from analysis of cells. 6. Sodium chloride solution, 0.9%, can be used instead of PBS. Phosphate (phosphoric acid) is derivatized by TBDMS and produces a peak in the MS that could potentially interfere with metabolite peaks of interest, but in the current protocol, it is well-separated from metabolite peaks. 7. For analysis of polar metabolites, scraping may not be necessary, as cells are likely permeabilized even though cell ghosts are still attached to plates. However, this is important for fatty acid analysis: yields may be much poorer (95%. 14. These PBS washes are necessary to remove serum proteins that are present in the FBS component of the tumor growth medium. CFSE binds to FBS proteins, and failure to remove these proteins will interfere with the subsequent CFSE staining of melanoma cells. 15. RPMI-1640 supplemented with 0.5% BSA is used here in place of complete growth medium containing 10% FBS in order to eliminate attachment factors found in FBS. Use of medium containing FBS at this stage might otherwise result in nonspecific adherence of melanoma cells to exposed tissue culture plastic should any small gaps in the lymphatic endothelial cell monolayer be exposed during wash steps in the subsequent adhesion assay. 16. Take care not to disturb the SV-LEC monolayer during medium removal and all wash steps. During washing, do not pipet the 1 PBS directly onto the monolayer. Rather, gently pipet 1 PBS onto the side of the wells in the 48-well plate, and allow it to slowly rinse across the bottom of the wells. When removing the 1 PBS, tilt the plate at a 45
angle and pipet out the solution from the bottom of the well. 17. As with the previous 1 PBS washes, do not pipet directly onto the monolayer but allow the RPMI-1640 medium with 0.5% BSA to slowly run from the side of the well down across the surface of the monolayer. Medium with BSA is again used in this step so that attachment factors found in FBS are not introduced into the assay system. 18. Again, pipet tumor cells gently along the side of the well so as not to disturb the SV-LEC monolayer. Once tumor cells have been added, the plate can be gently swirled and moved left-

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right and up-down to distribute the cells evenly within the well. The assay should be performed in triplicate for each cell type or experimental condition. 19. If cells have not completely detached within 10 min, they may be dislodged by holding the 48-well plate at a 45
angle and washing the wells by pipetting the Accutase solution across the entire surface of the wells several times. 20. Because it will be difficult to recover the full 200 μl volume of cells in Accutase solution from the wells without introducing varying levels of air bubbles in each sample, it is better to collect a consistent volume of 175 μl across each sample. Collecting equal volumes at this stage will allow for a consistent volumetric acquisition of samples during the subsequent flow cytometry and is crucial to accurate quantitation when comparing the number of adherent tumor cells across samples. 21. For each sample, collect only 150 μl of the 175 μl volume so as not to run the flow cytometer dry. As alluded to in Note 20, collecting equal volumes for each sample makes it possible during data analysis to compare across samples the number of melanoma cells that had adhered to the SV-LEC monolayer.

Acknowledgments D5.1G4 murine melanoma cells were a generous gift of Dr. Jerry Neiderkorn (University of Texas Southwestern Medical School). The SV-LEC murine melanoma cell line was kindly provided by Dr. Jonathan Alexander (Louisiana State University Health Sciences Center). This work was supported by funding from Virginia’s Commonwealth Health Research Board and a Jeffress Trust Awards Program in Interdisciplinary Research Grant from the Thomas F. and Kate Miller Jeffress Memorial Trust (Bank of America, N.A., Trustee) to KMH. We also thank Mr. Michael Hargadon and Mrs. Patricia Hargadon for generous donations to support the involvement of Hampden-Sydney College undergraduate students in this research. References 1. Luke JJ, Flaherty KT, Ribas A, Long GV (2017) Targeted agents and immunotherapies: optimizing outcomes in melanoma. Nat Rev Clin Oncol 14:463–482 2. Hargadon KM, Johnson CE, Williams CJ (2018) Immune checkpoint blockade therapy for cancer: an overview of FDA-approved immune checkpoint inhibitors. Int Immunopharmacol 62:29–39

3. Chen J, Xu Y, Zhou Y, Wang Y, Zhu H, Shi Y (2016) Prognostic role of sentinel lymph node biopsy for patients with cutaneous melanoma: a retrospective study of surveillance, epidemiology, and end-result population-based data. Oncotarget 7:45671–45677 4. Doepker MP, Thompson ZJ, Harb JN, Messina JL, Puleo CA, Egan KM, Sarnaik AA, Gonzalez RJ, Sondak VK, Zager JS (2016)

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Dermal melanoma: a report on prognosis, outcomes, and the utility of sentinel lymph node biopsy. J Surg Oncol 113:98–102 5. Pilko G, Zgajnar J, Music M, Hocevar M (2012) Lower tumour burden and better overall survival in melanoma patients with regional lymph node metastases and negative preoperative ultrasound. Radiol Oncol 46:60–68 6. Kim C, Economou S, Amatruda TT, Martin JC, Dudek AZ (2015) Prognostic significance of microscopic tumor burden in sentinel lymph node in patients with cutaneous melanoma. Anticancer Res 35:301–309 7. Ulmer A, Dietz K, Hodak I, Polzer B, Scheitler S, Yildiz M, Czyz Z, Lehnert P, Fehm T, Hafner C, Schanz S, Ro¨cken M, Garbe C, Breuninger H, Fierlbeck G, Klein CA (2014) Quantitative measurement of melanoma spread in sentinel lymph nodes and survival. PLoS Med 11:e1001604 8. Pereira ER, Kedrin D, Seano G, Gautier O, Meijer EFJ, Jones D, Chin SM, Kitahara S, Bouta EM, Chang J, Beech E, Jeong HS, Carroll MC, Taghian AG, Padera TP (2018) Lymph node metastases can invade local blood vessels, exit the node, and colonize distant organs in mice. Science 359:1403–1407 9. Brown M, Assen FP, Leithner A, Abe J, Schachner H, Asfour G, Bago-Horvath Z, Stein JV, Uhrin P, Sixt M, Kerjaschki D (2018) Lymph node blood vessels provide exit routes for metastatic tumor cell dissemination in mice. Science 359:1408–1411 10. Cianfarani F, Mastroeni S, Odorisio T, Passarelli F, Cattani C, Mannooranparampli TJ, Fortes C, Failla CM (2012) Expression of vascular endothelial growth factor-C in primary cutaneous melanoma predicts sentinel lymph node positivity. J Cutan Pathol 39:826–834 11. Peppicelli S, Bianchini F, Calorini L (2014) Inflammatory cytokines induce vascular endothelial growth factor-C expression in melanoma-associated macrophages and stimulate melanoma lymph node metastasis. Oncol Lett 8:1133–1138 12. Van Marck V, Stove C, Van Den Bossche K, Stove V, Paredes J, Vander Haegen Y, Bracke M (2005) P-cadherin promotes cell-cell adhesion and counteracts invasion in human melanoma. Cancer Res 65:8774–8783

13. Kreiseder B, Orel L, Bujnow C, Buschek S, Pflueger M, Scheutt W, Hundsberger H, de Martin R, Wiesner C (2013) α-Catulin downregulates E-cadherin and promotes melanoma progression and invasion. Int J Cancer 132:521–530 14. Botti G, Cerrone M, Scognamiglio G, Anniciello A, Ascierto PA, Cantile M (2013) Microenvironment and tumor progression of melanoma: new therapeutic prospectives. J Immunotoxicol 10:235–252 15. Jacquelot N, Duong CPM, Belz GT, Zitvogel L (2018) Targeting chemokines and chemokine receptors in melanoma and other cancers. Front Immunol 9:2480 16. Rebhun RB, Cheng H, Gershenwald JE, Fan D, Fidler IJ, Langley RR (2010) Constitutive expression of the alpha4 integrin correlates with tumorigenicity and lymph node metastasis of the B16 murine melanoma. Neoplasia 12:173–182 17. Jeong K, Murphy JM, Rodriguez YAR, Kim JS, Ahn EE, Lim SS (2019) FAK inhibition reduces metastasis of α4 integrin-expressing melanoma to lymph nodes by targeting lymphatic VCAM-1 expression. Biochem Biophys Res Commun 509:1034–1040 18. Ando T, Jordan P, Joh T, Wang Y, Jennings MH, Houghton J, Alexander JS (2005) Isolation and characterization of a novel mouse lymphatic endothelial cell line: SV-LEC. Lymphat Res Biol 3:105–115 19. Fidler IJ, Gersten DM, Budmen MB (1976) Characterization in vivo and in vitro of tumor cells selected for resistance to syngeneic lymphocyte-mediated cytotoxicity. Cancer Res 36:3160–3165 20. Overwijk WW, Restifo NP (2001) B16 as a mouse model for human melanoma. Curr Protoc Immunol 39:20.1.1–20.1.29 21. Knisely TL, Niederkorn JY (1990) Immunologic evaluation of spontaneous regression of an intraocular murine melanoma. Invest Ophthalmol Vis Sci 31:247–257 22. Hargadon KM, Forrest OA, Reddy PR (2012) Suppression of the maturation and activation of the dendritic cell line DC2.4 by melanomaderived factors. Cell Immunol 272:275–282

Part II 3D Cell Culture Systems for Studying Melanoma

Chapter 11 An Approach to Study Melanoma Invasion and Crosstalk with Lymphatic Endothelial Cell Spheroids in 3D Using Immunofluorescence Sanni Alve, Silvia Gramolelli, and P€aivi M. Ojala Abstract Three-dimensional (3D) cell culture has allowed a deeper understanding of complex pathological and physiological processes, overcoming some of the limitations of 2D cell culture on plastic and avoiding the costs and ethical issues related to experiments involving animals. Here we describe a protocol to embed single melanoma cells alone or together with primary human lymphatic endothelial cells in a 3D crosslinked matrix, to investigate the invasion and molecular crosstalk between these two cell types, respectively. After fixation and staining with antibodies and fluorescent conjugates, phenotypic changes in both cell types can be specifically analyzed by confocal microscopy. Key words Lymphatic endothelial cells, Melanoma, 3D cell culture, Co-culture, Invasion, Sprouting, Molecular crosstalk, Fibrin matrix, Immunofluorescence

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Introduction In living tissues, cells are organized in a three-dimensional (3D) environment where complex cell–cell and cell–matrix interactions occur in all directions and have a pivotal role in many physiological and pathological processes. Therefore, standard 2D monolayer cell culture approaches are insufficient to properly understand these processes and instead require the use of different animal models, which are costly and involve ethical issues. Despite the indisputable importance of these in vivo approaches, they do not easily allow dissection of molecular interactions between the tumor and its microenvironment. The development of 3D cell culture methodology has, to some extent, filled the gap between 2D cell culture and animal models, providing an experimental platform that resembles more closely the in vivo tissue in terms of cellular communication and organization, without the high costs and issues related to experiments involving animals. Several studies

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have highlighted how the morphology, proliferation, and differentiation, as well as the gene and protein expression, of many cell types is modified by the 3D environment, resembling more closely the in vivo situation (reviewed in [1, 2]). In 3D matrices, the diffusion of cytokines, nutrients, and growth factors also occurs in a more physiological fashion. Another remarkable advantage of this technique is that gene expression can be easily manipulated, and the effects of this manipulation can be readily observed when cells are embedded in 3D matrices [1]. The 3D cultures with relevant extracellular matrix components mimic the content of the actual microenvironment for the cancer and provide more relevant results. In particular, the 3D cell culture approach has provided a low-cost and efficient platform to better understand the progression of cancer and to find new molecules for cancer therapy [3, 4]. The 3D cell culture offers many advantages also when studying the effects of the molecular crosstalk between different cell types as it allows a deeper interaction and communication in all spatial dimensions. Cancer cells can be embedded in cross-linked matrices alone (as single cells or tumor spheroids) or together with other cell types that compose the tumor microenvironment. This has allowed researchers to reveal and dissect the contribution of the different stromal components (e.g., endothelial cells, fibroblasts, immune cells, different types of extracellular matrices) to cancer progression [4–8]. In light of the clinical observation that in solid cancers, such as melanoma, the presence of peri- and intratumoral lymphatic vessels predicts poor outcome due to metastatic dissemination (reviewed in [9]), we have established 2D and 3D co-culture models to investigate the crosstalk between melanoma and lymphatic endothelial cells (LECs) (Fig. 1) [7]. Besides representing a possible route for dissemination of the cancer cells from the primary tumor, LECs actively communicate with cancer cells inducing pro-metastatic changes and enhancing their ability to invade within the surrounding tissues [7, 10]. This co-culture system has made it possible to score not only for cancer cell growth but also invasion into lymphatics and changes in cell fate that could be directly connected to melanoma progression. Here we describe two different experimental protocols that allow the dissection of the molecular basis of the crosstalk between LECs and melanoma cells: 3D co-culture of melanoma cells with preformed LEC spheroids in cross-linked fibrin matrix (workflow described in Fig. 2a) and embedding of the melanoma cells after co-culturing with LECs in 2D or from monotypic cultures into 3D cross-linked fibrin matrix (workflow described in Fig. 2b). For these studies we chose fibrin matrix because it is commonly found in the melanoma perivascular tumor spaces. Cells are cultured in the fibrin droplets for 4 days, after which the droplets are fixed, stained with antibodies and/or fluorescent markers, and imaged by

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Fig. 1 Representative images of (a) GFP-expressing WM852 melanoma cells (green) co-cultured with LECs in 2D for 2 days. The dashed line indicates the LEC–melanoma border. Scale bar ¼ 25μm; (b) Preformed LEC spheroids embedded in a 3D cross-linked fibrin matrix for 4 days alone (right) or together (left) with single, GFP-expressing WM852 melanoma cells (green). Scale bar ¼ 200μm. Cells are stained as indicated, and nuclei are counterstained with Hoechst 33342

Fig. 2 Schematic diagram of the assays described. (a) Preformed LEC spheroids (shown in red) alone or together with single melanoma cells (shown in green) are embedded for 4 days either alone or together in 3D cross-linked fibrin matrix droplets. (b) Melanoma cells from a monotypic culture (dark green) or co-cultured with LECs in 2D (light green) are, after sorting and separation of the different cell types, embedded for 4 days in 3D cross-linked fibrin matrix. After fixation and staining, cells are imaged by confocal microscopy

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Fig. 3 Representative images of melanoma cells embedded in 3D cross-linked fibrin matrix for 4 days. Left panel: monotypic WM852 melanoma cells. Right panel: WM852 cells after the LEC co-culture. Cells are stained as indicated, nuclei are counterstained with Hoechst 33342. Scale bar ¼ 50μm

confocal microscopy (workflow described in Fig. 2). While the 3D co-culture allows to directly observe the interplay between the preformed LEC spheroids and the melanoma, the embedding of melanoma cells into 3D matrix, after the LEC co-culture in 2D (also called LEC priming), permits observation of the LEC interaction–induced phenotypic changes in melanoma. In fact, we observed that in the 3D co-cultures, the melanoma cells are attracted toward the LEC spheroids and adhere to them, likely mimicking the intravasation of the tumor cells into the peritumoral lymphatics (Fig. 1b). Furthermore, the pro-metastatic changes induced by the LEC priming in 2D induced the melanoma cells to change their growth phenotype in 3D from a sphere-like (typical for the monotypic not LEC-primed melanoma cells) to elongated and sprouting, suggestive of an increased invasive potential (Fig. 3).

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Materials Prepare all the solutions (thrombin and aprotinin aliquots, agarose stock solution, blocking buffer, and washing solutions) in sterilized, deionized water. Prepare all the stock solutions and reagents and perform all the procedures involving living cells in a cell culture hood (with laminar flow) using sterile equipment and reagents suitable for cell culture to avoid contamination. After fixation, staining of the fibrin droplets can be carried out under nonsterile conditions.

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In Fig. 3 we show an example of the phenotypic comparison of monotypic melanoma vs. LEC co-cultured, separated melanoma cells cultured in fibrin droplets (see Note 4). The melanoma cells can come from co-cultures consisting of different cell types, but the materials needed as well as the separation procedure works for most cell types. 1. Fluorescently labeled melanoma cells (e.g. melanoma cells expressing a fluorescent reporter protein such as EGFP or labeled with a fluorescent cell tracer prior to the start of co-culture). 2. Cell growth medium suitable for co-culture. For the melanoma–LEC 2D co-cultures we have used Endothelial cell growth medium (EGM-2 Basal Medium supplemented with EGM-2 MV Microvascular Endothelial Cell Growth Medium SingleQuots supplements but excluding VEGF). 3. Phosphate buffered saline (PBS). 4. FACS tubes complemented with a strainer cap. 5. Falcon tubes.

2.2 Preparation of Fibrin Droplets

Fibrin droplets are prepared for embedding of melanoma cells or LEC spheroids in fibrin and for establishing 3D melanoma–LEC co-cultures (Figs. 3 and 4). 1. Thrombin from human plasma: Prepare a 800 U/mL stock solution of thrombin in sterile, deionized water. Store in 10μL aliquots (¼8 U/aliquot) at 20  C. 2. Aprotinin: Dissolve aprotinin in sterile, deionized water to a final concentration of 20μg/μL. Store in 40μL aliquots (¼800μg/aliquot) at 20  C. 3. Human lyophilized fibrinogen, plasminogen depleted. 4. Hank’s balanced salt solution (HBSS): 140 mM NaCl, 5 mM KCl, 1 mM CaCl2, 0.4 mM MgSO4·7H2O, 0.5 mM MgCl2·6H2O, 0.3 mM Na2HPO4·2H2O, 0.4 mM KH2PO4, 6 mM D-glucose, 4 mM NaHCO3. 5. 6-well cell culture dishes. 6. Growth medium appropriate for the cells under study, supplemented as necessary (e.g. RPMI-1640 or DMEM supplemented with 10% fetal bovine serum [FBS]). 7. Trypsin. 8. Microcentrifuge tubes. 9. Falcon tubes.

2.3 Preparation of LEC Spheroids

1. Human dermal microvascular endothelial cells (primarily representing lymphatic endothelial cells). 2. 96-well U-bottom dishes.

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Fig. 4 Schematic diagram of the experimental procedure for embedding the preformed LEC spheroids with single melanoma cells in 3D cross-linked fibrin matrix. LEC spheroids are carefully resuspended in a thrombin + aprotinin solution (indicated as thrombin) while single melanoma cells are suspended in a fibrinogen solution using different pipette tips. The two solutions are mixed together (indicated as MIX), and the solution is then dispensed on a dish as single 50μL droplets

3. Low melting point agarose stock solution (0.5% low melting point agarose): Mix 0.5 g of low melting point agarose in 100 mL of sterile deionized water. Mix by using a magnetic stirrer and heat to around 50–70  C until fully dissolved. Store the stock at room temperature (see Note 1). 4. 200μL wide bore pipette tips. 5. Multichannel pipette. 6. Sterile multichannel plastic tray).

pipette

reagent

reservoir

(e.g.

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7. Endothelial cell growth medium: EGM-2 Basal Medium supplemented with EGM-2 MV Microvascular Endothelial Cell Growth Medium SingleQuots supplements but excluding VEGF. 2.4 Indirect Immunofluorescence (IF) Staining and Imaging of Fibrin Droplets

1. PBS. 2. 4% paraformaldehyde (PFA) in PBS. 3. 48-well dish. 4. Spatulas. 5. 1:2 ice-cold acetone-methanol: Prepare in advance and store at 20  C. 6. Hoechst solution: 1μg/mL of Hoechst 33342 in PBS. Store at 4  C, protected from light. 7. Texas-Red-X Phalloidin (Invitrogen), at a working dilution of 1:200 in blocking buffer. 8. Primary antibodies suitable for IF (see Note 2). For the detection of PECAM as shown in Fig. 2, we use mouse anti-human CD31, clone JC70A (Dako) at a working dilution of 1:500 in blocking buffer. 9. Secondary antibodies conjugated with fluorophores, for example Alexa Fluor (see Note 3). For detection of the primary antiPECAM antibody, we use Alexa Fluor 594 Goat anti-Mouse Cross-absorbed Secondary Antibody (Thermo Fisher) a working dilution of 1:800 in blocking buffer. 10. Parafilm. 11. Glass slides. 12. 50  20 mm coverslips. 13. Mowiol mounting medium. Dissolve 5 g of Mowiol 4-88 in 20 mL of 100 mM Tris–HCl, pH 8.0, in a glass beaker, cover the beaker, and mix by using a magnetic stirrer for 16 h. Add 10 mL of glycerol and continue mixing for an additional 16 h. Remove undissolved Mowiol by centrifugation at 11,000  g for 5 min. Store aliquots (1 or 2 mL) at 20  C. 14. Blocking buffer: 15% FBS, 0.3% Triton-X in PBS. Measure 7.5 mL of heat-inactivated FBS and 150μL of Triton-X 100. Add PBS to a volume of 50 mL, mix by using a magnetic stirrer until the solution is homogeneous. The same solution can be used for both primary and secondary antibody incubations, and it can be stored at 4  C overnight for 4 days. 15. Washing solution: 0.3% Triton-X in PBS. Add 150μL of Triton-X 100 to 50 mL of PBS. Store at 4  C. 16. Fluorescence microscope.

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Methods

3.1 Separation of the Two Cell Types in Melanoma–LEC 2D Co-cultures

1. Detach the cells in the monotypic and melanoma–LEC 2D co-cultures by trypsin treatment (170,000 U/mL for 5–8 min at 37  C) and collect them into EGM. 2. Centrifuge the cells at 450  g for 4 min. Remove supernatant. 3. Resuspend the cells into 200μL of PBS. If necessary, to obtain a single-cell suspension, filter the cells through a FACS tube complemented with a strainer cap. 4. Sort the cells with FACS using the filter sets compatible with the melanoma cell fluorescent label. Collect the cells into Falcon tubes containing 2 mL of EGM.

3.2 Embedding of Single Melanoma Cells in Fibrin (See Note 4)

1. Prepare fibrinogen solution just before the use. Dissolve fibrinogen in HBSS to the concentration of 6 mg/mL. For most experiments, a final volume of 3 mL is usually enough. Dissolve solution in a 37  C water bath for 10–15 min (see Note 5). 2. Prepare thrombin working solution just before the use. Mix one thrombin aliquot and one aprotinin aliquot in 2 mL of HBSS. Keep on ice until used. 3. Count the cells from Subheading 3.1 and resuspend them in growth medium to a concentration of 1  106 cells/mL. This will yield about 5000 cells per fibrin droplet, though the cell number can be adjusted, depending on the growth rate of the cells. To make four droplets with 5000 cells in each at once, transfer 20μL of the prepared cell dilution into a microcentrifuge tube (see Note 6). 4. Mix 100μL of the prepared fibrinogen solution with the cells by pipetting up and down. 5. Add 100μL of the thrombin working solution. Mix by pipetting up and down 5–6 times (see Note 7). 6. Using another pipette adjusted to 50μL in advance, pipet the mixture on a six-well cell culture dish as a droplet. From the mixture made above, it is possible to prepare four droplets at once, and these should fit on the same well (see Note 8). 7. Once you have pipetted all the droplets on the dish, incubate for 30 min in the cell incubator at 37  C with 5% CO2 without adding cell culture medium to allow proper fibrin formation. 8. Add cell culture medium supplemented with 50μg/mL of aprotinin (¼1 aliquot/16 mL of medium). 3 mL of medium/well is usually enough (see Note 9). 9. Culture the cells in the incubator at 37  C with 5% CO2 until the desired level of cell growth is achieved. In most cases, 4 days is optimal (see Note 10).

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1. Heat 0.5% agarose in a microwave oven. Heat at 400 W in few second pulses and mix by gently shaking the bottle in between. Avoid boiling (see Note 11). 2. Pipet the melted agarose on a 96-well U-shaped dish using a multichannel pipette and a sterile reagent reservoir. Dispense 100μL/well. 3. Wait for a few seconds and remove the agarose using the multichannel pipette. Be careful not to touch the bottom of the well. Instead, tilt the plate and remove the agarose from the walls of the plate. 4. Remove the remaining agarose by inverting the dish and tapping it a few times against a clean tissue paper. 5. Let the dish cool down without the lid for 15 min under the cell culture hood. Afterwards, insert the lid and keep the plate at RT overnight to let the agarose solidify (see Note 12). 6. Detach the LECs by trypsin treatment (170,000 U/mL for 5–8 min at 37  C) and collect them into EGM. After counting the cells, prepare a cell suspension of 5  104 cells/mL. 7. Dispense 100μL/well (¼5000 cells/well) of the cell suspension on the agarose-coated 96-well U-bottom dish using a multichannel pipette and a sterile reagent reservoir. Be careful not to scratch the bottom of the well. 8. Incubate the cells overnight in the cell incubator (at 37  C with 5% CO2) to allow spheroid formation (see Note 13). 9. Next day, prepare fibrinogen and thrombin working solutions as described in steps 1 and 2 of Subheading 3.2. 10. Collect the spheroids in microcentrifuge tubes, using a multichannel pipette and wide bore tips to avoid breaking them. You can collect around 4–6 spheroids into the same tube, and these can go into one fibrin droplet (see Note 14). 11. Centrifuge at 200  g for 2 min. Carefully remove the supernatant with suction (see Note 15). 12. Add 25μL of fibrinogen solution and 25μL of thrombin working solution to the LEC spheroids, and mix by pipetting up and down once using a wide bore tip (see Note 16). 13. Pipet one droplet to a six-well dish using wide bore tip. Although droplets are prepared and pipetted one at a time, four droplets can be pipetted per well. 14. Follow steps 7–9 from Subheading 3.2.

3.4 Preparation of 3D Melanoma–LEC Co-cultures

A schematic diagram of this workflow is shown in Fig. 4. 1. Prepare LEC spheroids as described in steps 1–11 of Subheading 3.3.

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2. Prepare fibrinogen and thrombin working solutions as described in steps 1 and 2 of Subheading 3.2. 3. Detach the melanoma cells by trypsin treatment (170,000 U/ mL) for 2–5 min at 37  C and collect into growth medium, count the cells, and prepare a cell suspension of 1  106 cells/ mL. 4. Next, mix in a new microcentrifuge tube or Falcon tube the melanoma cell suspension with fibrinogen working solution for as many droplets as needed. For each droplet, 5μL of the cell suspension (¼5000 cells/droplet) and 25μL of the fibrinogen working solution are needed. 5. Pipet 25μL of the thrombin working solution in the tube containing the LEC spheroids. Carefully pipet up and down to mix the solution without breaking the spheroids. 6. Add 25μL of the melanoma cell/fibrinogen solution prepared in step 4 of this section. Using a wide bore pipette tip, mix by pipetting up and down once. 7. Pipet the droplet onto a six-well dish using a wide bore tip. 8. Follow steps 7–9 of Subheading 3.2. 3.5 Staining of Fibrin Droplets

1. Remove growth medium and fix the cells with freshly thawed 4% PFA in PBS. Incubate for 20–30 min at RT. Make sure to add enough PFA-PBS to fully cover the droplets (5 mL/well are usually sufficient). 2. Remove PFA and wash once with 5 mL of PBS per well. Add at least 5 mL of PBS into the wells, so that the fixed droplets do not dry. The fixed droplets can be stored for a few weeks at 4  C in PBS. 3. Carefully remove the droplets from the six-well dish using a spatula and move them into a 48-well dish. Add 500μL of PBS. Two or three droplets of the same sample type can be put on the same well and stained with the same antibodies. 4. Remove PBS and add 500μL of ice-cold acetone–methanol mix. Incubate for 1 min, remove acetone–methanol, and wash twice with 500μL of PBS (see Note 17). 5. Remove PBS and add 500μL of blocking buffer. Incubate for 1 h at RT. 6. Meanwhile, prepare optimized reagents and primary antibody dilutions for proteins of interest (e.g., fluorescently labeled phalloidin to visualize the cell morphology or endothelial markers such as PECAM to label the LECs) in blocking buffer and keep on ice until use. For one well, 150μL volume of diluted primary antibodies is needed.

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7. Remove the blocking solution and add the diluted primary antibody to the wells containing the droplets. Carefully check that droplets are fully covered. Wrap the plate with parafilm to avoid evaporation of the buffer and incubate at 4  C overnight. 8. Remove primary antibody solution. Wash the droplets with washing solution, at least 500μL per wash. Do at least three washes for 15 min (see Note 18). 9. Prepare optimized secondary antibody dilutions in blocking buffer. Keep on ice until use. 10. Remove the washing buffer and add the diluted secondary antibodies in a volume of 150μL per well. Wrap the plate with parafilm to avoid evaporation of the buffer and incubate at 4  C overnight. From this point on, keep the droplets protected from light as much as possible (use aluminum foil to wrap the plate). 11. Remove the secondary antibody solution and wash at least 3 for 15 min with washing solution. 12. Incubate for 15–30 min in 500μL of Hoechst 33342 solution. 13. Wash at least 2 for 15 min with washing solution. 14. Wash 15 min in PBS. 15. Rinse the droplets with deionized water. 16. Using spatulas, transfer the droplets to a microscope glass slide (two or three droplets can be put onto the same slide). 17. Pipet thawed Mowiol mounting medium on top of the droplets, taking care to avoid making bubbles. Insert a glass objective slide on top (see Note 19). 18. Keep the slides at least overnight at RT to allow Mowiol to solidify. Keep the slides protected from light. 19. Image the droplets with a fluorescence microscope. The best choice is to use a confocal laser scanning microscope with a z-stack imaging option.

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Notes 1. 0.5% agarose solidifies when stored at room temperature (RT). Before use, the agarose solution can be heated in a 400 W microwave oven for a few times in few second pulses to have a liquid solution. Check the solution often and mix it by gentle shaking, being careful not to boil the liquid. If the liquid boils, wait for a couple of minutes for the agarose to cool down before proceeding.

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2. Many primary antibodies suitable for immunofluorescence usually work well also in 3D, but this always needs to be tested. Slightly higher concentrations are often needed in 3D when compared to 2D staining. If one wishes to observe the shapes and structures of the cell clusters, fluorescently conjugated phalloidin is a good choice. We have stained LEC spheroids with PECAM antibody to specifically visualize the LECs; however, any other (lymphatic) endothelial marker could be used. 3. Secondary antibodies can be used at a bit higher concentration than recommended for 2D immunofluorescence. In our experiments, 1:800 secondary antibody dilution (AlexaFluor) has worked well. 4. These experiments are easily adjustable, and different treatments can be applied to the melanoma cells prior to embedding into 3D (e.g., transfection, transduction, inhibitor and small molecule treatments, co-culture with different cell types followed by sorting). Figure 3 shows a comparison of the phenotype of parental melanoma cells grown as a monotypic culture (left panel) to cells after co-culture with LECs in 2D (right panel) prior to embedding into 3D matrix. 5. If it is not possible to weigh fibrinogen under sterile conditions, the fibrinogen-HBSS can be sterilized after dissolving by using a 0.45-μm filter head attached to a syringe. 6. The number of cells to be embedded into fibrin droplets depends largely on their growth rate. Droplets with 5000 cells are good for medium growth rate cells, but the cell number can be reduced to 3000 cells or even less for fast-growing cells. Mix the cell suspension carefully after trypsinization to ensure that you have a single-cell suspension. 7. This step needs to be done fast since fibrin starts to solidify immediately. The colder the enzyme mix is, the slower the fibrinogen will solidify, this is why it is important to keep the mix on ice. Be careful not to contaminate with the pipette tips the thrombin solution with fibrinogen solution and vice versa. 8. Because fibrin hardens and becomes insoluble very fast, one cannot use the same pipette that was used in the previous steps for preparing the droplets. Be quick while pipetting the droplets. It is also very important that the droplets do not touch each other or the walls of the well. 9. Although the droplets should be fully solidified by now, they are still fragile; be gentle while pipetting the cell growth medium. The droplets tend to first repel the added medium; to make sure that they will be fully covered, pipet an excess amount of medium in the well and then remove it.

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10. It is possible to add soluble drugs, inhibitors, etc. in the growth medium as desired for specific studies. 11. If the agarose solution has boiled during heating, let it cool down for a couple of minutes at RT. 12. If too little agarose is left on the plate, cells may attach on the bottom and will not form a proper spheroid. If 0.5% agarose boils during heating, it is very important to cool it down a bit, since very hot agarose forms a too thin layer on the well. On the other hand, if too much agarose is left on the well, it may be incorporated into the spheroid itself, resulting in peculiar, irregular spheroid shapes. 13. It is highly recommended to prepare a larger number of spheroids for each experiment than actually needed, since usually some are lost or broken during the processing. Some cells may require 48 h to form a proper, compact spheroid. 14. The spheroids are usually visible in the tip, as small white dots, when collected. The easiest way to collect spheroids is to take up some medium from the well into the tip, carefully pipet it back against the wall of the well and then pipet the excess medium back into the pipet tip. 15. Spheroids are very fragile and can be damaged by centrifugation. It is recommended to first test with only a couple of microcentrifuge tubes to ensure that the centrifugation force does not damage them. 16. This step might need a bit more mixing by pipetting up and down to make sure that the spheroids have not clumped together. However, too much mixing will result in the fragmentation of spheroids. Check the pipetting result often at the microscope while pipetting the droplets on the dish. 17. There is the risk to pipet the droplet together with the washing liquid since 1 mL pipette tips might have too wide ends to conveniently remove liquids from the well. We find it useful to attach a 10μL pipette tip (without filter) on top of a 1 mL pipette tip for easier removal of washing liquids. Placing the dish on a dark surface also helps to visualize where the droplets are located in the well. 18. Some antibodies may need more washing. If too much background disturbs the imaging, it is also possible to incubate the droplets in washing buffer overnight at 4  C between the primary and secondary antibody treatments. 19. Add enough mounting medium to fully cover the droplets. Sometimes droplets tend to move under the coverslip before mounting medium has solidified. Adding more mounting medium may help.

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References 1. Baker BM, Chen CS (2012) Deconstructing the third dimension: how 3D culture microenvironments alter cellular cues. J Cell Sci 125 (13):3015–3024. https://doi.org/10.1242/ jcs.079509 2. Justice BA, Badr NA, Felder RA (2009) 3D cell culture opens new dimensions in cell-based assays. Drug Discov Today 14(1-2):102–107. https://doi.org/10.1016/j.drudis.2008.11. 006 3. Ravi M, Sah S, Bhammar R (2014) Differences of SiHa (human cancer of cervix) and BMG-1 (brain glioma) cell lines as 2D and 3D cultures. J Cell Physiol 229(2):127–131. https://doi. org/10.1002/jcp.24433 4. Thoma CR, Zimmermann M, Agarkova I, Kelm JM, Krek W (2014) 3D cell culture systems modeling tumor growth determinants in cancer target discovery. Adv Drug Deliv Rev 69-70:29–41. https://doi.org/10.1016/j. addr.2014.03.001 5. Chen Y, Sun W, Kang L, Wang Y, Zhang M, Zhang H, Hu P (2019) Microfluidic co-culture of liver tumor spheroids with stellate cells for the investigation of drug resistance and intercellular interactions. Analyst 144 (14):4233–4240. https://doi.org/10.1039/ c9an00612e 6. Meier-Hubberten JC, Sanderson MP (2019) Establishment and analysis of a 3D co-culture spheroid model of pancreatic adenocarcinoma for application in drug discovery. In: Moll J,

Carotta S (eds) Target identification and validation in drug discovery. Methods Mol biol. Humana Press, New York, NY, p 1953 7. Pekkonen P, Alve S, Balistreri G, Gramolelli S, Tatti-Bugaeva O, Paatero I, Niiranen O, Tuohinto K, Perala N, Taiwo A, Zinovkina N, Repo P, Icay K, Ivaska J, Saharinen P, Hautaniemi S, Lehti K, Ojala PM (2018) Lymphatic endothelium stimulates melanoma metastasis and invasion via MMP14-dependent Notch3 and beta1-integrin activation. Elife 7: e32490. https://doi.org/10.7554/eLife. 32490 8. Alonso-Nocelo M, Raimondo TM, Vining KH, Lopez-Lopez R, de la Fuente M, Mooney DJ (2018) Matrix stiffness and tumorassociated macrophages modulate epithelial to mesenchymal transition of human adenocarcinoma cells. Biofabrication 10(3):035004. https://doi.org/10.1088/1758-5090/aaafbc 9. Alitalo A, Detmar M (2012) Interaction of tumor cells and lymphatic vessels in cancer progression. Oncogene 31(42):4499–4508. https://doi.org/10.1038/onc.2011.602 10. Ma Q, Dieterich LC, Ikenberg K, Bachmann SB, Mangana J, Proulx ST, Amann VC, Levesque MP, Dummer R, Baluk P, McDonald DM, Detmar M (2018) Unexpected contribution of lymphatic vessels to promotion of distant metastatic tumor spread. Sci Adv 4(8): eaat4758. https://doi.org/10.1126/sciadv. aat4758

Chapter 12 Evaluating Melanoma Viability and Proliferation in 3D Microenvironments Vasanth Siruvallur Murali, Murat Can Cobanoglu, and Erik S. Welf Abstract Researchers often aim to incorporate microenvironmental variables such as the dimensionality and composition of the extracellular matrix into their cell-based assays. A technical challenge created by introduction of these variables is quantification of single-cell measurements and control of environmental reproducibility. Here, we detail a methodology to quantify viability and proliferation of melanoma cells in 3D collagenbased culture platforms by automated microscopy and 3D image analysis to yield robust, high-throughput results of single-cell responses to drug treatment. Key words Drug screening, 3D cell culture, Collagen matrix, Image analysis, Extracellular matrix

1

Introduction The effects of gene disruption or pharmaceutical perturbation are often evaluated through cell biological studies performed under 2D in vitro conditions. However, ample experimental evidence highlights the importance of the dimensionality and stiffness of the extracellular environment for determining cells response to perturbation [1–6]. Furthermore, it is not clear what relevance studies relying exclusively on cells cultured in 2D plastic dishes have for in vivo predictions. This is due to environmental factors under in vivo conditions such as the three-dimensional shape of the cell, microenvironment, cell–cell interaction, cell–matrix interaction, and matrix stiffness. Introducing these complex conditions may complicate the readout for these perturbations. Thus, there is a need for assays that can evaluate the effects of complex microenvironments while retaining simplicity necessary for high-throughput measurements.

Electronic supplementary material: The online version of this chapter (https://doi.org/10.1007/978-1-07161205-7_12) contains supplementary material, which is available to authorized users. Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Here we developed a methodology to quantify cell fates in response to drug treatments and genetic perturbation using common lab equipment in high-throughput at a single-cell resolution. To do this, we used a microscopy-based approach using a commonly available epifluorescence microscope, and we identified userfriendly analytical software to facilitate adoption of this approach. Here, we describe the steps necessary to quantify proliferation and cell viability, but in principle, these readouts may be modified for other cell readouts such as differentiation or protein expression given appropriate labeling methods. We have previously implemented this pipeline for analyzing the effects of drug treatment on melanoma cells cultured as single cells and spheroids in 3D collagen cultures [7]. A challenge of using epi-fluorescence microscopy compared to confocal or light-sheet microscopy is the presence of out-out-focus light in the axial dimension (Fig. 1). Although sophisticated computational pipelines have been developed to segment cells or to identify cellular features in 3D image data [8–11], these algorithms often come with the cost of increased computational cost and human interaction, and/or they require sophisticated microscopy equipment. Thus, a way to circumvent these hurdles is performing ratiometric analysis sequentially through each optical slice. This approach facilitates a rapid, easily adopted means to quantify changes in cell viability, although it does not provide a direct cell count.

Fig. 1 Diagram of our ratiometric approach to analyzing 3D wide field microscopy images

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Materials Reagents

1. Phosphate-buffered saline (PBS): 1 mM KH2PO4, 154 mM NaCl, 5.2 mM Na2HPO4, pH 7.4. Store PBS at 4
C. Filter using a 0.2 μm polyvinylidene fluoride (PVDF) filter before experiment. 2. Petri dishes: 100 mm tissue culture–treated cell culture dishes for daily cell culture. 3. Flat-bottom 96-well dishes for terminal experiments. 4. Culture medium for A375 melanoma cells harboring the BRaf V600E mutation: Dulbecco’s modified Eagle’s medium (DMEM), with high glucose and L-glutamine, and supplemented with 10% fetal bovine serum (FBS). Appropriate culture medium should be chosen for the user’s specific choice of cells. 5. Phenol red-free culture medium. 6. Trypsin/EDTA. 7. Bovine collagen I with a molecular weight of ~300 kDa. 8. 1 M NaOH. 9. Sterile water. 10. Dimethyl sulfoxide (DMSO) solubilize. 11. Dabrafenib BRaf V600E targeting inhibitor solubilized in DMSO. 12. Trametinib MEK1/2 inhibitor solubilized in DMSO. 13. Ethidium Homodimer, a cell impermeant viability marker to identify complete cell death. 14. Apopxin, a phosphatidylserine marker to identify apoptotic cells. 15. Cell Event Caspase 3/7 biosensor. 16. Hoechst 33342 as a total nuclei marker for all cells. 17. Click iT-based imaging kit. 18. 40 ,6-Diamidino-2-phenylindole (DAPI) nuclear stain. 19. Paraformaldehyde (PFA): Prepare a 4% solution in 1 PBS. 20. Triton X100: Prepare a 0.5% Triton X100 solution from a 25% stock solution by diluting in the appropriate volume of PBS according to the number of samples treated. The 25% solution is made by dissolving 25 mL of Triton X100 stock in 75 mL of 1 PBS. Place the 25% solution in a 37
C water bath to solubilize the Triton X100 in PBS. 21. Epifluorescence microscope. 22. CellProfiler™ Cell Image Analysis Software (V2.2).

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Methods

3.1 Preparation of Melanoma Cells in 3D Collagen Matrices

The following procedure describes how to generate cultures of melanoma cells in 3D collagen-based matrices. The same procedure can be applied to any other choice of cells. However, the cells need to be evaluated for viability and proliferation upon culturing in 3D culture platforms. 1. Pre-warm all the cell culture reagents such as PBS, medium, and trypsin in a 37
C water bath. 2. Pre-warm the materials needed for 3D collagen matrix preparation: 10 PBS, 1 M NaOH, and sterile water in a 37
C water bath. Pre-warm a 96-well plate in a 37
C incubator. Pre-warm the bovine collagen to room temperature (see Note 1). 3. Culture melanoma cells in 100 mm tissue culture (TC) dishes (see Note 2). Aspirate the media from the dish and gently add PBS to the wall of the dish. Gently swirl the plate and aspirate the PBS form the cell culture dish (see Note 3). 4. To the adhered cells, add 1 mL of trypsin/EDTA (see Note 4) and gently swirl such that the trypsin is distributed uniformly throughout the dish. 5. Place the cells back in the incubator and inspect under the microscope after 2–3 min followed by gentle tapping on the side of the TC dish to dislodge the cells. If the cells are dislodged, proceed to the next step. If not put the dish back in the incubator for the trypsin to help dislodge the cells. 6. Once the cells are dislodged from the TC dish, add a tenfold volume of culture medium containing serum or an equal volume of trypsin neutralizer (see Note 5). 7. Centrifuge the cells at 300  g for 5 min and discard the supernatant with trypsin containing medium. 8. Gently resuspend the cells in fresh medium to have a cell suspension. 9. Count the cells either manually using a hemocytometer or an automated cell counter. 10. Calculate the final number of cells for the desired number of wells (see Note 6). 11. Pipette the required volume of cell suspension required to give the desired number of cells into a centrifuge tube and centrifuge the desired number of cells at 300  g for 5 min. For the work described herein, we use 30,000 cells per well of a 96-well plate (see Note 7). 12. Prepare collagen by mixing the pre-warmed 10 PBS, 1 M NaOH, and water. To prepare 1 mL of collagen at a concentration of 2 mg/mL, mix 100 μL of 10 PBS, 10 μL of 1 M

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NaOH, 250 μL of water, and 640 μL of collagen (see Note 8). It is important to add the water last and mix thoroughly before adding collagen in order to obtain a representative pH measurement in step 13. 13. Measure the pH of the final 2 mg/mL of collagen. The pH range should be between 7 and 7.4 (see Note 9). 14. Carefully aspirate the medium from the cell pellet created in step 11. Any remaining medium will dilute the collagen beyond the desired concentration. 15. Resuspend the cells in 2 mg/mL of collagen at a concentration of 30,000 cells per 100 μL. 16. Seed the cells at 30,000 cells per 96-well plate by adding the cells resuspended in collagen at 100 μL per well in the 96-well plate (see Note 7). 17. Place the plate in the incubator for the collagen to polymerize for 30–45 min (see Note 10). 18. Upon polymerization of the collagen (its color changes to translucent), gently add medium to the cells and incubate overnight (see Note 11). 3.2 Drug Treatment of Melanoma Cells in 3D Collagen Matrices

1. After overnight incubation in medium of cells cultured in 3D, gently aspirate the medium from the dish (see Note 12). 2. Prepare the drug samples by appropriately dissolving them in DMSO or respective solvent (see Note 13). Alternatively, if the motivation is to compare a genetic perturbation, set up the appropriate wild-type controls and skip to step 5 of this section. 3. Prepare a vehicle control with the same solvent in medium. 4. Add the inhibitor along with vehicle control to the culture sample and incubate for the desired period (see Note 14). 5. To evaluate the effect of inhibitors or genetic perturbation on proliferation, perform an EdU incorporation assay as described in Subheading 3.4, using EdU at a final concentration of 10 μM in the inhibitor solution added to the cells. Perform EdU treatment for 24 h or lesser time frames depending on the cell type (see Note 15). 6. To evaluate the effect of inhibitors or genetic perturbation on viability, perform a viability assay as described in Subheading 3.3.

3.3 Viability Assay in 3D Culture

Viability assays are performed using a combination of: (a) Apopxin, a phosphatidyl serine (PS) biosensor which determines PS flipping. PS is normally present on the inner leaflet of the cell’s plasma membrane but is moved to the outer leaflet of the membrane during apoptosis. An alternative apoptotic marker is Cell Event

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Caspase 3/7 biosensor which detects Caspase 3/7, both of which are early apoptotic markers in cells (see Note 16). (b) Ethidium Homodimer, a complete cell death marker which does not permeate healthy cells that have an intact nuclear membrane but which does permeate cells that are completely dead and have lost their nuclear membrane integrity. (c) Hoechst 33342, a nuclear marker to determine the total number of cells. 1. In phenol red-free medium (see Note 17), mix the reagents at final concentrations of 4 μM ethidium homodimer, 10 μM Cell Event Caspase 3/7 biosensor, and 10 μg/mL Hoechst 33342. 2. Gently aspirate the medium from the cells treated with controls and inhibitors. 3. Gently add the fluorophore mixture created in step 1 and incubate for 30 min in a 37
C incubator. 4. Perform 3D imaging of these cells on a widefield microscope. To determine the imaging volume, 100 μL of collagen in a 96-well plate gives a total matrix height of ~1000 μm. Travel along the z-plane to the center of the matrix and reset the position to 0 at the center. Measure the three channels for a total of 500 μm with a step size of 2.5 μm (see Note 18). 5. Determine the optimum exposure time for the three channels for apoptosis, complete cell death, and nuclear channel (see Note 19). 3.4 Proliferation Assay in 3D Culture Systems

1. After EdU incubation for 24 h as described in step 5 of Subheading 3.2, gently aspirate the medium containing the mixture of inhibitor and EdU and wash cells three times with 1 PBS. 2. Fix the cells with 4% PFA (see Note 20) by placing the plate in a 37
C incubator for 30 min. 3. Wash the cells three times with 1 PBS. 4. Permeabilize the cells with 0.5% Triton X100 for 30 min at room temperature (see Note 21). 5. Wash the cells three times with 1 PBS. 6. Perform Click-It reaction as per the manufacturer’s protocol, with the following exception: the final concentration of Alexa Fluor azide in the reaction mixture should be approximately 100-fold lower than indicated by the manufacturer. This reduction can be accomplished by diluting the manufacturer’s solution of Alexa Fluor azide 1:100 with DMSO prior to adding the azide to the reaction cocktail according to the manufacturer’s calculation (see Note 22). 7. Incubate the reaction for 30 min at room temperature in the dark.

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8. Wash the cells five times with 1 PBS and incubate the cells with DAPI at a final concentration of 1 μg/mL for 30 min. 9. Wash the cells and incubate with fresh 1 PBS overnight. Wash the cells three times with 1 PBS the following day and perform proliferation imaging the following day (see Note 19). 10. Perform 3D imaging of these cells on a widefield microscope as in steps 4 and 5 of Subheading 3.3. 3.5 3D Image Analysis Using Cell Profiler

Here we describe the method for 3D image analysis using Cell Profiler™ Image Analysis Software, a program that requires minimal computational experience. Screenshots of the data analysis workflow as well as sample images are provided as Electronic Supplementary Materials and are indicated where appropriate in the step-by-step procedure below: 1. Download Cell Profiler™ Image Analysis Software from http://cellprofiler.org [12–14]. We use Cell Profiler V2.2 (see Note 24). 2. Open the software (Screenshot 1). 3. Upon opening the software, there are four modules described in the “Input Modules,” which are Images, Metadata, Names And Types, and Groups (see Note 25). 4. Under the File List, right click using the mouse to “Browse For Images” (Screenshot 2). 5. Identify and select the images of your experimental data set (see Note 26). The example described here uses two sets from the proliferation data which is control (solvent in which the drug is dissolved) and drug-treated cells. 6. The EdU incorporation assay to evaluate proliferation has two channels: DAPI nuclear stain to evaluate the total number of cells and the Click-It reaction performed with Alexa Fluor azide to identify the proliferating cells. Select candidate images from various points and z-planes for both control and drug treatments (Screenshot 3). 7. Define the image channels. Proliferation imaging is two-channel imaging where in the case here c1 represents DAPI and c2 represents EDU (proliferation) channel (see Note 27). These definitions will then be used by the pipeline to segregate the different channels and analyze images for each channel. 8. Click on “Names And Types” under “Input Modules” (Screenshot 4). 9. Select “Images matching rules” under “Assign a name to.” In the “Select The Rule Criteria,” set parameters to “File Does Contain ‘c1’.” Define a channel name under “Name to assign these images” (Screenshot 5).

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10. To include the proliferation channel, click “Add another image” (Screenshot 6). 11. Like before, under “Select The Rule Criteria,” input “File does contain ‘c2’.” Define “Name to assign these images” with a name of your interest such as “Proliferation” (Screenshot 7). If there is a third channel of imaging, repeat the similar steps. 12. With the classification of the channels and the test images uploaded, click “Update” and the software will distribute the images based on channels (Screenshot 8). 13. Based on our classification, all c1 channel images are classified under DAPI and c2 under Proliferation. Since we had loaded three sample sets of control and three of drug treatment the software also indicates “Found 6 image sets” (Screenshot 8). 14. Once the images are selected and channels classified, create a pipeline to analyze the selected images under the “Analysis Modules.” 15. Right click in the “Analysis Modules,” and a variety of functions will be displayed (Screenshot 9). 16. Since this is a multichannel imaging, the first step is to align the images such that all the multichannel images of each plane lay over each other. 17. Right click under “Analysis Modules,” go to “Image Processing,” and select “Align” (Screenshot 10). 18. The selections below are optimized for the current image analysis. A vast number of settings can be changed as needed. Click the to acquire further information on any modules. 19. Upon selecting “Align” in the “Select the alignment method,” choose “Normalized Cross Correlation,” Crop mode “Pad Images,” Following these, “Select the first input image” as “DAPI” and provide an output name for the aligned images, such as “Name the first output image” as “AlignedDAPI.” Similarly, the second input image will be the proliferation channel “Select the second input image” and correspondingly “Name the second output image” which in this case is “AlignedProliferation.” If there are more than two channels, then click “Add another image” (Screenshot 11). 20. Ensure that all the information is input correctly by looking for a symbol in the “Analysis Modules.” 21. If an error is made, such as if the “Name the second output image” has no name in the pipeline (Screenshot 12), an error symbol indicated by will be displayed. This will inhibit processing of the pipeline when trying to “Analyze Images.”

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22. Perform illumination correction after aligning the images (see Note 28). The images acquired here are from a widefield epifluorescence microscope. There is a nonuniform illumination which needs to be flattened so that a uniform thresholding may be performed in subsequent steps. 23. Right click under “Analysis modules,” select “Add” ! “Image Processing” and “Correct Illumination Calculate” (Screenshot 13). 24. In the “Select the input image,” select the output image from the “Align” which is “AlignedDAPI.” Define an output name in “Name the output image.” In “Select how the illumination function is calculated,” select “Background,” “Rescale the illumination function” as “No,” and the “Smoothing Method” as “Fit Polynomial” (Screenshot 14). 25. Upon calculating the illumination correction, apply these calculations to the images. Right click under “Analysis modules” to ! “Add” ! “Image Processing” and select “Correct Illumination Apply” (Screenshot 15). 26. Under “Correct Illumination Apply,” input the image to which illumination correction needs to be performed and the illumination background calculated from “Correct Illumination Calculate” to get the illumination corrected image. In the “Select the input image,” choose the aligned image, in this case “Aligned DAPI,” as that was the output image which needs to be illumination corrected. Then in the “Select the illumination function,” select the output image from “Correct Illumination Calculate” which has the illumination corrected image, in this case “IllumDAPI.” Choose the method of illumination correction application under “Select how the illumination function is applied” (Screenshot 16). 27. After illumination correction, threshold the image. To implement thresholding, right click under “Analysis Modules” ! “Add” ! “Image Processing” ! “Apply Threshold” (Screenshot 17). 28. Select the output image from “Correct Illumination Apply” as the input image for thresholding. In the “Select the output image type,” choose “Grayscale” if you plan to implement “Threshold strategy” as “Automatic.” Otherwise, if using “Binary (black and white)” as “Select the output image type,” choose an alternate “Thresholding Strategy.” In this case, we use “Global” and Thresholding Method” as “Robust Background,” change “Use default parameters” to “Custom,” “Averaging Method” to “Mean,” “Variance Method” to “Standard Deviations,” and “Select the smoothing method for thresholding” to “Automatic.” The two important parameters which need to be adjusted as the method implemented

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is not automatic is “# of deviations” and “Threshold correction factor.” Both these parameters need to be tested (see Note 29). The larger the value implemented in both these parameters, the more background pixels will be eliminated (Screenshots 18 and 19). 29. After applying the threshold, identify the positive pixels, which in this case are the cells, by right clicking under “Analysis Modules” ! “Add” ! “Object Processing” ! “Identify Primary Objects” (Screenshot 20). 30. To identify the cells from the image, “select the input image” which in this case is the output image after thresholding, i.e., ThreshDAPI. Provide a name for the output file in “Name the primary objects to be identified.” For the “Typical diameter of objects in pixel units,” identify the diameter (in pixels) of the cells if either present as single cells or clumped in 3D (see Note 30). Set the “Threshold strategy” to “Binary Image” as that was the method used in “Apply Threshold,” and in “Select binary image,” again select the threshold output image. Use “Shape” as “Method to distinguish clumped objects” (Screenshot 21). 31. After identifying the objects, convert the object back to an image format. Right click under “Analysis Modules” ! “Add” ! “Object Processing” ! “Convert Objects To Image” (Screenshot 22). 32. In “Convert Objects To Image,” select the “Input Image” from the output of “Identify Primary Objects,” in this case “DAPI objects.” Provide an output name for the image, which in this case is “CellImage DAPI,” and “Select the color format” as “Binary (black and white)” (Screenshot 23). 33. Measure the total image area of the positive pixels of the cells in the image. Right click under “Analysis modules” ! “Add” ! “Measurement” ! “Measure Image Area Occupied” (Screenshot 24). 34. Select the “Measure the area occupied in a binary image, or in objects?” as “Binary image” and use the output image from “Convert Objects To Image” which is “CellImage DAPI” as the input image for “Select a binary image to measure” (Screenshot 25). 35. Apply the same steps for the next channel which in this case is for “Proliferation.” Select the steps from “Correct Illumination Calculate” to “Measure Image Area Occupied.” Right click the highlighted steps and select “Duplicate” (Screenshot 26). Go through the steps and reassign names from DAPI to Proliferation (Screenshot 27). If any names are similar, the pipeline may display an error.

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36. After duplicating the steps and renaming for proliferation, verify that the cells positive for proliferation are also positive for DAPI. If there are cells that show positivity for proliferation but not for DAPI (which in the case here is considered as the total cell count), it may alter the percent proliferating cells, and thus, it is important to eliminate any cells that are positive for proliferation but not for DAPI. To do that, mask both the images by right clicking in “Analysis Modules” ! “Add” ! “Image Processing” ! “Mask Image” (Screenshot 28). 37. In the “Mask Image,” “Select the input image” as the one which has the total cell population, which in the case presented here is “CellImageDAPI.” Select “Image” in “Use objects or an image as mask” and “Select image for mask” as the one which shows proliferating cells, which in the case here is “CellImageProliferation.” Provide an output file name in “Name the output image,” in this case “Intersection” (Screenshot 29). 38. Measure the area occupied by the masked image which yields results for true-positive proliferating cells by identifying cells that are both positive in signal for “DAPI” and “Proliferation.” Right click under “Analysis Modules” ! “Add” ! “Measurement” ! “Measure Image Area Occupied” (Screenshot 24). 39. To measure the area occupied by the masked image, “Select a binary image to measure,” in this case “Intersection.” Since the masked images were binary images, under “measure the area occupied in a binary image, or in objects?” choose “Binary Image” (Screenshot 30). 40. To acquire further information on the masked image, calculate the image overlap. Right click under “Analysis modules” ! “Add” ! “Measurement” ! “Calculate Image Overlap” (Screenshot 31). 41. In order to compare the overlapping cells, for “Compare segmented objects, or foreground/background?” select “Segmented objects.” Under “Select the objects to be used as the ground truth basis for calculating the amount of overlap,” choose the image that has the total number of cells, which in this case is “DAPI Objects.” Under “Select the objects to be tested for overlap against the ground truth,” which are the cells that are proliferating, input “Proliferation Objects” (Screenshot 32). 42. After quantifying these areas, export the data to a spreadsheet for analysis. Right click under “Analysis Modules” ! “Data Tools” ! “Export To Spreadsheet” (Screenshot 33) (see Note 31).

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43. To save the output images created from the steps performed above, right click under “Analysis Modules” ! “Add” ! “File Processing” ! “Save Images.” Duplicate this step to save images of both the channels and name appropriately (Screenshot 34). 44. To test the pipeline on the sample images that were uploaded under “File Lists” (Screenshot 3), click on “Start Test Mode” under “Output” (Screenshot 35). 45. The helps you visualize the steps being processed. Upon clicking on it, the icon appears as , which results in the step being processed but not visualized. 46. Upon clicking the “Start Test Mode,” there are five additional functions that are displayed: “Run” to go through all the steps for each set, “Step” that needs to be clicked every time to go from one step to the next, “Viewer” to visualize and edit the positive pixels of each step, “Exit Test Mode” to exit the process of testing images, and “Next Image Set” to go through the next set of images to be tested (Screenshot 36). 47. Upon initiating the “Test Mode” and upon clicking “Step,” go through each analysis step one at a time. The software starts at the very first step of “Align” with the first image data set containing the two channel images and it aligns them (Screenshot 37). 48. To go to the next step, click “Step,” and it will perform “Correct Illumination Calculate” to determine the value to correct uneven illumination (Screenshot 38). 49. In the next step of “Correct Illumination Apply,” the pipeline applies the “Correct Illumination Calculate” value and thereby generates a more even illuminated image (Screenshot 39). 50. After illumination correction, upon clicking “Step,” the pipeline applies threshold function to the illumination corrected image. The “# of deviations” and “Threshold correction factor” values need to be evaluated to acquire an optimal threshold image. For example, if we have a “# of deviations” as 3 and “Threshold correction factor” as 0.5, the number of identified positive pixels are far greater than the actual illumination corrected image (Screenshot 40). However, upon applying a more stringent “Threshold correction factor” of 1.2, a more robust threshold images is obtained (Screenshot 41). A few values need to be tried to get the optimum “Threshold correction factor” value. This value then will be implemented for all the control and drug-treated images in the c1 (DAPI) channel. 51. After thresholding upon clicking “Step,” the next step in the module is to “Identify Primary Objects,” which are the positive pixels in the threshold image. The “Typical diameter of objects,

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in pixel units (Min, Max)” may be adjusted to accommodate the larger clumps of cells which may be excluded if the “Max” value is smaller than the clumped group of pixels (which in this case are clumped cells) (Screenshot 42). 52. The next “Step” after accepting the value range for identifying objects is to “Convert Objects To Image” (Screenshot 43). 53. After converting back to image, the next “Step” is to “Measure Image Area Occupied,” which determines the area occupied by the positive pixels in the image (Screenshot 44). 54. Perform similar steps for the same image plane of the 3D z-stack but for channel c2 (Proliferation) image. Similar steps should be performed as before and “Calculate Image Area Occupied” (Screenshot 45). 55. The next “Step” is to identify the Proliferation channel (c2) that also is positive for DAPI (c1) and for that “Mask Image” test the two channels for that particular z-plane (Screenshot 46). 56. After evaluating the “Intersection,” the next step is to “Calculate Image Overlap,” which is considered the true proliferation-positive pixels (Screenshot 47). 57. There is a difference in area occupied by the “Intersection” and “CellImage Proliferation” that can be attributed to the fact that there were proliferation-positive cells that were not positive for DAPI. This was due to the camera chip imaging the DAPI channel, which shows a smaller area while the proliferation channel shows a larger area which is then corrected for. 58. Upon clicking “Next Image Set,” the pipeline goes through another image set uploaded to test. Test the same “Apply Threshold” and “Identify Primary Objects” values as for the first set. 59. Once all the images are tested, click “Exit Test Mode” and upload the entire image data set of both control and drug treatments under the “File List.” 60. Under “Output,” click on “View Output Settings” and select the destination folder where the data needs to be saved. 61. Save the project pipelines to review any settings used for the image analysis. Click on “File” and “Save Project As.” Select the destination folder where the file needs to be saved. Give a file name followed by “.cpproj.” For example, if the file name is “ABC,” then it will be “ABC.cpproj.” 62. Click “Analyze Images” to analyze all the image sets for the control and drug treatments. 63. Once the analysis is complete, the spreadsheet will be saved in the destination folder.

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Notes 1. Use of any 3D cell culture ingredients not pre-warmed increases the time to polymerize the collagen matrix. This will result in settling of the cells to the bottom of the dish and thus not having a 3D morphology. The pre-warm step may need to be modified if using alternate forms of collagen such as rat tail collagen or telocol. 2. The type of TC dish to be used may vary depending on the cell type in use. This may need to be optimized or changed depending on the cell type. 3. Add surplus PBS to dilute the FBS present in media. FBS interferes with trypsin/EDTA activity. Adding media or PBS directly on top of the cells may dislodge the cells from the dish. Thus, care should be administered when adding or aspirating media from the cell culture dish. 4. The amount and concentration of trypsin to dislodge cells depend on the cell type and need to be optimized. Excessive exposure of trypsin to cells can kill them so the cells need to be handled carefully after addition of trypsin. 5. If cells are cultured in serum-free medium, then the trypsin activity can be inhibited using trypsin neutralizer. 6. Calculate the number of cells for additional wells. For example, if one needs to culture at 30,000 cells per well for 10 wells, calculate for 12 wells to buffer for loss in tubes. 7. Appropriate seeding density needs to be determined depending on the cell type. For A375 melanoma cells, we use a seeding density of 30,000 cells per well in a 96-well plate. For larger cells such as fibroblasts, these number should be reduced. 8. The stock concentration of collagen that we use (Advanced Biomatrix) ranges from 3 to 3.2 mg/mL, which ultimately gives us ~2 mg/mL as the final concentration when prepared as we describe. 9. Measuring the pH is critical as it should range between 7 and 7.4. If the pH is not in the recommended range, it may alter collagen polymerization and may also result in cell death if it lies in an acidic or basic range. 10. The plate needs to be placed in the incubator immediately to initiate collagen polymerization. Leaving the plate at room temperature will lower the polymerization rate, resulting in cells at the bottom of the wells lacking the 3D morphology and thus altering any perturbation results. 11. Gently add medium to the wall of the well. Directly adding to the gel might disrupt/damage the collagen matrix.

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12. Avoid using a larger pipette tip (such as 1000 μL tips) in 96-well dishes and use gentle aspiration along the walls. If the tip directly comes into contact with collagen, it may disrupt or even aspirate the matrix, resulting in loss of the sample. 13. Check the manufacturer specification or MSDS to determine a suitable solvent for the inhibitor. 14. The optimal concentration and time of drug treatment should be determine by generating concentration and time curves. 15. EdU incubation may vary upon the cell type and needs to be optimized. 16. Determine a candidate apoptotic marker from the two depending on the cell type and intensity of perturbation. 17. Phenol red-free medium should be used to avoid any interference in the red 568 nm channel. 18. The 3D z-stack imaging height and an optimum range of z steps should be determined based on the cell type. It is best to have a microscope system with a triggered piezo as the motorized system helps acquire 3D images at a significantly greater speed. 19. An optimum exposure time may be determined by evaluating control cells that are not treated with fluorophores and show no autofluorescence under similar exposure settings as those used for cells treated with fluorescent markers that do yield a fluorescence signal. 20. PFA fumes are toxic, and care should be administered if making a stock PFA solution by dissolving in a fume hood. 21. Be sure the 0.5% Triton X100 is fully solubilized before use and place solution in a 37
C water bath prior to use if necessary. 22. Using the Alexa Fluor azide directly as per the manufacturer’s recommended concentration for 3D assays will result in significant background staining of the collagen, which interferes with the identification of the proliferated cells. 23. Incubating the 3D culture with 1 PBS and imaging the next day helps remove any background stain from the Alexa Fluor azide nonspecifically bound to collagen. 24. A newer version of Cell Profiler™ (Version 3.0) is available, and similar steps can be performed. 25. For simplicity and to remain focused on 3D image analysis, we focus here on the “Images” and “Names And Types” modules. 26. Before uploading an entire image data set, it is best to first optimize the image analysis parameters for a smaller data set of both treatment and controls. Remember to use the same analysis settings for the entire data set.

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27. Imaging should be performed such that within each well, multiple images (data points) are acquired. When using a Nikon epifluorescence microscope or similar, it yields images as “xy” indicating the different positions, “z” indicating the number of z slices, and “c” indicating the channels. 28. After the “Align” function, the subsequent steps must be performed for one channel at a time and repeated for all the channels imaged (in the case described here, the steps have to be repeated twice as this set is two-channel imaging). 29. This is where we test the sample images by running the “Start Test Mode” to see how well the values implemented in the pipeline perform. Once a set of values are determined that work well, use those for the entire set of images within that experiment. 30. Set a higher upper limit for cells in 3D as it is hard to predict the size of the largest clump of cells in the 3D space. 31. The “Export to spreadsheet” function is present both under “Data Tools” and “File Processing”; however, to our knowledge, they both are the same. References 1. Mohan AS, Dean KM, Isogai T et al (2019) Enhanced dendritic actin network formation in extended lamellipodia drives proliferation in growth-challenged Rac1P29S melanoma cells. Dev Cell 49(3):444–460.e9 2. Park JS, Burckhardt CJ, Lazcano R et al (2020) Mechanical regulation of glycolysis via cytoskeleton architecture. Nature 578 (7796):621–626 3. Kozlova N, Grossman JE, Iwanicki MP et al (2020) The interplay of the extracellular matrix and stromal cells as a drug target in stroma-rich cancers. Trends Pharmacol Sci 41(3):183–198 4. Qin X, Lv X, Li P et al (2020) Matrix stiffness modulates ILK-mediated YAP activation to control the drug resistance of breast cancer cells. Biochim Biophys Acta Mol basis Dis 1866(3):165625 5. Shea MP, O’Leary KA, Wegner KA et al (2018) High collagen density augments mTORdependent cancer stem cells in ERα+ mammary carcinomas, and increases mTOR-independent lung metastases. Cancer Lett 433:1–9 6. Singh A, Brito I, Lammerding J (2018) Beyond tissue stiffness and bioadhesivity: advanced biomaterials to model tumor microenvironments and drug resistance. Trends Cancer 4(4):281–291

7. Murali VS, Chang BJ, Fiolka R (2019) An image-based assay to quantify changes in proliferation and viability upon drug treatment in 3D microenvironments. BMC Cancer 19 (1):502 ˜ oz-Descalzo S et al 8. Mathew B, Schmitz A, Mun (2015) Robust and automated threedimensional segmentation of densely packed cell nuclei in different biological specimens with lines-of-sight decomposition. BMC Bioinformatics 16(1):187 9. Chittajallu DR, Florian S, Kohler RH et al (2015) In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 12(6):577–585 10. Xing F, Yang L (2016) Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev Biomed Eng 9:234–263 11. Driscoll MK, Welf ES, Jamieson AR et al (2019) Robust and automated detection of subcellular morphological motifs in 3D microscopy images. Nat Methods 16 (10):1037–1044 12. Lamprecht MR, Sabatini DM, Carpenter AE (2007) CellProfiler: free, versatile software for automated biological image analysis. BioTechniques 42(1):71–75

Melanoma Viability and Proliferation in 3D 13. McQuin C, Goodman A, Chernyshev V et al (2018) CellProfiler 3.0: next-generation image processing for biology. PLoS Biol 16(7): e2005970

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14. Carpenter AE, Jones TR, Lamprecht MR et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10):R100

Chapter 13 Preparation, Drug Treatment, and Immunohistological Analysis of Tri-Culture Spheroid 3D Melanoma-Like Models Maximilian E. A. Sch€afer, Julia Klicks, Mathias Hafner, and Ru¨diger Rudolf Abstract Most currently available three-dimensional melanoma models have either focused on simplicity or were optimized for physiological relevance. Accordingly, these paradigms have been either composed of malignant cells only or they were sophisticated human skin equivalents featuring multiple cell types and skin-like organization. Here, an intermediate spheroid-based assay system is presented, which uses tri-cultures of human CCD-1137Sk fibroblasts, HaCaT keratinocytes, and SK-MEL-28 melanoma cells. Being made of cell lines, these spheroids can be reliably reproduced without any special equipment using standard culture procedures, and they feature different aspects of skin and early stage melanoma. Therefore, this kind of model can be useful for lead-compound testing or addressing fundamental principles of early melanoma formation. Key words Melanoma, 3D model, Skin, Spheroid, Phenotypic screening, Pharmaceutical research, HaCaT, SK-MEL-28

1

Introduction Skin cancer is one of the most common forms of cancer worldwide [1, 2], and among these, melanoma is the most lethal form. Here, malignancy arises from irregular proliferation of pigment-containing melanocytes, which are normally embedded in the stratum basale adjacent to the basement membrane between the epidermal and dermal compartments of the skin [3, 4]. Malignant degeneration of melanocytes can be caused by a variety of risk factors such as genetic predisposition, exposure to UV radiation, age, gender, immune status, or number of nevi [5, 6]. Progression of melanoma is typically characterized by a sequence of radial growth phase, vertical growth phase, and metastatic melanoma [7–9]. Apart from uncontrolled proliferation, malignant cells

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_13, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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show in these phases first lateral spread, then occasional passage through the basement membrane, and, finally, entry and distribution through the bloodstream. While surgical removal of the tumor is a valid option in the radial growth phase and the early vertical growth phase, surgical removal is no longer an option in the later stages [10]. Signals from the microenvironment are crucially involved in distant metastasis and are therefore decisive for the therapy and the course of this disease [11]. Treatment for the disease after it has overcome radial growth includes immunotherapies like the anti-cytotoxic T-lymphocyte antigen-4 antibody ipilimumab and kinase inhibitors such as the BRAF inhibitor vemurafenib [12–14], but their success is rather limited and more appropriate options are being sought. To improve the screening and preclinical evaluation of novel therapeutics for melanoma treatment, the cellular environment and different cell types involved, including fibroblasts, keratinocytes, endothelial cells, and immune cells, should be taken into account [3]. This is also relevant for cell culture systems, where the cellular microenvironment and its spatial arrangement are critical for the reliability of preclinical studies [15, 16]. In particular, 3D cell culture models can partially bridge the large gap between the 2D in vitro and in vivo gene expression profiles [17]. Therefore, various 3D cell culture approaches such as spheroids, tumor spheres, human skin equivalents, and melanoma-on-chip assays have been developed [18] and have become indispensable for basic research and drug testing. However, in the form of melanoma spheroids, these models often consisted of one cell type. This increase in reproducibility and simplicity comes with a trade-off in terms of physiological relevance. Alternatively, very complex human skin equivalent models were introduced, which are rather close to the physiological situation but are often not reproducible without special equipment and are too time-consuming for intense industrial use [19]. In the following protocol, we give step-by-step instructions to a novel, simple, spheroid-based melanoma model of intermediate complexity composed of fibroblasts, keratinocytes, and melanoma cells (Fig. 1). The model enables the tracking of cellular behavior in a cell type-specific manner and shows various features of early melanoma stages. In brief, this tri-culture arranges in a skinlike structure, composed of a collagen-IV-rich fibroblast core, a ring of layered and partially differentiating keratinocytes, as well as clusters of highly proliferating melanoma cells on the spheroid surface and individual melanoma cells that enter the fibroblast core [20].

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Fig. 1 Schematic illustration of the melanoma tri-culture model. A collagen-IV-rich fibroblast core is surrounded by few layers of CK14-positive basal-like inner keratinocytes [21] and an outer layer of CK10positive more differentiated keratinocytes [22]. Highly proliferating and S100b-positive melanoma cells form agglomerates on the spheroid surface and are found in small numbers in the fibroblast-enriched core [20]

2 2.1

Materials Reagents

Prepare all solutions under sterile conditions. 1. Dulbecco’s modified Eagle medium (DMEM): Store at 4
C. 2. Iscove’s Modified Dulbecco’s Medium (IMDM): Store at 4
C. 3. Dulbecco’s Phosphate-Buffered Saline (PBS): Store at 4
C. 4. Fetal Bovine Serum (FBS): Store at 20
C. 5. Penicillin/Streptomycin (100) (P/S): Store at 20
C. 6. Trypsin–EDTA Solution: Store at 20
C. 7. GlutaMAX™ Supplement: Store at - 20
C. 8. Albumin Fraction V (BSA): Store at 4
C. 9. Triton X-100: Store at 20
C. 10. Dimethylsulfoxide (DMSO): Store at 20
C. 11. D-Sucrose: Store at 20
C. 12. Glycine. 13. Agarose. 14. Paraformaldehyde. 15. Sodium azide. 16. Glycerol. 17. FSC 22 Frozen Section Medium. 18. Mowiol. 19. CellTracker Red CMPTX Dye: Store at 20
C. 20. CellTracker Green CMFDA Dye: Store at 20
C.

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21. CellTracker Deep Red Dye: Store at 20
C. 22. DRAQ5 Fluorescent Probe Solution: Storage at 4
C. 23. 40 ,6-Diamidine-20 -phenylindole Store at 20
C. 2.2

Equipment

dihydrochloride

(DAPI):

1. Nunclon Sphera 96U-well plate. 2. μ-Slide: 18-well, flat, uncoated chamber slide. 3. 3D petri dish micro-mold spheroids. 4. Centrifuge. 5. CO2 incubator. 6. Vertical shaker. 7. Vi-Cell XR Cell Viability Analyzer (Beckman Coulter). 8. Tissue-Tek® Cryomold Standard. 9. Superfrost Plus™ Adhesion Microscope Slides. 10. Menzel Cover Glasses 24  60 mm, thickness I. 11. Scissors to trim pipette tips. 12. Cryostat CM-1950 (Leica Biosystems GmbH). 13. Leica TCS SP8 Microsystems GmbH).

DMi8

14. Axiovert 25 Inverted Microscopy GmbH).

microscope

Microscope

(Leica

(Carl

Zeiss

15. ImageJ software. 16. Leica Application Suite Microsystems GmbH). 2.3

Reagent Setup

X

software

(LAS

X;

Leica

1. Standard medium for CCD-1137Sk cell line: IMDM with Lglutamine, supplemented with 10% FBS and 1% penicillin/ streptomycin. 2. Standard medium for HaCaT and SK-MEL-28 cell lines: DMEM high glucose (4.5 g/L), with L-glutamine and sodium pyruvate supplemented with 10% FBS, and 1% penicillin/ streptomycin. 3. Cryoconservation medium: standard IMDM or DMEM (depending on the cell line) with 20% FBS and 5% DMSO.

2.4

Cell Lines

1. Human fibroblast cell line CCD-1137Sk. 2. Human keratinocyte cell line HaCaT. 3. Human malignant melanoma cell line SK-MEL-28.

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Methods We describe herein a low-attachment-based generation of spheroids composed of principal cell types involved in early melanoma.

3.1 Preparation of Tri-Culture Spheroids

All procedures should be performed under sterile conditions using a laminar flow hood. Cells are maintained at 37
C in 5% CO2 unless stated otherwise. 1. Start cultivating the individual cell lines for the melanoma tri-culture for at least two passages. For optimal cultivation and reproducibility, avoid over and under growth of the cells (see Note 1). 2. Prepare the cells for the co-culture by removing the medium and washing the cells with PBS. 3. Add Trypsin–EDTA solution to remove the cells from the T-75 cell culture flask. Incubate CCD-1137Sk and SK-MEL-28 at 37
C with 5% CO2 for 2–4 min. HaCaT cells need a 5–10 min incubation at 37
C with 5% CO2 to detach. 4. Inhibit trypsin activity by adding 8 ml of standard medium as appropriate for a particular cell line. 5. Before starting the tri-culture, check cell viability using a Vi-Cell XR Cell Viability Analyzer. The viability of the cells should be higher than 92%. 6. To prepare the first layer of the tri-culture melanoma model, seed CCD-1137Sk (1  104 cells/100 μl of medium) in each well of a 96U-well plate. 7. Centrifuge the cells for 4 min at 34  g and incubate the spheroids for 72 h at 37
C with 5% CO2. 8. Change 80% of the medium every 48 h. 9. After 3 days, seed HaCaT (1  104 cells/well) and SK-MEL-28 (2.5  103 cells/well) to the already formed CCD-1137Sk spheroids (achieve a 150 μl/well total volume of medium for the 96-well plate). 10. Centrifuge the cells for 4 min at 34  g and incubate the spheroids for 96 h at 37
C with 5% CO2. 11. Change 80% of the medium every 48 h. 12. After 4 days, the tri-culture melanoma model is ready for drug treatment or staining.

3.2

Drug Treatment

1. For tri-culture treatment in a 96-well plate, carefully remove 90% of the medium, wash three times with PBS, and continue with step 7 of this section.

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2. For tri-culture treatment in other reaction vessels, use a P1000 μl pipet. Cut 3 mm from the tip to enlarge the opening (see Note 2). 3. Use the pipet to transfer the total volume of the wells and their spheroids into a 1.5-ml reaction tube. 4. After harvesting four wells, wait for 1 min to allow spheroids to accumulate at the bottom of the 1.5-ml tube (see Note 3). 5. Remove as much medium as possible and avoid touching the spheroids. Continue this way until all spheroids are harvested. 6. Carefully wash the spheroids three times with PBS. In between the washing steps, wait for at least 1 min and allow spheroids to accumulate at the bottom of the 1.5-ml tube (see Note 3). 7. For the treatment of the tri-culture, follow the instructions according to the substances that shall be tested. Depending on the treatment, use FBS-free medium (see Note 4). 3.3 Agarose Mold Culture to Avoid Loss of Cells Upon Drug Treatment

For treatment with cancer drugs that can trigger apoptosis, necrosis, or autophagy, it is necessary to cultivate the melanoma model in a special environment. The ablation of cells can be prevented by the use of agarose 3D molds (Fig. 2).

Fig. 2 Handling of melanoma tri-culture spheroids treated with anticancer drug affects the detection of external melanoma cells. After the formation of the tri-culture in a cell-repellent plate, the spheroids were either fixed in agarose molds treated with 100 nM of docetaxel, sectioned, and stained (a–d) or transferred after docetaxel treatment in cell-repellent plate to reaction tubes, embedded, sectioned, and stained (e–h). (a/ e) Nuclei stained with DAPI (blue). (b/f) Keratinocytes stained with CellTracker Red CMPTX (red). (c/g) CellTracker Green CMFDA shows melanoma cells (green). (d) Agarose embedded tri-culture after drug treatment, melanoma cells are still in close proximity to the spheroid and in the core. (h) Tri-culture after drug treatment without agarose molds shows only melanoma cells in the spheroid core. Scale bar ¼ 100 μm

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1. Prepare the melanoma model as described in steps 1–11 of Subheading 3.1. 2. Five days after seeding the CCD-1137Sk, before treatment and staining, transfer the triculture spheroids to 3D agarose molds, prepared as follows: (a) Fill the inner part of the micro-molds with sterile and liquid molten 2% agarose. (b) Let the molds cool down for 24 h at 4
C. (c) Separate the gelled agarose from the micro-molds and place them in a multi-well plate of choice. 3. Flood the wells with medium and add the spheroids into the agarose molds. 4. Proceed with drug treatment in the micro-molds according to the substances that shall be tested. 5. Perform fixation, staining, and cutting in the micro-molds as described in steps 1–19 of Subheading 3.4.2. 3.4 Immunohistological Analysis 3.4.1 Sample Preparation for Whole Mount Staining and Imaging

1. To stain the spheroid-based tri-culture, transfer the spheroids by using a P1000 pipet with a cut-off tip (see Note 2). 2. Wash the spheroids 3 with PBS (see Note 3). 3. Fix samples in 4% wt/vol paraformaldehyde for 30 min at room temperature (RT) on a vertical shaker. 4. Wash samples 3 in PBS for at least 5 min each before further treatment (see Note 5). 5. Quench the spheroids with 0.5 M glycine for 1 h at RT on a vertical shaker. 6. Store samples in PBS + 0.05% sodium azide, or proceed with protocol. 7. Permeabilize samples by incubating in 0.4% Triton X-100 in PBS for 30 min at RT. 8. Wash samples 1 with PBS and 2 with 3% BSA (in PBS) at RT for 5 min each. 9. Block samples with 3% BSA at RT for 2 h on a vertical shaker. 10. If desirable, incubate samples in a primary antibody dilution of interest prepared in 3% BSA at 37
C for 24 h on a vertical shaker. 11. Wash sample 3 with 3% BSA at RT for 15 min each on a vertical shaker (see Note 3). 12. For secondary antibody, use an appropriate dilution prepared in 3% BSA and incubate at 37
C for 24 h on a vertical shaker in a dark chamber (see Note 3).

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13. After secondary antibody incubation, wash sample 3 for 5 min with PBS at RT (avoid light exposure) (see Note 3). 14. Wash samples 3 for 5 min with double-distilled water at RT (avoid light exposure) (see Note 3). 15. For refractive index matching, use 88% glycerol and adjust the index depending on the microscope you use (48 h, at RT, avoid light exposure). 16. To image the spheroids (Fig. 3), transfer them to a μ-slide 18-well plate by using a P1000 pipet with a cut-off tip to avoid damage (see Note 6).

Fig. 3 Whole-mount staining of melanoma tri-culture spheroid model. Spheroid clearing was performed with a simplified protocol based on Nu¨rnberg et al. [23]. (a) Nuclei stained with DAPI (blue). (b) CellTracker Deep Red marks keratinocytes (red). (c) Melanoma cells stained with CellTracker Green CMFDA (green). (d) Merged image with front cut-off to allow an insight into the core shows a layer structure of keratinocytes (red) and melanoma cells on the outer layer and in the core (green). Scale bar ¼ 100 μm

3D Melanoma-Like Tri-Culture Model 3.4.2 Sample Preparation for Section Staining and Imaging

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1. Collect the spheroids in a 1.5-ml reaction tube, by using a P1000 pipet with a cut-off tip (see Note 2). 2. Wash the spheroids 3 with PBS (see Note 3). 3. Fix samples in 4% wt/vol paraformaldehyde for 30 min at RT on a vertical shaker. 4. Incubate the spheroids for 12 h at 4
C in 15% sucrose in PBS (see Note 5). 5. Subsequently incubate the spheroids for 12 h at 4
C in 25% sucrose in PBS. 6. Embed the spheroids in FSC 22 frozen section medium and carefully transfer the spheroids into a Tissue-Tek® standard cryomold. 7. Cut the spheroids into 20-μm-thick sections using a cryostat (15
C). 8. Transfer sections on Superfrost Plus™ microscope slides. 9. Wash 1 with PBS to moisten the sections. 10. Permeabilize sections with 0.1% Triton X-100 in PBS for 10 min at RT. 11. Wash 2 with PBS and 1 with 3% BSA (in PBS), each for 5 min at RT. 12. Block with 3% BSA for 2 h at RT. 13. If desirable, incubate sections in a primary antibody dilution of interest that is prepared in 3% BSA for 2 h at RT. 14. Wash 3 with 3% BSA for 5 min at RT. 15. For secondary antibody, use a dilution prepared in 3% BSA and incubate sections for 3 h at RT in a dark chamber. 16. Use an established nuclei marker as well in a dilution prepared in 3% BSA, and incubate for 30 min at RT in a dark chamber. 17. Wash 3 with 3% BSA for 5 min at RT. 18. Wash sample 3 for 5 min with double-distilled water at RT (avoid light exposure). 19. Mount the section with Mowiol and a cover slip for confocal microscopy.

4

Notes 1. Avoid too high or too low confluence of the cell lines. CCD1137Sk should be subcultured between 2  104 and 7  104 cells/cm2. HaCaT cells grow best in a density between 1  104 cells/cm2 and 3  104 cells/cm2. It should be ensured that the HaCaT cells do not overgrow, otherwise they will start

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to differentiate. SK-MEL-28 should be cultured between 4  104 cells/cm2 and 7  104 cells/cm2. It is important to ensure that the cells are not seeded in too small numbers as they otherwise grow slowly. 2. For transferring spheroids, always use a P1000 pipet and cut 3 mm from the tip to enlarge the opening to obviate any damage to the spheroids. 3. Between each working step, except when culturing in a 96- or 384-well plate, allow the spheroids to settle down to the bottom of the tube. This ensures that no spheroids are lost between the individual steps. 4. Drug treatment of the tri-culture possibly leads to a detachment of the outer cells. To avoid the forfeiture, the culture should be transferred into agarose molds to stabilize the structure during treatment. 5. For better visibility of the spheroids, incubate them for 30 min with 5 μM Draq5 before embedding in FSC 22 frozen section medium. 6. When transferring spheroids embedded in glycerol, also use a P1000 pipet with cut-off tip and avoid air bubbles.

Acknowledgments This work was funded by the German Federal Ministry of Education and Research (BMBF) as part of the Innovation Partnership M2Aind, project M2OGA (03FH8I02IA) within the framework Starke Fachhochschulen–Impuls fu¨r die Region (FH-Impuls). This research project is a part of the Forschungscampus M2OLIE and funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Forschungscampus: public–private partnership for innovations. References 1. Laikova KV, Oberemok VV, Krasnodubets AM, Gal’chinsky NV, Useinov RZ, Novikov IA, Temirova ZZ, Gorlov MV, Shved NA, Kumeiko VV, Makalish TP, Bessalova EY, Fomochkina II, Esin AS, Volkov ME, Kubyshkin AV (2019) Advances in the understanding of skin cancer: ultraviolet radiation, mutations, and antisense oligonucleotides as anticancer drugs. Molecules 24:1516 2. Diepgen TL, Mahler V (2002) The epidemiology of skin cancer. Br J Dermatol 146:1–6

3. Herraiz C, Jime´nez-Cervantes C, Sa´nchezLaorden B, Garcı´a-Borro´n JC (2018) Functional interplay between secreted ligands and receptors in melanoma. Semin Cell Dev Biol 78:73–84 4. Erdei E, Torres SM (2010) A new understanding in the epidemiology of melanoma. Expert Rev Anticancer Ther 10:1811–1823 5. Coricovac D, Dehelean C, Moaca E-A, Pinzaru I, Bratu T, Navolan D, Boruga O (2018) Cutaneous melanoma—a long road

3D Melanoma-Like Tri-Culture Model from experimental models to clinical outcome: a review. Int J Mol Sci 19:1566 6. Carr S, Smith C, Wernberg J (2020) Epidemiology and risk factors of melanoma. Surg Clin North Am 100:1–12 7. Ciarletta P, Foret L, Ben Amar M (2011) The radial growth phase of malignant melanoma: multi-phase modelling, numerical simulations and linear stability analysis. J R Soc Interface 8:345–368 8. Haridas P, McGovern JA, McElwain SD, Simpson MJ (2017) Quantitative comparison of the spreading and invasion of radial growth phase and metastatic melanoma cells in a threedimensional human skin equivalent model. PeerJ 5:e3754 9. Qendro V, Lundgren DH, Rezaul K, Mahony F, Ferrell N, Bi A, Latifi A, Chowdhury D, Gygi S, Haas W, Wilson L, Murphy M, Han DK (2014) Large-scale proteomic characterization of melanoma expressed proteins reveals nestin and vimentin as biomarkers that can potentially distinguish melanoma subtypes. J Proteome Res 13:5031–5040 10. Balch CM, Buzaid AC, Soong S-J, Atkins MB, Cascinelli N, Coit DG, Fleming ID, Gershenwald JE, Houghton A Jr, Kirkwood JM, McMasters M, Mihm MF, Morton DL, Reintgen DS, Ross MI, Sober A, Thompson A, Thompson JF (2001) Final version of the American Joint Committee on Cancer staging system for cutaneous melanoma. J Clin Oncol 19:3635–3648 11. Shtivelman E, Davies MA, Hwu P, Yang J, Lotem M, Oren M, Flaherty KT, Fisher DE (2014) Pathways and therapeutic targets in melanoma. Oncotarget 5:1701 12. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, Dummer R, Garbe C, Testori A, Maio M, Hogg D, Lorigan P, Lebbe C, Jouary T, Schadendorf D, Ribas A, O’Day SJ, Sosman JA, Kirkwood JM, AMM E, Dreno B, Nolop K, Li J, Nelson B, Hou JH, Lee RJ, Flaherty KT, GA MA, BRIM-3 Study Group (2011) Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 364:2507–2516 13. Flaherty KT (2010) Narrative review: BRAF opens the door for therapeutic advances in melanoma. Ann Intern Med 153:587–591 14. Wolchok JD, Hodi FS, Weber JS, Allison JP, Urba WJ, Robert C, O’Day SJ, Hoos A, Humphrey R, Berman DM, Lonberg N,

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Korman AJ (2013) Development of ipilimumab: a novel immunotherapeutic approach for the treatment of advanced melanoma. Ann N Y Acad Sci 1291:1–13 15. Bourland J, Fradette J, Auger FA (2018) Tissue-engineered 3D melanoma model with blood and lymphatic capillaries for drug development. Sci Rep 8:1–13 16. Kapałczyn´ska M, Kolenda T, Przybyła W, Zaja˛czkowska M, Teresiak A, Filas V, Ibbs M, Bliz´niak R, Łuczewski Ł, Lamperska K (2018) 2D and 3D cell cultures–a comparison of different types of cancer cell cultures. Arch Med Sci 14:910–919 17. Ghosh S, Spagnoli GC, Martin I, Ploegert S, Demougin P, Heberer M, Reschner A (2005) Three-dimensional culture of melanoma cells profoundly affects gene expression profile: a high density oligonucleotide array study. J Cell Physiol 204:522–531 18. Marconi A, Quadri M, Saltari A, Pincelli C (2018) Progress in melanoma modelling in vitro. Exp Dermatol 27:578–586 19. Rodenhizer D, Dean T, D’Arcangelo E, McGuigan AP (2018) The current landscape of 3D in vitro tumor models: what cancer hallmarks are accessible for drug discovery? Adv Healthc Mater 7:e1701174 20. Klicks J, Masslo C, Kluth A, Rudolf R, Hafner M (2019) A novel spheroid-based co-culture model mimics loss of keratinocyte differentiation, melanoma cell invasion, and druginduced selection of ABCB5-expressing cells. BMC Cancer 19:402 21. Tsubokawa F, Nishisaka T, Takeshima Y, KJPi I (2002) Heterogeneity of expression of cytokeratin subtypes in squamous cell carcinoma of the lung: with special reference to CK14 overexpression in cancer of high-proliferative and lymphogenous metastatic potential. Pathol Int 52:286–293 22. Mackenzie I, Rittman G, Gao Z, Leigh I, Lane EB (1991) Patterns of cytokeratin expression in human gingival epithelia. J Periodontal Res 26:468–478 23. Nu¨rnberg E, Vitacolonna M, Klicks J, von Molitor E, Cesetti T, Keller F, Bruch R, Ertongur-Fauth T, Riedel K, Scholz P, Lau T, Schneider R, Meier J, Hafner M, Rudolf R (2020) Routine optical clearing of 3D-cell cultures: simplicity forward. Front Mol Biosci 7:20

Chapter 14 Enrichment of Melanoma Stem-Like Cells via Sphere Assays Nabanita Mukherjee, Karoline A. Lambert, David A. Norris, and Yiqun G. Shellman Abstract Sphere assays are widely used in vitro techniques to enrich and evaluate the stem-like cell behavior of both normal and cancer cells. Utilizing three-dimensional in vitro sphere culture conditions provide a better representation of tumor growth in vivo than the more common monolayer cultures. We describe how to perform primary and secondary sphere assays, used for the enrichment and self-renewability studies of melanoma/melanocyte stem-like cells. Spheres are generated by growing melanoma cells at low density in nonadherent conditions with stem cell media. We provide protocols for preparing inexpensive and versatile polyHEMA-coated plates, setting up primary and secondary sphere assays in almost any tissue culture format and quantification methods using standard inverted microscopy. Our protocol is easily adaptable to laboratories with basic cell culture capabilities, without the need for expensive fluidic instruments. Key words Melanoma stem cells, Cancer stem cell, Sphere assay, Cancer stemness, Melanomainitiating cells, Cancer-initiating cells, Tumor spheres

Abbreviations CICs CSCs MICs MSCs

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Cancer-initiating cells Cancer stem cells Melanoma-initiating cells Melanoma stem cells

Introduction The sphere formation assay is an in vitro functional assay for enriching adult stem cells and assessing their self-renewability potential [1–3]. Spheres have been widely used in stem and cancer research since the 1970s [2, 4–9] and are great functional assays

Nabanita Mukherjee and Karoline A. Lambert contributed equally to this chapter. Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_14, © The Author(s) 2021

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Fig. 1 Major scientific breakthroughs leading to the current sphere assays

complementary to assays based on cell surface stem-cell markers. A brief history of the development of this assay is provided in Fig. 1. In short, cells are grown in nonadherent, serum-free conditions to promote proliferation into three-dimensional spheres [1, 7, 9– 17]. The media contains growth factors selective for stem-like cells, and anchorage-independent cultureware is used to push anoikis of non-stem cells, which are often dependent on adhesion or the extracellular matrix. Cells are seeded at low density, allowing for single cells to clonogenically proliferate into spheres. Each sphere forms a microenvironment within itself, with cell layers of different oxygenation, nutrition, and CO2 removal [18, 19] (Fig. 2). Additionally, primary spheres can be dissociated and re-seeded into the secondary sphere assay; only stem-like cells able to self-renew are able to re-form spheres. Therefore, the sphere, as a threedimensional (3D) “tissue”, allows in vitro study of stem-like cells that closely resembles in vivo [2, 18, 20]. In cancer research, sphere assays are used to enrich the cancer stem cells (CSCs) of various solid cancers such as lung [21, 22], breast [23–26], ovarian [27–29], pancreatic [30–32], colon [33– 35], liver [36], prostate [3, 37, 38], and melanoma [10, 11, 39–47, 53, 54]. Various experimental treatments can be applied to sphere assays as a means of analyzing the effects on the stem-like populations of cancers, including melanoma. For instance, in vitro multiwell plates of spheres can be used to test the effects—such as killing, resistance, or proliferation in response to inhibitors, growth factors, or genetic manipulations on the stem-like populations [40, 41, 44, 46–50]. Similar questions can be tested in vivo, by enriching for stem-like cells and implanting in a modified mouse xenograft model. For example, the tumor initiating frequency of melanoma-

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Fig. 2 Schematic diagram of sphere 3D structure. Spheres have multiple layers of cells, each generating microenvironments to enable exchange of nutrient and waste products from the medium. (Figure adapted from [19])

stem or initiating cells (MSCs/MICs) can be studied by enriching MSCs/MICs, followed by mouse implantation of very low cell numbers by serial dilution [44, 45, 51]. Similarly, the metastatic potential of CSC can be assessed with injection in the lateral tail vein of immunodeficient NOD scid gamma mice followed by the detection of the metastatic incidence in lungs [49]. In this chapter, we describe the details of enriching MSC/MICs using sphere assays in suspension culture (Fig. 3). We have successfully used primary sphere assays to assess drug effects on melanoma stem-like cells [40, 44–46, 53, 54] and secondary sphere assays for self-renewal capacity [40, 44–46, 53, 54], and we have also implanted low number of cells from primary spheres in vivo to further assess tumor-initiating capacity of melanomas [44, 45]. Here, we explain the details of plate preparation, seeding, and quantification of sphere assays.

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Materials Whenever possible, use sterile technique and sterile reagents/ supplies in a biosafety hood.

2.1 Standard Materials Necessary Throughout the Procedure

1. Micropipettes. 2. Sterile aerosol-resistant micropipette tips. 3. Pipet aid. 4. Serological pipettes. 5. Conical tubes (15 and 50 ml). 6. Benchtop centrifuge.

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Fig. 3 Schematic diagram of sphere assays 2.2 Preparing Non-adherent Plates for Sphere Assays

Sphere assays utilize anchorage-independent conditions. Culture can be done either in commercially available ultra-low attachment plates or in standard plates (tissue culture-treated or untreated) coated with polyHEMA. Commercially available plates are only available in 96-well, 24-well, and 6-well formats, whereas polyHEMA coating allows versatility in cultureware and is less expensive. Below are the materials needed to coat plates with polyHEMA: 1. Culture plates. We typically use 12-well, 6-well, 6-cm, or 10-cm plates. 2. 95% ETOH, diluted from 200 proof using sterile molecular grade water. 3. PolyHEMA (10
): 12% (w/v) of PolyHEMA in 95% ETOH, prepared in a sterile brown glass bottle. Zero the bottle on a scale in the biosafety hood. Add a small amount of polyHEMA powder, and weigh again to determine the weight of polyHEMA. Dilute to 12% (w/v) with 95% ETOH. Shake in 65  C water bath for at least 4 h until visibly dissolved. Store at 4  C (see Note 1).

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The primary sphere assay enriches the MSCs/MICs from melanoma cells. We have successfully used this assay for commercially available melanoma cell lines, short-term cultures of patient samples, and tumors from mouse xenografts. The principle of this assay involves seeding the cells in nonadherent conditions using serumfree, stem cell medium to allow the formation of free-floating spheres. The size and number of spheres can be quantitated and compared between conditions, with or without treatments. The secondary sphere assay tests the self-renewal capacity of enriched MICs or is used for continuous maintenance of cells in sphere culture. We do the secondary sphere assay in 12-well non-adherent plates in triplicate for all conditions. These assays each take about 5–7 days (Fig. 3). 1. Melanoma cells (80–85% confluent). 2. Melanoma medium optimized for cells of interest (e.g., RPMI1640 with 10% FBS). 3. Detachment solution optimized for cells of interest (e.g., Trypsin-EDTA (0.25%)). 4. Serum-free trypsin neutralization solution (e.g., defined trypsin inhibitor (DTI)). 5. DMEM/F12 medium. 6. B-27 Supplement (serum-free). 7. Penicillin–streptomycin solution (100
). 8. 20 ng/ml of human recombinant basic fibroblast growth factor (bFGF), prepared and stored according to manufacturer. 9. 20 ng/ml of human or mouse recombinant epidermal growth factor (EGF), prepared and stored according to manufacturer. 10. 4 μg/ml of heparin, prepared and stored according to manufacturer. 11. Stem cell medium, prepared fresh each time: DMEM/F12 with 2% B-27, 2
Pen/Strep, 40 pg/ml bFGF, 4 pg/ml EGF, and 480 pg/ml Heparin. To 47.9 ml of DMEM/F12, add 1 ml of B-27, 1 ml of Pen/Strep, 100 μl of bFGF, 10 μl of EGF, and 6 μl of heparin. 12. PolyHEMA-coated plates (prepared just prior to assay as described in Subheading 3.1). 13. Sterile PBS (1
): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4. 14. 2 mM EDTA: Dilute 0.5 M EDTA stock with sterile PBS. 15. Inverted, optical light microscope with bright field and phase contrast options, with at least a 10
objective, ideally with a camera. 16. Automated cell counter or manual hand-held cell counter.

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17. Trypan blue. 18. Sphere viability detection reagents: IncuCyte® Caspase-3/7 Green Apoptosis Reagent, ethidium bromide, acridine orange, Annexin V, propidium iodide (optional). 19. IncuCyte® S3 Live Cell Analysis System (Sartorius) (optional). 20. IncuCyte® Cell-by-Cell Analysis Software Module (Sartorius Catalog #9600-0031) (optional).

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Methods

3.1 Preparing PolyHEMA-Coated Plates

1. Right before coating plates (see Note 1), dilute 10
polyHEMA 1:10 in 95% ETOH. Pipette 1
polyHEMA onto plates as follows: 300 μl per well in 12-well plate; 0.5 ml per well in 6-well plate; 2.5 ml per 6 cm dish; 5 ml per 10 cm dish. 2. Place lids on plates and incubate in a dry 37  C incubator (no water pan or cells) until dry. The small volumes in 12-well plates may dry overnight, but it may take 2–3 days to ensure all the ethanol has evaporated from larger plates. 3. Store coated plates at room temperature (plates can be stored for at least 6 months) and sterilize by UVC light right before seeding cells.

3.2 Seeding Primary Sphere Assay

The optimal seeding density is cell-specific (see Note 2) and ranges from 1000 cells/ml for fast-growing cell lines to 10,000 cells/ml for tumor samples. We recommend starting with a pilot study to determine the optimal density. 1. With the lids off, sterilize the polyHEMA-coated plates in biosafety hood UVC light for 20 min. 2. Freshly prepare the stem cell medium and warm up to 37 C in water bath. Volumes are standard for culture, e.g., 1 ml per well for 12-well plate, 2 ml per well for 6-well plate. 3. Detach adherent melanoma cells using method optimized for the particular cells, e.g., add trypsin–EDTA and place in the incubator for 5 min. For culturing spheres from human melanoma tumors and mouse tumors (see Notes 3 and 4). 4. Stop trypsin reaction with 2
volume of DTI. 5. Collect the cells in 15 ml tubes using about 8 ml of medium for a 10 cm dish of adherent cells. Spin conical tubes at 180
g and discard the supernatant. 6. Resuspend the pellet in medium (volume depends on pellet size) by pipetting up and down multiple times to ensure there are no clumps.

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7. Count cells and determine viability. We usually use a 1:10 dilution of cells while using an automated cell counter. Alternatively, manually count and determine viability with a trypan blue exclusion method, using a hemocytometer and a microscope. If cell clumping is evident during the cell count, pipette up and down and re-check for clumping. It is recommended that cells be at least 90% viable (see Note 5). 8. Plate viable cells at the recommended density (see Note 2) in stem cell medium (see Note 6). 9. Place the plates in a humidified incubator set at 37  C with 5% CO2 (this seeding day represents day 0 of the assay). 10. Do not disturb the plates for 48 h after seeding as the spheres are especially fragile during the formation period and may dissociate due to mechanical movements. Handle plates very gently if they must be viewed or imaged for experimental purposes. 3.3 Maintenance of Primary Sphere Cultures

1. Feed cells with freshly prepared stem cell medium every 3–4 days, or when pH indicator turns yellow, by adding one-fourth the starting volume of medium to the plates, e.g., 250 μl of medium per well of a 12-well plate (see Note 7). 2. If testing the effects of drugs or other experimental treatments on spheres, see Note 8 regarding the timing of treatment. 3. View cultures with an inverted microscope to assess the formation of doublets and triplets 24–72 h after seeding [14] (see Note 9).

3.4 Seeding Secondary Sphere Assay

1. Transfer the primary spheres from plates into sterile 15 or 50 ml conical tubes using a wide bore pipette, such as 5- or 10-ml serological pipettes. 2. Wash the plates with an equal volume of sterile PBS and carefully collect any remaining spheres. 3. Centrifuge gently at low speed typical for cells, such as 180
g, for 5 min at room temperature. 4. Aspirate off the media without disturbing the pellet. 5. Add 100–500 μl of 2 mM EDTA to the pellet and pipette up/down vigorously multiple times to dissociate the spheres into individual cells. The volume of EDTA depends on the pellet size. 6. Check the cells frequently under a light microscope to look for dissociation. Continue the process until at least 95% of spheres are dissociated. 7. Dilute the cell suspension with 1–10 ml of medium depending on the size of the pellet.

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8. Count cells and determine viability using automated cell counter, or manually count and determine viability with a trypan blue exclusion method, using a hemocytometer and a microscope. 9. Plate the cells at the recommended density (see Notes 10–12) with stem cell medium. 10. Check the plates under the microscope right after seeding to ensure uniform seeding of single cells. To ensure uniform seeding, gently pipette up and down and tilt the plate in a circular motion. 11. Place the plate in a humidified incubator set at 37  C with 5% CO2. 12. Maintain cultures and assess sphere formation according to the same timeline for Primary Sphere cultures described in Subheading 3.3 (see Fig. 3 and our publications [40, 44–46]). 3.5 Quantification of Spheres

1. Count the number of spheres in the entire well using an inverted microscope with a 10
objective, for a total magnification of 100
(see Note 13). 2. If analyzing differences in experimental treatments, report sphere data as a percentage by normalizing the sphere count of the treatment well respective to the sphere count of the control well [40, 44–46]. Alternatively, report the sphere efficiency as the number of spheres formed divided by the total number of cells seeded [14]. 3. Capture images of spheres with camera fitted to microscope if desirable.

3.6 Evaluation of Sphere Viability

It is often necessary to analyze the viability or apoptosis of the cells, especially in treatment experiments. Below are some easy and useful ways to evaluate viability of spheres. 1. Perform live-cell imaging with the IncuCyte® S3 Live Cell Analysis System or a related imaging system in order to visualize the formation and development of spheres over time—the addition of IncuCyte® Caspase-3/7 Green Apoptosis Reagent allows for the analysis of live/dead populations over time (see Note 14). 2. Perform dual ethidium bromide and acridine orange (EB/AO) staining, which is a low-cost alternative to live cell imaging that also distinguishes apoptotic cells from necrotic cells [52] (see Note 15). 3. Dissociate spheres into single cells and analyze by conventional assays, such as flow cytometry with Annexin-V/propidium iodide.

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Notes 1. If you are using a 10
stock of polyHEMA stored at 4  C, warm up by shaking at least 1 h in 65 C water bath. 2. For sphere assays, the cells need to be seeded at a lower density than typical for regular monolayer culture. The majority of commercially available melanoma cells form spheres when seeded at a density of 1000–5000 viable cells/ml. For instance, the fast-growing melanoma line A375 can be seeded at 1000 viable cells/ml, while the slower growing line HT-144 can be seeded at a density of 5000 viable cells/ml. For patient-derived melanoma cells, the density may need to be higher (5000–10,000 viable cells/ml) depending on the growth rate of cells. 3. For culturing spheres from human melanoma tumors, the tumors need to be minced thoroughly and may need to be enzymatically dissociated until a homogeneous mixture is obtained. The cells often need to be seeded at a higher density of 5000 cells per ml of stem cell medium [12]. 4. For culturing cells from mouse xenograft tumors of nude mice, the tumor tissue needs to be minced and dissociated as described in Note 3. After dissociation and cell count, it is important to separate out mouse cells from human cells by magnetic activated cell sorting (MACS), which we achieve with the commercially available Mouse Cell Depletion Kit (Miltenyi Biotec). 5. If cell clumping is evident during the cell count, pipette up and down and re-check for clumping. It is recommended that cells be at least 90% viable. 6. We recommend diluting cells in a 50 ml falcon tube, followed by gentle shaking or pipetting up and down to ensure mixing. Add the volume of cell of suspension to well or dish. It is helpful to tilt the plates and tap at the bottom gently to ensure spreading of cells. 7. Unlike standard procedures for regular 2D cell culture, do not attempt to exchange medium, as the mechanical stress of pipetting might dissociate the spheres. Instead, add extra medium gently from the side of the well or dish. 8. For testing the efficacy of certain drugs in MSC/MIC population, the drug should be added by day 5. This will enable better visualization of drug effect in vehicle and test wells without over-confluence of the vehicle wells. At least three replicates for each study/experimental condition are recommended.

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Fig. 4 Examples of primary and secondary spheres from melanoma cells. The ideal spheres are round and tight. The secondary spheres grow much faster and reach similar size in a shorter time. Scale bar ¼ 100 μm

Fig. 5 Examples of overgrown, difficult to count, spheres. (a) Overgrown spheres with necrotic areas. Spheres allowed to proliferate larger than 200 μm diameter often show necrotic centers (red arrow). (b) Spheres which are stuck together (red arrow) due to over proliferation. Scale bar ¼ 100 μm

9. Small compact spheroid masses of cells will be visible around day 4. By days 5–7, well-developed spheres of 50 μm or more in diameter should be visible (Fig. 4). This is the ideal time to count spheres. Once the spheres are of diameter 200 μm or larger, the cells will undergo apoptosis and appear dark in the center (Fig. 5). At this point, it is better to passage the cells into the secondary (or tertiary) sphere assay. 10. We recommend seeding the secondary spheres at the same density as primary spheres. However, for some cells, the secondary spheres grow faster (Fig. 4).

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11. Sphere assays can be conducted in any sized culture plates or flasks. However, 6-well or 12-well plates are recommended for quantification, and larger 10-cm dishes are recommended for protein lysates. 12. For secondary sphere assays, we find it ideal to do triplicate wells in 12-well plates for each condition. To ensure enough viable cells for the secondary sphere assay, it is important to seed cells for the primary spheres in larger plates. For instance, we typically set up primary sphere assays in 6-cm or 10-cm plates, which ensures enough cells for triplicate conditions in the secondary sphere assay, as well as for lysate collection. Also, if looking at the effect of drugs on primary spheres, expect much death, and seed the primary experiment accordingly. 13. Sharpies can be used to divide the plate into multiple areas for counting. For each well of 12-well plate, we divide the entire well into ten fractions for counting. The optimum time to count the spheres is typically between days 4 and 7. Spheres are easier to count if seeded in 6-well or 12-well plates, as it is easier to keep track of your place in the plate, thereby avoiding double counting. Pay attention to the sphere quality and relative size, and take images of the representative spheres if desired and able. It is important to distinguish spheres from loose cell aggregates. If there is doubt regarding a sphere versus an aggregate, it is useful to shake the plate very gently. A loose aggregate will dismantle, while a sphere will not. For faster growing cell lines, it is best to do the quantifications when the spheres are 100 μm in diameter. If the spheres are allowed to grow for longer, they over-proliferate and stick to one another. 14. Images are collected using phase contrast microscopy and a standard green fluorescence channel at time intervals, for multiple days. The same process can also be used to visualize the effects of compounds in disrupting the already formed spheres. The accompanying Incucyte® Cell-by-Cell Analysis Software Module is intuitive and automates sphere counts and viability analysis. 15. EB/AO is simply added to the culture media and immediately visualized under an inverted microscope with a standard green fluorescence channel. Live or necrotic cells fluoresce green or red, respectively, and apoptotic cells show green blebbing of the membrane. These methods have been described and quantitatively used in melanoma and other studies [40, 44, 45]. EB/AO are mutagenic compounds; care must be used when handling, and waste must be disposed of according to local regulations.

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Acknowledgments This work was supported in part by a merit grant from the Department of Veterans Affairs (Veterans Health Administration, Office of Research and Development, Biomedical Laboratory Research and Development) to D.A.N. and partly by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health to Y.G.S. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. These protocols have been used in multiple publications from our lab [40, 44–46, 53, 54]. References 1. Pastrana E, Silva-Vargas V, Doetsch F (2011) Eyes wide open: a critical review of sphereformation as an assay for stem cells. Cell Stem Cell 8(5):486–498. https://doi.org/10. 1016/j.stem.2011.04.007 2. Weiswald L-B, Bellet D, Dangles-Marie V (2015) Spherical cancer models in tumor biology. Neoplasia 17(1):1–15. https://doi.org/ 10.1016/j.neo.2014.12.004 3. Bahmad HF, Cheaito K, Chalhoub RM, Hadadeh O, Monzer A, Ballout F, El-Hajj A, Mukherji D, Liu Y-N, Daoud G, Abou-Kheir W (2018) Sphere-formation assay: threedimensional in vitro culturing of prostate cancer stem/progenitor sphere-forming cells. Front Oncol 8(347). https://doi.org/10. 3389/fonc.2018.00347 4. Inch WR, McCredie JA, Sutherland RM (1970) Growth of nodular carcinomas in rodents compared with multi-cell spheroids in tissue culture. Growth 34(3):271–282 5. Sutherland RM, Inch WR, McCredie JA, Kruuv J (1970) A multi-component radiation survival curve using an in vitro tumour model. Int J Radiat Biol Relat Stud Phys Chem Med 18(5):491–495. https://doi.org/10.1080/ 09553007014551401 6. Bjerkvig R, Tonnesen A, Laerum OD, Backlund EO (1990) Multicellular tumor spheroids from human gliomas maintained in organ culture. J Neurosurg 72(3):463–475. https://doi. org/10.3171/jns.1990.72.3.0463 7. Reynolds BA, Weiss S (1992) Generation of neurons and astrocytes from isolated cells of the adult mammalian central nervous system. Science 255(5052):1707. https://doi.org/10. 1126/science.1553558 8. Kaaijk P, Troost D, Das PK, Leenstra S, Bosch DA (1995) Long-term culture of organotypic

multicellular glioma spheroids: a good culture model for studying gliomas. Neuropathol Appl Neurobiol 21(5):386–391. https://doi.org/ 10.1111/j.1365-2990.1995.tb01075.x 9. Dou J, Pan M, Wen P, Li Y, Tang Q, Chu L, Zhao F, Jiang C, Hu W, Hu K, Ning G (2008) Isolation and identification of cancer stem like cells from murine melanoma cell lines. Cell Mol Immunol 4(6):467–472 10. Schatton T, Frank M (2010) The in vitro spheroid melanoma cell culture assay: cues on tumor initiation? J Invest Dermatol 130:1769–1771. https://doi.org/10.1038/ jid.2010.135 11. Girouard SD, Murphy GF (2011) Melanoma stem cells: not rare, but well done. Lab Invest 91(5):647–664. https://doi.org/10.1038/ labinvest.2011.50 12. Stecca B, Santini R, Pandolfi S, Penachioni JY (2013) Culture and isolation of melanomainitiating cells. Curr Protoc Stem Cell Biol Chapter 3:Unit 3.6. https://doi.org/10. 1002/9780470151808.sc0306s24 13. Nassar D, Blanpain C (2016) Cancer stem cells: basic concepts and therapeutic implications. Annu Rev Pathol 11(1):47–76. https:// doi.org/10.1146/annurev-pathol-012615044438 14. Tuccitto A, Beretta V, Rini F, Castelli C, Perego M (2017) Melanoma stem cell sphere formation assay. Bio-protocol 7. https://doi.org/ 10.21769/BioProtoc.2233 15. Phi LTH, Sari IN, Yang YG, Lee SH, Jun N, Kim KS, Lee YK, Kwon HY (2018) Cancer stem cells (CSCs) in drug resistance and their therapeutic implications in cancer treatment. Stem Cells Int 2018:5416923. https://doi. org/10.1155/2018/5416923

Sphere Assay Enrichment of Melanoma Cancer Stem Cells 16. Capp JP (2019) Cancer stem cells: from historical roots to a new perspective. J Oncol 2019:5189232. https://doi.org/10.1155/ 2019/5189232 17. Atashzar MR, Baharlou R, Karami J, Abdollahi H, Rezaei R, Pourramezan F, Zoljalali Moghaddam SH (2020) Cancer stem cells: a review from origin to therapeutic implications. J Cell Physiol 235(2):790–803. https://doi.org/10.1002/jcp.29044 18. Ham SL, Joshi R, Thakuri PS, Tavana H (2016) Liquid-based three-dimensional tumor models for cancer research and drug discovery. Exp Biol Med (Maywood) 241(9):939–954. https://doi.org/10.1177/ 1535370216643772 19. Chaicharoenaudomrung N, Kunhorm P, Noisa P (2019) Three-dimensional cell culture systems as an in vitro platform for cancer and stem cell modeling. World J Stem Cells 11 (12):1065–1083. https://doi.org/10.4252/ wjsc.v11.i12.1065 20. Deleyrolle LP, Ericksson G, Morrison BJ, Lopez JA, Burrage K, Burrage P, Vescovi A, Rietze RL, Reynolds BA (2011) Determination of somatic and cancer stem cell selfrenewing symmetric division rate using sphere assays. PLoS One 6(1):e15844–e15844. https://doi.org/10.1371/journal.pone. 0015844 21. Pan J, Zhang Q, Wang Y, You M (2010) 26S proteasome activity is down-regulated in lung cancer stem-like cells propagated in vitro. PLoS One 5(10):e13298–e13298. https://doi.org/ 10.1371/journal.pone.0013298 22. Herreros-Pomares A, de-Maya-Girones JD, ˜ as S, Lucas R, Martı´nez A, Calabuig-Farin Pardo-Sa´nchez JM, Alonso S, Blasco A, Guijarro R, Martorell M, Escorihuela E, Chiara MD, Dure´ndez E, Gandı´a C, Forteza J, Sirera R, Jantus-Lewintre E, Farra`s R, Camps C (2019) Lung tumor spheres reveal cancer stem cell-like properties and a score with prognostic impact in resected non-small-cell lung cancer. Cell Death Dis 10(9):660. https:// doi.org/10.1038/s41419-019-1898-1 23. Dontu G, Abdallah WM, Foley JM, Jackson KW, Clarke MF, Kawamura MJ, Wicha MS (2003) In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes Dev 17(10):1253–1270. https:// doi.org/10.1101/gad.1061803 24. Duru N, Fan M, Candas D, Menaa C, Liu H-C, Nantajit D, Wen Y, Xiao K, Eldridge A, Chromy BA, Li S, Spitz DR, Lam KS, Wicha MS, Li JJ (2012) HER2-associated radioresistance of breast cancer stem cells isolated from HER2-negative breast cancer cells. Clin Cancer

197

Res 18(24):6634–6647. https://doi.org/10. 1158/1078-0432.CCR-12-1436 25. Tsuyada A, Chow A, Wu J, Somlo G, Chu P, Loera S, Luu T, Li AX, Wu X, Ye W, Chen S, Zhou W, Yu Y, Wang Y-Z, Ren X, Li H, Scherle P, Kuroki Y, Wang SE (2012) CCL2 mediates cross-talk between cancer cells and stromal fibroblasts that regulates breast cancer stem cells. Cancer Res 72(11):2768–2779. https://doi.org/10.1158/0008-5472.CAN11-3567 26. Lombardo Y, de Giorgio A, Coombes CR, Stebbing J, Castellano L (2015) Mammosphere formation assay from human breast cancer tissues and cell lines. J Vis Exp 97:52671. https://doi.org/10.3791/52671 27. He Q, Luo X, Wang K, Zhou Q, Ao H, Yang Y, Sx L, Li Y, Zhu H, Duan T (2014) Isolation and characterization of cancer stem cells from high-grade serous ovarian carcinomas. Cell Physiol Biochem 33(1):173–184. https://doi. org/10.1159/000356660 28. Wang H, Paczulla A, Lengerke C (2015) Evaluation of stem cell properties in human ovarian carcinoma cells using multi and single cellbased spheres assays. J Vis Exp 95: e52259–e52259. https://doi.org/10.3791/ 52259 29. Dong P, Xiong Y, Watari H, Hanley SJB, Konno Y, Ihira K, Yamada T, Kudo M, Yue J, Sakuragi N (2016) MiR-137 and miR-34a directly target Snail and inhibit EMT, invasion and sphere-forming ability of ovarian cancer cells. J Exp Clin Cancer Res 35(1):132. https://doi.org/10.1186/s13046-016-0415y 30. Gou S, Liu T, Wang C, Yin T, Li K, Yang M, Zhou J (2007) Establishment of clonal colonyforming assay for propagation of pancreatic cancer cells with stem cell properties. Pancreas 34(4):429–435. https://doi.org/10.1097/ MPA.0b013e318033f9f4 31. Bao B, Azmi AS, Aboukameel A, Ahmad A, Bolling-Fischer A, Sethi S, Ali S, Li Y, Kong D, Banerjee S, Back J, Sarkar FH (2014) Pancreatic cancer stem-like cells display aggressive behavior mediated via activation of FoxQ1. J Biol Chem 289(21):14520–14533. https://doi.org/10.1074/jbc.M113.532887 32. Xu Z, Jia Y, Huang X, Feng N, Li Y (2018) Rapid induction of pancreatic cancer cells to cancer stem cells via heterochromatin modulation. Cell Cycle 17(12):1487–1495. https:// doi.org/10.1080/15384101.2018.1489180 33. Qureshi-Baig K, Ullmann P, Rodriguez F, Frasquilho S, Nazarov PV, Haan S, Letellier E (2016) What do we learn from spheroid culture systems? Insights from tumorspheres derived

198

Nabanita Mukherjee et al.

from primary colon cancer tissue. PLoS One 11(1):e0146052. https://doi.org/10.1371/ journal.pone.0146052 34. Olejniczak A, Szarynska M, Kmiec Z (2018) In vitro characterization of spheres derived from colorectal cancer cell lines. Int J Oncol 52 (2):599–612. https://doi.org/10.3892/ijo. 2017.4206 35. Zhao H, Yan C, Hu Y, Mu L, Huang K, Li Q, Li X, Tao D, Qin J (2019) Sphereforming assay vs. organoid culture: determining longterm stemness and the chemoresistant capacity of primary colorectal cancer cells. Int J Oncol 54(3):893–904. https://doi.org/10.3892/ ijo.2019.4683 36. Ma X-L, Sun Y-F, Wang B-L, Shen M-N, Zhou Y, Chen J-W, Hu B, Gong Z-J, Zhang X, Cao Y, Pan B-s, Zhou J, Fan J, Guo W, Yang X-R (2019) Sphere-forming culture enriches liver cancer stem cells and reveals Stearoyl-CoA desaturase 1 as a potential therapeutic target. BMC Cancer 19(1):760. https://doi.org/10.1186/s12885-019-5963z 37. Mulholland DJ, Xin L, Morim A, Lawson D, Witte O, Wu H (2009) Lin-Sca-1+CD49fhigh stem/progenitors are tumor-initiating cells in the Pten-null prostate cancer model. Cancer Res 69(22):8555–8562. https://doi.org/10. 1158/0008-5472.CAN-08-4673 38. Lukacs RU, Memarzadeh S, Wu H, Witte ON (2010) Bmi-1 is a crucial regulator of prostate stem cell self-renewal and malignant transformation. Cell Stem Cell 7(6):682–693. https:// doi.org/10.1016/j.stem.2010.11.013 39. Santini R, Vinci MC, Pandolfi S, Penachioni JY, Montagnani V, Olivito B, Gattai R, Pimpinelli N, Gerlini G, Borgognoni L, Stecca B (2012) Hedgehog-GLI signaling drives selfrenewal and tumorigenicity of human melanoma-initiating cells. Stem Cells 30 (9):1808–1818. https://doi.org/10.1002/ stem.1160 40. Mukherjee N, Reuland SN, Lu Y, Luo Y, Lambert K, Fujita M, Robinson WA, Robinson SE, Norris DA, Shellman YG (2015) Combining a BCL2 inhibitor with the retinoid derivative fenretinide targets melanoma cells including melanoma initiating cells. J Invest Dermatol 135(3):842–850. https://doi.org/ 10.1038/jid.2014.464 41. Marzagalli M, Moretti RM, Messi E, Marelli MM, Fontana F, Anastasia A, Bani MR, Beretta G, Limonta P (2018) Targeting melanoma stem cells with the vitamin E derivative

δ-tocotrienol. Sci Rep 8(1):587. https://doi. org/10.1038/s41598-017-19057-4 42. Mukherjee N, Schwan JV, Fujita M, Norris DA, Shellman YG (2015) Alternative treatments for melanoma: targeting BCL-2 family members to de-bulk and kill cancer stem cells. J Invest Dermatol 135(9):2155–2161. https:// doi.org/10.1038/jid.2015.145 43. Nguyen N, Couts KL, Luo Y, Fujita M (2015) Understanding melanoma stem cells. Melanoma Manag 2(2):179–188. https://doi.org/ 10.2217/mmt.15.4 44. Mukherjee N, Almeida A, Partyka KA, Lu Y, Schwan JV, Lambert K, Rogers M, Robinson WA, Robinson SE, Applegate AJ, Amato CM, Luo Y, Fujita M, Norris DA, Shellman YG (2016) Combining a GSI and BCL-2 inhibitor to overcome melanoma’s resistance to current treatments. Oncotarget 7(51):84594–84607. https://doi.org/10.18632/oncotarget.13141 45. Mukherjee N, Lu Y, Almeida A, Lambert K, Shiau CW, Su JC, Luo Y, Fujita M, Robinson WA, Robinson SE, Norris DA, Shellman YG (2017) Use of a MCL-1 inhibitor alone to de-bulk melanoma and in combination to kill melanoma initiating cells. Oncotarget 8 (29):46801–46817. https://doi.org/10. 18632/oncotarget.8695 46. Mukherjee N, Strosnider A, Vagher B, Lambert KA, Slaven S, Robinson WA, Amato CM, Couts KL, Bemis JGT, Turner JA, Norris DA, Shellman YG (2018) BH3 mimetics induce apoptosis independent of DRP-1 in melanoma. Cell Death Dis 9(9):907. https://doi.org/10. 1038/s41419-018-0932-z 47. Pietrobono S, Morandi A, Gagliardi S, Gerlini G, Borgognoni L, Chiarugi P, Arbiser JL, Stecca B (2016) Down-regulation of SOX2 underlies the inhibitory effects of the triphenylmethane gentian violet on melanoma cell selfrenewal and survival. J Invest Dermatol 136 (10):2059–2069. https://doi.org/10.1016/j. jid.2016.06.610 48. Yang F, Wei J, Zhang S, Zhang X (2017) Shrimp miR-S8 suppresses the stemness of human melanoma stem-like cells by targeting the transcription factor YB-1. Cancer Res 77 (20):5543–5553. https://doi.org/10.1158/ 0008-5472.Can-17-1375 49. Wang Y, Leonard M, Snyder D, Fisher M, Eckert R, Kaetzel D (2019) NME1 drives expansion of melanoma cells with enhanced tumor growth and metastatic properties. Mol Cancer Res 17(8):1665–1674. https://doi. org/10.1158/1541-7786.MCR-18-0019

Sphere Assay Enrichment of Melanoma Cancer Stem Cells 50. Tuccitto A, Tazzari M, Beretta V, Rini F, Miranda C, Greco A, Santinami M, Patuzzo R, Vergani B, Villa A, Manenti G, Cleris L, Giardiello D, Alison M, Rivoltini L, Castelli C, Perego M (2016) Immunomodulatory factors control the fate of melanoma tumor initiating cells. Stem Cells 34(10):2449–2460. https:// doi.org/10.1002/stem.2413 51. Roesch A, Fukunaga-Kalabis M, Schmidt EC, Zabierowski SE, Brafford PA, Vultur A, Basu D, Gimotty P, Vogt T, Herlyn M (2010) A temporarily distinct subpopulation of slowcycling melanoma cells is required for continuous tumor growth. Cell 141(4):583–594. https://doi.org/10.1016/j.cell.2010.04.020 52. Ribble D, Goldstein NB, Norris DA, Shellman YG (2005) A simple technique for quantifying apoptosis in 96-well plates. BMC Biotechnol 5:12. https://doi.org/10.1186/1472-67505-12

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53. Mukherjee N, Amato CM, Skees J, Todd KJ, Lambert KA, Robinson WA, Van Gulick R, Weight RM, Dart CR, Tobin RP, McCarter MD, Fujita M, Norris DA, Shellman YG (2020) Simultaneously Inhibiting BCL2 and MCL1 Is a Therapeutic Option for Patients with Advanced Melanoma. Cancers (Basel) 12 (8). https://doi. org/10.3390/cancers12082182 54. Mukherjee N, Skees J, Todd KJ, West DA, Lambert KA, Robinson WA, Amato CM, Couts KL, Van Gulick R, MacBeth M, Nassar K, Tan A-C, Zhai Z, Fujita M, Bagby SM, Dart CR, Lambert JR, Norris DA, Shellman YG (2020) MCL1 inhibitors S63845/ MIK665 plus Navitoclax synergistically kill difficult-to-treat melanoma cells. Cell death & disease 11 (6):443. https://doi.org/10.1038/ s41419-020-2646-2

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Part III Techniques for Isolating and Studying Circulating Melanoma Cells

Chapter 15 Capture and Isolation of Circulating Melanoma Cells Using Photoacoustic Flowmetry Robert H. Edgar, Justin Cook, Madeline Douglas, Anie-Pier Samson, and John A. Viator Abstract Early detection of cancer has been a goal of cancer research in general and melanoma research in particular (Birnbaum et al., Lancet Glob Health 6:e885–e893, 2018; Alendar et al., Bosnian J Basic Med Sci 9:77–80, 2009). Early detection of metastasis has been targeted as pivotal to increasing survival rates (Menezes et al., Adv Cancer Res 132:1–44, 2016). Melanoma, though curable in its early stages, has a dramatic decrease in survival rates once metastasis has occurred (Sharma et al., Biotechnol Adv 36:1063–1078, 2018). The transition to metastasis is not well understood and is an area of increasing interest. Metastasis is always premeditated by the shedding of circulating tumor cells (CTCs) from the primary tumor. The ability to isolate rare CTCs from the bloodstream has led to a host of new targets and therapies for cancer (Micalizzi et al., Genes Dev 31:1827–1840, 2017). Detection of CTCs also allows for disease progression to be tracked in real time while eliminating the need to wait for additional tumors to grow. Using a photoacoustic flowmeter, in which we induce ultrasonic responses from circulating melanoma cells (CMCs), we identify and quantify these cells in order to track disease progression. Additionally, these CMCs are captured and isolated allowing for future analysis such as RNA-Seq or microarray analysis. Key words Diagnostic, Metastasis, Optical, Optoacoustic, Ultrasound

1

Introduction Traditional melanoma treatment involves the removal of suspected melanoma tumors and close monitoring for additional tumor sites [1–5]. Aggressive melanoma treatment may involve interferon therapy or other chemotherapy and close monitoring for recurring tumors [6]. Each stage of treatment involves a substantial waiting period to assess disease progression or treatment success. CTCs are tumor cells that have separated from the main tumor and have entered the blood or lymphatic system. CTCs allow the cancer to spread and to develop secondary tumors, which is integral to metastatic progression [7]. Detection of CTCs allows for the real-time

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_15, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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assessment of a therapy’s efficiency and has a strong prognostic value for cancer advancement. Many methods for detecting CTCs have been developed including immunocytochemical, immunomagnetic, microfluidic, and molecular methods. Each of these methods has certain advantages and disadvantages. Some demonstrate excellent sensitivity while lacking the ability to recover identified CTCs. Similarly, a method might be able to capture cells but lack the ability to yield quantitative results, and each method may require a skilled technician or cytologist. Photoacoustic flow cytometry (PAFC) offers both a high degree of specificity and the ability to recover detected cells with high reproducibility [8]. The PAFC method in this chapter provides immediate, sensitive, and unambiguous detection and recovery of CMCs. Photoacoustics is a process using light to create sound. Photoacoustic flow cytometry generates ultrasonic waves resulting from absorption of light in particles under flow [9]. These ultrasonic waves are often created by thermoelastic expansion and contraction of an object that absorbed laser light [10, 11]. In our photoacoustic flow cytometry setup, a nanosecond laser operating at 532 nm is used to irradiate a sample under flow. The ultrasonic waves are detected by a piezoelectric transducer and recorded onto a computer. The advantage of using photoacoustic detection over conventional flow cytometry is that cells can be specifically targeted, and the ultrasonic wave generated by these cells is strong enough to allow for single-cell detection. Additionally, since the cells remain in suspension, recovery of the cells is straight forward with high CTC viability for later analysis.

2 2.1

Materials Blood Separation

1. BD Vacutainer® Blood Collection Tube with Sodium Heparin. 2. Histopaque®-1077 or Ficoll-Paque™PLUS. 3. Red Blood Cell (RBC) Lysis Buffer, 10
(Tonbo Biosciences). 4. Phosphate-buffered saline (PBS). 5. Neutral Density Solution (NDS): Prepare in a 50 ml Falcon tube by adding 0.56 ml of PBS to 49.44 ml of Histopaque® 1077. 6. DNase1. 7. 100–1000 μl micropipette. 8. 10-ml serological pipet. 9. 15-ml Falcon tube. 10. Centrifuge (capable of 414
g or greater).

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2.2 Photoacoustic Flowmetry

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1. Photoacoustic flowmetry system consisting of nanosecond laser, acoustic chamber, transducer, oscilloscope, and data recording device. 2. Mineral oil, light. 3. 5-ml syringe. 4. Sonotech LithoClear acoustic gel. 5. Tegam 4040B amplifier. 6. LabView software (National Instruments).

2.3 Capture and Isolation

1. Fraction collector, e.g., Gilson FC 203B fraction collector with three-way valve kit. 2. Microscope with micro injection system, e.g., DMI4000B with Eppendorf Microinjection System.

Leica

3. 96-Well flat-bottom plate. 2.4 Immunohistochemistry

1. PBS. 2. 1% bovine serum albumin (BSA) solution in PBS. 3. 10% normal mouse serum solution in PBS. 4. Formalin (4% formaldehyde). 5. Shaker. 6. Melan-A monoclonal primary antibody raised in rabbit (Invitrogen, Thermo Fisher MA5-15237). 7. Melan-A secondary antibody raised in mouse (Invitrogen, Thermo Fisher A-21235). 8. CD-45 monoclonal primary antibody raised in mouse (Invitrogen, Thermo Fisher MA1-19111). 9. CD-45 secondary antibody raised in mouse (Invitrogen, Thermo Fisher A28175). 10. 96-Well flat-bottom plate coated with 1% poly-L-lysine. 11. 0.1 μg/ml solution of DAPI in PBS.

2.5 RT-PCR and cDNA Library Preparation

1. SuperScript® VILO® cDNA Synthesis kit (Invitrogen). 2. 10% NP40. 3. SUPERase-in™. 4. T4 Gene 32 Protein. 5. Nuclease-free water. 6. Thermocycler. 7. 96-Well skirted PCR plate.

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Methods Blood Separation

1. Obtain blood samples in vacutainer tubes (see Note 1). 2. Place 10 ml of Histopaque 1077 in a 15-ml Falcon tube. 3. Carefully layer 3–4 ml of blood onto the Ficoll layer without breaking the surface tension of Ficoll. 4. Centrifuge the sample at 1600
g at 20  C for 30 min (see Note 2). 5. Carefully pipette out buffy coat which is situated above Ficoll and below plasma layer (see Note 3). 6. Pipette the white blood cell layer into a separate 15-ml Falcon tube. 7. Dilute one volume of extracted buffy coat into 10 volumes of 1
RBC lysis buffer. 8. Incubate buffy coat/RBC lysis buffer at 24  C for 15–30 min with periodic agitation or gentle vortexing (see Note 4). 9. Centrifuge the cells at 400
g at 20  C for 15 min, pipet out supernatant and resuspend cells in 1–4 ml of NDS. 10. Add 20 u of DNase1/ml and incubate at room temperature (RT) for 15 min.

3.2 Photoacoustic Flowmetry

Our PAFC system consists of an Nd:YAG laser coupled into a 1000 μm, 0.39 numerical aperture, optical fiber. This is used to produce 532 nm laser light with 5 ns pulses. Laser beam energy coupled through the optical fiber is maintained and measured from 1.9 to 2.1 mJ for melanoma CTC detection. Laser light is directed toward a quartz tube with 10-μm thick walls passing through a 3D-printed flow chamber (Fig. 1). The 10-μm thick walls allow for the propagation of ultrasonic waves, as well as provide an optically transparent pathway for the sample to flow through. The optical fiber is placed 5 mm from the quartz tube to create a detection volume of 0.04 μl. Laser beam shape is assumed Gaussian, and fluence is calculated to be 0.014 mJ/cm2. A 2.25 MHz transducer (immersion transducer, 0.50 in. element diameter F ¼ 0.80 in.) focused on the quartz sample tube is fitted to the base of the 3D-printed flow chamber. The transducer focal point is aligned to incorporate the quartz tube illuminated by the laser beam. The internal volume of the chamber is filled with Sonotech LithoClear acoustic gel to provide a medium for the propagation of acoustic waves. Syringe pumps create alternating flow of sample and mineral oil equal to a 60 μl/min flow rate. The introduction of sample and the immiscible mineral oil induces two-phase flow [12]. Two-phase flow is employed to allow for sample collection and further analysis while eliminating the possibility of samples becoming stuck or

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Acoustic Mineral Oil Matching Medium Optical Fiber Cell Suspension

Acoustic sensor Target Bacteria containing droplets

Fig. 1 Schematic of photoacoustic flow chamber with parts labeled for identification

Fig. 2 Schematic of photoacoustic flow setup with parts labeled for identification

delayed inside the tubing. Signals are amplified with a gain of 50 using a Tegam 4040B amplifier and sent to a desktop computer running a customized LabView program (Fig. 2). 1. Allow a 1% BSA solution to sit in the sample syringe, tubing, and the flow chamber for 1 h prior to use to create a hydrophobic barrier inside the tubing/syringe. 2. Turn on the laser system and allow it to establish thermal equilibrium. 3. Layer 1 ml of mineral oil on top of sample in a 5-ml syringe.

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4. Place sample syringe in first syringe pump and place second syringe with mineral oil in second syringe pump. 5. Turn on the syringe pumps and watch for air bubbles to pass through the quartz tube in the flow chamber before beginning test. 6. Start the computer interface and the fraction collector. 7. Begin the test once any air bubbles have cleared the flow chamber. 8. Set the oscilloscope to take a minimum of four averages to reduce noise. 9. Set the RITEC system on 32 dB amplification, with a 1 MHz high pass filter. 10. When capturing and isolating melanoma cells, collect detected volumes directly into a 96 well flat bottom plate coated with 1% poly-L-lysine (see Notes 5–7). Collected cell volumes can be further analyzed by visual inspection, RT-PCR, or immunocytochemistry. 3.3

Analysis of CTC

3.3.1 Immunocytochemistry

Once a CTC has been detected, it can be captured for additional testing. Either cells can be stained/imaged or RNA can be isolated for RT-PCR and the creation of a cDNA library for analysis. 1. Collect single droplets containing detected cells in 96 wells with bottom plate coated with 1% poly-L-lysine. 2. Place the 96-well plate containing cells into a 5% CO2 incubator set at 37  C for 15–60 min. 3. Centrifuge the plate at 100
g at RT for 3–5 min. 4. Using a micropipetter, gently remove the supernatant. 5. Add 100 μl of formalin (4% formaldehyde). 6. Place the 96-well plate on a shaker for 1 min. 7. Incubate on the bench top at RT for 15 min. 8. Centrifuge plate at 100
g for 2–3 min. 9. Using a micropipetter, gently remove supernatant. 10. Wash cells by adding 100 μl of PBS. 11. Centrifuge plate at 100
g at RT for 2–3 min. 12. Repeat cell washing (steps 9–11 from this section) three times. 13. Remove PBS and add 100 μl of 10% mouse serum for 1 h at RT. 14. Remove mouse serum solution and add 100 μl of the primary antibody solution (1% CD-45 antibody and 1% Melan-A antibody in 10% goat serum solution). Incubate at RT for 1 h. 15. Remove antibody solution using a micropipetter.

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Fig. 3 Captured melanoma cell stained with Melan-A antibody and AlexaFluor647 secondary antibody

16. Wash the cells three times with 100 μl of PBS for 10 min (repeat steps 9–11 from this section). 17. Remove PBS and add 100 μl of secondary antibody solution (1% CD-45 secondary antibody and 1% Melan-A secondary antibody in 10% mouse serum). Incubate at RT for 1 h. 18. Remove antibody solution with a micropipetter. 19. Wash the cells three times with 100 μl PBS for 10 min (repeat steps 9–11 from this section). 20. Add 0.1 μg/ml of DAPI. Incubate at RT for 1 min. 21. Remove DAPI solution and wash three times (repeat steps 9– 11 from this section). 22. Image cells in PBS with a fluorescence imaging microscope. An example of an isolated CTC is shown following staining for Melan-A in Fig. 3 (see Notes 8 and 9). 3.3.2 Isolation of CTC RNA and Generation of cDNA Library

1. For each 96-well plate, prepare the appropriate amount of RT Mix Solution 1 according to Table 1. This solution accomplishes both lysis of the cells and stabilization of isolated RNA. 2. Pipette 5 μl of RT Mix Solution 1 into each well to be used on the 96-well PCR plate. 3. Sort individual cells directly into the same plate containing RT Mix Solution 1. 4. Seal the plate and vortex thoroughly for 15 s. 5. Centrifuge plate for 30 s in a centrifuge pre-chilled at 4  C. 6. Immediately freeze (20  C) the sealed plate or immediately perform cDNA generation. 7. If previously frozen, thaw sealed plate on ice. 8. Centrifuge plate for 30 s in a centrifuge pre-chilled at 4  C. 9. Preheat thermocycler to 65  C.

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Table 1 RT Mix Solution 1 Volume (μl)

Component 5
VILO™ Reaction Mix

48 samples with overages (μl)

96 samples with overages (μl)

1.2

72.0

144.0

20 U/μl SUPERase-in™ 0.3

18.0

36.0

10% NP40

0.25

15.0

30.0

Nuclease-Free Water

3.25

195.0

390.0

Total

5.0

300.0

600.0

Volume (μl)

48 samples with overages (μl)

96 samples with overages (μl)

Table 2 RT Mix Solution 2

Component 10
SuperScript® Enzyme

0.15

9.0

18.0

T4 Gene 32 Protein

0.12

7.2

14.4

Nuclease-Free Water

0.73

43.8

87.6

Total

1.00

60.0

120.0

Table 3 RT thermocycler arameters Temperature

25  C

50  C

55  C

60  C

70  C

4 C

Time

5 min

30 min

25 min

5 min

10 min

Infinity

10. Transfer samples to thermocycler and incubate at 65  C for 90 s. 11. Immediately remove plate and snap chill on ice for 5 min. 12. Centrifuge plate for 30 s in a centrifuge pre-chilled at 4  C. 13. Prepare RT Mix Solution 2 according to Table 2. This solution contains the reagents necessary for the generation of cDNA. 14. Aliquot 1 μl into each well and centrifuge for 30 s in a centrifuge pre-chilled at 4  C. 15. Perform reverse transcriptase (RT) cycling in thermocycler according to conditions shown in Table 3. 16. Centrifuge plate for 30 s in a centrifuge pre-chilled at 4  C and store at 20  C. 17. Samples are now ready for cDNA sequencing.

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Notes 1. Blood must be processed within 24 h of specimen collection. 2. Blood must be 18–25  C for correct centrifugation to occur. 3. RBC will pellet through Ficoll to the bottom of the tube. Plasma and buffy coat containing white blood cells and CTCs will be layered on top of Ficoll. 4. If visible RBC contamination remains, ensure the cells are mixed up, so that the lysis buffer can lyse the RBC and repeat the lysis steps at RT before diluting cells into the desired amount of NDS buffer. 5. The fraction size containing a positively detected cell should be large enough to eliminate the possibility of dehydration before the test concludes. Droplet size from our PAFC system can range from 10 to 30 μl. 6. A consistent droplet size will increase the chances of appropriate staining of detected cells. 7. Each collected droplet is small and contains a CTC. There will also be other cells and cellular debris in the droplet. For some applications, the contaminating cells are inconsequential. For situations where CTC purity is necessary, the droplet can be diluted to 1 ml and the process repeated. 8. The typical staining of captured melanoma cells is shown in Fig. 3. Green fluorescence staining using Mart1 primary and green AlexaFluor secondary antibody delineates the captured melanoma cell. The image is created as an overlay using ImageJ software. The antibody concentration can be increased or decreased to modulate fluorescence and background staining. In contrast to CTC, cultured HS936 melanoma cells fully adhere to culture flasks (Fig. 4). When cultured cells adhere, they elongate and display a different morphology than circulating cells.

Fig. 4 Cultured HS936 melanoma cell shown fully adhered to culture flask without staining

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9. When staining cells, if image background looks washed out or overly green, try a 1:10, 1:100, and 1:1000 dilution series of both primary and secondary antibodies.

Acknowledgments We would like to acknowledge the support by the National Cancer Institute of the National Institutes of Health under award number 1R01CA161367-01 and our Pennsylvania Commonwealth Universal Research Enhancement (C.U.R.E.) grant. We also acknowledge Acousys Biodevices Inc. for their support. References 1. Birnbaum JK, Duggan C, Anderson BO, Etzioni R (2018) Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling study. Lancet Glob Health 6(8):e885–e893 2. Alendar F, Drljevic´ I, Drljevic´ K, Alendar T (2009) Early detection of melanoma skin cancer. Bosnian J Basic Med Sci 9(1):77–80 3. Menezes M, Das S, Minn I, Emdad L, Wang X-Y, Sarkar D, Pomper MG, Fisher P (2016) Detecting tumor metastases: the road to therapy starts here. Adv Cancer Res 132:1–44 4. Sharma S, Zhuang R, Long M, Pavlovic M, Kang Y, Ilyas A, Asghar W (2018) Circulating tumor cell isolation, culture, and downstream molecular analysis. Biotechnol Adv 36 (4):1063–1078 5. Micalizzi DS, Maheswaran S, Haber DA (2017) A conduit to metastasis: circulating tumor cell biology. Genes Dev 31 (18):1827–1840 6. P. A. T. E. Board (2019) Intraocular (uveal) melanoma treatment (pdq®). PDQ Cancer Information Summaries [Internet], National Cancer Institute (US) 7. Zhou L, Dicker DT, Matthew E, El-Deiry WS, Alpaugh RK (2017) Circulating tumor cells:

silent predictors of metastasis. F1000 Res 6: F1000 Faculty Rev-1445 8. Edgar RH, Cook J, Noel C, Minard A, Sajewski A, Fitzpatrick M, Fernandez R, Hempel JD, Kellum JA, Viator JA (2019) Bacteriophage-mediated identification of bacteria using photoacoustic ow cytometry. J Biomed Opt 24(11):1–7 9. Manohar S, Razansky D (2016) Photoacoustics: a historical review. Adv Opt Photon 8 (4):586–617 10. Paltauf G, Schmidt-Kloiber H, Guss H (1996) Light distribution measurements in absorbing materials by optical detection of laser-induced stress waves. Appl Phys Lett 69(11):1526–1528 11. Kruger RA, Liu P, Fang YR, Appledorn CR (1995) Photoacoustic ultrasound (PAUS) reconstruction tomography. Med Phys 22 (10):1605–1609 12. O’Brien CM, Rood K, Bhattacharyya KD, DeSouza T, Sengupta S, Gupta SK, Mosley J, Goldschmidt BS, Viator JA, Sharma N (2012) Capture of circulating tumor cells using photoacoustic flowmetry and two phase flow. J Biomed Opt 17(6):061221

Chapter 16 Multi-Marker Immunomagnetic Enrichment of Circulating Melanoma Cells Aaron B. Beasley, Emmanuel Acheampong, Weitao Lin, and Elin S. Gray Abstract Within the last decade, circulating tumor cells (CTCs) have emerged as a promising biomarker for prognostication, treatment monitoring, and detection of markers of treatment resistance, and their isolation can be used as a minimally invasive means of profiling tumors across multiple body sites. However, CTCs represent a minuscule fraction of the total circulating cells in a patient. Therefore, sensitive isolation methods are needed for the detection and downstream analysis of these cells. Herein we describe a sensitive method for melanoma CTC isolation using a multi-marker immunomagnetic bead method. This method has been purposely optimized to detect CTCs in melanoma patients. Key words Circulating tumor cells (CTCs), Immunomagnetic beads, Multi-marker

1

Introduction Circulating tumor cells (CTCs) are tumor-derived cells thought to be responsible for the hematogenous spread of cancer. Melanoma CTCs are exceptionally rare in the blood and harbor highly heterogenous marker expression, which further limits their detectability [1–3]. Many studies involving CTC detection initially utilized indirect measurements such as RT-PCR [4]. Recently, direct CTC measurement and isolation have become favorable, as they enable downstream DNA, RNA, or protein analysis, and studies have used many different techniques such as CellSearch immunomagnetic isolation [5], immunomagnetic beads [6], or microfluidics [7] to capture these cells. Once isolated, analysis of CTCs using various downstream analysis techniques has been shown to be useful for addressing clinical need. For example, enumeration of CTCs by means of immunostaining has been shown to provide prognostic information in metastatic breast cancer, where patients with greater than five CTCs per 7.5 mL of blood have a significantly shorter overall

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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survival than those with less than 5 [8], or similarly in metastatic castration-resistant prostate cancer [9], metastatic non-small cell lung cancer [10], and metastatic small cell lung cancer (thresholds of 50 rather than 5) [11]. Furthermore, enumeration of CTCs in primary non-metastatic breast cancer was predictive of decreased progression-free survival and overall survival [12]. Circulating tumor cells have also been shown to be prognostic of overall survival in metastatic cutaneous melanoma [5]. Beyond enumeration, the genetic analysis of CTCs can be additionally used to detect tumor-specific somatic copy number alterations associated with response to therapy [13], detection of druggable mutations driving acquired resistance to therapy [14], and detection of driver mutations [15]. Altogether, CTCs offer a minimally invasive method of profiling all tumors within a patient at a single time point and can be used to answer important clinically relevant questions. Although new methods of CTC analysis have been described, they still rely on the robust isolation of CTCs in most patients. While microfluidic techniques utilize label (marker)-free isolation to ensure that a heterogeneous population of cells is captured, there are several drawbacks. First, microfluidic capture requires the purchase of expensive machinery and the continued purchase of expensive microfluidic chips. Additionally, modification of run parameters is limited in this approach, and once the isolated CTCs are acquired, it requires either in-solution staining or techniques such as cytospin for immunostaining. Furthermore, although microfluidic devices are relatively hands-free for the isolation of CTCs, multiple machines are required due to the one-sample one-machine limitation of devices, and therefore, the purchase of multiple costly machines is necessary if high throughput is required. In contrast, immunomagnetic beads are cheap to use, and protocols can be easily modified and changed to improve capture or for different downstream analysis. This approach requires only some extra rare-earth magnetics, and it is easily scalable for multiple samples. Previous research, including that of our lab, has demonstrated that melanoma CTCs express highly heterogenous marker expression [1–3], and single capture markers are not suitable for the isolation of CTCs. Therefore, a multiplex marker immunomagnetic isolation protocol is beneficial. In melanoma, we have previously described an immunomagnetic isolation technique using a combination of antibodies targeting a range of melanoma, melanocyte, and stem cell markers (CD271, ABCB5, MCAM, and MCSP), which resulted in an increased melanoma CTC enrichment when compared to the individual markers. The protocol described below is utilized for the isolation of CTCs from melanoma patients.

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Materials

2.1 Blood Collection and PBMC Isolation

1. 9 mL K2 EDTA tubes (see Note 1). 2. Ficoll-Paque Plus. 3. Leucosep tube.

2.2 Immunomagnetic Beads

1. DynaBeads Antibody Coupling Kit (ThermoFisher Scientific). 2. Chondroitin Sulfate Antibody (Clone 9.2.27, Catalog #554275, BD Biosciences). 3. CD146 Antibody (Clone P1H12, Catalog #550314, BD Biosciences). 4. CD271 Antibody (Clone C40-1457, Catalog #557194, BD Biosciences). 5. ABCB5 Antibody (Clone 5H3C6, Catalog #ab140667, Abcam). 6. Make MCSP, MCAM, CD271, and ABCB5 immunomagnetic beads as described by the DynaBeads Antibody Coupling Kit handbook, with modifications (see Notes 2–4).

2.3 Immunomagnetic Enrichment and Immunostaining

1. Phosphate-Buffered Saline Tablets (1 PBS, pH 7.4): Dissolve two PBS tablets in 1 L of 18.2 MΩ·cm water (make 2 L). Filter sterilize using sterile top filter. For cost savings, more than 2 L of PBS can be filtered at one time (see Note 5). PBS can last for 2 months if kept at room temperature. 2. EDTA-Na2 dihydrate. 3. Bovine Serum Albumin (BSA), fraction V. 4. Bottle-top vacuum filters, 0.2 μm. 5. 50 mL CELLSTAR polypropylene conical tube. 6. Graduated 1.5-mL colorless microtubes. 7. Fix & Perm Cell Permeabilization Kit (Nordic MUBio). 8. Normal Donkey Serum. 9. FcR Blocking Reagent, human (Miltenyi Biotech). 10. Hoechst 33342, Trihydrochloride Trihydrate—FluoroPure Grade (diluted to 10 mg/mL in 1 PBS). 11. S100β Antibody (Clone EP1576Y, Catalog #ab52642, Abcam). 12. MLANA (MART1) Antibody (Clone EP1422Y, Catalog #ab51061, Abcam). 13. gp100 Antibody (Clone EPR4864, Catalog #ab137062, Abcam).

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14. CD16-AF647 Antibody (Clone 38G, Catalog #302049, BioLegend). 15. CD45-AF647 Antibody (Clone HI30, Catalog #304018, BioLegend). 16. Donkey Anti-Rabbit AF488 Secondary Antibody. 17. Glass slides. 18. Coverslips. 19. Neodymium smaller than coverslips. 20. Fluoromount-G Mounting Medium (No DAPI). 21. Magnetic Activated Cell Sorting (MACS) Buffer: Dissolve 5 g of BSA and 0.74 g of EDTA in 1 L of PBS, pH 7.2 to prepare this buffer with a concentration of 0.5% BSA and 2 mM EDTA. Filter sterilize solution (see Note 5) and store for up to 2 months at 4  C. 22. Primary antibody solution: Make fresh for each sample processed at step 3 of Subheading 3.3. Dilute 1 μL of S100 antibody in 10 μL of PBS. For one sample (scale up as required), add 39.5 μL of Perm Buffer B from the Fix & Perm Cell Permeabilization Kit to a 1.5 mL tube. Add the NDS and the remaining antibodies as shown in Table 1 to achieve a 50 μL staining volume per sample (work in the dark when adding fluorescent-conjugated antibodies). 23. Secondary Antibody Solution: Make fresh for each sample processed at step 7 of Subheading 3.3. Dilute 1 μL of antirabbit antibody and 1 μL of Hoechst in 10 μL of PBS. For one sample (scale up as required), add 46.5 μL of Perm Buffer B from the Fix & Perm kit to a 1.5 mL tube. Add the NDS and the diluted secondary antibody and Hoechst cocktail as shown in Table 2 to achieve a 50 μL staining volume per sample (work in the dark when adding fluorescent-conjugated antibodies).

Table 1 Preparation of primary antibody solution Antibody

Volume (μL)

Final dilution

NDS

2.5

1/20

Diluted S100β

1

1/500

MART-1

1

1/50

gp100

1

1/50

CD16-AF-647

2.5

1/20

CD45-AF-647

2.5

1/20

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Table 2 Preparation of secondary antibody solution

2.4

Instruments

Antibody

Volume (μL)

Final dilution

NDS

2.5

1/20

Diluted Anti-rabbit AF488 & Hoechst

1

1/500

1. DynaMag™-2 magnet. 2. Rotary Suspension Mixer. 3. Hot room or general lab incubator able to be set to 37  C. 4. Cold room or fridge that can fit Rotary Suspension Mixer. 5. Set of pipettes and tips. 6. Analytical balance (0.1 mg sensitivity). 7. Vortex. 8. Minicentrifuge. 9. Ultrapure water generator. 10. Laboratory vacuum (either built-in or stand-alone). 11. Centrifuge with swinging buckets that hold 50-mL tubes. 12. Non-magnetic tweezers. 13. Fluorescent microscope.

3

Methods

3.1 PBMC Isolation | Timing: 45 min

1. Spin blood tube at 300  g for 20 min. Remove plasma for storage, or other uses. 2. Add 15.2 mL of Ficoll-Paque Plus to a Leucosep tube. 3. Centrifuge at 1000  g for 30 s, and make sure that the space below the porous barrier is filled with Ficoll-Paque Plus. 4. Add approximately 5 mL of PBS to the Leucosep tube. 5. Pour blood into the Leucosep tube slowly, so that it runs down the side of the tube. Do not use pipettes to transfer the blood (see Note 6). 6. Pour about 3 mL of PBS into the blood tube, recap the blood tube, and mix until blood is removed from the sides of the tube. Pour into the same Leucosep tube. 7. Repeat step 6 of this section until Leucosep tube is filled to 50 mL and all blood is transferred. 8. Spin blood at 800  g for 15 min with slow deceleration.

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9. Pour separated PBMC layer from the Leucosep into a fresh 50 mL tube in one movement. Do not use pipettes to transfer the cells. 10. Add 5 mL of PBS to the Leucosep tube and mix to remove remaining PBMCs. Add to the new 50-mL tube. Top up to 50 mL with PBS if necessary. 11. Spin blood at 300  g for 8 min. During this time, label a new 1.5-mL microcentrifuge tube and add 500 μL of MACS Buffer. Invert the tube to coat the whole tube and set aside for step 4 of Subheading 3.2. A small number of RBCs will be left over with the PBMC layer due to becoming stuck on the top of the filter of the Leucosep tube. These do not interfere with CTC isolation (see Note 7). 3.2 CTC Isolation | Timing: 1 h 15 min

1. Remove the supernatant from the 50-mL centrifuge tube using a laboratory vacuum on low-medium power. Do not disturb the cell pellet. Leave about 100–200 μL of supernatant in the tube. 2. Remove MACS buffer from the 1.5-mL tube from step 11 of Subheading 3.1, and add 100 μL of MACS buffer to the cell pellet. 3. Mix pellet by tapping. 4. Transfer cells from the 50-mL centrifuge tube to the 1.5-mL microcentrifuge tube coated with MACS. Do not discard the tip. 5. With a different pipette, add enough MACS buffer to the 50-mL centrifuge tube to make the total volume in the microcentrifuge tube become approximately 700 μL. 6. Using the original pipette, move the remainder of the MACS buffer to the 1.5-mL tube. 7. Add 3 μL (each) of ABCB5, CD271, MCSP, and MCAM beads to the 1.5-mL tube. 8. Place on a roller at 4  C for 1 h. 9. Remove from the roller, spin quickly (~1 s), and place on a magnet for 2 min (brown beads should be visible in clear supernatant). 10. Remove supernatant. 11. Take off the magnet and add 500 μL of MACS buffer. Swirl gently to resuspend beads. Place on magnet for 2 min. 12. Repeat steps 10 and 11 of this section two more times. 13. Quickly spin the tube (~1 s) and place on the magnet for 2 min and remove supernatant.

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14. Remove from the magnet and add 50 μL of MACS buffer followed by 100 μL of Buffer A from the Fix & Perm Cell Permeabilization Kit (see Note 8). 15. Swirl to resuspend beads. Incubate for 10 min. 16. Place beads on the magnet with 2 min remaining on step 15 of this section. 17. Remove supernatant. Wash with 100 μL of PBS. Place on magnet for 2 min. 18. Repeat PBS wash once. 19. Resuspend in 100 μL of PBS. 20. If desired, store samples at 4  C overnight. Swirl to resuspend beads prior to continuing. 3.3 Immunostaining | Timing: 2 h

1. Add 5 μL of FcR Blocking Reagent and incubate for 10 min. 2. Place beads on the magnet with 2 min remaining on step 1 of this section. 3. Remove supernatant, then add 100 μL of PBS and place on the magnet for 2 min. 4. Prepare primary antibody solution as described in step 22 of Subheading 2.3 and Table 1 (see Note 9). 5. Remove supernatant, add 50 μL of Primary Antibody Solution, and incubate for 1 h in the dark, swirling to resuspend every 20 min (see Note 10). 6. Place on magnet for 2 min. Remove supernatant. 7. Wash with 100 μL of PBS. Place on magnet for 2 min. 8. Prepare secondary antibody solution as described in step 23 of Subheading 2.3 and Table 2. 9. Remove supernatant, add 50 μL of secondary antibody solution, and incubate for 30 min. 10. Place on magnet for 2 min. Remove supernatant. 11. Wash with 100 μL of PBS. Place on magnet for 2 min. 12. Repeat PBS wash step twice. 13. To mount cells for enumeration (see Note 11), pipette cells onto a slide while holding a magnet underneath (see Note 12). Use the PBS that is now on the slide to wash the 1.5-mL tube, pipette onto the slide, and repeat twice. 14. Remove as much PBS as possible by pipette. 15. Dry slide with a Kimwipe being careful to not touch the cells. 16. Let the slide dry in the dark for approximately 2 min.

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17. Apply 10 μL of Fluoromount-G onto the cells, with the magnet still underneath. Apply coverslip. Do not use magnetic tweezers. Store slides upright (see Note 13). 18. Under a fluorescent microscopy, there should be approximately 30,000 cells (patient dependent). More than 90% of PBMCs should be CD45/CD16 (AF647) positive (patient dependent). CTCs (MART1/gp100/S100β, AF488) generally are slightly larger than surrounding PBMCs; however, this is not always the case. CTC staining intensity can vary interand intra-patient. Number of CTCs varies heavily per patient and per time point (see Note 14).

4

Notes 1. K2 EDTA tube can be replaced with either 9 mL TransFix CTC-TVTs (CTC-TVT-09-50, Cytomark), 10 mL CellSave Preservative Tubes (7900005, Menarini Silicon Biosystems), or 10 mL Cell-Free DNA BCT (218962, Streck). These tubes can be used if the time between blood draw and CTC isolation is delayed. 2. Two milligram of beads can be made by reducing the volumes and weights on day 1 of the coupling protocol by 2.5. Ten microgram of antibody is generally suitable for coupling. 3. Check binding efficiency using cancer cell lines, such as A2058, in spike-in experiments. Detachment of cell lines from flasks should be done using EDTA to retain extracellular protein. Recovery should be approximately 80%. (Optional) Check binding efficiency using flow cytometry using anti-mouse secondary antibody incubated with 5 μL of beads for 15 min. Beads should be highly fluorescent when compared to beads without secondary antibody incubation. 4. Immunomagnetic beads can last for 6 months with sodium azide (as per DynaBeads handbook) at 4  C. 5. PBS and MACS buffer are not required to be sterile, but it is recommended to remove any debris for improved downstream analysis. Furthermore, as MACS buffer contains BSA, filter sterilizing will prevent microbiological growth. Keep MACS buffer at 4  C when not in use. 6. It is important to avoid loss of cells by pipetting as little as possible, and when pipetting, ensure that cells are washed from the tip. 7. A large number of RBC can be observed after PBMC isolation if the blood is old, but this is also patient dependent. A RBC lysis step could be included if necessary.

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8. Excessive clumping can be observed after fixation as a result of cells not being well resuspended. Make sure cells are well resuspended before fixation step. 9. Be careful not to introduce bubbles into the antibody solutions. Stop pipetting as soon as all liquid inside the tip is expelled, without introducing air into the solution. 10. Perform immunostaining in the dark to prevent unwanted photobleaching. 11. Alternatively, cells can also be used in other downstream pipelines such as single cell picking using the ALS CellCellector. 12. Neodymium or other rare earth magnet can be embedded into polystyrene (such as a lid from an antibody shipment), and slides can be taped to this as a “holder” if more convenient. 13. Using Fluoromount-G enables easy removal of the coverslip for further analysis such as single cell isolation. However, the coverslip is easily moved by gravity and needs level, upright storage to prevent coverslip-slippage. If no further downstream analysis is expected, Fluoromount-G can be replaced with a mountant such as Prolong Diamond Antifade Mountant. 14. Double-positive cells: Cells can be positive for CD45/CD16 and MART1/gp100/S100β. This is normal, and the number depends on individual patients and time points. These cells should not be counted as CTCs.

Acknowledgments A.B.B. and E.A. are supported by an Edith Cowan University Postgraduate Scholarship. A.B.B. is supported by a Cancer Council of Western Australia PhD Top-up Scholarship. E.S.G. holds a fellowship from the Cancer Council Western Australia. References 1. Gray ES, Reid AL, Bowyer S, Calapre L, Siew K, Pearce R, Cowell L, Frank MH, Millward M, Ziman M (2015) Circulating melanoma cell subpopulations: their heterogeneity and differential responses to treatment. J Invest Dermatol 135(8):2040–2048. https://doi. org/10.1038/jid.2015.127 2. Khoja L, Shenjere P, Hodgson C, Hodgetts J, Clack G, Hughes A, Lorigan P, Dive C (2014) Prevalence and heterogeneity of circulating tumour cells in metastatic cutaneous melanoma. Melanoma Res 24(1):40–46. https:// doi.org/10.1097/cmr.0000000000000025

3. Freeman JB, Gray ES, Millward M, Pearce R, Ziman M (2012) Evaluation of a multi-marker immunomagnetic enrichment assay for the quantification of circulating melanoma cells. J Transl Med 10(1):192. https://doi.org/10. 1186/1479-5876-10-192 4. Fusi A, Collette S, Busse A, Suciu S, Rietz A, Santinami M, Kruit WHJ, Testori A, Punt CJA, Dalgleish AG, Spatz A, Eggermont AMM, Keilholz U (2009) Circulating melanoma cells and distant metastasis-free survival in stage III melanoma patients with or without adjuvant interferon treatment (EORTC 18991 side

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Aaron B. Beasley et al.

study). Eur J Cancer 45(18):3189–3197. https://doi.org/10.1016/j.ejca.2009.09.004 5. Khoja L, Lorigan P, Zhou C, Lancashire M, Booth J, Cummings J, Califano R, Clack G, Hughes A, Dive C (2013) Biomarker utility of circulating tumor cells in metastatic cutaneous melanoma. J Invest Dermatol 133 (6):1582–1590. https://doi.org/10.1038/ jid.2012.468 6. Ulmer A, Schmidt-Kittler O, Fischer J, Ellwanger U, Rassner G, Riethmu¨ller G, Fierlbeck G, Klein CA (2004) Immunomagnetic enrichment, genomic characterization, and prognostic impact of circulating melanoma cells. Clin Cancer Res 10(2):531. https://doi. org/10.1158/1078-0432.CCR-0424-03 7. Aya-Bonilla CA, Morici M, Hong X, McEvoy AC, Sullivan RJ, Freeman J, Calapre L, Khattak MA, Meniawy T, Millward M, Ziman M, Gray ES (2020) Detection and prognostic role of heterogeneous populations of melanoma circulating tumour cells. Br J Cancer 122 (7):1059–1067. https://doi.org/10.1038/ s41416-020-0750-9 8. Cristofanilli M, Pierga JY, Reuben J, Rademaker A, Davis AA, Peeters DJ, Fehm T, Nole F, Gisbert-Criado R, Mavroudis D, Grisanti S, Giuliano M, Garcia-Saenz JA, Stebbing J, Caldas C, Gazzaniga P, Manso L, Zamarchi R, de Lascoiti AF, De MattosArruda L, Ignatiadis M, Cabel L, van Laere SJ, Meier-Stiegen F, Sandri MT, VidalMartinez J, Politaki E, Consoli F, Generali D, Cappelletti MR, Diaz-Rubio E, Krell J, Dawson SJ, Raimondi C, Rutten A, Janni W, Munzone E, Caranana V, Agelaki S, Almici C, Dirix L, Solomayer EF, Zorzino L, Darrigues L, Reis-Filho JS, Gerratana L, Michiels S, Bidard FC, Pantel K (2019) The clinical use of circulating tumor cells (CTCs) enumeration for staging of metastatic breast cancer (MBC): international expert consensus paper. Crit Rev Oncol Hematol 134:39–45. https://doi.org/10.1016/j.critrevonc.2018. 12.004 9. de Bono JS, Scher HI, Montgomery RB, Parker C, Miller MC, Tissing H, Doyle GV, Terstappen LWWM, Pienta KJ, Raghavan D (2008) Circulating tumor cells predict survival benefit from treatment in metastatic castrationresistant prostate cancer. Clin Cancer Res 14 (19):6302. https://doi.org/10.1158/10780432.CCR-08-0872

10. Krebs MG, Sloane R, Priest L, Lancashire L, Hou JM, Greystoke A, Ward TH, Ferraldeschi R, Hughes A, Clack G, Ransom M, Dive C, Blackhall FH (2011) Evaluation and prognostic significance of circulating tumor cells in patients with non–small-cell lung cancer. J Clin Oncol 29(12):1556–1563. https://doi.org/10.1200/JCO.2010.28. 7045 11. Hou JM, Krebs MG, Lancashire L, Sloane R, Backen A, Swain RK, Priest L, Greystoke A, Zhou C, Morris K, Ward T, Blackhall FH, Dive C (2012) Clinical significance and molecular characteristics of circulating tumor cells and circulating tumor microemboli in patients with small-cell lung cancer. J Clin Oncol 30 (5):525–532. https://doi.org/10.1200/JCO. 2010.33.3716 12. Lucci A, Hall CS, Lodhi AK, Bhattacharyya A, Anderson AE, Xiao L, Bedrosian I, Kuerer HM, Krishnamurthy S (2012) Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncol 13 (7):688–695. https://doi.org/10.1016/ S1470-2045(12)70209-7 13. Carter L, Rothwell DG, Mesquita B, Smowton C, Leong HS, Fernandez-GutierrezF, Li Y, Burt DJ, Antonello J, Morrow CJ, Hodgkinson CL, Morris K, Priest L, Carter M, Miller C, Hughes A, Blackhall F, Dive C, Brady G (2017) Molecular analysis of circulating tumor cells identifies distinct copynumber profiles in patients with chemosensitive and chemorefractory small-cell lung cancer. Nat Med 23(1):114–119. https://doi. org/10.1038/nm.4239 14. Maheswaran S, Sequist LV, Nagrath S, Ulkus L, Brannigan B, Collura CV, Inserra E, Diederichs S, Iafrate AJ, Bell DW, Digumarthy S, Muzikansky A, Irimia D, Settleman J, Tompkins RG, Lynch TJ, Toner M, Haber DA (2008) Detection of mutations in EGFR in circulating lung-cancer cells. N Engl J Med 359(4):366–377. https:// doi.org/10.1056/NEJMoa0800668 15. Reid AL, Freeman JB, Millward M, Ziman M, Gray ES (2015) Detection of BRAF-V600E and V600K in melanoma circulating tumour cells by droplet digital PCR. Clin Biochem 48 (15):999–1002. https://doi.org/10.1016/j. clinbiochem.2014.12.007

Chapter 17 PD-L1 Detection on Circulating Melanoma Cells Joseph W. Po, Yafeng Ma, Bavanthi Balakrishnar, Daniel Brungs, Farhad Azimi, Adam Cooper, Erin Saricilar, Vinay Murthy, Paul de Souza, and Therese M. Becker Abstract The advent of personalized medicines targeting cell signaling pathways has radically improved melanoma patient outcomes. More recently, immune-modulating therapies disrupting the PD-1/PD-L1 axis have become a powerful tool in the treatment of a range of melanoma, showing a profound improvement in the overall survival outcomes. However, immune checkpoint inhibitors (ICIs) are associated with considerable toxicities and appear to only be efficacious in a subset of melanoma patients. Therefore, there is an urgent need to identify biomarkers that can determine if patients will or will not respond to ICI therapy. Here, we describe an optimized method for analyzing PD-L1 expression on circulating melanoma cells following immunomagnetic enrichment from patient blood samples. Key words Circulating tumor cells (CTC), Melanoma, PD-L1, Immunotherapy, Liquid biopsy

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Introduction Circulating tumor cells (CTCs) are cells released into the circulation from primary or metastatic tumors with the potential to initiate metastases [1–3]. As key mediators facilitating hematogenous spread, CTCs have become an attractive biomaterial to analyze as they provide insight into disease progression and guide patient management. Moreover, compared with conventional tumor biopsies, the non-invasive nature of CTC analysis means longitudinal monitoring via serial sampling is entirely possible, allowing one to better monitor evolving cancer biology in real-time. With the increased availability of technologies and technological advancement, the utility of CTCs in predicting prognosis and survival continues to improve. CTC analyses have been adopted to detect useful molecular biomarkers that can inform personalized treatment and are increasingly used in clinics (reviewed by [2– 5]. Importantly, CTCs reflect tumor heterogeneity and allow characterization of cellular heterogeneity due to cell-specific expression

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of RNA or proteins [6–10]. Currently most carcinoma CTCs are isolated using capture and identification methods reliant on the targeting of epithelial epitopes [11, 12]. However, these CTC detection strategies cannot be used for non-epithelial derived cancers such as melanoma, brain cancer, or sarcomas. Consequently, the clinical utility of these CTCs has yet to be fully realized [13– 16]. The challenge in the detection of circulating melanoma cells is the marked heterogeneity in both the genetic and phenotypic landscape, leading to variable protein expression, disrupting efficient isolation or identification of CTCs. Thus, targeting multiple cell surface proteins for isolation and identification may be better suited for optimal melanoma CTC detection [17, 18]. The improvement in our understanding of the genetic landscape of melanoma led to the discovery and use of targeted therapies disrupting the MAPK pathway (BRAF/MEK inhibitors) and has since become the mainstay therapy for BRAF-mutated tumors [19–22]. More recently, immune checkpoint inhibitors (ICIs) targeting CTLA-4 or PD-1 antigens demonstrated potent and durable responses in melanoma patients independent of mutation status and have become an invaluable tool in treating melanoma patients [23, 24]. The durable response seen with ICI therapy, however, appears to only be efficacious in a subset of melanoma patients [25, 26], despite potential serious immune-related adverse events including colitis, myocarditis, and pancreatitis with sometimes fatal consequences [27]. Therefore, the ability to reliably identify the subset of patients that likely respond to ICIs—and importantly exclude those patients who will likely not respond—remains an important unmet need for melanoma patients. Currently, programmed death-ligand 1 (PD-L1) expression in patient tumor tissue is the primary biomarker used to predict response to PD-1 inhibition [28–30]. However, the utility of PD-L1 expression from tumor tissues is controversial as a significant number of patients with PD-L1 negative tumor tissues have responded well to PD-1 inhibition [29, 31, 32]. Moreover, PD-L1 expression may be highly variable due to intratumor or intertumor heterogeneity, and it may not be feasible to access tissue samples reliably throughout therapy [17]. CTC analysis provides an opportunity to overcome these challenges by offering a noninvasive method to longitudinally acquire tumor material and screen for PD-L1 expression. Additionally, CTC analysis may better represent tumor heterogeneity because they represent a sampling from multiple tumors releasing CTCs into circulation rather than single tumor nodes. There is a clear need to develop a robust method capable of detecting clinically relevant biomarkers to determine whether a patient will, or will not, respond to ICI therapy. The following protocol introduces an optimized method of analyzing PD-L1 expression on circulating melanoma cells after immunomagnetic enrichment from blood samples.

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Materials

2.1 Immunomagnetic Bead Preparation

1. Immunomagnetic beads from Isoflux Rare Cell Enrichment Kit (Fluxion). 2. Antibodies. (a) Mouse anti-human melanoma cell adhesion molecule (MCAM) antibody, clone P1H12. (b) Mouse anti-human melanoma-associated chondroitin sulfate proteoglycan (MCSP) antibody, clone 9.2.27. 3. Phosphate-buffered saline (PBS). 4. Lo-bind 1.5-mL microcentrifuge tubes. 5. Strong neodymium handheld magnet (19 mm diameter, height 10 mm, magnetic flux reading 4568 G). Alternatively, a magnetic rack can be used instead.

2.2 Peripheral Blood Mononuclear Cell (PBMC) Separation

1. 9-mL EDTA vacutubes. 2. SepMate 50-mL PBMC isolation tubes. 3. 50-mL centrifuge tubes with lid. 4. Lymphoprep gradient separation medium.

2.3 Melanoma Patient Circulating Tumor Cell (CTC) Enrichment

1. Isoflux CTC isolation platform (Fluxion). 2. Isoflux cartridge (Fluxion). 3. Fc Blocker, included in Isoflux Rare Cell Enrichment Kit. 4. Binding buffer, included in Isoflux Rare Cell Enrichment Kit. 5. Mounting medium. 6. Formaldehyde: Prepare in a 3.7% formaldehyde solution in PBS.

2.4 Immunodetection of PD-L1 Expression in Melanoma Patient CTCs

1. Antibodies: (a) Rabbit anti-human-PD-L1 antibody, clone E1L3N. (b) AlexaFluor555-conjugated goat-anti-rabbit secondary antibody. (c) AlexaFluor647-conjugated CD45 antibody, clone HI30. (d) Melanoma cell identification panel (Mel-ID). l

AlexaFluor488-conjugated anti-human GP100 antibody, clone DT101/BC199/HMB4.

l

AlexaFluor488-conjugated anti-human Melan-A antibody, clone SPM555.

l

FITC-conjugated anti-human S100-β antibody, clone 4C4.0 + S100B/1012.

2. Mouse gamma globulin.

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3. Triton X-100: Dilute to a 0.2% Triton X-100 solution in 1 PBS. 4. 10% fetal bovine serum (FBS). 5. 1 Hoechst. 6. Superfrost glass slides. 7. IX71 fluorescent microscope.

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Methods

3.1 Immunomagnetic Bead Preparation

1. Retrieve immunomagnetic beads, which are pre-coupled with anti-mouse IgG antibodies, from the Rare Cell Isolation Kit and bring beads into homogeneous suspension by gently agitating and inverting the tube. Then remove the desired volume of bead suspension, usually 300 μL per antibody per antibody binding preparation (see Note 1). 2. Equilibrate the bead suspension to binding buffer conditions: Pull beads down by magnetic force, discard supernatant, and wash in binding buffer with five times the original bead suspension volume, allowing beads to suspend; then pull beads down with magnetic force to remove supernatant and resuspend in binding buffer at the original suspension volume. 3. Incubate mouse anti-human MCAM or mouse anti-human MCSP antibodies (1 μg and 0.5 μg/50 μL of magnetic beads, respectively), with equilibrated magnetic beads on a rotating platform, agitating the samples gently, for 90 min at room temperature. Antibody-coupled beads may be stored until use at 4  C for 4 weeks.

3.2

PBMC Separation

1. Collect 9 mL of peripheral blood from melanoma patients into EDTA vacutubes. Gently invert tube 5–10 times to mix anticoagulant with blood. 2. Use Lymphoprep and SepMate tubes to separate the peripheral blood mononuclear cells (PBMCs), containing CTCs, according to manufacturer’s instructions as described below. All the following steps should be performed in a biosafety cabinet (see Note 2). 3. Transfer 13.5 mL of Lymphoprep into the lower compartment of the SepMate 50 mL PBMC separation falcon tube through the SepMate insert. Then carefully transfer 9 mL of patient blood to the SepMate tube by expelling blood down the sides of the tube. 4. Wash EDTA tube with 9 mL of PBS (equal volume to blood), rotating the tube as the PBS rinses the sides of the vacutube; transfer the PBS and residual blood to the SepMate tube.

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Fig. 1 Schematic of the CTC enrichment cartridge (Isoflux, Fluxion)

5. Centrifuge SepMate tube at 1200  g, for 10 min at room temperature with the brake ON. 6. Gently but quickly pour supernatant from the SepMate tube into a fresh 50-mL centrifuge tube. Gently pipette 10 mL of PBS down the sides of the SepMate tube, carefully swirling the tube, and then transfer to the centrifuge tube containing supernatant. Centrifuge at 280  g for 10 min with the brake ON. 7. Discard supernatant, wash PBMC cell pellet in 10 mL of PBS, and centrifuge again at 280  g for 10 min. 3.3 Melanoma CTC Enrichment

1. Discard supernatant and resuspend the PBMC cell pellet in 800 μL of binding buffer, 40 μL of Fc buffer, and 30 μL of both anti-MCAM and anti-MCSP antibody-bound immunomagnetic beads (prepared as described in Subheading 3.1; see Note 3). 2. Incubate cells for 90 min at 4  C on a rotating platform and then load the sample into binding buffer-primed IsoFlux cartridges (Fig. 1) for CTC enrichment using the Fluxion Standard Isolation Protocol Rev2 with collection into 200 μL recovery tubes. 3. Following run, immediately retrieve sample (see Note 4) and fix enriched CTC samples with 100 μL of 3.7% formaldehyde in PBS for 10 min. 4. Wash CTC samples in binding buffer, and store in 100 μL of PBS at 4  C for future immunocytostaining.

3.4 Immunodetection of PD-L1 Expression in Melanoma Patient CTCs

All following steps should be performed at room temperature, keeping samples in the dark to avoid photo-bleaching the fluorophore. Between each step, the immunomagnetic bead sample is “pulled down” by placing the handheld magnet at the base of the tube and waiting for about 10 s before removing the supernatant (see Notes 5 and 6).

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1. Retrieve enriched CTC samples and block with mouse globulins (diluted 1:10 in binding buffer) for 20 min at room temperature. 2. Block samples with 10% FBS (in PBS) for 20 min at room temperature. 3. Probe-enriched CTC samples with AlexaFluor647-conjugated CD45 antibody (diluted 1:100 in binding buffer) for 30 min at room temperature. 4. Following a 1 PBS wash, incubate samples with rabbit antiPD-L1 antibody (diluted 1:50 in binding buffer) for 30 min, then with AlexaFlour555-conjugated goat-anti-rabbit secondary antibody (diluted 1:2000 in binding buffer) for 30 min (see Note 7). 5. Permeabilize samples with 0.2% Triton X-100 and then probe for 30 min with the Mel-ID panel that includes: (a) AlexaFluor488-conjugated anti-human GP100; diluted 1:100 in blocking buffer. (b) AlexaFluor488-conjugated anti-human MelanA; diluted 1:100 in blocking buffer. (c) FITC-conjugated anti-human S100-β; diluted 1:500 in blocking buffer. 6. Following a wash with 1 PBS, mount sample onto a Superfrost glass slide with mounting medium with an added Hoechst constituent (see Note 8). 7. Image samples with the IX71 fluorescent microscope (we use the 20 objective lens). In the setup described here, CTCs are classified as Hoechst positive, Mel-ID positive, and CD45 negative (Hoescht+/Mel-ID+/CD45 ; Fig. 2). 8. Determine PD-L1 expression of all identified CTCs (see Note 9).

Fig. 2 PD-L1 expression detection on patient-derived circulating melanoma cell. Representative melanoma CTC identification staining with PD-L1 biomarker detection. Mel-ID: probed with cocktail of three fluorescently labeled melanoma identification antibodies as described in Subheading 3.4. (Image reused from [40] with permission)

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Notes 1. Optimal immunomagnetic bead preparation is dependent upon adequate interaction between immunomagnetic beads and antibodies. During incubation period for immunomagnetic bead preparation, users should ensure that appropriate solution volumes and lo-bind tubes are used to maintain constant mixing and to prevent drying out of beads or binding of antibodies to the tubes. To help preventing beads drying out, mixing volumes should be at least half of the tube volume, i.e., 1.5-mL lo-bind tube should have no less than 750 μL of buffers. Finally, during all bead transfer steps that require pipetting, prime all tips with binding buffer to reduce any bead loss. 2. During blood transfers during PBMC enrichment, it is recommended to transfer undiluted blood directly from blood tube to the SepMate 50-mL tube and then rinse the EDTA blood collection tube with a volume of PBS equal to the original blood collection volume. Then gently invert the blood tube and transfer PBS to the SepMate 50-mL tube. CTCs are in low abundance, and washing the tube in this way should minimize any CTC loss due to residual blood left in the collection tube. 3. Others have shown that MCAM and MCSP are highly suitable immunomagnetic targets for immunomagnetic targeted isolation of melanoma CTC isolation [18, 33]. In our lab, we compared immunomagnetic isolation of melanoma patient CTCs targeting MCAM alone, MCSP alone, and the combination of the two and demonstrated that an improved efficiency was achieved by targeting the combination of MCAM and MCSP (Fig. 3).

Fig. 3 Immunomagnetic melanoma cell isolation. Comparison of CTC enumeration from 14 advanced melanoma patients (16 blood draws, 3  9 mL each) following isolation by targeting with anti-human MCAM alone, anti-human MCSP alone, or the combination of both. CTC counts shown as box plot. (Image reused from [40] with permission)

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4. Sample is prone to drying out following CTC enrichment. In some cases, samples and beads may aggregate if samples are left too long (>10 min). To minimize drying out and aggregation, fill final collection tube with 100 μL of PBS. CTC samples should also be immediately retrieved following isolation with the Isoflux platform and resuspended in PBS buffer (run times are typically 35–45 min depending on sample volumes). 5. Ensure that between each of the immunostaining steps (including fixation, buffer washes, antibody incubation periods, etc.) that care is taken when “pulling down” immunomagnetic bead bound samples. Pull beads down for about 10 s each time, or longer if all beads have not visually settled, to ensure ALL sample is pulled down; otherwise, some CTC loss may occur. 6. Loss of CTCs may occur during sample manipulations. It is recommended to avoid unnecessary pipetting when suspending beads as cells may stick to plastic tips. Instead, the handheld magnet can be used to mix the sample throughout solution by moving its position to different sites of the tube in slight distance to it alternatively gently agitate tubes to resuspend samples. 7. There are many PD-L1 antibody clones commercially available that have demonstrated success in both clinical and research applications in terms of positive staining threshold correlating with prognostic outcomes [34, 35]. The anti-human PD-L1 clone E1L3N was optimized for this study as it shows good correlation between PD-L1 detection and response in non-small cell lung cancer [36], and it has previously been used successfully for PD-L1 probing in CTCs [37]. 8. When mounting the sample onto a slide, ensure that the beads are well spread out to allow for clear visualization of cells during fluorescent microscopy imaging/visualization. Beads “piling up” may obscure cells, especially low antigenexpressing cells (not uncommon for CTC analyses) that have bound less antibody. 9. Data indicate that PD-L1 expression is inducible (mediated via interferon gamma) in some melanoma cell populations due to sustained leukocyte–melanoma cell interactions [38, 39]. Data from our lab suggest that this is also true for melanoma cells in blood. Our in vitro data suggest that CTC detection with PD-L1 probing is best performed 24 h following blood collection.

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Acknowledgments This work was supported by the Cancer Institute New South Wales through the Centre for Oncology Education and Research Translation (CONCERT, grant ID: 13/TRC/1-01). Human ethics approval, HREC/13/LPOOL/158, was obtained and managed by the CONCERT Biobank. References 1. Ding PN, Becker TM, Bray VJ, Chua W, Ma YF, Lynch D, Po J, Luk AWS, Caixeiro N, de Souza P, Roberts TL (2019) The predictive and prognostic significance of liquid biopsy in advanced epidermal growth factor receptormutated non-small cell lung cancer: a prospective study. Lung Cancer 134:187–193. https://doi.org/10.1016/j.lungcan.2019.06. 021 2. Lim M, Kim CJ, Sunkara V, Kim MH, Cho YK (2018) Liquid biopsy in lung cancer: clinical applications of circulating biomarkers (CTCs and ctDNA). Micromachines (Basel) 9 (3):100. https://doi.org/10.3390/ mi9030100 3. Alix-Panabieres C, Pantel K (2016) Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov 6(5):479–491. https://doi.org/10. 1158/2159-8290.CD-15-1483 4. Becker TM, Caixeiro NJ, Lim SH, Tognela A, Kienzle N, Scott KF, Spring KJ, de Souza P (2014) New frontiers in circulating tumor cell analysis: a reference guide for biomolecular profiling toward translational clinical use. Int J Cancer 134(11):2523–2533. https://doi.org/ 10.1002/ijc.28516 5. Hwang WL, Pleskow HM, Miyamoto DT (2018) Molecular analysis of circulating tumors cells: biomarkers beyond enumeration. Adv Drug Deliv Rev 125:122–131. https:// doi.org/10.1016/j.addr.2018.01.003 6. Zhang S, Li L, Wang T, Bian L, Hu H, Xu C, Liu B, Liu Y, Cristofanilli M, Jiang Z (2016) Real-time HER2 status detected on circulating tumor cells predicts different outcomes of antiHER2 therapy in histologically HER2-positive metastatic breast cancer patients. BMC Cancer 16:526. https://doi.org/10.1186/s12885016-2578-5 7. Gray ES, Rizos H, Reid AL, Boyd SC, Pereira MR, Lo J, Tembe V, Freeman J, Lee JH, Scolyer RA, Siew K, Lomma C, Cooper A, Khattak MA, Meniawy TM, Long GV, Carlino MS, Millward M, Ziman M (2015) Circulating

tumor DNA to monitor treatment response and detect acquired resistance in patients with metastatic melanoma. Oncotarget 6 (39):42008–42018. https://doi.org/10. 18632/oncotarget.5788 8. Lee M, Kim EJ, Cho Y, Kim S, Chung HH, Park NH, Song YS (2017) Predictive value of circulating tumor cells (CTCs) captured by microfluidic device in patients with epithelial ovarian cancer. Gynecol Oncol 145 (2):361–365. https://doi.org/10.1016/j. ygyno.2017.02.042 9. Koyanagi K, Mori T, O’Day SJ, Martinez SR, Wang HJ, Hoon DS (2006) Association of circulating tumor cells with serum tumorrelated methylated DNA in peripheral blood of melanoma patients. Cancer Res 66 (12):6111–6117. https://doi.org/10.1158/ 0008-5472.CAN-05-4198 10. Aya-Bonilla CA, Marsavela G, Freeman JB, Lomma C, Frank MH, Khattak MA, Meniawy TM, Millward M, Warkiani ME, Gray ES, Ziman M (2017) Isolation and detection of circulating tumour cells from metastatic melanoma patients using a slanted spiral microfluidic device. Oncotarget 8(40):67355–67368. https://doi.org/10.18632/oncotarget.18641 11. Banko P, Lee SY, Nagygyorgy V, Zrinyi M, Chae CH, Cho DH, Telekes A (2019) Technologies for circulating tumor cell separation from whole blood. J Hematol Oncol 12(1):48. https://doi.org/10.1186/s13045-019-07354 12. Alix-Panabieres C, Pantel K (2013) Circulating tumor cells: liquid biopsy of cancer. Clin Chem 59(1):110–118. https://doi.org/10.1373/ clinchem.2012.194258 13. Satelli A, Mitra A, Cutrera JJ, Devarie M, Xia X, Ingram DR, Dibra D, Somaiah N, Torres KE, Ravi V, Ludwig JA, Kleinerman ES, Li S (2014) Universal marker and detection tool for human sarcoma circulating tumor cells. Cancer Res 74 (6):1645–1650. https://doi.org/10.1158/ 0008-5472.CAN-13-1739

232

Joseph W. Po et al.

14. Sullivan JP, Nahed BV, Madden MW, Oliveira SM, Springer S, Bhere D, Chi AS, Wakimoto H, Rothenberg SM, Sequist LV, Kapur R, Shah K, Iafrate AJ, Curry WT, Loeffler JS, Batchelor TT, Louis DN, Toner M, Maheswaran S, Haber DA (2014) Brain tumor cells in circulation are enriched for mesenchymal gene expression. Cancer Discov 4 (11):1299–1309. https://doi.org/10.1158/ 2159-8290.CD-14-0471 15. Salvianti F, Orlando C, Massi D, De Giorgi V, Grazzini M, Pazzagli M, Pinzani P (2015) Tumor-related methylated cell-free DNA and circulating tumor cells in melanoma. Front Mol Biosci 2:76. https://doi.org/10.3389/ fmolb.2015.00076 16. Ben-Izhak O, Stark P, Levy R, Bergman R, Lichtig C (1994) Epithelial markers in malignant melanoma. A study of primary lesions and their metastases. Am J Dermatopathol 16 (3):241–246. https://doi.org/10.1097/ 00000372-199406000-00003 17. Grzywa TM, Paskal W, Wlodarski PK (2017) Intratumor and intertumor heterogeneity in melanoma. Transl Oncol 10(6):956–975. https://doi.org/10.1016/j.tranon.2017.09. 007 18. Gray ES, Reid AL, Bowyer S, Calapre L, Siew K, Pearce R, Cowell L, Frank MH, Millward M, Ziman M (2015) Circulating melanoma cell subpopulations: their heterogeneity and differential responses to treatment. J Invest Dermatol 135(8):2040–2048. https://doi. org/10.1038/jid.2015.127 19. Vanella V, Festino L, Trojaniello C, Vitale MG, Sorrentino A, Paone M, Ascierto PA (2019) The role of BRAF-targeted therapy for advanced melanoma in the immunotherapy era. Curr Oncol Rep 21(9):76. https://doi. org/10.1007/s11912-019-0827-x 20. Mackiewicz J, Mackiewicz A (2018) BRAF and MEK inhibitors in the era of immunotherapy in melanoma patients. Contemp Oncol (Pozn) 22 (1A):68–72. https://doi.org/10.5114/wo. 2018.73890 21. Johnson DB, Estrada MV, Salgado R, Sanchez V, Doxie DB, Opalenik SR, Vilgelm AE, Feld E, Johnson AS, Greenplate AR, Sanders ME, Lovly CM, Frederick DT, Kelley MC, Richmond A, Irish JM, Shyr Y, Sullivan RJ, Puzanov I, Sosman JA, Balko JM (2016) Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nat Commun 7:10582. https://doi.org/ 10.1038/ncomms10582 22. Gowrishankar K, Snoyman S, Pupo GM, Becker TM, Kefford RF, Rizos H (2012)

Acquired resistance to BRAF inhibition can confer cross-resistance to combined BRAF/ MEK inhibition. J Invest Dermatol 132 (7):1850–1859. https://doi.org/10.1038/ jid.2012.63 23. Wyluda EJ, Cheng J, Schell TD, Haley JS, Mallon C, Neves RI, Robertson G, Sivik J, Mackley H, Talamo G, Drabick JJ (2015) Durable complete responses off all treatment in patients with metastatic malignant melanoma after sequential immunotherapy followed by a finite course of BRAF inhibitor therapy. Cancer Biol Ther 16(5):662–670. https://doi.org/10.1080/15384047.2015. 1026507 24. Domingues B, Lopes JM, Soares P, Populo H (2018) Melanoma treatment in review. Immunotargets Ther 7:35–49. https://doi.org/10. 2147/ITT.S134842 25. Quandt D, Dieter Zucht H, Amann A, WulfGoldenberg A, Borrebaeck C, Cannarile M, Lambrechts D, Oberacher H, Garrett J, Nayak T, Kazinski M, Massie C, Schwarzenbach H, Maio M, Prins R, Wendik B, Hockett R, Enderle D, Noerholm M, Hendriks H, Zwierzina H, Seliger B (2017) Implementing liquid biopsies into clinical decision making for cancer immunotherapy. Oncotarget 8(29):48507–48520. https://doi.org/10.18632/oncotarget.17397 26. Havel JJ, Chowell D, Chan TA (2019) The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19(3):133–150. https://doi.org/10. 1038/s41568-019-0116-x 27. Bajwa R, Cheema A, Khan T, Amirpour A, Paul A, Chaughtai S, Patel S, Patel T, Bramson J, Gupta V, Levitt M, Asif A, Hossain MA (2019) Adverse effects of immune checkpoint inhibitors (programmed death-1 inhibitors and cytotoxic t-lymphocyte-associated protein-4 inhibitors): results of a retrospective study. J Clin Med Res 11(4):225–236. https:// doi.org/10.14740/jocmr3750 28. Davis AA, Patel VG (2019) The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer 7(1):278. https://doi.org/10.1186/s40425-019-07689 29. Maleki Vareki S, Garrigos C, Duran I (2017) Biomarkers of response to PD-1/PD-L1 inhibition. Crit Rev Oncol Hematol 116:116–124. https://doi.org/10.1016/j.critrevonc.2017. 06.001 30. Gibney GT, Weiner LM, Atkins MB (2016) Predictive biomarkers for checkpoint

Circulating Melanoma Cells and Detection of PD-L1 inhibitor-based immunotherapy. Lancet Oncol 17(12):e542–e551. https://doi.org/10. 1016/S1470-2045(16)30406-5 31. Kee D, McArthur G (2017) Immunotherapy of melanoma. Eur J Surg Oncol 43(3):594–603. https://doi.org/10.1016/j.ejso.2016.07.014 32. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HE, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515 (7528):563–567. https://doi.org/10.1038/ nature14011 33. Freeman JB, Gray ES, Millward M, Pearce R, Ziman M (2012) Evaluation of a multi-marker immunomagnetic enrichment assay for the quantification of circulating melanoma cells. J Transl Med 10:192. https://doi.org/10. 1186/1479-5876-10-192 34. Sunshine JC, Nguyen PL, Kaunitz GJ, Cottrell TR, Berry S, Esandrio J, Xu H, Ogurtsova A, Bleich KB, Cornish TC, Lipson EJ, Anders RA, Taube JM (2017) PD-L1 expression in melanoma: a quantitative immunohistochemical antibody comparison. Clin Cancer Res 23 (16):4938–4944. https://doi.org/10.1158/ 1078-0432.CCR-16-1821 35. Kaunitz GJ, Cottrell TR, Lilo M, Muthappan V, Esandrio J, Berry S, Xu H, Ogurtsova A, Anders RA, Fischer AH, Kraft S, Gerstenblith MR, Thompson CL, Honda K, Cuda JD, Eberhart CG, Handa JT, Lipson EJ, Taube JM (2017) Melanoma subtypes demonstrate distinct PD-L1 expression profiles. Lab Investig 97(9):1063–1071. https://doi.org/10.1038/labinvest.2017.64

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36. Parra ER, Villalobos P, Mino B, RodriguezCanales J (2018) Comparison of different antibody clones for immunohistochemistry detection of programmed cell death ligand 1 (PD-L1) on non-small cell lung carcinoma. Appl Immunohistochem Mol Morphol 26 (2):83–93. https://doi.org/10.1097/PAI. 0000000000000531 37. Anantharaman A, Friedlander T, Lu D, Krupa R, Premasekharan G, Hough J, Edwards M, Paz R, Lindquist K, Graf R, Jendrisak A, Louw J, Dugan L, Baird S, Wang Y, Dittamore R, Paris PL (2016) Programmed death-ligand 1 (PD-L1) characterization of circulating tumor cells (CTCs) in muscle invasive and metastatic bladder cancer patients. BMC Cancer 16(1):744. https://doi. org/10.1186/s12885-016-2758-3 38. Mimura K, Teh JL, Okayama H, Shiraishi K, Kua LF, Koh V, Smoot DT, Ashktorab H, Oike T, Suzuki Y, Fazreen Z, Asuncion BR, Shabbir A, Yong WP, So J, Soong R, Kono K (2018) PD-L1 expression is mainly regulated by interferon gamma associated with JAK-STAT pathway in gastric cancer. Cancer Sci 109(1):43–53. https://doi.org/10.1111/ cas.13424 39. Castro F, Cardoso AP, Goncalves RM, Serre K, Oliveira MJ (2018) Interferon-gamma at the crossroads of tumor immune surveillance or evasion. Front Immunol 9:847. https://doi. org/10.3389/fimmu.2018.00847 40. Po JW, Ma Y, Balakrishna B, Brungs D, Azimi F, de Souza P, Becker TM (2019) Immunomagnetic isolation of circulating melanoma cells and detection of PD-L1 status. PLoS One 14(2):e0211866. https://doi.org/ 10.1371/journal.pone.0211866

Chapter 18 Transcript-Based Detection of Circulating Melanoma Cells Michael Morici, Weitao Lin, and Elin S. Gray Abstract Circulating tumor cells (CTCs) are cancer cells shed by the primary tumor or its metastases that circulate in the peripheral blood. CTCs are potential seeds for metastases, and their detection may allow early uncovering of metastatic dissemination and disease prognostication. To fully ascertain the biomarker potential of melanoma CTCs, sensitive and reliable methods are required. Melanoma-specific transcript analysis has been widely utilized as a standard approach for measuring the presence of CTCs. Here we describe a method for the analysis of CTCs through the detection of specific transcripts in CTC-enriched fractions. Key words Circulating tumor cell (CTC), Melanoma, Transcripts, Real-time PCR, Droplet digital PCR (ddPCR)

1

Introduction Circulating tumor cells (CTCs) have emerged as promising biomarkers for various cancers, including melanoma [1, 2]. However, as a result of the rarity and heterogeneity of these cells, specialized methods are required for isolation and detection [3–7]. CTC enrichment methods can be broadly divided into two categories: positive selection and negative selection [1, 2, 8]. Positive selection methods aim to select CTCs using known tumor cell surface markers or properties, while negative selection methods aim to deplete the sample of all blood cells which are not CTCs. Both strategies, however, typically result in a sample in which numerous non-CTC blood cells remain (see Note 1). Given the rare nature of melanoma CTCs, detection methods must be both sensitive and highly specific. The presence of tumor tissue-specific antigens, otherwise absent in the blood, is commonly used to detect the presence of CTCs in the enriched fraction. Generally, immunocytochemistry is used for the detection of proteins, or reverse transcription PCR (RT-PCR) is used for the detection of mRNA transcripts. For

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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melanoma, the detection of melanocytic markers can aid the detection of CTCs with high specificity [9]. However, given the heterogeneity of melanoma CTCs, multiple markers should be tested to increase sensitivity [5, 6, 10–12]. Here we describe a sensitive method for transcript-based detection of melanoma CTCs utilized by our team in previous studies [3, 4, 9, 10]. This method can be applied after CTCs have been enriched using a number of methodologies, including immunomagnetic beads and microfluidic device-based size selection. Notably, we tested CTC detection after negative selection using the Parsortix system (ANGLE plc), a microfluidic device which separates CTCs from other blood components through size exclusion [10, 13]. Following CTC enrichment, RNA and DNA are extracted from the enriched sample. Simultaneous RNA and DNA extraction yields quality RNA for transcript analysis while also facilitating genomic/mutational analysis of CTCs if desired [9]. Next, the RNA extracted from the captured cells is reverse transcribed. Following cDNA synthesis, targeted preamplification is employed to increase the sensitivity of the assay and enable the testing of multiple transcripts. Here we describe the detection of melanoma CTCs using a five-gene panel by either RT PCR or digital droplet PCR (ddPCR). The method can be adapted to include a larger number of targets [6]. The full procedure, from blood processing to the analysis of results, takes approximately 2 days.

2

Materials

2.1 RNA and DNA Extraction

1. Pure ethanol (96–100%). 2. Nuclease-free water. 3. 70% ethanol. 4. 80% ethanol. 5. RNeasy Plus Micro Kit (Qiagen), which includes all relevant buffers and supplies indicated in Subheading 3.1.1. 6. QIAamp DNA Micro Kit (Qiagen), which includes all relevant buffers and supplies indicated in Subheading 3.1.2. 7. Micropipettes and filtered pipette tips. 8. Microcentrifuge (capable of 300
g spin). 9. Microcentrifuge (capable of 20,000
g spin).

2.2 Reverse Transcription and Targeted Preamplification

1. SuperScript™ VILO™ Master Mix. 2. Nuclease-free water. 3. 0.2-ml PCR tubes. 4. TaqMan™ PreAmp Master Mix.

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Table 1 TaqMan assays for five-gene real-time PCR and ddPCR assays Gene symbol

Assay ID

TYR

Hs01099965_m1

MLANA

Hs00194133_m1

MAGEA3

Hs00366532_m1

ABCB5

Hs02889060_m1

PAX3

Hs00240950_m1

18S

Hs99999901_s1

5. TaqMan™ Gene Expression assays (ThermoFisher Scientific, Table 1). 6. TE buffer (1
): 10 mM Tris–HCl, 0.1 mM EDTA, pH 8.0. 7. Preamplification primer pool: Prepare just before use by diluting TaqMan assays 1:100, from 20
to 0.2
, in TE buffer. If custom primers are used, dilute them to a concentration of 0.5 μM. Do not include 18S primers in the preamplification mix. 8. T100™ Thermal Cycler or similar. 2.3 Real-Time PCR Transcript-Based CTC Detection Assay

1. 2
TaqMan™ Master Mix. 2. TaqMan™ Gene Expression assays (ThermoFisher Scientific, Table 1). 3. 96-Well PCR Plate for ABI, Clear, Low Profile. 4. Ultra-Clear Pressure Sensitive Sealing Film. 5. Mini Plate Spinner Centrifuge. 6. ViiA7 or QuantStudio Real-Time PCR System.

2.4 ddPCR Transcript-Based CTC Detection Assay

1. ddPCR™ Supermix for Probes (No dUTP). 2. TaqMan™ Gene Expression assays (ThermoFisher Scientific, Table 1). 3. Positive control material (see Note 2). 4. Nuclease-free water. 5. Microcentrifuge tubes. 6. Automated Droplet Generation Oil for Probes (140 ml). 7. DG32™ Automated Droplet Generator Cartridges. 8. Pipette Tips for the AutoDG™ System. 9. ddPCR™ 96-Well Plates. 10. Pipette Tip Waste Bins for the AutoDG™ System. 11. Pierceable Foil Heat Seal.

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12. ddPCR™ Droplet Reader Oil. 13. DG8 Droplet Generator Cartridges and Gaskets, for manual droplet generation (see Note 3). 14. DG8 Cartridge Holder, for manual droplet generation (see Note 3). 15. Automated Droplet Generator. Alternatively, a QX200™ manual droplet generator can be used (see Note 3). 16. QX200™ Droplet Reader. 17. C1000 Touch™ Thermal Cycler with 96-Deep Well Reaction Module. 18. PX1™ PCR Plate Sealer.

3

Methods CRITICAL POINT: Due to the rarity of melanoma CTCs and the instability of RNA, as well as the potential biohazard of clinical samples, several general practices are recommended (see Note 4).

3.1 RNA/DNA Extraction

3.1.1 RNA Extraction

The method for co-extraction of DNA and RNA is an in-house adaptation of the combined protocols of two Qiagen kits: the RNeasy Plus Micro Kit and the QIAamp DNA Micro Kit, as described below. 1. Pellet harvested cells by centrifuging for 5 min at 300
g in a 1.5-ml microcentrifuge tube. Carefully remove the supernatant by slowly aspirating with a pipette, following the meniscus down from the side opposite to the cell pellet. It is important to avoid disturbing the pellet. The goal is to reduce the volume to no more than 50 μl. 2. Disrupt the cells by adding 350 μl of Buffer RLT and pipetting repeatedly and thoroughly to ensure complete lysis. 3. Transfer the sample to a QIAamp MinElute column (from the QIAamp DNA Micro kit) and spin for 30 s at 8000
g. 4. Add 350 μl of 70% ethanol to the flow-through and mix well by pipetting. 5. Transfer the sample, including any precipitate, to an RNeasy MinElute spin column. Centrifuge for 30 s at 8000
g, then discard the flow-through. The QIAamp MinElute column can be saved for reuse in step 1 of Subheading 3.1.2. 6. Add 700 μl of Buffer RW1 to the RNeasy MinElute spin column, then centrifuge for 30 s at 8000
g. Discard the flow-through.

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7. Add 500 μl of Buffer RPE to the RNeasy MinElute spin column, then centrifuge for 30 s at 8000
g. Discard the flowthrough. 8. Add 500 μl of 80% ethanol to the RNeasy MinElute spin column, then centrifuge for 30 s at 8000
g. Discard the flow-through and place the column in a new collection tube. 9. Centrifuge at full speed (~20,000
g) for 2 min, discard flowthrough and place column in a labeled 1.5-ml tube. 10. Add 14 μl of RNase-free water to the column membrane and incubate for 1 min. 11. Centrifuge at full speed (~20,000
g) for 1 min to elute RNA into the 1.5-ml tube. 12. Use RNA immediately for cDNA synthesis, or store at 80  C. 3.1.2 DNA Extraction

1. Using the QIAamp MinElute column from steps 3–5 in Subheading 3.1.1, add 500 μl of Buffer AW1 to the column, then centrifuge 30 s at 8000
g. Discard the flow-through. 2. Add 500 μl of Buffer AW2, then centrifuge for 30 s at 8000
g. Discard the flow-through and place the column in a new collection tube. 3. Centrifuge at full speed (~20,000
g) for 2 min, discard flowthrough and place column in a labeled 1.5-ml tube. 4. Add 20 μl of nuclease-free water to the column membrane and incubate for 2 min at room temperature. 5. Centrifuge for 1 min at 8000
g to elute the DNA. 6. Store DNA at 80  C.

3.2 Reverse Transcription

1. Synthesize cDNA from the extracted RNA using SuperScript VILO Master Mix. Add 2 μl of SuperScript VILO Master Mix to 2 μl of RNase-free water in a labeled 0.2-ml PCR tube, then add 6 μl of eluted RNA. Mix gently (see Note 4(h)). 2. Run the following cycling conditions on a thermal cycler: Cycling conditions: 25  C—10 min. 42  C—60 min. 85  C—5 min. 4  C—hold. 3. Use cDNA product immediately in the targeted preamplification or store at 80  C.

3.3 Targeted Preamplification

1. Prepare the preamplification primer mix for the five-gene assay by diluting the Taq-Man assays (Table 1) 1:100 from 20
to 0.2
in TE Buffer. Do not include 18S primers in the preamplification mix.

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2. Mix the following in a PCR tube: 12.5 μl of TaqMan PreAmp Master Mix, 6.25 μl of 0.2
five-gene preamplification primer mix, and 1.25 μl of nuclease-free water (per preamplification reaction). 3. Add 5 μl of the cDNA to 20 μl of the preamplification mix, vortex, and quick spin. 4. Run the following cycling conditions on a thermal cycler: Cycling conditions: 95  C—10 min. 14 cycles: 95  C—15 s. 60  C—4 min. 99  C—10 min. 4  C—hold. 3.4 Real-Time PCR Assay

1. Prepare reactions. For each reaction add: 10 μl of TaqMan Gene Expression Master Mix (2
), 1.0 μl of the appropriate TaqMan Gene Expression Assay (20
), and 5 μl of nucleasefree water. 2. Dilute pre-amplified product 1:5 by adding 6 μl of pre-amplified sample to 24 μl of nuclease-free water, followed by mixing. 3. Add 4 μl of the diluted pre-amplified product to each reaction (4 μl of sample + 16 μl of reaction mix for a total reaction volume of 20 μl). 4. Set up appropriate control reactions, including no-template and positive controls. It may also be helpful to run samples derived from healthy controls (see Note 5). 5. Seal the plate with Ultra Clear Pressure Sensitive Sealing Film. 6. Centrifuge the plate in the Mini Plate Spinner Centrifuge. 7. Place the sealed plate in a ViiA7 or QuantStudio Real-Time PCR System. 8. Set up the plate template and define the experiment in the QuantStudio Software. 9. Run the following cycling conditions on the instrument: Cycling conditions: 95  C—10 min. 40 cycles: 95  C—15 s. 60  C—60 s (set to acquire data during this stage). 10. Analyze data from real-time PCR assay (see Notes 6 and 7).

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ddPCR Assay

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1. Prepare mixes for each assay. For each reaction, add: 10 μl of ddPCR™ Supermix for Probes (No dUTP), 1.0 μl of the respective ddPCR assay (20
), and 4 μl of nuclease-free water. 2. Dilute pre-amplified product 1:5 (e.g., by adding 6 μl of pre-amplified sample to 24 μl of nuclease-free water, followed by mixing). 3. Add 5 μl of the diluted pre-amplified product to each reaction (5 μl of sample + 15 μl of reaction mix ¼ 20 μl of total reaction volume). For positive controls, ensure that the sample is diluted to an appropriate level so the droplets are not saturated (see Note 8). 4. Place a Pierceable Heat Seal Foil (red line side facing upwards) over the plate and seal once using the PX1™ PCR Plate Sealer at 180  C. Do not leave the plate for long on the Sealer as it might decrease the sample volume through evaporation. 5. Centrifuge the plate briefly in a Mini Plate Spinner Centrifuge. Use an empty 96-well plate as balance. Carefully check the plate to ensure there are no bubbles inside each well. Centrifuge again if necessary. 6. Put the plate in AutoDG for droplet generation (see Note 3). Load consumables from the back to the front of the instrument to avoid contamination. Nothing should be placed on the instrument deck outside of the dedicated consumable holders. The lights on the holders will change from yellow to green on the deck and on the screen when consumables are placed correctly. Load the following: (a) Automated Droplet Generation Oil for Probes. (b) DG32™ Automated Droplet Generator Cartridges. (c) Pipette Tips for the AutoDG™ System. (d) Pipette Tip Waste Bins for the AutoDG™ System. (e) 96-Well PCR Plate containing prepared ddPCR™ reaction. (f) 96-Well PCR Plate and Cooling block. 7. Once all consumables are loaded, and the corresponding lights are all green both on the deck and touch screen, a blue “Start” button will appear at the bottom right of the screen. Press “Start droplet generation” to begin droplet generation. The door will automatically close at the beginning of the run and must remain closed during the duration. Opening the door may cause the instrument to terminate the run and the samples to be lost. 8. Once the droplets are generated and the plate is ready, the screen will display a “Finalising” note on the touch screen window followed by a blue “Droplets Ready” message with a timer showing time elapsed since completion.

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9. Carefully remove the plate containing the generated droplets and place a Pierceable Heat Seal Foil over the plate. Seal using the PX1™ PCR Plate Sealer at 180  C. 10. Place the sealed plate in the C1000 Touch™ Thermal Cycler and run the pre-set cycle. Begin the PCR cycle within 30 min following droplet generation, or alternatively store the plate at 4  C for a maximum of 4 h. Cycling conditions: 95  C (2.5  C/s ramp)—10 min. 40 cycles: 94  C (2.5  C/s ramp)—30 s. 55  C—60 s. 98  C (2.5  C/s ramp)—10 min. 4  C—hold. 11. Read the plate on the QX200 droplet reader. (a) Power on the QX200 droplet reader using the switch at the back. Allow it to warm up for 30 mins, then switch on the laptop and launch the “QuantaSoft” software. Details of this software analysis system are available in the QX200 Droplet Reader Manual, available for download at: http://www.bio-rad.com/webroot/web/pdf/lsr/litera ture/Bulletin_6407.pdf. (b) Set up the QuantaSoft template according to the experimental layout. (c) “Prime” the Droplet Reader before starting the run. The “Prime” function is on the right side of the QuantaSoft screen under “Instrument Routines.” (d) Once PCR is completed, place the 96-well plate containing the cycled droplets into the plate holder of the QX200 droplet reader. (e) Click “Run” to initiate data acquisition. This will generate a pop-up window where you will choose to acquire data by “columns” and select a dye set for probes in the dropdown menu between “FAM/HEX” and “FAM/VIC,” depending on your assay probes. 12. Analyze data from the ddPCR assay. (a) Once the run is completed, click “Analyse” in the lefthand side panel to analyze the data. (b) Click “1D Amplitude” to view Channel 1 (Ch1) and Channel 2 (Ch2) plots, and manually assign the appropriate thresholds for positive and negative populations.

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(c) Follow the general assay acceptance criteria that we have developed: l Droplet count should be 10,000.

4

l

Water controls must be negative.

l

Positive controls must be positive within predefined cut-off for specific assays.

l

Convert results from copies/well to copies/(ml of blood processed) to compare samples for which varying amounts of blood have been processed.

Notes 1. Differing CTC enrichment methodologies may yield differing levels of purity and bias in the pool of isolated cells. 2. We have found that RNA extracted and reverse transcribed to cDNA from melanoma cell lines can work well as a positive control. In particular, SK-MEL-5 cells work well, as they can provide acceptable signal across genes. The methods for extraction and reverse transcription described here can be applied to positive control cells. The preamplification step can be omitted, and the positive control cDNA is likely to require dilution so as not to saturate the assay. It is useful to prepare a large pool of material and store frozen aliquots, as the positive template control can be used to monitor the consistency of reaction. 3. For manual droplet generation, mix the 20 μl reaction mix samples from the 96-well plate prepared in steps 1–5 of Subheading 3.5, and transfer one column (eight samples) to the middle row of a DG8™ cartridge and 70 μl of Droplet Generation Oil into the bottom wells. Fill any unused well on the cartridge with AVE Buffer. Attach a gasket to the cartridge and transfer it to the Droplet Generator. After the droplets have been generated, transfer them gently to a new 96-well plate and proceed to step 9 of Subheading 3.5. 4. General recommendations for work with CTCs, RNA, and biohazardous clinical samples are as follows: (a) Wear gloves at all times while handling reagents, materials, and equipment to prevent cross-contamination. (b) Utilize appropriate personal protective equipment (e.g., lab coat, latex or nitrile gloves, and safety glasses) when processing biological samples, e.g., patient blood samples. (c) Observe proper microbiological technique when working with RNA (take care to avoid RNase contamination).

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(d) Clean the equipment and work surfaces with 70% ethanol prior to starting the experiments. For RNA work, clean with an RNase decontamination reagent. (e) Always label the tubes with the pertinent information, such as patient ID, type of cancer, type of CTC isolation method employed, and current date. (f) Clean the work surfaces with 70% ethanol and, if possible, treat the workspace with ultraviolet light (UV) after use (e.g., by working in a UV treatable room, biological safety hood, or cabinet). (g) As samples may have transcripts present at low levels and the assays involve preamplification, it is critical to work cleanly and to take steps to avoid contamination. Prepare the assay mixes in a template-free room or bench, using a UV-treatable PCR cabinet if possible, in order to avoid contamination. Then, transfer the plate or tubes to a separate room or bench space and add the template material there. (h) During dilution steps, ensure thorough mixing. Spin tubes briefly to collect liquid before opening. (i) Include a positive template control and a no-template control when performing PCR. 5. If new transcript targets are incorporated into the assay or there is a variation on the CTC isolation method, it is critical that the assay is tested using several (10–20) negative control samples (such as healthy donors’ blood) to determine the background threshold. 6. Targets with Ct values 35 are considered positive. The 18S assay is used to confirm the presence of amplifiable material in the sample, with an expected Ct value between 15 and 20 cycles, depending on the method of CTC enrichment. 7. There are a number of factors that influence interpretation of the real-time assay results, including the presence of varying amounts of total cells among samples. The assay is sensitive enough to detect as few as 1–3 melanoma cells in a background of 10,000 white blood cells, but as it is not possible to normalize to an endogenous control, the results are best interpreted as positive/negative or semi-quantitative. 8. The appropriate sample dilution may require empirical determination. Additionally, you may need to determine whether a lower number of cycles should be used in the preamplification step as highly expressed targets may saturate the signal.

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Acknowledgments W.L. is supported by a grant from the Cancer Research Trust. E.S.G. holds a fellowship from the Cancer Council Western Australia. This work was supported by a project grant from Cancer Council Western Australia. References 1. Marsavela G, Aya-Bonilla CA, Warkiani ME, Gray ES, Ziman M (2018) Melanoma circulating tumor cells: benefits and challenges required for clinical application. Cancer Lett 424:1–8. https://doi.org/10.1016/j.canlet. 2018.03.013 2. Pantel K, Alix-Panabieres C (2019) Liquid biopsy and minimal residual disease – latest advances and implications for cure. Nat Rev Clin Oncol 16(7):409–424. https://doi.org/ 10.1038/s41571-019-0187-3 3. Aya-Bonilla C, Gray ES, Manikandan J, Freeman JB, Zaenker P, Reid AL, Khattak MA, Frank MH, Millward M, Ziman M (2019) Immunomagnetic-enriched subpopulations of melanoma circulating tumour cells (CTCs) exhibit distinct transcriptome profiles. Cancers 11(2):157. https://doi.org/10.3390/ cancers11020157 4. Aya-Bonilla CA, Marsavela G, Freeman JB, Lomma C, Frank MH, Khattak MA, Meniawy TM, Millward M, Warkiani ME, Gray ES, Ziman M (2017) Isolation and detection of circulating tumour cells from metastatic melanoma patients using a slanted spiral microfluidic device. Oncotarget 8(40):67355–67368. https://doi.org/10.18632/oncotarget.18641 5. Gray ES, Reid AL, Bowyer S, Calapre L, Siew K, Pearce R, Cowell L, Frank MH, Millward M, Ziman M (2015) Circulating melanoma cell subpopulations: their heterogeneity and differential responses to treatment. J Invest Dermatol 135(8):2040–2048. https://doi. org/10.1038/jid.2015.127 6. Hong X, Sullivan RJ, Kalinich M, Kwan TT, Giobbie-Hurder A, Pan S, LiCausi JA, Milner JD, Nieman LT, Wittner BS, Ho U, Chen T, Kapur R, Lawrence DP, Flaherty KT, Sequist LV, Ramaswamy S, Miyamoto DT, Lawrence M, Toner M, Isselbacher KJ, Maheswaran S, Haber DA (2018) Molecular signatures of circulating melanoma cells for monitoring early response to immune checkpoint therapy. Proc Natl Acad Sci U S A 115 (10):2467–2472. https://doi.org/10.1073/ pnas.1719264115

7. Khoja L, Lorigan P, Dive C, Keilholz U, Fusi A (2015) Circulating tumour cells as tumour biomarkers in melanoma: detection methods and clinical relevance. Ann Oncol 26(1):33–39. https://doi.org/10.1093/annonc/mdu207 8. Fachin F, Spuhler P, Martel-Foley JM, Edd JF, Barber TA, Walsh J, Karabacak M, Pai V, Yu M, Smith K, Hwang H, Yang J, Shah S, Yarmush R, Sequist LV, Stott SL, Maheswaran S, Haber DA, Kapur R, Toner M (2017) Monolithic chip for high-throughput blood cell depletion to sort rare circulating tumor cells. Sci Rep 7(1):10936. https://doi. org/10.1038/s41598-017-11119-x 9. Reid AL, Freeman JB, Millward M, Ziman M, Gray ES (2015) Detection of BRAF-V600E and V600K in melanoma circulating tumour cells by droplet digital PCR. Clin Biochem 48 (15):999–1002. https://doi.org/10.1016/j. clinbiochem.2014.12.007 10. Aya-Bonilla CA, Morici M, Hong X, McEvoy AC, Sullivan RJ, Freeman J, Calapre L, Khattak MA, Meniawy T, Millward M, Ziman M, Gray ES (2020) Detection and prognostic role of heterogeneous populations of melanoma circulating tumour cells. Br J Cancer 122 (7):1059–1067. https://doi.org/10.1038/ s41416-020-0750-9 11. Freeman JB, Gray ES, Millward M, Pearce R, Ziman M (2012) Evaluation of a multi-marker immunomagnetic enrichment assay for the quantification of circulating melanoma cells. J Transl Med 10:192. https://doi.org/10. 1186/1479-5876-10-192 12. Khoja L, Shenjere P, Hodgson C, Hodgetts J, Clack G, Hughes A, Lorigan P, Dive C (2014) Prevalence and heterogeneity of circulating tumour cells in metastatic cutaneous melanoma. Melanoma Res 24(1):40–46. https:// doi.org/10.1097/CMR.0000000000000025 13. Xu L, Mao X, Imrali A, Syed F, Mutsvangwa K, Berney D, Cathcart P, Hines J, Shamash J, Lu YJ (2015) Optimization and evaluation of a novel size based circulating tumor cell isolation system. PLoS One 10(9):e0138032. https:// doi.org/10.1371/journal.pone.0138032

Chapter 19 Isolation and Quantification of Plasma Circulating Tumor DNA from Melanoma Patients Gabriela Marsavela, Anna Reid, Elin S. Gray, and Leslie Calapre Abstract In recent years, circulating tumor DNA (ctDNA) has emerged as a promising prognostic and monitoring biomarker of various cancers, including melanoma. However, sensitive methods are required for its preservation, isolation, and detection. Here we describe a sensitive method for plasma ctDNA isolation using a column-based extraction kit, followed by quantification using a single mutational target with a droplet digital PCR system. This sensitive protocol has been successfully used to quantify diverse mutations present in plasma-derived ctDNA from cancer patients. The full procedure, from blood processing to the analysis of results, takes approximately a day of work. Key words Circulating tumor DNA (ctDNA), Cell-free DNA (cfDNA), Droplet digital PCR (ddPCR)

1

Introduction Circulating tumor DNA (ctDNA) refers to plasma-derived cell-free DNA (cfDNA) fragments released by tumor cells through a variety of proposed mechanisms, including apoptosis or necrosis from nonviable tumor cells or secretion of tumor DNA in extracellular vesicles [1, 2]. Plasma ctDNA has been previously reported to be a reliable companion diagnostic biomarker in oncology. In melanoma, plasma ctDNA is a potential noninvasive alternative to tumor tissue biopsy for molecular profiling and longitudinal disease monitoring, especially in the metastatic setting [3–6]. The analysis of ctDNA through sensitive techniques increases its potential as a biomarker of therapeutic response. Positive ctDNA levels prior to starting systemic treatment followed by a rapid decline during therapy has been demonstrated as a predictor of tumor response and clinical benefit [7, 8]. Similarly, increase in ctDNA levels during treatment has been associated with treatment resistance and relapse [9–11]. Plasma ctDNA concentrations have also been found to correlate with tumor size and tumor metabolic activity [12], with

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_19, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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low disease volume equating to low ctDNA levels in blood. Moreover, recent studies have shown that the presence of ctDNA is strongly associated with shorter disease-free survival in melanoma patients with confirmed stage III disease [13, 14]. Altogether, these reports suggest that ctDNA enables identification of early signs of disseminating disease. Detection of ctDNA is impeded by limited blood volumes, low concentrations of total cfDNA, and low tumor-derived DNA fractions in plasma. Thus, optimal extraction protocols and sensitive methods are required to detect ctDNA in the plasma of cancer patients. In terms of isolation, multiple studies have assessed the preanalytical conditions required in order to optimize the blood collection process and maximize the yield of extracted cfDNA [15– 19]. Diverse methodologies for cfDNA isolation have been compared in the literature, particularly extraction protocols involving silica membrane spin columns versus magnetic beads. An important consideration for optimal plasma DNA extraction is the recovery of high and low molecular weight cfDNA fractions, which vary significantly between silica membrane spin columns and magnetic bead extraction technologies [20]. Nonetheless, the majority of studies showed a significant yield of total cfDNA using Qiagen QIAamp Circulating Nucleic Acid kit [21–23], which is now considered the gold standard for extraction. The analysis of ctDNA can be separated into two approaches: (a) examine a collective of genes to derive a comprehensive molecular profile, or (b) specifically target a specific mutation. So far, the selection of a particular method is context dependent and may vary between cancer types depending on the level of molecular information required. The rapid development of technologies based on nextgeneration sequencing (NGS) allows a broader study of the genome, enabling the monitoring of multiple tumor-specific mutations in a single assay. Targeted sequencing can be used to study small regions, such as individual exons, or a larger number of loci, expanding our ability to detect multiple genes of interest [24– 27]. Analysis of ctDNA using targeted NGS requires incorporation of unique molecular identifiers (UMI), which enable error suppression and detection of low-frequency mutations. The MassARRAY System, which is a nonfluorescent detection platform utilizing mass spectrometry to accurately measure PCR-derived amplicons, is also emerging as a viable alternative approach for multiplexing different mutational targets for comprehensive ctDNA analysis. The efficiency of UMI-based NGS and the MassArray technologies for ctDNA analysis in melanoma have been previously reported [28, 29]. Nevertheless, the high input requirement, cost, and lack of standardization of the analysis pipeline associated with these technologies limit their use. A single mutation target approach for ctDNA analysis relies on commercially available or custom designed assays specific for droplet-based PCR systems, particularly digital droplet PCR

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(ddPCR) and BEAMing. Due to its low limit of detection (0.01%), ddPCR has been recognized as one of the most accurate and reliable tools for detecting low frequency genetic aberrations, and is currently the gold standard for ctDNA analysis using a single mutation target in many cancers [10, 30–34]. The use of ddPCR allows calculation of absolute DNA quantity based upon the number of positive and negative droplets observed, which is done according to the Poisson distribution without the need for external reference standards or controls. The performance of every ddPCR assay is different, and for each given assay, the relative fluorescence signal that defines a true positive droplet from a negative droplet (and the extent to which droplets tend to fall between those values), can vary enormously. Factors including target locus sequence context, performance of the amplicon, cycling conditions employed, concentrations of key reagents, and the relative frequency of false positive droplets needs to be considered. Nevertheless, a significant advantage of ddPCR is that it affords a rapid, scalable, and costeffective method for ctDNA testing. For cancers where driver mutations have been identified in a high percentage of patients, such as the case for melanoma, ddPCR presents a highly viable method for ctDNA analysis in patients. In melanoma, 70% of patients harbor mutually exclusive BRAF V600 or NRAS Q61 mutations [35], which are primary mutational targets for ddPCR-based analysis of ctDNA in this cohort. However, melanoma patients that are wild-type (WT) for BRAF and NRAS (~30%) may carry mutations in RPS27 5’UTR [36], RAC1 (P29S) [37], MAPK2K1 (P124S) [38], IDH1 (R132C/H) [39], EZH2 (Y641N/H/S) [40], and the promoter regions of TERT (C228T and C250T) [41] or DPH3 (C8T and C9T) [42], making these useful targets for ctDNA analysis. However, targeting these mutations individually only provides a theoretical coverage of ~85% of all cutaneous melanoma patients [40, 43]. For patients who do not harbor these variants, sequencing of tumor samples may be required to identify private mutations that are viable mutational targets for ctDNA analysis by ddPCR. The protocol described below is for the isolation of total cfDNA from the plasma of metastatic melanoma patients using a silica membrane spin column and quantification of ctDNA using specific mutational targets by ddPCR.

2 2.1

Materials cfDNA Extraction

1. Pure ethanol (96–100%). 2. Isopropanol (100%). 3. Sterile phosphate buffered saline (PBS), pH 7.45. 4. QIAamp Circulating Nucleic Acid kit (Qiagen). 5. Micropipettes and filtered pipette tips.

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6. Automatic pipettor and serological pipettes. 7. Water bath or heating block capable of holding 50 mL centrifuge tubes at 60
C. 8. Heating block or similar at 56
C (capable of holding 2 mL collection tubes). 9. Microcentrifuge (required for max. speed of 20,000  g). 10. 50 mL centrifuge tubes—polypropylene conical tube 11. QIAvac 24 Plus vacuum manifold (Qiagen). 12. QIAvac Connecting System (Qiagen) or equivalent. 13. Vacuum pump capable of producing a vacuum of 800 to 900 mbar. 14. Laboratory grade ice dispenser—Crushed ice. 15. Benchtop vortex. 16. VacValves (Qiagen). 2.2 ctDNA Quantification

1. ddPCR™ Supermix for Probes—No dUTP (Bio-Rad), 2. ddPCR Assays (see Note 1 and Table 1), 3. Positive Controls (cell-linesor gBlocks). 4. Nuclease-Free Water. 5. Microcentrifuge tubes. 6. Buffer AVE (Qiagen). 7. cfDNA material for testing (isolated from 1 to 5 mL of plasma), 8. Automated Droplet Generation Oil for Probes (140 mL). 9. DG32™ Automated Droplet Generator Cartridges (Bio-Rad). 10. Pipette Tips for the AutoDG™ System (Bio-Rad). 11. ddPCR™ 96-Well Plates (Eppendorf), 12. Pipette Tip Waste Bins for the AutoDG™ System. 13. Pierceable Foil Heat Seal. 14. ddPCR™ Droplet Reader Oil (Bio-Rad), 15. Automated Droplet Generator (Bio-Rad); manual droplet generator QX200™ can be used alternatively (see Note 2). 16. DG8 Droplet Generator Cartridges and Gaskets (Bio-Rad) for manual droplet generation (see Note 2). 17. DG8 Cartridge Holder (Bio-Rad) for manual droplet generation (see Note 2). 18. C1000 Touch™ Thermal Cycler with 96-Deep Well Reaction Module (Bio-Rad). 19. PX1™ PCR Plate Sealer. 20. Mini Plate Spinner Centrifuge.

DPH3 C9T

DPH3 C8T

DPH3 WT

DPH3

BRAF p. V600E BRAF p. V600E (c. G > AA) BRAF p. V600K BRAF p. V600R BRAF p. K601E

BRAF WT

BRAF

Name of assay

50 -GGGCTCGGCATCATCAG-30

50 -CTACTGTTTTCCTTTACTTACTACACCT CAGA-30

Forward primer

50 -CCGCTACCGGTTATCCAT TT-30

50 -ATCCAGACAACTGTTCAAACT GATG-30

Reverse primer

Table 1 Primers and probes used for the common ddPCR assays utilized in melanoma

5HEX-TAGCCC TTC/ZEN/ CGGCGCA-3IABkFQ 56-FAM-TAGCTCTTC / ZEN/CGGCGCA3IABkFQ 56-FAM-TAGCTTTTC/ ZEN/CGGCGCA3IABkFQ

VIC-CTAGCTACAGT GAAATC-MGBNFQ 56-FAM-TAGCTACAGA GAAATC-MGBNFQ 56-FAM-TAGCTACA GAAAAATCMGBNFQ 56-FAM-TAGCTACAAA GAAATC-MGBNFQ 56-FAM-TAGCTACAAG GAAATC-MGBNFQ 56-FAM-TAGCTA CAGTGGAATCMGBNFQ

Probe

(continued)

[29]

[44]

Reference

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Forward primer

50 -AGCGCTGCCTGAAACTCG-30

Proprietary (Bio-Rad), Catalog Number: 186-3129

Proprietary (Bio-Rad), Catalog Number: 186-3128

Proprietary (Bio-Rad), Catalog Number: 186-3130

Proprietary (Bio-Rad), Catalog Number: 186-3127

NRAS p. Q61K

NRAS p. Q61L

NRAS p. Q61R

NRAS p. Q61H

Commercially available assays

TERT C250T/ C228T multiplex

TERT WT

TERT multiplex

Name of assay

Table 1 (continued)

50 -CCTGCCCCTTCACCTTCCAG-30

Reverse primer

5HEX-CCCCCTCCGG3IABkFQ 56-FAM-CCCCTTCCGG -3IABkFQ

Probe

[43]

Reference

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Methods

3.1 General Recommendations

1. Wear latex gloves at all times while handling reagents, materials, and equipment to prevent DNA cross-contamination. 2. Change gloves if there has been contact with DNA. 3. Utilize appropriate personal protective equipment (e.g., lab coat, latex gloves, and safety glasses) when processing biological samples (e.g., patient plasma). 4. Clean the equipment and work surfaces with 70% ethanol prior to starting the experiments. 5. Always label the tubes with the pertinent information, such as patient ID, date of blood collection, type of cancer, type of blood collection tube used, and current date. 6. Clean the work surfaces with 70% ethanol and, if possible, treat the workspace with ultraviolet light (UV) (e.g., a UV treatable room, biological safety hood or cabinet) after use. 7. Prepare ddPCR assays in a template-free room or bench, using a UV-treatable PCR cabinet if possible, in order to avoid DNA contamination of the master stock. Then, transfer your plate to a separate room or bench space and add your cfDNA template material there.

3.2

cfDNA Extraction

1. Allow plasma samples to thaw completely at room temperature for approximately 40 min (see Note 3 for our protocol for plasma separation from whole blood). 2. Preheat the heating block at 60
C for later use. 3. For each patient, label one 50 mL tube with the patient ID and the date of collection. Alternatively, each sample can be numbered from 1 to x, and these numbers can be used to reidentify the samples at the end of the protocol. The kit is designed to process plasma sample volumes between 1 and 5 mL. 4. Refer to QIAamp Circulating Nucleic Acid Manual Second Edition, specifically “Purification of Circulating Nucleic Acids in 1ml-5ml of Plasma” found in pages 26–35 [45] and follow the manufacturer’s instructions provided with the kit. 5. Elute RNA in RNase-free water or the appropriate buffer (see Note 4).

3.3 ctDNA Quantification 3.3.1 PCR Set Up

1. Define which assays will be used to detect the melanoma mutations in the samples (see Note 1). 2. Calculate how much master mix is required based on the number of samples for each assay, considering the addition of a nontemplate control (NTC), a positive control (see Note 5), and a negative control (or cfDNA from healthy individuals). For new users, see Note 6 [46].

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3. Thaw all components to room temperature. Mix thoroughly by vortexing the tube and briefly spin using a microcentrifuge. 4. Prepare master mix in either 1.5 mL or 0.2 mL microcentrifuge tubes, as per required volumes. 5. Briefly vortex and spin tubes. Aliquot 12 μL of master mix into corresponding wells in a 96-well plate. 6. Add 8 μL of cfDNA template (this amount is optimal given the low level of ctDNA detectable in plasma). If less than 8 μL is added, complete volume with dH2O or AVE buffer (whichever was used for elution of the cfDNA sample). 7. Place a pierceable heat seal foil (red line side facing upward) over the plate and seal using the PX1™ PCR plate sealer at 180
C. Do not leave the plate for long on the Sealer as it might decrease the sample volume through evaporation (see Note 7). 8. Centrifuge the plate briefly in a mini plate spinner centrifuge. Use an empty 96-well plate as balance. Carefully check the plate to ensure there are no bubbles inside each well. Centrifuge again if necessary. 9. Put the plate in AutoDG for droplet generation. Load consumables from the back to the front of the instrument to avoid contamination (see Note 8), as follows: (a) Automated Droplet Generation Oil for Probes. (b) DG32™ Automated Droplet Generator Cartridges. (c) Pipette Tips for the AutoDG™ System. (d) Pipette Tip Waste Bins for the AutoDG™ System. (e) 96-Well PCR Plate containing prepared ddPCR™ reaction (f) 96-Well PCR Plate and Cooling block 10. Once all consumables are loaded and the corresponding lights are all green both on the deck and touch screen, a blue “Start” button will appear at the bottom right of the screen. Press “Start droplet generation” to begin droplet generation (see Note 9). 11. Once the droplets are generated and the plate is ready (see Note 10), carefully remove the plate containing the generated droplets and place a pierceable heat seal foil over the plate. 12. Seal the plate using the PX1™ PCR plate sealer at 180
C. Again, do not leave the plate for long on the sealer as it might decrease the sample volume through evaporation. 13. Place the sealed plate in the C1000 Touch™ Thermal Cycler and run the preset cycle for each assay, as shown below (see Note 11):

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PCR cycling conditions 95
C (2.5
C/s ramp)

10 min

40 cycles 94
C (2.5
C/s ramp)


55 C


3.3.2 Droplet Reader Setup

30 s 60 s




98 C (2.5 C/s ramp)

10 min

4
C

Hold

1. Power on the QX200 droplet reader using the switch at the back. Allow it to warm up for 30 mins, then switch on the laptop and launch the “QuantaSoft” software [47] (see Note 12). Refer to QX200 Droplet Reader Manual for further details on droplet acquisition and software analysis [47]. 2. Set up a QuantaSoft template according to the experimental layout. 3. “Prime” the Droplet Reader before starting the run. The “Prime” function is on the right side of the QuantaSoft screen under “Instrument Routines.” 4. Once PCR is completed, place the 96-well plate containing generated droplets into the plate holder of the QX200 droplet reader. 5. Click “Run” to initiate data acquisition. 6. In the pop-up window, select to acquire data by “columns” and choose a dye set for probes in the dropdown menu between “FAM/HEX” or “FAM/VIC” depending on the specific assay probes used.

3.3.3 Data Analysis

1. Once the run is completed, click “Analyze” in the left-hand side panel to analyze the data. 2. Click “2D Amplitude” to view Channel 1 (Ch1) vs. Channel 2 (Ch2) clustering plots and manually assign the appropriate gating thresholds or lasso margins for positive, double-positive, and negative populations (Fig. 1). 3. Determine if data meet the following general assay acceptance criteria that we have developed: (a) Droplet count must be 10,000 but 8000 droplets are acceptable if 20 copies/mL of ctDNA is detected. (b) Buffer AVE or water controls must be negative. There must be zero droplets in the mutant quadrant but up to two droplets in WT are acceptable if droplet counts 10,000.

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Fig. 1 Visualization and analysis of ddPCR results. The 2D diagram denotes the separation of droplets containing mutant (Channel 1) and wild-type (Channel 2) DNA copies using lasso (a) BRAF p.V600E and (b) NRAS p.Q61K or multiwell thresholds, (c) NRAS Q61K, and (d) DPH3 C8T

(c) Positive control, positive within predefined cut off for specific assays. (d) Healthy controls, negative within predefined cut off for specific assay (see Note 13). 4. Report results in copies/mL of plasma or as frequency abundance (see Note 14).

4

Notes 1. Some companies, such as Bio-Rad, offer a wide array of assays that have been optimized for use with ddPCR Supermix for Probes (no dUTP). These ddPCR Mutation Detection Assays contain primers and probes and are available either in single tube (containing both WT and mutant) or two-tube formats (WT and mutant separately). However, some mutations are not commercially available, and in such cases, specific assays will require customization. In our case, customized probes are synthesized either by Integrated DNA Technologies or designed using the Bio-Rad Mutation Detection Software [48]. In addition, multiplex assays can also be commercially obtained or customized in-house. A multiplex assay has been developed for simultaneous detection of TERT promoter mutations C250T and C228T [43]. The addition of specific additives such as Q-solution (Qiagen) and 7-Deaza-dGTP has

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Fig. 2 A Schema of the ctDNA analysis pipeline for BRAF or NRAS WT melanoma patients

allowed a better separation of the populations, increasing the performance of the assay [43, 49]. Please refer to Table 1 for more details on the primers and probes used for ctDNA detection by ddPCR. We have also established a pipeline for custom next-generation sequencing of commonly mutated loci in melanoma, from which private driver mutations are identified via sequencing of tumor tissue biopsy and selected for customized ddPCR assays to track ctDNA levels (Fig. 2). This pipeline was particularly useful in patients without the mutations mentioned above. Specific annealing temperatures may need optimisation for new customized assays. In order to determine optimal annealing temperature, a temperature gradient experiment will have to be performed prior to selecting the PCR cycling conditions. 2. For manual droplet generation, mix the 20 μL reaction mix samples from the 96-well plate prepared in step 6 of Subheading 3.3.1 and transfer one column (8 samples) to the middle row of a DG8™ cartridge and 70 μL of Droplet Generation Oil into the bottom wells. Fill any unused well on the cartridge with AVE Buffer. Attach a gasket to the cartridge and transfer it to the Droplet Generator. After the droplets have been generated, transfer them gently to a new 96-well plate and proceed to step 11 of Subheading 3.3.1. 3. Blood samples should be collected using EDTA vacutainer or Cell-Free DNA BCT® (Streck) tubes. Other cfDNA preserving tubes may be appropriate [17, 19]. Plasma should be separated within 2 h from collection in EDTA tubes to avoid an increase in background wild-type cfDNA. However, we found that

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ctDNA absolute copies do not decrease if plasma is separated within 24 h of blood collection. According to the manufacturer’s instructions, ctDNA is stable in Cell-Free DNA BCT® tubes kept at room temperature for up to 14 days. We separate plasma by centrifugation at 300  g for 20 min, followed by a second centrifugation at 4700  g for 10 min. However, methods vary widely in the literature and between blood collection tube manufacturers [16]. Isolated plasma must be stored at 80
C until extraction. 4. The provided AVE buffer is used for elution; however, RNasefree water or EB buffer can also be used depending on the downstream analyses. Always reelute the flowthrough in the column when extracting patient cfDNA to increase final DNA yields. Important, we previously optimized the elution step accordingly: for 1–3 mL of plasma extraction, elute in 30 μL of AVE buffer, and for 4–5 mL plasma extraction, elute in 40 μL of AVE buffer. 5. The addition of positive controls to each assay is required to validate the positive results from your samples. These positive mutation references can be obtained from gDNA Reference Standards (Horizon Discovery), synthetic double-stranded DNA (gBlocks, Integrated DNA Technologies), or gDNA from cell lines known to carry the mutation(s) of interest [43]. Total copies obtained from these positive controls can be monitored over time to ensure accurate results. Levey–Jennings control charts are very useful for this purpose. 6. Additional detailed information about assay development can be found at Rare Mutation Detection Best Practices Guidelines [46]. 7. To ensure that sample volume of the wells is not affected by evaporation, preheat the Sealer to 180
C ensuring that the plate holder is kept outside to keep it at room temperature during sealing. 8. Nothing should be placed on the instrument deck outside of the dedicated consumable holders. The lights on the holders will change from yellow to green on the deck and on the screen when consumables are placed correctly. 9. The door will automatically close at the beginning of the run and must remain closed during the duration. Opening the door may cause the instrument to terminate the run and samples to be lost. 10. The screen will display a “Finalising” note on the touchscreen window followed by a blue “Droplets Ready” message with a timer showing time elapsed since completion.

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11. Begin the PCR cycle within 30 min following droplet generation or alternatively store the plate at 4
C for a maximum of 4 h. 12. For new users, guidance on droplet acquisition and software analysis are available in QX200 Droplet Reader Manual [47]. 13. Limit of blank must be determined prior to defining the analytical threshold for each assay. In our laboratory, we run at least 10 healthy controls for each new assay to define the limit of blank of the assay. Positive results will be considered only if it is above the set threshold [29]. The limit of detection (LOD) is defined as the lowest PCR copy number concentration that can be distinguished from zero with a level of confidence of 95%, and in order to define the LOD, a sensitivity curve needs to be run, using serially diluted mutant DNA into WT DNA [50]. 14. Results can be reported as copies of mutant DNA/mL of plasma or mutant allele frequency abundance. The release of normal wild-type cfDNA can strongly vary due to preanalytical conditions, such as the patient’s physical status (e.g., inflammation, posttraumatic, postexercise, chronic illness), the type of anticoagulant in the collection tubes, and the time from blood draw to plasma processing [16, 51]. An increase in cfDNA concentration will result in a more diluted ctDNA, affecting the frequency abundance reported if sample processing is not tightly controlled. Reporting the results in copies/ mL of plasma could provide more robust and comparable results, as this factor is less affected by preanalytical processing. References 1. Heitzer E, Haque IS, Roberts CES, Speicher MR (2019) Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet 20(2):71–88. https://doi. org/10.1038/s41576-018-0071-5 2. Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J (2016) Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164(1–2):57–68. https://doi.org/10.1016/j.cell.2015.11.050 3. Diefenbach RJ, Lee JH, Rizos H (2019) Monitoring melanoma using circulating free DNA. Am J Clin Dermatol 20(1):1–12. https://doi. org/10.1007/s40257-018-0398-x 4. Valpione S, Gremel G, Mundra P, Middlehurst P, Galvani E, Girotti MR, Lee RJ, Garner G, Dhomen N, Lorigan PC, Marais R (2018) Plasma total cell-free DNA (cfDNA) is a surrogate biomarker for tumour burden and a prognostic biomarker for survival in metastatic melanoma patients. Eur J Cancer

88:1–9. https://doi.org/10.1016/j.ejca. 2017.10.029 5. Seremet T, Planken S, Schreuer M, Jansen Y, Delaunoy M, El Housni H, Lienard D, Del Marmol V, Heimann P, Neyns B (2018) Illustrative cases for monitoring by quantitative analysis of BRAF/NRAS ctDNA mutations in liquid biopsies of metastatic melanoma patients who gained clinical benefits from anti-PD1 antibody therapy. Melanoma Res 28 (1):65–70. https://doi.org/10.1097/cmr. 0000000000000415 6. Rowe SP, Luber B, Makell M, Brothers P, Santmyer J, Schollenberger MD, Quinn H, Edelstein DL, Jones FS, Bleich KB, Sharfman WH, Lipson EJ (2018) From validity to clinical utility: the influence of circulating tumor DNA on melanoma patient management in a realworld setting. Mol Oncol 12(10):1661–1672. https://doi.org/10.1002/1878-0261.12373

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7. Herbreteau G, Vallee A, Knol AC, Theoleyre S, Quereux G, Varey E, Khammari A, Dreno B, Denis MG (2018) Quantitative monitoring of circulating tumor DNA predicts response of cutaneous metastatic melanoma to anti-PD1 immunotherapy. Oncotarget 9 (38):25265–25276. https://doi.org/10. 18632/oncotarget.25404 8. Lee JH, Long GV, Boyd S, Lo S, Menzies AM, Tembe V, Guminski A, Jakrot V, Scolyer RA, Mann GJ, Kefford RF, Carlino MS, Rizos H (2017) Circulating tumour DNA predicts response to anti-PD1 antibodies in metastatic melanoma. Ann Oncol 28(5):1130–1136. https://doi.org/10.1093/annonc/mdx026 9. Wong SQ, Raleigh JM, Callahan J, Vergara IA, Ftouni S, Hatzimihalis A, Colebatch AJ, Li J, Semple T, Doig K, Mintoff C, Sinha D, Yeh P, Silva MJ, Alsop K, Thorne H, Bowtell DD, Gyorki DE, Arnau GM, Cullinane C, Kee D, Brady B, Kelleher F, Dawson MA, Papenfuss AT, Shackleton M, Hicks RJ, McArthur GA, Sandhu S, Dawson S-J (2017) Circulating tumor DNA analysis and functional imaging provide complementary approaches for comprehensive disease monitoring in metastatic melanoma. JCO Precis Oncol 1:1–14. https://doi.org/10.1200/po.16.00009 10. Gray ES, Rizos H, Reid AL, Boyd SC, Pereira MR, Lo J, Tembe V, Freeman J, Lee JH, Scolyer RA, Siew K, Lomma C, Cooper A, Khattak MA, Meniawy TM, Long GV, Carlino MS, Millward M, Ziman M (2015) Circulating tumor DNA to monitor treatment response and detect acquired resistance in patients with metastatic melanoma. Oncotarget 6 (39):42008–42018. https://doi.org/10. 18632/oncotarget.5788 11. Santiago-Walker A, Gagnon R, Mazumdar J, Casey M, Long GV, Schadendorf D, Flaherty K, Kefford R, Hauschild A, Hwu P, Haney P, O’Hagan A, Carver J, Goodman V, Legos J, Martin AM (2016) Correlation of BRAF mutation status in circulating-free dna and tumor and association with clinical outcome across four BRAFi and MEKi clinical trials. Clin Cancer Res 22(3):567–574. https://doi.org/10.1158/1078-0432.Ccr15-0321 12. McEvoy AC, Warburton L, Al-Ogaili Z, Celliers L, Calapre L, Pereira MR, Khattak MA, Meniawy TM, Millward M, Ziman M, Gray ES (2018) Correlation between circulating tumour DNA and metabolic tumour burden in metastatic melanoma patients. BMC Cancer 18(1):726. https://doi.org/10.1186/ s12885-018-4637-6

13. Lee JH, Saw RP, Thompson JF, Lo S, Spillane AJ, Shannon KF, Stretch J, Howle J, Menzies AM, Carlino MS, Kefford RF, Long GV, Scolyer RA, Rizos H (2019) Pre-operative ctDNA predicts survival in high-risk stage III cutaneous melanoma patients. Ann Oncol 30 (5):815–822. https://doi.org/10.1093/ annonc/mdz075 14. Tan L, Sandhu S, Lee RJ, Li J, Callahan J, Ftouni S, Dhomen N, Middlehurst P, Wallace A, Raleigh J, Hatzimihalis A, Henderson MA, Shackleton M, Haydon A, Mar V, Gyorki DE, Oudit D, Dawson MA, Hicks RJ, Lorigan P, McArthur GA, Marais R, Wong SQ, Dawson SJ (2019) Prediction and monitoring of relapse in stage III melanoma using circulating tumor DNA. Ann Oncol 30(5):804–814. https://doi.org/10.1093/annonc/mdz048 15. Cavallone L, Aldamry M, Lafleur J, Lan C, Gonzalez Ginestet P, Alirezaie N, Ferrario C, Aguilar-Mahecha A, Basik M (2019) A study of pre-analytical variables and optimization of extraction method for circulating tumor DNA measurements by digital droplet PCR. Cancer Epidemiol Biomark Prev 28(5):909–916. https://doi.org/10.1158/1055-9965.Epi18-0586 16. El Messaoudi S, Rolet F, Mouliere F, Thierry AR (2013) Circulating cell free DNA: preanalytical considerations. Clin Chim Acta 424:222–230. https://doi.org/10.1016/j. cca.2013.05.022 17. Ward Gahlawat A, Lenhardt J, Witte T, Keitel D, Kaufhold A, Maass KK, Pajtler KW, Sohn C, Schott S (2019) Evaluation of storage tubes for combined analysis of circulating nucleic acids in liquid biopsies. Int J Mol Sci 20(3):704. https://doi.org/10.3390/ ijms20030704 18. Kang Q, Henry NL, Paoletti C, Jiang H, Vats P, Chinnaiyan AM, Hayes DF, Merajver SD, Rae JM, Tewari M (2016) Comparative analysis of circulating tumor DNA stability in K(3)EDTA, Streck, and CellSave blood collection tubes. Clin Biochem 49(18):1354–1360. https://doi.org/10.1016/j.clinbiochem. 2016.03.012 19. Lampignano R, Neumann MHD, Weber S, Kloten V, Herdean A, Voss T, Groelz D, Babayan A, Tibbesma M, Schlumpberger M, Chemi F, Rothwell DG, Wikman H, Galizzi JP, Bergheim IR, Russnes H, Mussolin B, Bonin S, Voigt C, Musa H, Pinzani P, Lianidou E, Brady G, Speicher MR, Pantel K, Betsou F, Schuuring E, Kubista M, Ammerlaan W, Sprenger-Haussels M,

Analysis of Melanoma Circulating Tumour DNA Schlange T, Heitzer E (2019) Multicenter evaluation of circulating cell-free DNA extraction and downstream analyses for the development of standardized (pre)analytical work flows. Clin Chem 66(1):149–160. https://doi.org/10. 1373/clinchem.2019.306837 20. Kloten V, Ru¨chel N, Bru¨chle NO, Gasthaus J, Freudenmacher N, Steib F, Mijnes J, Eschenbruch J, Binnebo¨sel M, Knu¨chel R, Dahl E (2017) Liquid biopsy in colon cancer: comparison of different circulating DNA extraction systems following absolute quantification of KRAS mutations using Intplex allelespecific PCR. Oncotarget 8 (49):86253–86263. https://doi.org/10. 18632/oncotarget.21134 21. Sorber L, Zwaenepoel K, Deschoolmeester V, Roeyen G, Lardon F, Rolfo C, Pauwels P (2017) A comparison of cell-free DNA isolation kits: isolation and quantification of cellfree dna in plasma. J Mol Diagn 19 (1):162–168. https://doi.org/10.1016/j. jmoldx.2016.09.009 22. Streubel A, Stenzinger A, Stephan-Falkenau S, Kollmeier J, Misch D, Blum TG, Bauer T, Landt O, Am Ende A, Schirmacher P, Mairinger T, Endris V (2019) Comparison of different semi-automated cfDNA extraction methods in combination with UMI-based targeted sequencing. Oncotarget 10 (55):5690–5702. https://doi.org/10.18632/ oncotarget.27183 23. Holmberg RC, Gindlesperger A, Stokes T, Lopez D, Hyman L, Freed M, Belgrader P, Harvey J, Li Z (2013) Akonni TruTip(®) and Qiagen(®) methods for extraction of fetal circulating DNA--evaluation by real-time and digital PCR. PLoS One 8(8):e73068–e73068. https://doi.org/10.1371/journal.pone. 0073068 24. Dawson SJ, Tsui DW, Murtaza M, Biggs H, Rueda OM, Chin SF, Dunning MJ, Gale D, Forshew T, Mahler-Araujo B, Rajan S, Humphray S, Becq J, Halsall D, Wallis M, Bentley D, Caldas C, Rosenfeld N (2013) Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 368 (13):1199–1209. https://doi.org/10.1056/ NEJMoa1213261 25. Leary RJ, Kinde I, Diehl F, Schmidt K, Clouser C, Duncan C, Antipova A, Lee C, McKernan K, De La Vega FM, Kinzler KW, Vogelstein B, Diaz LA, Velculescu VE (2010) Development of personalized tumor biomarkers using massively parallel sequencing. Sci Transl Med 2(20):20ra14. https://doi.org/ 10.1126/scitranslmed.3000702

261

26. Leary RJ, Sausen M, Kinde I, Papadopoulos N, Carpten JD, Craig D, O’Shaughnessy J, Kinzler KW, Parmigiani G, Vogelstein B, Diaz LA, Velculescu VE (2012) Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci Transl Med 4(162):162ra154. https://doi. org/10.1126/scitranslmed.3004742 27. Murtaza M, Dawson S-J, Tsui DWY, Gale D, Forshew T, Piskorz AM, Parkinson C, Chin S-F, Kingsbury Z, Wong ASC, Marass F, Humphray S, Hadfield J, Bentley D, Chin TM, Brenton JD, Caldas C, Rosenfeld N (2013) Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497:108. https://doi. org/10.1038/nature12065 28. Gray ES, Witkowski T, Pereira M, Calapre L, Herron K, Irwin D, Chapman B, Khattak MA, Raleigh J, Hatzimihalis A, Cebon J, Sandhu S, McArthur GA, Millward M, Ziman M, Dobrovic A, Wong SQ (2019) Genomic analysis of circulating tumor DNA using a melanoma-specific UltraSEEK oncogene panel. J Mol Diagn 21(3):418–426. https:// doi.org/10.1016/j.jmoldx.2018.12.001 29. Calapre L, Giardina T, Robinson C, Reid AL, Al-Ogaili Z, Pereira MR, McEvoy AC, Warburton L, Hayward NK, Khattak MA, Meniawy TM, Millward M, Amanuel B, Ziman M, Gray ES (2019) Locus-specific concordance of genomic alterations between tissue and plasma circulating tumor DNA in metastatic melanoma. Mol Oncol 13(2):171–184. https://doi.org/10.1002/1878-0261.12391 30. Olmedillas-Lopez S, Garcia-Arranz M, GarciaOlmo D (2017) Current and emerging applications of droplet digital PCR in oncology. Mol Diagn Ther 21(5):493–510. https://doi.org/ 10.1007/s40291-017-0278-8 31. Ascierto PA, Minor D, Ribas A, Lebbe C, O’Hagan A, Arya N, Guckert M, Schadendorf D, Kefford RF, Grob JJ, Hamid O, Amaravadi R, Simeone E, Wilhelm T, Kim KB, Long GV, Martin AM, Mazumdar J, Goodman VL, Trefzer U (2013) Phase II trial (BREAK-2) of the BRAF inhibitor dabrafenib (GSK2118436) in patients with metastatic melanoma. J Clin Oncol 31(26):3205–3211. https://doi.org/ 10.1200/jco.2013.49.8691 32. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM, Antonarakis ES, Azad NS, Bardelli A, Brem H, Cameron JL, Lee CC, Fecher LA, Gallia GL, Gibbs P, Le D, Giuntoli RL, Goggins M, Hogarty MD,

262

Gabriela Marsavela et al.

Holdhoff M, Hong SM, Jiao Y, Juhl HH, Kim JJ, Siravegna G, Laheru DA, Lauricella C, Lim M, Lipson EJ, Marie SK, Netto GJ, Oliner KS, Olivi A, Olsson L, Riggins GJ, SartoreBianchi A, Schmidt K, Shih l M, Oba-Shinjo SM, Siena S, Theodorescu D, Tie J, Harkins TT, Veronese S, Wang TL, Weingart JD, Wolfgang CL, Wood LD, Xing D, Hruban RH, Wu J, Allen PJ, Schmidt CM, Choti MA, Velculescu VE, Kinzler KW, Vogelstein B, Papadopoulos N, Diaz LA Jr (2014) Detection of circulating tumor DNA in early- and latestage human malignancies. Sci Transl Med 6 (224):224ra224. https://doi.org/10.1126/ scitranslmed.3007094 33. Sanmamed MF, Fernandez-Landazuri S, Rodriguez C, Zarate R, Lozano MD, Zubiri L, Perez-Gracia JL, Martin-Algarra S, Gonzalez A (2015) Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors. Clin Chem 61(1):297–304. https://doi.org/10.1373/clinchem.2014. 230235 34. Chang GA, Tadepalli JS, Shao Y, Zhang Y, Weiss S, Robinson E, Spittle C, Furtado M, Shelton DN, Karlin-Neumann G, Pavlick A, Osman I, Polsky D (2016) Sensitivity of plasma BRAFmutant and NRASmutant cell-free DNA assays to detect metastatic melanoma in patients with low RECIST scores and non-RECIST disease progression. Mol Oncol 10(1):157–165. https://doi.org/10.1016/j. molonc.2015.09.005 35. Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat JP, Nickerson E, Auclair D, Li L, Place C, Dicara D, Ramos AH, Lawrence MS, Cibulskis K, Sivachenko A, Voet D, Saksena G, Stransky N, Onofrio RC, Winckler W, Ardlie K, Wagle N, Wargo J, Chong K, Morton DL, StemkeHale K, Chen G, Noble M, Meyerson M, Ladbury JE, Davies MA, Gershenwald JE, Wagner SN, Hoon DS, Schadendorf D, Lander ES, Gabriel SB, Getz G, Garraway LA, Chin L (2012) A landscape of driver mutations in melanoma. Cell 150(2):251–263. https://doi. org/10.1016/j.cell.2012.06.024 36. Dutton-Regester K, Gartner JJ, Emmanuel R, Qutob N, Davies MA, Gershenwald JE, Robinson W, Robinson S, Rosenberg SA, Scolyer RA, Mann GJ, Thompson JF, Hayward NK, Samuels Y (2014) A highly recurrent RPS27 5’UTR mutation in melanoma. Oncotarget 5(10):2912–2917. https://doi.org/10. 18632/oncotarget.2048

37. Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker JP, Cheng E, Davis MJ, Goh G, Choi M, Ariyan S, Narayan D, Dutton-Regester K, Capatana A, Holman EC, Bosenberg M, Sznol M, Kluger HM, Brash DE, Stern DF, Materin MA, Lo RS, Mane S, Ma S, Kidd KK, Hayward NK, Lifton RP, Schlessinger J, Boggon TJ, Halaban R (2012) Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 44 (9):1006–1014. https://doi.org/10.1038/ ng.2359 38. Nikolaev S, Rimoldi D, Iseli C, Valsesia A, Robyr D, Gehrig C, Harshman K, Guipponi M, Bukach O, Zoete V, Michielin O, Muehlethaler K, Speiser D, Beckmann J, Xenarios I, Halazonetis T, Jongeneel C, Stevenson B, Antonarakis S (2011) Exome sequencing identifies recurrent somatic MAP 2K1 and MAP 2K2 mutations in melanoma. Nat Genet 44(2):133–139. https://doi.org/10.1038/ng.1026 39. Lopez GY, Reitman ZJ, Solomon D, Waldman T, Bigner DD, McLendon RE, Rosenberg SA, Samuels Y, Yan H (2010) IDH1(R132) mutation identified in one human melanoma metastasis, but not correlated with metastases to the brain. Biochem Biophys Res Commun 398(3):585–587. https://doi.org/10.1016/j.bbrc.2010.06. 125 40. Cancer Genome Atlas Network (2015) Genomic classification of cutaneous melanoma. Cell 161(7):1681–1696. https://doi.org/10. 1016/j.cell.2015.05.044 41. Horn S, Figl A, Rachakonda PS, Fischer C, Sucker A, Gast A, Kadel S, Moll I, Nagore E, Hemminki K, Schadendorf D, Kumar R (2013) TERT promoter mutations in familial and sporadic melanoma. Science 339 (6122):959–961. https://doi.org/10.1126/ science.1230062 42. Denisova E, Heidenreich B, Nagore E, Rachakonda PS, Hosen I, Akrap I, Traves V, GarciaCasado Z, Lopez-Guerrero JA, Requena C, Sanmartin O, Serra-Guillen C, Llombart B, Guillen C, Ferrando J, Gimeno E, Nordheim A, Hemminki K, Kumar R (2015) Frequent DPH3 promoter mutations in skin cancers. Oncotarget 6(34):35922–35930. https://doi.org/10.18632/oncotarget.5771 43. McEvoy AC, Calapre L, Pereira MR, Giardina T, Robinson C, Khattak MA, Meniawy TM, Pritchard AL, Hayward NK, Amanuel B, Millward M, Ziman M, Gray ES (2017) Sensitive droplet digital PCR method

Analysis of Melanoma Circulating Tumour DNA for detection of TERT promoter mutations in cell free DNA from patients with metastatic melanoma. Oncotarget 8(45):78890–78900. https://doi.org/10.18632/oncotarget.20354 44. Reid AL, Freeman JB, Millward M, Ziman M, Gray ES (2015) Detection of BRAF-V600E and V600K in melanoma circulating tumour cells by droplet digital PCR. Clin Biochem 48 (15):999–1002. https://doi.org/10.1016/j. clinbiochem.2014.12.007 45. QIAamp circulating nucleic acid handbook. https://www.qiagen.com/au/ resources/resourcedetail?id¼0c4b31ab-f4fb425f-99bf-10ab9538c061&lang¼en. 46. Bio-Rad Rare Mutation Detection Best Practices Guidelines. https://www.bio-rad.com/ webroot/web/pdf/lsr/literature/Bulletin_6628.pdf. 47. QX200 droplet reader and QuantaSoft software instruction manual. https://www.biorad.com/webroot/web/pdf/lsr/literature/ 10031906.pdf 48. Bio-Rad Bio-Rad Mutation Detection Software. https://www.bio-rad.com/digital-

263

assays/#/assays-create/mutation. Bio-Rad Laboratories, Inc. Accessed 2020 49. Colebatch AJ, Witkowski T, Waring PM, McArthur GA, Wong SQ, Dobrovic A (2018) Optimizing amplification of the GC-rich TERT promoter region using 7-Deaza-dGTP for droplet digital PCR quantification of TERT promoter mutations. Clin Chem 64 (4):745–747. https://doi.org/10.1373/ clinchem.2017.284257 50. Milbury CA, Zhong Q, Lin J, Williams M, Olson J, Link DR, Hutchison B (2014) Determining lower limits of detection of digital PCR assays for cancer-related gene mutations. Biomol Detect Quantif 1(1):8–22. https://doi. org/10.1016/j.bdq.2014.08.001 51. van der Meer AJ, Kroeze A, Hoogendijk AJ, Soussan AA, Ellen van der Schoot C, Wuillemin WA, Voermans C, van der Poll T, Zeerleder S (2019) Systemic inflammation induces release of cell-free DNA from hematopoietic and parenchymal cells in mice and humans. Blood Adv 3(5):724–728. https://doi.org/ 10.1182/bloodadvances.2018018895

Chapter 20 Simultaneous BRAFV600E Protein and DNA Aberration Detection in Circulating Melanoma Cells Using an Integrated Multimolecular Sensor Alain Wuethrich, Shuvashis Dey, Kevin M. Koo, Abu A. I. Sina, and Matt Trau Abstract Liquid biopsy has emerged as the next generation target for diagnostics and therapeutic monitoring of many diseases including cancer. Liquid biopsy offers noninvasive analysis of aberrant biomolecular changes (e.g., aberrant protein expression, DNA mutation) which can provide crucial information on disease stages and therapy responses. As a diagnostically important biomarker for melanoma, the detection of the BRAF V600E aberration at the DNA and protein level in liquid biopsies confers an attractive option. This method describes the preparation and operation of an integrated multimolecular sensor (IMMS) for simultaneous detection of the BRAFV600E aberration in both molecular forms from circulating melanoma cells in liquid biopsy. IMMS integrates specific melanoma cell capture, cell release, cell lysis, and electrochemical BRAF V600E detection on a single device. IMMS is demonstrated for a sample-to-answer workflow of plasma spiked with melanoma cells. Key words Microfluidics, Biosensing, Immunoassay, Liquid biopsy, Melanoma, Lab on a chip

1

Introduction A main oncogenic driver in melanoma is the BRAFV600E aberration that leads to an overactivation of the mitogen-activated protein kinase (MAPK) pathway and stimulation of uncontrolled cell growth [1]. Diagnostically, the BRAF V600E aberration can be detected at the protein and DNA level, which is commonly performed from a tissue sample. Representing only a relatively small tumor area at a given time point, tissue sampling has limitations for obtaining accurate information on multimolecular levels due to tumor heterogeneity. Tissue biopsy is also inapplicable for frequent sampling that would be required in melanoma screening and monitoring of targeted therapy. A promising and minimally invasive alternative to tissue biopsy is liquid biopsy such as blood or plasma.

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_20, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The concept of detecting BRAF V600E in circulating tumor cells (CTCs) in liquid biopsies at the DNA and protein levels offers distinct diagnostic advantages. However, to explore the diagnostic potential of circulating BRAF V600E, highly sensitive and specific methods are required to reliably analyse the rare abundance of CTCs (typically, 1–100 CTC per mL of blood). In the case of the single base mutation BRAF V600E, DNA analysis requires highly specific probes. On the protein level, anti-BRAF V600E antibodies are available that provide 100% target specificity; however, the sensitivity of antibody-based immunoassays is often insufficient to match the required detection limit for circulating BRAF V600E protein detection. Furthermore, the detection of a single molecular form (i.e., either DNA or protein) has its shortcomings in terms of being susceptible to false-positive or false-negative results, as liquid biopsy consists of sample matrix rich in nontarget molecules that can interfere with the diagnostic results. To address these challenges, the simultaneous detection of both molecular forms on a single device could improve diagnostic accuracy. In this protocol, we describe an integrated multimolecular sensor (IMMS) for combined detection of BRAF V600E in melanoma cells at both DNA and protein levels [2]. IMMS provides on a single device multilevel monitoring information of melanoma such as (a) detection of CTCs which could inform on the metastatic state of the disease, (b) monitoring BRAF V600E expression levels which could provide crucial information on the activation of aberrant signaling pathways, and (c) detection of BRAFV600E mutation at the DNA level that would help to select the right drug for patients and understand the therapy responses at the genomic level. IMMS consists of a serpentine microfluidic structure to capture and release melanoma cells, a chamber for electrical lysis of isolated cells, and two separate electrodes for electrochemical detection of both molecular forms of BRAFV600E. Figure 1 shows the schematic sample-to-answer workflow for BRAF V600E DNA and protein detection using the IMMS. To capture melanoma cells specifically, the serpentine microfluidic structure was conjugated with melanoma-associated chondroitin sulfate proteoglycan (MCSP) (Fig. 1a). Specific melanoma cell capture was facilitated by alternating current electrohydrodynamic (ac-EHD) fluid mixing that increased cell interaction with the serpentine structure and simultaneously reduced nonspecific binding [3]. Subsequently, the captured melanoma cells were released by an electrical pulse and transferred to the lysis chamber, where a direct current electric field was applied to rupture the cell membrane and release the intracellular content (Fig. 1b). In the following, the cell lysate was transferred to the detection zones. For DNA analysis (Fig. 1c-i), after magnetic DNA isolation a selective ligaseassisted amplification was applied prior to horseradish peroxidase– mediated amperometric detection. For protein detection

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Fig. 1 Schematic workflow for simultaneous detection of BRAF V600E DNA mutation and protein using the integrated multimolecular sensor (IMMS). (Reproduced from [2] with permission from the Royal Society of Chemistry)

(Fig. 1c-ii), an anti-BRAF V600E antibody functionalized electrode enabled the specific protein detection by differential pulse voltammetry in the presence of ferricyanide–ferrocyanide redox couple. Both detection schemes applied ac-EHD mixing to accelerate binding kinetics and improve detection specificity [4]. Through testing on plasma samples spiked with designated concentrations of melanoma cells, we used the IMMS to demonstrate a sample-to-answer workflow of capturing CTCs at diagnostically relevant concentrations followed by subsequent multimolecular analysis. Being capable of integrated and miniaturized analysis, the IMMS could lead to the emergence of a new generation of cancer biosensors for multimolecular analysis in liquid biopsies [5].

2 2.1

Materials Cell Culture

1. RPMI-1640 2. Fetal bovine serum 3. GlutaMAX 4. Penicillin–streptomycin 5. Melanoma cell lines (SK-MEL-28 and SK-MEL-35) and breast cancer cell line (MD-MBA-231) 6. Cell culture flask

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7. Cell surface stain solution: 3,30 -dioctadecyloxacarbocyanine perchlorate (DiO), Vybrant™ 8. Cell nuclei stain solution: 1 mg/mL of 40 ,6-diamidino-2-phenylindole (DAPI) 2.2 IMMS Device Fabrication

1. Microfluidic design software, for example, L-Edit (Tanner Research) 2. Soda lime chrome mask, 5-by-5 in. 3. 4-inch glass wafer: 1 mm thick, Schott Borofloat® 33 4. Negative photoresist for electrode structures: AZnLOF 2070 5. Negative photoresist for microfluidic structures: SU-82150 photoresist 6. Direct laser write system μPG 101 (Heidelberg Instruments) 7. Spin coater, for example, Cee 200 Precision Spin Coater (Brewer Science) 8. Hot plate 9. Mask aligner: EVG 620 mask aligner 10. Developer solution: AZ726 MIF Developer 11. Reactive ion Instruments)

etching

system:

PlasmaPro

80

(Oxford

12. Physical vapor deposition system: Temescal FC-2000 evaporator (Ferrotec) 13. Lift off solution: Remover PG 14. Polydimethylsiloxane (PDMS) elastomer kit: Sylgard™ 184 Silicone Elastomer Kit (includes elastomer solution and curing agent) 2.3 IMMS Cell Capture, Release, and Lysis

1. IMMS microfluidic chip 2. Signal and wave generator: waveform generator 33510B 3. Biotin–bovine serum albumin (BSA) solution: 250 μL of 200 μg/mL biotinylated BSA in 1 phosphate-buffered saline (1 PBS) 4. Streptavidin solution: 250 μL of 100 μg/mL streptavidin in 1 PBS 5. Capture antibody solution: 250 μL of 250 μg/mL biotinylated anti-MCSP antibody in 1 PBS 6. Fluorescence microscope Ti-U (Nikon)

2.4 IMMS BRAFV600E DNA Detection

1. Agencourt AMPure XP SPRI kit 2. 70% ethanol 3. UltraPure™ DNase/RNase-free distilled water

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Table 1 Oligonucleotides used in experimentsa Oligos

50 -Sequence-30

BRAFV600E forward ligation probe

-PO-AGACATCGATCTG GACGAGGGAAAGAGTTGTACCTAAAT

BRAFV600E reverse ligation probe TGTATAGGAATCCCACTGAATTTTTCCCATCGAGATTTC BRAFV600E forward primer

Biotin-C3-ATTTAGGTACAACTCTTTCCCTCGTC

BRAF

TGTATAGGAATCCCACTGAATTTTTC

V600E

reverse primer

a

Underlined and bold represent unique primer recognition sequences on ligation probes

4. Neodymium bar magnet 5. T4 DNA ligase 6. T4 DNA ligase reaction buffer, 10 (New England Biolabs) 7. Heat block (see Note 1) 8. 1.5 mL 3810 tubes 9. Streptavidin-labelled magnetic beads 10. Binding and washing (B&W) buffer: 10 mM Tris–HCl, 1 mM EDTA, 2 M NaCl, pH 7.5 11. BRAF V600E forward and reverse ligation probe oligonucleotides with unique primer recognition sequences (Table 1) 12. Biotinylated BRAF (Table 1) 13. Thermomixer, (Eppendorf)

for

V600E

forward primer oligonucleotides

example,

Thermomixer

Comfort

14. TwistAmp® Liquid Basic kit 15. Biotinylated dUTPs 16. BRAFV600E reverse primer oligonucleotides (Table 1) 17. Streptavidin–horseradish peroxidase (HRP) conjugate 18. 1 PBS with 0.5% Triton X-100 19. 1-Step™ 3,30 ,5,50 -tetramethylbenzidine (Thermo Fisher Scientific)

(TMB)

solution

20. 500 mM sulfuric acid (H2SO4) 21. CHI650D electrochemical workstation (CH Instruments) 2.5 IMMS BRAFV600E Protein Detection

1. Biotin solution: 2 ng/mL of biotinylated BSA in 1 PBS (see Note 2) 2. Streptavidin solution: 1 ng/mL streptavidin in 1 PBS 3. Spacer solution: 0.1 μmol/L of 6-mercapto-1-hexanol in purified water

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4. Antibody solution: 0.1 ng/mL of monoclonal BRAF V600E antibody (Abcam Catalog #ab200535) in 1 PBS 5. Ferricyanide–ferrocyanide redox couple solution: 2.5 mM [Fe (CN)6]3/[Fe(CN)6]4 and 0.1 M KCl in 1 PBS at pH 7.4

3

Methods Figure 2 shows a schematic of the IMMS’s layout with dedicated zones for melanoma cell capture and release in a serpentine microfluidic channel, electrical cell lysis chamber, and separate wells for BRAF V600E DNA and protein detection. To perform target cell capture, release, lysis, and BRAFV600E DNA and protein detection, the IMMS is connected to the waveform signal generator via the connecting pads of the IMMS. The signal generator delivers the required frequency, amplitude, and electric field required for stimulating the ac-EHD induced mixing for target cell capture, release, and lysis. The sample is transferred manually by pipetting the obtained solution from one zone to the next.

3.1 IMMS Device Fabrication

The IMMS microfluidic device consists of a glass wafer patterned with gold electrode structures bonded to PDMS microfluidic structures, which are made by standard soft lithography and photolithography. The detailed electrode geometry and mask design are reported elsewhere [2].

3.1.1 Preparation of Electrode Pattern on Glass Wafer

1. Rinse the silicon wafer with isopropanol, acetone, and water, and dry using a flow of nitrogen. 2. Bake the glass wafer at 150  C for 20 min.

Fig. 2 Schematic of IMMS device with zones for capture/release, lysis, and detection of BRAF V600E DNA and protein. (1) Connecting pads; (2) lysis well; (3) BRAF V600E DNA probe ligation well; (4) BRAF V600E DNA amplification and detection well; and (5) BRAFV600E protein detection well. (Adopted from [2] with permission from the Royal Society of Chemistry)

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3. After the glass wafer reaches room temperature (RT), pour 4 mL of AZnLOF 2070 to the middle of the wafer and spincoat for 30 s at 1008  g (this is the equivalent of 1008  g if using the Cee 200 Precision Spin Coater). 4. Transfer the photoresist-coated wafer to a hotplate and bake for 2 min at 110  C. 5. After cooling to RT, align the coated wafer with the photomask and UV-expose with a constant dose of 200 mJ/cm2 using the mask aligner. 6. Transfer the exposed wafer to a hotplate and bake for 2 min at 110  C. 7. After cooling to RT, develop wafer in development solution for 75 s (see Note 3). 8. Insert the wafer into a physical vapor deposition instrument and coat a layer of 10 nm Ti followed by a layer of 200 nm Au onto the wafer. 9. Complete the patterning of the wafer with the gold electrode structures by overnight lift-off in Remover PG, and clean the wafer by rinsing with isopropanol, acetone, and water. 3.1.2 Preparation of Microfluidic Structures

1. Prepare PDMS solution by mixing and degassing 50 g of PDMS elastomer and 5 g of curing agent. 2. Pour the PDMS solution over the master mold placed in the center of a petri dish. 3. Cure the PDMS solution at 80  C for 20 min. 4. Carefully separate the cured PDMS carrying the microfluidic structures from the master mold. 5. Use the PDMS puncher to punch the sample inlet and outlet reservoirs. 6. Align the microfluidic structure with the electrode pattern. 7. Complete the device fabrication process by plasma bonding at 50 W for 45 s of the microfluidic structures with electrode pattern.

3.2 IMMS Cell Capture, Release, and Lysis

1. Functionalize the cell capture domain of the microfluidic chip with capture antibody in a layer-by-layer protein immobilization technique. (a) Add 250 μL of 200 μg/mL biotinylated BSA solution to the microfluidic capture zone and incubate for 2 h at 37  C. (b) Wash microfluidic chip three times with 1 PBS to remove unbound molecules from the surface. (c) Add 250 μL of 100 μg/mL streptavidin solution to the capture domain and incubate for another 1 h at 37  C.

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(d) Repeat 1 PBS wash three times to remove unbound molecules from the surface. (e) Add 250 μL of 250 μg/mL biotinylated anti-MCSP solution to the streptavidin functionalized chip and incubate for 1 h at 37  C for antibody immobilization. (f) Repeat 1 PBS wash three times to remove unbound molecules from the surface. 2. Stain target cell membranes with 5 μL of DiO solution per 106 cells and incubate for 10 min at 37  C. 3. Pass stained target cells in healthy plasma (200 μL) through the antibody functionalized microfluidic capture zone under optimized ac-EHD condition ( f ¼ 600 Hz, Vpp ¼ 100 mV) for 45 min. 4. Rinse the capture domain three times with 1 PBS solution to remove nonspecifically adsorbed molecules. 5. Determine cell capture efficiency. (a) Permeabilize captured cells by adding 0.2% Triton X-100 in 1 PBS in the microchannel for 10 min (b) Wash with 1 PBS (c) Stain with nuclear staining dye by adding DAPI solution for 15 min. (d) Wash the capture zone with 1 PBS before imaging the cells with the multichannel fluorescence microscope. 6. Fill the capture domain with 0.1 PBS and electrochemically release the captured cells by applying a dc potential (V ¼ 1.4 V and t ¼ 200 s). 7. Transfer the released cells in 0.1 PBS to the cell lysis zone and lyse by applying 5 V dc potential for 4 min. 3.3 IMMS BRAFV600E DNA Detection

1. Purify the DNA from cell lysates magnetically using Agencourt AMPure XP magnetic beads according to manufacturer’s instructions in lysis well. (a) Add 90 μL of AMpure XP magnetic beads to 50 μL of cell lysate and incubated for 10 min. (b) Place the neodymium bar magnet under “lysis” well to separate beads+DNA from contaminants in the liquid supernatant (see Note 4). (c) Wash the beads twice with 200 μL of 70% ethanol to fully remove contaminants (see Note 5), and air-dry beads for 5 min (see Note 6). (d) Add 5 μL of ultrapure water and incubate for 5 min to elute DNA off beads, and transfer eluate solution to ligation well by using the magnet to separate beads.

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2. Ligate the probes to BRAFV600E using T4 DNA ligase [6]: Add 10 μL of ligation mixture containing 100 nM of each ligation probe, 40 U of T4 DNA ligase, and 1 T4 DNA ligase reaction buffer in “ligation” well. 3. Heat the ligation mixture to 95  C for 3 min by placing the IMMS on a heating block. 4. Cool the mixture to RT for 15 min to facilitate hybridization and ligation under ac-EHD ( f ¼ 500 Hz, Vpp ¼ 800 mV). 5. Prepare forward primer-modified magnetic beads before isothermal amplification. (a) Wash 10 μL of streptavidin-labelled magnetic beads with 200 μL of B&W buffer. (b) Resuspend the washed magnetic beads in 25 μL of B&W buffer in a 1.5 mL tube. (c) Incubate the resuspended beads with 5 μM biotinylated forward primer oligonucleotides on a thermomixer at RT for 30 min under 252  g mixing (this is the equivalent of 252  g if using the Thermomixer Comfort unit). (d) Wash the modified beads three times with 200 μL of B&W buffer and resuspend in 5 μL of ultrapure water. 6. Perform isothermal amplification on primer-modified magnetic beads in “amplification and detection” well using components of TwistAmp® Liquid Basic kit with modifications to manufacturer’s instructions. (a) Add 12.5 μL of 2 Reaction Buffer, 4.6 μL of 1.8 mM dNTPs/20 nM biotinylated dUTPs, 2.5 μL of 10 Basic E-mix, and 1.2 μL of 500 nM reverse primers to the well. (b) Mix via ac-EHD ( f ¼ 500 Hz, Vpp ¼ 800 mV) for 30 s. (c) Add 1.25 μL of 20 Core Reaction Mix and mix via ac-EHD ( f ¼ 500 Hz, Vpp ¼ 800 mV) for 30 s. (d) Add 5 μL of forward primer-modified magnetic beads, 1.25 μL of 280 mM MgOAc, and 1 μL of ligation mixture (from the “ligation” well). (e) Incubate at 43  C for 15 min on heat block under ac-EHD ( f ¼ 500 Hz, Vpp ¼ 800 mV). 7. Wash post-amplification modified magnetic beads twice with 200 μL of B&W buffer by placing neodymium bar magnet under “lysis” well. 8. Resuspend washed beads in 10 μL of 1 PBS with 0.5% Triton X-100. 9. Label amplicons on bead surface with streptavidin-HRP [7]: Add 1 μL of 1:1000 diluted streptavidin–HRP to resuspended beads and incubate for 5 min.

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10. Wash labeled beads twice with 200 μL of B&W buffer by placing neodymium bar magnet under “lysis” well. 11. Resuspend washed beads in 15 μL of 1-Step™ TMB solution 12. Magnetically load resuspended beads onto working electrode surface in “amplification and detection” well (see Note 7) and incubate for 5 min. 13. Add 500 mM H2SO4 into “amplification and detection” well. 14. Perform amperometric detection using the electrochemical workstation of HRP-labeled amplicons on bead surface by graphing the current at 150 mV for 30 s at RT. 3.4 IMMS BRAFV600E Protein Detection

1. Functionalize the protein detection zone with anti-BRAFV600E antibodies using biotin–avidin conjugation chemistry. (a) Pipet 40 μL of 2 ng/mL biotinylated bovine serum albumin on the protein detection electrode and incubate for 2 h at RT. (b) Wash electrode three times with 1 PBS. (c) Add 40 μL of 1 ng/mL streptavidin and incubate for 1 h at RT. (d) Wash electrode three times with 1 PBS. (e) Add 40 μL of 0.1 μM 6-mercapto-1-hexanol and incubate for 10 min at RT. (f) Wash electrode three times with 1 PBS. (g) Complete electrode functionalization by adding 40 μL of 0.1 ng/mL monoclonal BRAF V600E antibody and incubating for 1 h at RT (see Note 8). (h) Wash electrode three times with 1 PBS. 2. Add 20 μL of ferricyanide–ferrocyanide redox couple solution to electrode, and connect the IMMS to the electrochemical workstation. 3. Measure the baseline peak current (ibaseline) of the electrode by differential pulse voltammetry using the measurement parameters as follows: pulse amplitude of 50 mV; pulse width of 50 ms; potential step of 5 mV; and pulse period of 100 ms (see Note 9). 4. Rinse the electrode with 1 PBS, and pipet 20 μL of lysed melanoma cells onto the antibody-conjugated electrode (see Note 10). 5. Disconnect the IMMS from the electrochemical workstation and connect to the signal generator. 6. Apply ac-EHD induced fluidic mixing for 3 min at 500 Hz and 800 mV. 7. Wash the electrode three times with 1 PBS.

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8. Add 20 μL of ferricyanide–ferrocyanide redox couple solution to the IMMS. 9. Perform electrochemical readout to determine target adsorption (itarget) by differential pulse voltammetry with the same parameter as in step 3. 10. Calculate BRAF V600E target adsorption as relative current i i target change (%ir) following equation %ir ¼ baseline  100%. i baseline

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Notes 1. It is recommended for the heat block to have a solid flat surface to place the IMMS on for even heat transfer. 2. PBS should be freshly prepared and filtered through a 0.22 μm syringe filter prior to use. 3. Inspect the wafer under the microscope. The electrode structures should be fully developed. If not, repeat the development process for another 20–30 s. 4. It is recommended to concentrate magnetic beads to a single side of the well and remove liquid supernatant without disturbing the beads to maximize purified DNA yield. 5. To ensure DNA purity and yield, pipet beads up and down up to five times gently to fully resuspend beads and avoid disturbing magnetically isolated beads when removing ethanol wash solution. 6. Ensure as much ethanol wash solution is removed from the well as possible for maximal bead drying. Avoid overdrying beads beyond 5 min to maximize DNA yield. 7. Maintain neodymium bar magnet in stationary position under “amplification and detection” well to keep beads magnetically loaded in place without disturbance until end of amperometric detection. 8. The antibody functionalized surface should always be kept wet and never allowed to dry completely. 9. The stepwise functionalization of the BRAF V600E protein detection electrode can be followed by a decrease in DPV peak current. 10. In addition to the sample analysis, it is recommended to investigate the BRAFV600E protein detection specificity by analysing cell lysate obtained from BRAF wild-type cell lines (e.g., SK-MEL-35) and BRAF V600E cell lines (SK-MEL-28) [2]. Measurement of the BRAF wild-type cell lysate sample is expected to result in negligible changes in peak current (%ir) obtained by differential pulse voltammetry.

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Acknowledgments The authors acknowledge grants received by our laboratory from the National Breast Cancer Foundation of Australia (CG-12-07) and the ARC DP (160102836 and 140104006). These grants have significantly contributed to the environment to stimulate the research described here. A.W. and A.A.I.S. thank the National Health and Medical Research Council of Australia for funding (APP1173669 and APP1175047). References 1. Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S (2018) Melanoma. Lancet 392:971–984 2. Dey S, Koo K, Wang Z, Sina AAI, Wuethrich A, Trau M (2019) An integrated multi-molecular sensor for simultaneous BRAFV600E protein and DNA single point mutation detection in circulating tumour cells. Lab Chip 19:738–748 3. Khondakar KR, Dey S, Wuethrich A, Sina AAI, Trau M (2019) Toward personalized cancer treatment: from diagnostics to therapy monitoring in miniaturized electrohydrodynamic systems. Acc Chem Res 52:2113–2123 4. Wuethrich A, Howard CB, Trau M (2018) Geometric optimisation of electrohydrodynamic

fluid flows for enhanced biosensing. Microchem J 137:231–237 5. Li J, Wuethrich A, Dey S, Lane RE, Sina AAI, Wang J, Wang Y, Puttick S, Koo KM, Trau M (2020) The growing impact of micro/nanomaterial-based systems in precision oncology: translating “Multiomics” technologies. Adv Funct Mater 1909306:1–37 6. Koo KM, Wee EJH, Trau M (2017) High-speed biosensing strategy for non-invasive profiling of multiple cancer fusion genes in urine. Biosens Bioelectron 89:715–720 7. Koo KM, Wee EJH, Trau M (2016) Colorimetric TMPRSS2-ERG gene fusion detection in prostate cancer urinary samples via recombinase polymerase amplification. Theranostics 6:1415–1424

Chapter 21 Single-Cell Analysis of BRAFV600E and NRASQ61R Mutation Status in Melanoma Cell Lines as Method Generation for Circulating Melanoma Cells Joseph W. Po, Yafeng Ma, Alison W. S. Luk, David Lynch, Bavanthi Balakrishnar, Daniel Brungs, Farhad Azimi, Adam Cooper, Erin Saricilar, Vinay Murthy, Paul de Souza, and Therese M. Becker Abstract Molecular testing of tumor biopsies allows for the identification of the key mutations driving a patient’s cancer. However, this is limited to singular nodes and may not accurately reflect cancer heterogeneity. Circulating tumor cell (CTC) analyses offer a noninvasive method of interrogating the genomic profile of patient-derived tumor material. To date, molecular analysis of CTCs has relied on the characterization of bulk or pooled CTC lysates, limiting the detection of minor tumorigenic CTC subclones. Here, we show a workflow enabling BRAFV600E/NRASQ61R mutation detection from single cultured melanoma cells by combining micromanipulation and genomic material amplification methods. This workflow can be directly integrated into circulating tumor cell analysis applications. Key words Circulating tumor cells (CTC), Melanoma, Cell lines, Whole-genome amplification (WGA), Liquid biopsy

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Introduction A new era of precision medicine has revolutionized the approach to managing cancer patient care in the past decade, driven by a deeper understanding of the underlying molecular mechanisms driving cancer development and progression [1, 2]. Currently, molecular interrogation of tumor biopsies can be performed to identify oncogenic driver mutations that may be treatment targets [3]. Therapy is then selected to target the identified genetic mutations, such as HER2 for breast cancer, EGFR for lung cancer, BRAF for melanoma, and KRAS for colorectal cancer [4]. Conventional tumor biopsies, however, are associated with risk to the patient, are invasive by nature, and may be limited by difficulty in accessing the tumor site. Moreover, a single node biopsy does not address the

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_21, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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issues of intertumor and intratumor heterogeneity, and it therefore may not accurately reflect the genetic diversity of a patient’s cancer or the evolution of a patient’s disease over time [5, 6]. Circulating tumor cells (CTCs) are cells that have detached from primary or metastatic tumor deposits and invaded the circulation, where they may seed metastases at distant sites. Compared to conventional biopsies, CTC analyses offer a noninvasive and costeffective means of acquiring tumor material from blood [7, 8]. The prognostic value of CTCs is well established, with higher CTC numbers portending a poorer outcome in many cancers, including breast, colorectal, prostate, lung, melanoma, and pancreatic cancers [9–13]. CTC analyses have also moved beyond enumeration toward genomic profiling to maximize the clinical utility of CTCs within the precision medicine era. Initial efforts to characterize CTCs have typically relied on bulk-cell analysis either from enriched or pooled CTCs lysates [14–19]. Enriched samples, however, carry a high number of background leukocytes that may limit the detectability of clinically relevant mutations within minor CTC subpopulations [20]. Substantial efforts to enable genomic profiling of single CTCs are taking place and may provide an opportunity to improve genetic profiling resolution, allowing dynamic changes in heterogeneity to be monitored over the course of therapy [21–25]. Importantly though, the technological hurdles of the extreme rarity of CTCs in circulation and the low starting genomic material must be overcome before single cell analyses can become a routinely used diagnostic tool in the era of precision medicine and targeted therapies. There is a clear need for a robust method to detect clinically relevant mutations and enable genomic profiling of a single CTC or cell. Here, we describe a method of combining micromanipulation and genomic material amplification technologies to achieve singlecell mutation detection of BRAFV600E and NRASQ61R from cultured melanoma cell lines. Importantly, these methods can be directly integrated to CTC analysis applications (Fig. 1).

Fig. 1 Workflow illustrating proposed single cell workflow. Schematic of proposed end-to-end workflow for mutation detection of single CTCs. From left to right: Blood collection from the patient; CTC enrichment processes; single CTC micromanipulation; single CTC whole genomic amplification; mutation detection via droplet digital PCR

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Materials

2.1 Culture and Preparation

1. Complete Dulbecco’s Modified Eagle’s Medium (DMEM): Supplement base DMEM medium with 10% fetal bovine serum (FBS), 4 nM L-glutamine, and 20 mM HEPES. 2. Melanoma cell lines: 501mel and MelRM. 3. Phosphate buffered saline (PBS). 4. Ethylenediaminetetraacetic acid (EDTA): Prepare a 0.2 mM EDTA solution in PBS. 5. Cell scraper. 6. Hemocytometer. 7. Superfrost glass slides. 8. BX53 microscope. 9. Centrifuge tubes. 10. Tabletop centrifuge.

2.2 Single Cell Micromanipulation

1. CellCelector™ platform for single cell picking. 2. CKX41 fluorescent microscope. 3. XM10 CCD camera system. 4. Aerosol barrier tips. 5. Custom glass slide for CellCelector system. 6. Custom slide adaptor for CellCelector system. 7. Ethanol, 100%. 8. Bovine serum albumin (BSA): Prepare a 2% BSA solution in PBS. 9. PCR tubes. 10. PCR-grade water.

2.3 Whole-Genome Amplification of DNA Derived from Single Cells

1. Ampli1 WGA-Kit (Silicon Biosystems).

2.4 Mutation Detection Using Droplet Digital PCR (ddPCR)

1. PrimePCR™ ddPCR™ Mutation Assay Kit: BRAF WT for p. V600E, and BRAF p.V600E (Bio-Rad, Human #1863100).

2. GenElute PCR cleanup kit (Sigma). 3. NanoDrop™ 2000 spectrophotometer.

2. NRASQ61R PCR primers and probes: (a) Forward primer: 5’-GGTGAAACCTGTTTGTTGGACA TAC-3’. (b) Reverse primer: 5’-TGGTCTCTCATGGCACTGTACT-30 . (c) Q61R: 50 -/HEX/ACAGCTGGACAAGAAG/BHQ1/3’. (d) WT: 50 -/6FAM/ACAGCTGGACGAGAAG/BHQ1/30 .

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3. QX200 ddPCR suite (Bio-Rad): (a) QX200 Droplet Generator. (b) C1000 Touch Thermocycler. (c) QX200 Droplet Reader. 4. 96-well PCR plate (Thermo Scientific) 5. QuantaSoft Software V1.7.4 (Bio-Rad).

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Methods

3.1 Culture and Preparation of Melanoma Cell Lines

1. Seed 501mel and MelRM melanoma cell lines at 15–20% confluency in complete DMEM medium and culture in a humidified atmosphere with 5% CO2 at 37  C (see Note 1). 2. Following culture for 72–96 h, remove residual medium, wash with 1 PBS and harvest adherent cells by incubating culture flasks with 0.2 mM EDTA (in PBS) for 10 min at 37  C. If adherent cells have not detached within 10 min, use cell scrapers to manually detach cells (see Note 2). 3. Transfer cell suspension to 15 mL centrifuge tube, add 5 mL of complete DMEM medium, and centrifuge at 218  g for 10 min. 4. Remove supernatant, resuspend cell pellet in 1 mL of 1 PBS, and determine cell concentration using a hemocytometer. Adjust volume (using 1X PBS as a diluent) to achieve a concentration of ~1  103 cell/mL (see Note 3). 5. Add 1 Hoechst for downstream nuclear imaging.

3.2 Single Cell Micromanipulation

All micromanipulation steps described herein are applicable for use with the CellCelector™ platform. The CellCelector™ is a semiautomated micromanipulator consisting of an inverted CKX41 fluorescent microscope with an XM10 CCD camera system and a robotic arm with a vertical glass capillary of 30 μm diameter. 1. Retrieve the custom glass slide (hydrophobic border surrounded visual area) and coat visual field with 200 μL of 2% BSA for 2 min to minimize the cells settling, then remove (see Notes 4 and 5). Slot glass slide into custom slide adaptor. 2. Carefully transfer cell suspension onto a custom glass slide using aerosol barrier tips and place into the custom slide adaptor on the automatic stage. Allow cells to settle. 3. For micromanipulation, manually identify and target Hoechst positive events (threshold settings for DAPI channel set no higher than 50 ms) under the 20 objective. During cell micromanipulation, perform imaging in the DAPI channel to ensure target cells are indeed aspirated and no adjacent cells are mistakenly coaspirated (see Note 6).

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4. Expel aspirated cells into a 200 μL PCR tube containing 100 μL of PCR-grade water. 5. Centrifuge samples at 5000  g for 10 min, and remove all supernatant except for 1 μL. 6. Process cells within 24 h. 3.3 Whole Genome Amplification of DNA Derived from Single Cells

1. To amplify genomic DNA from isolated single cells, use the Ampli1 WGA-Kit according to the manufacturer’s instructions (see Note 7). Briefly, this procedure consists of four steps: (1) Lysis, (2) Digestion, (3) Ligation and (4) Amplification. Minor modifications are as follows: extend lysis to an overnight incubation and digestion to 3 h [26]. 2. Purify whole genome amplification samples using the GenElute PCR Clean-Up kit as per manufacturer’s instructions to remove short fragments and salts. 3. Determine DNA concentration using a NanoDrop™ 2000 spectrophotometer. 4. Perform quality control of whole genome amplification products prior to downstream analysis (see Note 8).

3.4 Mutation Detection Using Droplet Digital PCR

1. For BRAF/NRAS mutation detection, use the QX200 ddPCR suite. 2. Prepare the ddPCR reactions mixtures as follows. (a) For BRAF WT and BRAFV600E detection, retrieve the PrimePCR™ ddPCR™ Mutation Assay Kit: BRAF WT for p.V600E, and BRAF p.V600E. Prepare a 20 μL reaction mixture consisting of 10 μL of Supermix, 0.5 μL of both primer/probe mixes for BRAF WT/V600E, 1 μL of DNA (at 50 ng/μL concentration) derived from the single cell whole genome amplification products, and 8 μL of PCR-grade H20. (b) For NRAS WT and NRASQ61R, prepare a 20 μL reaction mixture consisting of 10 μL of Supermix, 0.5 μL of both NRAS FWD/REV primers, 0.25 μL of HEX probes, 0.5 μL of FAM probes, 1 μL of DNA (at 50 ng/μL concentration) derived from single cell whole genome amplification products, and 7.25 μL of PCR-grade H20. 3. Prepare emulsified oil and reaction mixtures using the QX200 droplet generator as below. (a) Fill the DG8 cartridge with 20 μL of ddPCR MasterMixes as prepared above and 70 μL of droplet generation oil for each sample. (b) Following emulsification, carefully transfer 40 μL of the droplet suspension to a 96-well PCR plate (see Note 9).

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Fig. 2 1D ddPCR blots of mutation detection using whole genome amplification products from variable cell numbers. Representative 1D blots illustrating BRAF/NRAS mutation detection from whole genome amplification products yielded from the indicated number of micromanipulated cultured melanoma cells. Left 1D blots represent BRAF WT (top) and V600E (bottom) detection from 1, 5, and 10 501mel cells (wells 1–6). Right 1D blots represent NRAS WT (top) and Q61R (bottom) detection from 1, 5, and 10 MelRM cells (wells 1–6). All samples were run in duplicate; DNA extracted from respective cell lines was used as positive controls (bulk); PCR-grade H20 was used as no template control (NTC)

(c) Seal the 96-well plate samples by covering with the foil seal. 4. Process both mutation detection samples using the thermal cycler settings as follows (noting different annealing temperatures for BRAF/NRAS). (a) 1  cycle: 95  C, 10 s (b) 40  cycles: denaturation 94  C, 30 s; annealing/extension 55  C for BRAF or 53  C for NRAS, respectively, 60 s (c) 1  cycle: final extension 98  C, 10 min (d) Hold at 4  C. 5. Read amplified PCR products (fluorescent droplets) using the QX200 droplet reader, and analyze data with QuantaSoft software V1.7.4 (Figs. 2 and 3).

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Notes 1. Ensure cell lines are STR authenticated, confirmed to be free of mycoplasma, and of low passage number to maintain reliability of phenotypes/genotypes. 2. It is recommended to avoid use of potent proteolytic enzymes such as trypsin to detach cells from culture flasks. These enzymes may disrupt/damage cell membranes and potentially affect downstream analyses.

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Fig. 3 2D ddPCR blots of BRAF and NRAS mutation detection. Representative 2D blots of (a) 501Mel cell line DNA (BRAFV600E) and (c) MelRM cell line DNA (NRASQ61R). (b) and (d) represent single cells subjected to whole genome amplification. Events circled in red indicate mutant positive events

3. The addition of EDTA/FBS to the cell suspension volume is recommended to minimize cell settling and adhesion to the slide during the subsequent micromanipulation. Cell settling beyond 30 min will result in increased cell adherence to the glass slides, making micromanipulation/picking more difficult. While more abrasive physical handling may allow for picking of single cells that have attached to the slide, the efficiency of successful downstream whole genome amplification is affected. 4. If an alternative micromanipulator system is used and the user cannot get access to a custom glass slide with hydrophobic border, any hydrophobic marker can be used. Ideally, the hydrophobic border should be narrow enough to minimize spreading of cells across the slide and retain buffer to prevent the sample from drying out. Any dehydrating or drying out of sample will dramatically reduce successful transfer/micromanipulation of single cells. 5. Coating of the slide with BSA should be performed immediately prior to transferring the cell suspension in order to minimize cell adhesion to the slide surface. BSA should be made fresh and aliquoted as sterile 1-use stocks that are kept frozen to avoid contamination and its effects on downstream analyses. 6. The “picking” process, or micromanipulation, requires calibration of the capillary with respect to x, y, and z coordinates in order to center and position the capillary above the glass slide. Aspirate cells with 20–100 nL solution using a 30 μm capillary. Replace the capillary for every sample, and sterilize in ethanol immediately before picking each cell.

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7. Other commercial kits relying on different genomic amplification technologies are available such as GenomePlex (Sigma), PicoPlex (Rubicon Genomics Inc.), and Repli-G (Qiagen), offering variable genomic coverage, amplification biases, and/or DNA yields that may be more appropriate for users depending on intended downstream applications. Here, we use the Ampli1 WGA kit which utilizes a randomized oligonucleotide-primed PCR method followed by a sitespecific DNA restriction to generate a representative amplification of genomic DNA. Below are suggested considerations to improve whole-genome amplification sample processing. (a) Pipette each whole genome amplification reaction mixture onto the side of a PCR tube with sample, and quick-spin before putting into the thermal cycler. Make sure lids are firmly closed in thermal cycler. (b) Keep ALL enzymes at 20  C until immediately before transferring into reaction mixes. (c) Please be aware that whole genome amplification requires handling of very small volumes (90% of the events included in the bead region. Use this gate to set up the acquisition stop condition in 2000 events. 3. Acquire data: Within the APC-positive bead region, open a histogram of fluorescence in the PE channel. This region will measure the percentage of beads that captured EVs expressing the protein of interest. The fluorescence PMT voltages should be established to adjust the negative peak with the Ig-isotype control and then test samples should be run without modifying this parameter. 4. Run positive controls and adjust compensation between different channels (PE, APC, PE-Cy5). For melanoma, positive control EVs can either be purchased or enriched from cell lines (see Subheading 3.2.1). Typically, capture with CD63 and detection with CD81 yields a strong positive signal. 3.7 Flow Cytometry Analysis

Once the samples have been acquired by the flow cytometer, data files are generated for each sample. In order to analyze the obtained data, a flow cytometry analysis software is required. An example of the analysis workflow is shown in Fig. 5b.

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1. Open the data files in the analysis software and follow the same gating strategy as for sample acquisition (Subheading 3.6). 2. Obtain the data for percentage of positive beads, Median Fluorescence Intensity (MFI), and Standard Deviation (SD) for each analyzed sample. MFI values reflect both the number of EVs carrying the marker used for capture and the expression levels of the marker used for detection in each individual EV. It should be noted that this detection method is a bulk analysis in which the signal corresponding to several EVs is detected as a single bead and, therefore, as a single event by the flow cytometer. Usually the histogram appears as a single peak that can be broader or thinner depending on the number of EVs captured by each bead. Thus, the best way of analyzing these data is comparing the MFI and SD of the whole population. 3. For analysis, overlay plots can be built, but a quantitation of the positive signal can be numerically obtained: calculate the relative fluorescence intensity (RFI) and the stain index (SI) for each tested sample (see Note 22): RFI ¼

SI ¼

MFI sample MFI isotype control ðIgGÞ

ðMFI sample  MFI isotype controlÞ ð2x SD isotypeÞ

RFI > 1 and SI > 0 correspond to positive signal for the tested protein on the EVs. 4. Export all the data for statistical analysis and graphical expression of the results.

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Notes 1. e.g. Beckman Coulter Ultracentrifuge tubes Ultra-Clear tubes 25  89 mm for volumes up to 30 ml with SW28 rotor or Beckman Coulter Ultracentrifuge tubes 13  51 mm for volumes up to 5 ml with SW55 Ti rotor. For more information on rotors and tube types see Table 3.22.1 in [27] or Beckman Coulter “Rotors Tubes and Accessories ULTRACENTRI FUGE” Catalog. 2. When lyophilizing EVs, use screwcap tubes for convenience. 3. Metastatic melanoma cell lines are usually adherent so the protocols described here are specific for adherent cells. For suspension cells, washes involve centrifugation at 200  g. 4. FBS ultracentrifuged for 18–20 h at 110,000  g under sterile conditions. Aliquots can be stored frozen and thawed when needed.

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5. Negative and positive controls should always be run in parallel [3, 30]: for each antibody-coated bead test, an irrelevant antibody (Ig isotype control) should be included. When planning the experiment, it is important to include several tetraspanin combinations, since the molecule of interest could be in any subpopulation of vesicles. 6. Biotinylated antibodies are not recommended for the analysis of plasma. 7. It is important to have a healthy monolayer of cells, usually not more than 70% confluent when changed to 1% EV-free cell culture medium. The number of EVs produced varies with the cell type. For ultracentrifugation, it is recommended to prepare at least two 150 mm plates to recover 30 ml of supernatant as a start. The number of cells in the monolayer will determine the amount of EVs recovered; thus, depending on the downstream application, a larger number of plates might be necessary. 8. Cells remaining on the plate should be detached and counted to know the number of EVs recovered per cell in each condition tested, for example, if comparing treatment with different drugs that could affect cell viability. A cell lysate can also be prepared for comparison in western blot, as a control for EV expression of a protein. It is also recommended to check for the presence of any apoptotic cells. 9. This pellet contains microvesicles, apoptotic vesicles, and other large organelles. Alternatively, this centrifugation step can be performed at 14,000  g for 10 min. 10. In scientific articles, plasma collection and processing methods should be reported according to MISEV2018 [3]. When obtaining plasma, make sure that the sample has been centrifuged at 500  g to remove all cells. For more ISEV recommendations on sample collection, isolation, and analysis methods in extracellular vesicle research, see [31, 32]. Because of its viscosity, plasma needs to be diluted and certain centrifugation steps need higher speed and length. 11. Take care not to disturb any pellet while recovering supernatants. To ensure that no particles from the pellet are recovered with the supernatant, it is advisable to leave a small volume behind in each step. A second centrifugation round at the same speed ensures complete removal of the cells. If, at the end of this step, the isolation process cannot be continued the same day, the supernatant can be kept at 4
C up to a week or frozen at 80
C. However, this is not recommended because it may lead to EV loss. Pellets in all the centrifugation steps can be stored if necessary, for analysis by Western blot of the fractionation process.

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12. For the high-speed ultracentrifugation, all tubes should be at least three-quarters full and weight carefully compensated. Reminder: ultracentrifugation without brake will take around an extra hour to stop. For certain applications (not necessary for flow cytometry), an extra step could be added to remove impurities and high-molecular-weight complexes copurifying with EVs. The most common is size exclusion chromatography (SEC). Another purification option (not necessary for flow cytometry) is to rinse the EV pellet by repeating the ultracentrifugation, once resuspended in PBS or HBS, for 70 min at 4
C with no brake. With this step some impurities might be eliminated but material loss can also happen. 13. The remaining liquid on the sides and the mouth of the tube can be cleaned up with a serviette. Make sure not to leave any vesicles behind by solubilizing the pellet and recovering the liquid in consecutive steps (e.g., adding and recovering 30 μl three times, instead of 90 μl at once). A concentration of around 4–8  109 particles per million cells is usually obtained, depending on the cell line and EV accumulation time. In our hands, HBS buffer is a better choice than PBS because it generates less particle precipitation. 14. For longer term storage, EVs can be lyophilized. For lyophilization, add sucrose to a final concentration of 8% and prepare aliquots of the desired volume (e.g., 15 μl) in microcentrifuge tubes covered with Parafilm. Freeze at 80
C overnight, perforate Parafilm and lyophilize following the instrument recommendations. Lyophilized samples can be stored at 4
C. EVs should be rehydrated in the same volume included in each tube. For scientific papers, storage, time, lyophilization, and reconstitution conditions and buffer(s) should be reported. 15. NTA technology, measuring dynamic light scattering (DLS), allows EV size and concentration measurement, based on analysis of the Brownian motion of particles in suspension. Other methods use resistive pulse sensing (RPS) based on conductivity [33], surface plasmon resonance (SPR), or surface enhanced Raman spectroscopy (SERS) [34, 35]. Conventional protein concentration tests (e.g., BCA or Bradford), or lipid concentration tests [36], combined with particle concentration measurement allows for complementary information on the purity of the enriched preparation [37, 38]. Protein concentration per number of particles will vary depending on the purity of the preparation, the cell type, and culture conditions. 16. Transmission electron microscopy (TEM) and Scanning Electron Microscopy (SEM) can provide size, composition, and topology information. Cryo-TEM or atomic force microscopy (AFM) also allow EV imaging, overcoming sample dehydration produced by conventional TEM sample processing methods.

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17. When using EV-enriched preparations from melanoma cell lines, typically, a concentration per test tube of around 1  107 particles/μl provides a good signal for detection of the EV tetraspanins CD63, CD9, or CD81 (see Note 15). However, if the purpose of the assay is to analyze the presence of tumor antigens (e.g., MICA or PD-L1) on EVs, it is highly recommended to add at least twice the amount of sample, since tumor antigens are usually expressed less than tetraspanins and may be present only on a subpopulation of EVs. 18. Addition of the EV sample must not increase the final volume of incubation for the capture step. This is a crucial parameter in these experiments. If EVs are very diluted, the master solution of capture beads should take into account the volume of EVs that will be added to the tube. 19. Rotation or low temperature do not significantly enhance capture to the antibody coated beads. 20. In the rack used for our experiments, the magnet is located on the side, therefore the magnetic beads will be collected on the side of the tube. If using a different magnet, each user should find out where the magnetic field is applied and avoid disturbing the captured beads. 21. Fluorescent molecules are very sensitive to light, so ensure that the tubes are covered at all times and avoid direct sunlight exposure. 22. Calculation of these parameters allows the expression of binding as a function of the signal-to-noise ratio, that is, to compare the mean fluorescence intensity (MFI) of the positive sample peak with the MFI of the negative control peak (nonspecific binding). The stain index (SI) also takes into account the standard deviation (peak width) of the negative control, thus considering the instrument adjustments. See more on this topic in [39, 40].

Acknowledgments We would like to thank Prof. J.L. Carrascosa, CNB-CSIC, for Electron Microscopy analysis of EVs immunocaptured on microspheres; Dr. V. Horejsi (Institute of Molecular Genetics of the ASCR, Czech Republic) for the gift of CD63, CD81, and CD9 antibodies; Prof A. Paschen (University Hospital Essen, Germany) for melanoma cell lines; H. Peinado (Spanish National Centre for Oncological Research (CNIO) for the use of Nanosight; C. Moreno and S. Escudero, from the Flow Cytometry Service, ˜ o, from the Electron Microscopy Service, CNB-CSIC; and C. Patin CNB-CSIC. Conflict of Interest: R.J. is CEO of Immunostep, S.L.

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References 1. Yanez-Mo M, Siljander PR, Andreu Z, Zavec AB, Borras FE, Buzas EI, Buzas K, Casal E, Cappello F, Carvalho J, Colas E, Cordeiro-da Silva A, Fais S, Falcon-Perez JM, Ghobrial IM, Giebel B, Gimona M, Graner M, Gursel I, Gursel M, Heegaard NH, Hendrix A, Kierulf P, Kokubun K, Kosanovic M, KraljIglic V, Kramer-Albers EM, Laitinen S, Lasser C, Lener T, Ligeti E, Line A, Lipps G, Llorente A, Lotvall J, Mancek-Keber M, Marcilla A, Mittelbrunn M, Nazarenko I, Nolte-’t Hoen EN, Nyman TA, O’Driscoll L, Olivan M, Oliveira C, Pallinger E, Del Portillo HA, Reventos J, Rigau M, Rohde E, Sammar M, Sanchez-Madrid F, Santarem N, Schallmoser K, Ostenfeld MS, Stoorvogel W, Stukelj R, Van der Grein SG, Vasconcelos MH, Wauben MH, De Wever O (2015) Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 4:27066. https://doi.org/10.3402/jev.v4. 27066 2. van Niel G, D’Angelo G, Raposo G (2018) Shedding light on the cell biology of extracellular vesicles. Nat Rev Mol Cell Biol 19 (4):213–228. https://doi.org/10.1038/nrm. 2017.125 3. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, Ayre DC, Bach JM, Bachurski D, Baharvand H, Balaj L, Baldacchino S, Bauer NN, Baxter AA, Bebawy M, Beckham C, Bedina Zavec A, Benmoussa A, Berardi AC, Bergese P, Bielska E, Blenkiron C, BobisWozowicz S, Boilard E, Boireau W, Bongiovanni A, Borras FE, Bosch S, Boulanger CM, Breakefield X, Breglio AM, Brennan MA, Brigstock DR, Brisson A, Broekman ML, Bromberg JF, Bryl-Gorecka P, Buch S, Buck AH, Burger D, Busatto S, Buschmann D, Bussolati B, Buzas EI, Byrd JB, Camussi G, Carter DR, Caruso S, Chamley LW, Chang YT, Chen C, Chen S, Cheng L, Chin AR, Clayton A, Clerici SP, Cocks A, Cocucci E, Coffey RJ, Cordeiro-da-Silva A, Couch Y, Coumans FA, Coyle B, Crescitelli R, Criado MF, D’Souza-Schorey C, Das S, Datta Chaudhuri A, de Candia P, De Santana EF, De Wever O, Del Portillo HA, Demaret T, Deville S, Devitt A, Dhondt B, Di Vizio D, Dieterich LC, Dolo V, Dominguez Rubio AP, Dominici M, Dourado MR, Driedonks TA, Duarte FV, Duncan HM, Eichenberger RM, Ekstrom K, El Andaloussi S, Elie-Caille C, Erdbrugger U, Falcon-Perez JM, Fatima F, Fish JE, Flores-Bellver M, Forsonits A, Frelet-

Barrand A, Fricke F, Fuhrmann G, Gabrielsson S, Gamez-Valero A, Gardiner C, Gartner K, Gaudin R, Gho YS, Giebel B, Gilbert C, Gimona M, Giusti I, Goberdhan DC, Gorgens A, Gorski SM, Greening DW, Gross JC, Gualerzi A, Gupta GN, Gustafson D, Handberg A, Haraszti RA, Harrison P, Hegyesi H, Hendrix A, Hill AF, Hochberg FH, Hoffmann KF, Holder B, Holthofer H, Hosseinkhani B, Hu G, Huang Y, Huber V, Hunt S, Ibrahim AG, Ikezu T, Inal JM, Isin M, Ivanova A, Jackson HK, Jacobsen S, Jay SM, Jayachandran M, Jenster G, Jiang L, Johnson SM, Jones JC, Jong A, Jovanovic-Talisman T, Jung S, Kalluri R, Kano SI, Kaur S, Kawamura Y, Keller ET, Khamari D, Khomyakova E, Khvorova A, Kierulf P, Kim KP, Kislinger T, Klingeborn M, Klinke DJ 2nd, Kornek M, Kosanovic MM, Kovacs AF, Kramer-Albers EM, Krasemann S, Krause M, Kurochkin IV, Kusuma GD, Kuypers S, Laitinen S, Langevin SM, Languino LR, Lannigan J, Lasser C, Laurent LC, Lavieu G, Lazaro-Ibanez E, Le Lay S, Lee MS, Lee YXF, Lemos DS, Lenassi M, Leszczynska A, Li IT, Liao K, Libregts SF, Ligeti E, Lim R, Lim SK, Line A, Linnemannstons K, Llorente A, Lombard CA, Lorenowicz MJ, Lorincz AM, Lotvall J, Lovett J, Lowry MC, Loyer X, Lu Q, Lukomska B, Lunavat TR, Maas SL, Malhi H, Marcilla A, Mariani J, Mariscal J, MartensUzunova ES, Martin-Jaular L, Martinez MC, Martins VR, Mathieu M, Mathivanan S, Maugeri M, McGinnis LK, McVey MJ, Meckes DG Jr, Meehan KL, Mertens I, Minciacchi VR, Moller A, Moller Jorgensen M, MoralesKastresana A, Morhayim J, Mullier F, Muraca M, Musante L, Mussack V, Muth DC, Myburgh KH, Najrana T, Nawaz M, Nazarenko I, Nejsum P, Neri C, Neri T, Nieuwland R, Nimrichter L, Nolan JP, Nolte’t Hoen EN, Noren Hooten N, O’Driscoll L, O’Grady T, O’Loghlen A, Ochiya T, Olivier M, Ortiz A, Ortiz LA, Osteikoetxea X, Ostergaard O, Ostrowski M, Park J, Pegtel DM, Peinado H, Perut F, Pfaffl MW, Phinney DG, Pieters BC, Pink RC, Pisetsky DS, Pogge von Strandmann E, Polakovicova I, Poon IK, Powell BH, Prada I, Pulliam L, Quesenberry P, Radeghieri A, Raffai RL, Raimondo S, Rak J, Ramirez MI, Raposo G, Rayyan MS, RegevRudzki N, Ricklefs FL, Robbins PD, Roberts DD, Rodrigues SC, Rohde E, Rome S, Rouschop KM, Rughetti A, Russell AE, Saa P, Sahoo S, Salas-Huenuleo E, Sanchez C, Saugstad JA, Saul MJ, Schiffelers RM, Schneider R,

342

Carmen Campos-Silva et al.

Schoyen TH, Scott A, Shahaj E, Sharma S, Shatnyeva O, Shekari F, Shelke GV, Shetty AK, Shiba K, Siljander PR, Silva AM, Skowronek A, Snyder OL 2nd, Soares RP, Sodar BW, Soekmadji C, Sotillo J, Stahl PD, Stoorvogel W, Stott SL, Strasser EF, Swift S, Tahara H, Tewari M, Timms K, Tiwari S, Tixeira R, Tkach M, Toh WS, Tomasini R, Torrecilhas AC, Tosar JP, Toxavidis V, Urbanelli L, Vader P, van Balkom BW, van der Grein SG, Van Deun J, van Herwijnen MJ, Van Keuren-Jensen K, van Niel G, van Royen ME, van Wijnen AJ, Vasconcelos MH, Vechetti IJ Jr, Veit TD, Vella LJ, Velot E, Verweij FJ, Vestad B, Vinas JL, Visnovitz T, Vukman KV, Wahlgren J, Watson DC, Wauben MH, Weaver A, Webber JP, Weber V, Wehman AM, Weiss DJ, Welsh JA, Wendt S, Wheelock AM, Wiener Z, Witte L, Wolfram J, Xagorari A, Xander P, Xu J, Yan X, Yanez-Mo M, Yin H, Yuana Y, Zappulli V, Zarubova J, Zekas V, Zhang JY, Zhao Z, Zheng L, Zheutlin AR, Zickler AM, Zimmermann P, Zivkovic AM, Zocco D, Zuba-Surma EK (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7(1):1535750. https://doi. org/10.1080/20013078.2018.1535750 4. Hood JL, San RS, Wickline SA (2011) Exosomes released by melanoma cells prepare sentinel lymph nodes for tumor metastasis. Cancer Res 71(11):3792–3801. https://doi.org/10. 1158/0008-5472.CAN-10-4455 5. Peinado H, Aleckovic M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, GarciaSantos G, Ghajar C, Nitadori-Hoshino A, Hoffman C, Badal K, Garcia BA, Callahan MK, Yuan J, Martins VR, Skog J, Kaplan RN, Brady MS, Wolchok JD, Chapman PB, Kang Y, Bromberg J, Lyden D (2012) Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med 18(6):883–891. https://doi. org/10.1038/nm.2753, [pii] nm.2753 6. Ashiru O, Boutet P, Fernandez-Messina L, Aguera-Gonzalez S, Skepper JN, ValesGomez M, Reyburn HT (2010) Natural killer cell cytotoxicity is suppressed by exposure to the human NKG2D ligand MICA*008 that is shed by tumor cells in exosomes. Cancer Res 70(2):481–489. https://doi.org/10.1158/ 0008-5472.CAN-09-1688. [pii] 0008-5472. CAN-09-1688 7. Sharma P, Diergaarde B, Ferrone S, Kirkwood JM, Whiteside TL (2020) Melanoma cellderived exosomes in plasma of melanoma

patients suppress functions of immune effector cells. Sci Rep 10(1):92. https://doi.org/10. 1038/s41598-019-56542-4 8. Lopez-Cobo S, Campos-Silva C, Moyano A, Oliveira-Rodriguez M, Paschen A, YanezMo M, Blanco-Lopez MC, Vales-Gomez M (2018) Immunoassays for scarce tumourantigens in exosomes: detection of the human NKG2D-Ligand, MICA, in tetraspanincontaining nanovesicles from melanoma. J Nanobiotechnol 16(1):47. https://doi.org/ 10.1186/s12951-018-0372-z 9. Daassi D, Mahoney KM, Freeman GJ (2020) The importance of exosomal PDL1 in tumour immune evasion. Nat Rev Immunol 20 (4):209–215. https://doi.org/10.1038/ s41577-019-0264-y 10. Chen G, Huang AC, Zhang W, Zhang G, Wu M, Xu W, Yu Z, Yang J, Wang B, Sun H, Xia H, Man Q, Zhong W, Antelo LF, Wu B, Xiong X, Liu X, Guan L, Li T, Liu S, Yang R, Lu Y, Dong L, McGettigan S, Somasundaram R, Radhakrishnan R, Mills G, Lu Y, Kim J, Chen YH, Dong H, Zhao Y, Karakousis GC, Mitchell TC, Schuchter LM, Herlyn M, Wherry EJ, Xu X, Guo W (2018) Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 560(7718):382–386. https://doi.org/10.1038/s41586-018-03928 11. Poggio M, Hu T, Pai CC, Chu B, Belair CD, Chang A, Montabana E, Lang UE, Fu Q, Fong L, Blelloch R (2019) Suppression of exosomal PD-L1 induces systemic anti-tumor immunity and memory. Cell 177(2):414–427 e413. https://doi.org/10.1016/j.cell.2019. 02.016 12. Oliveira-Rodriguez M, Lopez-Cobo S, Reyburn HT, Costa-Garcia A, Lopez-Martin S, Yanez-Mo M, Cernuda-Morollon E, Paschen A, Vales-Gomez M, Blanco-Lopez MC (2016) Development of a rapid lateral flow immunoassay test for detection of exosomes previously enriched from cell culture medium and body fluids. J Extracell Vesicles 5:31803 13. Koliha N, Wiencek Y, Heider U, Jungst C, Kladt N, Krauthauser S, Johnston IC, Bosio A, Schauss A, Wild S (2016) A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. J Extracell Vesicles 5:29975. https://doi.org/10.3402/ jev.v5.29975 14. Suarez H, Gamez-Valero A, Reyes R, LopezMartin S, Rodriguez MJ, Carrascosa JL, Cabanas C, Borras FE, Yanez-Mo M (2017) A bead-assisted flow cytometry method for

Immunocapture and Flow Cytometry of Melanoma-Derived EVs the semi-quantitative analysis of extracellular vesicles. Sci Rep 7(1):11271. https://doi. org/10.1038/s41598-017-11249-2 15. Morales-Kastresana A, Jones JC (2017) Flow cytometric analysis of extracellular vesicles. In: Hill A (ed) Exosomes and microvesicles, Methods mol biol, vol 1545. Humana Press, New York 16. Wiklander OPB, Bostancioglu RB, Welsh JA, Zickler AM, Murke F, Corso G, Felldin U, Hagey DW, Evertsson B, Liang XM, Gustafsson MO, Mohammad DK, Wiek C, Hanenberg H, Bremer M, Gupta D, Bjornstedt M, Giebel B, Nordin JZ, Jones JC, El Andaloussi S, Gorgens A (2018) Systematic methodological evaluation of a multiplex beadbased flow cytometry assay for detection of extracellular vesicle surface signatures. Front Immunol 9:1326. https://doi.org/10.3389/ fimmu.2018.01326 17. van der Vlist EJ, Nolte-’t Hoen EN, Stoorvogel W, Arkesteijn GJ, Wauben MH (2012) Fluorescent labeling of nano-sized vesicles released by cells and subsequent quantitative and qualitative analysis by high-resolution flow cytometry. Nat Protoc 7(7):1311–1326. https://doi.org/10.1038/nprot.2012.065 18. Nolan JP, Duggan E (2018) Analysis of individual extracellular vesicles by flow cytometry. In: Hawley T, Hawley R (eds) Flow cytometry protocols, Methods mol biol, vol 1678. Humana Press, New York 19. Gorgens A, Bremer M, Ferrer-Tur R, Murke F, Tertel T, Horn PA, Thalmann S, Welsh JA, Probst C, Guerin C, Boulanger CM, Jones JC, Hanenberg H, Erdbrugger U, Lannigan J, Ricklefs FL, El-Andaloussi S, Giebel B (2019) Optimisation of imaging flow cytometry for the analysis of single extracellular vesicles by using fluorescence-tagged vesicles as biological reference material. J Extracell Vesicles 8(1):1587567. https://doi.org/10.1080/ 20013078.2019.1587567 20. Andreu Z, Yanez-Mo M (2014) Tetraspanins in extracellular vesicle formation and function. Front Immunol 5:442. https://doi.org/10. 3389/fimmu.2014.00442 21. Lopez-Cobo S, Pieper N, Campos-Silva C, Garcia-Cuesta EM, Reyburn HT, Paschen A, Vales-Gomez M (2018) Impaired NK cell recognition of vemurafenib-treated melanoma cells is overcome by simultaneous application of histone deacetylase inhibitors. Onco Targets Ther 7(2):e1392426. https://doi.org/10. 1080/2162402X.2017.1392426 22. Sharma P, Ludwig S, Muller L, Hong CS, Kirkwood JM, Ferrone S, Whiteside TL (2018) Immunoaffinity-based isolation of melanoma

343

cell-derived exosomes from plasma of patients with melanoma. J Extracell Vesicles 7 (1):1435138. https://doi.org/10.1080/ 20013078.2018.1435138 23. Andre F, Schartz NE, Movassagh M, Flament C, Pautier P, Morice P, Pomel C, Lhomme C, Escudier B, Le Chevalier T, Tursz T, Amigorena S, Raposo G, Angevin E, Zitvogel L (2002) Malignant effusions and immunogenic tumour-derived exosomes. Lancet 360(9329):295–305. https://doi.org/10. 1016/S0140-6736(02)09552-1 24. Campos-Silva C, Sua´rez H, Jara-Acevedo R, ˜ eiro L, Linares-Espino´s E, Martinez-Pin ˜ ez-Mo´ M, Vale´s-Go´mez M (2019) High Ya´n sensitivity detection of extracellular vesicles immune-captured from urine by conventional flow cytometry. Sci Rep 9(1):2042. https:// doi.org/10.1038/s41598-019-38516-8 25. Ugurel S, Thirumaran RK, Bloethner S, Gast A, Sucker A, Mueller-Berghaus J, Rittgen W, Hemminki K, Becker JC, Kumar R, Schadendorf D (2007) B-RAF and N-RAS mutations are preserved during short time in vitro propagation and differentially impact prognosis. PLoS One 2(2):e236. https://doi.org/10.1371/journal.pone. 0000236 26. Zhao F, Sucker A, Horn S, Heeke C, Bielefeld N, Schrors B, Bicker A, Lindemann M, Roesch A, Gaudernack G, Stiller M, Becker JC, Lennerz V, Wolfel T, Schadendorf D, Griewank K, Paschen A (2016) Melanoma lesions independently acquire T-cell resistance during metastatic latency. Cancer Res 76(15):4347–4358. https://doi.org/10.1158/0008-5472.CAN16-0008 27. Thery 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 28. Momen-Heravi F (2017) Isolation of extracellular vesicles by ultracentrifugation. In: Kuo WP, Jia S (eds) Extracellular vesicles, Methods mol biol, vol 1660. Humana Press, New York 29. Hill A (2017) Exosomes and microvesicles, Methods mol biol, vol 1545. Humana Press, New York 30. Welsh JA, Van Der Pol E, Arkesteijn GJA, Bremer M, Brisson A, Coumans F, DignatGeorge F, Duggan E, Ghiran I, Giebel B, Gorgens A, Hendrix A, Lacroix R, Lannigan J, Libregts S, Lozano-Andres E, Morales-Kastresana A, Robert S, De Rond L, Tertel T, Tigges J, De Wever O, Yan X,

344

Carmen Campos-Silva et al.

Nieuwland R, Wauben MHM, Nolan JP, Jones JC (2020) MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments. J Extracell Vesicles 9(1):1713526. https://doi.org/10.1080/ 20013078.2020.1713526 31. Witwer KW, Buzas EI, Bemis LT, Bora A, Lasser C, Lotvall J, Nolte-’t Hoen EN, Piper MG, Sivaraman S, Skog J, Thery C, Wauben MH, Hochberg F (2013) Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J Extracell Vesicles 2. https://doi.org/10.3402/jev.v2i0. 20360 32. Yuana Y, Boing AN, Grootemaat AE, van der Pol E, Hau CM, Cizmar P, Buhr E, Sturk A, Nieuwland R (2015) Handling and storage of human body fluids for analysis of extracellular vesicles. J Extracell Vesicles 4:29260. https:// doi.org/10.3402/jev.v4.29260 33. Vogel R, Coumans FA, Maltesen RG, Boing AN, Bonnington KE, Broekman ML, Broom MF, Buzas EI, Christiansen G, Hajji N, Kristensen SR, Kuehn MJ, Lund SM, Maas SL, Nieuwland R, Osteikoetxea X, Schnoor R, Scicluna BJ, Shambrook M, de Vrij J, Mann SI, Hill AF, Pedersen S (2016) A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing. J Extracell Vesicles 5:31242. https://doi.org/10.3402/jev.v5.31242 34. Rojalin T, Phong B, Koster HJ, Carney RP (2019) Nanoplasmonic approaches for sensitive detection and molecular characterization of extracellular vesicles. Front Chem 7:279. https://doi.org/10.3389/fchem.2019.00279 35. Gualerzi A, Kooijmans SAA, Niada S, Picciolini S, Brini AT, Camussi G, Bedoni M

(2019) Raman spectroscopy as a quick tool to assess purity of extracellular vesicle preparations and predict their functionality. J Extracell Vesicles 8(1):1568780. https://doi.org/10. 1080/20013078.2019.1568780 36. Visnovitz T, Osteikoetxea X, Sodar BW, Mihaly J, Lorincz P, Vukman KV, Toth EA, Koncz A, Szekacs I, Horvath R, Varga Z, Buzas EI (2019) An improved 96 well plate format lipid quantification assay for standardisation of experiments with extracellular vesicles. J Extracell Vesicles 8(1):1565263. https://doi.org/10.1080/20013078.2019. 1565263 37. Osteikoetxea X, Balogh A, Szabo-Taylor K, Nemeth A, Szabo TG, Paloczi K, Sodar B, Kittel A, Gyorgy B, Pallinger E, Matko J, Buzas EI (2015) Improved characterization of EV preparations based on protein to lipid ratio and lipid properties. PLoS One 10(3): e0121184. https://doi.org/10.1371/journal. pone.0121184 38. Webber J, Clayton A (2013) How pure are your vesicles? J Extracell Vesicles 2. https:// doi.org/10.3402/jev.v2i0.19861 39. Baumgarth N, Bigos M (2004) Optimization of emission optics for multicolor flow cytometry. In: Darzynkiewicz Z, Roederer M, Tank H (eds) Cytometry, New developments, Methods cell biol, vol 75, 4th edn. Academic, Redwood City, CA 40. Maecker HT, Frey T, Nomura LE, Trotter J (2004) Selecting fluorochrome conjugates for maximum sensitivity. Cytometry A 62 (2):169–173. https://doi.org/10.1002/cyto. a.20092

Chapter 25 Postlymphadenectomy Analysis of Exosomes from Lymphatic Exudate/Exudative Seroma of Melanoma Patients Susana Garcı´a-Silva, Pilar Xime´nez-Embu´n, Javier Mun˜oz, and He´ctor Peinado Abstract Circulating extracellular vesicles in biofluids have become an interesting approach to analyse disease biomarkers. There are multiple methods for isolation of extracellular vesicles, though differential ultracentrifugation is still considered as the gold-standard isolation technique for exosomes. Furthermore, exosomes purified by this method have been demonstrated to display functional activity in vitro and in vivo and exhibit great versatility for subsequent analysis including proteomics, electron microscopy, mass spectrometry, or nucleic acid analysis. Here, we describe the method for isolation of exosomes from lymphatic exudate (seroma) obtained postlymphadenectomy for liquid biopsy approaches. Key words Lymphatic exudate, Lymphadenectomy, Extracellular vesicles, Exosomes, Ultracentrifugation, Iodixanol gradient

1

Introduction Extracellular vesicles (EVs) are cell-secreted vesicles with variable sizes. A recent classification based on size divided them into small EVs (sEVs) and large EVs (lEVs) [1]. Exosomes, exomeres, and other vesicles with a size around 100 nm are considered sEVs. Microvesicles (200 nm to 1 μm), apoptotic bodies (1–5 μm), and oncosomes (1–10 μm) are considered lEVs [1, 2]. Essentially, all EVs are composed of a lipid bilayer containing cytosolic and membrane-anchored proteins and glycoproteins, lipids, nucleic acids, and metabolites [3]. Large EVs are generated by budding from the plasma membrane, and exosomes are generated through the endosomal pathway [4]. More specifically, exosomes arise from the formation of the multivesicular bodies (MVBs) that subsequently fuse with the plasma membrane releasing exosomes to the extracellular medium [4, 5].

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EVs are considered as a mechanism of cell–cell communication regulating paracrine and distal cell communication. According to this, EVs have been detected in most biological fluids [6]. Most of the knowledge about EVs has been focused on exosomes, and subsequently the methods and applications developed for liquid biopsy are mostly based on these vesicles. Exosomes have been involved in numerous physiological and pathological processes such as adaptive immune response [7], mother–fetus communication [8], neurodegenerative processes [9], and cancer progression [10], including the formation of the premetastatic niche (PMN) [11, 12] and metastatic organotropism [13]. Several methods have been described for the purification of EVs. Differential ultracentrifugation is a widely used technique for the isolation of exosomes from cell cultures and biological fluids such as serum, plasma, cerebrospinal fluid, or urine [14, 15]. This method is based on the different densities of bodies under a rotary force. Several steps with increasing centrifugation forces allow for the removal of cellular debris and large EVs such as microvesicles [14]. Ultracentrifugation at 100,000  g yields a pellet enriched in exosomes. Due to the presence of protein aggregates, apolipoproteins, and other contaminants in the exosomal fraction, an additional step of centrifugation using an iodixanol density-gradient medium could be performed for the recovery of highly purified exosomes [16, 17]. Although differential ultracentrifugation allows for a high recovery yield of vesicles, it is a time-consuming technique and a certain degree of damage can occur to the exosomes [14]. Alternative methods have been developed to purify exosomes [15]. Size-exclusion chromatography (SEC) employs columns containing heterogeneous pores [18, 19]. Another size-based isolation method is ultrafiltration that segregates vesicles using membranes with specific cut-off molecular weights [20]. These methods have the caveat of limitations in the in/out volumes and still contaminants with the same size of exosomes can be present. Precipitation with hydrophobic polymers such as Polyethylene glycol (PEG) precipitates exosomes [21]. Based on this principle, several commercial kits propose an easy purification of exosomes; however, the concomitant precipitation of contaminants can interfere with subsequent analyses. A high-purity isolation of exosomes can be performed using immunoaffinity techniques based on the use of exosome-specific antibodies attached to magnetic beads or other matrices [22]. Although this approach provides isolation of specific subpopulations of exosomes, it is not suitable for the purification of a large amount of EVs. Recently, new techniques based on microfluidic devices are being developed. These platforms apply strategies based on immunoaffinity-based capture, size-based sorting, or dynamic separation mostly applying electrical or acoustical forces to achieve particle separation [23].

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Isolation of exosomes allows for the subsequent analysis of their content that is defined by the cell of origin of the vesicle. Besides that, the stability of extracellular vesicles in body fluids facilitate their analysis in biobanked samples [17]. Thus, exosomes are powerful tools to provide information in a noninvasive way about diseases such as cancer, neurological syndromes, or cardiovascular diseases [24]. Thanks to their complex cargo, exosomes can be exploited for the detection of mutations, SNPs, and epigenetic changes [24–26] as well as RNA, miRNA [27], and other noncoding RNA profiles [28] apart from the analysis of protein, lipid, and metabolic biomarkers [29–31]. Due to the presence of nucleic acids in EVs, detection of mutations located in the coding region has been shown in exosomal RNA/DNA derived from cell cultures [32]. In liquid biopsies, the use of EV-derived DNA has been first focused on the analysis of EGFR in lung cancer and glioblastoma [25, 26] and KRAS and TP53 in pancreatic cancer [33, 34]. Concerning protein biomarkers, there are many studies proposing the detection of exosomeassociated proteins as cancer diagnostic or prognostic tools. For example, the levels of PD-L1 in plasma-derived exosomes correlated with the response to immunotherapy in melanoma patients [30]. The analysis of prostate-specific antigen (PSA) in plasmaderived exosomes from prostate cancer patients has been shown to be a more reliable marker than serum PSA [31, 35]. Tumor progression into a metastatic phase is the deadliest stage of cancer diseases with an estimation of 90% of cancer-related deaths caused by metastatic spread and growth. Although not life-threatening, lymph node (LN) metastases are associated with poor prognosis in cancer patients and the number of LNs with metastasis is a strong prognostic factor [36]. Exudative seroma (lymphatic exudate) is a biofluid obtained post-lymphadenectomy, a technique frequently performed in cancer patients for the removal of regional sentinel lymph nodes after tumor resection [37]. This seroma is a lymph-like fluid with some similarities to plasma and a wound exudate [38]. In the clinical setting, this fluid has been used to profile disease markers in melanoma showing better discrimination of patients with high risk of relapse than an equivalent analysis in blood [39–41]. Remarkably, circulation of exosomes through the lymph and the participation of exosomes in the formation of the PMN in the sentinel LNs have been described [42–45]. In agreement with these reports, seroma is a biofluid highly enriched in sEVs that contain proteins and microRNAs associated with melanoma progression [46, 47]. Furthermore, a combined approach analyzing seroma-derived EVs and circulating nucleic acids estimated that the genetic material obtained from seroma is around 650 times more abundant compared to plasma. We have recently

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shown that the analysis of BRAFV600E mutation in this liquid biopsy test showed a strong prognostic value in a small cohort of melanoma patients [47]. In summary, the purification of exosomes from seroma described here can be a potential highly sensitive approach for liquid biopsies aimed at the diagnosis of cancer patients or the detection of minimal residual disease. In this chapter we describe in details the procedure to analyze exosomes in the exudative seroma obtained postlymphadenectomy in melanoma patients. This approach could serve to profile biomarkers in the exudative seroma and other biofluids obtained in other pathologies.

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Materials 1. Surgical drain tubes for postoperative fluid collection. 2. Commercially available red blood cell lysis buffer (see Note 1). 3. Refrigerated ultracentrifuge and swinging-bucket or fixedangle rotor (see Note 2). 4. Ultracentrifuge-suitable tubes (see Note 3). 5. 15 mL collection Falcon tubes. 6. Phosphate saline buffer (PBS) without Mg+ and Ca2+ (1): 137 mM NaCl, 10 mM Na2HPO4, 2.7 mM KCl, 1.8 mM KH2PO4, pH 7.4. 7. OptiPrep™ (containing iodixanol) density gradient medium (60% w/v) (see Note 4). 8. Diluent mix: 0.25 M sucrose, 1 mM EDTA, 10 mM Tris-HCl, pH 7.5. 9. Iodixanol gradient solutions: Prepare 40%, 20%, 10%, and 5% w/v iodixanol solutions by diluting a stock solution of OptiPrep™ in the diluent mix. 10. QIAamp DNA Mini kit (see Note 5). 11. MilliQ water. 12. Bicinchoninic acid–based assay kit (see Note 6). 13. Standard spectrophotometer or plate reader. 14. Nanoparticle tracking analysis (NTA) system. 15. Glow-discharged carbon-coated 400 mesh copper electron microscopy (EM) grids. 16. Uranyl acetate (1%). 17. Transmission electron microscope. 18. Freshly prepared solubilization solution: 8 M urea in 100 mM Tris-HCl (pH 8.0).

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19. Filter units Microcon-30. 20. 2 M urea in 100 mM Tris-HCl (pH 8.0). 21. 1 M Tris(2-carboxyethyl)phosphine (TCEP). 22. 0.5 M 2-chloroacetamide (CAA). 23. Lys-C and trypsin proteases. 24. Mass spectrometer.

3

Methods Carry out all procedures at room temperature, unless otherwise specified. We encourage careful attention to the Notes in each step.

3.1 Purification of Exosomes from Human Exudative Seroma/ Lymphatic Exudate by Differential Ultracentrifugation

(A summary of the experimental workflow described here is shown in Fig. 1). 1. Collect seroma (3–10 mL) from postoperative surgical drains during 24–48 h after radical lymphadenectomy. 2. Centrifuge for 10 min at 500  g at RT. 3. Discard the pellet and incubate the supernatant with red blood cell lysis buffer for 10 min on ice. 4. Spin samples for 10 min at 1100  g at RT. 5. Transfer the supernatant fraction into a new collection tube and immediately freeze at 80  C for storage (see Note 7, Fig. 1 upper panel). 6. When ready for analysis (see Note 8), allow samples to thaw at 37  C for 3–5 min. 7. Spin samples for 10 min at 3000  g. 8. Place the supernatants into 13.5 mL or 4 mL polycarbonate thick wall tubes (see Note 9). 9. Place a mark on the upper part of the tube and align it to the exterior part of the rotor (see Note 10). 10. Centrifuge at 12,000  g for 20 min at 10  C (see Note 11). 11. Measure particles in the supernatant by any NTA technique (see Subheading 3.4). 12. Transfer the supernatants into new polycarbonate thick wall tubes. Place a mark on the upper part of the tube and align it to the exterior part of the rotor (see Note 12). Ultracentrifuge at 100,000  g for 70 min at 10  C. 13. Discard the supernatants by carefully swinging the tubes and keep the tube upside down for 1–2 min onto a paper towel to dry (see Note 13).

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Fig. 1 Workflow of the isolation of exosomes by differential ultracentrifugation and additional purification by density gradient-based ultracentrifugation. Exudative seroma is collected through surgical drains located at the site of the lymphadenectomy wound. After 24–48 h lymphatic drainage is removed for sample processing. Red blood cell lysis and cell removal through centrifugation prepare the sample for subsequent ultracentrifugation steps. These steps will remove large vesicles, protein aggregates, etc. Additional density gradientbased ultracentrifugation allows a further purification of vesicles

14. Wash the pellets by slowly adding 20 mL of 1 PBS on the tube side contrary to the pellet to avoid disturbing it. Introduce the tube into the rotor, carefully aligning the mark again with the exterior part of the rotor (see Note 14). 15. Centrifuge at 100,000  g for 70 min at 10  C. 16. Discard supernatants by carefully swinging the tube. Important: the pellet contains purified exosomes. Let the tube stand upside down for 2–3 min to remove the remaining liquid. 17. Resuspend the pellet of exosomes in 100 μL of 1 PBS (see Note 15). 18. Measure exosome protein concentration (see Subheading 3.3) and particle number (see Subheading 3.4).

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3.2 Preparing Highly Purified Exosomes by Density GradientBased Ultracentrifugation

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1. Generate a discontinuous iodixanol gradient by sequentially layering 2 mL of each of the 40%, 20%, 10%, and 5% (w/v) iodixanol solutions (see Note 16). 2. Place the exosome sample obtained in step 17 from Subheading 3.1 on top of the iodixanol gradient. 3. Centrifuge at 100,000  g for 16 h at 10  C. 4. Collect fractions of 1 mL starting from the top of the gradient and measure particles by NTA analysis (see Subheading 3.4). 5. Dilute exosome positive fractions (usually fractions 5, 6, and 7) in 20 mL of 1 PBS and centrifuge at 100,000  g for 70 min at 10  C (see Note 17). 6. Resuspend the pellets in 100 μL of 1 PBS (see Note 18). 7. Store (see Note 19) or proceed with analysis.

3.3 Exosome Protein Measurement

1. Determine exosome protein concentration with a BCA assay, using 5–10 μL of the exosome sample (see Note 20). 2. Measure absorbance in a standard spectrophotometer or plate reader at 562 nm (see Note 21).

3.4 Exosome Particles Measurement

1. Dilute 1 μL of sEV sample in 1 mL of 1 PBS.

3.5 Analysis of Purified Exosomes by Electron Microscopy

1. Apply purified exosomes onto freshly glow-discharged carboncoated 400 mesh copper electron microscopy (EM) grids at a concentration of 4  107 particles/mL and incubate for 5 min at RT.

2. Determine the number and average size of particles by NTA (see Note 22).

2. Place the grids consecutively on top of three distinct 50 μL drops of MilliQ water, rinse gently for 2 s, and lay on the top of two different 50 μL drops of 1% uranyl acetate. 3. Stain sample for 1 min. 4. Rinse the grids gently for 5 s and air dry. 5. Visualize grids with a transmission electron microscope (see Note 23). Images can be recorded at 21,900 nominal magnification with a 4k  4k TemCam-F416 CMOS camera (Fig. 2). 3.6 Proteomic Analysis

1. Solubilize samples using 8 M urea in 100 mM Tris-HCl (pH 8.0). Samples (7.5 μg of protein amount) are digested using filter units (see Note 24). 2. Reduce with 15 mM TCEP and alkylate with 30 mM CAA the proteins for 30 min in the dark. 3. Centrifuge the filter units at 12,000  g for 15 min and discard flow-through.

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Fig. 2 Electron microscopy images of exosomes purified from exudative seroma by differential ultracentrifugation (a) and by differential ultracentrifugation plus iodixanol gradient ultracentrifugation (b)

4. Remove the excess of reagents adding 2 M urea in 100 mM Tris-HCl. 5. Centrifuge the filter units at 12,000  g for 15 min and discard the flow-through. 6. Sequentially digest the samples with Lys-C (protein–enzyme ratio 1:50) overnight and with trypsin (protein–enzyme ratio 1:100) 6 h at 37  C. 7. Centrifuge the filter units at 12,000  g for 8 min and keep the flow-through. 8. Desalt the resulting peptides using stage-tips. 9. Perform mass spectrometry analysis (see Note 25). 10. Assess the purity of sEV preparations by performing GO-based analysis using the Cellular Component (CC) annotation (see Note 26). 11. Probe the identified proteins against sEV repositories such as ExoCarta (www.exocarta.org), and check for the presence of well-known sEV markers such as CD9, CD63, and CD81. 3.7

DNA Extraction

DNA extracted from the exosome pellet can be analyzed for quantity, quality, or further characterization (see Note 27). 1. Dilute sample up to 200 μL with 1 PBS. 2. Extract DNA using the QiaAMP DNA Mini kit. 3. Elute DNA from the column in 50 μL of MilliQ H2O. 4. Measure DNA concentration (see Note 28) and test DNA quality (see Note 29).

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3.8 Analysis of DNA Mutations (e.g., BRAF V600E/K)

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For analysis of mutations, a combination of filtration and PCR analysis could be performed [47]. 1. Use 0.5–1 mL of fluid from each seroma sample for coisolation of exosomal RNA and DNA along with any cfDNA (see Note 30). 2. Perform a reverse transcription reaction with the total nucleic acid purified sample. 3. Analyze by allele-specific qPCR both wild-type BRAF as well as V600E and V600K mutant BRAF [48].

4

Notes 1. For example, ACK lysing buffer can be purchased from many suppliers. 2. Some recommendations are: (a) Ultracentrifuge OPTIMA XPN 100-IVD (BeckmanCoulter). (b) Rotor 50.4 Ti (Beckman-Coulter), for 4 mL polycarbonate thick wall tubes. (c) Rotor 70.1 Ti (Beckman-Coulter), for 13.5 mL polycarbonate thick wall tubes. 3. Some recommendations are: (a) For volumes up to 3.5 mL of seroma samples: 4 mL polycarbonate thick wall tubes. (b) For volumes up to 10 mL of seroma samples: 13.5 mL polycarbonate thick wall tubes. 4. This material is required for an optional subsequent purification step. Purification applying a further density gradient-based procedure increases the purity of exosomes but the yield is substantially lower. 5. This kit yields more DNA than others tested. 6. We recommend the Pierce BCA Protein assay kit. 7. After collecting the seroma from the drainage, centrifugation and freezing of the seroma sample should be performed within 1 h to preserve the quality and avoid variability. Caution: Work with human samples carries a risk of infection if the material is not handled with care. Make sure you have the proper training and authorization to work with human samples, and wear required personal protection material to handle human samples. It is compulsory to have an approved ethical protocol before the use of any sample involving patient-derived material.

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8. We recommend to collect all samples and to keep them frozen until further analysis. The exosome isolation procedure should be performed ideally by batches to reduce experimental variability. 9. Do not include more than 10 mL of sample in 13.5 mL tubes and more than 3.5 mL of sample in 4 mL tubes to avoid spilling during the centrifugation. It is also important to fill at least three-quarters of the tube with the sample. If necessary, complete the volume with 1 PBS. 10. This mark will help to identify the pellet of microvesicles (usually with a yellowish color). Try not to disturb the pellet when collecting the supernatant. 11. Depending on the rotor the equivalence between rpm and  g can be calculated. Some specific calculations have been published [14]. 12. If the amount of exosomes is low, the pellet may not be visible. The mark and orientation in the rotor help to identify the position of the pellet in the tube. For swinging-bucket rotors, the pellet is at the bottom of the tube. For fixed-angle rotors, the pellet is on the side of the tube facing up near the bottom of the tube. 13. Do not put the tube back in vertical position before it dries for 1 min because the remaining fluid could disturb the pellet. Important: the pellet contains exosomes. It has a yellowish color. 14. It is very important to keep orienting the tube within the rotor since this additional washing step yields a more transparent pellet difficult to see if not trained. 15. For resuspension, pipet up and down for at least 20 times with the pipette ejecting the liquid directly to the pellet. Try to avoid foam formation. Sometimes the pellet displays a mucinous consistence or is hard to resuspend. In these cases, increase the PBS volume 2–5 times. Samples can be stored at 4  C for 48 h. For longer times, samples can be stored at 80  C for at least 1 year. 16. On the bottom of the tube add the 40% iodixanol solution and continue layering the more diluted solutions on top of it by carefully placing the pipette where the liquid surface and the wall of the tube meet and slowly adding the solution. This will help not to disturb the solution gradient. 17. This step will help to remove the iodixanol and sucrose and concentrate the exosome content. Remember again to mark the tube for identifying the position of the pellet since it could be totally transparent after this step.

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18. This sample can also be characterized for protein and particle number and visualized by EM. 19. Samples can be stored at 4  C for 48 h. For longer times, samples can be stored at 80  C for at least 1 year. 20. Do not forget to include a standard BSA curve. 21. The expected total amount of protein is highly variable depending on the source of sEVs. For plasma and seroma, an expected yield is 50–150 μg. 22. Size of particles should be between 90 and 150 nm. You may compare particles obtained in step 17 of Subheading 3.1 with particles obtained in step 11 of the same section to calculate the isolation yield. 23. A Tecnai 12 transmission electron microscope with a lanthanum hexaboride cathode operated at 120 keV can be used here. 24. We recommend collaboration with a Mass Spectrometry facility for this step and the following ones. Our analysis is normally accomplished in collaboration with the CNIO Proteomics Unit. See additional technical details if needed in: https:// epic-xs.eu/download/purification-of-small-extracellularvesicles-by-ultracentrifugation-and-their-mass-spectrometricanalysis/ 25. Exosome samples have medium/low complexity in terms of number of proteins. Hence, it is advisable a 60–90 min effective gradient when injecting 1–1.5 μg of total protein in a Q Exactive Mass Spectrometer (or similar) instrument. The number of peptides and proteins identified is highly variable and mostly depends of the biological source of the exosomes. For exosomes purified from seroma, 300–400 proteins are often identified using this procedure. 26. An over-representation of terms related to “extracellular vesicles” is expected. 27. Studies have demonstrated the presence of genomic DNA (gDNA) and nuclear proteins within exosomes and sEVs [32, 49, 50]. The mechanisms by which nuclear components are present in exosomes remain poorly understood and have been related to mechanisms such as maintenance of cell homeostasis or micronuclei secretion, for example [51, 52]. Still today there is controversy about the origin and source of DNA in exosomes. It has been proposed that extracellular DNA is secreted through an autophagy- and multivesicular-endosome-dependent but exosomeindependent mechanism [53]. Regardless of the origin and the molecular mechanism involved, tumor-derived DNA is

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found in the exosomal fraction and can be used as a biomarker to detect mutations and other modifications found in tumors by liquid biopsy approaches in real time with good sensibility [25, 26, 33, 34, 47]. 28. DNA concentration can be measured with a fluorometer such as Qubit® 2.0. 29. DNA quality can be checked on a Bioanalyzer or a 1% agarose gel. High quality genomic DNA shows a major band of 10–20 Kb on the gel. Circulating free DNA is characterized by bands at 150, 300, and 450 bp, corresponding to DNA fragmented into nucleosome units. 30. We have collaborated with Exosome Diagnostics Inc. for these assays, using the ExoLution™ Plus extraction technology followed by nucleic acid purification.

Acknowledgments H.P. RETOS SAF2017-82924-R (AEI/10.13039/501100011 033/FEDER-UE), Fundacio´n AECC: LABAE19027PEIN, Fundacio´n Ramo´n Areces and MICINN-Translational NeTwork for the CLinical application of Extracellular VesicleS, TeNTaCLES. RED2018-102411-T. J.M. EPIC-XS, project number 823839, funded by the Horizon 2020 programme of the European Union. References 1. Witwer KW, The´ry C (2019) Extracellular vesicles or exosomes? On primacy, precision, and popularity influencing a choice of nomenclature. J Extracell Vesicles 8(1):1648167 2. Jaiswal R, Sedger LM (2019) Intercellular vesicular transfer by exosomes, microparticles and oncosomes—implications for cancer biology and treatments. Front Oncol 9:125 3. Raposo G, Stoorvogel W (2013) Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol 200(4):373–383 4. Colombo M, Raposo G, Thery C (2014) Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol 30:255–289 5. Tkach M, The´ry C (2016) Communication by extracellular vesicles: where we are and where we need to go. Cell 164(6):1226–1232 6. Wiklander OPB, Brennan M, Lo¨tvall J, Breakefield XO, El Andaloussi S (2019) Advances in therapeutic applications of extracellular vesicles. Sci Transl Med 11(492):eaav8521

7. Gutie´rrez-Va´zquez C, Villarroya-Beltri C, Mittelbrunn M, Sa´nchez-Madrid F (2013) Transfer of extracellular vesicles during immune cell-cell interactions. Immunol Rev 251(1):125–142 8. Zhang J, Li H, Fan B, Xu W, Zhang X (2020) Extracellular vesicles in normal pregnancy and pregnancy-related diseases. J Cell Mol Med 24 (8):4377–4388 9. Mathews PM, Levy E (2019) Exosome production is key to neuronal endosomal pathway integrity in neurodegenerative diseases. Front Neurosci 13:1347 10. Becker A, Thakur BK, Weiss JM, Kim HS, Peinado H, Lyden D (2016) Extracellular vesicles in cancer: cell-to-cell mediators of metastasis. Cancer Cell 30(6):836–848 11. Costa-Silva B, Aiello NM, Ocean AJ, Singh S, Zhang H, Thakur BK, Becker A, Hoshino A, Mark MT, Molina H, Xiang J, Zhang T, Theilen TM, Garcia-Santos G, Williams C, Ararso Y, Huang Y, Rodrigues G, Shen TL,

Analysis of Exosomes in Exudative Seroma Labori KJ, Lothe IM, Kure EH, Hernandez J, Doussot A, Ebbesen SH, Grandgenett PM, Hollingsworth MA, Jain M, Mallya K, Batra SK, Jarnagin WR, Schwartz RE, Matei I, Peinado H, Stanger BZ, Bromberg J, Lyden D (2015) Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver. Nat Cell Biol 17(6):816–826 12. Peinado H, Aleckovic M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, GarciaSantos G, Ghajar C, Nitadori-Hoshino A, Hoffman C, Badal K, Garcia BA, Callahan MK, Yuan J, Martins VR, Skog J, Kaplan RN, Brady MS, Wolchok JD, Chapman PB, Kang Y, Bromberg J, Lyden D (2012) Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med 18(6):883–891 13. Hoshino A, Costa-Silva B, Shen TL, Rodrigues G, Hashimoto A, Tesic Mark M, Molina H, Kohsaka S, Di Giannatale A, Ceder S, Singh S, Williams C, Soplop N, Uryu K, Pharmer L, King T, Bojmar L, Davies AE, Ararso Y, Zhang T, Zhang H, Hernandez J, Weiss JM, Dumont-Cole VD, Kramer K, Wexler LH, Narendran A, Schwartz GK, Healey JH, Sandstrom P, Jorgen Labori K, Kure EH, Grandgenett PM, Hollingsworth MA, de Sousa M, Kaur S, Jain M, Mallya K, Batra SK, Jarnagin WR, Brady MS, Fodstad O, Muller V, Pantel K, Minn AJ, Bissell MJ, Garcia BA, Kang Y, Rajasekhar VK, Ghajar CM, Matei I, Peinado H, Bromberg J, Lyden D (2015) Tumour exosome integrins determine organotropic metastasis. Nature 527 (7578):329–335 14. 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 30 (1):3.22.21–23.22.29 15. Yang X-X, Sun C, Wang L, Guo X-L (2019) New insight into isolation, identification techniques and medical applications of exosomes. J Control Release 308:119–129 16. Cantin R, Diou J, Be´langer D, Tremblay AM, Gilbert C (2008) Discrimination between exosomes and HIV-1: purification of both vesicles from cell-free supernatants. J Immunol Methods 338(1–2):21–30 17. Kalra H, Adda CG, Liem M, Ang CS, Mechler A, Simpson RJ, Hulett MD, Mathivanan S (2013) Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics 13 (22):3354–3364

357

18. Bo¨ing AN, van der Pol E, Grootemaat AE, Coumans FA, Sturk A, Nieuwland R (2014) Single-step isolation of extracellular vesicles by size-exclusion chromatography. J Extracell Vesicles 3:23430 19. Nordin JZ, Lee Y, Vader P, M€ager I, Johansson HJ, Heusermann W, Wiklander OP, H€allbrink M, Seow Y, Bultema JJ, Gilthorpe J, Davies T, Fairchild PJ, Gabrielsson S, Meisner-Kober NC, Lehtio¨ J, Smith CI, Wood MJ, El Andaloussi S (2015) Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties. Nanomedicine 11 (4):879–883 20. Cheruvanky A, Zhou H, Pisitkun T, Kopp JB, Knepper MA, Yuen PS, Star RA (2007) Rapid isolation of urinary exosomal biomarkers using a nanomembrane ultrafiltration concentrator. Am J Physiol Renal Physiol 292(5): F1657–F1661 21. Rider MA, Hurwitz SN, Meckes DG Jr (2016) ExtraPEG: a polyethylene glycol-based method for enrichment of extracellular vesicles. Sci Rep 6:23978 22. Greening DW, Xu R, Ji H, Tauro BJ, Simpson RJ (2015) A protocol for exosome isolation and characterization: evaluation of ultracentrifugation, density-gradient separation, and immunoaffinity capture methods. In: Posch A (ed) Proteomic profiling, Methods in molecular biology, vol 1295. Springer, Clifton, NJ 23. Li G, Tang W, Yang F (2020) Cancer liquid biopsy using integrated microfluidic exosome analysis platforms. Biotechnol J 15:e1900225 24. Urbanelli L, Buratta S, Sagini K, Ferrara G, Lanni M, Emiliani C (2015) Exosome-based strategies for diagnosis and therapy. Recent Pat CNS Drug Discov 10(1):10–27 25. Castellanos-Rizaldos E, Grimm DG, Tadigotla V, Hurley J, Healy J, Neal PL, Sher M, Venkatesan R, Karlovich C, Raponi M, Krug AK, Noerholm M, Tannous J, Tannous BA, Raez LE, Skog J (2018) Exosome-based detection of EGFR T790M in plasma from non-small cell lung cancer patients. Clin Cancer Res 24 (12):2944–2950 26. Figueroa JM, Skog J, Akers J, Li H, Komotar R, Jensen R, Ringel F, Yang I, Kalkanis S, Thompson R, LoGuidice L, Berghoff E, Parsa A, Liau L, Curry W, Cahill D, Bettegowda C, Lang FF, Chiocca EA, Henson J, Kim R, Breakefield X, Chen C, Messer K, Hochberg F, Carter BS (2017) Detection of wild-type EGFR amplification and EGFRvIII mutation in CSF-derived

358

Susana Garcı´a-Silva et al.

extracellular vesicles of glioblastoma patients. Neuro-Oncology 19(11):1494–1502 27. Wang M, Yu F, Ding H, Wang Y, Li P, Wang K (2019) Emerging function and clinical values of exosomal MicroRNAs in cancer. Mol Ther Nucleic Acids 16:791–804 28. Jiang N, Pan J, Fang S, Zhou C, Han Y, Chen J, Meng X, Jin X, Gong Z (2019) Liquid biopsy: circulating exosomal long noncoding RNAs in cancer. Clin Chim Acta 495:331–337 29. Kalishwaralal K, Kwon WY, Park KS (2019) Exosomes for non-invasive cancer monitoring. Biotechnol J 14(1):e1800430 30. Chen G, Huang AC, Zhang W, Zhang G, Wu M, Xu W, Yu Z, Yang J, Wang B, Sun H, Xia H, Man Q, Zhong W, Antelo LF, Wu B, Xiong X, Liu X, Guan L, Li T, Liu S, Yang R, Lu Y, Dong L, McGettigan S, Somasundaram R, Radhakrishnan R, Mills G, Lu Y, Kim J, Chen YH, Dong H, Zhao Y, Karakousis GC, Mitchell TC, Schuchter LM, Herlyn M, Wherry EJ, Xu X, Guo W (2018) Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 560(7718):382–386 31. Pang B, Zhu Y, Ni J, Thompson J, Malouf D, Bucci J, Graham P, Li Y (2020) Extracellular vesicles: the next generation of biomarkers for liquid biopsy-based prostate cancer diagnosis. Theranostics 10(5):2309–2326 32. Thakur BK, Zhang H, Becker A, Matei I, Huang Y, Costa-Silva B, Zheng Y, Hoshino A, Brazier H, Xiang J, Williams C, Rodriguez-Barrueco R, Silva JM, Zhang W, Hearn S, Elemento O, Paknejad N, ManovaTodorova K, Welte K, Bromberg J, Peinado H, Lyden D (2014) Double-stranded DNA in exosomes: a novel biomarker in cancer detection. Cell Res 24(6):766–769 33. Yang S, Che SP, Kurywchak P, Tavormina JL, Gansmo LB, Correa de Sampaio P, Tachezy M, Bockhorn M, Gebauer F, Haltom AR, Melo SA, LeBleu VS, Kalluri R (2017) Detection of mutant KRAS and TP53 DNA in circulating exosomes from healthy individuals and patients with pancreatic cancer. Cancer Biol Ther 18 (3):158–165 34. Allenson K, Castillo J, San Lucas FA, Scelo G, Kim DU, Bernard V, Davis G, Kumar T, Katz M, Overman MJ, Foretova L, Fabianova E, Holcatova I, Janout V, MericBernstam F, Gascoyne P, Wistuba I, Varadhachary G, Brennan P, Hanash S, Li D, Maitra A, Alvarez H (2017) High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann Oncol 28(4):741–747

35. Logozzi M, Angelini DF, Iessi E, Mizzoni D, Di Raimo R, Federici C, Lugini L, Borsellino G, Gentilucci A, Pierella F, Marzio V, Sciarra A, Battistini L, Fais S (2017) Increased PSA expression on prostate cancer exosomes in in vitro condition and in cancer patients. Cancer Lett 403:318–329 36. Van Trappen PO, Pepper MS (2002) Lymphatic dissemination of tumour cells and the formation of micrometastases. Lancet Oncol 3 (1):44–52 37. Leiter U, Stadler R, Mauch C, Hohenberger W, Brockmeyer N, Berking C, Sunderko¨tter C, Kaatz M, Schulte KW, Lehmann P, Vogt T, Ulrich J, Herbst R, Gehring W, Simon JC, Keim U, Martus P, Garbe C (2016) Complete lymph node dissection versus no dissection in patients with sentinel lymph node biopsy positive melanoma (DeCOG-SLT): a multicentre, randomised, phase 3 trial. Lancet Oncol 17(6):757–767 38. McCaul JA, Aslaam A, Spooner RJ, Louden I, Cavanagh T, Purushotham AD (2000) Aetiology of seroma formation in patients undergoing surgery for breast cancer. Breast 9 (3):144–148 39. Rutkowski P, Nowecki ZI, Kulik J, Ruka W, Siedlecki JA (2008) Molecular staging by multimarker reverse transcriptase-polymerase chain reaction assay of lymphatic drainage and blood from melanoma patients after lymph node dissection. Melanoma Res 18(4):246–252 40. Nowecki ZI, Rutkowski P, Kulik J, Siedlecki JA, Ruka W (2008) Molecular and biochemical testing in stage III melanoma: multimarker reverse transcriptase-polymerase chain reaction assay of lymph fluid after lymph node dissection and preoperative serum lactate dehydrogenase level. Br J Dermatol 159(3):597–605 41. Wlodzimierz R, Rutkowski P, Nowecki ZI, Kulik J, Nasierowska-Guttmejer A, Siedlecki JA (2004) Detection of melanoma cells in the lymphatic drainage after lymph node dissection in melanoma patients by using two-marker reverse transcriptase-polymerase chain reaction assay. Ann Surg Oncol 11(11):988–997 42. Hood JL (2017) The association of exosomes with lymph nodes. Semin Cell Dev Biol 67:29–38 43. Pucci F, Garris C, Lai CP, Newton A, Pfirschke C, Engblom C, Alvarez D, Sprachman M, Evavold C, Magnuson A, von Andrian UH, Glatz K, Breakefield XO, Mempel TR, Weissleder R, Pittet MJ (2016) SCS macrophages suppress melanoma by restricting tumor-derived vesicle-B cell interactions. Science 352(6282):242–246

Analysis of Exosomes in Exudative Seroma 44. Srinivasan S, Vannberg FO, Dixon JB (2016) Lymphatic transport of exosomes as a rapid route of information dissemination to the lymph node. Sci Rep 6:24436 45. Sun B, Zhou Y, Fang Y, Li Z, Gu X, Xiang J (2019) Colorectal cancer exosomes induce lymphatic network remodeling in lymph nodes. Int J Cancer 145(6):1648–1659 46. Broggi MAS, Maillat L, Clement CC, Bordry N, Corthesy P, Auger A, Matter M, Hamelin R, Potin L, Demurtas D, Romano E, Harari A, Speiser DE, Santambrogio L, Swartz MA (2019) Tumorassociated factors are enriched in lymphatic exudate compared to plasma in metastatic melanoma patients. J Exp Med 216(5):1091–1107 47. Garcia-Silva S, Benito-Martin A, SanchezRedondo S, Hernandez-Barranco A, Ximenez-Embun P, Nogues L, Mazariegos MS, Brinkmann K, Amor Lopez A, Meyer L, Rodriguez C, Garcia-Martin C, Boskovic J, Leton R, Montero C, Robledo M, Santambrogio L, Sue Brady M, SzumeraCieckiewicz A, Kalinowska I, Skog J, Noerholm M, Munoz J, Ortiz-Romero PL, Ruano Y, Rodriguez-Peralto JL, Rutkowski P, Peinado H (2019) Use of extracellular vesicles from lymphatic drainage as surrogate markers of melanoma progression and BRAF (V600E) mutation. J Exp Med 216(5):1061–1070 48. Brinkman K, Meyer L, Bickel A, Enderle D, Berking C, Skog J, Noerholm M (2020) Extracellular vesicles from plasma have higher tumour RNA fraction than platelets. J Extracell Vesicles 9(1):1741176

359

49. Kahlert C, Melo SA, Protopopov A, Tang J, Seth S, Koch M, Zhang J, Weitz J, Chin L, Futreal A, Kalluri R (2014) Identification of double-stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J Biol Chem 289 (7):3869–3875 50. Lazaro-Ibanez E, Lasser C, Shelke GV, Crescitelli R, Jang SC, Cvjetkovic A, GarciaRodriguez A, Lotvall J (2019) DNA analysis of low- and high-density fractions defines heterogeneous subpopulations of small extracellular vesicles based on their DNA cargo and topology. J Extracell Vesicles 8(1):1656993 51. Yokoi A, Villar-Prados A, Oliphint PA, Zhang J, Song X, De Hoff P, Morey R, Liu J, Roszik J, Clise-Dwyer K, Burks JK, O’Halloran TJ, Laurent LC, Sood AK (2019) Mechanisms of nuclear content loading to exosomes. Sci Adv 5(11):eaax8849 52. Takahashi A, Okada R, Nagao K, Kawamata Y, Hanyu A, Yoshimoto S, Takasugi M, Watanabe S, Kanemaki MT, Obuse C, Hara E (2017) Exosomes maintain cellular homeostasis by excreting harmful DNA from cells. Nat Commun 8:15287 53. Jeppesen DK, Fenix AM, Franklin JL, Higginbotham JN, Zhang Q, Zimmerman LJ, Liebler DC, Ping J, Liu Q, Evans R, Fissell WH, Patton JG, Rome LH, Burnette DT, Coffey RJ (2019) Reassessment of exosome composition. Cell 177(2):428–445

Part V Assessing Melanoma Metastasis in Pre-Clinical and Clinical Settings

Chapter 26 Fate Mapping of Cancer Cells in Metastatic Lymph Nodes Using Photoconvertible Proteins Ethel R. Pereira, Dmitriy Kedrin, and Timothy P. Padera Abstract The lymph node microenvironment is extremely dynamic and responds to immune stimuli in the host by reprogramming immune, stromal, and endothelial cells. In normal physiological conditions, the lymph node will initiate an appropriate immune response to clear external threats that the host may experience. However, in metastatic disease, cancer cells often colonize local lymph nodes, disrupt immune function, and even leave the lymph node to create additional metastases. Understanding how cancer cells enter, colonize, survive, proliferate, and interact with other cell types in the lymph node is challenging. Here, we describe the use of photoconvertible fluorescent proteins to label and trace the fate of cancer cells once they enter the lymph node. Key words Photoconvertible proteins, Lymph node, Metastasis, Intravital imaging, Circulating tumor cells, Photodiode, Dendra2, Confocal microscopy

1

Introduction Metastatic cancer cells exit the primary tumor via blood and lymphatic vessels. Oftentimes the first site of colonization is the regional lymph node [1, 2]. Once tumor cells enter the lymph node through an afferent lymphatic vessel, they begin to proliferate in the subcapsular sinus and gradually invade into the parenchyma as the metastatic colony gets larger [3]. Not all cancer cells that enter the lymph node survive the new microenvironment, particularly as the lymph node is a major site for the initiation of immune responses [4]. The difficulty in studying the biology of spontaneous lymph node metastasis has left many questions unanswered. What makes some cancer cells thrive in this microenvironment versus others that do not? How do cancer cells interact with other cell types in the lymph node, including immune, stromal, and endothelial cells? What makes some cancer cells exit the node to colonize distant organs [4, 5]?

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_26, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Given the dynamic response of the lymph node to changes in the host during cancer progression, it is extremely challenging to address these unanswered questions, each of which have clinical implications for eradicating cancer from lymph nodes, generating anti-tumor immune responses, and inhibiting cancer progression. Several studies have begun to elucidate the role of signaling pathways, including chemokine-chemokine receptor signaling, that regulate cancer cell entry into the lymph node and further migration in the node [5–7]. Further studies are warranted to understand the molecular mechanisms that regulate cancer cell survival, proliferation, and migration in the lymph node. The dynamic and highly orchestrated process of cancer cell metastasis is challenging to visualize in real-time. However, recent advances in imaging technologies have made it possible to study this process in a live animal [8, 9]. Once cancer cells in the primary site invade vessels and colonize a secondary site, it can be difficult to follow the fate of metastatic cancer cells longitudinally. To overcome this challenge, light-responsive proteins can be used as a molecular tag to identify cancer cells that arrive in a secondary site [10, 11]. There are three kinds of light-responsive fluorescent proteins that can be used [12, 13]. First, photoactivatable fluorescent proteins can be switched “on” or turned from a nonfluorescent state to emit fluorescence at a specific wavelength by irradiation with light in the blue/violet spectrum. Examples of these fluorescent proteins are PA-GFP, PAmCherry, and PAmKate. Second, photoconvertible fluorescent proteins such as Dendra2, Kaede, and EosFP can change their fluorescence emission maximum from one wavelength to another (switch colors) after conversion with light at a specific wavelength. Finally, photoswitchable fluorescent proteins can be switched “on” and “off” by specific light pulses at different wavelengths. Examples of this type of light-responsive fluorescent protein include Dronpa and Kindling FP. Additional details for each category of fluorescent proteins are listed in Table 1. Several advances have been made in intravital microscopy that enable real-time visualization of cell-cell interaction in a live animal. In the case of cancer, understanding how tumor cells interact with each other or with other cell types in a specific organ is critical to advancing our knowledge and exploiting dependent pathways for cancer therapy. In this chapter, we describe a method for tracing cancer cell fate and progression to distant organs following their arrival in a lymph node (Fig. 1). Cancer cells engineered to express a photoconvertible protein that enables short-term fate mapping are used in this method. We generated stable cancer cell lines expressing Dendra2 [14], a photoconvertible protein that natively fluoresces green but, upon light activation, converts to red fluorescence. This technology allows specifically labeled cells that entered the lymph node to be

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Table 1 Details for light-responsive fluorescent proteins Protein

Exmax (nm)

Emmax (nm)

Structure

Activation Wavelength

Photoactivatable proteins Photoactivatable proteins often display reduced brightness compared to traditional fluorophores and have reduced photostability. PA-GFP

400 504

515 517

Monomer

Violet

PAmCherry

404 564

595

Monomer

350–400 nm

PAmKate

586

628

Monomer

405 nm

Photoconvertible proteins These proteins emit fluorescence in the nonconverted state, making it easier to define regions of interest. Tetrameric proteins may not be optimal when expressed in cells. Dendra2

490 553

507 573

Monomer

405 nm or 488 nm

Kaede

508 572

518 580

Tetramer

Violet

EosFP

506 571

516 581

Tetramer

390 nm

Photoswitchable proteins Versatile when switching between the “on” and “off” states without photobleaching. Dronpa

503

518

Monomer

Violet/Blue

Kindling FP

580

600

Tetramer

Green/450 nm

monitored for their migratory behavior and interaction with other cell types in the lymph node. In addition, this method provides the ability to track the movement of metastatic cancer cells from the lymph node to distant organs (Fig. 1).

2

Materials

2.1 Animal Models and Cell Lines

1. Mice: C57Bl/6, Balb/c, and C3H strains (see Note 1). 2. Cell lines: B16F10 melanoma cells, 4T1 mammary carcinoma cells, and SCCVII squamous cell carcinoma cells (originally established in the Edwin Steele Laboratories, Massachusetts General Hospital [MGH], Boston). Store cell line stocks in aliquots of 1  106 cells/cryogenic vial in liquid nitrogen. 3. Complete cell culture medium: Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS).

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Fig. 1 Fate-mapping cancer cells in the lymph node. To trace cancer cells from the lymph node to distant organs, we engineer murine cancer cell lines to express the photoconvertible protein Dendra2. In the absence of light conversion, the primary tumor fluoresces green. Spontaneous lymphatic metastasis from the primary tumor results in cancer cell colonization in the lymph node in the mouse models we tested, which include B16F10 (melanoma), 4T1 (breast cancer), and SCCVII (squamous cell carcinoma). Green fluorescently labeled cancer cells in the lymph node are photoconverted to red fluorescence using a 405 nm photodiode. The red fluorescently labeled cancer cells in the lymph node are followed over time to trace their path inside the lymph node and beyond to distant organs such as the lung and liver. This is a powerful technology that can be utilized to trace the movement of cancer cells from a specific location through the complex metastatic cascade to their eventual colonization of secondary sites 2.2 General Purpose and Animal Surgery Materials

1. Gauze sponge. 2. Scalpel. 3. Chronic lymph node window (CLNW) for longitudinal intravital imaging [15], consisting of custom-made titanium frame, custom-made titanium ring, and tension C-ring insert. 4. 5-0 polypropylene sutures. 5. Sterile cotton swabs. 6. 11.7 mm round coverslips. 7. 1 ml insulin syringes. 8. Microdissection scissors. 9. Surgical scissors. 10. 2 Dumont forceps. 11. Heating pad. 12. Needle holder. 13. Surgical tray. 14. Nitrile gloves.

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15. Cautery. 16. Surgical bright-field microscope. 17. Shaving device. 18. Laminar flow hood. 19. Surgical tape. 20. Sterile saline for injection. 21. Sterile water. 22. Scale. 23. Ketamine (controlled substance). 24. Xylazine. 25. Buprenorphine hydrochloride. 26. Acetaminophen. 27. Ophthalmic ointment. 28. Hair removal cream. 29. Glue. 30. Tetramethylrhodamine-dextran (two million MW) for labeling blood vessels. 31. Flow cytometry buffer: 5% fetal bovine serum (FBS) in phosphate buffered saline (PBS). 2.3 Equipment and Materials for Photoconversion and Intravital Imaging

1. Photodiode emitting 405 nm light custom-made to focus light on the lymph node of interest, with a rheostat to control light intensity and time. 2. Aluminum foil. 3. Tape. 4. Plasmid containing Dendra2 for cytoplasmic overexpression in cancer cell lines (see Note 2). 5. Plasmid containing Dendra2-H2B for nuclear overexpression in cancer cell lines (see Note 2). 6. Custom-built multiphoton laser-scanning microscope (adapted from the Olympus 300) [16] and a broadband femtosecond laser source (i.e., high performance Mai Tai®).

2.4 Tissue Staining and Clearing

1. Anti-CD31 antibody (Clone 390, BioLegend): secondary antibody, fluorescent tagged anti-rat. 2. Anti-cytokeratin antibody (Clone C-11, Sigma): FITC conjugated antibody, no secondary antibody required. 3. Anti-podoplanin antibody (Clone 8.1.1): secondary antibody, fluorescent tagged anti-Syrian hamster.

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4. Anti-Lyve-1 antibody (polyclonal; Catalog #NB600-1008, Novus Biologicals): secondary antibody, florescent tagged anti-rabbit. 5. 4% formaldehyde. 6. Phosphate buffered saline (PBS): NaCl (0.137 M), KCl (0.0027 M), Na2HPO4 (0.01 M), KH2PO4 (0.0018 M). 7. Red blood cell (RBC) Lysing Buffer: Proprietary Buffer (ThermoFisher Scientific). 8. 45 μm nylon mesh filter. 9. Permeabilization buffer: 0.5% Triton X-100 in PBS. 10. PBS glycine buffer (10): Add NaCl (3.8 g), Na2HPO4 (9.38 g), NaH2PO4 (2.07 g), and glycine (37.5 g) to 500 ml of distilled water. Adjust pH to 7.4. When making working solution, dilute to 1 using distilled water. 11. Immunofluorescence (IF) wash buffer (10): Add NaCl (38 g), Na2HPO4 (9.38 g), NaH2PO4 (2.07 g), NaN3 (2.5 g), bovine serum albumin (BSA) (5 g), Triton X-100 (10 ml), and Tween 20 (2.05 ml) to 500 ml of distilled water. Adjust pH to 7.4. When making working solution, dilute to 1 using distilled water. 12. Blocking buffer: Prepare 1 IF wash buffer supplemented with 10% normal donkey serum. 13. Tetrahydrofuran (THF). 14. Dichloromethane (DCM). 15. 1:2 solution of benzyl alcohol and benzyl benzoate.

3

Methods

3.1 In Vitro Measurement of Photoconversion Efficiency

The use of photoconvertible proteins to trace cancer cells in vivo is a powerful method to address unanswered questions in the complex metastatic process. We have validated the method described here in experiments with syngeneic mouse models that promote spontaneous lymphatic metastasis from the primary tumor to the draining lymph node. We describe the steps involved in the major procedures for this method, including in vitro measurement of photoconversion efficiency, tumor inoculation/growth and spontaneous lymph node metastasis, CLNW implantation, photoconversion of cancer cells in the lymph node (see Note 3), intravital microscopy, and tissue staining and clearing. 1. Engineer cancer cell lines to stably express Dendra2 (cytosolic) or Dendra2H2B (nuclear) protein (see Notes 1 and 2). Prior to light activation, these cell lines fluoresce by emitting green light (507 nm).

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2. Test the photoconversion efficiency of the Dendra2-expressing cell lines in vitro prior to inoculation in the mouse. (a) Grow Dendra2-expressing cell lines in a monolayer in a 6-well tissue culture-treated plate. (b) Confirm by confocal microscopy that the majority of cells express green fluorescently labeled Dendra2 (cytosolic) or Dendra2-H2B (nuclear) prior to photoconversion. (c) Perform a focused light activation in a predetermined region of interest using either the 405 nm or 488 nm laser, and measure the photoconversion efficiency in vitro (see Note 4). 3.2 Tumor Inoculation, Growth, and Spontaneous Lymph Node Formation

1. Once the efficiency and duration of the photoconversion are established in vitro, inoculate preswitched tumor cells into mice. 2. Perform orthotopic implantation of tumor cells by injecting a cell pellet (1  105 cells in 30 μl of DMEM medium) into syngeneic mouse strains (see Note 5). 3. Measure the primary tumor three times per week and once it reaches a size of ~250 mm3 (approximately Day 7–10 postinoculation of B16-F10 melanoma cells), resect the tumor mass using aseptic techniques, taking care to minimize damage to the surrounding tissue (see Note 6). Keep the wound site sterile and close with sutures. 4. Allow time for lymph node metastases to become established. B16-F10 cells should colonize and become established in the draining lymph node as a result of spontaneous lymphatic metastasis from the primary tumor ~4–5 days after primary tumor resection. Lymph node metastasis is typically observed in ~70% of B16F10 implanted mice.

3.3 Chronic Lymph Node Window Implantation

1. Prepare the mouse with lymph node metastasis for surgery by placing it on a scale to weigh, and administer ketamine-xylazine mixture at 100 mg/10 mg per kg of body weight). 2. Place the mouse on a heating pad for the remainder of the surgery to maintain the body temperature at 37  C. 3. Identify the location of the tumor draining lymph node by holding skin over the inguinal lymph node away from the body and using a bright light (see Note 7). 4. Place two screws through the most lateral holes in the CLNW, and secure them by tightening a nut to the frame. Position the lymph node in the center of the titanium window and pierce a hole in the skin using a scalpel. Place the screw through the skin-hole and repeat for the second screw.

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5. Place the second CLNW on top of the first with the skin in between and secure the CLNW with two additional nuts. 6. Place the mouse under a bright-field surgical microscope, and carefully remove the top ventral skin (~5 mm diameter) with microdissection scissors. The lymph node will be visible once the upper dermis is open and the surrounding fat tissue is cleared by blunt dissection. 7. Apply sterile saline to the exposed lymph node area to prevent dehydration. Carefully place a coverslip that fits in the groove of the CLNW and secure with a tension C-ring (see Note 8). 8. Subcutaneously administer buprenorphine hydrochloride at 10 ml/kg of body weight once post-operatively right before the animal wakes up. Monitor the mice daily post-surgery and administer buprenorphine if the animals show signs of pain or distress. 3.4 Photoconversion of Cancer Cells in the Lymph Node

1. Prior to photoconversion, anesthetize the mouse using ~3–4% isoflurane for induction. Then decrease the level to ~1.5% isoflurane after confirming the mouse is relaxed and has a steady respiration rate. Maintain a constant supply of isoflurane by using a nose cone fitted snugly around the nose of the mouse (see Note 9). 2. Place the anesthetized mouse on its dorsal side with the inguinal lymph node and the CLNW facing upward. Secure the animal by taping its legs to a platform on the lab bench. 3. Once the mouse is correctly positioned, assemble the 405 nm photodiode by connecting it to the external power supply (see Note 10). 4. Optimize the timing and intensity of light activation by performing the steps below on multiple mice with different settings (see Note 11): (a) Image each experimental mouse on a multiphoton microscope to confirm the presence of cancer cells and the absence of any spontaneous photoconversion. To detect lymph node metastasis, the laser should be set to 840 nm with 30 mW power on the sample. The 405 nm filter is used to detect the lymph node capsule by second harmonic generation (SHG). Nonconverted green cancer cells can be detected with a 488 nm filter, and photoconverted red cancer cells can be detected using a 540 nm filter. (b) Verify that all cancer cells fluoresce green prior to light activation. To determine the conditions that yield the most efficient photoconversion in a specific system (see Note 12), a recommendation of timing and 405 nm photodiode power is provided in Table 2.

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Table 2 Recommended timing and 405 nm photodiode power to test when optimizing conditions that yield the most efficient photoconversion in a specific system Experimental mouse

Exposure time (in seconds)

Power intensity (mW)

1

30

11

2

60

11

3

90

11

4

180

11

5

180

7

6

180

9

7

180

11

8

180

13

5. Post-photoconversion, place each mouse back on the multiphoton microscope and obtain images using 840 nm laser light at 30 mW on-sample power to confirm photoconversion efficiency. (a) Images should be obtained using a 488 nm filter (to monitor green fluorescence in cancer cells), a 540 nm filter (to monitor red fluorescence in cancer cells postphotoconversion), and a 405 nm filter to visualize the collagen fibers in the capsule of the lymph node. (b) The distinct pattern of the collagen fibers in the capsule enable this structural feature of the node to be identified and serve as a landmark when repeated imaging is performed on the same node. 6. Quantify the fraction of photoconverted cells (see Note 13) by calculating the number of red cancer cells (photoconverted cells)/number of green or red cancer cells (total number of metastatic cancer cells). 7. Obtain repeated multiphoton images 4–5 days post light activation to confirm the presence of photoconverted cells in the lymph node longitudinally. 8. To determine whether cancer cells from the lymph node escape, collect blood and distant organs (such as lungs and liver) to monitor the presence/absence of green and red circulating tumor cells (CTCs). 9. To analyze if CTCs from the blood have transited the lymph node, collect whole blood by cardiac puncture from mice that had their lymph nodes photoconverted. As control, collect whole blood from mice that did not have light activation of the lymph node.

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(a) Transfer whole blood (~1 ml) immediately to a roundbottom polystyrene tube and add 4 ml of 1 RBC lysing buffer. Mix the contents in the tube well while making sure not to vortex or pipette vigorously as that may damage any CTCs in the sample. (b) Incubate the tube contents for 4 min at room temperature. (c) After lysis, centrifuge the tube at 500  g for 5 min, and decant the supernatant. (d) Repeat the RBC lysing procedure until the supernatant is clear and lysing is complete. (e) Spin down at 500  g for 5 min, and resuspend the cell pellet in PBS. (f) After counting the cells in the pellet, resuspend at a concentration of 1  106 cells/ml in flow cytometry buffer. The cells should be filtered through a 45 μm nylon mesh filter and stored on ice prior to flow cytometry. (g) Analyze the resuspended cells by loading and processing the samples on a flow cytometer (see Note 14). 10. To analyze distant organs such as the liver and lung for the presence or absence of photoconverted cancer cells, rinse the organs in PBS following harvest and store in a petri dish on ice until further processing. (a) Gross examination of metastatic lesions can be assessed using a fluorescence dissection microscope, or higher resolution images can be obtained by confocal microscopy. (b) Record images of metastatic lesions, and quantify the presence or absence of photoconverted (red) cancer cells (see Note 15). (c) Detailed analysis of distant organs can be further performed by clearing the tissues as detailed in Subheading 3.5, followed by imaging the cleared tissue to detect the presence of micrometastatic lesions. 3.5 Tissue Staining and Clearing

Once the experiment is complete and the animals are sacrificed, the lymph nodes and other organs should be harvested and further processed for staining and clearing. Although the lymph node microenvironment is constantly changing throughout the experiment, staining the tissue at the end-point to observe blood vessels, lymphatic vessels, and fibroblastic reticular cells (FRCs) will give a clear snapshot of the interaction of these cell types with the cancer cells. Based on the mouse model used, distant organs can be harvested and metastatic colonies analyzed for the presence or absence of photoconverted cancer cells.

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1. Fix the lymph nodes (or any other organ that requires clearing, staining and imaging to assess distant metastases) with 4% formaldehyde for 1 h at room temperature. 2. Rinse with PBS and permeabilize with 0.5% Triton X-100 in PBS at 4  C overnight. 3. Rinse three times with 1 PBS–Glycine wash buffer for 20 min each at room temperature. 4. Incubate samples with Blocking buffer for 1 h at room temperature. 5. Incubate samples with primary antibody at 4  C for 48–72 h. For the metastatic lymph node, stain cancer cells (anticytokeratin), blood vessels (anti-CD31), FRCs (antipodoplanin), and lymphatic vessels (anti-Lyve-1) to clarify the location of cancer cells as well as any cellular interaction cancer cells may have with other structures in the lymph node. 6. Wash 3 with 1 IF wash buffer for 3 min/wash. 7. Incubate samples with appropriate secondary antibody at 4  C overnight. 8. Rinse 3 with PBS for 3 min/rinse. 9. For tissue clearance, incubate tissues in the following solutions in glass containers: 50% THF for 30 min, 80% THF for 30 min, 100% THF for 30 min, 100% THF for 30 min, 100% THF for 30 min, 100% DCM for 30 min. 10. Store tissue in 1:2 solution of benzyl alcohol and benzyl benzoate until the time of imaging.

4

Notes 1. For relevant studies of lymph node metastasis, it is critical to use immune-competent mice with a complete immune system. We use 8–10-week-old or older mice that weigh at least 25 g. For our tumor models, we have used syngeneic mouse strains paired with murine cancer cell lines, including C57Bl/6 mice for B16F10 (melanoma), Balb/c mice for 4T1 (breast cancer) and C3H mice for SCCVII (squamous cell carcinoma). All mice were bred and housed in our facility at MGH. 2. The choice of subcellular localization of Dendra2 fluorescent protein will depend on the goal of the experiment. If Dendra2 will be used to detect the presence or absence of photoconverted cells in a specific organ, then engineering the cancer cells to express Dendra2 localized to the nucleus (Dendra2H2B) is preferred. Nuclear localization of Dendra2 intensifies the

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fluorescence in a confined space. If Dendra2 will be used to visualize cells by intravital microscopy, then using the cytosolic version of the protein is preferred. 3. The photoconvertible protein-Dendra2 fluoresces green prior to light activation and converts to red fluorescence post light activation. Although the conversion of the protein is permanent, the gene is not altered so the cell and its daughter cells will continue to make Dendra2 that fluoresces green. As photoconverted cancer cells divide, the red fluorescence will dilute out with each cell division. In addition, photoconverted protein will degrade with time. This is a major limitation of this technology and hence cannot be used for long-term cell fate mapping. 4. In vitro, cancer cells should efficiently switch from green to red fluorescence in 30 s to 1 min using 30% laser power on a confocal microscope. The duration and laser power for the photoconversion can be optimized for each specific cell line and system. 5. B16F10 is implanted intradermally in the flank of C57BL/6 mice, 4T1 is implanted in the fourth mammary fat pad of Balb/ c mice, and SCCVII is implanted subcutaneously in the flank of C3H mice. As an example for highlighting the implementation of this protocol, we will describe its use in experiments with the B16F10 melanoma cell line. Each cancer line will have its own growth kinetics and rate of spontaneous metastasis. 6. There is a constant inflow of cancer cells into the lymph node from the primary tumor as a result of spontaneous lymphatic metastasis. Hence, we first resect the primary tumor prior to photoconversion of metastatic cancer cells in the lymph node. This allows us to restrict any potential new cancer cells from arriving in the node after light activation. 7. The tumor draining lymph node is usually inflamed and hence should be easy to identify by palpation. 8. The photoconversion of the lymph node can be done in the presence or absence of the CLNW. It is advised to perform this procedure initially with a window so the photoconversion efficiency of cancer cells in the lymph node can be assessed in realtime. Once the exposure time and light intensity have been established, the procedure can be performed in the absence of the window by focusing the diode directly on the lymph node through the skin. Care must be taken to remove any hair that might obscure light penetration to the lymph node. 9. It is advised to perform the photoconversion of cancer cells 2–3 days post CLNW implantation to allow the animal to recover from surgery, minimize inflammation, and enable any ruptures in capillary blood vessels to heal.

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10. Make sure to wear UV protective glasses while handling the photodiode. Always take care to position the photodiode away from personnel. Additionally, the process of light activation in the lymph node should be carefully carried out to avoid light penetration to any other part of the animal’s body. We cover the mouse with aluminum foil and expose only the lymph node area to the photodiode. The photodiode can be engineered to focus the light in a limited area and restrict photoconversion of any circulating cancer cells outside the node. 11. The settings that work best in our laboratory for photoconverting cancer cells in the lymph node with a 405 nm photodiode are 11 mW for 180 s for 5 consecutive days. 12. Photoconversion of Dendra2-expressing cancer cells in vitro is achievable in 30 s to 1 min using either a photodiode or focused confocal laser at 405 nm or 488 nm. However, the same parameters cannot be used for in vivo photoconversion. The time and laser intensity needs to be optimized for photoconversion in vivo. The photoconversion settings depend on several factors including depth of cancer cells from the surface, obstruction of light due to surrounding tissue and fat, and the number of metastatic cancer cells in the lymph node. 13. The photoconversion efficiency of cancer cells in the lymph node is important to quantify by counting the number of green and red cancer cells post-photoconversion. It is important to note that photoconverted cells will fluoresce green and red, though the intensity of the green fluorescence is reduced compared to that in prephotoconverted cells. Information on photoconversion efficiency will enable one to correctly interpret data obtained from CTCs and distant organs. 14. The Amnis ImageStream imaging flow cytometer has a highspeed camera that captures an image of every event that passes through the flow cytometer. This technology enables identification of CTCs based on size exclusion using flow cytometry as well as identification of green and red cancer cells (CTCs) from the images obtained. 15. Due to the limited time that photoconverted cancer cells retain red fluorescence, it is not always possible to determine if a large green metastatic lesion identified in the lung or liver initially fluoresced red. Once cancer cells are photoconverted and fluoresce red, they do not pass on the red fluorescence to the daughter cells. As a photoconverted cancer cell keeps dividing, the red fluorescence dilutes out. To overcome this caveat in the system, a permanent photoconversion technology must be engineered for long-term fate-mapping.

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References 1. Cady B (2007) Regional lymph node metastases, a singular manifestation of the process of clinical metastases in cancer: contemporary animal research and clinical reports suggest unifying concepts. Cancer Treat Res 135:185–201 2. Ferris RL, Lotze MT, Leong SP et al (2012) Lymphatics, lymph nodes and the immune system: barriers and gateways for cancer spread. Clin Exp Metastasis 29(7):729–736 3. Jeong HS, Jones D, Liao S et al (2015) Investigation of the lack of angiogenesis in the formation of lymph node metastases. J Natl Cancer Inst 107(9):djv155 4. Pereira ER, Jones D, Jung K et al (2015) The lymph node microenvironment and its role in the progression of metastatic cancer. Semin Cell Dev Biol 38:98–105 5. Kawada K, Taketo MM (2011) Significance and mechanism of lymph node metastasis in cancer progression. Cancer Res 71 (4):1214–1218 6. Podgrabinska S, Skobe M (2014) Role of lymphatic vasculature in regional and distant metastases. Microvasc Res 95:46–52 7. Das S, Sarrou E, Podgrabinska S et al (2013) Tumor cell entry into the lymph node is controlled by CCL1 chemokine expressed by lymph node lymphatic sinuses. J Exp Med 210(8):1509–1528 8. Pereira ER, Kedrin D, Seano G et al (2018) Lymph node metastases can invade local blood vessels, exit the node, and colonize distant organs in mice. Science 359 (6382):1403–1407 9. Brown M, Assen FP, Leithner A et al (2018) Lymph node blood vessels provide exit routes

for metastatic tumor cell dissemination in mice. Science 359(6382):1408–1411 10. Kedrin D, Gligorijevic B, Wyckoff J et al (2008) Intravital imaging of metastatic behavior through a mammary imaging window. Nat Methods 5(12):1019–1021 11. Hulit J, Kedrin D, Gligorijevic B et al (2012) The use of fluorescent proteins for intravital imaging of cancer cell invasion. In: Hoffman R (ed) In vivo cellular imaging using fluorescent proteins. Methods mol biol. Humana Press, Totowa, NJ 12. McKinney SA, Murphy CS, Hazelwood KL et al (2009) A bright and photostable photoconvertible fluorescent protein. Nat Methods 6 (2):131–133 13. Chudakov DM, Matz MV, Lukyanov S et al (2010) Fluorescent proteins and their applications in imaging living cells and tissues. Physiol Rev 90(3):1103–1163 14. Gurskaya NG, Verkhusha VV, Shcheglov AS et al (2006) Engineering of a monomeric green-to-red photoactivatable fluorescent protein induced by blue light. Nat Biotechnol 24 (4):461–465 15. Meijer EFJ, Jeong HS, Pereira ER et al (2017) Murine chronic lymph node window for longitudinal intravital lymph node imaging. Nat Protoc 12(8):1513–1520 16. Brown EB, Campbell RB, Tsuzuki Y et al (2001) In vivo measurement of gene expression, angiogenesis and physiological function in tumors using multiphoton laser scanning microscopy. Nat Med 7(7):864–868

Chapter 27 Detection of Melanoma Cells in Lymphatic Drainage (LD) After Lymph Nodes Dissection Via Nested RT-PCR Analysis of Molecular Melanocytic Markers Aleksandra Gos, Piotr Rutkowski, and Janusz A. Siedlecki Abstract We present the assay based on multimarker analysis of mRNA transcripts associated with melanocytic cells detected in lymphatic fluid collected after lymph node dissection. Positive results of reverse transcriptase polymerase chain reaction (RT-PCR) test have a strong relationship with melanoma recurrence and diseasespecific survival time in stage III melanoma. Key words RT-PCR, Lymphatic drainage, Melanoma, Lymph node metastases

1

Introduction The presented assay involves multimarker analysis of mRNA transcripts associated with melanocytic cells in lymphatic fluid collected routinely after lymph node dissection. Positive results of reverse transcriptase polymerase chain reaction (RT-PCR) test have a strong relationship with melanoma recurrence and disease specific survival time in melanoma with regional lymph node metastases, detected with sentinel node biopsy or clinically. The test aims to detect occult melanoma cells and subclinical residual disease. It might serve as early warning assay of relapse, and for identifying patients who could benefit from more aggressive adjuvant treatment before the onset of clinically detectable metastatic disease, as well as allow for enhanced accuracy of melanoma staging. Lymph drainage analysis provides unique possibility of testing large quantity of body fluid and increases chances of detection of single metastatic cells, without causing additional discomfort for the patient. It was proven by us to be more informative than peripheral blood based test [1]. Analysis is performed via nested RT-PCR, that was proven to be sensitive enough to detect single melanoma cell in the background of normal cells. Lymphatic

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_27, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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drainage was collected after lymph node dissection performed therapeutically in stage III melanoma patients (both after positive sentinel lymph node and in clinically/palpable detected metastases in regional lymph nodes). Markers chosen to be analyzed were tyrosinase, MART-1, and uMAGE. Tyrosinase is the key enzyme involved in melanin synthesis that is actively expressed in melanocytes. However, its value as melanoma cells marker is decreased as it might be expressed by other cells such as Schwann cells which limits its tissue specificity. Additionally, tyrosinase expression is sometimes lost in advanced cases [2]. MART-1 (melanoma antigen recognized by T cells) is frequently expressed by over 85% by melanoma cells and is not expressed in nonmelanoma malignancies or healthy individuals. It was reported to have similar sensitivity of specificity like tyrosinase, but it is associated with a different subgroup of patients (with locoregional rather than distant metastases) [3]. u-MAGE (Melanoma antigen E) has high specificity and expression in various malignancies [4].

2

Materials 1. RBC lysis solution (1
): Prepare a 10
stock solution containing 0.15 M NH4Cl, 10 mM NaHCO3, and 0.1 mM EDTA in distilled water. Filter through 0.45 μm filter, and store at room temperature. Dilute stock solution 1:10 in distilled water prior to use. 2. Denaturing solution (solution D): 4 M guanidinium thiocyanate, 25 mM sodium citrate, pH 7.0, 0.5% (wt/vol) N-lauroylsarcosine (Sarkosyl), 0.1 M 2-mercaptoethanol. Prepare a stock solution by dissolving 250 g guanidinium thiocyanate in 293 ml water at 65  C. Add 17.6 ml of 0.75 M sodium citrate, pH 7.0, and 26.4 ml of 10% Sarkosyl. The stock solution can be stored up to 3 months at room temperature. To prepare the working solution D, add 0.36 ml of 98% 2-mercaptoethanol to 50 ml of stock solution. Working solution D can be stored up to 1 month at room temperature. 3. 2 M sodium acetate, pH 4.0: Add 16.42 g of sodium acetate (anhydrous) to 40 ml of water and 35 ml of glacial acetic acid. Adjust to a pH of 4.0 with glacial acetic acid and bring to a final volume of 100 ml with DEPC-treated water. 4. Water-saturated phenol. 5. Chloroform–isoamyl alcohol (49:1 mix). 6. Isopropanol. 7. 75% ethanol.

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Table 1 Primers specific for GAPDH1, MART-1, and Tyrosinase mRNA used in our studies GAPDH 1

50 GGTCGGAGTCAACGGATTTG

GAPDH 2

50 ATGAGCCCCAGCCTTCTCCAT

HTYR1

50 TTGGCAGATTGTCTGTAGCC

HTYR2

50 AGGCATTGTGCTTGCTGCTT

HTYR3

50 GTCTTTATGCAATGGAACGC

HTYR4

50 GCTATCCCAGTAAGTGGACT

MART1.1

50 TGACCCTACAAGATGCCAAG

MART1.2

50 TCAGCATGTCTCAGGTGTCT

MART1.3

50 TCATCTATGGTTACCCCAAC

MART1.4

50 TCATAAGCAGGTGGAGCAT

uMAGE-A1

50 CTCGGTGAGGAGGCAAGGT

uMAGE-A2

50 GGCCGAAGGAACCTGACCC

uMAGE-A3

50 CTGGAGGCTCCCTGAGGACT

8. DNase I, RNase-free (1 U/μl). 9. DEPC-treated water. 10. SuperScript™ II Reverse Transcriptase. 11. AmpliTaq Gold™ DNA Polymerase with Buffer II and MgCl2. 12. Formaldehyde Agarose Gel for RNA quality analysis. 13. Sodium Borate (SB) Buffer (20
): Prepare solution by mixing 8 g of NaOH with 45 g of boric acid and bringing to 1 l with MilliQ water. Dilute to 1
with MilliQ water prior to use. 14. 2% agarose gel in 1
SB buffer for assay analysis. 15. pUC19 plasmid digested with DdeI for weight marker. 16. Primer sequences as shown in Table 1.

3

Methods

3.1 Lymph Fluid Collection

Lymph fluid routinely collected after lymph node dissection is to be analyzed. Drainage fluid can be collected after radical lymphadenectomy from routinely used sucking drainage. Only process fraction of the fluid collected between 24 and 72 h (see Note 1). The first phase of protocol should be performed as soon as possible after collection (see Note 2). 1. Collect 50–100 ml of lymph fluid (see Note 3). 2. Distribute lymph fluid to 50 ml centrifuge tubes.

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3. Centrifuge lymph fluid at 500
g, 10 min. 4. Incubate pellet with RBC lysis solution for 10 min on ice. Centrifuge again (500
g, 10 min). 5. The collected pellet can be stored at 70  C or directly used for RNA isolation (see Note 4). 3.2

RNA Isolation

RNA could be prepared from fresh or frozen pellets according to standard protocol of Chomczynski method [5]. RNA should be further purified from residual DNA (see Note 5). 1. Resuspend pellet in 1 ml of solution D. 2. Add 0.1 ml of 2 M sodium acetate, pH 4.0, mix thoroughly by inversion. 3. Add 1 ml of water-saturated phenol, mix thoroughly by inversion. 4. Add 0.2 ml of chloroform–isoamyl alcohol (49:1), shake vigorously by hand for 10 s. 5. Cool the samples on ice for 15 min. 6. Centrifuge samples for 20 min at 10,000
g at 4  C. Transfer the upper aqueous phase, which contains mostly RNA, to a clean tube. 7. Add 1 ml of isopropanol to precipitate the RNA. Incubate the samples for at least 1 h at 20  C. 8. Centrifuge for 20 min at 10,000
g at 4  C. Discard the supernatant. The RNA precipitate should form a gel-like pellet. 9. Dissolve the RNA pellet in 0.3 ml of solution D. Transfer to a 1.5-ml microcentrifuge tube. 10. Add 0.3 ml of isopropanol. Incubate the samples for at least 30 min at 20  C. 11. Centrifuge for 10 min at 10,000
g at 4  C and discard the supernatant. 12. Resuspend the RNA pellet with 0.5–1 ml of 75% ethanol and vortex for a few seconds. 13. Incubate samples for 10–15 min at room temperature to dissolve possible residual traces of guanidinium. 14. Centrifuge for 5 min at 10,000
g at 4  C and discard the supernatant. 15. Air-dry the RNA pellet for 5–10 min at room temperature. 16. Dissolve the RNA pellet in 100–200 μl of DEPC-treated water. 17. Incubate RNA 10–15 min at 60  C to ensure complete solubilization. Store RNA at 80  C or proceed directly to the next steps.

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18. Purify RNA form residual DNA: Prepare reaction mixture containing 1 μg of RNA, 0.25 μl of DNase I, 1 μl of reaction buffer, and DEPC-treated water up to 10 μl. 19. Incubate at room temperature for 15 min, then heat-inactivate DNase at 65  C for 10 min. Purify solution via phenol extraction. 20. Measure RNA concentration spectrophotometrically. Verify integrity and quality of RNA by denaturing agarose gel electrophoresis. Only samples with appropriate 28S and 18S bands should be included in subsequent analysis. 21. Check RNA for DNA contamination in PCR assay with human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) primers (see Note 5). 3.3

RT-PCR

This assay is based on detection of cDNA of transcripts of tyrosinase, MART-1, and uMAGE via nested PCR reaction. The drain fluid was assumed to be positive if at least one marker was expressed (see Notes 6 and 7). The method outlined below has been validated in four clinical settings (see Notes 8–11). 1. Use 2 μg of RNA (DNA free) for reverse transcription. Perform reverse transcription using SuperScript II™ Reverse Transcriptase according to the manufacturer’s manual. 2. Purify cDNA by phenol/chloroform extraction and precipitation in 2.5
volume of 96% ethanol. Dissolved in 15 μl of DEPC-treated water. Verify quality of cDNA by control PCR with GAPDH primers (Table 1). cDNA can be stored at 70  C or directly used for PCR assay (see Note 4). 3. Using AmpliTaq Gold™ DNA Polymerase with Buffer II and MgCl2, perform first PCR reaction in mixture containing: 1
PCR buffer, 1.5 nM MgCl2, 50 ng of primers, 5 μl of purified cDNA solution, and 2.5 U of Taq polymerase in a total volume of 25 μl. Use amplification condition as described in Table 2. Primers for first PCR are included in Table 1: HTYR1 and HTYR2 for tyrosinase, MART1.1 and MART1.2 for MART1, and uMAGE-A1 and uMAGE-A3 for uMAGE. 4. Perform reamplification with nested primers. Use same conditions as above with 0.5 μl of first PCR as template (with exception of second round of MART-1 amplification performed in final concentration of 2 nM MgCl2 and with annealing temperature reduced to 55  C). Primers for second PCR are included in Table 1: HTYR3 and HTYR4 for tyrosinase, MART1.3 and MART1.4 for MART-1, and uMAGE-A2 and uMAGE-A3 for u-MAGE. 5. Analyze products on 2% agarose gel with pUC19 digested with DdeI as molecular weight marker.

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Table 2 Condition for amplification Temp [ C] 94

4

Time 2

0

Cycles 1

00

94 60 72

45 4500 4500

30

72

100

1

4

1

Notes 1. To ensure that the sample of the lymph fluid undergoing analysis does not contain any surgical debris or contamination, the first 24 h batch of fluid should be discarded. 2. General rules of working with RNA should be followed to ensure successful performance of this protocol. All reagents should be RNase-free, and ideally a separate set of pipette tips should be used while working with RNA. Lab surfaces should be cleaned and gloves should be worn at all times. 3. Minimal value of drainage to be analyzed is 50 ml. 50–100 ml depending on the sample features was usually processed. 4. Repeat thawing should be avoided. 5. Avoiding DNA contamination is a crucial step to avoid false positive results. Repeat DNase digestion protocol if trace of DNA is detected. 6. We used human melanoma cell line MeW151 as positive control and normal (FN180) and fetal (RC5) fibroblast cell lines as negative control. As it was possible, samples of lymphatic drainage taken from patients undergoing surgery due to another type of tumor were tested as an additional control. Such samples should be negative for markers used in the assay. RNA from control samples was also isolated according to Chomczynski method [2], and further processed as described above. 7. Primers specific for MART-1 and tyrosinase mRNA were designed in our laboratory and their sequences are introduced in Table 1. Primers for uMAGE transcript were used according to [6]. 8. Validation of the test. Assay 1. (tyrosinase and MART-1) [6]. 93 patients were analyzed. RT-PCR assays were positive in 18 (19.4%): 8 cases for tyrosinase only, 7 for MART-1 only,

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and 3 for both. Positive RT-PCR results correlated with number of metastatic lymph nodes (P < 0.0001) and extracapsular extension (P ¼ 0.03). Higher rate of recurrence was observed in patients with positive RT-PCR results (83.3% vs. 35%; P ¼ 0.0001). Estimated 18 month OS rate for the whole group was 63.5%. Estimated 18-month OS rates were significantly lower in the RT-PCR positive group (23% vs. 76%; P ¼ 0.001). Estimated 18-month DFS rate for the whole group was 49.5%. Estimated 18-month DFS rates were significantly lower in the RT-PCR positive group 10% vs. 55%; P ¼ 0.0001). Positive RT-PCR were found to be independent predictors of poor OS and DFS in both univariate (P ¼ 0.0001 and P ¼ 0.0001) the multivariate analyses. (P ¼ 0.01 and P ¼ 0.0004). 9. Validation of the test. Assay 2. (tyrosinase, MART-1, uMAGE) [7]. 255 patients were analyzed. RT-PCR assays were positive in 82 (32%): 19 cases for tyrosinase only, 23 for MART-1 only, and 14 for uMAGE. 17 cases were positive for two markers, and 9 were positive for all markers assessed. Positive RT-PCR results correlated with older age (P ¼ 0.03) and male sex ( p ¼ 0.043), as well as number of metastatic lymph nodes (P < 0.0001) and extracapsular extension (P ¼ 0.02). Higher rate of recurrence was observed in patients with positive RT-PCR results (71% vs. 54%; P ¼ 0.01). Estimated 3-year OS rate for the whole group was 48.5%. Estimated 3-year OS rates were significantly lower in the RT-PCR positive group (16.5% vs. 58.2%; P ¼ 0.007). Estimated 3-year DFS rate for the whole group was 31.6%. Estimated 3-year DFS rates were significantly lower in the RT-PCR positive group (18% vs. 39.5%; P ¼ 0.00004). Positive RT-PCR results were found to be independent predictors of poor OS and DFS in both univariate (P ¼ 0.0001 and P ¼ 0.01) the multivariate analysis. (P ¼ 0.0001 and P ¼ 0.003). 10. Validation of the test. Assay 3. (tyrosinase, MART-1, uMAGE in blood and lymphatic drainage) [1]. 107 patients were analyzed based on both blood and lymphatic drainage RT-PCR tests. RT-PCR assays of lymphatic drainage were positive in 40 casas (37.4%): 9 cases for tyrosinase only, 6 for MART-1 only, and 11 for uMAGE. 11 cases were positive for two markers, and three were positive for all markers assessed. In blood assays, positive results for at least one marker were found in 28 cases (26.2%). Positive RT-PCR results correlated with number of metastatic lymph nodes (P ¼ 0.04) and extracapsular extension (P ¼ 0.002) as well as with therapeutic lymphadenectomy due to macroscopic metastases (P ¼ 0.005).

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Estimated 24-month DFS rates were significantly lower in the lymphatic drainage RT-PCR positive group (18.9% vs. 42.1%; P ¼ 0.04). This parameter did not reach statistical significance for analysis performed on peripheral blood. 11. Validation of the test. Assay 4. (tyrosinase, MART-1, uMAGE in patients with positive sentinel node biopsy) [8]. 137 patients were analyzed. RT-PCR assays were positive for at least one marker in 38 (27.7%). Positive RT-PCR results correlated with higher Breslow thickness (P < 0.01), number of metastatic lymph nodes (P ¼ 0.01), and older age (P ¼ 0.01). Higher rate of recurrence was observed in patients with positive RT-PCR results (71% vs. 49.5%; P ¼ 0.02). Estimated 5-year DFS rates were significantly lower in the RT-PCR positive group (21.1% vs. 39%; P ¼ 0.005). References 1. Rutkowski P, Nowecki ZI, Kulik J, Ruka W, Siedlecki JA (2008) Molecular staging by multimarker reverse transcriptase-polymerase chain reaction assay of lymphatic drainage and blood from melanoma patients after lymph node dissection. Melanoma Res 18(4):246–252. https://doi.org/10.1097/CMR. 0b013e328307bf3f 2. Tsao H, Nadiminti U, Sober AJ et al (2001) A meta-analysis of reverse transcriptasepolymerase chain reaction for tyrosinase mRNA as a marker for circulating tumor cells in cutaneous melanoma. Arch Dermatol 137(3):325–330 3. Curry BJ, Myers K, Hersey P (1999) MART-1 is expressed less frequently on circulating melanoma cells in patients who develop distant compared with locoregional metastases. J Clin Oncol 19:3622–3634 4. Miyashiro I, Kuo C, Huynh K et al (2001) Molecular strategy for detecting metastatic cancers with use of multiple tumor-specific MAGEA genes. Clin Chem 47:505–512 5. Chomczynski P, Sacchi N (1987) Single step method of RNA isolation by acid guanidinium

thiocyanate-phenol-chloroform extraction. Anal Biochem 162:156–159 6. Włodzimierz R, Rutkowski P, Nowecki ZI et al (2004) Detection of melanoma cells in the lymphatic drainage after lymph node dissection in melanoma patients by using two-marker reverse transcriptase-polymerase chain reaction assay. Ann Surg Oncol 11(11):988–997 7. Nowecki ZI, Rutkowski P, Kulik J, Siedlecki JA, Ruka W (2008) Molecular and biochemical testing in stage III melanoma: multimarker reverse transcriptase-polymerase chain reaction assay of lymph fluid after lymph node dissection and preoperative serum lactate dehydrogenase level. Br J Dermatol 159(3):597–605. https://doi.org/ 10.1111/j.1365-2133.2008.08710.x 8. Rutkowski P, Nowecki ZI, van Akkooi AC et al (2010) Multimarker reverse transcriptasepolymerase chain reaction assay in lymphatic drainage and sentinel node tumor burden. Ann Surg Oncol 17(12):3314–3323. https://doi. org/10.1245/s10434-010-1142-9

Chapter 28 A Clonogenic Assay to Quantify Melanoma Micrometastases in Pulmonary Tissue Fabrizio Mattei, Sara Andreone, and Giovanna Schiavoni Abstract Metastatic melanoma is one of the most aggressive types of cancers, diffused worldwide and with a significant percentage of lethality. The employment of animal models to test therapeutic strategies against melanoma growth and metastatic spread is of key relevance for cancer biologists. In this regard, the count of metastatic foci in murine lung tissue is one of the recognized methods to monitor macrometastases of melanoma. Here, we illustrate a clonogenic assay method to detect with high sensitivity the presence of single melanoma cells (micrometastases) at the pulmonary level when metastatic foci are still not detectable in the tissue. This method allows for high precision detection and quantification of melanoma metastatic spread to the lung at early stages. Key words Micrometastasis, Crystal Violet, B16.F10, Melanoma, Mouse model, Clonogenic assay, ImageJ macros, R scripts

1

Introduction Metastatic melanoma is a worldwide threatening malignancy with a poor survival rate and high frequency of relapse due to the insurgence of resistance during first and second line therapies [1, 2]. The gradual development and refinement of mouse models has significantly improved the discovery of mechanisms and dynamics at the basis of melanoma progression and therapy response in vivo [3]. Despite the development of ad hoc protocols for melanoma studies, little is known regarding the early mechanisms leading to metastatic spread. For this reason, there is an urgent need to develop affordable and quantifiable methodologies to determine the presence of single melanoma cells metastasizing in target organs, such as the pulmonary tissue. In this chapter we describe

Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-1-07161205-7_28) contains supplementary material, which is available to authorized users. Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_28, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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a high sensitivity ex vivo clonogenic assay method to quantify the presence of melanoma cell clones within lung tissue [4]. For the sample study described here, we used an in vivo melanoma mouse model for experimental metastases based on an intravenous (i.v.) injection of B16.F10 melanoma cells in the tail vein. Mice were treated in vivo with four intranasal administrations of IL-33, an alarmin recently shown to exert antitumor activities through stimulation of several immune cell effectors [5]. Treated and control mice were sacrificed 19 days after B16.F10 melanoma cell transfer and lung tissue was harvested. Pulmonary cell suspensions were prepared and plated to allow for the growth of melanoma cell clones. Micrographs were then acquired by visible light microscope every 24 h and melanoma cells were quantified after fixation of cells with Crystal Violet staining method. We demonstrate that this method is an affordable and sensitive method that allows to detect the presence of B16.F10 melanoma cells (micrometastasis) at early stage, when foci (macrometastasis) are still undetectable in the pulmonary tissue.

2

Materials All solutions and buffers must be prepared with ultrapure deionized water with a sensitivity of about 18 MΩ at 25
C. It is also recommended to appropriately waste the reagents as indicated by manufacturer’s instructions.

2.1 Materials and Reagents for the Generation and Monitoring of Melanoma Mouse Model

1. Complete medium for murine melanoma cell growth: High Glucose content Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% Fetal Bovine Serum (see Note 1), 1% penicillin, 1% streptomycin, 1% amphotericin B, and 1% glutamine. Store at 4
C until use. 2. Trypsin-EDTA cell detachment solution (10): Sterile stock reagent composed of 5 g/L of porcine trypsin and 2 g/L EDTA. Use at a final concentration of 1 (the two reagents at a concentration of 0.5 g/L and 0.2 g/L, respectively). Store at 4
C until use. 3. B16.F10 murine melanoma cells (see Note 2). 4. 15 mL Falcon tubes. 5. Phosphate-buffered saline (10): Mix 80 g/L of sodium chloride, 2 g/L of potassium chloride, 14.4 g/L of disodium hydrogen phosphate, and 2.4 g/L of potassium dihydrogen phosphate in ultrapure deionized water. Adjust pH to 7.4 with a digital pH meter, autoclave solution, and store at 4
C.

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6. IL-33 protein solution: Dissolve recombinant IL-33 protein to a concentration of 1–2μg per 10μL of 1 PBS for each intranasal installation per mouse. 7. C57BL/6 mice (see Note 3). 2.2 Solutions for Lung Tissue Dissection, Ex Vivo B16 Plating, Cell Staining, and Image Acquisition

1. Digestion solution: Prepare a solution containing 325 kUnits of DNase I and 1 g/L of Type III Collagenase in PBS (see Note 4). 2. Lysis buffer: 140 mM ammonium chloride, 17 mM Tris–HCl, pH 7.2 (see Note 5). This solution serves to lyse erythrocytes present in the lung tissue with minimal effects on leukocytes. 3. Crystal Violet solution: 0.1% (w/v) Crystal Violet in 20% (v/v) Ethanol. Store at room temperature in a dark bottle to avoid visible light penetration.

2.3 Other Supplies and Equipment

1. Sterile polystyrene 6 well plate. 2. Refrigerated tabletop centrifuge. 3. Motorized horizontal rotor. 4. 70μm filter. 5. EVOS-FL microscopy system. 6. ImageJ software. 7. GraphPad Prism software. 8. R software package.

3

Methods

3.1 Melanoma Cell Culture

1. Quickly thaw a stock vial of B16.F10 murine melanoma cells by warming at 37
C with a water bath. 2. Transfer the cells to a 15 mL Falcon tube and wash them by adding 10 mL of Complete DMEM. 3. Centrifuge at 400  g for 5 min at 4
C. 4. Discard the supernatant, resuspend B16.F10 cells in 5 mL of ice-cold Complete DMEM, and transfer to a 25 cm2 cell culture-treated flask. 5. Grow cells in an incubator at 37
C in the presence of 5% CO2 until subconfluence is reached (see Note 6). 6. Detach cells from flask substrate by removing growth medium and incubating them with 2 mL of trypsin–EDTA solution for 5 min at 37
C. 7. Centrifuge cells at 4
C for 5 min at 400  g. Discard the supernatant and passage cells at least twice prior to use in experiments (see Note 7).

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3.2 Mice Handling and In Vivo Treatments

The aim of the melanoma experimental metastasis model described herein is to monitor and quantify the early appearance of single melanoma cells in lungs of mice, which we have specifically evaluated in the context of immunotherapy with IL-33 [4]. Lunginfiltrating melanoma cells that are visually undetectable are referred to as micrometastases, as opposed to macrometastases represented by metastatic foci that are easily visible by eye observation of the lung tissues. Though this general protocol can be applied to evaluate lung metastases in many contexts, we present specific modifications to the protocol below to illustrate an application of this method for assessing the impact of IL-33 on lung metastasis. For this purpose, as schematically illustrated in Fig. 1, B16.F10 bearing mice were subdivided into three groups: IL-33-1 (mice administered with 1μg of IL-33), IL-33-2 (mice administered with 2μg of IL-33), and the PBS control group (mice administered with PBS only). To obtain the data shown herein, we used a total of nine animals, three per experimental group. 1. Treat mice as appropriate prior to injection of melanoma cells. For the sample study described here, each mouse received a single intranasal [i.n.] administration of either murine IL-33 protein (1μg or 2μg in 10μL of PBS) or PBS vehicle for three consecutive days prior to injection of melanoma cells.

Fig. 1 In vivo treatments of mice. Operational workflow recapitulating the overall treatments of C57BL/6 mice and the indicated experimental conditions for the sample experiment we use to highlight this protocol. The PBS group represents the control group of mice. The micrometastasis assay is performed with lungs excised 19 days after the intravenous transfer of B16.F10 cells. Each experimental group consists of three mice

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2. Collect melanoma cells from culture, wash cells in PBS, and resuspend in PBS at a concentration of 3  105 cells/200μL. 3. Inject melanoma cells intravenously (i.v.) in a 200μL volume into the tail vein of each animal using a 1 mL sterile plastic syringe. 4. Continue to treat mice as necessary depending on your specific experimental protocol. For the sample study described here, on day 6 post-B16 injection mice received another i.n. dose of either IL-33 (1μg or 2μg in 10μL of PBS) or PBS alone (see Note 8). 3.3 Mice Sacrifice and Pulmonary Tissue Dissection

1. Sacrifice mice by cervical dislocation [6] at desired days postB16 injection to evaluate the presence of melanoma cells in pulmonary tissues. For the study presented herein, we sacrificed mice on day 19 post-B16 injection. Dispose of pulmonary tissues appropriately (see Note 9). 2. Excise the lung tissues using sterile surgical scissors and transfer to a sterile 50 mL Falcon tube containing ice-cold PBS to deeply rinse the tissue (Fig. 2). 3. Transfer lungs to a petri dish containing ice-cold complete DMEM. Using sterile surgical scissors, cut the tissue into small pieces and then transfer to a digestion solution containing DNase I and Type III Collagenase (Fig. 2). 4. Incubate the minced tissues for 25 min at room temperature with constant and gentle agitation by using a motorized horizontal rotor (Supplementary Material 1). 5. At the end of this incubation, add 1 mL of 0.1 M EDTA (pH 7.2) to the suspension and incubate for an additional 5 min at room temperature with agitation. Do not exceed this incubation time (see Note 10). 6. Filter the obtained homogenate through a 70μm filter placed on top of a 50 mL vial to obtain single cell suspensions (Fig. 2). This step is pivotal to discard little pieces of tissue that remain undigested. 7. Wash the cell suspensions with ice-cold complete DMEM medium by centrifugation at 4
C for 5 min at 400  g using a table centrifuge, and then discard the supernatant. 8. Add 3 mL of Lysis Buffer to the cell pellet and incubate for 3 min at room temperature to destroy erythrocytes. Be careful not to exceed this incubation time (see Note 11). 9. Stop the reaction by adding at least 3 volumes of ice-cold complete DMEM medium (i.e., 9 mL). Centrifuge the cells at 400  g for 5 min at 4
C and discard the supernatant. 10. Resuspend the cells appropriately in complete DMEM medium for use in the micrometastasis clonogenic assay (see Note 12).

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Fig. 2 Clonogenic assay for the quantification of micrometastasis into lungs of mice. The protocol workflow is illustrated with the particular steps of the experimental workplan. The protocol starts with the dissection of the lungs from in vivo treated mice and terminates with the extrapolation of micrometastasis quantification results derived from the analysis of the acquired images. Acquisition of the images is performed in visible light by an EVOS-FL microscope equipped with a 4x 20–40 high contrast objective. Light blue texts denote the main steps of the workflow 3.4 Clonogenic Assay Plating Setup

1. Plate the lung cell suspensions, which contain potential invading B16.F10 melanoma cells that are not visible at this stage, in a sterile polystyrene 6 well plate as depicted in the workflow of Fig. 2 (see Note 13). 2. After plating, incubate the cells at 37
C in the presence of 5% CO2 and replace the medium with 2.5 mL of newly fresh Complete DMEM medium every 24 h for 5 consecutive days (see Note 14). 3. Allow the cultures to grow for a minimum of 6 to 9 days, depending on the growth speed of tumor cells. Terminate the incubation when the cell cultures from the control group reach subconfluence. 4. Quickly rinse wells with 1 mL of PBS and stain with 400μL of Crystal Violet solution (Fig. 3 illustrates the chemical structure of this dye) for 2 min by manual agitation to spread the dye to the entire well area (see Notes 15 and 16).

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Fig. 3 Chemical structure of Crystal Violet. Official 2D (a) and 3D (b) molecular structures of the dye Crystal Violet {[Tris(4-(dimethylamino)phenyl)methylium]chloride}, CAS number 548-62-9] provided by the MolView software (ID: 11057). Gray spheres, carbon atoms; white spheres, hydrogen atoms; blue spheres, nitrogen atoms. The green sphere represents the chloride anion associated to the positively charged dimethylamino group. Lines represent the chemical bounds between atoms

5. Vigorously rinse the cells with distilled water until the dye residuals are completely flushed away. 6. Allow wells to dry overnight, thus obtaining clean wells with spots (representing lung micrometastases) similar to that shown in Fig. 2. 3.5 Micrometastasis Determination and Quantification of the Stained Micrographs

Once the stained plates have dried, acquisition of images can be carried out by two different modes. In the first manner, images are acquired under visible light and serve as qualitative information to be displayed. The second way involves the acquisition of images with an EVOS-FL microscopy system and a 4 objective with a 20–40 high contrast phase light (Fig. 2). This facilitates the subsequent quantitative analysis, as the instrument’s software can discriminate the empty areas from the cell-filled regions.

3.5.1 Manual Analysis of the Acquired Images

We recommend that a set of 5 images be acquired for each of the well replicates using an EVOS-FL microscopy system. Hence, in the example analysis included here, we obtained 15 images for each of the three experimental conditions, for a total of 45 images. These images were then analyzed with the open source software ImageJ and its mask analysis plugin [7–9] to allow for visualization and quantification of the B16.F10 clones (Fig. 2). 1. Open the acquired images with ImageJ and subject them to auto-threshold processing [10] (see Note 17) with the optimal thresholding method (Fig. 4a, b).

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Fig. 4 Operative workflow for the analysis and quantification of the acquired images by ImageJ/Fiji software. A sample image was used to illustrate how ImageJ works to process and quantify the images in terms of clone area percentage referred to the total area of the image. An example microphotograph from the IL-33-1 condition is provided. (a) Starting raw image obtained by 20–40 phase contrast light and 4 magnification objective. Distinct B16.F10 clones are represented by the spheroidal bodies in this illustration. Images are 1280  960 pixel sized. (b) Thresholding steps. Selection of the Autothreshold method by the Adjust submenu inside the Image Menu. Except for the IL-33-2 images, where the selected method was MaxEntropy, the Huang algorithm with the indicated operational parameters is depicted and used for the other images (red arrows). (c) Thresholded image processed with the Huang method. The white particles represent the correctly thresholded B16.F10 cell clones. (d) Particle analysis steps. Menu view and selection of the Analyze Particles plugin within the Analyze Menu. The indicated parameters to be returned to the Analyzer (red arrows) are employed for each analyzed image. (e) Image displaying the masks (in black) representing the B16.F10 clones correctly quantified. (f) Summary window returning the relevant values of the mask analysis, such as the total area (in square micrometers) covered by the B16.F10 cell clones (underlined in red). Scale bar, 200μm

2. Process the obtained thresholded image (Fig. 4c) with the Analyze particle plugin (see Note 18). Insert the optimal parameters in the plugin window (Fig. 4d). 3. At the end of the particle analysis process, ImageJ returns a mask image displaying all the particles, representing tumor cell clones (Fig. 4e) and a Summary window (see Note 19) comprising relevant morphometric values (Fig. 4f). 4. Save this Summary window as an Excel dataset representing the overall morphometric factors of each image.

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The manual sequence depicted in Fig. 4 and in Subheading 3.5.1 can also been automated by running a macro within ImageJ environment (Fig. 5a, see Note 20). This command, reachable in the Macro main menu of ImageJ, serves to open and execute a macro listing generated by the internal IJ1 language editor (Fig. 5b, Supplementary Material 2), written by user for specific routines to execute in batch mode those instances. When completed, this macro listing, named “automated analysis”, can be saved as an IJM file and used in this format to be automatically executed with the Run command in the Macros submenu (Fig. 5a). The resulting datasets (see Note 21) associated to each mask image can be automatically saved in a folder previously defined by the user during the execution of the first part of the macro. 1. The first steps of the macro code (see Note 22) do initialize some variables (Input Images, Final Results, and Image) and allow the user to select in a dialog window the folder containing the starting EVOS-FL images from the experimental conditions as well as a second folder containing the resulting final datasets of the analysis (Supplementary Material 2, lines 5–7). 2. Another customized dialog window is created and allows the user to insert the thresholding method to be employed. The user can exclusively choose the Huang and the Max Entropy methods. (see Note 23 and Supplementary Material 2, lines 10–14). 3. The true boolean argument assigned to the instruction SetBatchMode prevents ImageJ from opening all the input and output images and tables, thus saving memory and speedingup the execution of the macro (Supplementary Material 2, line 15). 4. At this point, a for. . .if iteration (Supplementary Material 2, lines 17–28) serves for the conversion of the images in an 8-bit binary form and subsequently into mask images. Being that these mask images are part of the final results, they should be stored as TIFF images in the path assigned to the FinalResults variable (see Note 24). 5. The mask images are then cyclically processed in the third part of the macro, a for. . .if iteration containing a nested if. . . logical operator (Supplementary Material 2, lines 30–52). Here, each thresholded mask is opened and sequentially subjected to scaling, and measurements are also fixed (Supplementary Material 2, lines 39–40). The Analyze Particle tool of ImageJ is then called and cyclically executed for each image with the appropriate values assigned to the size interval of the tool (Supplementary Material 2, line 42). This instructs ImageJ to automatically select the cell clones with area values greater than 30μm2. During these nested iterations, a Summary

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Fig. 5 Selection of the Macro environment to run an automated analysis of images. (a) Running a macro from the ImageJ software. The Run command is indicated by red arrow. (b) Example of macro developing environment and editing with the internal ImageJ IJ1 Editor. The text represents the code for the execution of the indicated user-defined macro entitled «Example» and saved as an IJM file. Brown text, commands; purple text, String text or numbers; Black text, variables. Blue text: assigned name of the macro

window remains open in background mode and is gradually updated with the morphometric parameters extrapolated for each mask image. This window resembles that shown in Fig. 4f. Similarly, a Results window saves the datasets for each processed mask image (Supplementary Material 2, lines 43–48). 6. At the end of macro execution at row 54 of the code, a dataset containing results extrapolated from the mask and a summary dataset representing the average values of this mask image are generated for each starting image.

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The summary dataset generated by the automated analysis can be evaluated by heatmap graphs to determine whether or not the data fits with the findings such as those displayed in Fig. 6, which illustrates some representative microphotographs for each experimental condition. This is done with the R programming environment and their packages readxl, ggplot2, RColorBrewer, SciViews-R, and pheatmap [11–16]. The source code (Supplementary Material 3) allows the user to organize a single morphometric parameter (e.g., total perimeter or Feret’s diameter) by stratifying it as per experimental condition used (see Note 25) and to compute a heatmap for that factor (Fig. 7). 1. The code, named “Heatmap-R” and saved as R file (“R” extension), is conveniently executed by using RStudio, without the need to execute it as a command line. 2. The R routine begins by loading external R libraries needed for the correct execution of the R routine (Supplementary Material 3, lines 5–24). 3. A variable Data is then assigned a numeric array extrapolated from an Excel file, with a title for the heatmap chosen by the user. Then, the names of the fields are separated from the numeric array (Supplementary Material 3, lines 25–31).

Fig. 6 Qualitative evaluation of the clonogenic micrometastasis assay. Microphotographs acquired with visible light after plating of the lung cell suspensions and Crystal violet staining for the qualitative evaluation of the assay. The indicated experimental conditions refer to the in vivo treatments received by mice. Three representative images per experimental condition are shown. Scale bars, 200μm

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Fig. 7 Preliminary heatmap overview of morphometric factor features computed by ImageJ. Heatmaps were obtained from the indicated numerical datasets (upper text) by R software executed in RStudio environment. Average linkage cluster analysis was done on the three indicated treatments (lower text). Heatmaps were generated by using the Datasets yielded by ImageJ automated analysis via the «Automated-analysis» macro. Rep1 to Rep15, replicate values of each experimental condition

4. The function pheatmap() will generate the heatmap layout for the specified morphometric factor. The layout format will be defined by several variables and subfunctions called by the pheatmap() main function (Supplementary Material 3, lines 40–55), thus allowing the user to customize the heatmap layouts (see Note 26). 5. Regarding the statistics, users can establish whether to do these computations. Indeed, setting the Boolean variables cluster_cols to TRUE and cluster_rows to FALSE indicates that only a column (experimental conditions) clustering analysis must be computed (Supplementary Material 3, line 51). The clustering analysis method used during the execution of R is the Average Linkage method, assigned by default. 3.7 Statistical Analysis and Plotting of Data 3.7.1 Statistical Analysis

The table datasets obtained by ImageJ analysis can be plotted and statistically evaluated by using the GraphPad Prism software. In our example analysis, the statistical analysis method and post-tests were chosen by taking into account the presence of three experimental groups. One-way analysis of variance (ANOVA) with the Bonferroni’s post-test was used to perform statistical evaluation of the results [17]. Statistical analysis on heatmap columns was performed by R via the hierarchical clustering, using the Average Linkage method [18, 19].

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Even though the software GraphPad enables the user to obtain box–whisker plots, we use R software in order to draw plots as informative as possible. Indeed, R allows the user to merge the data in order to visualize them in dot and box–whisker plot formats. The graphs are obtained by the R package ggplot2 [12] (the associated R code is shown in Supplementary Material 4). We used this code to automatically draw the five box plots for each of the morphometric parameters shown in Fig. 8, stratifying by each experimental condition. 1. Open RStudio and write and save the code in an R format file (a file with the “R” extension), naming it “Dot&BoxPlots-R. r”, as illustrated in Supplementary Material 4.

Fig. 8 Quantitative evaluation of the B16.F10 micrometastasis clonogenic assay. (a) Representative mask images for each of the three indicated experimental groups (n ¼ 15). Each image represents B16.F10 cells (black color) obtained at the end of auto-thresholding and particle analysis processes. The covered area values and the auto-thresholding method used for each image are indicated. Gray scale bar, 400μm. (b) Dot and box-whisker plots denoting the calculated data occupied by B16.F10 cells or cell aggregates referred to each of the indicated parameter. Each group (PBS, IL-33-1, IL-33-2) is represented by n ¼ 15 replicate images. Calculations were obtained by the “Automated-analysis” user-defined ImageJ macro. Lines and bars represent Mean values  standard deviation. *, p < 0.01; **, p < 0.001; ***, p < 0.0001 (One-way ANOVA with Bonferroni’s post test)

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2. Be sure that the Excel file containing the overall dataset (morphometric data stratified into the individual experimental conditions) is correctly formatted and ready to be opened. 3. Manually change the values of the trio variables morph, Pointsize, and ymax. This user-defined R code needs to be manually changed for each morphometric parameter to be plotted prior to executing it (see Note 27). This is explained in detail in the red text of Supplementary Material 4. 4. Execute the code for each morphometric variable to be plotted, starting by line 1. This will load the readxl and ggplot2 libraries in the R environment. The first library is necessary for importing numeric data from the Excel file, whereas the latter loads all the ggplot instructions in the environment (lines 5 and 6). 5. The Excel file containing the morphometric data in a folder defined by the user is assigned to the numeric array variable Data, having the same structure of the Excel table. 6. Lines 22–24 assign the appropriate values to the trio morph, Pointsize, and ymax (see Note 27). 7. At this point, the code in lines 28–62 (Supplementary Material 4) collects all the data in the ggplot() function by calling its subfunctions stat_boxplot(), theme(), geom_boxplot(), geom_dotplot(), and ylim() (see Note 28). These instructions represent the most important part of the code as they control the plotting process of the graphs. 8. The resulting plot is then drawn as an image in the graph layout window of the RStudio environment and can be easily exported with a Copy&Paste operation. 9. Repeat steps 3–8 from this section to draw a dot and box– whisker graph associated with a new morphometric factor. 10. Interpret data once plots have been created and statistical analyses have been performed (see Note 29 for interpretation of the example experiment highlighted throughout this protocol).

4

Notes 1. The serum must be heat-inactivated before use. To do so, place the serum bottle in a water bath at 56
C for 30 min. Then, aliquot the serum in 50 mL Falcon tubes and store at 20
C until use. 2. B16.F10 are derived from a mouse skin melanoma on a C57BL/6 background and are endowed with the ability to metastasize to distant organs when injected in mice of the same background [4]. B16.F10 cells are cultured in complete

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DMEM medium until they reach subconfluence in adherence. In this phase, the medium may appear dark-brown or black, due to melanin secretion, especially when the cells are approaching confluence [20]. For culture preparation, 5  105 B16.F10 cells can be resuspended in 5 mL of medium and grown in a 25 cm2 tissue culture-treated flask, followed by passaging to another flask when cells reach subconfluence. New fresh complete DMEM medium (5 mL) is used for each passage. 3. C57BL/6 mice represent a widely used inbred strain and are featured by a competent immune system. Mice should be housed in appropriate animal facilities and used at a minimal number sufficient for the planned in vivo experiments. For the studies presented herein, mice were house at the Istituto Superiore di Sanita` (Rome, Italy) and handled in accordance with the Local Ethical Committee guidelines. Mice procedures in our animal facility are compliant with the European Commission Directive 2010/63/EU. For our experiments we used 5–7 week-old female mice. 4. The Digestion solution cleaves the tissue matrix components via the Collagenase and DNase I enzymes. Type III Collagenase is an enzyme isolated from Clostridium histolyticum that digests native collagen fibers [21]. DNase I is a nuclease that cleaves DNA preferentially at the phosphodiester binding sites adjacent to a pyrimidine nucleotide [22]. DNase I minimizes cell clumping due to DNA released by damaged cells. This mixed protein/DNA digestion allows efficient tissue dissociation and release of cells into living cell suspensions. 5. This solution serves to lyse erythrocytes present in the lung tissue with minimal effects on leukocytes. The pH of the Lysis buffer should be measured carefully. pH values higher or lower than 7.2 can potentially compromise the lysing activity of the solution and this can reflect in a parallel lysis of non-erythrocytic cells. 6. When B16.F10 cells reach subconfluence, passage cells in another 25 cm2 flask at 1/10 dilution. 7. The number of passages (normally twice weekly) is limited to a maximum of four in order to avoid the insurgence of potential mutations that can modify the B16.F10 phenotype, growth and function. Melanoma cells should routinely be tested for morphology and absence of Mycoplasma contamination [23]. 8. If performing i.n. treatments as part of the protocol, prior to each i.n. treatment, anesthetize mice with an intraperitoneal administration of a Rompun/Zoletil mix (0.25 mL/mouse of a 1:1 mix of Rompun [diluted 1:12.5] and Zoletil [diluted 1:25] in PBS) [24, 25]. When the animals appear unconscious,

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perform the i.n. instillations. Anesthesia not only avoids discomfort in mice but also allows the routing of the treatment into the intra-tracheal channel. 9. For each group three mice were considered, for a total of nine animals used in this experimental design. Each animal was injected intravenously with 3  105 B16.F10 melanoma cells in 200μL of PBS and treated i.n. with either PBS or IL-33 as delineated in the workflow (Fig. 1). 10. This step is extremely important to allow disruption of cell conjugates and to separate cells by the rest of proteins and fibers that compose the matrix of the pulmonary tissue. 11. This incubation step is optional and should be carried out only when the cell pellet is markedly red-colored, thus indicating the presence of high concentrations of erythrocytes. In any case, do not exceed the 3 min incubation time: a longer incubation interval may cause the destruction of tumor cells present in the suspension. 12. The volume of Complete DMEM medium is dependent on the number of wells to process for the clonogenic assay. We use 2 mL of medium for each well in a 6-well plate. 13. The exact volumes and dilutions for plating may vary for each experiment, but usually a 1:10 dilution with a total volume of 2.5 mL is optimal for a 3  2 well plate, as illustrated in Fig. 2. Specifically, each cell suspension is derived from individual animals belonging to the different experimental groups sacrificed as described above, for a total of nine mice (three mice per three treatment groups). Hence, a total of two plates to culture the cell suspensions are necessary if using this setup. Dilute the suspensions with Complete DMEM and culture in the well plates in a regime of 2.5 mL of total volume, as indicated in the workflow (Fig. 2). 14. This series of washing steps is of key relevance for a gradual discard of dying lung-derived primary non-adherent cells in order to select adherent melanoma cells possibly present in the original pulmonary tissue. If needed, this time interval can be shortened or prolonged, depending on the starting dilution or on the absolute number of cells present in the suspension to be plated. At the end of the washing steps, the wells are devoid of pulmonary tissue-derived cells (primary cells) and enriched in B16.F10 melanoma cells. 15. This step allows adherent cells to be fixed in the polystyrene substrate of the well by ethanol, which is present in the solution and favors the Crystal Violet cell staining (Fig. 2).

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16. Crystal Violet (Fig. 3) is a triarylmethane dye first used for the qualitative evaluation of the filling materials during dental restorations [26]. Its use was then extended to the Gram’s classification of bacteria and in histological examination [27]. This dye is now also widely used to stain primary cells, including tumor cells, and is suitable for morphological and quantitative studies [28]. 17. ImageJ is equipped with different autothreshold algorithms, to be used in dependence of the image type and quality. In this regard, the Huang algorithm is optimal for images where the number of cells visible in the acquired fields is appropriately elevated, such as those derived from the PBS and IL-33-1 experimental conditions. The Huang algorithm [29] did not work with the images from the IL-33-2 condition, due to the fact that these images displayed very low cell growth with a very low contrast. These 1280  960 pixel-sized images were then subjected to auto-threshold with a more appropriate method, such as the MaxEntropy algorithm, optimized for images where the particles are smaller and with low or very low contrast [30]. In parallel, each image was re-scaled taking into account that 460 pixels from 4 EVOS-FL images represent a distance of 1000μm. 18. Setting the Analyze Particle plugin parameters appropriately will increase the computational precision of the total area quantitation associated to the clone size and growth. To this extent, the minimal and maximal size area values should be inserted so that the Particle Analysis algorithm will exclude clones or tumor cell aggregates below a 30μm2 value. 19. The morphometric values usually employed are represented by the area of the image expressed as square-micrometers and as percent of area covered by the particles in that image (Fig. 4f). The selected parameters should be used for all of the analyzed images. 20. Conceptually, the identification of the B16 cell clones in a well sample after Crystal Violet staining denotes the presence of a single micrometastasis in the original pulmonary tissue [4]. However, when single metastatic cells are numerous and/or fast growing, the forming clones may overlap in the plate, thus limiting an accurate distinction. Thus, the overall quantification of the number of clones and their surface area reflects the growth entity (i.e., aggressiveness) of micrometastasis in lung. This sequential process requires a long time to be manually performed. Moreover, manual execution is subjected to a high probability of erroneous computations and userdependent errors.

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21. The aim of the macro listed in Supplementary Material 2 is to automatically perform the steps illustrated in Fig. 4 and described in Subheading 3.5.1. This approach quickly provides the results of image evaluations as a series of annotated Excel compliant datasets. All of the 45 images from our example analysis were processed in seconds, avoiding user-dependent errors and time-consuming issues. 22. The 51-line macro code is composed of three main sequential components. The first part is devoted to the selection of folders containing the starting images and the folder containing the final datasets, whereas the last two parts are dedicated to the iterative execution of the thresholding and particle analysis processes. This is illustrated in Supplementary Material 2 (red text). 23. As already mentioned, the methods Huang and MaxEntropy are the most optimized algorithms for the analysis of mask images. Therefore, in our example analysis, we created a folder for images from PBS and IL-33-1 and another folder for images derived from the IL-33-2 experimental conditions. These two folders were separately analyzed with the two methods (Huang and MaxEntropy, respectively). This is a helpful tip as the user will be able to maintain the datasets ordered in distinct and separated locations. Other threshold methods (e.g., Default) can return lacking or bad values for one or more images and this can negatively affect the subsequent macro code for particle analysis. This means that the optimal threshold method to be used is strictly dependent on the image quality and the microscopy system adopted. 24. Images in TIFF format are preferred to JPEG ones due to the lossless format of TIFF files [31]. Use the JPEG format only in the case where a TIFF file would be very large (>80–100 MB). 25. The stratification of data associated to each morphometric factor can be done manually (e.g., by Copy&Paste iterations to transfer the data in a new Excel table) or with an ad hoc routine made with Excel macro code or R program. 26. The heatmaps generated by this R code can be easily customized for an optimized readability of the layouts. For example, the sizes of each rectangle shown inside the heatmap are defined by the variables cellwidth and cellheight (Supplementary Material 3, lines 46–47). 27. The user can manage the trio variables Pointsize, ymax, and morph (see red text on Supplementary Material 4). The values for this trio should be empirically fixed for each morphometric factor. The Pointsize and ymax variables contain single numeric data, whereas the morph variable is dependent from the Data dataset. For this reason, morph is composed of two parts

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separated by a “$” symbol, as per R rules. The name in the left denotes the dataset to be used (Data) and the name in the right indicates the morphometric factor to be plotted (e.g. Feret’s diameter, named Feret in the dataset Data). For example, if the user wishes to draw a box and whisker plot associated to the Feret’s diameter data, the variables Pointsize and ymax (Supplementary Material 4, lines 23–24) must be manually assigned with the values 5 and 100, respectively. In parallel, the right field value to be manually inserted in the morph variable to inform R that data should be plotted relative to Feret’s diameter is a “$Feret” string (Supplementary Material 4, line 22). In these settings, the variable morph recovers the numeric field “Feret” by the numeric array Data. Lines 28–62 generate the plot being all the variables correctly defined (Supplementary Material 4). This workflow needs to be activated for each morphometric parameter to obtain dot and box–whisker plots. 28. The ggplot() subfunctions stat_boxplot(), theme(), geom_boxplot (), geom_dotplot(), and ylim() are deputed to specific aims during the graph plotting process. Indeed, the stat_boxplot() computes all the line values needed to construct the boxplot with the geom_boxplot() function. Moreover, the subfunction geom_dotplot() draws the values of the current morphometric factor as dots. Finally, the theme() function contains all the information to format the graph (colors, axes sizes, label sizes, etc.) and the ylim() function defines the lower and higher limit of the Y-axis. 29. The growth extent of B16.F10 melanoma cells is increased in lung samples from mice receiving PBS as compared to those from animals treated with IL-33 (Fig. 6). While the number of clones quantitatively indicates single metastatic cells in the lungs, the clone size reflects the growth rate of tumor cells, namely their aggressiveness. Notably, the in vivo treatment with IL-33 markedly reduced the clone size and number of melanoma cells in a dose-dependent manner (Fig. 6). In order to quantify the growth extent of melanoma cells in the Crystal Violet stains, we employed ImageJ to analyze the high phase contrast 4 magnification images acquired with an EVOS-FL microscope. An example of high phase contrast 4 image is illustrated in Fig. 4a. First, the summary morphometric datasets returned by macro execution were evaluated by a preliminary at-a-glance observation with heatmap visual graphs (Fig. 7). In our sample experiment, the average linkage analysis performed on the experimental conditions by executing the R code “Heatmaps-R.r” (Supplementary Material 3) clearly indicates that the control (PBS) is distinctly clustered compared to the treated (IL-33-1 and IL-33-2) conditions, except for the

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Minimum Feret’s diameter (Fig. 7). Moreover, the ImageJ masks (an example in Fig. 4e) returned by the analysis clearly indicate that increasing doses of IL-33 markedly diminish the extent of micrometastasis formation, namely B16.F10 cell covered area, as evidenced in Fig. 8a. Of note, while melanoma clone expansion is clearly evident in the control (PBS) group, the anti-tumor treatment (IL-33) reduced the formation of large clones in a dose-dependent fashion Fig. 8a. We then plotted the image quantitative analysis returned by running the ImageJ macro “Automated-analysis” (Supplementary Material 2). As shown in Fig. 8b, in vivo treatment with IL-33 caused a significant and dose-dependent reduction of B16.F10 melanoma cell colonization of lungs in comparison to control mice. Indeed, this is clearly illustrated by percent of cell area covered, average total perimeter, average particle size, and Feret’s parameters (see Note 30). Compared to control mice, all the evaluated parameters, except for the average value of Minimum Feret’s diameter, were significantly decreased in the treated (IL-33-1 and IL-33-2) groups, indicative of a markedly relevant delay in metastatic foci generation in mice (Fig. 8b). Taken together, covered area and the other described morphometric factors may represent useful parameters for quantitative evaluation of pulmonary micrometastasis in mice when using this clonogenic assay coupled with Crystal Violet staining. In addition, ImageJ macros and R programming, within the RStudio environment, represent an added value to these kinds of analyses, as they avoid user-dependent errors and allow for automation of computations. 30. Feret’s parameters are considered an added set of values to be aligned to the traditional morphometric factors (e.g., perimeter, area, diameter, radius), easily computed for regular-shaped polygons. Feret’s factors are helpful tools when one deals with irregular-shaped polygons, whose morphology is very difficult to be manually assigned. Many software packages such as ImageJ are equipped with internal codes that automatically determine the morphometric values of irregular polygons. For these reasons, Feret’s morphometric factors are extensively used to follow shape changes of tumor cells [32–34].

Acknowledgments This work was supported by the Italian Association for Cancer Research (AIRC) grant no. 21366 to G.S.

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References 1. Dummer R, Brase JC, Garrett J, Campbell CD, Gasal E, Squires M et al (2020) Adjuvant dabrafenib plus trametinib versus placebo in patients with resected, BRAFV600-mutant, stage III melanoma (COMBI-AD): exploratory biomarker analyses from a randomised, phase 3 trial. Lancet Oncol 21(3):358–372. https://doi.org/10.1016/S1470-2045(20) :30062-0 2. Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Niksˇic´ M et al (2018) Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 populationbased registries in 71 countries. Lancet 391 (10125):1023–1075. https://doi.org/10. 1016/S0140-6736(17)33326-3 3. Saleh J (2018) Murine models of melanoma. Pathol Res Pract 214(9):1235–1238. https:// doi.org/10.1016/j.prp.2018.07.008 4. Lucarini V, Ziccheddu G, Macchia I, La Sorsa V, Peschiaroli F, Buccione C et al (2017) IL-33 restricts tumor growth and inhibits pulmonary metastasis in melanomabearing mice through eosinophils. OncoImmunology 6(6):e1317420. https://doi.org/ 10.1080/2162402X.2017.1317420 5. Afferni C, Buccione C, Andreone S, Galdiero MR, Varricchi G, Marone G et al (2018) The pleiotropic immunomodulatory functions of il-33 and its implications in tumor immunity. Front Immunol 9:2601. https://doi.org/10. 3389/fimmu.2018.02601 6. Carbone L, Carbone ET, Yi EM, Bauer DB, Lindstrom KA, Parker JM et al (2012) Assessing cervical dislocation as a humane euthanasia method in mice. J Am Assoc Lab Anim Sci 51 (3):352–356 7. Bosch M (2019) Introduction to ImageJ macro language in a particle counting analysis: automation matters. In: Rebollo E, Bosch M (eds) Computer optimized microscopy, Meth Mol Biol, vol 2040. Humana, New York. https://doi.org/10.1007/978-1-4939-96865_4 ˇ epa M (2018) Segmentation of total cell area 8. C in brightfield microscopy images. Methods Protoc 1(4):E43. https://doi.org/10.3390/ mps1040043 9. Grishagin IV (2015) Automatic cell counting with ImageJ. Anal Biochem 473:63–65. https://doi.org/10.1016/j.ab.2014.12.007 10. Guirado R, Carceller H, Castillo-Go´mez E, Castre´n E, Nacher J (2018) Automated

analysis of images for molecular quantification in immunohistochemistry. Heliyon 4(6): e00669. https://doi.org/10.1016/j.heliyon. 2018.e00669 11. Kolde R. pheatmap: Pretty Heatmaps. 2019 12. Wickham H (2009) ggplot2—Elegant Graphics for Data Analysis. Use R! Springer Nature 13. Wickham H, Bryan J (2019) readxl: Read Excel Files. In: CRAN package readxl. https://cran. r-project.org/web/packages/readxl/index. html. Accessed 13 Mar 2019 14. Neuwirth E, Brewer C (2014) RcolorBrewer: ColorBrewer palettes. In: CRAN package RcoloBrewer. https://cran.r-project.org/web/ packages/RColorBrewer/index.html. Accessed 07 Dec 2014 15. Grosjean P (2019) SciViews: SciViews - Main package. In: CRAN Package SciViews. https:// cran.r-project.org/web/packages/SciViews/ index.html. Accessed 16 Nov 2019 16. R Core Team (2014) The R manuals. In: CRAN: manuals. https://cran.r-project.org/ manuals.html. Accessed 1 Jan 2014 17. Armstrong RA (2014) When to use the Bonferroni correction. Ophthalmic Physiol Opt 34 (5):502–508. https://doi.org/10.1111/opo. 12131 18. Hulot A, Chiquet J, Jaffre´zic F, Rigaill G (2020) Fast tree aggregation for consensus hierarchical clustering. BMC Bioinformatics 21(1):120. https://doi.org/10.1186/ s12859-020-3453-6 19. Booma PM, Prabhakaran S, Dhanalakshmi R (2014) An improved Pearson’s correlation proximity-based hierarchical clustering for mining biological association between genes. ScientificWorldJournal 2014:357873. https:// doi.org/10.1155/2014/357873 20. Bhatnagar V, Srirangam A, Abburi R (1998) In vitro modulation of proliferation and melanization of melanoma cells by citrate. Mol Cell Biochem 187(1–2):57–65. https://doi.org/ 10.1023/a:1006870621424 21. Yoshida S, Yamagata Y, Murayama K, Watanabe K, Imura T, Igarashi Y et al (2014) The influence of collagen III expression on the efficiency of cell isolation with the use of collagenase H. Transplant Proc 46(6):1942–1944. https://doi.org/10.1016/j.transproceed. 2014.06.007 22. Nadano D, Yasuda T, Kishi K (1991) Purification and characterization of genetically polymorphic deoxyribonuclease I from human kidney. J Biochem 110(3):321–323. https://

406

Fabrizio Mattei et al.

doi.org/10.1093/oxfordjournals.jbchem. a123578 23. Corral-Va´zquez C, Aguilar-Quesada R, Catalina P, Lucena-Aguilar G, Ligero G, Miranda B et al (2017) Cell lines authentication and mycoplasma detection as minimun quality control of cell lines in biobanking. Cell Tissue Bank 18(2):271–280. https://doi.org/ 10.1007/s10561-017-9617-6 24. Kim JY, Lee JI, Jeong JH, Fang Y, Ju MK, Kim SJ et al (2010) Improved yield and functional parameters of rat pancreas islets isolated under intramuscular anesthesia. Cell Transplant 19 (6):743–750. https://doi.org/10.3727/ 096368910X508843 25. Arras M, Autenried P, Rettich A, Spaeni D, Ru¨licke T (2001) Optimization of intraperitoneal injection anesthesia in mice: drugs, dosages, adverse effects, and anesthesia depth. Comp Med 51(5):443–456 26. Going RE, Massler M, Dute HL (1960) Marginal penetrations of dental restorations as studied by crystal violet dye and 1131. J Am Dent Assoc 61:285–300. https://doi.org/10. 14219/jada.archive.1960.0200 27. Coico R (2005) Gram staining. Curr Protoc Microbiol. Appendix 3:Appendix 3C. https:// doi.org/10.1002/9780471729259. mca03cs00 28. Yung WK (1989) In vitro chemosensitivity testing and its clinical application in human gliomas. Neurosurg Rev 12(3):197–203. https://doi.org/10.1007/bf01743984 29. Terheyden JH, Wintergerst MWM, Falahat P, Berger M, Holz FG, Finger RP (2020)

Automated thresholding algorithms outperform manual thresholding in macular optical coherence tomography angiography image analysis. PLoS One 15(3):e0230260. https:// doi.org/10.1371/journal.pone.0230260 30. Siritantikorn S, Jintaworn S, Noisakran S, Suputtamongkol Y, Paris DH, Blacksell SD (2012) Application of ImageJ program to the enumeration of Orientia tsutsugamushi organisms cultured in vitro. Trans R Soc Trop Med Hyg 106(10):632–635. https://doi.org/10. 1016/j.trstmh.2012.05.004 31. Kabachinski J (2007) TIFF, GIF, and PNG: get the picture? Biomed Instrum Technol 41 (4):297–300. https://doi.org/10.2345/ 0899-8205(2007)41[297:tgapgt]2.0.co;2 32. Bittinger A, Barth PJ, Nerreter V (1994) Nuclear morphometry of soft tissue tumors of fibrous origin. Zentralbl Pathol 140 (4–5):351–356 33. Ilic IR, Stojanovic´ NM, Randjelovic´ PJ, Mihajlovic´ MN, Radulovic´ NS, Ilic´ RS (2016) Evaluation of pathological parameters and morphometric data of desmoplastic lobular breast carcinoma. Indian J Pathol Microbiol 59(4):463–468. https://doi.org/10.4103/ 0377-4929.191775 34. Boussadia Z, Lamberti J, Mattei F, Pizzi E, Puglisi R, Zanetti C et al (2018) Acidic microenvironment plays a key role in human melanoma progression through a sustained exosome mediated transfer of clinically relevant metastatic molecules. J Exp Clin Cancer Res 37 (1):245. https://doi.org/10.1186/s13046018-0915-z

Chapter 29 PET Imaging of Melanoma Using Melanin-Targeted Probe Xiaowei Ma and Zhen Cheng Abstract Melanin exists in the most of melanoma lesions. Melanin plays an important role in melanoma progression, metastasis, therapy response, and the overall survival of patients. Therefore, melanin is a critical target for melanoma diagnosis and therapy. Many melanin targeting probes, such as radioisotope-labeled benzamide analogs, have been developed for melanoma diagnosis using positron emission tomography (PET). The N(2-(diethylamino)-ethyl)-18F-5-fluoropicolinamide (18F-P3BZA) probe is one of the benzamide analogs and has been preliminarily tested for clinical diagnosis of melanoma in our recent studies. It has shown high specificity and favorable in vivo performance for PET of melanoma. Herein, we describe the detailed synthesis protocol of 18F-P3BZA and PET/CT imaging procedure for animal models and patients. Key words Melanin, Positron emission tomography, Melanoma, Targeted probes

1

Introduction Melanin is a dark biological pigment that is synthesized and secreted by melanocytes and melanoma cells. Most melanomas are melanotic, in which melanin pigmentation is highly increased because tyrosinase activity is significantly elevated [1, 2]. Only 2–8% of melanomas are amelanotic (lacking either pigment on visual inspection or melanin in the pathology) [3, 4]. Many studies reported that melanin regulates epidermal homeostasis and plays an important role in melanoma metastasis, the outcome of therapy, and the overall survival of patients with metastatic melanomas [5– 7]. Melanin is a highly specific biomarker for melanoma, and it allows differentiation of melanomas from other tumors. It also affects the behavior of melanoma cells and their surrounding environment by modifying cellular metabolism, generating an oxidative environment, or hypoxia. Furthermore, melanin is related to immunosuppression and resistance to chemotherapies and radiotherapy [5, 6, 8]. Melanin is a very attractive target for melanoma diagnosis and therapeutic effect evaluation. A variety of

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_29, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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radiolabeled melanin targeting probes have been developed for melanoma PET imaging or radionuclide therapy [9–14]. PET/CT is a powerful molecular imaging tool for melanoma diagnosis and therapy response evaluation with high sensitivity and accuracy [15, 16]. Recently, we have developed a series of radiolabeled benzamide analogs for melanin targeted PET imaging, of which N-(2-(diethylamino)-ethyl)-18F-5-fluoropicolinamide 18 ( F-P3BZA) shows high targeting specificity and binding capability to the melanin in melanoma xenografts [17]. 18F-P3BZA has been further studied to understand its radiation dosimetry and pharmaceutical kinetics in human volunteers. Meanwhile, melanoma patients have been recruited for PET/CT imaging of 18 F-P3BZA and 18F-FDG. Our studies have demonstrated that 18 F-P3BZA is safe and shows a statistically higher specificity than 18 F-FDG for diagnosis of melanoma metastases [18]. 18F-P3BZA has high potential for PET of melanoma by targeting of melanin. In this chapter, we describe the detailed synthesis protocol for the precursor and standard of 18F-P3BZA, radiochemistry, and PET/CT procedures in melanoma mouse models and patients.

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Materials 1. Chemicals for synthesis of Br-P3BZA 19 F-P3BZA standard compound (Fig. 1):

precursor

and

(a) 5-bromopyridine-2-caboxylic acid (CAS: 30766-11-1). (b) 5-fluoropyridine-2-carboxylic acid (CAS: 107504-08-5). (c) N,N,N0 ,N0 -tetramethyl-O-(N-succinimidyl)uronium tetrafluoroborate (TSTU) (CAS: 105832-38-0). (d) N,N-diisopropylethylamine (DIPEA) (CAS: 7087-68-5). (e) N,N-diethylethylenediamine (CAS: 100-36-7). 2. Dimethylformamide (DMF). 3. Anhydrous Dimethyl sulfoxide (DMSO). 4. Helium or Nitrogen. 5. Anhydrous acetonitrile. 6. Acetonitrile. 7. Ethanol. 8. Deionized water (di-water). 9. K2.2.2/K2CO3 solution (1 mL): 15 mg Kryptofix2.2.2 (K2.2.2) and 3.5 mg K2CO3 in 0.9 mL of acetonitrile and 0.1 mL of di-water. Store at 4  C and use within 1 month. 10.

18 

F , produced by cyclotron.

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Fig. 1 The chemical structures of the materials. (a) 5-bromopyridine-2-caboxylic acid (CAS: 30766-11-1); (b) 5-fluoropyridine-2-carboxylic acid (CAS: 10750408-5); (c) N,N-diethylethylenediamine (CAS: 100-36-7); (d) N,N,N0 ,N0 -tetramethyl-O-(N-succinimidyl)uronium tetrafluoroborate (TSTU) (CAS: 105832-38-0); (e) N,N-diisopropylethylamine (DIPEA) (CAS: 7087-68-5)

11. 0.01 M (NH4)HCO3 solution: Dissolve 0.79 g Ammonium bicarbonate (99.8%) in 1 L of Millipore water. Then filter through 0.45 μm pore size PTFE filter. 12. Saline (0.9% NaCl). 13. QMA cartridge. 14. C18 plus Sep-Pak cartridge. 15. 0.22 μm sterile filter (PTFE) 16. Cell culture medium: Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS). 17. Phosphate buffered saline (PBS). 18. Trypsin. 19. Incubator. 20. Sterile petri dishes. 21. Hemocytometer. 22. C57BL/6 mice, 4-6 weeks old. 23. Anesthesia machine, oxygen, and isoflurane. 24. 1 cm3 tuberculin syringe with 21G needle. 25. Trypan Blue solution: Dilute to 0.8 mM in PBS. Store at room temperature. Stable for 1 month. 26. High-performance liquid chromatography (HPLC). 27. Thin layer chromatography (TLC) instrument.

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28. Radioactivity detector (for detecting radioactivity of the TLC strip). 29. TLC developing solvent: Mix methanol–(NH4)HCO3 at a 6:4 (v:v) ratio. 30. Paper or silica gel coated sheets for TLC. 31. PET scanners (micro-PET/CT and clinical PET/CT).

3

Methods

3.1 Synthesis of the Precursor (Br-P3BZA) and Standard (19F-P3BZA)

The reaction for this synthesis scheme is shown in Fig. 2. For the reagent equivalents in this reaction, see Note 1. 1. Dissolve 5-Bromopyridine-2-caboxylic acid (or 5-Fluoropyridine-2-carboxylic acid for synthesizing standard) in DMF at a concentration of ~100 nM (in 500 μL of DMF). 2. Add TSTU to above solution. 3. Add DIPEA to above solution. 4. Add N,N-diethylethylenediamine to the reaction. 5. Incubate the reaction for 2 h at room temperature (20–25  C). 6. Check the reaction with HPLC and mass spectrum (MS). 7. Load the mixture to HPLC for purification: set absorption wavelength at 254 nm. Mobile phases are acetonitrile and di-water. 8. Collect the product. 9. Check the molecular weight, structure, and purity of the product with mass spectrometry (MS), 1H-NMR, 13C-NMR, and HPLC. 10. Lyophilize and weigh the product. 11. Dissolve product into 10% ethanol to make a 30 mg/mL solution. 12. Inject the solution through the 0.22 μm filter under a biosafety cabinet.

Fig. 2 Synthesis scheme of Br-P3BZA and 19F-P3BZA

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Fig. 3 The radiochemistry scheme of 18F-P3BZA

13. Recalculate the concentration with spectrometer at absorption wavelength 254 nm. 14. Aliquot to 3 mg per vial. 15. Lyophilize in a sterile room or in a biosafety cabinet. 16. Seal: sealed with helium protection. 17. Store at 20  C and for animal experiment use only. For human use, the precursor must be synthesized by a manufacture with GMP environment and certifications. 3.2 Radiochemistry of the 18F-P3BZA Probe

The radiochemistry of 18F-P3BZA is shown in Fig. 3. 1. Dissolve 3 mg of anhydrous Br-P3BZA in 1 mL of anhydrous DMSO. 2. Prepare and place reagent and materials to the right position of the radiosynthesis module and double check everything is ready (see Notes 2 and 3). 3. Produce 18F on cyclotron and transfer to the module. 4. Load 18F on QMA cartridge. 5. Elute 18F from QMA with 1 mL K2.2.2/K2CO3 solution to reactor. 6. Dry the 18F solution completely under helium or nitrogen flow (see Note 4). 7. Add anhydrous Br-P3BZA solution to the reactor. 8. Rapidly heat up to 150  C and keep for 10 min. 9. Cool down the reactor to 45  C. 10. Add 6 mL of (NH4)HCO3 solution to dilute the reacted solution. 11. Transfer the solution to HPLC for purification (see Note 5). 12. Monitor the radioactive channel of the HPLC chromatography and collect the product to an empty vial. The retention time of product is about few minutes shorter than precursor. 13. Dilute the product solution with 20 mL of di-water (The volume of the di-water could be adjusted to make sure the original product solution is diluted at least ten times). 14. Inject the mixture through a C18 plus Sep-Pak slowly (about 10 mL per minute).

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15. Inject 10 mL of di-water through the C18 plus Sep-Pak slowly to remove solvent residue (about 10 mL per minute). 16. Elute desired product from the C18 plus Sep-Pak with 1 mL of Ethanol and then 10 mL of saline and pass through a 0.22 μm sterile filter to a sterile vial (see Note 6). 17. Mix 3.7 MBq of product with 20 μg of 19F-P3BZA and then inject to HPLC to test the retention time (see Note 7). 18. Perform routine radiopharmaceutical quality control (QC) tests (include radionuclide identity, radionuclide purity, radiochemical purity, chemical purity, visual examination, content of solvents, sterility) of the product (see Note 8). 19. The product is ready for use once all QCs are passed. 3.3 PET/CT Imaging of Melanoma in Mouse Models

1. Grow B16F10 melanoma cells in DMEM containing 10% FBS. 2. When cells are 70–80% confluent, remove medium and wash cells with PBS. 3. Harvest cells grown in monolayer culture during the exponential growth phase using trypsin. 4. Add culture medium and disperse cells. 5. Centrifuge cells immediately at 225  g for 5 min and wash twice with PBS. 6. Resuspend cells in PBS. Mix 20 μL of cell suspension with 20 μL of trypan blue solution and count cell number using a hemocytometer to calculate the cell concentration. Viable cells exclude trypan blue, while dead cells are stained with blue color due to trypan blue uptake. 7. Mix cells and draw 1.0  106 cells (50 μL) into a syringe without a needle. 8. Clean and sterilize the lower flank of 4–6 week old C57BL/6 mice and subcutaneously inject B16F10 cells using a 1-cm3 syringe with 21 G needle. 9. Start imaging experiments when the tumors have reached an average volume of 100 mm3. 10. Anesthetize mice with 2.5% isoflurane. 11. Inject about ~3.7 MBq of 18F-P3BZA (100 μL) into the mice through the tail vein. 12. Acquire PET/CT images at 20–40 min post-injection. Imaging parameters should be set according to the manual of the PET scanner. 13. Process and analyze the imaging data (see Note 9). A PET image of the melanoma in a mouse model is shown in Fig. 4.

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Fig. 4 Decay-corrected static micro-PET images of C57BL/6 mice bearing B16F10 melanoma tumors at 0.5 and 1 h after injection of 18F-P3BZA. Tumors are indicated by arrows. 18F-P3BZA physiological uptake could be found in eyes because of melanin distribution in the retina

Fig. 5 PET images of a melanoma patient at 60 min after 18F-P3BZA injection. The primary melanoma (red arrow), lymph node metastasis (green arrow), and bone metastasis (white arrow) could be observed on the PET imaging 3.4 PET/CT Imaging of Melanoma in Human Patients

Extra precautions must be taken when using the 18F-P3BZA probe in clinical settings (see Note 10). 1. Recruit patients according to the clinical trial regulation. 2. Inject 18F-P3BZA (3.5 MBq/kg body weight) to patient through cubital vein or hand vein. 3. Acquire PET/CT images at 20–40 min post-injection. The scan and reconstruction parameters should be set according to the manual of the PET scanner. 4. Process and analyze the imaging data. A PET image from a melanoma patient is shown in Fig. 5.

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Notes 1. The reagent equivalents for this reaction are as follows: 5-bromopyridine-2-caboxylic acid or 5-fluoropyridine-2-carboxylic acid ¼ 1; TSTU ¼ 1.5; DIPEA ¼ 4; N,NDiethylethylenediamine ¼ 2. 2. Activate QMA cartridge with 1 mL of ethanol, then 1 mL of di-water, then 10 mL of air, and then place it into the synthesis system. 3. Activate C18 cartridges with 10 mL of ethanol, then 10 mL of di-water, then place them in the synthesis system. 4. The radiolabeling yield is critically relative to the drying degree of 18F and precursor. Make sure the 18F solution is completely dried. Sometimes, an extra drying with anhydrous acetonitrile is necessary. 5. The mobile phase is 20% acetonitrile and 80% 0.01 M (NH4) HCO3 solution, and a reversed phase 5 μm C18 column is preferred. The retention time of the produce is significant shorter than precursor. A prepurification step could be added to the protocol: load the diluted reaction solution to a C18 plus Sep-Pak cartridge and elute the C18 with 1 mL acetonitrile and fowling 5 mL 0.01 M (NH4)HCO3. Then inject the elution to HPLC for further purification. 6. Sometimes the product cannot trap on the C18 plus Sep-Pak cartridge. In that case, check the pH value (~7.5) of the solution and the ratio of acetonitrile. 7. Theoretically, the retention times on 254 nm ultraviolet channel and radioactive channel should be the same. However, due to the dead volume of the tubing between the ultraviolet detector and radioactive detector, the retention times are different. Researches can calculate the time difference based on the speed of the mobile phase and the volume of the tubing. 8. The TLC developing solvent is methanol and 0.01 M (NH4) HCO3 (v:v ¼ 6:4). The Rf is between 0.5 and 0.8. For human use, the quality must higher than the radiopharmaceutical standard. 9. The retina uptake is a distinctive property of the 18F-P3BZA. Significant retina uptake should be observed on a successful 18 F-P3BZA PET imaging. 10. For human PET/CT imaging using 18F-P3BZA, the precursor must be synthesized under a Good Manufacturing Practice (GMP) environment and pass the strict quality controls. Each batch of production of 18F-P3BZA must be subjected for

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quality control according to the requirements of the radiopharmaceutical standard. Also, an Institutional Review Board (IRB) approval is needed for human study of 18F-P3BZA.

Acknowledgments This work was supported by grants from the National Natural Science Foundation of China (81501501). We acknowledge Dr. Feng He and the Xiangya Second Hospital of Central South University for testing and optimizing the radiochemistry of the 18 F-P3BZA based on the method published by our group. References 1. Dadachova E, Casadevall A (2005) Melanin as a potential target for radionuclide therapy of metastatic melanoma. Future Oncol 1 (4):541–549. https://doi.org/10.2217/ 14796694.1.4.541 2. Thompson JF, Scolyer RA, Kefford RF (2005) Cutaneous melanoma. Lancet 365 (9460):687–701. https://doi.org/10.1016/ S0140-6736(05)17951-3 3. Thomas NE, Kricker A, Waxweiler WT, Dillon PM, Busman KJ, From L, Groben PA, Armstrong BK, Anton-Culver H, Gruber SB, Marrett LD, Gallagher RP, Zanetti R, Rosso S, Dwyer T, Venn A, Kanetsky PA, Orlow I, Paine S, Ollila DW, Reiner AS, Luo L, Hao H, Frank JS, Begg CB, Berwick M, Genes E, Melanoma Study G (2014) Comparison of clinicopathologic features and survival of histopathologically amelanotic and pigmented melanomas: a population-based study. JAMA Dermatol 150(12):1306–1314. https://doi.org/10.1001/jamadermatol. 2014.1348 4. Gualandri L, Betti R, Crosti C (2009) Clinical features of 36 cases of amelanotic melanomas and considerations about the relationship between histologic subtypes and diagnostic delay. J Eur Acad Dermatol Venereol 23 (3):283–287. https://doi.org/10.1111/j. 1468-3083.2008.03041.x 5. Brozyna AA, Jozwicki W, Roszkowski K, Filipiak J, Slominski AT (2016) Melanin content in melanoma metastases affects the outcome of radiotherapy. Oncotarget 7 (14):17844–17853. https://doi.org/10. 18632/oncotarget.7528 6. Slominski RM, Zmijewski MA, Slominski AT (2015) The role of melanin pigment in melanoma. Exp Dermatol 24(4):258–259. https:// doi.org/10.1111/exd.12618

7. Broz˙yna AA, Jo´z´wicki W, Carlson JA, Slominski AT (2013) Melanogenesis affects overall and disease-free survival in patients with stage III and IV melanoma. Hum Pathol 44 (10):2071–2074. https://doi.org/10.1016/j. humpath.2013.02.022 8. Shain AH, Bastian BC (2016) From melanocytes to melanomas. Nat Rev Cancer 16 (6):345–358. https://doi.org/10.1038/nrc. 2016.37 9. Roberts MP, Nguyen V, Ashford ME, Berghofer P, Wyatt NA, Krause-Heuer AM, Pham TQ, Taylor SR, Hogan L, Jiang CD, Fraser BH, Lengkeek NA, Matesic L, Gregoire MC, Denoyer D, Hicks RJ, Katsifis A, Greguric I (2015) Synthesis and in vivo evaluation of [123I]melanin-targeted agents. J Med Chem 58(15):6214–6224. https://doi.org/10. 1021/acs.jmedchem.5b00777 10. Cachin F, Miot-Noirault E, Gillet B, Isnardi V, Labeille B, Payoux P, Meyer N, Cammilleri S, Gaudy C, Razzouk-Cadet M, Lacour JP, Granel-Brocard F, Tychyj C, Benbouzid F, Grange JD, Baulieu F, Kelly A, Merlin C, Mestas D, Gachon F, Chezal JM, Degoul F, D’Incan M (2014) (123)I-BZA2 as a melanintargeted radiotracer for the identification of melanoma metastases: results and perspectives of a multicenter phase III clinical trial. J Nucl Med 55(1):15–22. https://doi.org/10.2967/ jnumed.113.123554 11. Sillaire-Houtmann I, Bonafous J, Veyre A, Mestas D, D’Incan M, Moins N, Kemeny JL, Chossat F, Bacin F (2004) Phase 2 clinical study of 123I-N-(2-diethylaminoethyl)-2iodobenzamide in the diagnostic of primary and metastatic ocular melanoma. J Fr Ophtalmol 27(1):34–39. https://doi.org/10.1016/ s0181-5512(04)96089-5

416

Xiaowei Ma and Zhen Cheng

12. Kertesz I, Vida A, Nagy G, Emri M, Farkas A, Kis A, Angyal J, Denes N, Szabo JP, Kovacs T, Bai P, Trencsenyi G (2017) In vivo imaging of experimental melanoma tumors using the novel radiotracer (68)Ga-NODAGA-procainamide (PCA). J Cancer 8(5):774–785. https:// doi.org/10.7150/jca.17550 13. Garg PK, Nazih R, Wu Y, Grinevich VP, Garg S (2017) Selective targeting of melanoma using N-(2-diethylaminoethyl) 4-[(18)F] fluoroethoxy benzamide (4-[(18)F]FEBZA): a novel PET imaging probe. EJNMMI Res 7 (1):61. https://doi.org/10.1186/s13550017-0311-2 14. Rizzo-Padoin N, Chaussard M, Vignal N, Kotula E, Tsoupko-Sitnikov V, Vaz S, Hontonnou F, Liu WQ, Poyet JL, Vidal M, Merlet P, Hosten B, Sarda-Mantel L (2016) [(18)F]MEL050 as a melanin-targeted PET tracer: fully automated radiosynthesis and comparison to (18)F-FDG for the detection of pigmented melanoma in mice primary subcutaneous tumors and pulmonary metastases. Nucl Med Biol 43(12):773–780. https://doi.org/10.1016/j.nucmedbio.2016. 08.010 15. Mena E, Sanli Y, Marcus C, Subramaniam RM (2017) Precision medicine and

PET/computed tomography in melanoma. PET Clin 12(4):449–458. https://doi.org/ 10.1016/j.cpet.2017.05.002 16. Petersen H, Holdgaard PC, Madsen PH, Knudsen LM, Gad D, Gravergaard AE, Rohde M, Godballe C, Engelmann BE, Bech K, Teilmann-Jorgensen D, Mogensen O, Karstoft J, Johansen J, Christensen JB, Johansen A, Hoilund-Carlsen PF, Denmark PCTFotRoS (2016) FDG PET/CT in cancer: comparison of actual use with literature-based recommendations. Eur J Nucl Med Mol Imaging 43(4):695–706. https:// doi.org/10.1007/s00259-015-3217-0 17. Liu H, Liu S, Miao Z, Deng Z, Shen B, Hong X, Cheng Z (2013) Development of 18 F-labeled picolinamide probes for PET imaging of malignant melanoma. J Med Chem 56 (3):895–901. https://doi.org/10.1021/ jm301740k 18. Ma X, Wang S, Wang S, Liu D, Zhao X, Chen H, Kang F, Yang W, Wang J, Cheng Z (2019) Biodistribution, radiation dosimetry, and clinical application of a melanin-targeted PET probe, (18)F-P3BZA, in patients. J Nucl Med 60(1):16–22. https://doi.org/10.2967/ jnumed.118.209643

Chapter 30 Imaging and Isolation of Extravasation-Participating Endothelial and Melanoma Cells During Angiopellosis Tyler A. Allen and Ke Cheng Abstract Cancer mortality rates are primarily a result of cancer metastasis. Recent advances in microscopy technology allow for the imaging of circulating tumor cells (CTCs) as they extravasate (exit) blood vessels, a key step in the metastasis process. Here, we describe the use of intravital microscopy techniques to image and isolate both extravasating melanoma CTCs and the extravasation-participating endothelial cells. These techniques can be used as a means to study cancer metastasis and as a screening tool for anticancer therapeutics. Key words Circulating tumor cells, Metastasis, Intravital microscopy, Cancer Exodus Hypothesis, Extravasation, Angiopellosis, Zebrafish, Tumor infusion, Light sheet microscopy, Tumor cell isolation

1

Introduction Metastasis remains the leading cause of cancer-related deaths worldwide [1]. This complex process involves tumor cell (s) disassociation from the primary site, intravasation (entering) into the circulation, and extravasation (exiting) through the blood- and lymph- vessels at distant sites. The molecular mechanisms regulating this process remain poorly understood, but the process is known to depend on tumor cells’ ability to transmigrate the blood vessel wall from the circulation [2, 3]. Only a small percent of all tumor cells ever successfully make this journey, and even fewer actually survive following extravasation to form secondary tumors. However, this small percent of metastasis cases account for the overwhelming majority of cancer-related deaths [4]. Limitations of imaging technology prevent the imaging of circulating tumor cells (CTCs) in cancer patients at the needed resolution to characterize the metastasis process fully [5–8]. Our lab developed an alternative method to image CTC extravasation in real-time using a transgenic zebrafish model [9]. Zebrafish allow for unique imaging and transplantation capabilities as their embryos are both transparent and immunodeficient, allowing for the

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introduction, imaging, and growth of human/mammalian tumor cells [10–12]. Using this approach, we discovered CTCs utilize the recently characterized extravasation method, angiopellosis, to exit blood vessels [13]. Additionally, we found CTC clusters exclusively extravasate through angiopellosis leading to an increased propensity for tumor growth at distant sites, a phenomenon known as the cancer exodus hypothesis [9]. Here, we demonstrate the use of a transgenic zebrafish model allowing for the targeted imaging and isolation of extravasationparticipating endothelial cells and extravasating melanoma cells. By using light sheet microscopy, we were able to image infused melanoma cells and observe their movement and extravasation behaviors. Then, using the photoconvertible EosFP, exclusively expressed in endothelial cells and melanoma cells, we developed a method to select for extravasation-positive/participating cells [14]. These techniques are applicable to multiple cancer types and can be used to interrogate the effect of molecules, drugs, and genes at the cellular and molecular levels [15–18].

2

Materials

2.1 Zebrafish Tumor Injections

1. Transgenic zebrafish lines to use include Tg(fli1a:EGFP) and Tg(kdrl:eosFP). 2. E3 Medium for zebrafish embryo: Prepare a working concentration of medium containing 5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, 0.33 mM MgSO4, and 10
5% Methylene Blue in ddH2O. 3. 0.2 mM N-phenylthiourea (PTU). 4. Tricaine (3-amino benzoic acid ethylester). 5. PV830 Pneumatic Pico pump and a manipulator (WPI). 6. OmniPur® Agarose, Low Melting. 7. Zebrafish Microinjection & Transplantation Kit (AgnThos). 8. Borosilicate glass capillary needles (1 mm o.d.  0.78 mm i.d.). 9. 200  15 mm petri dishes coated with 3% agarose.

2.2 Melanoma Cell Culture and Preparation

1. Tumor cell growth medium: IMDM medium supplemented with 10% (v/v) fetal bovine serum, 2 mM L-glutamine, 100 U/mL penicillin, and100 μg/mL streptomycin. 2. Fluorescent DiI Membrane Stain.

2.3 Isolation of Injected Melanoma Cells from Zebrafish

1. Phosphate-buffered saline (PBS) containing 50 U/mL penicillin and 0.05 mg/mL streptomycin (PBS/PS). 2. TrypLE™ Express Enzyme (1). 3. TRIzol.

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4. Thermomixer. 5. 96-Well plate. 6. Fluorescent Microscope with adjustable field of UV-penetration/targeting.

3

Methods

3.1 Tumor Cell Injection into Zebrafish Embryo

1. Dechorionize zebrafish embryos 48 h postfertilization (hpf) prior to injection. 2. Anaesthetize embryos with 0.016% tricaine and position on a 200  15 mm Petri dish coated with 3% agarose (see Notes 1 and 2). 3. Prepare melanoma cell suspensions at a concentration of 2.0  106 cells/mL in PBS for injection, keep at room temperature before implantation, and implant within 2 h (see Note 3). 4. If using nonfluorescent cells, label with the fluorescent cell tracker DiI according to the manufacturer’s instructions (see Note 4). 5. Load up to 5 μL of the cell suspension into a single borosilicate glass capillary needle (see Note 5). 6. Perform injection using a PV830 Pneumatic Pico pump and a manipulator (see Notes 6 and 7). Inject cells approximately 50 μm above the ventral end of the duct of Cuvier where it opens into the heart (Fig. 1).

Fig. 1 Representative illustration of zebrafish tumor injection and orthographic time-lapse images of a single tumor cell cluster (cyan) following extravasation at 24 h postinjection, then an image of the same cell at 96 h postinjection. Scale bar ¼ 20 μm

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Fig. 2 Illustration of the light sheet chamber zebrafish embryos were placed in during the intravital time-lapse imaging

7. After implantation with mammalian cells, maintain zebrafish embryos (including nonimplanted controls) at 32  C (see Note 8). 8. At the conclusion of the experiment, euthanize the remaining embryos through tricaine overdose (see Note 9). 3.2 Light Sheet Imaging of Injected Zebrafish Embryos

1. Anaesthetize injected embryos using 0.016% tricaine and then embed in 1.3% low-melting-temperature (LM) agarose prepared in E3 medium (see Note 10). 2. Suck the embryo in the agarose into a glass tube. Using a plastic miniplunger, push the solidified agarose section containing the embryo out of the glass tube (Fig. 2; see Notes 11–13). 3. Fill the light sheet sample chamber with E3 medium containing 0.016% tricaine. Maintain the system temperature at 32  C throughout the imaging period. 4. Perform the fluorescent imaging using the Zeiss Lightsheet Z.1. Z-stacks. For time-lapse (4D) images, take z-stack images every 5–15 min for a total time of up to 24 h with a step number between 50 and 200 and step size of 0.3–2.0 μm. Confirmation of injected cell migration from inside of the

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lumen to surrounding tissue can be done using the Zeiss ZEN software 3D retendering capability. 3.3 Isolation of Injected Tumor Cells from Zebrafish Embryos for Culturing

1. Dissect the portion of the embryo containing the extravasated tumor cells. Transfer injected embryos to PBS/PS for at least 15 min (see Note 14). 2. Refresh the PBS/PS solution once and transfer the individual embryos to a sterile tube (embryo + 500 μL of PBS/PS) for 5 min (see Note 15). 3. Transfer the embryos to 200 μL of a 1% bleaching solution for 5 min. Then, replace the bleaching solution with PBS/PS immediately and incubate for 5 min (see Note 16). 4. Centrifuge the embryos at 1200  g for 2 min at room temperature and discard the supernatant carefully. 5. Add 300 μL of TrypLE and incubate for 45 min at 37  C in a thermomixer, while gently mixing (see Note 17). 6. Next, pipette the embryo–TrypLE mixture, with a 200-μL tip, several times up and down under sterile conditions; centrifuge immediately (4 min, 1200  g at room temperature). 7. Discard the supernatant, resuspend with 400 μL of PBS, and centrifuge (4 min, 1200  g at room temperature). 8. Resuspend the cell pellets in 200 μL of tumor cell growth medium and transfer cell suspension to a 96-well plate (200 μL of medium per well). 9. Allow cells to grow for at least 1 week (see Note 18).

3.4 Isolation of Extravasating Tumor Cells and ExtravasationParticipating Endothelial Cells for Direct Analysis

1. Inject tumor cells stably expressing EosFP into the circulation of Tg(fli1a:EGFP) or Tg(kdrl:eosFP) zebrafish embryos at 48 h postfertilization. 2. Following the observation of CTC cluster extravasation, confirm the placement of the tumor cell as outside the blood vessel lumen using intravital microscopy (see Note 19). 3. Use targeted UV-light activation with a focused UV penetration diameter of 10 μM to photoconvert either extravasating tumor cells or extravasation-participating endothelial cells for at least 30 s. Visually confirm the complete transition of EosFP from GFP to RFP expression (Fig. 3; see Note 20). 4. Following activation of targeted cells, disassociate the embryos through trypsinization as previously described in steps 1–7 of Subheading 3.3, and sort through fluorescence-activated cell sorting (FACS) directly into a TRIzol, or similar, solution (see Note 21).

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Fig. 3 Illustration of the targeted UV photoconversion of the EosFP transduced melanoma cells following angiopellosis extravasation

4

Notes 1. To prevent the embryo from being displaced during the injection, use an agar mold to allow the embryo to nest gently inside, with the yolk sac facing the inside of the ridge. 2. Higher amounts of tricaine will slow the heart rate and blood flow down drastically and make the injection of the cells into the circulation difficult. 3. The concentration of cells infused correlates with the type of tumor formation in the circulation. Lower concentrations will produce higher amounts of individual tumor cells, whereas higher concentrations will produce more clustered tumor cells. 4. If using a fluorescent membrane stain (such as DiO and DiI) to label the cells, be sure to wash the cells thoroughly prior to injection. If not thoroughly washed, the fluorescent particles will shed off into the blood and interfere with imaging. 5. Break the tip to create a hole just large enough to let cells comfortably out, but not too large that the solution the cells are in comes out. 6. The approximate PV830 injection parameters are: injection pressure ¼ 300 p.s.i., holding pressure ¼ 10 p.s.i., injection time ¼ 0.2 s. Injection parameters should be adjusted based on the size and amount of cells you are aiming to inject. 7. Injected tumor cells will normally be seen entering the vasculature within 5–30 min after injection, and they begin to arrest in the vessels of the tail within 1 h of injection.

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8. Normally, embryos are kept at 28  C, and the human tumor cells at 37  C. Following injection, keeping the embryos at 32  C allows for the tumor cells to survive and proliferate while also allowing the embryo to continue developing for several days. 9. Experiments were discarded when the survival rate of the control group was 1.0. 12. A tight band of high molecular weight DNA indicates high integrity, whereas smearing indicates degraded DNA. 13. For gDNA quantification, a fluorescence-based assay (e.g., Qubit) is required for downstream applications such as MSS. Spectrophotometric methods of quantification, such as NanoDrop, are not as accurate, as results can be affected by the detection of single-stranded DNA or other contaminants.

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14. Given the high volatility of certain gut metabolites, flashfreezing of fecal specimens and immediate storage at 80  C will ensure the most accurate snapshot of the gut metabolome. Left at room temperature for even several minutes, the metabolomics profile of the fecal specimen can change drastically. 15. The number of samples processed at once should be limited to the maximum number of samples that can fit into the centrifuge. 16. The dried fecal matter should break apart after vortexing. If fecal matter does not break apart, the sample should be vortexed again.

Acknowledgments This study was supported by the Roberta I. and Norman L. Pollock Fund (A.Y.K) and NIH R01 CA231303 (A.Y.K). References 1. Spor A, Koren O, Ley R (2011) Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol 9(4):279–290 2. Hooper LV, Littman DR, Macpherson AJ (2012) Interactions between the microbiota and the immune system. Science 336 (6086):1268–1273 3. Iida N, Dzutsev A, Stewart CA et al (2013) Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342(6161):967–970 4. Sivan A, Corrales L, Hubert N et al (2015) Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350(6264):1084–1089 5. Ve´tizou M, Pitt JM, Daille`re R et al (2015) Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350 (6264):1079–1084 6. Frankel AE, Coughlin LA, Kim J et al (2017) Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human Gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 19 (10):848–855 7. Chaput N, Lepage P, Coutzac C et al (2017) Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann Oncol 28(6):1368–1379

8. Gopalakrishnan V, Spencer CN, Nezi L (2018) Gut microbiome modulates response to antiPD-1 immunotherapy in melanoma patients. Science 359(6371):97–103 9. Matson V, Fessler J, Bao R (2018) The commensal microbiome is associated with anti-PD1 efficacy in metastatic melanoma patients. Science 359(6371):104–108 10. Routy B, Le Chatelier E, Derosa L (2018) Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359(6371):91–97 11. Larkin J, Chiarion-Sileni V, Gonzalez R et al (2015) Combined Nivolumab and Ipilimumab or monotherapy in untreated melanoma. N Engl J Med 373(1):23–34 12. Mathewson ND, Jenq R, Mathew AV et al (2016) Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat Immunol 17(5):505–513 13. Smith PM, Howitt MR, Panikov N et al (2013) The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341(6145):569–573 14. Carroll IM, Ringel-Kulka T, Siddle JP et al (2012) Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage. PLoS One 7(10):e46953 15. Minich JJ, Zhu Q, Janssen S et al (2018) KatharoSeq enables high-throughput

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microbiome analysis from low-biomass samples. mSystems 3(3):e00218–e00217 16. Ye SH, Siddle KJ, Park DJ et al (2019) Benchmarking metagenomics tools for taxonomic classification. Cell 178(4):779–794 17. Segata N, Waldron L, Ballarini A et al (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9(8):811–814 18. Abubucker S, Segata N, Goll J et al (2012) Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol 8(6):e1002358 19. Kim J, Kim MS, Yoh AY et al (2016) FMAP: functional mapping and analysis pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics 17(1):420 20. Simms-Waldrip TR, Sunkersett G, Coughlin LA et al (2017) Antibiotic-induced depletion

of anti-inflammatory clostridia is associated with the development of graft-versus-host disease in pediatric stem cell transplantation patients. Biol Blood Marrow Transplant 23 (5):820–829 21. Segata N, Izard J, Waldron L et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12(6):R60 22. Song SJ, Amir A, Metcalf JL (2016) Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1(3):e00021–e00016 23. Witchley JN, Penumetcha P, Abon NV et al (2019) Candida albicans morphogenesis programs control the balance between gut commensalism and invasive infection. Cell Host Microbe 25(3):432–443.e6

Chapter 34 Assessment of Cell-Free microRNA by NGS Whole-Transcriptome Analysis in Cutaneous Melanoma Patients’ Blood Kevin D. Tran, Rebecca Gross, Negin Rahimzadeh, Shanthy Chenathukattil, Dave S. B. Hoon, and Matias A. Bustos Abstract MicroRNAs (miRs) are small RNA molecules (18–22 nucleotides) that regulate the transcriptome at a posttranscriptional level by affecting the expression of specific genes. This regulatory mechanism is critical to maintain cell homeostasis and specific functions. Aberrant expression of miRs have been associated with pathobiological processes including cancer. There are few technologies available that are able to profile whole-genome miR expression using minimal amounts of blood samples and without the need for timeconsuming extraction steps. Here, we describe the HTG EdgeSeq miR Whole-Transcriptome Assay (WTA) in serum and plasma samples. To identify specific cell-free miR (cfmiR) patterns we have first focused on the analysis of normal donor samples and have then compared these to patients with cutaneous melanoma. The identification of specific cfmiR for melanoma patients will allow for better patient surveillance during targeted and/or checkpoint inhibitor immunotherapy (CII) treatment. Key words miRNA, Metastatic melanoma, Serum, Plasma, miR Whole Transcriptome Assay

1

Introduction Metastatic melanoma is a rapidly progressing cancer that can metastasize to distant organs such as the liver, lung, and brain [1]. Early detection of melanoma metastasis remains a key factor for treatment decisions such as targeted therapy, CII, and radiological or surgical interventions [2]. In the past few years, new CII have significantly improved survival outcomes for metastatic melanoma patients and have become the standard of care [2]. However, there is still a need for biomarker assay development as this would allow for the evaluation of disease progression in melanoma patients undergoing treatment [2]. Detection assays of cell-free nucleic acids (cfNA) found in metastatic cutaneous melanoma patient samples have improved in

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recent years, particularly in using circulating cell-free DNA (cfDNA) to target specific mutations [3–5]. However, the detection of cfDNA in the blood is still limited due to degradation. On the contrary, microRNAs (miRs) are short stable RNA molecules (18–22 nucleotides) that can control gene expression posttranscriptionally [6, 7]. Specific miRs are found in both the serum and plasma of cutaneous melanoma patients, as our group and others have demonstrated [7–12]. Several diagnostic and prognostic cfmiRs have been proposed as biomarkers, both individually and in combination, for assessment of cutaneous melanoma patients [7]. However, the specificity of diagnostic and prognostic cfmiRs identified in melanoma and other solid tumors is of major concern. Single cfNA biomarkers are not reliable for detection due to specificity and frequency. This is because most of the cfmiRs are associated with normal physiological functions and/or are associated with other diseases [7]. To increase the specificity of cfmiRs as biomarkers, we have focused on the identification of cfmiRs signatures in melanoma patients compared to normal cancer-free blood samples. In this regard, we have used the HTG WTA probe-based and Next-Generation Sequencing (NGS)-based detection platform. HTG WTA is designed to detect 2083 miRs with minimal sample input requirements, reduced preprocessing isolation steps, and high reproducibility. Using HTG WTA, we have described the profiling of serum and plasma samples taken from normal healthy donors to identify cfmiR signatures, and we have performed a comparative analysis of these signatures with those obtained from melanoma patients. With this approach, we have found that specific cfmiRs are differentially expressed in normal (cancer-free) donor plasma and serum samples (Fig. 1) and that specific cfmiRs are found in plasma samples from

Fig. 1 cfmiR comparison between normal serum and plasma samples. Shown is a principal component analysis (PCA) plot for the top 100 cfmiRs DE in serum compared to plasma samples of normal (cancer-free) donors. The figure shows that the top 100 cfmiRs may distinguish serum from plasma samples in this analysis

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Fig. 2 cfmiRs serve as a marker for cutaneous melanoma treatment. The top 5 differentially expressed cfmiRs in pretreatment samples of melanoma patients receiving CII compared to plasma samples of normal (cancerfree) healthy donors. This is a list of cfmiRs that were found DE in the pretreatment blood samples of melanoma patients receiving CII. The approach described herein will allow evaluation of these cfmiRs over time and may identify cfmiR biomarkers that can be used to monitor therapeutic efficacy and tumor response during the course of treatment

pretreatment melanoma patients receiving CII treatment when compared to plasma samples obtained from normal (cancer-free) donors (Fig. 2). This technology may therefore lead to the identification of cfmiR biomarkers that can be evaluated over time to monitor therapeutic efficacy and tumor response during the course of treatment.

2 2.1

Materials Serum Isolation

1. Corvac™ red/gray mottled stopper serum separator tubes (referred to as tiger-top tubes). 2. BD Vacutainer™ Venous Blood Collection Tubes: SST™ Serum Separation Tubes: Conventional Stopper (referred to as red-top tubes). 3. Serum Filter Fisherbrand™ 16 mm OD X 2–3/4 in. 4. 2 mL transfer pipettes. 5. 2 mL sterile cryovials.

2.2

Plasma Isolation

1. BD Vacutainer™ Glass Blood Collection Tubes: Buffered Sodium Citrate (referred to as blue-top tubes). 2. 15 mL conical polypropylene centrifuge tubes. 3. Glass or plastic transfer pipettes. 4. 2 mL sterile cryovials.

2.3 Library Preparation and Cleanup

1. HTG EdgeSeq instrument (HTG Molecular). 2. HTG Edge System software (HTG Molecular). 3. HTG EdgeSeq miRNA sample prep pack containing biofluids lysis buffer, plasma lysis buffer, and proteinase K (HTG Molecular). 4. HTG EdgeSeq assay reagent pack and HTG EdgeSeq plate pack containing sample plate and stop plate (HTG Molecular).

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5. Pipette tip pack. 6. Parafilm®. 7. Human brain total RNA. 8. 10 mM dNTP. 9. Hemo Klentaq® reaction buffer (5): When diluted to 1 with other reaction reagents, this buffer contains 60 mM Tricine, 5 mM (NH4)2SO4, 3.5 mM MgCl2, 6% glycerol, pH 8.7 (at 25  C). 10. Hemo Klentaq® DNA polymerase. 11. Molecular biology grade water. 12. HTG EdgeSeq sequencing tag pack containing forward and reverse primers (HTG Molecular). 13. AMPure XP beads. 14. DynaMag™-96 Side Skirted magnet. 15. 200 proof ethanol. 16. 10 mM Tris–HCl buffer, pH 8.0. 17. 1.5 mL LoBind tubes. 18. Twin.tec® PCR plate 96, unskirted, clear. 19. Microseal ‘B’ seal. 20. T100™ Thermal cycler or any modern equivalent thermal cycler. 21. Orbital shaker with planar mixing orbit for 1.5 mL centrifuge tubes. 2.4 Library Quality Check

1. High Sensitivity (HS) D1000 ScreenTape. 2. HS D1000 sample buffer (Agilent Technologies Inc., proprietary buffer). 3. HS D1000 DNA ladder. 4. 2200 TapeStation Instrument (Agilent Technologies Inc.). 5. 8-strip optical tube. 6. 8-strip optical Cap. 7. Loading tips (1pk). 8. KAPA library Quant kit (Illumina) universal qPCR mix kit. 9. Multiplate® PCR plates™ 96-well. 10. Tween 20. 11. 1 M Tris–HCl buffer, pH 8.0. 12. DNA dilution buffer: 10 mM Tris–HCl, pH 8.0 + 0.05% Tween 20. 13. Modern day thermocycler Real-Time PCR Detection System.

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1. HTG EdgeSeq Library Calculator Input Template (HTG Molecular). 2. HTG EdgeSeq RUO Library Calculator (HTG Molecular). 3. MiSeq™ instrument (Illumina). 4. NextSeq™ 550 instrument (Illumina). 5. MiSeq® V3 (150 Cycle) kit (Illumina). 6. NextSeq 500/550 Mid Output kit v2.5 (75 cycles) (Illumina). 7. NextSeq Accessory Box v2 (Illumina). 8. PhiX Control v3 Library (Illumina). 9. 10 mM Tris–HCl buffer, pH 8.5. 10. 200 mM Tris–HCl buffer, pH 7.0. 11. 2 N NaOH. 12. 2 N HCl. 13. HTG EdgeSeq ILM Seq primer kit containing ILM MiSeq primer, HTG ILM NextSeq Index primer, and ILM NextSeq Read1 primer (HTG Molecular). 14. HTG EdgeSeq Parser software (HTG Molecular).

3 3.1

Methods Serum Isolation

1. Collect blood in tiger-top tubes and centrifuge at 1300  g at room temperature (RT) for 10 min (see Notes 1 and 2). 2. Obtain a red-top tube. Remove the red-top cap while keeping the 7 mL vacutainer inside the sterile biological safety cabinet. Discard the red-top cap. 3. Using a transfer pipette, transfer serum from the spun-down serum separator tube to the 7 mL red-top vacutainer. Avoid poking the gel layer with the transfer pipette. 4. Discard transfer pipette and serum separator tube. 5. Obtain a serum filter. Avoid touching the bottom filter portion. 6. Insert the serum filter into the 7 mL vacutainer and push it down without covering the entire mouth of the serum filter. Be careful not to over-flow the serum filter. 7. Use a new transfer pipette to transfer the filtered serum from the serum filter to a 2 mL sterile cryovial (see Note 3). 8. Discard the serum filter, 7 mL vacutainer, and transfer pipette. 9. Store all 2 mL serum cryovials at 80  C for long-term storage.

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Plasma Isolation

1. Centrifuge the blue-top tube at 1300  g for 10 min at RT (see Note 2). 2. Gently remove the blue-top tube from the centrifuge to a tube rack. 3. Slowly transfer the plasma from the blue-top tube to a 15 mL conical polypropylene centrifuge tube (see Note 4). 4. Centrifuge the 15 mL conical centrifuge tube containing plasma at 1300  g for 10 min at RT (see Note 2). 5. Transfer the clarified plasma from the 15 mL conical centrifuge tube to a 2 mL sterile cryovial. 6. Store all 2 mL plasma cryovials at

3.3 Library Preparation and Cleanup

80  C.

1. Remove cryovials from freezer. Thaw isolated serum/plasma on ice until completely defrosted. Pipet to mix the samples thoroughly to obtain a homogenous sample (see Notes 5 and 6). 2. Thaw biofluids lysis buffer (for serum) and/or plasma lysis buffer (for plasma) by placing the tubes on a 50  C heat block for 30 min to ensure complete solubility of any buffer precipitates. 3. Aliquot 15 μL of serum/plasma into a separate 1.5 mL LoBind tube. 4. Prepare to lyse the samples by adding 15 μL of biofluids lysis buffer or plasma lysis buffer into each serum or plasma sample, respectively. Add 3 μL of proteinase K to each sample and pipet to mix thoroughly. Quick spin for 5 s to collect the residual lysis buffer at the bottom of the tube. 5. Lyse the samples by incubating on a preheated 50  C orbital shaker set to 500 rpm for 3 h (see Note 7). 6. Aliquot 25 μL of lysate per well into an HTG sample plate (see Note 8). Load the HTG EdgeSeq Instrument with sample plate, stop plate, HTG WTA reagent cartridge, and pipette tips according to the schematic provided by the HTG Edge System software. Initiate the run according to the HTG EdgeSeq user manual. Allow the HTG WTA to perform automated probe capture for 20 h. 7. Following the 20-h processing period, remove the samples now located in the stop plate and store them at 20  C until they are ready for library preparation or move onto library preparation steps immediately (see Note 9). Clean the instrument according to the HTG EdgeSeq User Manual. 8. Carefully mix samples in the stop plate with a pipette set to 10 μL to obtain a homogenous aqueous mixture (see Note 10).

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9. Prepare the PCR mixture on ice in a twin.tec® PCR plate using 21 μL of master mix (0.6 μL of 10 mM dNTP, 6 μL of 5 Hemo Klentaq® Reaction buffer, 2.4 μL of Hemo Klentaq® DNA Polymerase, and 12 μL of molecular biology grade water), 6 μL of primers from HTG EdgeSeq sequencing tag pack (3 μL of forward primer, 3 μL of reverse primer) and 3 μL of sample (see Note 11). Seal plate with a Microseal® ‘B’ seal. 10. On a preheated thermal cycler, run PCR using the following settings. (a) 95  C for 4 min. (b) 95  C for 15 s, 56  C for 45 s, 68  C for 45 s (repeat for 16 total cycles). (c) 68  C for 10 min. (d) Hold at 4  C. 11. Perform library cleanup by thoroughly pipetting to mix 37.5 μL of AMPure XP beads with 15 μL of PCR-amplified libraries in a new twin.tec® PCR Plate. Incubate for 5 min at RT. 12. In the meantime, prepare 400 μL of 80% ethanol per sample (see Note 12). 13. Place plate on a DynaMag™ 96 Side Skirted Magnet for 2 min or until the supernatant becomes completely clear. 14. While still on the magnetic plate, remove supernatant without disturbing the library-bound beads and add 200 μL of 80% ethanol to wash. Incubate at RT for 30 s (see Note 13). 15. Repeat 80% ethanol wash. 16. Remove supernatant (ethanol) and let the beads dry on the magnet for 2 min (see Note 14). 17. Remove the plate from the magnet and thoroughly resuspend the beads with 40 μL of 10 mM Tris–HCl, pH 8.0 to elute libraries. 18. Incubate at room temperature for 2 min. 19. Place the plate back on the magnet for 2 min or until the supernatant becomes completely clear. 20. Transfer the supernatant (containing libraries) without disturbing the beads (see Note 15) and store in a new plate. 21. Store at 20  C for up to 2 weeks, or storage until ready to sequence. 3.4 Library Quality Check

80  C for longer

1. Perform library fragment-size analysis by first allowing the Agilent HS D1000 ScreenTape and reagents to equilibrate to RT for 30 min. Vortex and quick spin the Agilent HS D1000 sample buffer and HS D1000 DNA ladder for at least 5 s.

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2. Pipet and mix 2 μL of each library and 2 μL of sample buffer into each well of the 8-strip optical tube. Briefly vortex and quick spin for 5 s. 3. Load Agilent 2200 TapeStation Instrument with 8-strip tubes, loading tips, and equilibrated HS D1000 ScreenTape. Initialize instrument program as specified in the Agilent 2200 TapeStation System User Manual. 4. Confirm the library fragment size is between 150 and 170 base pairs (bp). 5. To quantitate the libraries, perform qPCR with the KAPA Library Quant kit Universal qPCR mix kit. 6. Thaw Universal qPCR master mix and standards in a 4  C refrigerator (see Note 16). 7. Prepare 30 mL of DNA dilution buffer (10 mM Tris–HCl, pH 8.0 + 0.05% Tween 20) by mixing 29.55 mL of molecular-grade water, 300 μL of 1 M Tris–HCl, pH 8.0, and 150 μL of 10% Tween 20 (see Note 17). 8. Prepare a 10,000 dilution series for each sample by diluting the samples by 100 (2 μL of sample + 198 μL of DNA dilution buffer) twice in a twin.tec® PCR plate. Carefully mix the samples in between dilutions. 9. Prepare 16 μL of master mix (12 μL of qPCR master mix and 4 μL of molecular-grade water) per sample, standard, and nontemplate control (NTC, negative control) (see Note 18). 10. Aliquot 16 μL of master mix into each well of the Multiplate® PCR plates™. Add 4 μL of library, standard, or DNA dilution buffer (NTC) into respective wells. 11. Perform qPCRs under the following conditions. (a) 95  C for 5 min. (b) 95  C for 30 s, 60  C for 45 s (repeat for 35 total cycles). 3.5 Library Normalization, Pooling, and Next-Generation Sequencing

1. Remove MiSeq® V3 (150 Cycle) kit or NextSeq 500/550 Mid Output kit v2.5 (75 cycles) from 20  C storage and place reagent cartridge and HT1 buffer in a water bath at RT to thaw before beginning preparations for NGS (see Note 19). Remove HTG ILM MiSeq primer (MiSeq) or HTG ILM NextSeq index primer and ILM NextSeq Read1 primer (NextSeq) to thaw on ice. 2. Fill out the HTG EdgeSeq Library Calculator Input Template with the sample concentrations as determined by the qPCR results. Select the correct sequencing instrument (MiSeq or NextSeq) and input the intended final loading concentration into the HTG EdgeSeq RUO Library Calculator software. Upload the input template into the software and generate the pooling and denaturing protocol.

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3. Quantitated libraries are diluted, normalized, pooled, and denatured according to the protocol generated by the HTG EdgeSeq RUO Library Calculator software. The following steps are performed with calculated volumes provided in the protocol generated by the HTG EdgeSeq RUO Library Calculator software. 4. Dilute libraries with 10 mM Tris–HCl, pH 8.5. 5. Pool diluted libraries together. 6. Denature the normalized pool of libraries with 2 N NaOH for 8 min at RT. 7. Add cold HT1 buffer. Invert the tube to mix. 8. Neutralize the NaOH with 2 N HCl. Vortex to mix well and quick spin for 5 s. 9. Dilute and denature stock PhiX Control v3 libraries by adding 5 μL of 0.2 N NaOH to 5 μL of 4 nM PhiX. Vortex to mix and quick spin for 5 s. 10. Incubate at RT for 5 min. 11. Add 5 μL of 200 mM Tris–HCl, pH 7.0 to neutralize the NaOH. 12. Dilute PhiX to 20 pM by adding 985 μL of prechilled HT1 buffer (see Note 20). 13. Dilute 20 pM PhiX to 12.5 pM by adding 32.5 μL of 20 pM PhiX to 19.5 μL of HT1 buffer. 14. Spike denatured libraries with 12.5 pM PhiX control libraries at the volume specified by the HTG-generated protocol. 15. Heat-denature pooled libraries and PhiX at 98  C for 4 min. 16. Immediately chill on ice for a minimum of 5 min. 17. Load the entire denatured library pool and custom primer into the reagent cartridges of the respective MiSeq® V3 (150 Cycle) kit or NextSeq 500/550 Mid Output kit v2.5 (75 cycles) as instructed in the HTG user manual (see Note 21). 18. Load sequencer as instructed in the respective Illumina system guide (see Notes 22 and 23). 19. After sequencing, generate FASTQ files by uploading the sequencing run files and sample sheet into the Illumina Local Run Manager module (see Note 24). 20. Upload FASTQ files and sample sheet (see Note 23) into the HTG EdgeSeq Parser software and begin parsing and alignment. Save the generated .xls file containing the final counts for 2083 miRs per each sample for downstream data analysis.

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Data Analysis

Data analysis will depend on the specific needs and experimental goals of the user. In general, though, data analysis for the sequenced miRs includes the following steps: (1) raw data processing and quality check, (2) mapping miRs to 2083 reference miRs, (3) acquiring read counts for each miR respectively, (4) identification of differentially expressed (DE) miRs, and (5) performing exploratory analysis for comparisons of DE miRs. The HTG platform accounts for the first four steps of this analysis. Exploratory analysis using datasets obtained from the HTG Reveal software can be performed in the interactive platform Jupyter (https://jupyter. org). Some of the python libraries/packages used for this analysis include: scikit-learn (https://scikit-learn.org/) for data classification; Matplotlib (https://matplotlib.org/) for generating 2D plots; Seaborn (https://seaborn.pydata.org/) for creating hierarchically clustered heatmaps; NumPy (https://numpy.org/) and SciPy (https://www.scipy.org/) for statistical analysis.

Notes 1. Allow blood specimens to sit for 1 h at RT before processing them for serum isolation. Process blood under a clean HEPAfiltered laboratory hood (BSL2 or higher level) to avoid contamination. 2. During centrifugation, label cryovials with the specimen number, protocol ID, and the date the blood was drawn. 3. Cap tightly and do not exceed the 1.8 mL volume of each cryovial. 4. Avoid disturbing/pipetting the solid blood layer near the top surface of the plasma. 5. Turn on and set up the HTG Instrument according to the HTG EdgeSeq user manual before processing the blood specimens. 6. Designate the human brain total RNA to be the experimental positive control. Leave one well empty on the sample plate to load the positive control. 7. A heat block can be used instead of the orbital shaker, but samples must be mixed every hour. 8. There are three formats of HTG plates (8, 24, or 96-well sample plates) and selection is made according to the number of samples. 9. Take care not to invert the stop plate. If done, it will spill the samples and risk cross-contamination. If storing at 20  C, parafilm the lid to prevent frost from entering the plate.

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10. The HTG EdgeSeq instrument performs a denaturation oil overlay on the samples in the stop plate. Mix the samples while taking care to avoid aspirating the oil overlay. Mixing the oil into the aqueous sample may inhibit downstream PCR amplification. 11. Arrange forward and reverse primers to ensure unique barcode combinations for each sample. For example, for 24 samples, 3 forward X 8 reverse primers allow for 24 unique forwardreverse primer combinations. 12. Prepare extra ethanol accounting for pipetting errors to ensure there is enough for all samples, including the standard and NTC. 13. Use a multichannel pipette when working with more than 8 samples at a time to prevent the beads from drying while working. 14. Do not allow beads to over-dry. Look for a matte finish on the beads. Cracks are indicative of over-drying and will lead to lower library yields. 15. Use a 20 μL pipette to reduce bead carryover. 16. Add 1 mL of 10 primer mix to 5 mL of qPCR master mix upon first use of the qPCR with the KAPA Library Quant kit Universal qPCR mix kit. Also, qPCR master mix is lightsensitive. 17. Prepare 10% Tween 20 by diluting 15 μL of stock Tween 20 with 135 μL of molecular-grade water. 18. Perform qPCR on samples, standards, and NTC in at least duplicates. 19. For 24 samples, use MiSeq™ instrument and reagents. For 96 samples, use NextSeq™ 550 instrument. For NextSeq kits, HT1 buffer is provided in the NextSeq accessory box v2. 20. 20 pM denatured PhiX may be stored at 20  C for up to 2 weeks. Save and store extra PhiX for other HTG WTA sequencing runs. PhiX is the positive sequencing control. 21. For MiSeq runs, load custom primer ILM MiSeq primer. For NextSeq runs, load HTG ILM NextSeq index primer and ILM NextSeq Read1 primer. 22. Set-up sequencing parameters to be 1 50 bp read length for either instrument. MiSeq® V3 kit (150 cycle) is the lowest cycle kit validated with HTG WTA. 23. The sample sheet for either the MiSeq or NextSeq runs is located in the “Reports” tab of the HTG Edge System software. This sample sheet may be used for setting up MiSeq (required) or NextSeq (optional) runs.

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24. The MiSeq™ instrument has automatic FASTQ generation. Local Run Manager is only required for NextSeq sequencing. Alternatively, if the user has an Illumina BaseSpace account, FASTQ generation may occur automatically for NextSeq runs.

Acknowledgments The authors thank the Dept. of Translational Molecular Medicine staff at JWCI for their kind advisory and technical assistance. This research was funded by Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (D.S.B.H.) and Gonda Foundation (D.S. B.H.). References 1. Gershenwald JE, Balch CM, Soong SJ, Thompson JF (2009) Prognostic factors and natural history of melanoma. In: Balch CM, Houghton AN, Sober AJ, Soong SJ, Atkins MB, Thompson JF (eds) Cutaneous melanoma, 5th edn. Quality Medical Publishing, St. Louis 2. Weiss SA, Wolchok JD, Sznol M (2019) Immunotherapy of melanoma: facts and hopes. Clin Cancer Res 25(17):5191–5201. https://doi.org/10.1158/1078-0432.Ccr18-1550 3. Lin SY, Huang SK, Huynh KT, Salomon MP, Chang S-C, Marzese DM, Lanman RB, Talasaz A, Hoon DSB (2018) Multiplex gene profiling of cell-free DNA in patients with metastatic melanoma for monitoring disease. JCO Precis Oncol 2:1–30. https://doi.org/10. 1200/po.17.00225 4. Lanidou E, Hoon D (2017) Circulating tumor cells and circulating tumor DNA as a real time liquid biopsy approach. In: Wittner C, Rafai N, Horvath R (eds) Tietz textbook clinical chemistry and molecular diagnostics, 6th edn. Elsevier, St. Louis, MO, pp 1145–1155 5. Lanidou E, Hoon D (2018) Circulating tumor cells and circulating tumor DNA. In: Wittner C, Park S, Rafai N, Horvath R (eds) Tietz textbook of clinical chemistry and molecular diagnostics, 6thedn edn. Elsevier, St. Louis, MO, pp 1111–1144 6. Fleming NH, Zhong J, da Silva IP, Vega-Saenz de Miera E, Brady B, Han SW, Hanniford D, Wang J, Shapiro RL, Hernando E, Osman I (2015) Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients. Cancer 121(1):51–59. https://doi. org/10.1002/cncr.28981

7. Mumford SL, Towler BP, Pashler AL, Gilleard O, Martin Y, Newbury SF (2018) Circulating microRNA biomarkers in melanoma: tools and challenges in personalised medicine. Biomol Ther 8(2):21. https://doi.org/10. 3390/biom8020021 8. Heitzer E, Haque IS, Roberts CES, Speicher MR (2019) Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet 20(2):71–88. https://doi. org/10.1038/s41576-018-0071-5 9. Leidinger P, Keller A, Borries A, Reichrath J, Rass K, Jager SU, Lenhof HP, Meese E (2010) High-throughput miRNA profiling of human melanoma blood samples. BMC Cancer 10:262. https://doi.org/10.1186/14712407-10-262 10. Van Laar R, Lincoln M, Van Laar B (2018) Development and validation of a plasma-based melanoma biomarker suitable for clinical use. Br J Cancer 118(6):857–866. https://doi. org/10.1038/bjc.2017.477 11. Bustos MA, Ono S, Marzese DM, Oyama T, Iida Y, Cheung G, Nelson N, Hsu SC, Yu Q, Hoon DSB (2017) MiR-200a regulates CDK4/6 inhibitor effect by targeting CDK6 in metastatic melanoma. J Invest Dermatol 137 (9):1955–1964. https://doi.org/10.1016/j. jid.2017.03.039 12. Iida Y, Ciechanover A, Marzese DM, Hata K, Bustos M, Ono S, Wang J, Salomon MP, Tran K, Lam S, Hsu S, Nelson N, KravtsovaIvantsiv Y, Mills GB, Davies MA, Hoon DSB (2017) Epigenetic regulation of KPC1 ubiquitin ligase affects the NF-kappaB pathway in melanoma. Clin Cancer Res 23 (16):4831–4842. https://doi.org/10.1158/ 1078-0432.ccr-17-0146

Chapter 35 High-Throughput Identification of miRNA–Target Interactions in Melanoma Using miR-CATCHv2.0 Andrea Marranci, Romina D’Aurizio, Milena Rizzo, Catherine M. Greene, and Laura Poliseno Abstract MicroRNAs (miRNAs) can regulate the expression of potentially every transcript in the cell, and the definition of miRNA–target interactions is crucial to understand their role in all biological processes. However, the identification of the miRNAs that target a specific mRNA remains a challenge. Here, we describe an innovative method called miR-CATCHv2.0 for the high-throughput identification of the miRNA species bound to an RNA of interest. We also describe how this method can overcome the limitations of the current computational and experimental methods available in this field. Key words MicroRNAs, microRNA–target interaction, Post-transcriptional regulation, High throughput

1

Introduction Post-transcriptional regulation of mRNAs plays a crucial role in every cellular process and is mainly mediated by noncoding RNAs, in particular microRNAs (miRNAs) [1], and RNA binding proteins (RBPs) [2]. miRNAs are short single stranded noncoding RNAs (21–25 nt) that physically interact with their target mRNAs and control their expression. In the vast majority of cases, miRNAs inhibit protein production by translational repression or mRNA degradation [3], though in some cases they can sustain their target expression by inducing RNA stability or translation [4, 5]. Since the late 90’s, an increasing amount of experimental evidence has clearly established that miRNAs are involved in several cellular processes and, as a consequence, that their deregulation is associated with different diseases, including melanoma [6] and other cancers [7, 8]. In cancer, miRNAs have been reported to regulate the expression of tumor-suppressor genes and oncogenes, thus modulating crucial phenotypes such as proliferation, cell

Kristian M. Hargadon (ed.), Melanoma: Methods and Protocols, Methods in Molecular Biology, vol. 2265, https://doi.org/10.1007/978-1-0716-1205-7_35, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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death, and invasion [9]. Moreover, miRNAs represent an important tool in diagnostics, monitoring, and therapeutics [10]. The identification of miRNA–target interactions is the crucial precondition for a full understanding of their role in the cell and, despite the advancements of recent years, remains a challenging issue. In the last decade a considerable number of bioinformatic tools have been developed to predict such interactions [11, 12]. Computational methods are easy-to-use and not timeconsuming, but they are limited by high false-positive rates (25–70%) and can miss some true-positive interactions that do not follow the algorithms’ “rules” [13, 14]. On the other hand, experimental methods can directly identify miRNAs physically bound to an RNA of interest and overcome the limitations of the in silico approaches. In some methods, the transcript of interest (generally the 30 UTR) is used as bait and is administered as a 30 -end biotinylated molecule [15] or as a fusion transcript with MS2 RNA hairpin motifs [16, 17]. Alternatively, the miR-CATCH technique [18–22] gives a more faithful representation of the intracellular target-miRNA interactions, by the use of biotinylated oligo probes that allow the pull down of a selected endogenous mRNA, without any external perturbation of its expression level. Here, we describe the technique miR-CATCHv2.0 that was coupled with small-RNA sequencing [23] and used for the first time in melanoma to identify miRNA regulators of the BRAFX1 transcript isoform [24, 25]. This innovative upgrade of the miR-CATCH approach takes advantage of a cross-linking step, RNA fragmentation by sonication, and two sets of biotinylated tiling probes (ODD and EVEN). miR-CATCHv2.0 yields a heavy enrichment of target-related miRNAs for subsequent identification by small-RNA sequencing. Besides providing a step-by-step description of the capture procedure, in this chapter we provide an ad hoc analytical workflow to analyze sequencing data, and we describe several molecular assays useful for validating the microRNA regulation of the transcript of interest.

2

Materials

2.1 Materials for miR-CATCHv2.0 Protocol

For all Buffer preparations, use concentrated stock solutions. Treat water and concentrated stock solutions (Tris–HCl, NaCl) with diethyl pyrocarbonate (DEPC) to neutralize RNAse (see Note 1), then use those solutions for buffer preparation. 1. 1% formaldehyde solution in PBS. Always prepare fresh from paraformaldehyde powder (see Note 2). 2. 1.25 M Glycine solution in PBS. 3. 30 biotinylated DNA probes (30 BIOTEG oligos).

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4. Basic Local Alignment Search Tool (BLAST), available at https://blast.ncbi.nlm.nih.gov. 5. ChIRP Probe Designer software, available at https://www. biosearchtech.com/chirpdesigner. 6. Hybridization Buffer: 750 mM NaCl; 1% SDS; 50 mM Tris– HCl, pH 7.0; 1 mM EDTA; 15% formamide. Treat concentrated NaCl stock solution and water with DEPC to neutralized RNase (see Note 1), then use those solutions for buffer. Adjust pH before adding EDTA and SDS. Supplement buffer just before use by adding fresh formamide, 1 mM PMSF, 1 protease inhibitor cocktail, and 80 U/ml RNasin. 7. Lysis Buffer: 50 mM Tris–HCl, pH 7.0; 5 mM EDTA; 1% sodium dodecyl sulfate (SDS). Adjust pH before adding EDTA and SDS. To make complete Lysis Buffer from this unsupplemented Lysis Buffer, add 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 protease inhibitor cocktail, and 80 U/ml of RNasin fresh before use where required. 8. Magnet. 9. mfold Web Server, available at http://unafold.rna.albany. edu/?q¼mfold. 10. Proteinase K Buffer: 100 mM NaCl; 10 mM Tris–HCl, pH 7.0; 1 mM EDTA; 0.5% SDS. Follow the recommendation stated above for preparation. 11. Proteinase K. 12. Sonicator. 13. Streptavidin magnetic Streptavidin C1).

beads

(i.e.,

Dynabeads

myOne

14. Supplies for cell culture. 15. TE Buffer: 1 mM EDTA, pH 8.0; 10 mM Tris–HCl, pH 8.0. 16. Wash Buffer: 2 saline sodium citrate (SSC) Buffer; 0.5% SDS. Dilute commercially available SSC buffer stock solution with DEPC water and add SDS. Add 1 mM PMSF before use. 2.2 Materials for Small-RNA seq (Library Construction and Sequencing)

1. 4.5% Mini Protean® TBE gel—50 μl (Bio-Rad) (see Note 3). 2. Bioanalyzer 2100 (Agilent). 3. General lab supplies. 4. High Sensitivity DNA Kit (Agilent). 5. Kapa Library Quantification Kit (Roche). 6. MiSeq sequencer (Illumina). 7. MiSeqReagent Kit V3 150 cycle (Illumina). 8. Nanosep MF centrifugal device with GHP membrane and 0.45 μm pore size (PALL Life Sciences) (see Note 3).

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9. PhiXControl V3 Kit (Illumina). 10. SuperScript Scientific).

II

Reverse

Transcriptase

(Thermo

Fisher

11. T4 RNA Ligase 2, Deletion Mutant (Epicentre, Lucigen). 12. TruSeq® Small RNA Library preparation kit (Illumina). 2.3 Hardware and Software for Bioinformatic Analysis

As prerequisites, a computer running an UNIX-based operating system with at least 4 GB of RAM is needed, with the following installed software: 1. SAMtools, which can be downloaded at https://www.htslib. org/download/. 2. Burrows–Wheeler Aligner (BWA), available at http://bio-bwa. sourceforge.net. 3. R, which can be downloaded at CRAN (https://cran.r-project. org) and, eventually, RStudio [26], the open-source integrated development environment (IDE) for R.

2.4 Materials for miRNA Validation Assays

1. Lipofectamine 2000. 2. Actinomycin D. 3. Anti-HA Sepharose beads. 4. Dual-Glo® Luciferase Assay System. 5. Lipofectamine 2000 transfection reagent. 6. miRNA mimics. 7. miRNeasy Micro Kit (Qiagen). 8. NT2 Buffer: 50 mM Tris–HCl, pH 7.5; 150 mM NaCl; 1 mM MgCl2; 0.05% NP-40. Follow the recommendations for buffer preparation stated above. When required, supplement the base buffer to make complete NT2 Buffer by adding Protease Inhibitor Cocktail, RNase OUT (RNase inhibitor), and 10 mM DTT prior to use. 9. pMIR-report Vector. 10. pMS2 Vector. 11. pMS2-BP Vector. 12. Polyethylenimine (PEI) transfection reagent. 13. Polysome Buffer: 20 mM Tris–HCl, pH 7.5; 100 mM KCl; 5 mM MgCl2; 0.5% NP-40. Follow the recommendations for buffer preparation stated above. Prior to use, supplement the base buffer to make complete Polysome Buffer by adding Protease Inhibitor Cocktail, RNase OUT (RNase inhibitor), and 10 mM dithiothreitol (DTT). 14. pRL-TK Vector.

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15. qRT-PCR primers, including those for Firefly and Renilla Luciferase mRNAs: (a) Firefly Forward primer (50 –30 ): CCCGCCGCCGTTGT TGTTTTGG. (b) Firefly Reverse primer (50 –30 ): CGCGCAACTTTTTCG CGGTTGT. (c) Renilla Forward primer (50 –30 ): GAAAGTGAAGGGCCT GCACTTC. (d) Renilla Reverse primer (50 –30 ): CACTGCTCGTTCTT CAGCACCC. 16. QuantiTect Reverse Transcription Kit (Qiagen). 17. QuikChange II Site-directed Mutagenesis Kit (Agilent). 18. Restriction enzymes for cloning. 19. Trizol.

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Methods For all the steps of miR-CATCHv2.0 protocol, refer to the schematic representation in Fig. 1.

3.1

miR-CATCHv2.0

3.1.1 Probe Design and Preparation

Before starting with probe design, check carefully the length, sequence, and expression of the RNA target that you want to study in your biological system (see Note 4). 1. Design probes for the capture of the RNA of interest using the ChIRP Probe Designer online software. Probes should be 20 nt long and designed to specifically target loci every ~100 nt along the transcript (Fig. 1a). 2. Check the probes to avoid using those that show noticeable match to off-target genes, using BLAST to evaluate complementarity against the genome of interest. 3. Avoid probes that form thermodynamically stable self-dimers, which can be assessed using the mfold Web Server. 4. Design probes to have an extended spacer arm (triethyleneglycol) plus a biotin (Biotin-TEG) molecule at their 30 ends (many companies can synthesize ready to use oligos with this functionalization). 5. Resuspend lyophilized probes in TE buffer following the manufacturer’s instructions. Pool the Odd probes and pool the Even probes, bringing each probe set to a 100 μM final concentration.

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Fig. 1 miR-CATCHv2.0 experimental protocol. (a) Schematic representation of antisense biotinylated tiling probe location, referred to target RNA sequence. Probes are located approximately 100 nt apart along the target sequence and are pooled into “ODD” and “EVEN” sets based on their position. (b) Workflow of miR-CATCHv2.0 protocol. Cells are cross-linked and lysed by sonication to obtain RNA fragmentation. ODD probes and EVEN probes are used for independent target RNA captures. miRNA–target complexes are purified using magnetic streptavidin beads and, after stringent washes, RNA is eluted and processed for small-RNA seq 3.1.2 Cross-Linking

1. Harvest 1  107 cells into a 50 ml tube and wash once with PBS (depending on the morphology and on the availability of the cells, a lower number can be used). Harvest also the same amount of cells for a pellet to be used as baseline (precapture sample for a total miRNome or further qRT-PCR analyses). 2. Prepare 10 ml of fresh 1% formaldehyde in PBS from paraformaldehyde powder (see Note 2).

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3. Centrifuge the cells and resuspend the pellet with 10 ml of 1% formaldehyde in PBS. Incubate the tube on a shaker for 10 min at room temperature. 4. Quench the reaction by adding 1 ml of 1.25 M glycine solution and incubate for 5 min at room temperature. 5. Centrifuge at 2000  g for 5 min at 4  C. Discard supernatant. 6. Resuspend the pellet with 50 ml of ice-cold PBS and centrifuge at 2000  g for 5 min at 4  C. Discard supernatant. 7. Resuspend the cell pellet in 600 μl of ice-cold PBS and transfer to a 1.5 ml screw-cap microtube. To harvest any remaining cells in the 50 ml tube, add an extra 600 μl of ice-cold PBS to the tube and transfer that volume to the same 1.5 ml screw-cap microtube. 8. Centrifuge at 2000  g for 5 min at 4  C, and discard the supernatant (remove carefully all the supernatant using a 20 μl pipette). 9. Flash-freeze the cell pellet in liquid nitrogen. 10. Stopping point: the flash-frozen cell pellet can be stored at 80  C until use. 3.1.3 Lysis and Sonication

1. Quickly thaw the cell pellets at room temperature and flick to dislodge. Spin down the pellet at 2000  g for 3 min at 4  C. Remove any supernatant using a 20 μl pipette. 2. Resuspend the pellet in 1 ml of complete Lysis Buffer, and transfer the solution to a V-bottom 15 ml tube (see Note 5). Place the tube in an ice water bath with constant convection during the sonication. 3. Put the sonic probe in the tube and sonicate for 12 rounds at 70% amplitude for 30 s pulses, with 45 s cool down pauses in between. During this step, RNA fragments should reach a length between 200–1000 nt. Different types of cells may need a different number of rounds of sonication to obtain this range (see Notes 5 and 6). Flash freeze the samples in liquid nitrogen and store at 80  C. 4. Stopping point: flash-frozen lysate can be stored at 80  C until use.

3.1.4 Probes Hybridization (Capture)

1. Thaw tubes of lysate at room temperature and collect together sonicated lysates to have a minimum of 1 ml for the ODD capture and 1 ml for the EVEN capture. 2. Wash 60 μl (30 μl for each pool of probes) of streptavidin magnetic beads three times with 1 ml of unsupplemented Lysis Buffer, and resuspend the beads in 60 μl of complete Lysis Buffer (see Note 7).

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3. Add 30 μl of beads to 1 ml of lysate in a 1.5 ml tube and incubate in a 37  C hybridization oven for 30 min under constant upside/down mixing on a rocker. 4. Clear the lysate from beads twice, using the magnetic stand and new 1.5 ml tubes, and then transfer the lysate to a 5 ml roundbottom tube (see Note 8). 5. Add 2 ml of supplemented Hybridization Buffer and a total amount of 100 pmol of probes (1 μl from the 100 μM stock probes pool) to each tube (one tube for Odd probes and one tube for Even probes). 6. Incubate the tubes under constant upside/down mixing in a 37  C hybridization oven for 4 h. 7. Wash 200 μl of beads three times with 1 ml of unsupplemented Lysis Buffer and resuspend in 200 μl of supplemented Lysis buffer (see Note 7). 8. Add 100 μl of beads to the ODD and to the EVEN capture and place again the tubes in the hybridization oven for 30 min at 37  C under constant upside/down mixing. 9. Pellet the beads with the magnet and resuspend them in 1 ml of Wash Buffer prewarmed to 37  C. 10. Transfer beads/buffer to a new 1.5 ml tube. Wash again the 5 ml tube with an additional 300 μl of Wash Buffer to collect any remaining beads, and add this volume to the 1.5 ml tube. Place the tubes under constant upside/down mixing in a 37  C hybridization oven for 5 min. 11. Wash the beads another four times as described above, using 1 ml of prewarmed Wash Buffer for a total of five washes (see Note 9). 12. Spin down beads after the last wash and remove all remaining Wash buffer using a micropipette. 3.1.5 Target Affinity Purification and Elution

1. Resuspend the beads in 185 μl of Proteinase K Buffer, and add 15 μl of 20 mg/ml Proteinase K. Treat in the same way a sample of the original lysate, as an input control. 2. Incubate the samples at 45  C for 1 h under constant and vigorous agitation. 3. Incubate the samples at 95  C for 10 min. 4. Add 1 ml of TRIzol directly to the beads (and input), vortex, and incubate for 10 min at room temperature. 5. Proceed with RNA isolation using the miRNeasy Micro Kit (or equivalent kit), by following the manufacturer’s instructions (see Note 10) or store at 80  C.

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6. Using an aliquot of purified RNA, assess the enrichment of the target RNA over the precaptured samples (total RNA) by qRT-PCR. Then, perform small-RNA seq, qRT-PCR, other analyses, or store RNA at 80  C. 3.2

Small-RNA Seq

For next-generation sequencing (NGS), we use the Illumina platform, generating cDNA libraries with the TruSeq® Small RNA Library Preparation Kit and sequencing samples on a MiSeq sequencer (Fig. 2). In the procedure below, only the steps differing from the standard Illumina protocol are described in more detail. Where we add these details, we refer to the specific Illumina protocol and underline the steps (i.e., step 1) from the publicly available TruSeq® Small RNA Library Prep Reference Guide (Document #15004197).

Fig. 2 Small-RNA seq. Schematic representation of the generation of the library for small-RNA seq (see Subheading 3.2 for details)

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1. Lyophilize the RNA samples, obtained with themiRCATCHv2.0 procedure, to reduce the volume to 5 μl. 2. Use the 5 μl of RNA for the ligation of 30 (RA3) and 50 (RA5) adapters (step 1, Ligate Adapters). 3. Use 6 μl of the adapters-ligated RNA (step 1) for reverse transcription (step 2, Reverse Transcribe Libraries) and amplification (step 3, Amplify Libraries) steps. Perform all steps following the manufacturer’s instructions for the Illumina protocol. During the amplification step increase the number of cycles to 15. To pool and run together in a single lane of the MiSeq sequencer the ODD and EVEN samples of more than one independent capture (three in our case), use one specific RNA PCR Primer index (RPI) for each sample during amplification (step 3). At the end, one cDNA library (50 μl) for each sample will be obtained. 4. Pool the cDNA libraries obtained from the ODD and EVEN samples (50 μl + 50 μl) of the same capture and perform size selection (step 4, Purify cDNA Construct) using a 5% MP TBE gel (50 μl) (Bio-Rad), 1 TBE buffer, and Mini-Protean apparatus (Bio-Rad). Load and run the pools according to the Illumina protocol. Each pool must be loaded in more than one well, and the gel slice excised from the wells belonging to the same pool is further processed following the Illumina protocol (see Notes 11 and 12). 5. Concentrate the pools using the protocol described in the “Concentrate Final Library” paragraph of the Illumina protocol (we do not use the 1 Pellet Paint NF Coprecipitant). Importantly, the fragmented gel slices of the different pools must be shaken overnight with 300 μl of ultrapure water to increase cDNA elution. At the end size selected/purified pools are obtained. 6. To check the quality of the pools (step 5, Check Libraries), load 1 μl of each pool onto a High Sensitivity DNA chip (Agilent) and run the chip in the Bioanalyzer 2100 following the manufacturer’s indication. The resulting peak must be about 147 bp. 7. On the basis of the molarity estimated by the Bioanalyzer, construct a “final pool” composed of an equimolar quantity of the pools of the independent captures. 8. Before sequencing, evaluate the “final pool” molarity by qRT-PCR, using the Kapa Library Quantification Kit according to the manufacturer’s protocol (see Note 13). 9. Process the “final pool” for sequencing, following the Illumina protocol (Preparing Libraries for Sequencing on the MiSeq). We sequence 18 pM (see Note 13) of the “final pool” on a

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MiSeq V3 flow cell (MiSeq reagent kit V3 150 cycle), using the “FASTQ only” workflow in single read mode (36 bp) on a MiSeq sequencer. 3.3 Bionformatic Analysis

In this section we describe how to select miRNAs that are likely physically bound to the RNA of interest, starting from the full set of miRNAs identified by small-RNA sequencing (as described in Subheading 3.2). A schematic representation of the analytical workflow is shown in Fig. 3 and encompasses three main steps: #1. targetabundance scaling correction, #2. miR-CATCHv2.0 miRNAs selection, and #3. classification of miR-CATCHv2.0 enriched miRNAs (refer to #1, #2, and #3 in Fig. 3). The identification of miRNAs and their quantification from a small-RNA seq experiment is not part of this protocol, which is devoted to the peculiarities of analyzing miR-CATCHv2.0 experiments. Many different bioinformatic tools have been developed in the last decades for the analysis of small-RNA high-throughput sequencing data. Most of them are stand-alone tool suites to be run locally with separated or combined features, such as reads preprocessing, mapping, and differential expression analysis (e.g., mirExpress [27], miRDeep2 [28], or miRanalyzer [29]). Web platforms with integrated tools for independent pipelines are also available, such as sRNAtoolbox [30]. Details on these procedures have been previously described [31]. The standard output of all miRNA identification tools is a reads count (expression units) matrix of each miRNA (rows) per sample (columns), which provides a digital measure of the abundance of an miRNA/gene. Furthermore, most of the tools provide also the library size normalized counts which are necessary to remove technical biases in sequenced data due to the different size of libraries produced by nonuniform depth of sequencing (higher sequencing depth produces higher read counts for genes expressed at the same level). The normalized read counts matrix, reads per million (RPM), is the input data of the analytical workflow that we describe in this section (see Note 14). Before proceeding with the downstream analysis, one should check the quality of the produced data. Indeed, the pool of ODD probes and that of EVEN probes can show differences in binding efficiencies, resulting in a subset of microRNAs being identified by only one pool (see Note 15).

3.3.1 Target Abundance Scaling Correction

The first step (Fig. 3, #1) of the analysis accounts for the influence of the abundance of miRNAs by the relative abundance of the captured target, for example X1 30 UTR. Since all RNA molecules are cut into small pieces during the sonication step, also the target mRNA is captured by the small-RNA sequencing library procedure and can be quantified by counting the number of reads aligning to its sequence for each capture sample. To identify and quantify the

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Fig. 3 Bioinformatic analysis of the microRNAs identified through small-RNA seq. Schematic representation of the analytical steps followed to select the microRNAs to be validated (see text for details)

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sequenced reads of the target, use BWA alignment tool with the following command lines starting from the .fasta file with the sequence of the target and the .fastq file with the sequenced reads of each capture sample: > bwa index 3UTR_BRAF-X1.fa > bwa aln 3UTR_BRAF-X1.fa 115-ODD9_S1_L001_R1_001.T.fastq > 115-ODD9.sai > bwa samse 3UTR_BRAF-X1.fa 115-ODD9.sai 3UTR_BRAF-X1.fa | samtools view -S -b -o 115-ODD9.bam > samtools flagstat 115-ODD9.bam > 115-ODD9.stat

In the .stat output file there is the total number of reads of the library and the number of mapped reads to calculate the normalized counts for the target (RPMT) (see Note 14). Calculate the target scaled counts of each miRNAs using those values as scaling factors for the original miRNAs RPM and the formula: RPM, Target scaled of a microRNA ¼

3.3.2 miR-CATCHv2.0 miRNA Selection

RPM of a microRNA RPMT

The lists of microRNAs identified by ODD and EVEN probes must be compared to select only those that (i) are common to both capture probe sets; (ii) show low-variance among replicates of the same probe set; and (iii) are concordant between ODD and EVEN captures. The selection steps described below could be implemented using R codes, starting from a normalized count matrix of miRNAs from a miR-CATCHv2.0 experiment (Fig. 3, #2). In the working example used here, we use the normalized count matrix from GEO accession number GSE117640, which can be downloaded from the GEO repository through the link: https://www. ncbi.nlm.nih.gov/geo/query/acc.cgi?acc¼GSE117640. The page shows details about the full dataset, which includes 6 samples (ODD and EVEN probe captures for the 3 independent experiments and the scrambled probes capture). The GSE117640_rpm. txt.gz is a tab-delimited file with a matrix with normalized RPM values for each identified microRNA for each sample, by rows and columns, respectively. 1. Load the tab-delimited file with microRNA RPMs. > GSE117640_rpm RPMT miR-CATCH.target indODD indEVEN miR-CATCH.common miR-CATCH.STAT indS miR-CATCH.S miR-CATCH.S.rank miR-CATCH.S2