Handbook of ELISPOT: Methods and Protocols, 2nd Edition (Methods in Molecular Biology 792)

METHODS IN MOLECULAR BIOLOGY™ Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfi...

Author: Alexander E. Kalyuzhny

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METHODS

IN

MOLECULAR BIOLOGY™

Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK

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

Handbook of ELISPOT Methods and Protocols Second Edition Edited by

Alexander E. Kalyuzhny R & D Systems, Inc., Minneapolis, MN, USA

Editor Alexander E. Kalyuzhny, Ph.D R & D Systems, Inc. Minneapolis, MN, USA [email protected]

ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-61779-324-0 e-ISBN 978-1-61779-325-7 DOI 10.1007/978-1-61779-325-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011936016 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

Preface Enzyme-linked immunospot assay (ELISPOT) has been known for almost three decades as a unique state-of-the-art technique for studying the cytokine-secreting activity of immune system cells. ELISPOT appears to be one of the fast growing applications in biomedical research and has become an indispensable tool in vaccine development, HIV research, transplantation studies, and cancer and allergy research. After publishing the 1st edition of the Handbook of ELISPOT in 2005 which received a strong positive feedback from novices, advanced users, and ELISPOT experts, a wealth of new experience with this assay has been accumulated, bringing about the 2nd edition. The very fact that almost twice as many ELISPOT papers were published in 2010 than when the ﬁrst edition was written in 2004, suggests that ELISPOT is gaining popularity as a must-have research tool. In addition, ELISPOT appears to be a very dynamic technique that can be modiﬁed and adapted for a large variety of diverse research tasks. In spite of its apparent simplicity, ELISPOT is complicated and capricious, and setting up an assay and executing it requires a great deal of understanding of its chemical and biological aspects. Furthermore, even knowing the latter is not sufﬁcient enough because it is also critically important to understand the principles of analyzing ELISPOT images, spot quantiﬁcation, extracting the biological information from the images of spots, and performing a statistical analysis. The 2nd edition of the Handbook of ELISPOT is not just a reformatted 1st edition but rather an extension of the former. It is only the second book in the ﬁeld which is entirely dedicated to ELISPOT assay, helping researchers not only to learn it but also to advance and become experts. In addition, this book is also intended to assist both novice and experienced researchers from other areas of biomedical science, including stem cells, neuroscience, and endocrinology who are looking for additional cell-based research tools. Part I of the Handbook of ELISPOT includes two chapters introducing the reader to the strengths (Chapter 1) and challenges (Chapter 2) of ELISPOT assay. Part II covers veterinary applications of the ELISPOT assay with equine (Chapter 3), feline (Chapter 4), and canine (Chapter 5) species. Advanced applications are grouped in Part III, covering multicolor ﬂuorescent ELISPOT (Fluorospot, Chapter 6), as well as using ELISPOT for such novel applications as studying oxidative stress (Chapter 7) and secretory activity of microglial cells (Chapter 8), stem cell research (Chapter 9), and combining ELISPOT with ELISA to measure amounts of cytokine secreted by a single cell (Chapter 10). Principles of ELISPOT image analysis are presented in Part IV (Chapters 11–13) along with protocols on statistical data analysis (Chapters 14 and 15). Finally, Part V concludes this volume with chapters on using ELISPOT for vaccine development (Chapters 16 and 17), a diagnostic tool (Chapter 18), and it ends with an overview of membranes and membrane plates used for research and diagnostic ELISPOT applications (Chapter 19). As with the 1st edition, the ultimate goal of putting the current volume together was a compilation of a technical reference and a troubleshooting guide for researchers worldwide. The material presented in this book is written by the leading scientists in their ﬁelds who

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translated their hands-on experience into concise how-to protocols, walking the reader step-by-step through their merits and shortcomings. Even after dedicating 10 years to this ﬁeld with more than 50 developed ELISPOT assays and considering myself an expert, I found contributed chapters as excellent educational materials with a lot of new tricks and hints to learn. I wish to thank contributing authors for sharing their knowledge and expertise with the rest of us, and spending a lot of time (often their personal) on writing and reviewing their chapters to make them both highly informative and easy to comprehend by researchers at different knowledge levels and training skills. I also hope that protocols presented in this volume will serve as food for thought for inquisitive minds in their attempts to develop the next generation of ELISPOT assays that can better meet the challenges presented by the biomedical science. Minneapolis, MN

Alexander E. Kalyuzhny

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

PART I

SETTING AND PERFORMING ELISPOT ASSAY

1 Unique Strengths of ELISPOT for T Cell Diagnostics . . . . . . . . . . . . . . . . . . . . . . Paul V. Lehmann and Wenji Zhang 2 The Impact of Harmonization on ELISPOT Assay Performance . . . . . . . . . . . . . . . Sylvia Janetzki and Cedrik M. Britten

PART II

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ELISPOT FOR VETERINARY RESEARCH

3 Equine ELISPOT Assay to Study Secretion of IFNg and IL-4 from Peripheral Blood Mononuclear Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jodi Hagen, Chris Hartnett, Jeffrey P. Houchins, Steeve Giguère, and Alexander E. Kalyuzhny 4 Utilization of Feline ELISPOT for Mapping Vaccine Epitopes . . . . . . . . . . . . . . . . Jeffrey R. Abbott, Ruiyu Pu, James K. Coleman, and Janet K. Yamamoto 5 Analyzing Cellular Immunity to AAV in a Canine Model Using ELISPOT Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zejing Wang, Rainer Storb, Stephen J. Tapscott, and Stanley Riddell

PART III

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ADVANCED ELISPOT TECHNIQUES

6 Dual- and Triple-Color Fluorospot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Niklas Ahlborg and Bernt Axelsson 7 ELISPOT Assay as a Tool to Study Oxidative Stress in Lymphocytes. . . . . . . . . . . . Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny 8 ELISPOT Assay for Neuroscience Research: Studying TNFA Secretion from Microglial Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny 9 ELISPOT Assay as a Tool to Study the Effects of Stem Cells on Cytokine Secretion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun-Seop Shin and Chung-Gyu Park 10 Combining ELISPOT and ELISA to Measure Amounts of Cytokines Secreted by a Single Cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny

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PART IV

IMAGE AND DATA ANALYSIS

11 How ELISPOT Morphology Reflects on the Productivity and Kinetics of Cells’ Secretory Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexey Y. Karulin and Paul V. Lehmann 12 Mathematical Algorithms for Automatic Search, Recognition, and Detection of Spots in ELISPOT Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey S. Zadorozhny and Nikolai N. Martynov 13 Objective, User-Independent ELISPOT Data Analysis Based on Scientifically Validated Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenji Zhang and Paul V. Lehmann 14 Statistical Analysis of ELISPOT Assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcus Dittrich and Paul V. Lehmann 15 Response Determination Criteria for ELISPOT: Toward a Standard that Can Be Applied Across Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zoe Moodie, Leah Price, Sylvia Janetzki, and Cedrik M. Britten

PART V

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ELISPOT ASSAY FOR VACCINE DEVELOPMENT AND DIAGNOSTICS

16 Detection of Vaccinia Virus-Specific IFNG and IL-10 Secretion from Human PBMCs and CD8+ T Cells by ELISPOT . . . . . . . . . . . . . . . . . . . . . . 199 Benjamin J. Umlauf, Norman A. Pinsky, Inna G. Ovsyannikova, and Gregory A. Poland 17 ELISPOT Assays to Enumerate Bovine IFN-G-Secreting Cells for the Development of Novel Vaccines Against Bovine Tuberculosis . . . . . . . . . . . 219 Martin Vordermeier and Adam O. Whelan 18 IL-7 Addition Increases Spot Size and Number as Measured by T-SPOT.TB ® . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Marsha L. Feske, Miguel Medina, Edward A. Graviss, and Dorothy E. Lewis 19 Overview of Membranes and Membrane Plates Used in Research and Diagnostic ELISPOT Assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Alan J. Weiss Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257

Contributors JEFFREY R. ABBOTT s Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA NIKLAS AHLBORG s MABTECH AB, Nacka Strand, Sweden BERNT AXELSSON s MABTECH AB, Nacka Strand, Sweden CEDRIK M. BRITTEN s Department of Internal Medicine, University Medical Center, Johannes Gutenberg-University Mainz III, Mainz, Germany JAMES K. COLEMAN s Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA MARCUS DITTRICH s Department of Bioinformatics, Biocenter, University of Wuerzburg, Würzburg, Germany MARSHA L. FESKE s Center for Molecular and Translational Human Infectious Disease Research/Molecular Tuberculosis Laboratory, Methodist Hospital Research Institute, Houston, TX, USA STEEVE GIGUÈRE s Department of Large Animal Medicine, College of Veterinary Medicine, University of Georgia, Athens, GA, USA EDWARD A. GRAVISS s Center for Molecular and Translational Human Infectious Disease Research/Molecular Tuberculosis Laboratory, Methodist Hospital Research Institute, Houston, TX, USA JODI HAGEN s R&D Systems, Inc., Minneapolis, MN, USA CHRIS HARTNETT s R&D Systems, Inc., Minneapolis, MN, USA JEFFREY P. HOUCHINS s R&D Systems, Inc., Minneapolis, MN, USA SYLVIA JANETZKI s ZellNet Consulting, Inc., Fort Lee, NJ, USA ALEXANDER E. KALYUZHNY s R&D Systems, Inc., Minneapolis, MN, USA ALEXEY Y. KARULIN s Cellular Technology Limited, Shaker Heights, OH, USA PAUL V. LEHMANN s Cellular Technology Limited, Shaker Heights, OH, USA DOROTHY E. LEWIS s Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA NIKOLAI N. MARTYNOV s MZ Computers, Ltd, Moscow, Russia MIGUEL MEDINA s Department of Internal medicine, Health Division Infection Diseases, Houston, TX, USA ZOE MOODIE s Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA, USA INNA G. OVSYANNIKOVA s Mayo Vaccine Research Group, Mayo Clinic and Foundation, Rochester, MN, USA CHUNG-GYU PARK s Department of Microbiology and Immunology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea; Xenotransplantation Research Center, Seoul National University College of Medicine, Seoul, South Korea; Transplantation Research Institute, Seoul National University College of Medicine, Seoul, South Korea

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NORMAN A. PINSKY s Mayo Vaccine Research Group, Mayo Clinic and Foundation, Rochester, MN, USA GREGORY A. POLAND s Mayo Vaccine Research Group, Mayo Clinic and Foundation, Rochester, MN, USA; Program in Translational Immunovirology and Biodefense, Mayo Clinic and Foundation, Rochester, MN, USA LEAH PRICE s Division of Biostatistics, Department of Environmental Medicine, New York University School of Medicine, New York, NY, USA RUIYU PU s Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA STANLEY RIDDELL s Program in Immunology, Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA JUN-SEOP SHIN s Department of Microbiology and Immunology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea; Xenotransplantation Research Center, Seoul National University College of Medicine, Seoul, South Korea; Transplantation Research Institute, Seoul National University College of Medicine, Seoul, South Korea RAINER STORB s Program in Transplantation Biology, Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA STEPHEN J. TAPSCOTT s Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA BENJAMIN J. UMLAUF s Mayo Vaccine Research Group, Mayo Clinic and Foundation, Rochester, MN, USA MARTIN VORDERMEIER s Department of Bacteriology, TB Research Group, Veterinary Laboratories Agency, WeybridgeAddlestone, Surrey, UK ZEJING WANG s Program in Transplantation Biology, Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, USA ALAN J. WEISS s Strategy and Business Development, EMD Millipore, A Division of Merck, Acton, MA, USA ADAM O. WHELAN s Department of Bacteriology, TB Research Group, Veterinary Laboratories Agency, WeybridgeAddlestone, Surrey, UK JANET K. YAMAMOTO s Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA SERGEY S. ZADOROZHNY s MZ Computers, Ltd, Moscow, Russia WENJI ZHANG s Cellular Technology Limited, Shaker Heights, OH, USA

Part I Setting and Performing ELISPOT Assay

Chapter 1 Unique Strengths of ELISPOT for T Cell Diagnostics Paul V. Lehmann and Wenji Zhang Abstract The T cell system plays an essential role in infections, allergic reactions, tumor and transplant rejection, as well as autoimmune diseases. It does so by the selective engagement of different antigen-speciﬁc effector cell lineages that differentially secrete cytokines and other effector molecules. These T cell subsets may or may not have cytolytic activity, can preferentially migrate to different tissues, and display variable capabilities to expand clonally. The quest of T cell immune diagnostics is to understand which speciﬁc effector function and T cell lineage is associated with a given clinical outcome, be it positive or adverse. No single assay can measure all of the relevant parameters. In this chapter, we review the unique contributions that ELISPOT assays can make toward understanding T cell-mediated immunity. ELISPOT assays have an unsurpassed sensitivity in detecting low frequency antigen-speciﬁc T cells that secrete effector molecules, including granzyme and perforin. They provide robust, highly reproducible data – even by ﬁrst time users. Because ELISPOT assays require roughly tenfold less cell material than ﬂow cytometry, ELISPOT is ideally suited for all measurements requiring parallel testing under multiple conditions. These include deﬁning (a) T cell reactivity to individual peptides of extensive libraries, thereby establishing the ﬁne–speciﬁcity of the response, and determinant mapping; (b) reactivity to different concentrations of the antigen in serial dilutions to measure the avidity of the T cell response; or (c) different secretory products released by T cells which deﬁne their respective effector lineage/functions. Further, because T cells survive ELISPOT assays unaffected, they can be retested for the acquisition of additional information in follow-up assays. These strengths of ELISPOT assays the weaknesses of ﬂow cytometrybased measurements. Thus, the two assays systems compliment each other in the quest to understand T cell-mediated immunity in vivo. Key words: ELISPOT, Flow cytometry, Intracellular cytokine staining, Tetramers, Pentamers, Multimers, Cytokine bead array, Luminex, ELISA, T cell-mediated immunity, Cellular immune response, Immune monitoring, T cell afﬁnity, T cell avidity, Determinant mapping, Epitope mapping, High-throughput T cell testing, Multiplexing, Cytokines, Frequency measurements, Single cell analysis

1. Introduction The ultimate goal of T cell diagnostics is to reliably and reproducibly measure those T cells which are mediators of clinical correlates of interest; for example, the speciﬁc T cell type that mediates protection Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_1, © Springer Science+Business Media, LLC 2012

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against a certain infection, or causes transplant rejection, autoimmune disease, allergy, etc. Until recently, efforts to identify such T cells had been misled by a dichotomous concept of T cell effector functions being either Th1 (type 1) or Th2 (type 2). Thus, it was assumed that the measurements of IFN-G producing T cells by IFN-G ELISPOT assays would detect all pro-inﬂammatory T cells, including CD8 T cells that mediate cytotoxicity. As such, IFN-G ELISPOT assays have been widely used to measure, e.g., the HIVspeciﬁc “cellular immune response.” The danger of equating CD8 T cell-mediated immunity with IFN-G measurements was recently brought to the spotlight by a high proﬁle HIV vaccine trial in which induction of HIV-speciﬁc IFN-G producing T cells was detected without the induction of protective immunity (1). While a central role for T cells in controlling HIV infection has been abundantly documented, measurements of IFN-G or other cytokines (that also had been assessed in that trial) failed to identify the protective T cell class. While we now know that T cells can differentiate into a multitude of effector lineages, each exerting unique effector functions, we still do not know which of these functions are of particular relevance for a speciﬁc condition, such as the control of HIV or other viruses. For HIV, it is tempting to speculate that the cytolytic potential of CD8 T cells rather than their cytokine production capacity is critical for controlling the virus. Cytotoxic activity of CD8 cells, however, is not necessarily associated with IFN-G secretion. We have recently shown that immunizations with different adjuvants can induce CD8 T cells that produce IFN-G and other cytokines (TNF-A, IL-2, and IL-17) and mediate delayed type hypersensitivity (DTH) but are noncytolytic, while immunizations with other adjuvants can induce CD8 T cells that are highly cytolytic, but do not produce IFN-G or other cytokines (TNF-A, IL-2, and IL-17) and do not mediate DTH (2). The measurement of IFN-G production by antigen-speciﬁc T cells does not permit to conclude whether cytolytic T cells had been induced, that, if induced, might have mediated protective immunity against HIV, and it should not matter which assay platform is utilized for the measurement of IFN-y production by T cells. It would be utterly wrong to conclude. That the ELISPOT assay itself is unsuitable for detecting clinical correlates of HIV protection (3). The correct conclusion is that IFN-G measurement per se (irrespective of the method used for detection) is not sufﬁcient to reveal the protective T cell class in HIV because apparently T cell functions other than IFN-G production are essential for controlling HIV. Measurement of cytolytic activity might have provided the sought after information which could have been done with granzyme B or perforin ELISPOT assays (4–6). Furthermore, TNF-related apoptosis-inducing ligand (TRAIL) ELISPOT assays could have revealed whether the HIV antigenspeciﬁc CD8 T cells are “helped,” functional effector cells (7).

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These measurements could be done only by ELISPOT assays measuring the actual secretion of these molecules. As we start to understand more and more about the complexity of the T cell system (and we are apparently still at the beginning of the learning curve (8)), we also learn to appreciate the importance to account for this complexity in T cell diagnostics. Isolated observations within any such complex system are likely to trigger fundamentally wrong conclusions. This generally applicable wisdom has been captured in the ancient Indian metaphor “The blind men and the elephant” (see Fig. 1). As long as we do not know what the critical effector functions are for a certain clinical condition, it should be wise to attempt to measure multiple facets of T cell immunity: their production of various cytokines, cytolytic, proliferative, and migrational properties, including their abilities to control virus (1, 9). Various assays are needed for the comprehensive measurement of different T cell functions. In ﬂow cytometry-based measurements, cells need to be “poisoned” (Golgi inhibitors) and “killed” (permeabilized) for the detection of secretory products, as such, ﬂow-based measurements tell us more about physical phenotypes

Fig. 1. The danger of relying on single parameter measurements. Inspired by the ancient Indian parable of “The blind men and the elephant.” Graphic artist: Gabor Pesthy.

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Fig. 2. Measuring T cell functions by ﬂow cytometry vs. ELISPOT. Left panel: For the detection of secretory products by ICS, the cells need to be “poisoned” ﬁrst with Golgi inhibitors to prevent secretion, then permeabilized and ﬁxed/“mummiﬁed.” The subsequent standard ﬂow cytometric analysis does not make the distinction whether the analyte is indeed bound for secretion and thus is biologically active, or is retained in/on the cell. Right panel: In contrast, ELISPOT measures the actual secretory activity of pharmacologically untreated, living cells. The cells survive ELISPOT assays unharmed, and can be retested, phenotyped, expanded, cloned, or cryopreserved. Graphic artist: Gabor Pesthy.

of cells than their biological function (see Fig. 2). ELISPOT does not allow examining of cell surface or introcytoplasmic markers, or sorting of cells based on physical characteristics, however, unlike ﬂow cytometry, it enables single cell measurements of the actual secretion of bioactive molecules. Cell surface marker positive cell populations can be readily obtained and tested in ELISPOT, should it be important to deﬁne the cell surface phenotype of the analyte secreting T cell. Not only the choice of “what” to measure is critical, the “how” is equally important. Antigen-speciﬁc T cells normally occur in low frequencies (1/100,000–1,000,000) in the test material, typically peripheral blood, and detecting them can be a major challenge. Because of the low frequency of antigen-speciﬁc T cells, and because of the need to measure their function in complex assay systems, particular consideration needs to be given to the reliability and reproducibility of T cell measurements. Finally,

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feasibility issues are also critical when selecting an approach for T cell immune monitoring, such as the number of cells needed and labor and cost involved in the procedure of data analysis. Among the approaches available for T cell immune monitoring, this chapter focuses on the ELISPOT technique’s unique contributions to T cell diagnostics.

2. Materials 1. PBMC: Cryopreserved PBMC, high resolution HLA-typed, characterized for peptide and protein antigen reactivity. 2. CTL-CryoABC™ Kit: PBMC freezing medium for loss-free cryopreservation of PBMC without the component of serum. 3. CTL-AntiAggregate™ Wash 20×: PBMC thawing solution with anti-DNAse without the need of serum supplement. 4. CTL-Test™ Medium. ELISPOT assay medium, optimized for low background and high signal without the need to supplement with serum. 5. CEF – MHC Class I Control Peptide Pool “Plus”. 6. CMV – MHC Class I Control Peptide Pool. 7. EBV – MHC Class I Control Peptide Pool. 8. CEFT – MHC class II Control Peptide Pool “Plus”. 9. ImmunoSpot® Analyzer. 10. PBMC Reference Sample QC set. 11. Practical suggestions for standardized ELISPOT work can be found in Notes 1–17.

3. Methods 3.1. Unique Strengths of ELISPOT 3.1.1. ELISPOT Measures the Functionality of Single Cells via Their Secretory Activities

ELISPOT is the only technique that allows for the quantiﬁcation of the actual secretory activity of individual cells. Intracellular cytokine staining (ICS) detects, as the name tells, intracellular analyte. The detection of actually secreted vs. intra cellular analyte can be critical for understanding functional properties of T cells. For example, a cytokine which is posttranslationally regulated will be detected upon de novo synthesis by ICS, or by measuring mRNA, but it will not exert biological effects unless it is actually secreted.

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Similarly, some highly relevant molecules are stored in granules of T cells – perforin and granzyme being prime examples. The speciﬁc release of these molecules upon antigen activation permits to selectively detect antigen-speciﬁc cytolytic CD8 effector cells by granzyme B or perforin ELISPOT assays (4–7). In contrast, by ICS all effector memory cells stain positive irrespective of their antigenspeciﬁcity, i.e., up to 20% of all CD8 T cells will be positive. Furthermore, several cell surface molecules important for T cell diagnostics become bioactive only after being cleaved and released from the cells – TNF family members, including TRAIL, fall in this category. ELISPOT detects only the functionally-relevant released molecules upon speciﬁc antigen activation. Flow cytometry measures the cell surface molecules, thus, leading to false positive results concerning functional information (7). Therefore, one needs to be thoughtful when interpreting what has been measured by ﬂow cytometry: is it functionally relevant information, or is it a phenotype that possibly bears no functional signiﬁcance. In all of the above situations, ELISPOT allows the investigator to detect the secreted, bioactive analytes. 3.1.2. ELISPOT Provides High Content Information on Analyte Secretion at Single Cell Resolution

With the advanced platform that recently have become available for ELISPOT data analysis, scientists now can gain information on the quantity and kinetics of analyte secretion as reﬂected by the size and density of the spots (see Chapter 11 and 13 on this topic). Such information can provide critical insights for T cell diagnostics beyond the frequency measurements. For example, T cells that have been activated recently in vivo, show increased per cell IFN-G productivity, i.e., produce larger and denser IFN-G spots (10). This observation made in the context of vaccinations might help to distinguish between long-term T cell memory and ongoing T cell activity. This distinction is especially important for the T cell diagnostic of autoimmune diseases, allergies, or chronic infections, including hepatitis and tuberculosis. Under conditions of immune suppression, T cells show a decreased per cell IFN-G productivity rate (11). High avidity T cells produce signiﬁcantly more cytokine than low avidity T cells (12). Per cell productivity information cannot be obtained by supernatant-based measurements, including ELISAs or cytokine bead arrays (CBA/Luminex). The latter assays measure only the net amount of analyte produced, without revealing how many cells produced it, and at what rate.

3.1.3. ELISPOT Is the Most Sensitive Technique for Single Cell Functional Analysis

In systematic comparisons with ELISPOT, ICS was found to be less sensitive with a detection limit around 0.02% (13). In typical ELISPOT assays, 400,000 PBMC are tested per well, in which case the detection limit is 0.00025% (1 analyte producing cell in 400,000 bystander cells) (12). ELISPOT per se is inherently without a detection limit. In regular 96-well plates, the numbers of PBMC plated and spots detected are linear in the range from

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100,000 to 800,000 PBMC per well (14). Thus, by plating one million PBMC per well, the lower detection limit of ELISPOT assays can be readily extended to 0.0001%. ELISPOT assays can be performed in larger than 96-well plate format, like in 6-well plates with ten million cells per well, lowering the detection limit to 0.00001%. Practically, the number of cells available for testing is the only limiting factor when it comes to conﬁguring ELISPOT assays for ultra low frequency measurements (but keep in mind, T cells survive ELISPOT assay intact and can be retested in a secondary ELISPOT or any other assays). Further, when compared to measurements of soluble analyte in supernatant, e.g., by ELISA, CBA or Luminex, ELISPOT has been shown to outperform the latter by far in sensitivity (12). There are two main reasons for this. First, in ELISPOT assays, the analyte is captured around the secreting cell before it is diluted into the supernatant, degraded or captured by receptors of bystander cells. Supernatant-based assays, in contrast, need to detect the analyte after dilution, absorption, and degradation has occurred. Second, unlike in supernatant-based assays that measure net analyte produced by all cells, in ELISPOT assays the secretory activity of individual cells is detected. Due to this quantitative nature of the ELISPOT measurements, even a moderate increase in the numbers of secreting cells becomes detectable, and can provide a statistically highly signiﬁcant result identifying a T cell response (see Chapters 13–15). The ability to reliably detect rare antigen-speciﬁc T cells is at the very core of immune diagnostic. T cells each express a unique T cell receptor (TCR) which is speciﬁc for a single antigen. In order to be able to recognize the universe of antigens, the T cell system relies on an astronomical number (~1012) of various T cell speciﬁcities. Subsequently, the frequencies of T cells recognizing individual antigens are very low. While the frequency of antigenspeciﬁc effector T cells can transiently rise to as high as 1:100 after acute infections, it typically settles in the range of under 1:10,000 (0.01%) in chronic infections, or after the antigen is cleared (1, 2, 5–7, 10, 11, 14, 15). This frequency is at the lower detection limit of ﬂow cytometry-based techniques, such as ICS, but is well within the linear detection range of standard ELISPOT measurements. 3.1.4. ELISPOT Is Most Economic in Sample Utilization

In ELISPOT assays, every single cell plated is being measured – no cells are lost, as for example, in the tubing of the ﬂow cytometer. While for ﬂow cytometry typically one million PBMC are stained per assay condition, for ELISPOT assays one tenth that number is required (100,000 PBMC per well). Furthermore, ELISPOT assays can be performed with even fewer cells. PVDF plates have become available in the 384-well format, permitting to downscale the cell numbers 1:4, thus 25,000 PBMC per well. Recently, we published a study in which ELISPOT assays were done with a

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single drop of blood obtained from the tail vein of mice: the cells obtained from each drop of blood were tested for medium background control and antigen-induced production of IFN-G and IL-17 in a dual color ELISPOT assay (15). Moreover, when antigen-presenting cells (APC) are provided as a monolayer, even single T cells can be studied in ELISPOT assays (12). Similarly, ELISPOT assays are well suited to run functional tests on the few T cells obtained by needle biopsy. The economic utilization of cells in ELISPOT compared with ﬂow cytometry-based techniques is critical when either the numbers of cells available are limiting (which is the case with essentially any clinical trial, in particular for pediatric studies or with immune suppressed test subjects) or when several antigens or assay conditions need to be tested for determinant mapping, for measurements of functional afﬁnity, or multiplexing (see below). PBMC can be efﬁciently frozen without loss of function when tested in ELISPOT assays (16). For valuable samples, it is wise to freeze them in aliquots so that data can be independently reproduced, or the range of measurements/analytes extended. Freezing away aliquots, however, cuts down on the cell material available for each test, which can make PBMC limiting even from healthy donors. Here again, the efﬁcient cell utilization of ELISPOT assays is of major advantage. 3.1.5. T Cells Survive ELISPOT Assays, Intact, and Can Be Further Utilized

In ELISPOT assays, PBMC are cultured with antigen and remain otherwise untreated. While the cells are typically discarded after an initial incubation period (the optimal duration of which is different for different analytes, Fig. 3), they can be transferred to regular tissue culture plates for later testing. In one such example, we utilized only 11 million PBMC from subjects with type 1 diabetes to study their T cell reactivity to 70 individual peptides ﬁrst ex vivo, and then again after 12 days of antigen-driven in vitro expansion while measuring IFN-G and IL-4 in a dual color assay at both time points (17). In Parallel, on day 12 ELISPOT testing was done with the cells transferred from the day 0 ELISPOT assay. We found that the results of the secondary ELISPOT testing were identical for such cells rescued from a primary ELISPOT testing, and PBMC that have been cultured in regular tissue culture plates in parallel (without initially performing an ELISPOT assay on them), further conﬁrming that the T cells survived the primary ELISPOT assay unharmed for further utilization. While we retested them in ELISPOT, they could have been tested by ﬂow cytometry, grown into T cell lines, or frozen down for further characterization at a later time. This “recycling” strategy can be very useful when one works with valuable clinical samples. It cannot be applied to assays in which the primary testing is done

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Fig. 3. The different time course of cytomegalovirus (CMV )-induced cytokine production. PBMC were plated with (or without, not show since negative) heat inactivated CMV into IL-2, -4, -5, -17, or IFN-G coated PVDF plates, and cultured in an incubator for the time period speciﬁed before the respective detection antibodies were added, and the analyte was visualized. Since the maximal numbers of spots differed for each cytokine, the maximal number was set as one. Note, the different cytokines each have very different secretion kinetics, which needs to be accounted for when measuring these cytokines.

by ﬂow cytometry, because in such cases, the cells need to be killed (ﬁxed, permealized) for analysis. Occasionally, the frequency of antigen-speciﬁc T cells is very low ex vivo, even below the detection limit of standard 96-well ELISPOT assays, where normally 100,000–500,000 PBMC are plated per well. This has been seen with some cancer vaccines or after immunizations with protein antigens. In such situations, scientists frequently rely on in vitro T cell expansion strategies: the PBMCs are ﬁrst cultured with antigen plus T cell growth factors for a longer time period (typically 1–2 weeks) in the attempt to detect the antigen-reactive T cells following this expansion. However, frequencies measured after expansion do not necessarily match up with ex vivo frequencies, (17) because different T cell populations do not have uniform expansion potential. Thus, when tested after expansion, the ex vivo measurement is clouded by the proliferative capacity of the T cells. The expansion strategy is advisable only if no ex vivo signal can be obtained via an ex vivo ELISPOT assay. The two approaches can be elegantly combined, however. The fact that the T cells can be harvested without loss after an initial ex vivo ELISPOT assay makes it feasible to test a sample ﬁrst ex vivo and then again, after expansion. Thus, the PBMC can be ﬁrst tested in an ELISPOT assay in a 6-well membrane plate at ten million PBMC per well. After the 24-h incubation of an ex vivo ELISPOT assay, the cells can be transferred into 6-well tissue culture plates for further expansion, and after 14 days

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of cell culture, can be retested in ELISPOT and/or other assays. In this way, the chances of obtaining direct ex vivo frequency measurements are maximized while still maintaining the option of learning about the frequencies after expansion via retesting. Moreover, by comparing the ex vivo frequencies with the frequencies after expansion, one can learn about the proliferative potential of the antigen-speciﬁc T cells, assessing an additional important parameter of T cell-mediated immunity, which one-time measurements by ELISPOT or ﬂow cytometry cannot provide. 3.1.6. ELISPOT Is an Ideal Technique for High-Throughput Testing and Screening

A combination of qualities makes the ELISPOT assay the primary choice for high-throughput testing, e.g., for screening of PBMC for reactivity to a multitude of antigens/peptides (i.e., determinant mapping) or establishing antigen dose–response curves (i.e., T cell avidity measurements), or for testing a high number of donor samples (in CTL’s GLP lab, we test up to 300 PBMC samples per day), or for multiplexing by ELISPOT. One important quality that enables high-throughput testing by ELISPOT is the efﬁcient cell utilization in this assay. An example was provided above (17) where only 11 million PBMC were used to test T cell reactivity to 70 individual peptide pools, measuring two cytokines, in that case even testing the cells repeatedly. Second, the simplicity of the assay favors high-throughput testing – the cells and reagents can all be handled in 96-well format, all being pipetted in batches. (The afore mentioned experiments were performed by one single student within a few days). Third, ELISPOT data analysis, including spot recognition and gating, can all be done in a fully automated and walk-away fashion (see Chapter 13 dedicated to this issue in this volume). For the above example of testing 70 peptides individually for two cytokines per test subject, the ImmunoSpot Analyzer requires less than 2 min. These 2 min include the fully automated process of acquiring the images from the wells, analyzing them for two colors, feeding the counts to a database while also saving raw and counted images for audit trails, and automatically preparing the publication-ready graph with the results. By ﬂow cytometry, it would take many hours of intense manual work of highly experienced personal to accomplish the same. Finally, the low cost of ELISPOT assays relative to ﬂow cytometric measurements has also contributed to it being the method of choice for high-throughput testing and screening.

3.1.7. ELISPOT Is the Ideal Technique for Determinant Mapping

T cells recognize peptide fragments of antigens presented on MHC molecules. MHC molecules are polymorphic (there are hundreds of alleles for each locus in the human population), whereby each allele has a unique antigen-peptide binding pattern. Moreover, MHC molecules are polygenic (T cells use several class I and class II gene products as restriction elements). As a consequence, antigenic peptide recognition by T cells in different individuals is highly individualized, being dictated by MHC polymorphism/polygenism,

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and other yet poorly understood rules of antigen processing and repertoire selection. This diversity is an insurmountable hurdle for comprehensive tetramer analysis. The peptides of an antigen that are recognized in the context of an MHC molecule are called determinants (or epitopes). Due to its high-throughput capability, ELISPOT is ideally suited for determinant (epitope) mapping, whereby extensive libraries of overlapping peptides are screened (18). The validity of the ELISPOT approach for determinant mapping was ﬁrst validated on inbred mice using model antigens, such as hen egg-white lysozyme (HEL) or ovalbumin (OVA), whose determinant recognition in the context of different MHC haplotypes had been well established (19). Since then, screening large peptide libraries has become a standard method for testing the ﬁne speciﬁcity of T cell responses and has been applied to many ﬁelds of T cell diagnostics. Here, we would like to give an illustration of the feasibility of high-throughput determinant mapping by ELISPOT – and why ELISPOT is the only technique currently available that can realistically accomplish this. The assumed task is the detection of T cell responses to an entire pathogen’s proteome using a library of overlapping peptides. For HIV, for example, a total of 410 peptides of 18 amino acid length, overlapping by 10 amino acids, are sufﬁcient to cover the entire HIV proteome. Testing of these 410 peptides on, e.g., ten donors by ELISPOT requires a simple blood draw of about 40 ml from each individual (41 million PBMC if the PBMC are tested at 100,000 cells/well) or 10 ml of blood if the test is done in the corresponding 384-well format. The plating of the cells and developing the plates can be done by a single experienced scientist (assuming the peptides had been pre-aliquoted) – and it would not even ﬁll his/her work day. The fully automated scanning, analysis and graphing time would be 10 min per test subject, thus less than 2 h for all ten subjects. The entire test could be easily done by a single investigator in 3 days, as a part time effort. If the mapping would be done by ICS, about 400 ml blood would be needed from each donor, and the analysis time alone would take days for the ten test subjects. Supernatant measurements by ELISAs or CBA/Luminex are high-throughput assays; however, these techniques are not sensitive enough to detect the peptide-induced production of cytokine by the low frequency T cells. 3.1.8. ELISPOT Is the Ideal Technique for Measurements of Functional T Cell Avidity

Typically, in functional T cell assays, antigens/peptides are tested at a single dose. This pragmatic approach misses important information about the T cell’s afﬁnity/avidity for antigen. (Avidity is the appropriate term, since during T cell activation multiple TCRs bind to multiple MHC-peptide ligands on the APC, whereby the off-rate contributes more to T cell activation than the on-rate.) In practical terms, T cell avidity can be readily measured by titrating

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Fig. 4. The different functional avidities of antigen-reactive T cells. PBMC of an HLA-A2 positive subject were plated with different concentrations of individual A-2 restricted CEF peptides, as speciﬁed by the different symbols. A standard 24 h IFN-G ELISPOT assay was performed. Note how far apart the maximum stimulatory concentrations of the different peptides are.

the peptide dose while measuring T cell activation (12). Figure 4 provides an example of the dose–response curves obtained when PBMC are tested for reactivity to different doses of peptides. Some peptides activate T cells only at relatively high concentration (in the 1–10 Mg/ml range), other peptide can cause full-blown T cell activation at concentrations as low as 1 pg/ml. High avidity T cells will be stimulated by trace amounts of antigen on APC in vivo, and are likely to exert effector functions. In contrast, the high peptide concentrations that can lead to the stimulation of low avidity T cells in vitro may not be reached in vivo – such T cells might be “ignorant” of the antigen in vivo. These considerations are of particular relevance for studies of autoimmunity and tumor immunity. We showed, using the example of myelin basic protein (MBP), that T cells in wild-type mice require 10,000-fold higher antigen doses to become activated, relative to T cells in MBP gene defective “shiverer” mice (20). In the wild-type mice, MBP is a “self-antigen” that causes negative selection of the high avidity MBP-speciﬁc T cell repertoire; whereas in the MBP deﬁcient mice it is a foreign antigen encountering an unselected T cell repertoire. Due to negative selection, most tumor antigens (that are self-antigens) are recognized by low avidity T cells. Thus, when immunizing with such antigens, there is the danger of loading APC with a higher concentration of the antigen/ peptide than that which is present on the tumor cell. This would

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result in the induction of low avidity antigen-speciﬁc T cells. Such T cells would be detected ex vivo when a high concentration of the antigen is used for their activation. The data would truthfully show the induction of a tumor antigen-speciﬁc T cell response, but, will not reveal whether those T cells could also recognize lower concentrations of the peptide on the tumor cells, i.e., whether they could function as effector cells. Measurements of T cell avidity by titrating the peptide in the recall assay will add an extra dimension to these tests providing important information toward the latter. T cell avidity measurements require functional assays that are highly efﬁcient in cell utilization to permit testing of antigen in serial dilution while at the same time being sensitive enough to detect low frequency T cells. Among T cell assays, ELISPOT is the only technique that readily fulﬁlls these requirements. 3.1.9. ELISPOT Is Readily Standardized and Validated for Immune Monitoring

Ever since T cell assays have been around, they have been surrounded by the stigma of being an art form that only few can successfully perform after a high level of specialization. Also there has been a perception that data from such assays are hard to reproduce. Indeed, the magnitude of this problem has been recently highlighted by a multicenter assay harmonization attempt (21). The same PBMC were tested in different laboratories for reactivity to the same antigen, yet the frequency measurements were more than 3,000% apart. It remains unclear to what extent this alarming variation resulted from the different level of expertise and training by the participants, the variations of protocols and reagents that were permitted to be used, subjective analysis of the data, or whether such variations are inherent to complex biological assays. Are T cell assays really so complex and their results so hard to reproduce? The authors of this chapter helped provide evidence that ELISPOT assays can produce very reproducible data among different laboratories, even in the hands of ﬁrst time users, if all assay parameters are standardized and the data analysis is performed with scientiﬁcally validated principles (14). Expertise and GLP structure were found to be not critical, only the adherence to an optimized protocol that eliminates the variables in the ELISPOT assay, and importantly the utilization of an automated, scientiﬁcally validated algorithm for user-independent analysis of the test results. Note, the same PBMC tested in this study, along with reagents, are available from CTL to anyone who wishes to reproduce this claim. The ﬁnding in this study is also particularly encouraging for anyone who would like to get started with ELISPOT. Alerted by the high level of variation caused by the subjectivity of ﬂow cytometry data analysis – which is still done manually – the iSBTc/SITC recently announced an “ICS Gating Panel” which invites scientists experienced in ICS to develop a gating harmonization strategy. While the ﬁeld is struggling to come up with a software that is capable of automated, objective analysis of ﬂow

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cytometry data, this goal has been accomplished for ELISPOT with the ImmunoSpot platform. Scientiﬁcally validated and statistical-based analysis is used by ImmunoSpot® analyzers to deﬁne spot recognition parameters and to set gates automatically, making sure that the results are objective and user independent, hence ELISPOT data become reproducible between laboratories (see Chapter 13). Thus, as the ﬁrst among T cell assays, ELISPOT has transited from an “art” form into an exact science – a technique that provides solid, reproducible measurements. 3.1.10. ELISPOT Is Well Suited for Multiplexing

Because T cells occur in many different effector classes, and because most of the time we do not know which of the effector functions are relevant, it is important to measure as many parameters as we can (see Fig. 1). Bead-based multiple analyte measurements in supernatants (CBA/Luminex) seem to be one of the ways to proceed in these efforts. However, being supernatantbased, they are most of the time not sensitive enough to reliably detect antigen-speciﬁc T cell activities that occur at low frequencies. Multiparameter ﬂow cytometry is also an option for such measurements. However, anything more than four colors is presently an art form – even “high art” – such measurements can be reliably performed and reproduced by few researchers. Moreover, by the very nature of the measurements, ﬂow cytometry excels in deﬁning phenotypes of cells, not their functions. For many key functions however, such as antigen-speciﬁc killing, we have no reliable corresponding phenotypes. Dual color ELISPOT assay has been established since a decade (22). Cytokine combinations have been deﬁned that, when measured in the double color format, provide the same spot count for each color as the corresponding analytes measured in parallel in single color ELISPOT assay (23). Also, cytokine coexpression can be studied by dual color ELISPOT, detecting coexpressing cells with the same frequency as measured by ICS (22). Double Color ELISPOT, therefore is well suited for detecting polyfunctional T cells that coexpress cytokines. Fully-automated double color analysis software largely facilitates such studies. Double color ELISPOT analysis can be done via the classical enzymatic approach using precipitating red and blue substrates, or by ﬂuorescent detection (ﬂuorospot). Both approaches provide equal sensitivity in the detection of two analytes simultaneously, and coproducers. Fluorospot becomes indispensable, however, when it comes to detecting more than two analytes. Fully-automated instrumentation and software for up to 8-color multiplexing via ﬂuorospot analysis is already available from CTL. We believe that reliable, readily applicable and standardized 8-color ﬂuorospot analysis will be sooner realized than 8 parameter ﬂow cytometry with the ELISPOT-based approach having the additional advantage

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of high sensitivity for the detection of low frequency cells, economy with cells, high-throughput capacity, and being a functional assay that measures biologically relevant secreted analyte. It also should be noted that “multiplexing” T cell measurements by ELISPOT, can be readily done by running multiple single- or double-color assays in parallel, or in succession. Since ELISPOT requires only 100,000 PBMC as a standard sample size, with one million PBMC, that a standard ﬂow cytometry sample requires, one can obtain 10 single- or ten double color ELISPOT measurements, detecting 10 or 20 analytes, respectively – a target that is hard to match by multiparameter ﬂow cytometry. One can further increase the number of analytes measured in ELISPOT assays by testing cells in succession. For example, granzyme and perforin are released within 4 h after antigen stimulation while the production of IL-4, IL-5, or IL-17 requires a longer activation period. Thus, the cells can be tested in a granzyme/perforin assay ﬁrst, and then transferred into an IL-4/5 assay, doubling the number of analytes measured with one sample of 100,000 PBMC. One can also easily combine ELISPOT assays with proliferation assays. Because the cells can be retrieved from the ELISPOT assay unaffected, they can be transferred afterward into a proliferation assay or used for measuring other functions or for identifying phenotypes. Cells treated with Golgi inhibitors, permeabilized and ﬁxed, in contrast, will no longer provide functional information. 3.2. Concluding Remarks

Clearly, reliable measurements of several key T cell functions will be required for a better understanding of these cells’ roles in diverse immune processes, and for mediating different clinical outcomes. These parameters include the type of cytokine, chemokine, and other mediators T cells produce, their cytolytic activity, migratory properties, proliferative potential, and their functional avidity. It will take the thoughtful utilization and combination of several different techniques to assess these functions. ELISPOT will continue to be the technique of choice for screening, measurements of effector functions mediated by secretory products, ﬁne speciﬁcity, and avidity. Flow cytometry will continue to be indispensable for multiparameter phenotypic analysis. Neither of the two, however, will obviate the need for a new generation of killer assays, or migration assays. Each of these techniques excels in providing a speciﬁc type of information – and does not permit interpretations beyond what actually is being measured. Interpreting only one type of read-out inherently goes with the danger of being one of the “blind men studying the elephant.” The sum of the information gained, however, can help reveal the true nature of the “beast” studied. When used to its full potential, ELISPOT will continue to make major contributions to this quest.

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4. Notes In the following, we provide some practical suggestions for ELISPOT work: 1. Blood draw: The use of heparin as anticoagulant is recommended. 2. Blood storage/shipping: Never chill blood or the PBMC! Keep at room temperature. Store in dark. If shipping in winter, add warm packs to keep at ambient temperature. Do not use cold media for Ficoll gradient separation or washing – it is better to prewarm media in a water bath to 37°C. If handled in this way, PBMC can be stored/shipped for 24 h without signiﬁcant loss of CD4 or CD8 cell function (more than 90% of reactivity being retained after 24 h). 3. Media: Do not use untested serum for testing or even for washing or freezing – even brief exposure to a mitogenic or suppressive serum can ruin an assay (14). It is best to use special serum-free media that have been developed speciﬁcally for ELISPOT work for freezing, thawing, washing, and testing. Such media are available from CTL (14). Do not use PBS or similar minimal buffers for washing cells – it can ruin your assay! 4. Freezing and thawing: PBMC can be frozen without any loss in function, i.e., the frequencies of antigen-induced CD4 or CD8 cells producing IFN-G, IL-2, IL-4, IL-5, and IL-17 are identical in freshly isolated PBMC, and after freeze–thawing (16). To achieve this result, speciﬁc protocols that are available from CTL need to be followed. One of the key factors for success is that the freezing medium and the cells need to be at room temperature when mixed, and not chilled on ice, as commonly recommended (16). Also for thawing, the cells need to be warmed up to 37°C and warm washing media needs to be added, slowly, to avoid osmotic lysis of cells. Use pretested (ideally) serum-free media for processing the cells. Detailed protocols are available from CTL. 5. Final storage temperature: After rate-controlled freezing of cells in a −80°C freezer (e.g., using Mr. Frosty cryo-freezing container, Nalgene), or sealed, plastic wrapped Styrofoam racks, transfer them to liquid nitrogen within 48 h – do not store them at −80°C after freezing, or for short- or long-term storage – they will gradually lose functionality. Also for shipping, use dry ice only for overnight shipping – ship in vapor nitrogen containers. 6. Resting of PBMC: For work with freeze–thawed PBMC, the notion has been put forth that a resting period (keeping the cells in a tissue culture incubator overnight before recounting

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and plating them for the assay) would increase the spot counts without increasing the medium background, thus resulting in a higher signal to noise ratio. We have tested this many times – different members of the lab on different PBMC samples – and we could not verify a real beneﬁt of resting. We occasionally observed a <10% increase which can be explained by the depletion of apoptotic cells in a not well-cryopreserved PBMC samples. Resting may be more beneﬁcial if the freezing conditions have not been optimized. We recommend verifying whether resting indeed improves your results because resting prolongs the assay, adds labor, and leads to loss of precious cells materials. 7. Cell counting: Once reagents are standardized, cell counting introduces the largest variability into ELIPSOT assays. Trypan blue exclusion is not ideal for cell counting because apoptotic cells are still alive when the counting occurs: they will be counted as live cells, but they will be dead by the time the assay is performed. Ideally, dyes should be used for counting which permit the distinction between live, dead and apoptotic cells under a UV microscope, or by an automated reader (CTL’s latest UV readers have three color live/dead/apoptotic cell counting functionality in addition to ELISPOT data analysis) or by ﬂow cytometry. 8. Counting apoptotic cells is not only important for establishing the correct number of viable cells, but it is also an excellent indicator of the overall quality of the PBMC sample. If the cells were damaged during shipment, or freeze–thawing, it will become evident by an increase in the numbers of apoptotic cells. In contrast, dead cells frequently lyse, so they either cannot be detected or become indirectly evident by cell clumping caused by free strands of DNA that are released. In those cases, including a DNAse into the washing solution improves cell recovery. 9. Membranes: The use of PVDF plates is recommended – T cell ELISPOT assays started to perform robustly only after we introduced these plates (24). 10. Prewetting of membranes with ethanol. While recommended by some reagent manufactures, prewetting with ethanol is only required for monoclonal antibodies that are low in hydrophobicity (the PVDF membrane is hydrophobic). The antibodies offered by the different vendors speciﬁc for different analytes largely vary in their hydrophobicity. At CTL, we prefer to use antibodies that are hydrophobic, so they have robust performance in ELISPOT assays without prewetting. Since prewetting can cause severe membrane leakage, and prewetting adds six additional steps to your assay (prewetting itself followed by ﬁve washing steps), it is recommended to test whether prewetting indeed improves the performance of the assay in question.

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11. Cell numbers plated: Three to ﬁve hundred thousand cells per well is a good starting point if the frequency of antigenspeciﬁc T cells is unknown. However, because of the linearity between cell numbers plated and spot counts in the 100,000 to 800,000 cells/well range, one can readily make adjustments as needed. Since cells of clinical samples are precious, valuable results could be obtained by testing 4–5 times more conditions using PBMC at 100,000 cells per well. On the other hand, if frequencies are low, reliable measurements may require increasing the cell number, and the number of replicates (see below). 12. Adding APC: PBMC contain abundant APC that are capable of stimulating T cells. Macrophages, B cells, and dendritic cells (DC) are similarly stimulatory – while the activation on DC is faster, and the per cell cytokine production is increased compared to B cell or macrophages, by the end of the 24-h activation period of a standard ELISPOT assay, these differences disappear (25). For a standard ELISPOT assay, adding DC as APC may not improve the results, and because of the substantial additional effort involved, it is recommended to verify whether adding DC indeed improves the results of a particular assay. 13. Serum-free media: Serum is a limited, unique biological product. The only serum which is suited for ELISPOT assays is that which has been thoroughly tested, i.e., supports the maximal induction of T cells while it does not induce an elevation of the background. The different sera used in laboratories are a prime source of substantial interlaboratory variation of ELISPOT results (14). For standardized ELISPOT testing, CTL has been the ﬁrst to develop a complete serum-free media platform to freeze, wash, and test human PBMCs while achieving optimal results in ELISPOT assays. Serum-free media developed by CTL perform equally or better for ELISPOT assays than the best sera selected for T cell work (14). 14. Numbers of replicates: Because ELISPOT lends itself to highthroughput testing, and because it is efﬁcient with cell utilization, generally assay conditions are run in triplicates. If response levels are unknown, triplicates are a good practice. If frequencies are high, single measurements are sufﬁcient. If high accuracy is desired for frequency measurements, one should plate cells in serial dilution. Spot counts are linear between 800,000 and 100,000 PBMC per well (14). (If results are not linear in this range, the analyte is most likely not T cell derived (26).) If spot counts indicate a borderline positive result, retesting at higher cell number, up to one million per well, and/or increasing the number of replicates, can lead to more deﬁnitive conclusion. 15. ELISPOT counting and audit trails: T cell-derived spots follow, at the population level, log normal distributions. This notion

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allows automatic identiﬁcation (with 99% conﬁdence) of the lower and upper “gates” for the spots that are produced by T cells, thus properly identifying cell clusters and excluding background spots. Such analysis is done automatically by the ImmunoSpot® software (see Chapter 13). 16. Evaluating ELISPOT data: The spot counts per se need to be a solid starting point (see Note 14). It is a questionable practice to determine the number of antigen-speciﬁc spots by subtracting the number of background spots from the number of spots found after addition of the antigen. With minor limitations, the T-test is suited for statistical evaluation of ELISPOT data (see Chapters 13–15). 17. PBMC reference samples: CTL offers PBMC that are high resolution HLA-typed, and their T cell reactivity to various viral peptides and protein antigens characterized with ELISPOT assays. The cytokine proﬁle of these responses and their functional avidity is also deﬁned. Since such PBMC have predefined reactivity types and levels, they are well suited (a) for newly establishing ELISPOT assays in a laboratory, (b) for expanding the range of analytes in a laboratory (e.g., selecting PBMC that display an antigen-speciﬁc IL-17 T cell response), (c) for testing the ELISPOT proﬁciency of a new lab member, (d) for being used as a reference standard in GLP or exploratory research settings to assess inter assay variations within a laboratory, or to compare interlaboratory variations, and (e) for developing assay variants with increased performance (e.g., to test whether the inclusion of co-stimulatory antibodies, resting, in vitro expansion strategies, or addition of DC enhance the assay). 18. Measuring cytokine coexpression or switching: In ELISPOT assays, the analyte is continuously captured around the secreting cell during the assay’s entire duration, thereby providing an integral of analyte produced over time. Even if the secretion kinetics of different analytes is asynchronous, which frequently is the case (see Fig. 3), multicolor ELISPOT assays of several days duration will detect each analyte. Assays that rely on killing the cells at a certain time point, like mRNA or ICS measurements, provide information about that time point only. For example, since IL-17 production by T cells does not even start by 48 h after antigen stimulation, while IL-2 production is ﬁnished by 48 h, ICS or mRNA measurements done at 24 h would miss IL-17, and measurements done at 48 h would fail to detect IL-2. If ICS or mRNA assays were done at both times, IL-2 and IL-17 would be detected, but one could not tell whether T cells switch from IL-2 production to IL-17 or whether different cell lineages produce the two cytokines: even if the cells would switch, they would appear as IL-2 single positive if killed early on, and IL-17 single positive

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if killed at the later timepoint. In a double color ELISPOT assay of 3-day duration, cells that do not switch will appear IL-2 or IL-17 single positive, while cells that switch will appear IL-2/IL-17 double positive. Because Golgi inhibitors are highly toxic to the cells, they cannot be used for measurements over such extended periods of time to reveal this information by ICS. References 1. Sekaly, R.P. (2008) The failed HIV Merck vaccine study: a step back or a launching point for future vaccine development? J. Exp. Med 205, 7–12. 2. Tigno, J.T., Lehmann, P.V., and Tary-Lehmann, M. (2009) Dissociated induction of cytotoxicity and DTH by CFA and CpG. J Immunother 32, 389–394. 3. Streeck, H., Frahm, N., and Walker, B.D. (2009) The role of IFN-gamma Elispot assay in HIV vaccine research. Nat Protoc 4, 461–469. 4. Rininsland, F. Helms, T., Asaad, R.J., Boehm, B.O., and Tary-Lehmann, M. (2000) Granzyme B ELISPOT assay for ex vivo measurements of T cell immunity. J Immunol Methods 240, 143–155. 5. Kleen, T. O., Asaad, R.J., Landry, S.J., Boehm, B.O., and Tary-Lehmann, M. (2005) Tc1 effector diversity shows dissociated expression of granzyme B and interferon-gamma in HIV infection. AIDS 18, 383–392. 6. Kuerten, S., Kleen, T., Assad, R.J., Lehmann, P.V., and Tary-Lehmann, M. (2007) Dissociated production of perforin, granzyme B and IFN-G by HIV-speciﬁc CD8+ cells in HIV infection. AIDS Research and Human Retroviruses 24, 62–71. 7. S., Asaad, R.J., Schoenberger, S.P., Lehmann, P.V., and Tary-Lehmann, M. (2008) The TRAIL of helpless CD8+ T cells in HIV infection. AIDS Res Hum Retroviruses 24, 1–9. 8. Zhu, J., Yamane, and H., Paul, W.E. (2010) Differentiation of effector T cell populations. Annu. Rev. Immunol 28, 445–489. 9. McElrath, M.J., De Rosa, S.C., Moodie, Z., Dubey, S., Kierstead. L,, Janes, H., et al. (2008) HIV-1 vaccine-induced immunity in the testof-concept Step Study: a case-cohort analysis. Lancet 372, 1894–905. 10. Schlingmann, T.R., Shive, C.L., Targoni, O.S., Tary-Lehmann, M., Lehmann, P.V. (2009) Increased per cell IFN-gamma productivity indicates recent in vivo activation of T cells. Cellular Immunology 258,131–137. 11. Helms, T., Boehm, B.O., Assad, R.J, Trezza, R.T., Lehmann, P.V., and Tary-Lehmann, M.

(2000) Direct visualization of cytokineproducing, recall antigen-speciﬁc CD4 memory T cells in healthy individuals and HIV patients. J Immunol 164, 3723–3732. 12. Hesse, M.D., Karulin, A.Y., Boehm, B.O., Lehmann, P.V., and Tary-Lehmann, M. (2001) A T cell clone’s avidity is a function of its activation state, J Immunol 167, 1353–1361. 13. Schmittel, A., Keilholz, U., and Scheibenbogen, C. (1997) Evaluation of the interferon-gamma ELISPOT-assay for quantiﬁcation of peptide speciﬁc T lymphocytes from peripheral blood. J Immunol Methods. 210, 67–74. 14. Zhang, W., Caspell, R., Karulin, A.Y., Ahmad, M., Haicheur, N., Abdelsalam, A., et al. (2009) ELISPOT assays provide reproducible results among different laboratories for T-cell immune monitoring--even in hands of ELISPOTinexperienced investigators, J Immunotoxicol 6, 227–234. 15. Kuerten, S., Rottlaender, A., Rodi, M., Velasco, V.B. Jr, Schroeter, M., Kaiser, C., et al. (2010) The clinical course of EAE is reﬂected by the dynamics of the neuroantigen-speciﬁc T cell compartment in the blood. Clin Immunol. 137:422–432. 16. Kreher, C. R., Dittrich, M. T., Guerkov, R., Boehm, B. O., and Tary-Lehmann, M. (2003) CD4+ and CD8+ cells in cryopreserved human PBMC maintain full functionality in cytokine ELISPOT assays, J Immunol Methods 278, 79–93. 17. Ott, P.A., Herzog, B.A., Quast, S., Hofstetter, H.H., Boehm, B.O., Tary-Lehmann, M., et al. (2005) Islet-cell antigen-reactive T cells show different expansion rates and Th1/Th2 differentiation in type 1 diabetic patients and healthy controls. Clin Immunol 115, 102–114. 18. Anthony, D. D., and Lehmann, P.V. (2003) T-cell epitope mapping using the ELISPOT approach. Methods 29, 260–271. 19. Yip, H. C., Karulin, A.Y., Tary-Lehmann, M., Hesse, M.D., Radeke, H., Heeger, P.S., et al. (1999) Adjuvant-guided type-1 and type-2 immunity: infectious/noninfectious dichotomy deﬁnes the class of response. J Immunol 162, 3942–3949.

1 20. Targoni, O. S., and Lehmann, P.V. (1998) Endogenous myelin basic protein inactivates the high avidity T cell repertoire. J Exp Med 187, 2055–2063. 21. Janetzki. S., Britten, C.M., Kalos, M., Levitsky, H.I., Maecker, H.T., Melief, C.J., et al. (2009) “MIATA”-minimal information about T cell assays. Immunity 31, 527–528. 22. Karulin, A. Y., Hesse, M.D., Tary-Lehmann, M., and Lehmann, P.V. (2000) Single-cytokineproducing CD4 memory cells predominate in type 1 and type 2 immunity. J Immunol 164, 1862–1872. 23. Quast, S., Zhang, W., Shive, C., Kovalovski, D., Ott, P.A., Herzog, B.A, et al. (2005) IL-2 absorption affects IFN-gamma and IL-5, but

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not IL-4 producing memory T cells in double color cytokine ELISPOT assays. Cell Immunol 237, 28–36. 24. Forsthuber, T., Yip, H. C., and Lehmann, P. V. (1996) Induction of TH1 and TH2 immunity in neonatal mice. Science 271, 1728–1730. 25. Ott, P. A., Tary-Lehmann, M., and Lehmann, P. V. (2007) The secretory IFN-gamma response of single CD4 memory cells after activation on different antigen presenting cell types. Clin Immunol 124, 267–276. 26. Hofstetter H.H., Karulin A., Forsthuber, T.G., Ott, P.A., Tary-Lehmann, M., and Lehmann P.V. (2005) The cytokine signature of MOGspeciﬁc CD4 cells in the EAE of C57BL/6 mice. J Neuroimmunol 170, 105–114.

Chapter 2 The Impact of Harmonization on ELISPOT Assay Performance Sylvia Janetzki and Cedrik M. Britten Abstract During more than 25 years of application in immunological sciences, ELISPOT has been established as a routine, robust, versatile, and reliable assay. From basic research to clinical immune monitoring, ELISPOT is being used to address the quantiﬁcation and (to a lesser extent) functional characterization of immune cells secreting different molecules in the context of health and disease, immune intervention, and therapy in humans and other species [Kalyuzhny (Ed.) (2005) Handbook of Elispot: methods and protocols, Vol. 302, Humana Press Inc., Totowa, NJ]. Over the last decade, ELISPOT assays have been increasingly implemented as an immune-monitoring tool in clinical trials [Schmittel et al. J Immunother 23:289–295, 2000; Whiteside Immunol Invest 29:149–162, 2000; Nagata et al. Ann N Y Acad Sci 1037:10–15, 2004; Cox et al. (2005) Cellular immune assays for evaluation of vaccine efﬁcacy in developing countries., In Manual of Clinical Immunology Laboratory (Rose, N. R., Hamilton, R. G., and Detrick, B., Eds.), p 301, ASM Press, Washington, DC; Cox et al. Methods 38:274–282, 2006]. While the principles of the original protocol have changed little since its ﬁrst introduction [Czerkinsky J Immunol Methods 110:29–36, 1988], individual laboratories have adapted assay procedures based on experimental needs, availability of reagents and equipment, obtained recommendations, and gained experience, leading to a wide disparity of applied ELISPOT protocols with inevitable consequences. This chapter addresses the resulting challenges for ELISPOT use in clinical trial settings, and discusses the inﬂuence of harmonization strategies as a tool for overcoming these challenges. Furthermore, harmonization is discussed in the context of assay standardization and validation strategies. Key words: ELISPOT, Harmonization, Proﬁciency panel, Standardization, Validation

1. Introduction 1.1. ELISPOT Assay: Achievements

The strength of the ELISPOT technique lies in its outstanding sensitivity to detect antigen-speciﬁc T and B cells in even very low frequencies, on a single cell level (8). In most scenarios, the assay can be performed without any in vitro expansion of cells or addition of exogenous cytokines, offering the possibility to attain a precise estimate of reactive cells in a donor. Further, these measurements

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can be achieved in relatively short time with a straightforward protocol that can be standardized and exposed to qualiﬁcation and validation procedures following available guidance (9–11). The assay can be adapted to high-throughput sample screening which is supported by the demonstration that cryopreserved cells can perform comparable to fresh cells in ELISPOT assays (12). Further, a wide range of qualiﬁed reagents, materials, and equipment exists, and various controls and quality assurance parameters have been described and made available to scientists performing the assay (13–15). While the advantage of ELISPOT testing is its superb screening ability for cells secreting a speciﬁc cytokine (most commonly, IFN-G), it has to be noted that it can be adapted to the simultaneous detection of two cytokines (16, 17), as well as a variety of secreted molecules, including granzyme B (18) and perforin (19). 1.2. ELISPOT Assay: Challenges

As in every assay, the outcome is dependent on the protocol choices made (9) and the established laboratory environment the assay is conducted in (20). It is well-known and reviewed elsewhere that choices, like ELISPOT plate, antibody coating concentration, spot development system, and other protocol variables, can inﬂuence the ﬁnal spot numbers (9). Further adding to possible sources of result variation is the ﬁnal analysis approach of ELISPOT plates (21). Another complicating issue is the nonlinearity of responses in dependence of the cell number plated in a well. While linearity is preserved within a speciﬁc cell range (typically, <150,000 effector cells per well) if sufﬁcient costimulation as well as antigen presentation by separate cells are provided, there is only a limited linearity range existent when peripheral blood mononuclear cells (PBMCs) are used as effectors and antigen presenters at the same time. This observation is most likely inﬂuenced by the fact that less than 200,000 PBMCs per well do not guarantee optimal antigen presentation conditions while more than 200,000 cells start to pile up on each other, thus providing good cell-to-cell contact, but limiting the percentage of cells with direct contact to the coating antibody bound to the well membrane, which is essential for spot formation. These ﬁndings are not new, and the ﬁeld has responded with the establishment of Standardized Operating Procedures (SOPs), especially in clinical immune-monitoring labs. During this process, labs typically test variations of different protocol choices and select those with the most desired outcome as the standard to adhere to. A logical conclusion would be that all standardized laboratories have similar protocols since it can be assumed that each one opted for the most desired results (highest speciﬁc spot numbers, lowest background reactivity levels, and lowest variability within replicates), which should be achievable with the most optimized reagents, materials, and general protocol procedures.

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Nonetheless, countless different SOPs exist, even for closely related experimental requirements. Certainly, some of this divergence can be explained by factors already mentioned earlier, like local availability or preference of reagents and materials and their vendors, previous experience of operators, or recommendations obtained from collaborators. However, parts of this development might be accounted for by the predicament of the lack of a true gold reference standard for ELISPOT. Some groups attempt to solve this challenge by using T-cell lines or clones, others PBMC reference samples. While the ﬁrst option is of limited wider applicability, the latter one does not truly represent a reference standard since the actual number of antigen-speciﬁc T cells able to secrete a given cytokine in these preparations is not precisely known. PBMC reference samples can be an excellent tool for standardization and validation approaches, as well as external controls for ELISPOT experiments; they are not, however, a reference standard for the amount of analyte or, in the case of ELISPOT, the number of antigen-speciﬁc cytokinesecreting cells. Hence, the question always remains: Is the measurement perceived as optimal with a given protocol indeed the correct measurement? Or with other words: Does the protocol permit optimal sensitivity and speciﬁcity (all cells detected without false-positive signals)? The key question that arises from these challenges is: How comparable are ELISPOT measurements across laboratories?

2. Materials 1. SOP for human IFN-G ELISPOT assay. 2. PBMC. 3. CEF peptide pool (consisting of a panel of 8–11mers derived from Cytomegalovirus (CMV), Epstein–Barr virus (E BV), and Inﬂuenza virus (F lu) epitopes (14)). 4. CMV pp65 peptide pool (consisting of 15mers overlapping by 11 amino acids, spanning the entire protein (13)). 5. Ongoing ELISPOT proﬁciency panel program.

3. Methods 3.1. The Dual Impact of Proﬁciency Panel Testing

Proﬁciency panel programs are typically conducted to provide participants a feedback about their test performance relative to a predeﬁned reference value (see Note 1). This feedback can be of additional importance, as regular and successful (e.g., results

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within a given range) participation in proﬁciency panel programs might be requested by regulatory frameworks, depending on the speciﬁc setting. In addition to quality assessment, proﬁciency panel programs can serve as a tool to deﬁne the extent and speciﬁcs of assay harmonization necessary. In order to allow the identiﬁcation of critical assay steps that inﬂuence the assay outcome and to generate harmonization guidelines, proﬁciency panels need to be properly designed and conducted in such a way that a large enough number of representative data sets are obtained. Successful assay harmonization can ﬁrst and foremost increase the comparability of results generated across institutions. This goal clearly is of high interest to the scientiﬁc community, but might not be the main interest of participating labs. Here, the question how individual labs can beneﬁt from participating in harmonization activities is addressed. 3.2. Quality Assessment

It has been clearly stated that each method implemented for patient testing needs to undergo an external quality assessment via proﬁciency panel testing (22). For such testing, the same samples are sent to participating laboratories, where they need to be tested with the established assay. Results are centrally collected and analyzed. A feedback about each lab’s performance is given in comparison to the entire panel. If a lab’s performance is not in acceptable consensus with the overall panel results, necessary steps to correct and improve the assay outcome within that lab need to be taken. It has been suggested that the expected accuracy for proﬁciency panel testing should be >90% (22). However, as accuracy describes the closeness of results to the true value, determining the accuracy level for ELISPOT testing is a challenge due to the difﬁculty to ascertain the actual number of antigen-speciﬁc cells in PBMC samples. A solution to this impediment could be offered by the proﬁciency panel itself. Given a well-designed panel with a sufﬁcient number of participating laboratories with their own established protocol (providing an acceptable cross-section of applied protocols in the ﬁeld), it can be assumed that the measurement median of the entire panel for a given sample provides a representative estimate of antigen-reactive cells in that sample. In fact, an accumulation of participants’ measurements around the panel median has been demonstrated for previously conducted ELISPOT and other proﬁciency panels (Fig. 1) (23, 24). With this in mind, it appears reasonable to propose that the median measurement values of large, open panels could provide a range for an alternative reference standard for certain biological assays, like ELISPOT (see Note 2). The use of ELISPOT assays as an immune-monitoring tool in clinical trial context has consequently led to the initiation of various large international proﬁciency panel programs (23, 25, 26). A main goal of these panels is to offer an external quality assessment

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Fig. 1. Accumulation of ELISPOT measurement values around the panel median. The measurement values of all panelists from the 4th ELISPOT proﬁciency panel of the CIC/CRI for one donor against the CEF peptide pool are shown. Each lab employed their own SOP, but plated 200,000 cells per well and 1 Mg/ml peptide pool. The graph illustrates individual results as box plots with maximum, minimum, mean (triangle within box ), and median (line within box ) values of six replicate measurements. The panel median (64 spots) is presented as a line marked by an arrow. Most measurements accumulate around the panel median while some measurements are clearly out of range.

for laboratories using ELISPOT for patient testing in the cancer and HIV vaccine and related ﬁelds. A central aspect of these programs is their thought-out design that allows comparability of results while including laboratories with different protocols in place. Not surprisingly, the interlaboratory variability observed was high, and labs were identiﬁed that were not able to detect all responses even on a yes/no basis. Recent harmonization efforts evolving out of these activities have dramatically improved these initial observations (see Subheading 3.3). Furthermore, panels with strict overall standardization as required in speciﬁc vaccine networks were able to demonstrate encouraging concordance of results (see Note 3) (27). 3.3. Assay Harmonization

Several smaller ELISPOT proﬁciency panels with a limited number of participating centers were conducted by groups in the ﬁeld of cancer, autoimmunity, and infectious diseases (28–30). Larger, more systematic approaches to identify critical assay variables were initiated in 2005 and mainly driven by the HIV and cancer vaccine ﬁeld (23, 25, 26, 31, 32). The design of large international ELISPOT proﬁciency panels with the inclusion of labs employing different SOPs has opened the door to a process that allows the investigation of crucial protocol variables which inﬂuence the assay outcome in either direction. Once such variables have been identiﬁed, measures can be taken to

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A. Establish lab Elispot SOP for: A1. Counting method for apoptotic cells in order to determine adequate cell dilution for plating A2. Overnight resting of cells prior to plating B. Use only pretested serum with optimal signal:noise ratio C. Establish SOP for plate reading, including: C1. Human auditing during reading process C2. Adequate adjustment for technical artifacts D. Only let well trained personnel conduct assay

Fig. 2. Initial ELISPOT harmonization guidelines (adapted from ref. 23). The results of the ﬁrst two ELISPOT proﬁciency panels of the CIC/CRI led to the establishment of initial ELISPOT harmonization guidelines (23), which address general ELISPOT process steps and do not aim at imposing strict standardization on labs implementing these guidelines.

harmonize the ﬁeld toward a uniform approach of dealing with them. During the past few years, two collaborating programs have made signiﬁcant contributions to the harmonization of ELISPOT testing: the proﬁciency panel program of the Cancer Immunotherapy Consortium of the Cancer Research Institute (CIC/CRI) and the Cancer Immunoguiding Program (CIP) of the Association for Cancer Immunotherapy (CIMT). Both programs were able to systematically investigate speciﬁc protocol variables for their inﬂuence on ELISPOT testing by analyzing data and protocol speciﬁcs obtained from their recurring large-scale proﬁciency panels. Their ﬁndings are summarized in initial ELISPOT harmonization guidelines which were made available to the community (23, 26). Interestingly, these initial guidelines address rather general assay steps, which do not require major protocol changes and, importantly, do not impose strict overall standardization measures to the ﬁeld (Fig. 2). Most importantly, they are continuously being adapted by panelists, and their implementation has assisted remarkably in improving the overall panel outcome (Fig. 3) (33). Notably, these harmonization efforts do not end with the publication of initial guidelines, but continue with constant reﬁnements (see Note 4). For instance, both programs have initiated a thorough investigation of the inﬂuence of serum and the use of serum-free media for the ELISPOT assay. It could be shown that serum is not required for ELISPOT performance (34) and that commercially available serum-free media can perform at least equally well in human IFN-G ELISPOT assays as extensively pretested serum-supplemented media (35). A logical next study is underway testing the inﬂuence of different freezing media on ELISPOT outcome. In addition, proﬁciency panel projects have demonstrated that even after an experiment was done and spots were counted, considerable variation can occur due to the interpretation of raw data

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% of labs missing weak responder

1st Elispot panel, no recommendations

2nd Elispot panel, first recommendation based on findings from 1st panel 3rd panel, initial harmonization guidelines deduced from panel 1 and 2 4th panel, increased implementation of harmonization guidelines

Fig. 3. Improvement of ELISPOT performance during the harmonization process. The percentage of panelists missing to detect the weak responder in dependence of the stage of the ELISPOT proﬁciency panel program of the CIC/CRI is depicted as the black pie part to the right. This number decreased with increasing harmonization from 47 to 14 to 7% of participants.

from ELISPOT assays (25). As various methods for response determination lead to variable outcomes (36), harmonization of ELISPOT assays has to include the harmonization of response determination as addressed in Chapter 15 in this book. The assay harmonization efforts conducted over the past 5 years led to the identiﬁcation of several critical experimental process steps based on the analysis of large, representative data sets. Obviously, any published report of ELISPOT experiments should include sufﬁcient information on critical test variables and process steps (see Note 5). To this end, the MIATA project was launched which addresses the minimal information that needs to be published when reporting results from T-cell assays (see Note 6) (24, 37). 3.4. Integration of Assay Harmonization into the Regular Workﬂow of Assay Progression

The typical evolution of assay development can be divided into six subsequent steps (Fig. 4): 1. Development 2. Optimization 3. Standardization 4. Prevalidation 5. Validation 6. Implementation Assay development begins even prior to the ﬁrst experiment by deﬁning the actual assay (what will it measure, how it will be measured) and the ﬁrst selection of reagents, materials, and protocol variables.

S. Janetzki and C.M. Britten Development Optimization

Harmonization

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Standardization Pre-Validation Validation Implementation

Re-validation

Fig. 4. Assay evolution and beneﬁts from harmonization. The classical steps of assay evolution are shown. The arrows depict the relative beneﬁt of assay harmonization for various stages of assay evolution (arrows pointing to assay stage) and the relative contribution of labs to assay harmonization (arrows pointing to harmonization), in dependence of their stage during the assay evolution.

The initial assay test runs are typically followed by systematic benchmarking studies for all (or the most critical) assay variables/ steps. Results from internal benchmarking studies can be used to further optimize the protocol. Obviously, investigators in these early stages of assay evolution can beneﬁt considerably from integrating recommendations and guidelines deduced from harmonization efforts (Fig. 4). Protocol optimization is followed by standardization which is typically achieved by generating and implementing SOPs. Once a working SOP is in place, assay qualiﬁcation and validation can be tackled which is supported by ﬁrst describing the purpose and design of planned validation studies and how each of the critical parameters is addressed in detail (validation plan). The prevalidation stage establishes the parameters for qualifying the assay by performing a series of exploratory experiments addressing each of the deﬁned validation parameters. The validation stage involves conducting a series of experiments to determine whether the speciﬁcations established during the prevalidation stage can be consistently met. The organizers of proﬁciency panels acknowledge that the most advanced labs generally contribute best to harmonization efforts as they can generate robust data sets. Nevertheless, labs with newly developed and nonvalidated assay protocols can also achieve outstanding test sensitivity and performance and thus contribute valuable data sets (Fig. 4). Obviously, an investigator who has already validated an ELISPOT assay might prefer not to change any assay component as this would ask for time-consuming revalidation of the new protocol. However, in more than one instance, panel participation was regarded as an eye opener and has led to modiﬁcations in even long-established protocols (see Note 7).

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The increased comparability of results generated across institutions that can be reached by harmonization efforts represents a clear advancement for the scientiﬁc community. Without doubt, even investigators who use validated assays within clinical studies can value the possibility to better compare own results with data sets that were generated by peers who use similar antigens and drug formats and treat similar patient groups. In addition, it seems reasonable to argue that participation in proﬁciency panels can beneﬁt a lab’s assay development independent of its stage. During assay evolution, one constantly needs to compare assay performance to a reference standard. Proﬁciency panels can offer an alternative reference standard, as described earlier, thus providing a solution to the lack of a true gold reference standard in ELISPOT. Further, participation in proﬁciency testing projects allows the performance comparison with the ﬁeld at each step of assay evolution. This contributes to enhanced conﬁdence in optimization and standardization procedures and the actual performance of appointed staff members. In summary, the output from harmonization activities can help to develop and optimize an assay at early stages of assay evolution. By repetitively comparing the performance of many different protocols, large data sets are generated which can be used to deﬁne typical and extreme performance characteristics for the ELISPOT assay. This knowledge can be used to set speciﬁcations for assay validation. Finally, even experienced and validated labs can proﬁt from participating in harmonization activities due to the feedback of performance they obtain, which would expose the quality of their assay performance.

4. Notes 1. For assays for which no accepted gold standard exists, the feedback may also be expressed as test performance relative to the performance of other panel participants. 2. However, the actual number of antigen-reactive cells remains to be determined. 3. While standardization across the immune monitoring ﬁeld would be desirable, it has to be recognized that this is not feasible due to a variety of circumstances and testing requirements, and not at last by the ever-present question of which standard is the “best” standard. 4. These reﬁnements are based on the outcome of new panels, during which guidelines are investigated in detail where applicable to provide further guidance to the ﬁeld.

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5. Nevertheless, reports which lack considerable parts of the critical information can frequently be found in the published literature. 6. The immune monitoring ﬁeld can actively contribute to shaping these guidelines by participating in the public consultation process which can be accessed at the project-oriented Web site (37). 7. The experience from previous proﬁciency panels revealed that even labs that were effectively using an assay for several years had to face the fact that certain protocol steps used in the ﬁeld, but not addressed in their own SOP, could induce less background spot production in medium controls and a higher number of antigen-speciﬁc spots in the experimental wells. References 1. Kalyuzhny, A. E., (Ed.) (2005) Handbook of Elispot: Methods and Protocols, Vol. 302, Humana Press Inc., Totowa, NJ. 2. Schmittel, A., Keilholz, U., Thiel, E., and Scheibenbogen, C. (2000) Quantiﬁcation of tumor-speciﬁc T lymphocytes with the ELISPOT assay., J Immunother 23, p289–295. 3. Whiteside, T. L. (2000) Immunologic monitoring of clinical trials in patients with cancer: technology versus common sense, Immunol Invest 29, 149–162. 4. Nagata, M., Kotani, R., Moriyama, H., Yokono, K., Roep, B. O., and Peakman, M. (2004) Detection of autoreactive T cells in type 1 diabetes using coded autoantigens and an immunoglobulin-free cytokine ELISPOT assay: report from the fourth immunology of diabetes society T cell workshop, Ann N Y Acad Sci 1037, 10–15. 5. Cox, J. H., D’Souza, M., Ratto-Kim, S., Ferrari, G., Weinhold, K., and Birx, D. L. (2005) Cellular immune assays for evaluation of vaccine efﬁcacy in developing countries., In Manual of Clinical Immunology Laboratory (Rose, N. R., Hamilton, R. G., and Detrick, B., Eds.), p 301, ASM Press, Washington, DC. 6. Cox, J. H., Ferrari, G., and Janetzki, S. (2006) Measurement of cytokine release at the single cell level using the ELISPOT assay, Methods 38, 274–282. 7. Czerkinsky, C., Andersson, G., Ekre, H. P., Nilsson, L. A., Klareskog, L., and Ouchterlony, O. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of gamma-interferon-secreting cells, J Immunol Methods 110, 29–36. 8. Helms, T., Boehm, B. O., Asaad, R. J., Trezza, R. P., Lehmann, P. V., and Tary-Lehmann, M.

(2000) Direct Visualization of CytokineProducing Recall Antigen-Speciﬁc CD4 Memory T Cells in Healthy Individuals and HIV Patients, J Immunol 164, 3723–3732. 9. Janetzki, S., Cox, J. H., Oden, N., and Ferrari, G. (2005) Standardization and validation issues of the ELISPOT assay, Methods Mol Biol 302, 51–86. 10. Maecker, H. T., Hassler, J., Payne, J. K., Summers, A., Comatas, K., Ghanayem, et al. (2008) Precision and linearity targets for validation of an IFNgamma ELISPOT, cytokine ﬂow cytometry, and tetramer assay using CMV peptides, BMC Immunol 9, 9. 11. Prabhakar, U., and Kelley, M. (2008) Validation of cell-based assays in the GLP setting: A practical guide. John Wiley, Chichester, UK. 12. Kreher, C. R., Dittrich, M. T., Guerkov, R., Boehm, B. O., and Tary-Lehmann, M. (2003) CD4+ and CD8+ cells in cryopreserved human PBMC maintain full functionality in cytokine ELISPOT assays, J Immunol Methods 278, 79–93. 13. Maecker, H. T., Dunn, H. S., Suni, M. A., Khatamzas, E., Pitcher, C. J., Bunde, T., et al. (2001) Use of overlapping peptide mixtures as antigens for cytokine ﬂow cytometry, J Immunol Methods 255, 27–40. 14. Currier, J. R., Kuta, E. G., Turk, E., Earhart, L. B., Loomis-Price, L., Janetzki, S., et al. (2002) A panel of MHC class I restricted viral peptides for use as a quality control for vaccine trial ELISPOT assays, J Immunol Methods 260, 157–172. 15. Smith, J. G., Joseph, H. R., Green, T., Field, J. A., Wooters, M., Kaufhold, R. M., et al. (2007) Establishing acceptance criteria for cell-mediated-immunity assays using frozen peripheral

2 blood mononuclear cells stored under optimal and suboptimal conditions, Clin Vaccine Immunol 14, 527–537. 16. Gazagne, A., Claret, E., Wijdenes, J., Yssel, H., Bousquet, F., Levy, E., et al. (2003) A Fluorospot assay to detect single T lymphocytes simultaneously producing multiple cytokines, J Immunol Methods 283, 91–98. 17. Boulet, S., Ndongala, M. L., Peretz, Y., Boisvert, M. P., Boulassel, M. R., Tremblay, C., et al. (2007) A dual color ELISPOT method for the simultaneous detection of IL-2 and IFN-gamma HIV-speciﬁc immune responses, J Immunol Methods 320, 18–29. 18. Shafer-Weaver, K., Rosenberg, S., Strobl, S., Gregory Alvord, W., Baseler, M., and Malyguine, A. (2006) Application of the granzyme B ELISPOT assay for monitoring cancer vaccine trials, J Immunother 29, 328–335. 19. Zuber, B., Levitsky, V., Jonsson, G., Paulie, S., Samarina, A., Grundstrom, et al. (2005) Detection of human perforin by ELISpot and ELISA: ex vivo identiﬁcation of virus-speciﬁc cells, J Immunol Methods 302, 13–25. 20. Kalos, M. (2010) An integrative paradigm to impart quality to correlative science, J Transl Med 8, 26. 21. Janetzki, S., Schaed, S., Blachere, N. E., BenPorat, L., Houghton, A. N., and Panageas, K. S. (2004) Evaluation of Elispot assays: inﬂuence of method and operator on variability of results, J Immunol Methods 291, 175–183. 22. NCCLS. (2004) Performance of Single Cell Immune Response Assays; Approved Guideline., In NCCLS document I/LA26-A, NCCLS, 940 West Valley Road, Suite 1400, Wayne, PA, USA. 23. Janetzki, S., Panageas, K. S., Ben-Porat, L., Boyer, J., Britten, C. M., Clay, T. M., et al. (2008) Results and harmonization guidelines from two large-scale international Elispot proﬁciency panels conducted by the Cancer Vaccine Consortium (CVC/SVI), Cancer Immunol Immunother 57, 303–315. 24. Janetzki, S., Britten, C. M., Kalos, M., Levitsky, H. I., Maecker, H. T., Melief, C. J. M., et al. (2009) “MIATA”-minimal information about T cell assays, Immunity 31, 527–528. 25. Cox, J. H., Ferrari, G., Kalams, S. A., Lopaczynski, W., Oden, N., and D’Souza M, P. (2005) Results of an ELISPOT proﬁciency panel conducted in 11 laboratories participating in international human immunodeﬁciency virus type 1 vaccine trials, AIDS Res Hum Retroviruses 21, 68–81. 26. Britten, C. M., Gouttefangeas, C., Welters, M. J., Pawelec, G., Koch, S., Ottensmeier, C., et al. (2008) The CIMT-monitoring panel: a twostep approach to harmonize the enumeration

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of antigen-speciﬁc CD8+ T lymphocytes by structural and functional assays, Cancer Immunol Immunother 57, 289–302. 27. Boaz, M. J., Hayes, P., Tarragona, T., Seamons, L., Cooper, A., Birungi, J., et al. (2009) Concordant proﬁciency in measurement of T-cell immunity in human immunodeﬁciency virus vaccine clinical trials by peripheral blood mononuclear cell and enzyme-linked immunospot assays in laboratories from three continents, Clin Vaccine Immunol 16, 147–155. 28. Scheibenbogen, C., Romero, P., Rivoltini, L., Herr, W., Schmittel, A., Cerottini, J. C., et al. (2000) Quantitation of antigen-reactive T cells in peripheral blood by IFNgamma-ELISPOT assay and chromium-release assay: a four-centre comparative trial, J Immunol Methods 244, 81–89. 29. Schloot, N. C., Meierhoff, G., Karlsson Faresjo, M., Ott, P., Putnam, A., Lehmann, P., et al. (2003) Comparison of cytokine ELISpot assay formats for the detection of islet antigen autoreactive T cells. Report of the third immunology of diabetes society T-cell workshop, J Autoimmun 21, 365–376. 30. Smith, S. G., Joosten, S. A., Verscheure, V., Pathan, A. A., McShane, H., Ottenhoff, T. H., et al. (2009) Identiﬁcation of major factors inﬂuencing ELISpot-based monitoring of cellular responses to antigens from Mycobacterium tuberculosis, PLoS One 4, e7972. 31. Britten, C. M., Janetzki, S., Ben-Porat, L., Clay, T. M., Kalos, M., Maecker, H., et al. (2009) Harmonization guidelines for HLApeptide multimer assays derived from results of a large scale international proﬁciency panel of the Cancer Vaccine Consortium, Cancer Immunol Immunother 58, 1701–1713. 32. Britten, C. M., Janetzki, S., van der Burg, S. H., Gouttefangeas, C., and Hoos, A. (2008) Toward the harmonization of immune monitoring in clinical trials: quo vadis?, Cancer Immunol Immunother 57, 285–288. 33. Hoos, A., Eggermont, A., Janetzki, S., Hodi, S., Ibrahim, R., Andersen, A., et al. (2010) Improved Endpoints for Cancer Immunotherapy Trials, J Natl Cancer Inst 102,1388–1397. 34. Mander, A., Gouttefangeas, C., Ottensmeier, C., Welters, M. J., Low, L., van der Burg, S. H., et al. (2010) Serum is not required for ex vivo IFN-gamma ELISPOT: a collaborative study of different protocols from the European CIMT Immunoguiding Program, Cancer Immunol Immunother 59, 619–627. 35. Janetzki, S., Price, L., Britten, C. M., van der Burg, S. H., Caterini, J., Currier, J. R., et al. (2010) Performance of serum-supplemented

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and serum-free media in IFNgamma Elispot Assays for human T cells, Cancer Immunol Immunother 59, 609–618. 36. Moodie, Z., Price, L., Gouttefangeas, C., Mander, A., Janetzki, S., Lower, M., et al.

(2010) Response deﬁnition criteria for ELISPOT assays revisited, Cancer Immunol Immunother 59, 1489–1501. 37. The MIATA Project. http://www.miataproject. org. Accessed July 27, 2011.

Part II ELISPOT for Veterinary Research

Chapter 3 Equine ELISPOT Assay to Study Secretion of IFNg and IL-4 from Peripheral Blood Mononuclear Cells Jodi Hagen, Chris Hartnett, Jeffrey P. Houchins, Steeve Giguère, and Alexander E. Kalyuzhny Abstract Human and mouse immune system cells are the most frequently used specimens in ELISPOT assays. In an effect to expand the application of ELISPOT assay to other species, we developed matched antibody pairs for ready-to-use kits designed for studying the frequency of equine IFNG- and IL-4-secreting peripheral blood mononuclear cells (PBMCs). Equine PBMCs were stimulated with either concanavalin A (Con A) or calcium ionomycin mixed with phorbol 12-myristate 13-acetate (CaI + PMA). We found that Con A, in general, had a more profound stimulating effect than CaI + PMA on IL-4 secretion, whereas both stimulatory and inhibitory effects were observed on IFNG secretion. Our data demonstrate a large dynamic range in IFNG and IL-4 secretion among different donors, which may reﬂect animal health and serve as a valuable diagnostic marker. Key words: ELISPOT, Equine PBMCs, IFNG, IL-4, Diagnostics, Con A and PMA stimulation, Multidonor study, Cytokine secretion, Dynamic range

1. Introduction The vast majority of ELISPOT assays is performed on human and mouse or rat immune system cells, with far fewer assays of primate, feline, and canine cells. Evidence of this can be obtained by visiting the Web site http://www.ncbi.nlm.nih.gov/pubmed/ and performing a search using such keywords as “ELISPOT,” “human” (or “mouse,” “feline,” etc.), and “2009” (or any other “year”) linked by and operator. Because of the valuable and unique information that can be collected using ELISPOT assays (1–5), however, they can also be utilized to study many other species. Cells from

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both domesticated livestock and wild animals can be tested to monitor their health and to diagnose immune system status. Until fairly recently, most studies evaluating cytokine induction in horses have relied on bioassays (6, 7), ELISA (6, 8), or methods assessing mRNA expression (9, 10). The development of a cell-based immunoassay, such as ELISPOT, has been delayed due to the lack of species-speciﬁc antibodies and cytokine standards. To bridge the gap and to expand the utility of ELISPOT assays for equine research, we have developed antibodies against equine cytokines and designed ready-to-use ELISPOT kits (11) to study the frequency of IFNG and IL-4 secretion from equine peripheral blood mononuclear cells (PBMCs) in a multidonor study. This chapter describes an equine ELISPOT protocol, and discuses important details for the performance of a successful assay.

2. Materials 2.1. Isolation of Equine PBMCs

1. Ficoll-Paque™ PLUS. 2. 50 mM phosphate-buffered saline (PBS), pH 7.2. 3. Red blood cell lysing solution: 155 mM NH4Cl, 10 mM, NaHCO3, and 0.1 mM EDTA. 4. RPMI complete culture medium: RPMI1640 (1 L) supplemented with 50 mL of heat-inactivated fetal calf serum, 1.19 g HEPES, 2.0 g of sodium bicarbonate, 3.5 ML of beta-mercaptoethanol, 50 mg Gentamicin Reagent Solution (see Notes 1 and 2). 5. Centrifuge for spinning 50-mL culture tubes at 500 × g. 6. Hemacytometer to count PBMCs under the microscope to determine cell dilution. 7. Trypan blue dye. 8. Upright microscope equipped with bright-ﬁeld illumination and phase-contrast condenser.

2.2. ELISPOT Assay

1. Commercially available, ready-to-use ELISPOT kits to measure secretion of equine IFNG (EL1586; R&D Systems) and equine IL-4 (EL1809; R&D Systems). Each kit includes a dry, 96-well, PVDF membrane-backed plate precoated with capture antibody, a concentrated solution of detection antibody, a concentrated solution of streptavidin-conjugated alkaline phosphatase, BCIP/NBT chromogenic substrate, and wash and dilution buffers. 2. Mitogens to stimulate release of cytokines from cultured PBMCs: Calcium ionomycin (CaI) and phorbol 12-myristate 13-acetate (PMA) or concanavalin A (Con A).

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3. Hand-held Nunc-Immuno™ 12-plate washer. 4. Membrane-removal device (http://www.mvspaciﬁc.com). 5. ELISPOT plate reader QuantiHub (http://www.mvspaciﬁc.com).

3. Methods 3.1. Isolation of Equine Peripheral Blood Lymphocytes

1. Collect blood samples from healthy donors in sodium heparin. Centrifuge whole blood in a 50-mL centrifuge tube at 500 × g for 10 min. 2. Discard upper plasma layer after centrifugation, and reﬁll 50-mL tube with sterile PBS. Using density centrifugation separation, layer 25 mL of blood/PBS mixture on 20 mL of 1.077 g/mL Ficoll-Paque™ PLUS at 25°C (see Note 3) and centrifuge at 500 × g for 30 min. 3. Discard the top layer after centrifugation and transfer PBMCs (buffy coat layer) into two sterile 50-mL tubes. 4. PBMCs are then resuspended in 45 mL of sterile PBS and centrifuged for 5 min at 500 × g. 5. Discard supernatant and resuspend the pellet in 10 mL of red blood cell lysing solution and incubate for 5 min at room temperature. 6. After lysing, add sterile PBS to reach 50-mL graduation mark on the tube to resuspend PBMCs. 7. Centrifuge tubes for 5 min at 500 × g. 8. Discard supernatants and add 30–40 mL of RPMI complete to the tubes with PBMCs. 9. Mix a small sample of cells 1:2 with trypan blue dye and pipette 10 ML of this mixture into each side of a hemacytometer under a coverslip (see Note 4). Count cells under the microscope using 20× lens and phase-contrast condenser.

3.2. ELISPOT Assay

1. Plate PBMCs (100 ML/well; six wells per group) into the ELISPOT plates at cell concentrations of 105 and 106 cells/ mL (see Notes 5 and 6). 2. Stimulate PBMCs with either 0.5 Mg/mL of calcium ionomycin and 50 ng/mL of PMA or 4 Mg/mL of Con A added directly to cells in ELISPOT plates and incubated in a CO2 incubator at 37°C for 18 h (see Notes 7 and 8). 3. After ﬁnishing the incubation, aspirate PBMCs from the plates and wash the plate by rinsing wells four times with wash buffer (see Notes 9 and 10). 4. Make working solutions of detection antibodies by diluting concentrated detection antibody 1:120 with dilution buffer.

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5. Add 100 ML of detection antibody working solution into each well and incubate ELISPOT plates overnight at 4°C. 6. Wash plates three times with the wash buffer. 7. Prepare working solution of streptavidin–alkaline phosphatase by mixing the concentrated stock solution 1:120 with corresponding dilution buffer. 8. Add 100 ML of streptavidin–alkaline phosphatase working solution into each well and incubate for 2 h at room temperature. 9. Wash plates three times with wash buffer. 10. Add 100 ML of ready-to-use BCIP/NBT substrate into each well and incubate for 30–60 min at room temperature in a place protected from direct light. 11. Wash plates three times with distilled water and let them dry completely (see Note 11). 12. Quantify spots using automated ELISPOT reader. Our study demonstrated that Con A had a more profound stimulating effect than CaI + PMA on IL-4 secretion from equine PBMCs, whereas both stimulating and inhibitory effects were observed on PBMCs from the same animals for IFNG secretion (see Table 1). As shown in Table 1, Con A in general had a stronger stimulating effect on secretion of IFNG and IL-4 than CaI + PMA, but the responses can vary signiﬁcantly among individual donors. Our study demonstrated that ELISPOT assay can be successfully used to determine the frequency of cytokine-secreting equine PBMCs. We have also shown that ELISPOT is a very convenient

Table 1 Effects of different mitogens on IFNg and IL-4 secretion from equine PBMCs Spot-forming cells (SFCs) ± SD IFNg

IL-4

Con A

CaI + PMA

Con A

CaI + PMA

Horse 1

15 ± 1.7

73.3 ± 10.6

97.7 ± 2.9

72 ± 2

Horse 2

75 ± 6.6

108.7 ± 6.4

61.3 ± 7.6

14 ± 4

Horse 3

71.7 ± 4.2

59.7 ± 6.0

104.3 ± 19.5

66.3 ± 13.4

Horse 4

39.3 ± 4.7

36.0 ± 4.4

105.3 ± 12.3

13.3 ± 8.4

Horse 5

108 ± 2.7

37.7 ± 0.6

197 ± 5.0

40.0 ± 8.2

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Fig. 1. Typical ELISPOT images of equine IFNG and IL-4. The number of spot-forming cells (SFCs) produced by the same number of plated cells from the same animal can vary depending on the cell-stimulating reagents.

and easy-to-use assay capable of covering a large dynamic range in IFNG and IL-4 secretion among different donors which may reﬂect animal health conditions and/or serve as a valuable diagnostic tool (Fig. 1).

4. Notes 1. Sterilize RPMI complete culture medium and reagents that are used to separate out the white blood cells through a 0.2-Mm sterile ﬁlter. 2. When using fetal calf serum, it is important to heat inactivate the serum at 56°C for 30 min. After heat inactivation, the serum should be ﬁltered. 3. To obtain the best separation and highest yield of PBMCs, when layering Ficoll, make sure that it does not mix with the blood. 4. Overﬁlling the hemacytometer with cell solution may result in inaccurate cell quantiﬁcation. When counting cells on a hemacytometer, ﬁrst locate the middle square that contains 25 smaller squares and count cells in 5 of them. Calculate the

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average, multiply by 25 (total number of squares in that area), then multiply by 2 (cell dilution factor), and then multiply by 10,000 to determine the number of cells in 1 mL of original cell suspension. The resulting number should be used for calculating serial dilutions of PBMCs. 5. Making serial dilutions of cells allows the user to avoid overdevelopment of the ELISPOT plate and obtain a quantiﬁable number of spots that can be counted either manually or by using an automated ELISPOT reader. 6. For better well-to-well reproducibility, cells need to be mixed thoroughly before adding them into the wells. This may require systematically shaking the tube with cells after ﬁlling every four wells in ELISPOT plate. 7. Plates can be wrapped in aluminum foil to provide even heat distribution across the bottom of the ELISPOT plate during their incubation. This helps to improve well-to-well spot consistency across the plate (12). Aluminum foil also helps to reduce background staining. This is a very simple procedure that can be done as follows: before plating cells, the ELISPOT plate is placed onto a 13 × 16-cm piece of aluminum foil (e.g., Reynolds Wrap Quality Aluminum Foil, Consumer Products Division of Reynolds Metal, Richmond, VA). After the cells are added into the wells and the plate is covered with its lid, the edges of the foil are shaped loosely around the edges of the plate to wrap it. After ﬁnishing the incubation of the cells, the foil can be removed and either discarded or saved and used on the next ELISPOT plate. 8. Shelves in the CO2 incubator must be level to avoid moving cells drifting toward one side of the well. This may produce either under- or overdeveloped parts of the well and hinder quantiﬁcation of spots. It is also important to avoid disturbing cultured cells (e.g., by slamming the door of the incubator) during the incubation which may cause the development of weakly stained fuzzy spots. 9. Make sure that the height of prongs in the handheld plate washer is properly adjusted so that prongs do not touch the membranes on the bottom of the ELISPOT plate. PVDF membranes on the ELISPOT plate are fragile and can be easily punctured by protruding prongs. 10. Between washes, it is important to tap out any excess liquid in the well onto a paper towel to prevent diluting the subsequent reagents added into the plate. 11. ELISPOT plates must be completely dried before analysis because wet membranes appear dark and obscure detection and quantiﬁcation of spots.

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References 1. Sedgwick, J. D., and Holt, P. G. (1983) A solid-phase immunoenzymatic technique for the enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 57, 301–319. 2. Czerkinsky, C. C., Nilsson, L. A., Nygren, H., Ouchterlony, O., and Tarkowski, A. (1983) A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 65, 109–121. 3. Sedgwick, J. D. (2005) ELISPOT assay: a personal retrospective Methods Mol Biol 302, 3–14. 4. Kalyuzhny, A. E. (2005) Handbook of Elispot: Methods and Protocols, Vol. 302, 1 ed., Humana Press, Totowa, New Jersey. 5. Kalyuzhny, A. E. (2009) ELISPOT assay on membrane microplates. Methods Mol Biol 536, 355–365. 6. Wagner, B., Hillegas, J. M., Flaminio, M. J., and Wattrang, E. (2008) Monoclonal antibodies to equine interferon-alpha (IFN-alpha): new tools to neutralize IFN-activity and to detect secreted IFN-alpha. Vet Immunol Immunopathol 125, 315–325. 7. Clutterbuck, A. L., Mobasheri, A., Shakibaei, M., Allaway, D., and Harris, P. (2009) Interleukin-1beta-induced extracellular matrix degradation and glycosaminoglycan release is

inhibited by curcumin in an explant model of cartilage inﬂammation. Ann N Y Acad Sci 1171, 428–435. 8. Wagner, B., and Freer, H. (2009) Development of a bead-based multiplex assay for simultaneous quantiﬁcation of cytokines in horses. Vet Immunol Immunopathol 127, 242–248. 9. Davidson, A. J., Edwards, G. B., Proudman, C. J., Cripps, P. J., and Matthews, J. B. (2002) Cytokine mRNA expression pattern in horses with large intestinal disease. Res Vet Sci 72, 177–185. 10. Davidson, A. J., Hodgkinson, J. E., Proudman, C. J., and Matthews, J. B. (2005) Cytokine responses to Cyathostominae larvae in the equine large intestinal wall. Res Vet Sci 78, 169–176. 11. Ryan, C., Giguere, S., Hagen, J., Hartnett, C., and Kalyuzhny, A. E. (2010) Effect of age and mitogen on the frequency of interleukin-4 and interferon gamma secreting cells in foals and adult horses as assessed by an equine-speciﬁc ELISPOT assay. Vet Immunol Immunopathol 133, 66–71. 12. Kalyuzhny, A., and Stark, S. (2001) A simple method to reduce the background and improve well-to-well reproducibility of staining in ELISPOT assays. J Immunol Methods 257, 93–97.

Chapter 4 Utilization of Feline ELISPOT for Mapping Vaccine Epitopes Jeffrey R. Abbott, Ruiyu Pu, James K. Coleman, and Janet K. Yamamoto Abstract A commercial feline immunodeﬁciency virus (FIV) vaccine consisting of inactivated dual-subtype viruses was released in the USA in 2002 and released subsequently over the next 6 years in Canada, Australia, New Zealand, and Japan. Based on the genetic, morphologic, and biochemical similarities between FIV and human immunodeﬁciency virus-1 (HIV-1), FIV infection of domestic cats is being used as a small animal model of HIV/AIDS vaccine. Studies on prototype and commercial FIV vaccines provide new insights to the types of immunity and the vaccine epitopes required for an effective human HIV-1 vaccine. ELISPOT assays to detect cytokines, chemokines, and cytolytic mediators are widely used to measure the magnitude and the types of cellular immunity produced by vaccination. Moreover, such approach has identiﬁed regions on both HIV-1 and FIV proteins that induce robust antiviral cellular immunity in infected hosts. Using the same strategy, cats immunized with prototype and commercial FIV vaccines are being analyzed by feline interferon-J and IL-2 ELISPOT systems to identify the vaccine epitope repertoire for prophylaxis. Key words: Feline interferon-J ELISPOT, Feline IL-2 ELISPOT, Vaccine epitope mapping, Feline immunodeﬁciency virus, FIV vaccine, HIV-1 vaccine

1. Introduction Numerous human immunodeﬁciency virus (HIV)/AIDS researchers have used the ELISPOT analysis system to evaluate the epitopes on the human immunodeﬁciency virus-1 (HIV-1) viruses that induce mediators of cell-mediated immunity (CMI), such as interferon-J (IFNJ), IFND, IL-2, tumor necrosis factor-D (TNFD), and cytolytic mediators (e.g., perforins and granzymes) (1–3). The most widely used ELISPOT studies in HIV/feline immunodeﬁciency virus (FIV) research detect the cytokines IFNJ and IL-2. IFNJ and IL-2 ELISPOT assays are among the initial ELISPOT systems to be commercially available for both humans and cats.

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IFNJ is an important mediator of cellular immunity expressed by CD4+ T-helper 1 (Th1) cells, NK cells, and cytotoxic T lymphocytes (CTLs) (4). IL-2 is produced by T cells (both CD4+ and CD8+ T cells), but the predominant T-cell population that produces this cytokine is the Th1 cells (5). The general function of IL-2 is to promote the proliferation and differentiation of effector cells, such as T cells, NK cells, and even B cells, as well as autocrine effects promoting proliferation of Th cells (5). The generation of FIV- or HIV-speciﬁc antibodies is also mediated by cytokines, such as IFNJ and IL-2 (5, 6). Peripheral blood mononuclear cells (PBMCs) from HIV-1-infected subjects and FIV-infected cats typically express IFNJ after stimulation with HIV-1 and FIV peptides, respectively (7–9). More recently, HIV-speciﬁc IL-2 responses of T cells alone or in combination with IFNJ responses correlated with the decreased HIV-1 load in infected individuals (10). Studies are being performed on infected hosts with an expectation that the immunity for controlling infection may be utilized in the immunity for vaccine prophylaxis. However, controversies exist on whether IFNJ is involved in vaccine protection against these viruses (1, 10–12). Taking a conservative viewpoint, effective FIV and HIV-1 vaccines need to generate both robust CTL activity and virus-neutralizing antibodies (VNAs) against the respective viruses (13, 14). Thus, because IL-2 and IFNJ play a critical role in both humoral and cellular immunities, they are important cytokines to monitor during AIDS lentivirus infection as well as in vaccine responses to be leveraged for vaccine development. The ELISPOT system is a powerful screening tool that allows for the identiﬁcation of functional T-cell responses to various antigens, including whole organisms, proteins, and peptides. The use of protein-derived, overlapping peptide antigens within the ELISPOT system facilitates the identiﬁcation of peptide-speciﬁc epitopes based on the peptide-speciﬁc immune responses generated by the PBMC. In the culture milieu of the ELISPOT well, T cells recognize peptides presented by major histocompatibility complex (MHC), which in turn triggers the T cells to express CMI mediators, such as IL-2 and IFNJ. Sufﬁcient levels of CMI mediator expressed during short-term antigen stimulation surrounding an individual cell are captured by the mediator-speciﬁc antibodies on the ELISPOT membrane and detected by a chemical reaction similar to that of ELISA. A unique aspect of the ELISPOT system is the one-to-one relation between the mediator-expressing cell and the “spot” resulting from such reaction. The direct use of puriﬁed cell populations, such as CD4+ T cells and CD8+ T cells, to the ELISPOT system has aided in identifying the cell type(s) expressing the CMI mediators. In the case of IFNJ, this step determines the numbers of CD4+ Th1 cells, CD4+ CTL, or CD8+ CTL producing the IFNJ in response to stimulation of speciﬁc epitopes (15, 16).

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IFNJ and IL-2 ELISPOT assays are being used to identify and map speciﬁc epitope(s) on individual viral peptides that stimulate these cytokine productions by the T cells from vaccinated animals and humans (17–20). The sizes of the peptides generally used for initial screening are those that can generate both MHC-I- and MHC-II-mediated responses. MHC-I presents small peptides of 8–11 amino acid (aa) to the T-cell receptor (TCR) of CD8+ T cells while MHC-II presents large peptides of 10–30 aa to TCR of CD4+ T cells (5). Thus, peptides of about 15 aa are typically used to generate both MHC-I and -II presentations of a peptide to CD8+ T cells and CD4+ T cells, respectively (20). To assure that all epitopes on the full length of the targeted viral protein are evaluated, overlapping 15-mer peptide with 8–11 aa overlaps is generated using a computational algorithm that generates peptide sequences based on the rules for CTL epitopes, such as the PeptGen Peptide Generator tool (Los Alamos National Laboratory [LANL], http://www.hiv.lanl.gov/content/sequence/PEPTGEN/ peptgen. html) (20). Pools of peptides can be used as a ﬁrst screening tool to identify regions of the protein that initiate a positive mediator response. Following the use of pooled peptides, individual peptides can be used to further localize epitopes. Upon detection of positive mediator response, the speciﬁc epitope(s) on the 15-mer peptide are identiﬁed for the MHC-I-mediated responses by testing with smaller peptide sequences, whereas the exact peptide size for the MHC-II-mediated response can be determined by using different sequence overlaps of the peptide (21). Candidate CTL epitopes in combination with Th1 epitopes are being used as an HIV vaccine immunogen in multipeptide complexes (22). This vaccine approach can be tested against lentivirus challenge, utilizing animal models, such as FIV vaccine in cats. To achieve this goal, IFNJ and IL-2 ELISPOT systems of humans and cats are being applied.

2. Materials 2.1. FIV Vaccines

1. Prototype dual-subtype FIV vaccine (hereon called prototype IWV or IWV vaccine) consists of 250 Pg each of 0.1% paraformaldehyde inactivated FIVPet and FIVShi viruses (IWV) mixed in 1 mL of FD-1 adjuvant (Fort Dodge Animal Health, Fort Dodge, IA) and supplemented with human or feline IL-12 at 5 Pg per dose (23). 2. Commercial dual-subtype FIV vaccine called Fel-O-Vax® FIV (Fort Dodge Animal Heath) was purchased from Veterinary Medicine Teaching Hospital at the University of Florida. This vaccine consists of 2 × 107 cells/dose of inactivated FIVPetinfected cells and inactivated FIVShi-infected cells and d50 Pg/ dose of inactivated whole viruses of FIVPet and FIVShi (9, 24).

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2.2. Peripheral Blood Mononuclear Cell Preparation and T Cell Isolation

1. Ficoll-hypaque. 2. Hanks’ balanced salt solution (HBSS) with 5% EDTA. 3. Dynabeads® Untouched™ Human CD4+ T Cells (Invitrogen, Oslo, Norway) for isolation of CD4+ T cells. This kit includes Depletion MyOne™ Dynabeads and antibody mix (human CD4+ T cells). 4. Dynabeads® Untouched™ Human CD8+ T Cells (Invitrogen, Oslo, Norway) for isolation of CD8+ T cells. This kit includes Depletion MyOne™ SA Dynabeads and antibody mix (human CD8+ T cells). 5. Isolation buffer: Ca2+- and Mg2+-free phosphate-buffered saline (PBS) supplemented with 0.1% bovine serum albumin (BSA) and 2 mM EDTA.

2.3. IFNg and IL-2 ELISPOT for Humans and Cats

1. 96-Well polyvinylidene ﬂuoride (PVDF)-membrane plates (Millipore MultiScreen plates, Millipore, Billerica, MA). 2. AIM-V® medium (GIBCO, Grand Island, NY) supplemented with gentamycin (25 Pg/mL) (Mediatech, Inc., Herndon, VA) and with either 10% heat-inactivated pooled HIVseronegative human sera or 10% heat-inactivated pooled speciﬁc-pathogen-free (SPF) feline sera for use with the human or feline PBMC, respectively (see Note 1). 3. PBS – 137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4, 1.5 mM KH2PO4, pH 7.4. 4. Reagent diluent: PBS supplemented with 1% (w/v) fraction V BSA for use in reconstituting lyophilized antibodies (see Note 1). 5. Blocking buffer – 1% BSA, 5% (w/v) sucrose in PBS (see Note 1). 6. FeIFNJ ELISPOT Development Module (R&D Systems Inc., Minneapolis, MN). The development module includes a matched pair of antibodies used for capture and detection: (a) Feline IFN-J Capture Antibody Concentrate reconstituted in 1 mL PBS and diluted 1 in 50 parts reagent diluent. (b) Feline IFN-J Detection Antibody Concentrate reconstituted in 1 mL PBS and diluted 1 in 50 parts reagent diluent. 7. FeIL-2 ELISPOT Development Module (R&D Systems, Inc.,). The development module includes a matched pair of antibodies used for capture and detection: (a) Feline IL-2 Capture Antibody Concentrate reconstituted in 1 mL PBS and diluted 1 in 50 parts reagent diluent. (b) Feline IL-2 Detection Antibody Concentrate reconstituted in 1 mL PBS and diluted 1 in 50 parts reagent diluent.

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8. ELISPOT Blue Color Module or equivalent (R&D Systems, Inc.): The color module contains the following: (a) Streptavidin–AP concentrate (streptavidin conjugated to alkaline phosphatase) diluted 1 in 50 parts with Reagent Diluent (R&D Systems, Inc., Part #845100) (see Note 2). (b) 5-bromo-4-chloro-3c indolylphosphate p-toluidine salt (BCIP)/nitro blue tetrazolium (NBT) chromogen consists of BCIP and NBT chloride in organic solvent (R&D Systems, Inc., Part #895866) (see Note 2). 9. Human IFNJ ELISPOT Kit (MABTECH AB, Nacka Strand, Sweden) for Fig. 3a and Human IFNJ ELISPOT Development Module (R&D Systems Inc.) for Fig. 4. Both MABTECH and R&D kits contain a matched pair of antibodies to be used for capture and detection, but only MABTECH kit includes streptavidin–AP solution and BCIP/NBT chromogen for detection [see above step 8 (a and b) for R&D System, Inc.]. 2.4. Antigens

1. Overlapping 15-mer peptides diluted in AIM-V® medium: The overlapping peptides were derived from the following proteins and synthesized at the respective laboratories. (a) HIV-1HXB2 subtype-B p24 (National Institutes of Health AIDS Research & Reference Reagent Program, Rockville, MD; or RS Synthesis LLC, Louisville, KY). (b) FIVBang (subtype-Agag/pol/envV1–V3 and subtype-BenvV4–V9 recombinant virus) (SynPep, Corp., Dublin, CA; or RS Synthesis LLC, Louisville, KY). 2. Concanavalin A (ConA) (Sigma-Aldrich Co., St. Louis, MO) diluted in AIM-V® medium for use as a positive control. 3. Recombinant HIV-1 and FIV p24 proteins (QIAexpressionist, Qiagen Inc., Valencia, CA) and FIV IWV (same as vaccine immunogen, but inactivated with UV and heat) are diluted in AIM-V® medium for use as positive controls.

3. Methods Two strategies using the FIV–cat model have been undertaken to identify the vaccine epitopes essential for developing an effective HIV-1 vaccine for humans (13). The ﬁrst approach is to identify the vaccine epitopes and the speciﬁc immune responses induced by the prototype and commercial FIV vaccines. These vaccines have been shown to confer protection against global FIV subtypes and recombinants (13, 20, 25). This approach is based on the concept that the overall humoral and cell-mediated immune responses, including the epitopes that generate appropriate protective

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immune responses determined from this model, can translate to an effective HIV-1 vaccine for humans. The counterpart HIV-1 epitopes are anticipated to induce similar protective immune responses. The ﬁrst step toward analyzing the protective vaccine immunity is to measure the magnitude of the immune responses stimulated by the vaccine. Vaccine-induced VNAs to FIV have recently been evaluated for both prototype and commercial FIV vaccines (25). In these studies, vaccine-induced VNAs conferred protection against viruses with similar envelope (Env) sequences, but not against viruses with distinctly heterologous Env sequences, such as those from subtypes different from the vaccine viruses. These ﬁndings suggest that protection against heterologous subtype viruses and recombinant viruses likely require a robust T-cell immunity to multiple viral proteins (25). The induction of FIV-speciﬁc T-cell immunity has been reported for only prototype FIV vaccine using both mRNA and biological tests to detect T-cell functions (26). The mRNA assays for T-cell cytokines and cytolytic mediators are an indirect approach and do not always correspond with the protein levels of these molecules (27). Biological assays are ideal, but many are impractical for analyzing a multitude of epitopes on the viral proteins. The current trend is to combine epitopes identiﬁed by in silico immunoinformatics to be later tested with a rapid biological conﬁrmatory system, such as ELISPOT (28, 29). Since IFNJ and IL-2 mRNA analyses, FACS intracellular staining (ICS) for IFNJ, and proliferation assay for IL-2 detected robust IFNJ and IL-2 expressions by the PBMC from prototype FIV-vaccinated cats (26), the next approach was to determine the viral epitopes responsible for vaccine protection. HIV-1 core p24 and reverse transcriptase (RT) have a large number of CTL epitopes according to LANL database (http://hiv-web.lanl.gov/content/immunology/maps/maps. html). The viral protein counterparts of HIV-1 also exist for FIV (13). However, counterparts for a few HIV-1 regulatory proteins do not exist for FIV, such as HIV Nef. Thus, T-cell epitopes on FIV p24 and RT were screened by IFNJ ELISPOT to (1) determine whether the commercial dual-subtype FIV-infected cell vaccine can induce potent cellular immunity as described for prototype dual-subtype IWV vaccine (Fig. 1), (2) identify potential CTL epitopes on viral proteins that are recognized by the T cells from vaccinated cats (19), and evaluate whether FIV p24-speciﬁc IFNJ responses of PBMC correlates with IL-2 responses (Fig. 2). The second approach is to identify evolutionarily conserved vaccine epitopes on HIV-1 and FIV that confer protection against these viruses (13). This approach is founded on a concept that conserved regions are present on both viruses to maintain either the structural or the functional stability of the virus, which is less likely to be mutable and more likely to be broadly conserved among all subtypes of the viruses (13). In support of this approach, recent

Fig. 1. FIV p24-speciﬁc IFNJ responses of PBMC from Fel-O-Vax® FIV- and prototype IWV-vaccinated cats. The IFNJ responses of the PBMC from cats vaccinated 2× to 4× with Fel-O-Vax® FIV (commercial dual-subtype FIV vaccine) (a) were compared to those from prototype IWV-vaccinated cats (b). All results were performed in duplicate and adjusted to spot-forming units (SFUs) per 106 cells, after subtracting the background derived from nonspeciﬁc peptide control or media control, whichever was higher in value. The number below the peptide pool, FIV p24, FIV IWV (same as vaccine immunogen, but inactivated with UV and heat), or T-cell mitogen (concanavalin A, ConA) is the total number of responders (t50 SFU threshold, dotted red line) with a responder frequency of t54%. The numbers in dark color represent those peptide-pool responses common between the two vaccine groups while the numbers in light color are observed predominantly by the corresponding vaccine group than the other group. Thus, FIV p24 peptide pools Fp1, Fp3, Fp4, and Fp7 were the conserved peptide pools common between the vaccine groups. The IFNJ responses to Fp6 were detected predominantly in the Fel-O-Vax® FIV-vaccinated group, whereas those to Fp8, Fp10, and Fp13 were observed predominantly in the prototype IWV-vaccinated group. In summary, the IFNJ response proﬁle to FIV p24 appears to be similar between the two vaccine groups.

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Fig. 2. FIV p24-speciﬁc IL-2 and IFNJ responses of PBMC from prototype IWV-vaccinated cats. The IL-2 (a) and IFNJ (b) responses of the PBMC were from four cats vaccinated with prototype IWV vaccine for 7× to 9× over the course of 4 years. The number below the peptide pool, FIV p24, FIV IWV, or ConA is the total number of responders (t50 SFU threshold) with a responder frequency of t75%. The numbers in dark color represent those peptide-pool responses common between the two cytokine groups while the numbers in light color are observed predominantly by the corresponding cytokine group than the other. Many cats had both IL-2 and IFNJ responses to Fp6, Fp10, and Fp12. The peptide pools Fp14 and Fp16 induced predominantly IL-2 responses in majority of cats while Fp3, Fp5, and Fp17 induced predominantly IFNJ responses. Overall, FIV p24-speciﬁc IFNJ responses of the PBMC from IWV-vaccinated cats correlate with IL-2 responses for certain peptide pools. When the IFNJ results of these cats (BDA, BDM, QVD, QVF) after 4 years of vaccination (post 8–9 vaccinations) (b) are compared to their responses from the ﬁrst year (post 2–4 vaccinations) in Fig. 1b, IFNJ responses to Fp3 and Fp10 are clearly retained over 4 years while those to Fp1 and Fp13 are lost with only one cat each retaining a response to a peptide pool long term. An interesting observation was that responses to Fp7 and Fp8 are lost by QVD and QVF (QVs are siblings), but not by BDA and BDM (BDs are from same parents but different litters), indicating a genetic inﬂuence. Thus, ELISPOT results provide information on the immune modulation over a period of time.

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studies demonstrate cross-protection of HIV-1 p24-immunized cats against low-dose FIV challenge (19). Additional studies show that PBMC from HIV-infected human subjects and PBMC from HIV-1 p24-vaccinated cats are stimulated by similar HIV-1 p24 peptides (Fig. 3) and most remarkably to a number of FIV p24 peptides. Interestingly, these are similar FIV p24 peptides recognized by the prototype IWV-vaccinated cats (Fig. 4) (13, 30). Preliminary results suggest that PBMCs from HIV-infected subjects also respond to conserved FIV RT peptide pools (Fig. 4, last column) (30). Conserved epitopes on these viral proteins are currently being assessed by phenotype/function-based ICS and ELISPOT analyses for their ability to induce CTL activity. These techniques in combination with MHC-based immunoinformatics expedite the identiﬁcation of CTL and Th epitopes essential for formulating the vaccine immunogen against HIV-1 and FIV. More importantly, such techniques are also being used to characterize vaccine epitopes for a wide variety of pathogens. 3.1. Human and Animal Subjects

1. SPF cats were vaccinated 2× to 4× with Fel-O-Vax® FIV or prototype IWV at intervals of 3–4 weeks. In one study (Fig. 2), SPF cats were vaccinated 7× to 9× over the course of 4 years initially with IWV vaccine and later on occasionally in combination with Fel-O-Vax® FIV. Since the initial four vaccinations in the ﬁrst year used only IWV vaccine and all vaccinations subsequently contained IWV vaccine, these cats are described as IWV-vaccinated in Figs. 1b and 2. Blood samples were collected in heparin at 1–3 weeks after the third or fourth vaccination and used fresh. 2. HIV-1 positive Caucasian male subjects from the University of California at San Francisco (UCSF) cohort study were used in current studies. These subjects consisted of 12 HIV-1-infected long-term survivors who were not on antiretroviral therapy (ART), an HIV-1 long-time-infected subject who was on ART, and an HIV-1 short-time-infected subject who was not on ART. Blood samples were collected in heparin and used fresh. 3. All human studies were approved by the Committee on Human Research at the UCSF and at the University of Florida. All animal studies were approved by the Institutional Animal Care and Use Committee at the University of Florida.

3.2. Isolation of Peripheral Blood Mononuclear Cells

1. The human PBMCs are isolated from blood by Ficoll-hypaquebased gradient puriﬁcation method as previously described (30). The CD4+ and CD8+ T-cell populations are obtained from PBMC by positive selection using magnetic beads bearing anti-human CD4 monoclonal antibodies or anti-human CD8 monoclonal antibodies (Invitrogen, Oslo, Norway) according to the manufacturer’s instruction (see Note 3).

Fig. 3. IFNJ responses of PBMC from HIV-1-infected subjects and HIV-1 p24-immunized cats. Responses from 11 HIV-1 positive (HIV+) long-term survivors (LTSs) on no antiretroviral therapy (a) were compared to those from ten cats immunized 2× to 4× with HIV-1 p24 in adjuvant (b). The immunization frequency (Vac#) is designated for each cat in the legend insert. The number below the HIV-1 p24 peptide pool or HIV-1 p24 protein is the total number of responders (t50 SFU threshold) with a responder frequency of t30%. The color coding of these numbers is the same as previous ﬁgures. As expected, HIV-1-infected LTSs had IFNJ responses to more peptide pools than HIV-1 p24-immunized cats. Three peptide pools Hp6, Hp11, and Hp14 were recognized by the PBMC from both HIV+ LTS and immunized cats, whereas responses to Hp12 were observed only in the immunized cats.

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1. Overlapping peptides based on the p24 protein of FIV and HIV are derived by using PeptGen Peptide Generator tool (Los Alamos National Laboratory, http://www.hiv.lanl.gov/ content/sequence/PEPTGEN/peptgen.html), which generates peptide sequences based on the rules for CTL epitopes as previously described (30). This peptide generator tool is used to construct overlapping 15-mer peptide sequences with 11 aa overlaps for HIV-1 p24. The FIV p24 peptide pools were generally 15-aa peptide (median of 15 aas and range of 12–17 aas) with 11 aa overlap (median of 11 aas and range of 7–12 aa overlaps). 2. With the exception of peptide-pool Hp1 consisting of four overlapping peptides from the amino-terminal, each peptide pool from amino-end Hp2 to carboxyl-end Hp18 contains three consecutive overlapping peptides. The three 15-mer peptides in each peptide pool have the peptide designation of HCP# for HIV-1 Core p24 Peptide number. 3. Peptides used to stimulate PBMC are used at a concentration of 2 Pg peptide each per well for pooled peptide analysis and 5 Pg peptide per well for individual peptide analysis. 4. Positive controls, recombinant HIV-1LAV p24 (identical in p24 sequence to HIV-1HXB2) and recombinant subtype-A FIVPet, and FIVBang p24 proteins are expressed in E. coli M15 and puriﬁed as previously described (19). 5. Recombinant FIV and HIV p24 proteins are used to stimulate PBMC at concentrations of 2 Pg protein per well. 6. FIV IWV (same as vaccine immunogen, but inactivated with UV and heat) is used to stimulate PBMC at 2 Pg per well. 7. T-cell mitogen, Con A, is used to stimulate PBMC at 2.5 Pg per well. 8. FIVBang p24 10-mer peptide (VGSPGYKMQLL) (17) is used as a negative control for HIV-1 p24 peptides as this peptide had little to no IFNJ response with PBMC from HIV-1 p24immunized cats and HIV-seronegative subjects. 9. Lipopolysaccharide (LPS) from E. coli M15 is used as a control for both recombinant HIV-1LAV and FIV p24 proteins. E. coli LPS is used at 5 and 50 EU per well.

3.4. Feline and Human IFNg ELISPOT Analyses

1. 96-well PVDF membrane plates are primed by adding 30 PL of 35% ethanol in each well and incubating at room temperature for 60 s. 2. The wells are washed 3× with PBS (250 PL per well) using a squirt bottle, multichannel pipette, or plate washer. Remove any remaining PBS by inverting the plate and blotting it against a clean paper towel (see Note 4). A plate washer can also be used if available.

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Fig. 4. IFNJ responses to FIV and HIV-1 p24 and reverse transcriptase (RT) peptide pools by HIV-1-infected subjects, IWV-vaccinated cat, and HIV-1 p24-immunized cat. Top ﬁve rows are actual IFNJ ELISPOT results from PBMC or T-cell populations of HIV-infected subjects with no stimulation (column 3 ) or after stimulation with the FIV-p24 peptide pool Fp14 (column 1 ), counterpart HIV-p24 peptide pool Hp15 (column 2 ; sequence counterpart of Fp14), FIV-RT peptide pool FRT3 (column 6 ), counterpart HIV-1 peptide pool HRT3 (column 5, counterpart of FRT3), or T-cell mitogen (positive control concanavalin A, ConA) (column 4). The description of the subject or the cat is shown on the left along with the multiplication

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3. Once the plates are primed, immediately (see Note 5) add 100 PL of Capture Antibody Solution at 1:50 dilution to each well, cover, and incubate for at least 10 h at 2–8°C. 4. Aspirate Capture Antibody Solution from each well and wash 3× with PBS (250 PL per well). After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel (see Note 5). 5. Block the membranes by adding 250 PL of AIM-V® medium with gentamycin and 10% serum (either feline or human) to each well. Incubate for 2 h at 37°C. 6. Aspirate blocking medium: Wash 3× with PBS (250 PL per well) and tapping-dry the plates. Rinse wells with AIM-V® medium with gentamycin. 7. AIM-V® medium with gentamycin (50 PL) is added to each well. Incubate at room temperature until addition of antigen and PBMC. 8. Antigen (peptide, peptide pool, recombinant protein, or controls) or medium diluted only in AIM-V® medium with gentamycin (50 PL) is added to each well (see Note 6). 9. PBMC (100 PL) is added at a concentration of 1 × 106 to 5 × 106 cells per mL diluted in AIM-V® medium with 10% serum (either feline or human) (see Note 7). 10. Incubate the covered plates for 24 to 36 h at 37°C in 5% CO2 (see Note 8). 11. The wells are washed 5× with PBS (250 PL per well). After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 12. Detection Antibody Solution (100 PL) is added at 1:50 dilution to each well and incubated for 12 h or overnight at 2–8°C. 13. The wells are washed 5× with PBS (250 PL per well). After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 14. Streptavidin–AP solution (100 PL) is added to each well and incubated for 2 h at room temperature.

Fig. 4. (continued) factor (#X) to obtain an SFU result for 106 cells. The results are from a long-term survivor (LTS19), an HIV+ long-time-infected subject (HL20), and an HIV+ short-time-infected subject (HS23) with 12, 23, and 7 years of infection, respectively. The SFU number of each result is shown immediately below the corresponding ELISPOT well. The last two rows are the results from PBMC of an IWV-vaccinated cat (row 6 ) and an HIV-1 p24-immunized cat (row 7 ). The not tested results are shown as (ND). The PBMC from LTS19, HL20, HS23, and cat BDA have moderate to large responses to FIV-p24 peptide pool Fp14 while their responses to HIV-p24 counterpart Hp15 are moderate to none. Subject HS23 has a robust response to FIV-RT peptide pool FRT3, but not to the counterpart HRT3. Subject LTS19 and cat BDA have moderate to high responses to FRT3. Furthermore, CD8+ T cells from HS23 responded to Fp14 and FRT3 more than the individual’s CD4+ T cells. Hence, the most striking observation was that PBMC from a number of HIV-infected subjects was stimulated more by the FIV peptide pools than by the counterpart HIV peptide pools.

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15. Wash the wells 5× with PBS (250 PL per well). After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 16. BCIP/NBT chromogen solution (100 PL) is added to each well, covered, and incubated for 45–60 min at room temperature in the dark. 17. Rinse with deionized water. Invert plate and tap to remove excess water. Allow the plate to dry at room temperature or at 37°C (approximately 2 h). 18. Once the plates are fully developed, the spots are quantitated either manually using a dissecting microscope or using an automatic spot counter and photographs taken (see Note 9). 19. Following quantiﬁcation, the number of spots in the negative controls is subtracted from all sample wells to determine the spot-forming units (SFUs) (see Note 10) and typically reported as SFU per 106 cells. Greater than 50 SFU per 106 PBMC is considered signiﬁcant. 20. Peptide pools that return positive results are then retested as individual peptides.

4. Notes 1. These reagents should be ﬁlter sterilized with a 0.2-Pm ﬁlter before use. 2. These reagents should be stored at 2–8°C with caution not to freeze. 3. The purity of the magnetic-bead puriﬁed human CD8+ T-cell population in our study ranged from 69 to 88% CD3+CD8+ T cells with 0.3–10% CD3+CD4+ T cells, and the human CD4+ T-cell population was 90–92% CD3+CD4+ T cells with 1–10% CD3+CD8+ T cells. 4. Do not dry the plates before the Capture Antibody Solution is ready. 5. Do not touch the membranes during washing to avoid damage. 6. The concentration of the working solutions for each stimulant is fourfold higher than the ﬁnal concentration in the well. 7. The ﬁnal volume after addition of antigen and PBMC is 200 PL per well, and the cell concentration is 0.1 × 106 to 0.5 × 106 cells per well. 8. Care should be taken not to move, bump, or expose plates to vibrations that may cause the cells to move, as the assay is dependent on cells remaining in place to create a “spot.”

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9. The uses of the dissecting microscope and the automatic reader both have pros and cons. With the dissecting microscope, the investigator has more individual and controlled input regarding what is deemed a “spot,” making it easier to sort out artifactual marks and dealing with varying background intensities that are common in ELISPOT. However, because the determination is subjective, consistency can be difﬁcult. For example, in wells with few spots, small pale spots are more obvious than in wells with numerous intense spots. In addition, manual counting is extremely labor intensive. Automatic readers are quick (approximately 4 min per 96-well plate) and are able to read each well with the same thresholds and algorithms enhancing consistency. When using an automatic reader, it is imperative to set thresholds and spot check spot numbers for each plate or assay to avoid underestimating or overestimating spots. Automatic readers often have problems with artifacts if threads or dust get in the wells; however, built-in algorithms attempt to detect gradients in the spots to distinguish between artifacts and true “spots” as well as subdividing coalescing spots. 10. SFUs are considered to each represent a single cell secreting IFNJ. Although not typically considered in the current literature, the size and intensity of the spots may add additional information regarding the level of IFNJ secretion as well as the number of secreting cells. The SFU in positive controls especially often coalesces and makes enumerating individual spots difﬁcult or impossible. In these cases, the percent saturation of the well (e.g., 80%) can be used as a measurement (some automatic readers generate this data).

Acknowledgment This work was supported by NIH R01-AI65276, R01-AI30904, and JKY Miscellaneous Donors Fund. We thank Missa P. Sanou for his technical assistance and editorial comments. J.K.Y. is the inventor of record on a patent held by the University of Florida and may be entitled to royalties from companies developing commercial products related to the research described in this chapter. References 1. Addo, M. M., Yu, X. G., Rathod, A., Cohen, D., Eldridge, R. L., Strick, D., et al. (2003) Comprehensive epitope analysis of human immunodeﬁciency virus type 1 (HIV-1)-speciﬁc T-cell responses directed against the entire

expressed HIV-1 genome demonstrate broadly directed responses, but no correlation to viral load. J. Virol. 77, 2081–2092. 2. Boulet, S., Ndongala, M. L., and Bernard, N. F. (2010) Dual-color ELISPOT assay for the

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simultaneous detection of IL-2 and/or IFNgamma secreting T cells. Cold Spring Harb. Protoc. Jan;2010(1):pdb.prot5369. 3. Kuerten, S., Nowacki, T. M., Kleen, T. O., Asaad, R. J., Lehmann, P. V., and TaryLehmann, M. (2008) Dissociated production of perforin, granzyme B, and IFN-gamma by HIV-speciﬁc CD8(+) cells in HIV infection. AIDS Res. Hum. Retroviruses 24, 62–71. 4. Schoenbarn, J. R., and Wilson, C. B. (2007) Regulation of interferon-gamma during innate and adaptive immune responses. Adv. Immunol. 96, 41–101. 5. Abbas, A.K., Lichtman, A.H., and Pillai, S. In: Cellular and Molecular Immunology. 6th Ed. Philadelphia, PA, 2010; pp. 97–111, 267–301. 6. Purkerson, J. and Isakson, P. (1992) A twosignal model for regulation of immunoglobulin isotype switching. FASEB J. 6, 3245–3252. 7. Cao, J., McNevin, J., Holte, S., Fink, L., Corey, L., and McElrath, M. J. (2003) Comprehensive analysis of human immunodeﬁciency virus type 1 (HIV-1)-speciﬁc gamma interferon-secreting CD8+ T cells in primary HIV-1 infection. J. Virol. 77, 6867–6878. 8. McKinnon, L. R., Ball, T. B., Wachihi, C., Chinga, N., Maingi, A., Luo, M., et al. (2008) Substantial intrapatient differences in the breadth and speciﬁcity of HIV-speciﬁc CD8+ T-cell interferon-J and proliferation responses. J. Acquir. Immune Deﬁc. Syndr. 49, 123–127. 9. Uhl, E. W., Martin, M., Coleman, J., and Yamamoto, J. K. (2008) Advances in FIV vaccine technology. Vet. Immunol. Immunopath. 123, 65–80. 10. Betts, M. R., Ambrozak, D. R., Douek, D. C., Bonhoeffer, S., Brenchley, J. M., Casazza, J. P., et al. (2001) Analysis of total human immunodeﬁciency virus (HIV)-speciﬁc CD4+ and CD8+ T-cell responses: relationship to viral load in untreated HIV infection. J. Virol. 75, 11983–11991. 11. Boaz, M. J., Waters, A., Murad, S., Easterbrook, P. J., and Vyakarnam, A. (2002) Presence of HIV-1 gag-speciﬁc IFN-J+IL-2+ and CD28+IL-2+ CD4+ T cell responses is associated with nonprogression in HIV-1 infection. J. Immunol. 169, 6376–6385. 12. Migueles, S. A., Laborico, A. C., Shupert, W. L., Sabbaghian, M. S., Rabin, R., Hallahan, C. W., et al. (2002) HIV-speciﬁc CD8+ T cell proliferation is coupled to perforin expression and is maintained in nonprogressors. Nat. Immunol. 3, 1061–1068.

13. Yamamoto, J. K., Sanou, M. P., Abbott, J. R., and Coleman, J. K. (2010) Feline immunodeﬁciency virus model for designing HIV/AIDS vaccines. Curr. HIV Res. 8, 14–25. 14. Robinson, H. L. (2007) HIV/AIDS vaccines: 2007. Clin. Pharmacol. Ther. 82, 686–693. 15. Streeck, H., Frahm, N., and Walker, B. D. (2009) The role of IFN-gamma Elispot assay in HIV vaccine research. Nat. Protoc. 4, 461–469. 16. Brown, D. M. (2010) Cytolytic CD4 cells: Direct mediators in infectious disease and malignancy. Cell. Immunol. 262, 89–95. 17. Buchbinder, S. P., Mehrotra, D. V., Duerr, A. D., Fitzgerald, W., Mogg, R., Li, D., et al. (2008) Efﬁcacy assessment of a cell-mediated immunity HIV-1 vaccine (the Step Study): a double-blind, randomised, placebo-controlled, test-of-concept trial. Lancet. 372, 1881–1893. 18. Rerks-Ngarm, S., Pitisuttithum, P., Nitayaphan, S., Kaewkungwal, J., Chiu, J, Paris, R., et al. (2009) Vaccination with ALVAC and AIDSVAX to prevent HIV-1 infection in Thailand. N Engl. J. Med. 361, 2209–2219. 19. Coleman, J. K., Pu, R., Martin, M., Sato, E., and Yamamoto, J. K. (2005) HIV-1 p24 vaccine protects cats against FIV. AIDS. 19, 1457–1466. 20. Coleman, J. K., Pu, R., and Yamamoto, J. K. (2009) Efﬁcacy of HIV-1 and FIV subunit vaccines against FIV. J. Immunol. (Immunology 2009 Meeting Abstract) 182, 132.3. 21. Marsh S. G. E., Parham, P., and Barber, L. D. HLA polymorphism, peptide-binding motifs and T-cell epitopes. In: The HLA FactsBook. Acadmic Press, San Diego, CA, 2000; pp. 61–78. 22. Spearman, P., Kalams, S., Elizaga, M., Metch, B., Chiu, Y. L., Allen, M., et al. (2009) Safety and immunogenicity of a CTL multiepitope peptide vaccine for HIV with or without GM-CSF in a phase I trial. Vaccine 27, 243–249. 23. Pu, R., Coleman, J., Omori, M., Mison, M., Huang, C., Arai, M., et al. (2001) Dual-subtype FIV vaccine protects cats against in vivo swarms of both homologous and heterologous subtype FIV isolates. AIDS 15, 1225–1237. 24. Uhl, E. W., Heaton-Jones, T., Pu, R., and Yamamoto, J. K. (2002) FIV vaccine development and its importance to veterinary and human medicine: a review. Vet. Immunol. Immunopath. 90, 113–132. 25. Yamamoto, J. K., Pu, R., Sato, E., and Hohdatsu, T. (2007) Feline immunodeﬁciency virus pathogenesis and development of a

4 dual-subtype feline-immunodeﬁency-virus vaccine. AIDS 21, 547–563. 26. Omori, M., Pu, R., Tanabe, T., Hou, W., Coleman, J. K., Arai, M., et al. (2004) Cellular immune responses to feline immunodeﬁciency virus (FIV) induced by dual-subtype FIV vaccine. Vaccine 23, 386–398. 27. Gygi, S. P., Rochon, Y., Franza, B. R., and Aebersold, R. (1999) Correlation between protein and mRNA abundance in yeast. Mol. Cell Biol. 19, 1720–1730. 28. De Groot, A. S., and Martin, W. (2003) From immunome to vaccine: epitope mapping and

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vaccine design tools. Novartis Found. Symp. 254, 57–72. 29. Weber, C. A., Mehta, P. J., Ardito, M., Moise, L., Martin, B., and De Groot, A. S. (2009) T cell epitope: friend or foe? Immunogenicity of biologics in context. Adv. Drug Deliv. Rev. 61, 965–976. 30. Sanou, M., Coleman, J., Levy, J. A., and Yamamoto, J. K. (2011) Selection of conserved HIV-1 vaccine epitopes based on cross-reactivity to feline immunodeﬁciency virus. J. Immunol. (Immunology 2011 Meeting Abstract) 186, 53.17.

Chapter 5 Analyzing Cellular Immunity to AAV in a Canine Model Using ELISPOT Assay Zejing Wang, Rainer Storb, Stephen J. Tapscott, and Stanley Riddell Abstract Adeno-associated viral (AAV) vector-mediated gene transfer represents a promising gene replacement strategy for treating various genetic diseases. One obstacle in using viral-derived vectors for in vivo gene delivery is the development of host immune responses to the vector. Recent studies have demonstrated cellular immune responses speciﬁc to capsid proteins of various AAV serotypes in animal models and in human trials for different diseases. We developed a canine-speciﬁc ELISPOT assay to detect such immunity in dogs received AAV treatment. Here, we describe in detail the use of a constructed panel of overlapping peptides spanning the entire VP1 sequence of AAV capsid protein to detect speciﬁc T-cell responses in peripheral blood in dogs following intramuscular injection of AAV. This high-throughput method allows the identiﬁcation of T-cell epitopes without the need for large cell numbers and the need for major histocompatibility complex molecule-matched cell lines. Key words: Adeno-associated virus, AAV, Peptide library, Dog, ELISPOT assay

1. Introduction Vectors based on the adeno-associated virus (AAV) have shown promise for gene therapy of a number of acquired and inherited diseases in preclinical studies and clinical trials (1–6). However, one major hurdle in using viral vectors for in vivo gene delivery is the development of host cellular immune responses to the vector which may lead to elimination of transgene expression (7–12). Cellular immune responses are mediated through the recognition of peptide epitopes presented by major histocompatibility complex (MHC) molecules on antigen-presenting cells by T-cell receptors, and can be assessed by several approaches. The chromium release assay has been a standard to measure speciﬁc

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cytotoxic T lymphocyte (CTL) activity (13). However, this assay requires knowledge of the peptide epitopes that are recognized and MHC molecule-matched target cells. The chromium release assay has low sensitivity and is not practical for assessing consecutive blood samples or for high-throughput screening to identify new epitopes. The use of a ﬂow cytometry-based staining assay with MHC tetramers is more sensitive for detecting speciﬁc T cells than a chromium release assay, but depends on prior knowledge of both the MHC-restricting allele and the immunogenic epitopes (14, 15). The other disadvantage is that MHC tetramer staining measures only the frequency of T cells with a speciﬁc T-cell receptor and not the function of the T cell. Intracellular cytokine staining (ICS) is another developed ﬂow cytometry-based assay that can detect cytokine expression from responding T cells stimulated with peptides or peptide pools (16, 17). It is a sensitive assay without the need of knowing the precise MHC-restricting allele, and when combined with multiparameter staining with antibodies to diverse cell surface markers can simultaneously detect the phenotypes of the responding T cells. However, the technique does not lend itself to high-throughput testing. Interferon gamma (IFNG) ELISPOT assay is a cytokine enzyme-linked immunospot assay that has many advantages over other methods (18–22). It can be used on blood samples without the need to add additional MHC molecule-matched target cells. IFNG ELISPOT can be designed in a high-throughput format to screen all potential epitopes in a given viral antigen using an overlapping peptide library, and thereby identify novel immunogenic epitopes. It is both sensitive and quantitative, and does not require large number of cells. Furthermore, the ELISPOT assay can be performed on frozen cells which would allow testing of multiple samples from different subjects or conditions or consecutive samples from the same subject within the same assay (23, 24). It has been applied in analyzing peptide-speciﬁc immune responses in patients with various diseases, including vaccine trials for monitoring cell immune responses (25–29). Here, we describe the use of INFG ELISPOT assay with a constructed panel of overlapping peptides spanning the entire VP1 sequence of AAV6 capsid protein to detect speciﬁc T-cell responses in peripheral blood in dogs given AAV6 vectors.

2. Materials 2.1. Intramuscular Vector Injection

1. AAV vectors carrying CMV-canine factor IX (cFIX) (provided by Dr. Jeffrey Chamberlain, University of Washington, Seattle, WA, and Dusty Miller, Fred Hutchinson Cancer Research Center, Seattle, WA). The vectors were puriﬁed either by afﬁnity

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puriﬁcation through a HiTrap heparin column (Amersham, Piscataway, NJ), or by double CsCl gradient centrifugation followed by dialysis into Hank’s buffered salt solution (HBSS). 2. HBSS. 3. 31-Gauge syringes. 4. Anesthesia reagents: acepromazine (Fort Dodge Animal Health, Fort Dodge, IA, USA); atropine (Baxter Health Corporation, Deerﬁeld, IL, USA); butorphanol (Fort Dodge Animal Health); glycopyrrolate (American Regent, Inc, Shirly, NY, USA); propofol (Abbott Laboratories, North Chicago, IL, USA); lidocaine hydrochloride, injectable-2% at the site (VEDCO, Inc, St. Joseph, MO, USA). 5. Surgical instruments and sutures: sterile forceps and scissors (World Precision Instruments, Inc. Sarasota, FL, USA), 4–0 Maxon (US Surgical, Tyco Healthcare LP, Norwalk, CT); 4–0 Prolene non-absorbable suture, blue microﬁlament (Ethicon, Inc. Somerville, NJ, USA), scalpel #10 blade (Medline Industries, Inc. Mundelein, IL, USA), skin stapler (US Surgical, Tyco Healthcare LP, Norwalk, CT, USA). 2.2. Peripheral Blood Mononuclear Cells Collection

1. Heparin sulfate (APP Pharmaceuticals, LLC Schaumburg, IL, USA). 2. 1× Dulbecco’s phosphate-buffered saline (PBS). 3. Ficoll-Hypaque (D = 0.074, FHCRC, Seattle). 4. Tongue depressor. 5. Waymouth medium. 6. Nonessential amino acids (Mediatech Inc., Herndon, VA). 7. 50-mL Corning tubes. 8. Centrifuge – Sorvall Legend Heraeus, 750064446 B. 9. Sterile plastic transfer pipette.

2.3. ELISPOT Assay for Detecting T Cells to AAV Capsid Protein

1. Peptide panel encompassing the entire AAV VP1 capsid protein sequence synthesized. 2. Dimethyl sulfoxide (DMSO). 3. Canine IFN-development module (store at 4°C, R&D, Minneapolis, MN). 4. ELISPOT blue color module (store at 4°C, R&D, Minneapolis, MN). 5. ISCOVE medium. 6. Waymouth medium. 7. Nonessential amino acids (Mediatech Inc., Herndon, VA). 8. Sodium pyruvate.

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9. Penicillin streptomycin. 10. L-Glutamine. 11. Heat inactivated dog serum (HIND, Gemini Bio-Products, West Sacramento, CA). 12. Phytohemagglutinin, PHA (store at −20°C, Sigma, St. Louis, MO). 13. Sucrose. 14. 96-Well PVDF microplates (Millipore, Bedford, MA). 15. Tween 20. 16. Automated ELISPOT reader (CTL, Cleveland, OH).

3. Methods 3.1. Intramuscular AAV Vector Injection

All research experiments performed on dogs follow the Guide for Laboratory Animal Facilities and Care prepared by the National Academy of Sciences, National Research Council and after approval by the Institutional Animal Care and Use Committee. All dogs are immunized for leptospirosis, distemper, hepatitis, papillomavirus, and parvovirus and dewormed. Three muscles in hind limbs are chosen for intramuscular AAV injection for easy access: biceps femoris, semitendinosus, and semimembranosus. 1. Shave injection sites. 2. Dogs are under general anesthesia then placed in a lateral decubitus position; the site of incision is inﬁltrated with 2% lidocaine hydrochloride S.C. 3. A 4–6-cm incision is made in the skin along the longitudinal axis of the hind limb to expose selected muscles. 4. Nonabsorbable 4–0 sutures are placed in the belly of the muscle as marker for each injection site. 5. Slowly inject 1 × 1011 to 1 × 1012 vector genome of rAAV vectors in 250 ML of HBSS per site into the muscle belly right underneath each suture using 31-gauge syringes. 6. Close skin, and monitor animals daily for recovery.

3.2. Peripheral Blood Mononuclear Cells Collection

To examine cellular immune responses to AAV capsid proteins, blood samples are collected for isolation of PBMC before vector injection (pre) and at 4 and 12 weeks after AAV injection or at a desired time of your study. 1. Collect 30 mL of blood containing 10% heparin as anticoagulant. 2. Dilute blood in 30 mL prewarmed PBS (37°C).

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3. Transfer 15 mL of Ficoll into each 50-mL corning tubes, tilt the tube, and layer 30 mL of diluted blood very slowly on top of the Ficoll by gently pipetting the blood onto the side of the tube. 4. Centrifuge at 365 r g for 40 min at room temperature (RT) with the lowest speed acceleration rate. 5. Remove the lymphocyte layer that collects above the Ficoll with a sterile plastic pipette, transfer to a 15-mL tube, and then ﬁll the tube with Waymouth medium supplemented with 2% nonessential amino acid (Way-N) to 15 mL and centrifuge at room temperature for 10 min at 280 r g. 6. Remove supernatant, resuspend the cell pellet, and combine cells from all tubes and transfer to a new 15-ml tube. Fill the tube with Way-N and centrifuge at RT for 10 min at 224 r g. 7. Remove supernatant, and resuspend cells at 2 r 106 cells/mL in 50% Waymouth, 50% ISCOVE medium supplemented with 1% nonessential amino acids, 1% sodium pyruvate, 1% penicillin streptomycin, 5% L-glutamine, and 10% HIDS (Way-ISC). Or alternatively, freeze down in 10% DMSO and 90% HIDS at a concentration of 10–15 × 106 cells/mL in liquid nitrogen. 3.3. ELISPOT Assay for Detecting T Cells to AAV Capsid Protein

3.3.1. Generation of Peptide Pools

To examine cellular immune responses to AAV capsid proteins, blood samples are collected at different time points described above for isolation of PBMC, and each sample is subjected for stimulation with peptides. PBMC collected before vector treatment stimulated with peptides and medium only and PBMC collected after vector treatment stimulated with medium only are used as negative controls. PHA stimulation is used as positive control for each sample. 1. Obtain a peptide library consisting of 15-mer peptides each overlapping by 11 amino acids with adjacent peptides. HPLCpuriﬁed peptides (purity >90%) are ideal (Reutlingen, Germany; or Sigma, St. Louis, MO). 2. Dissolve each peptide in 100% DMSO at a concentration of 20 mg/mL. Number the peptide stocks sequentially and store at −80°C. 3. Design a two-dimensional array for the individual peptides. For AAV6 VP1, there are 182 peptides in the panel, so a 14 × 13 array was designed (Table 1). 4. Generate a peptide pool by combining equal quantity of each peptide stock along an axis and a volume of DMSO sufﬁcient to result in a ﬁnal concentration of 2 mg/mL/peptide in each pool. For example, pool 1 contains all peptides in the ﬁrst column, and pool 15 contains all the peptides in the top row. There are total 27 pools for AAV6 VP1 (Table 1). Store pools at −80°C until use.

16

30

44

58

72

86

100

114

128

142

156

170

15

29

43

57

71

85

99

113

127

141

155

169

16

17

18

19

20

21

22

23

24

25

26

27

87

73

59

45

31

17

3

171

157

143

129

115

101

3

88

74

60

46

32

18

4

172

158

144

130

116

102

4

89

75

61

47

33

19

5

173

159

145

131

117

103

5

90

76

62

48

34

20

6

174

160

146

132

118

104

6

91

77

63

49

35

21

7

175

161

147

133

119

105

7

92

78

64

50

36

22

8

176

162

148

134

120

106

8

93

79

65

51

37

23

9

177

163

149

135

121

107

9

178

164

150

136

122

108

94

80

66

52

38

24

10

10

179

165

151

137

123

109

95

81

67

53

39

25

11

11

180

166

152

138

124

110

96

82

68

54

40

26

12

12

181

167

153

139

125

111

97

83

69

55

41

27

13

13

182

168

154

140

126

112

98

84

70

56

42

28

14

14

A total of 182 peptides was synthesized each of which is 15 amino acids long and overlapping by 11 amino acids with adjacent peptides. Twenty-seven peptide pools are generated containing all peptides in their corresponding columns or rows

2

2

1

1

15

È

Pools Æ

Table 1 AAV6 VP1 peptide panel

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1. Add 50 ML of 70% ethanol to each well. 2. Wash immediately 4× 200 ML of sterile PBS. 3. Calculate the total volume of capture antibody needed and dilute to the working concentration using PBS. 4. Add 100 ML diluted capture antibody to each well, cover, and incubate overnight at 4°C. 5. Aspirate capture antibody and wash four times with wash buffer (0.05% Tween 20 in PBS, 350 ML/well). After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 6. Block plates with 200 ML of blocking buffer (1% BSA, 5% sucrose in PBS) for 2 h at RT. 7. During incubation, thaw peptide pool stocks and dilute each pool in culture medium (Way-ISC) to 4 Mg/mL. The culture medium is used as negative control, and 5 Mg/mL PHA is used as positive control. 8. Aspirate blocking buffer, and wash the plates once with 350 ML of culture medium. 9. Aspirate medium, and ﬁll with 100 ML/well of each peptide pools or controls. 10. Add 100 ML of PBMC suspensions (2 × 106 cells/mL) to each well (ﬁnal concentration of 2 × 105 cells/well, see Notes 1 and 2) and incubate overnight at 37°C (see Note 3). 11. Aspirate and wash plates four times with 350 ML wash buffer. After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 12. Calculate the total volume of detection antibody needed and dilute to the working concentration using reagent diluent (1% BSA in PBS). 13. Add 100 ML of the diluted detection antibody per well. Cover the plate with the lid and incubate overnight at 4°C. 14. Aspirate and wash plates four times with 350 ML wash buffer. After the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel.

3.3.3. Color Development

1. Calculate total volume of streptavidin–AP needed and dilute 1:60 in diluent reagent. 2. Add 100 ML to each well and incubate for 2 h at RT. 3. Wash the plates 4× 350 ML wash buffer, rinse again with deionized water; after the ﬁnal wash, remove any remaining liquid by inverting the plate and blotting it against a clean paper towel. 4. Add 100 ML/well BCIP/NBT solution, cover, incubate for 30 min in dark at RT.

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5. Rinse six times with deionized water, invert plate, and tap to remove excess water. 6. Allow plates to dry at RT for at least 3 h. 7. Count spots using automated ELISPOT reader (see Notes 4 and 5).

4. Notes 1. When using frozen cells, treat cells with 50 U/mL DNase for 3 min at RT after thaw to prevent cell aggregates. Then, spin down at 1,000 × g for 5 min, wash two times with 10 ml of the culture medium, spin at 800 × g for 7 min, and resuspend cell pellet in appropriate volume that gives 2 × 106/mL. 2. Cell numbers per well should be determined empirically using 2 × 105 cells/well as a starting point. Too much background would require using reduced number of cells or increasing cell numbers to obtain sufﬁcient positive spots. Or alternatively, expand antigen-speciﬁc T cells before subjecting to ELISPOT assay (see Note 5 below). 3. Incubation time can range from overnight to 3 days depending on the number or size of positive spots. 4. Once a positive pool is identiﬁed, each peptide within the pool should be subjected to a second round of ELISPOT assay as stimulant for identifying individual antigenic peptides in a positive pool. 5. To reduce background and increase the number of antigenspeciﬁc T cells, in vitro T cell expansion can be performed as follows: – Plate 1.5 × 106 cells/well in 500 ML medium in a 48-well plate. – Dilute peptide pools in culture medium and add 500 ML to the cells for ﬁnal concentration of 4 Mg/mL. Incubate at 37°C for 48 h. – Take out 500 ML of the medium, and add IL-2 in 500 ML medium for ﬁnal concentration of 2 U/mL. Incubate at 37°C for 5 days. – Spin down cells at 1,000 rpm for 5 min. – Resuspend in 350 ML of medium, and add 100 ML/well for ELISPOT assay.

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Acknowledgments We thank Drs. Jeffery Chamberlain and Dusty Miller for providing vectors used in the protocols and Dr. Kathy High for providing the cFIX plasmid. We thank Drs. Christine Halbert and Carolina Berger for technical advice, E. Zellmer and E. Finn for technical assistance, A. Joslyn, and the canine team, and M. Spector, DVM, and J Duncan, DVM, for their care of the dogs. We further thank S. Carbonneau, H. Crawford, B. Larson, K. Carbonneau, and D. Gayle for administrative assistance and manuscript preparation. This work was supported by NIH R01 AR056949-01A1, NIH CA15704, and by Career Development Award for Z. Wang from the Muscular Dystrophy Association (MDA 114979). References 1. Athanasopoulos, T., Fabb, S., and Dickson, G. (2000) Gene therapy vectors based on adenoassociated virus: characteristics and applications to acquired and inherited diseases (review). Int J Mol Med 6, 363–375. 2. Sun, B., Zhang, H., Franco, L. M., Young, S. P., Schneider, A., Bird, A., et al. (2005) Efﬁcacy of an adeno-associated virus 8-pseudotyped vector in glycogen storage disease type II. Molecular Therapy 11, 57–65. 3. Warrington, K. H., Jr., and Herzog, R. W. (2006) Treatment of human disease by adenoassociated viral gene transfer. Hum Genet 119, 571–603. 4. Bostick, B., Yue, Y., Lai, Y., Long, C., Li, D., and Duan, D. (2008) Adeno-associated virus serotype-9 microdystrophin gene therapy ameliorates electrocardiographic abnormalities in mdx mice. Hum Gene Ther 19, 851–856. 5. Yue, Y., Ghosh, A., Long, C., Bostick, B., Smith, B. F., Kornegay, J. N., et al. (2008) A single intravenous injection of adeno-associated virus serotype-9 leads to whole body skeletal muscle transduction in dogs. Molecular Therapy 16, 1944–1952. 6. Athanasopoulos, T., Graham, I. R., Foster, H., and Dickson, G. (2004) Recombinant adenoassociated viral (rAAV) vectors as therapeutic tools for Duchenne muscular dystrophy (DMD) (Review). Gene Ther 11 Suppl 1, S109–S121. 7. Sabatino, D. E., Mingozzi, F., Hui, D. J., Chen, H., Colosi, P., Ertl, H. C., et al. (2005) Identiﬁcation of mouse AAV capsid-speciﬁc CD8+ T cell epitopes. Molecular Therapy 12, 1023–1033. 8. Gao, G., Lu Y., Calcedo, R., Grant, R. L., Bell, P., Wang, L., et al. (2006) Biology of AAV

serotype vectors in liver-directed gene transfer to nonhuman primates. Molecular Therapy 13, 77–87. 9. Manno C. S., Pierce G. F., Arruda V. R., Glader B., Ragni M., Rasko J. J., et al. (2006) Successful transduction of liver in hemophilia by AAVFactor IX and limitations imposed by the host immune response (erratum appears in Nat Med. 2006 May;12(5):592). Nat Med 12, 342–347. 10. Wang, Z., Allen, J. M., Riddell, S. R., Gregorevic, P., Storb, R., Tapscott, S. J., et al. (2007) Immunity to adeno-associated virusmediated gene transfer in a random-bred canine model of Duchenne muscular dystrophy. Hum Gene Ther 18, 18–26. 11. Brantly, M. L., Chulay, J. D., Wang, L., Mueller, C., Humphries, M., Spencer, L. T., et al. (2009) Sustained transgene expression despite T lymphocyte responses in a clinical trial of rAAV1AAT gene therapy (Erratum appears in Proc Natl Acad Sci U S A. 2009 Oct 13;106(41):17606). Proc Natl Acad Sci USA 106, 16363–16368. 12. Halbert, C. L., Madtes, D. K., Vaughan, A. E., Wang, Z., Storb, R., Tapscott, S. J., et al. (2010) Expression of human A1-antitrypsin in mice and dogs following AAV6 vector-mediated gene transfer to the lungs. Molecular Therapy 18, 1165–1172. 13. Engers, H. D., Thomas, K., Cerottini, J. C., and Brunner, K. T. (1975) Generation of cytotoxic T lymphocytes in vitro. V. Response of normal and immune spleen cells to subcellular alloantigens. J Immunol 115, 356–360. 14. Altman, J. D., Moss, P. A., Goulder, P. J., Barouch, D. H., McHeyzer-Williams, M. G., Bell, J. I., et al. (1996) Phenotypic analysis of

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antigen-speciﬁc T lymphocytes Science 274, 94–96. 15. Appay, V., and Rowland-Jones, S. L. (2002) The assessment of antigen-speciﬁc CD8+ T cells through the combination of MHC class I tetramer and intracellular staining (Review). J Immunol Methods 268, 9–19. 16. Murali-Krishna, K., Altman, J. D., Suresh, M., Sourdive, D. J., Zajac, A. J., Miller, J. D., et al. (1998) Counting antigen-speciﬁc CD8 T cells: a reevaluation of bystander activation during viral infection. Immunity 8, 177–187. 17. Kern F., Faulhaber, N., Frommel, C., Khatamzas, E., Prosch, S., Schonemann, C., et al. (2000) Analysis of CD8 T cell reactivity to cytomegalovirus using protein-spanning pools of overlapping pentadecapeptides. Eur J Immunol 30, 1676–1682. 18. Lalvani, A., Brookes, R., Hambleton, S., Britton, W. J., Hill, A. V., and McMichael, A. J. (1997) Rapid effector function in CD8+ memory T cells. J Exp Med 186, 859–865. 19. Tobery, T. W., Wang, S., Wang, X. M., Neeper, M. P., Jansen, K. U., McClements, W. L., et al. (2001) A simple and efﬁcient method for the monitoring of antigen-speciﬁc T cell responses using peptide pool arrays in a modiﬁed ELISpot assay. J Immunol Methods 254, 59–66. 20. Schmittel, A., Keilholz, U., and Scheibenbogen, C. (1997) Evaluation of the interferon-gamma ELISPOT-assay for quantiﬁcation of peptide speciﬁc T lymphocytes from peripheral blood. J Immunol Methods 210, 167–174. 21. Kumar, A., Weiss, W., Tine, J. A., Hoffman, S. L., and Rogers, W. O. (2001) ELISPOT assay for detection of peptide speciﬁc interferongamma secreting cells in rhesus macaques. J Immunol Methods 247, 49–60. 22. Tobery, T. W., and Caulﬁeld, M. J. (2004) Identiﬁcation of T-cell epitopes using ELISpot and peptide pool arrays. Methods in Molecular Medicine 94, 121–132.

23. Maecker, H. T., Moon, J., Bhatia, S., Ghanekar, S. A., Maino, V. C., Payne, J. K., et al. (2005) Impact of cryopreservation on tetramer, cytokine ﬂow cytometry, and ELISPOT. BMC Immunology 6, 17. 24. Smith, J. G., Joseph, H. R., Green, T., Field, J. A., Wooters, M., Kaufhold, R. M., et al. (2007) Establishing acceptance criteria for cell-mediated-immunity assays using frozen peripheral blood mononuclear cells stored under optimal and suboptimal conditions. Clinical and Vaccine Immunology 14, 527–537. 25. Arlen, P., Tsang, K. Y., Marshall, J. L., Chen, A., Steinberg, S. M., Poole, D., et al. (2000) The use of a rapid ELISPOT assay to analyze peptidespeciﬁc immune responses in carcinoma patients to peptide vs. recombinant poxvirus vaccines. Cancer Immunol Immunother 49, 517–529. 26. Kaufhold, R. M., Field, J. A., Caulﬁeld, M. J., Wang, S., Joseph, H., Wooters M. A., et al. (2005) Memory T-cell response to rotavirus detected with a gamma interferon enzyme-linked immunospot assay. J Virol 79, 5684–5694. 27. Larsson, M., Jin, X., Ramratnam, B., Ogg, G. S., Engelmayer, J., Demoitie, M. A., et al. (1999) A recombinant vaccinia virus based ELISPOT assay detects high frequencies of Pol-speciﬁc CD8 T cells in HIV-1-positive individuals. AIDS 13, 767–777. 28. Wang, R., Richie, T. L., Baraceros, M. F., Rahardjo, N., Gay, T., Banania, J. G., et al. (2005) Boosting of DNA vaccine-elicited gamma interferon responses in humans by exposure to malaria parasites. Infection & Immunity 73, 2863–2872. 29. Firbas, C., Jilma, B., Tauber, E., Buerger, V., Jelovcan, S., Lingnau, K., et al. (2006) Immunogenicity and safety of a novel therapeutic hepatitis C virus (HCV) peptide vaccine: a randomized, placebo controlled trial for dose optimization in 128 healthy subjects. Vaccine 24, 4343–4353.

Part III Advanced ELISPOT Techniques

Chapter 6 Dual- and Triple-Color Fluorospot Niklas Ahlborg and Bernt Axelsson Abstract Cytokine ELISPOT has become a powerful routine tool for the analysis of disease- as well as vaccine-induced T-cell responses. The method is limited, however, in that only one cytokine at a time is assessed. Fluorospot is based on the principle of ELISPOT, but facilitates the analysis of single cells secreting several cytokines, e.g., polyfunctional T cells, suggested to be of protective importance in various infectious diseases. By detecting each cytokine with a speciﬁc ﬂuorophore and analyzing differentially colored spots by ﬂuorophore-speciﬁc ﬁlter systems, cells producing single or multiple cytokines are identiﬁed. Fluorospot maintains the simplicity and sensitivity of the ELISPOT while taking the analysis a step forward toward multiplex analysis. Key words: Fluorospot, ELISPOT, Cytokine, T cell, Immune response, Interferon-gamma, Interleukin-2

1. Introduction ELISPOT has, due to its high sensitivity and simplicity, proven valuable for assessing antigen-speciﬁc T-cell responses, both with regard to speciﬁcity and magnitude. One limitation of the regular ELISPOT method is, however, that only one cytokine at a time is measured. Still, in many settings, it is desirable to measure the production of multiple cytokines in a single well. For example, in studies of HIV, TB, and malaria infection or in the development of vaccines against these and other diseases, enumeration of antigen-speciﬁc T cells secreting, e.g., IFN-G, may not yield a complete picture of the quality of the immune response. Recent studies in the ﬁeld have highlighted the importance of polyfunctional T cells that secrete multiple cytokines. The ability of CD4+ and CD8+ T cells to respond to antigen with a combination of, e.g., IFN-G, IL-2, and TNF-A, rather than only one of the cytokines, has been associated with enhanced protective immunity in viral, bacterial, as well as parasitic diseases (1–4).

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An ELISPOT-based assay analyzing multiple cytokines would not only be useful for deﬁning polyfunctional T cells, it may as well be used for simultaneous measurement of functionally distinct cell populations of various types, e.g., T-cell populations, predominantly secreting single key cytokines representing, e.g., Th1, Th2, Th17, or Tregs. The possibility to measure multiple cytokines simultaneously in the same well also has other advantages, such as a need for less sample cells, valuable, e.g., in studies of mucosaderived cells or studies on newborns and children (5). Over the years, efforts to broaden the ELISPOT technique to include staining with substrates of two colors have been made, ﬁrst for B cells secreting different Ig isotypes (6) and later for analysis of cytokines (7). Although successfully used in several studies (8, 9), the dual ELISPOT technique can be technically challenging to perform, suffers from several inherent analytical difﬁculties, and is limited to the analysis of two cytokines. Building on the principle of ELISPOT, but using detection based on ﬂuorescence instead of substrates, the ﬂuorospot assay was developed (10). At present, two-color ﬂuorospot has been described in several publications and reagents/kits for various cytokine combinations and species are commercially available. The most common protocol for ﬂuorospot includes the use of biotinylated detection antibodies for one cytokine and an FITC-labeled detection antibody for the other cytokine (Fig. 1). As a second step in the detection, streptavidin conjugated to a red ﬂuorophore (Cy3) and anti-FITC antibodies labeled with a green ﬂuorophore, respectively, are used (10, 11). The resulting spots are subsequently analyzed using an automated reader equipped with ﬁlters for FITC and Cy3. Fluorescent detection can be as sensitive as ELISPOT, or even more sensitive, and offers several advantages compared to dual ELISPOT. Most prominently, by using readers equipped with several narrow-band ﬂuorophore ﬁlters, spots derived from cells secreting multiple cytokines are identiﬁed by the colocalization of single-colored spots in an overlay analysis of images from different ﬁlters (Fig. 2). Importantly, this enables the analysis of not only two, but also three and potentially even more cytokines simultaneously. Experimental systems for triple-color ﬂuorospot have been described (12), but the method may need further development before commercial reagents become available. The major limitation is the availability of additional ampliﬁcation systems compatible with, e.g., FITC/anti-FITC and biotin/avidin and, in particular, automated readers designed for the analysis of three-colored spots. One ampliﬁcation strategy that has been evaluated is to use a third detection antibody from a unique species that can be detected by ﬂuorophore-labeled species-speciﬁc anti-Ig antibodies, reactive only with the third detection antibody (12). However, in a wider perspective, this may be a limiting factor since most highly functional

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2# detection reagent Detection mAb

Cytokine secreted by cells

IL-10

IFN-G

Capture mAb

a

b

c

Fig. 1. Principle of dual and triple ﬂuorospot. For dual ﬂuorospot, a combination of two capture mAbs is used for coating. The cell incubation step is followed by addition of two detection mAbs and the corresponding secondary detection reagents (a, b). In the example above, IFN-G is detected by an FITC-labeled mAb followed by an anti-FITC mAb labeled with a green ﬂuorophore emitting light at the same wavelength as FITC, whereas IL-2 is detected by a biotinylated mAb in combination with streptavidin (SA)-Cy3. To enable detection of a third cytokine, an additional capture antibody is included. The detection requires the use of a third detection mAb combined with yet another secondary detection reagent (c). For the third cytokine, here IL-10, the detection mAb is labeled with a tag recognized by an antitag mAb labeled with Cy5. Several different tags and antitag mAbs are presently being evaluated to optimize the triple ﬂuorospot.

Fig. 2. Dual ﬂuorospot for the detection of antigen-speciﬁc T cells secreting IFN-G and/or IL-2. Human PBMCs (250,000 cells/well) were stimulated with a class I-restricted EBV-derived 9-mer peptide. Images show IFN-G (green), IFN-G/IL-2 (yellow), and IL-2 (red ) spots after stimulation. The image in the middle, displaying IFN-G and/or IL-2 spots, is an overlay of the ﬂanking IFN-G and IL-2 spot images. Note that the double-positive spots, visualized here in yellow, are identiﬁed by the position of red and green spots in a computerized overlay and not by their color. The bars below indicate the number of single positive IFN-G (green) and IL-2 (red ) spots. The yellow bar indicates double-positive spots. In wells with unstimulated cells, single green and red spots were <5 and double-stained spots <1.

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antibody pairs are derived from a few species. Another strategy being employed for triple staining is the use of additional tag/anti-tag systems other than FITC/anti-FITC (Fig. 1).

2. Materials 1. Transparent, ﬂat-bottomed, 96-well plates with polyvinylidene diﬂuoride (PVDF) membranes designed for low autoﬂuorescence (IPFL; Millipore, Cork, Ireland). 2. Blocking reagent after coating: 10% fetal calf serum (FCS) in phosphate-buffered saline (PBS), pH 7.4. 3. Cells suspended in complete culture medium, i.e., RPMI 1640 containing 10% FCS supplemented with glutamic acid, pyruvate, HEPES, and penicillin/streptomycin. 4. Monoclonal antibodies (mAbs) to cytokines: Mouse mAb to human IFN-G; mAb 1-D1K for coating and FITC-labeled mAb 7-B6-1 for detection. Mouse mAb to human IL-2; mAbs IL2I/249 for coating and biotinylated mAb IL2-II for detection. If a third cytokine is included in the analysis, e.g., IL-10, rat mAb 9D7 is used for coating and tag (different from FITC and biotin)-labeled rat mAb 12G8 is used for detection. All mAbs from Mabtech, Nacka Strand, Sweden. 5. Secondary reagents for ampliﬁcation: Mouse anti-FITC mAb labeled with green ﬂuorophore emitting light at the same wavelength as FITC, streptavidin (SA) labeled with Cy3, and if three cytokines are analyzed, a mouse anti-tag mAb labeled with Cy5 (Mabtech). 6. Mouse mAb anti-human CD28 at 0.1 mg/ml in sterile ﬁltered (0.2 Mm) PBS (Mabtech). 7. Analysis of spots is performed with an iSpot Spectrum FluoroSpot Reader from AID Diagnostika GmbH, Strassberg, Germany, equipped with a motorized stage, a digital ﬁrewire camera of 5.2 MP, and a 7&1 ﬁlter wheel equipped with narrow band ﬁlters for DAPI, FITC, Cy3, and Cy5. 8. Fluorescence Enhancer (Mabtech) is used to improve the ﬂuorescent signal.

3. Methods The protocol describes double staining for the detection of human IFN-G and IL-2. PVDF plates are coated with mAbs to IFN-G and IL-2. Cells ± stimuli are added, and secreted IFN-G and IL-2 are

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captured by the speciﬁc mAbs. After cell removal, spots are detected in two steps. First, a mixture of mAb anti-IFN-G-FITC and antiIL-2-biotin is added. Secondly, spots are detected by adding a mixture of mAb anti-FITC-green ﬂuorophore (for IFN-G) and SA-Cy3 (red ﬂuorescence; for IL-2). The protocol can be adapted to triple staining by using three coating antibodies, three detection antibodies labeled with different tags, and ﬁnally three different antitag ampliﬁcation reagents labeled with different ﬂuorophores (green, Cy3 and Cy5). The protocol for double staining is well-established, whereas the protocol for and analysis of triple staining have to be further optimized. The protocol can also be adjusted to single staining by using reagents for only one cytokine. 3.1. Dual-Color Fluorospot

Preparation of the plates and coating for double staining (sterile conditions) 1. Pre-wet a sterile 96-well PVDF plate with 20 Ml of 35% ethanol for 1 min at room temperature and wash the plate ﬁve times with 200 Ml of sterile water per well. 2. Mix coating mAbs for IFN-G and IL-2 in the same tube in sterile PBS, pH 7.4, at a ﬁnal concentration of 15 Mg/ml each. Add 100 Ml/well of the mixed mAb solution and incubate overnight at +4–8°C (see Note 1). 3. After coating, empty the plate and wash ﬁve times with sterile PBS (200 Ml/well) to remove excess mAb. Block the plate for at least 30 min with 200 Ml/well of 10% FCS (same serum as used for the cell suspensions) in PBS (see Note 2). Incubation of cells in the plates (sterile conditions) 4. Suspend the cells in complete culture medium (see Note 3) and add the suspension, including stimulatory agents of choice and anti-CD28 (0.1 Mg/ml; see Note 4), in a ﬁnal volume of 100–150 Ml/well. The number of cells per well is dependent on the stimulating agent used (see Note 5). 5. Incubate the plate overnight at +37°C in a humidiﬁed atmosphere with 5% carbon dioxide. It is important to wrap the plate in aluminum foil to avoid evaporation and to avoid moving the plate during this time. Incubation times may vary depending on the cytokine to be detected (see Note 6). Detection of spots 6. Discard the cells and wash the plate ﬁve times with PBS in a plate washer (or by hand 200 Ml/well; see Note 7). 7. In the same tube, dilute the detection mAbs for IFN-G and IL-2 labeled with FITC and biotin, respectively, in PBS containing 0.1% bovine serum albumin (BSA) (see Note 8). Add 100 Ml/well and leave the plate for 2 h at room temperature.

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8. Wash the plate as in step 6 above. 9. Dilute secondary detection reagents, i.e., mAb anti-FITC labeled with green ﬂuorophore and SA labeled with Cy3, in the same tube to their respective working concentrations in PBS–0.1% BSA (see Note 8). Add 100 Ml/well and leave the plate for 1 h at room temperature. 10. Wash the plate as in step 6 above, tap the plate against tissue paper, and add thereafter ﬂuorescence enhancer solution for 15 min at room temperature (100 Ml/well). Finally, tap the plate again against clean tissue paper. Remove the soft plastic underdrain and let the plate dry in the dark before analysis. Further storage of the dry plate should be at room temperature in the dark. 3.2. Analysis of Spots

1. The plate should be completely dry before analysis. We recommend the use of an automated reader with ﬁlters for FITC and Cy3. Green spots represent IFN-G-producing cells and red spots IL-2-producing cells. Double-stained spots are then identiﬁed based on the position of the green and red spots in a computerized overlay of IL-2 and IFN-G spot images from the same well. Similarly, in triple staining, the reader has to be able to identify and analyze yet another ﬂuorophore, in this case Cy5. 2. Fluorescent spots, like any ﬂuorescent signal, may fade due to excessive exposure to light. Although the ﬂuorescence in the dry plate has been found to be surprisingly stable (plates can be saved for months), for best result it is recommended to analyze the plate within 1 week of development as some brightening of the membrane, especially in green staining, takes place with time. 3. To ensure that no “bleeding over” between ﬁlters occurs, wells where only one detection system has been used can be included; analysis with ﬁlters for the other ﬂuorophore should result in an image with no detectable spots. 4. Random dual spots may occur, i.e., two adjacent cells secreting either of the two cytokines of interest may be counted as one dual spot. The number of random dual spots is primarily dependent on the total number of spots of each cytokine in the well and the deﬁnition criteria for multistained spots set in the reader (maximal distance between spot centers). If this distance is set to about 15 Mm, experiments in our laboratory have shown that 100 spots of each cytokine per well will generate less than 1% random events. Similarly, at about 500 spots of each cytokine per well, the percentage of random events does not exceed 2%. One way to tentatively estimate the number of random spots in any given experiment is to analyze the number of dual spots in overlays of unrelated green and red images from different wells within, e.g., a triplicate (12). In a similar

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manner, different combinations of random double-positive spots can be estimated in triple staining experiments. Also random triple-positive spots, which occur at a very low frequency, can be estimated in this way.

4. Notes 1. Plates: Transparent plates with low-ﬂuorescent PVDF-based membrane are recommended. To obtain maximal antibody binding capacity, these plates need to be pre-wetted with ethanol as described. It is essential to keep the membrane wet after ethanol treatment. If the membrane is let to dry, the prewetting needs to be repeated before addition of the coating antibodies. To avoid artifactual staining, working antibody solutions should preferably be ﬁltered (0.2 Mm). 2. Blocking: Lower membrane autoﬂuorescence is obtained if blocking is performed with PBS containing 10% FCS instead of complete medium. 3. Serum in cell medium: A serum pre-evaluated for cell culture and that yields low spontaneous cytokine secretion should be used in the medium; FCS is recommended. 4. Effect of IL-2 capture and costimulation with anti-CD28: When IFN-G and IL-2 are measured in the same well, capture of secreted IL-2 by coated mAbs to IL-2 may have a negative impact on the production of IFN-G and other cytokines (13). A proper control for that potential effect is to include wells coated with a single capture mAb. Costimulation using antiCD28 mAb enhances IL-2 production which can restore IFN-G production to the same level as when IFN-G is analyzed separately. Inclusion of anti-CD28 in the cell suspension added to double-coated wells is recommended. Further optimization may be necessary, depending on which cells and stimuli are used. A too high concentration of anti-CD28 mAb may result in elevation of nonspeciﬁc cytokine secretion, in particular IL-2. The costimulatory effects of anti-CD28 mAb, as well as a possible impact on nonspeciﬁc spots, can be assessed by comparing cells cultured with or without anti-CD28 mAb. An alternative strategy to avoid IL-2 capture effects is to preincubate the cells in vials prior to adding them to the coated wells. In this case, the cells are activated before they are exposed to IL-2 capture antibodies. 5. Cells: Both freshly prepared and cryopreserved cells may be used in the assay with good results. Triplicates or duplicates of 250,000 peripheral blood mononuclear cells (PBMCs) per well are often used to assess antigen-speciﬁc T-cell responses.

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If cryopreserved cells are used, they should be rested for a minimum of 1 h at 37°C in 5% CO2 before being counted and mixed with stimulatory agents. The number of cells responding to antigen stimulation is often compared to the number of cells spontaneously producing cytokine. Spontaneous production is determined by incubating the same number of cells in the absence of antigen. A polyclonal activator, such as anti-CD3 mAb (100 ng/ml) or phytohemagglutinin (PHA; 1–10 Mg/ ml), should normally be used as a control for cell viability and functionality of the test system. For polyclonal activators, the number of PBMC per well may have to be reduced in order to avoid conﬂuent spot formation. Typical cell numbers for stimulation with anti-CD3 or PHA are 50,000–100,000 PBMC per well. If T-cell clones, mixtures of separated cell fractions, etc. are used, conditions may have to be modiﬁed. In situations where most cells secrete the cytokine of interest, i.e., cell lines, the number of cells per well should be even further reduced. 6. Kinetics: When two cytokines are investigated, the cell incubation time has to be adapted to ﬁt both. In T-cell responses manifested by IFN-G and IL-2 production, both cytokines are rapidly induced and can be detected already after 10 h. In cases where the cytokines have different kinetics, e.g., analysis of IFN-G together with a cytokine like IL-4, the incubation time may have to be extended. For triple staining, the kinetics of all three cytokines have to be considered. 7. Plate washing: Washing of plates can be done using a multichannel device. In washing steps not requiring sterile conditions (from “Detection of spots”, step 6 and forward), a regular ELISA plate washer with a washing head adjusted to ELISPOT/ ﬂuorospot plates can be used. 8. Concentration of detection reagents: Suitable working concentrations for both the primary and secondary detection reagents should be determined in pilot tests. In the doublestaining protocol described, working concentrations for the respective reagents were mAb anti-IFN-G-FITC (1 Mg/ml), mAb anti-IL-2-biotin (2 Mg/ml), mAb anti-FITC-green (1 Mg/ml), and SA-Cy3 (0.5 Mg/ml).

Acknowledgments The authors would like to thank Thomas Ernemar, Eva Gelius, and Staffan Paulie for helpful discussions.

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References 1. Betts, M.R., Nason, M.C., West, S.M., De Rosa S.C., Migueles, S.A., Abraham, J., et al. (2006) HIV nonprogressors preferentially maintain highly functional HIV-speciﬁc CD8+ T cells. Blood 107, 4781–4789. 2. Darrah, P.A., Patel, D.T., De Luca, P.M., Lindsay, R.W., Davey, D.F., Flynn, B.J., et al. (2007) Multifunctional TH1 cells deﬁne a correlate of vaccine-mediated protection against Leishmania major. Nat Med 13, 843–850. 3. Day, C.L., Mkhwanazi, N., Reddy, S., Mncube, Z., van der Stok, M., Klenerman, P., et al. (2008) Detection of polyfunctional Mycobacterium tuberculosis-speciﬁc T cells and association with viral load in HIV-1infected persons. J Infect Dis 197, 990–999. 4. Duvall, M.G., Precopio, M.L., Ambrozak, D.A., Jaye, A., McMichael, A.J., Whittle, H.C., et al. (2008) Polyfunctional T cell responses are a hallmark of HIV-2 infection. Eur J Immunol 38, 350–363. 5. Lee, F.E., Walsh, E.E., Falsey, A.R., Lumb, M.E., Okam, N.V., Liu, N., et al. (2007) Human infant respiratory syncytial virus (RSV)speciﬁc type 1 and 2 cytokine responses ex vivo during primary RSV infection. J Infect Dis 195, 1779–1788. 6. Czerkinsky, C., Moldoveanu, Z., Mestecky, J., Nilsson, L.A., and Ouchterlony O. (1988) A novel two colour ELISPOT assay. I. Simultaneous detection of distinct types of antibody-secreting cells. J Immunol Methods 115, 31–37. 7. Okamoto, Y., Abe, T., Niwa, T., Mizuhashi, S. and Nishida, M. (1998) Development of a dual color enzyme-linked immunospot assay for simultaneous detection of murine T helper type

1- and T helper type 2-cells. Immunopharmacology 39, 107–116. 8. Karulin, A.Y., Hesse, M.D., Tary-Lehmann, M. and Lehmann, P.V. (2000) Single-cytokineproducing CD4 memory cells predominate in type 1 and type 2 immunity. J Immunol 164, 1862–1872. 9. Boulet, S., Ndongala, M.L., Peretz, Y., Boisvert, M.P., Boulassel, M.R., Tremblay, C., et al. (2007) A dual color ELISPOT method for the simultaneous detection of IL-2 and IFNgamma HIV-speciﬁc immune responses. J Immunol Methods 320, 18–29. 10. Gazagne, A., Claret, E., Wijdenes, J., Yssel, H., Bousquet, F., Levy, E., et al. (2003) A Fluorospot assay to detect single T lymphocytes simultaneously producing multiple cytokines. J Immunol Methods 283, 91–98. 11. Divekar, A.A., Zaiss, D.M., Lee, F.E., Liu, D., Topham, D.J., Sijts, A.J. et al. (2006) Protein vaccines induce uncommitted IL-2-secreting human and mouse CD4 T cells, whereas infections induce more IFN-gamma-secreting cells. J Immunol 176, 1465–1473. 12. Rebhahn, J.A., Bishop, C., Divekar, A.A., Jiminez-Garcia, K., Kobie, J.J., Lee, F.E., et al. (2008) Automated analysis of two- and threecolor ﬂuorescent Elispot (Fluorospot) assays for cytokine secretion. Comput Methods Programs Biomed 92, 54–65. 13. Quast, S., Zhang, W., Shive, C., Kovalovski, D., Ott, P.A., Herzog, B.A., et al. (2005) IL-2 absorption affects IFN-gamma and IL-5, but not IL-4 producing memory T cells in double color cytokine ELISPOT assays. Cell Immunol 237, 28–36.

Chapter 7 ELISPOT Assay as a Tool to Study Oxidative Stress in Lymphocytes Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny Abstract Enzyme-linked immuno spot (ELISPOT) assay is widely used for vaccine development, cancer and AIDS research, and autoimmune disease studies. The output of ELISPOT assay is a formation of colored spots which appear at the sites of cells releasing cytokines, with each individual spot representing a single cytokinereleasing cell. We worked out a protocol to study oxidative stress in human peripheral blood lymphocytes by determining their potency to secrete IFN-gamma, IL-2, IL-4, IL-5, IL-8, and TNF-alpha in response to acute treatment with hydrogen peroxide. We show that hydrogen peroxide-induced oxidative stress can cause a ~twofold decrease in the number of lymphocytes secreting the TH1 cytokines IFN-gamma and IL-2, as well as chemokines IL-8 and TNF-alpha. However, the number of cells secreting TH2 cytokines IL-4 and IL-5 in hydrogen peroxide-treated group did not change. It appears that oxidative stress may affect TH1–TH2 cytokine secretion balance which, in turn, may underlie developments of various pathological conditions. This protocol can be easily modiﬁed to study the effects of many other oxidative stress compounds. Key words: ELISPOT, Oxidative stress, Peripheral blood mononuclear cells, PBMCs, IFN-gamma, TNF alpha, IL-2, IL-8, TH1 and TH2 cytokines, Reactive oxygen species, ROS

1. Introduction Reactive oxygen species (ROS), such as hydrogen peroxide (H2O2), can be generated in live cells and tissues either via normal oxidative intracellular metabolism or induced by extracellular toxins which inhibit activity of antioxidant enzymes. Reduced endogenous antioxidant capacity causes natural overproduction of ROS which, in turn, leads to oxidative stress. Hydrogen peroxide can affect different biochemical reactions in human lymphocytes, including alterations in enzymatic activities, lipid peroxidation, and damage to DNA, and it was reported that short-term exposure of

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lymphocytes to hydrogen peroxide suppresses NFKB, AP-1, and NFAT transcription factors which play important roles in regulating production of cytokines (1). Enzyme-linked immuno spot (ELISPOT) allows detection of just a few cytokine-releasing cells out of tens of thousands making this technique a method of choice for vaccine development (2–4), cancer research (5), AIDS research (6–8), allergy research (9), and autoimmune disease studies (10–12). We ﬁnd that ELISPOT assay can be easily adopted for studying oxidative stress in human lymphocytes and, if combined with multidonor testing (13), ELISPOT can be used for high-throughput screening of multiple oxidative stress substances. The objective of this study was to examine the effect of oxidative stress induced by hydrogen peroxide in unstimulated peripheral blood mononuclear cells (PBMCs) on their capacity to secrete IFN-gamma, IL-2, IL-4, IL-5, IL-8, and TNF-alpha. Our oxidative stress model utilized short-term treatment of lymphocytes cultured in vitro with hydrogen peroxide in a concentration that does not impair lymphocyte viability (14). We employed ELISPOT assays (15–17) which are more sensitive than ELISA (18) and permit determination of frequency of cytokine-secreting cells. Our results indicate that hydrogen peroxide-induced oxidative stress signiﬁcantly inhibits secretion of TH1 cytokines IFN-gamma and IL-2, as well as proinﬂammatory chemokine IL-8 and inﬂammatory cytokine TNF-alpha (Table 1, Fig. 1). It appeared that acute oxidative stress induced by hydrogen peroxide did not affect the frequency of cell secreting TH2 cytokines: the regulator of adaptive and humoral immunity IL-4 and B-cell activator IL-5. Interestingly, there was no linear correlation between the number of cultured cells and the number of spots for each cytokine tested (Table 1). The lack of such a correlation is not completely understood and additional studies are required to address this issue.

2. Materials 2.1. Isolation of Human PBMCs

1. Ficoll-Paque PLUS (GE Biosciences, St. Giles, UK). 2. 50 mM phosphate-buffered saline (PBS), pH 7.2. 3. Red blood cell lysing solution: 155 mM NH4Cl, 10 mM NaHCO3, and 0.1 mM EDTA. 4. RPMI complete culture medium: RPMI1640 (1 L) (GibcoBRL, Grand Island, NY) supplemented with 50 mL of heat-inactivated fetal calf serum (Sigma Chemical Co., St Louis, MI), 1.19 g HEPES, 2 g of sodium bicarbonate, 3.5 ML of beta-mercaptoethanol, 50 mg Gentamicin Reagent Solution (Gibco-BRL, Grand Island, NY) (see Notes 1 and 2).

H2O2

Treatment

H2O2

Treatment

Control

H2O2

2 × 105

H2O2

Control

H2O2

5 × 103

TNF-alpha

Control

H2O2

5 × 104 Control

Control

66.2 ± 13.1

Control

H2O2

2 × 106

15.8 ± 4

H2O2

5 × 105

IL-4

H2O2

Control

H2O2

5 × 106

17.3 ± 2.4 9.2 ± 2.2

Control

106

IL-5

Control

7 ± 1.1

Control

Secretory capacity of PBMCs was evaluated in ELISPOT assays by counting the number of spots formed by secreted proteins on the bottom of the 96-well plate

233.3 ± 12.6 401.8 ± 22.3 58.7 ± 9.5 52.8 ± 4.3 29.7 ± 3.3 28.7 ± 4.5

H2O2

5 × 104

329.2 ± 41.8 66.3 ± 9.5 131.3 ± 12.3 41.8 ± 21.7

Control

104

IL-8

Number of 195.7 ± 13 282.7 ± 24.6 236.6 ± 111.6 693.3 ± 24.2 234.2 ± 23 347 ± 33.8 spots ± SD

105

Cells/mL

Number of 23.8 ± 1.6 73.2 ± 12.4 207.7 ± 7.5 spots ± SD

H2O2

5 × 104

104

Cells/mL

Control

IL-2

IFN-gamma

Analyte

Table 1 Effect of hydrogen peroxide-induced oxidative stress on secretory capacity of various cytokines from human PBMCs

Fig. 1. Typical ELISPOT images of secreted cytokines from human PBMCs. Note the prominent inhibitory effect of oxidative stress on secretion of all cytokines, except IL-4 and IL5.

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5. Centrifuge allowing spinning of 50-mL culture tubes at 500 × g. 6. Hemacytometer to count lymphocytes under the microscope. 7. Trypan blue dye. 8. Upright microscope equipped with bright-ﬁeld illumination and phase-contrast condenser. 2.2. Induction of Oxidative Stress

1. Reagent to induce oxidative stress: First, prepare 1 mM solution of H2O2 by adding 0.5 ML of 30% H2O2 to 8.8 mL of Hank’s balanced salt solution (HBSS). Then, prepare 10 MM solution of H2O2 by adding 100 ML of 1 mM H2O2 solution to 9.9 mL of RPMI complete culture medium.

2.3. ELISPOT Assays

1. Commercially available, ready-to-use ELISPOT assay kits (R&D Systems, Inc.) to study secretion of human IFN-G (Cat # EL285), IL-2 (Cat # EL202), IL-4 (Cat # EL204), IL-5 (Cat # EL205), IL-8 (Cat # EL208), and TNF-A (Cat # EL210). Each kit includes a dry 96-well, PVDF, membranebacked plate precoated with capture antibody, a concentrated solution of detection antibody, a concentrated solution of streptavidin-conjugated alkaline phosphatase, BCIP/NBT chromogenic substrate, and wash and dilution buffers. 2. Mitogens to stimulate release of cytokines from cultured PBMCs: Calcium ionomycin (CaI; #C-7522; Sigma Chemical Co., St Louis, MI), Phorbol 12-myristate 13-Acetate (PMA; #P-8139; Sigma Chemical Co., St Louis, MI), Phaseous Vulgaris Red Kidney Bean Phytohaemagglutinin (PHA; #L-3897; Sigma Chemical Co., St Louis, MI). 3. Hand-held Nunc-Immuno™ 12-plate washer (Thermo Fisher Scientiﬁc, Rochester, NY). 4. Membrane-removal device (MVS Paciﬁc, Minneapolis, MN). 5. ELISPOT plate reader QHub (MVS Paciﬁc, Minneapolis, MN).

3. Methods 3.1. Isolation of Human Peripheral Blood Lymphocytes

1. Collect blood samples from healthy donors in standard citratephosphate-dextrose unit bags (Leukopack, Memorial Blood Centers of Minnesota) and separate PBMCs using density centrifugation (500 × g for 30 min) of 25 mL of blood layered on 20 mL of 1.077 g/mL Ficoll-Paque Plus at 25°C (see Note 3). 2. Discard the upper plasma layer after centrifugation and transfer PBMCs into two sterile 50-mL tubes.

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3. PBMCs were then resuspended in 45 mL of sterile PBS and centrifuged for 5 min at 500 × g. 4. Discard supernatant, resuspend the pellet in 10 mL of red blood cells lysing solution, and incubate for 5 min at room temperature. 5. After lysing, add sterile PBS to reach 50 mL graduation mark on the tube to resuspend PBMCs. 6. Centrifuge tubes for 5 min at 500 × g. 7. Discard supernatants and add 30–40 mL of RPMI complete medium to the tubes with PBMCs. 8. Mix cells 1:2 with Trypan blue dye and pipette 10 ML of that mixture into each side of a hemacytometer under a coverslip (see Note 4). Count cells under the microscope using 20× lens and phase-contrast condenser. 3.2. Induction of Oxidative Stress

1. Add PBMCs directly to RPMI complete culture medium containing hydrogen peroxide and incubate in 37°C/CO2 humidiﬁed incubator for 30 min. 2. Transfer PBMCs from the incubator into a sterile hood, discard culture media with hydrogen peroxide, and rinse cells three times with sterile culture media. 3. Prepare several serial dilutions of PBMCs and mix them with corresponding mitogens (see Note 5).

3.3. ELISPOT Assays

1. Plate PBMCs (100 ML/well; six wells per group) into the ELISPOT plates at following cell concentrations (see Note 6): (a) IFN-gamma assay: 104 and 105 cells/mL (b) IL-2 assay: 5 × 104 and 2 × 105 cells/mL (c) IL-8 assay: 104 and 5 × 104 cells/mL (d) TNF-alpha assay: 5 × 103 and 5 × 104 cells/mL (e) IL-4 assay: 5 × 105 and 2 × 106 cells/mL (f) IL-5 assay: 106 and 5 × 106 cells/mL 2. Stimulate PBMCs with mitogens added directly to cells in ELISPOT plates and incubate in a CO2 incubator at 37°C (see Notes 7 and 8). Use the following stimulation and incubation interval: (a) IFN-gamma, IL-2, IL-5, and IL-8: Stimulate with a mixture of 0.5 Mg/mL of calcium ionomycin and 50 ng/mL of PMA for 18 h. (b) IL-4: Stimulate with 3 Mg/mL of PHA for 18 h. (c) TNF-alpha: No mitogens added, and incubate for 18 h.

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3. After ﬁnishing the incubation, remove PBMCs from the plates by rinsing wells four times with wash buffer (see Notes 9 and 10). 4. Make working solutions of detection antibodies by mixing concentrated detection antibodies 1:120 with dilution buffer. 5. Add 100 ML of detection antibody working solution into each well and incubate ELISPOT plates overnight at 4°C. 6. Wash plates three times with the wash buffer. 7. Prepare working solution of streptavidin–alkaline phosphatase by mixing the concentrated stock solution 1:120 with corresponding dilution buffer. 8. Add 100 ML of streptavidin–alkaline phosphatase working solution into each well and incubate for 2 h at room temperature. 9. Wash plates three times with wash buffer. 10. Add 100 ML of ready-to-use BCIP/NBT substrate into each well and incubate for 30–60 min at room temperature in a place protected from direct light. 11. Wash plates three times with distilled water and let them dry completely (see Note 11). 12. Quantify spots using automated ELISPOT reader. Treatment of human PBMCs with H2O2 caused almost twofold decrease in the number of lymphocytes secreting the TH1 cytokines IFN-gamma and IL-2, as well as chemokines IL-8 and TNFalpha (see Fig. 1, Table 1). However, the number of cells secreting TH2 cytokines IL-4 and IL-5 in hydrogen peroxide-treated group did not change (see Fig. 1, Table 1). Results of this study suggest that oxidative stress can change the balance between TH1 and TH2 cytokine secretion which, in turn, may underlie developments of various pathological conditions. It appears that ELISPOT assay can be used as a convenient research tool for studying the effects of oxidative stress on PBMCs and turned into a high-throughput platform to screen various oxidative stress compounds.

4. Notes 1. Sterilize RPMI complete culture medium and reagents that are used to separate out the white blood cells through 0.2-Mm sterile ﬁlter to allow their long-term storage. 2. When using fetal calf serum, it is important to heat inactivate the serum at 56° for 30 min. After the heat inactivation, the serum should be ﬁltered.

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3. When layering Ficoll, make sure that the blood does not mix with the Ficoll to gain the best separation and highest yield of PBMCs. 4. Overﬁlling hemacytometer with cell solution may result in inaccurate cell quantiﬁcation. While counting cells on a hemacytometer, ﬁrst ﬁnd the middle square which contains 25 smaller squares and count cells in 5 of them. Calculate the average and multiply by 25 (total number of squares in that area), then multiply by 2 (cell dilution factor), and multiply by 10,000 to determine the number of cells in 1 mL of original cell suspension. The resulting number should be used for calculating serial dilutions of PBMCs. 5. Making serial dilutions of cells allows avoidance of overdevelopment of ELISPOT plate and obtains a quantiﬁable number of spots that can be counted either manually or using automated ELISPOT plate readers. 6. For better well-to-well reproducibility, cells need to be mixed thoroughly before adding them into the wells. This may require shaking the tube with cells after ﬁlling every four wells in ELISPOT plate. 7. Plates can be wrapped into aluminum foil to provide even heat distribution across the bottom of the ELISPOT plates during their incubation. This helps to improve well-to-well spot consistency across the plate (described by Kalyuzhny and Stark (19) and in kit’s insert). Aluminum foil also helps to reduce a background staining. This is a very simple procedure which can be done as follows: before plating cells, ELISPOT plate is placed onto 13 × 16-cm piece of aluminum foil (e.g., Reynolds Wrap Quality Aluminum Foil, Consumer Products Division of Reynolds Metal, Richmond, VA); after that, cells are added into the wells, plate is covered with the lid, and edges of the foil are shaped loosely around the edges of the plate to wrap it. After ﬁnishing the incubation of the cells, the foil can be removed and either discarded or saved and used on the next ELISPOT plate. 8. Shelves in the CO2 incubator must be level to avoid moving cells toward one side of the well: this may produce under- and overdeveloped parts of the well and hinder quantiﬁcation of spots. It is also important to avoid disturbing cultured cells (e.g., by slamming the door of the incubator) during the incubation which may cause developing of weakly stained fuzzy spots. 9. Make sure that the height of prongs in the handheld plate washer is properly adjusted so that prongs do not touch the membranes on the bottom of the ELISPOT plate: PVDF membranes are fragile and can be easily punctured by protruding prongs.

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10. Between washes, it is important to tap out the excess liquid in the well onto a paper towel to prevent diluting the sequential reagents added to the plate. 11. Plates must be completely dried before analysis because wet membranes appear dark and obscure detection and quantiﬁcation of spots.

Acknowledgments Christopher Hartnett for assistance with isolation of peripheral blood lymphocytes. References 1. Flescher, E., Tripoli, H., Salnikow, K., and Burns F. J. (1998) Oxidative stress suppresses transcription factor activities in stimulated lymphocytes. Clin Exp Immunol 112, 242–247. 2. Pass, H. A., Schwarz, S. L., Wunderlich, J. R., and Rosenberg S. A. (1998) Immunization of patients with melanoma peptide vaccines: immunologic assessment using the ELISPOT assay. Cancer J Sci Am 4, 316–323. 3. Asai, T., Storkus, W. J., and Whiteside T. L. (2000) Evaluation of the modiﬁed ELISPOT assay for gamma interferon production in cancer patients receiving antitumor vaccines. Clin Diagn Lab Immunol 7, 145–154. 4. Kamath, A. T., Groat, N. L., Bean, A. G., and Britton W. J. (2000) Protective effect of DNA immunization against mycobacterial infection is associated with the early emergence of interferon-gamma (IFN-gamma)-secreting lymphocytes. Clin Exp Immunol 120, 476–482. 5. Schmittel, A., Keilholz, U., Thiel, E., and Scheibenbogen C. (2000) Quantiﬁcation of tumor-speciﬁc T lymphocytes with the ELISPOT assay. J Immunother 23, 289–295. 6. Keane, N. M., Price, P., Stone, S. F., John, M., Murray, R. J., and French M. A. (2000) Assessment of immune function by lymphoproliferation underestimates lymphocyte functional capacity in HIV patients treated with highly active antiretroviral therapy. AIDS Res Hum Retroviruses 16, 1991–1996. 7. Chapman, A. L., Munkanta, M., Wilkinson, K. A., Pathan, A. A., Ewer, K., Ayles, H., Reece, W. H., Mwinga, A., Godfrey-Faussett, P., and Lalvani A. (2002) Rapid detection of active and latent tuberculosis infection in HIV-positive individuals by enumeration of Mycobacterium

tuberculosis-speciﬁc T cells. Aids 16, 2285–2293. 8. Eriksson, K., Nordstrom, I., Horal, P., Jeansson, S., Svennerholm, B., Vahlne, A., Holmgren, J., and Czerkinsky C. (1992) Ampliﬁed ELISPOT assay for the detection of HIV-speciﬁc antibody-secreting cells in subhuman primates. J Immunol Methods 153, 107–113. 9. Jakobson, E., Masjedi, K., Ahlborg, N., Lundeberg, L., Karlberg, A. T., and Scheynius A. (2002) Cytokine production in nickel-sensitized individuals analysed with enzyme-linked immunospot assay: possible implication for diagnosis. Br J Dermatol 147, 442–449. 10. Pelfrey, C. M., Cotleur, A. C., Lee, J. C. and Rudick R. A. (2002) Sex differences in cytokine responses to myelin peptides in multiple sclerosis. J Neuroimmunol 130, 211–223. 11. Bienvenu, J., Monneret, G., Fabien, N., and Revillard J. P. (2000) The clinical usefulness of the measurement of cytokines. Clin Chem Lab Med 38, 267–285. 12. Okamoto, Y., Gotoh, Y., Tokui, H., Mizuno, A., Kobayashi, Y. and Nishida M. (2000) Characterization of the cytokine network at a single cell level in mice with collagen-induced arthritis using a dual color ELISPOT assay. J Interferon Cytokine Res 20, 55–61. 13. Bailey, T., Stark, S., Grant, A., Hartnett, C., Tsang, M., and Kalyuzhny A. (2002) A multidonor ELISPOT study of IL-1beta, IL-2, IL-4, IL-6, IL-13, IFN-gamma and TNF-alpha release by cryopreserved human peripheral blood mononuclear cells. J Immunol Methods 270, 171–182. 14. Flescher, E., Bowlin, T. L., Ballester, A., Houk, R., and Talal N. (1989) Increased polyamines

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may downregulate interleukin 2 production in rheumatoid arthritis. J Clin Invest 83, 1356–1362. 15. Czerkinsky, C. C., Nilsson, L. A., Nygren, H., Ouchterlony, O., and Tarkowski A. (1983) A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 65, 109–121. 16. Sedgwick, J. D. and Holt P. G. (1983) A solidphase immunoenzymatic technique for the enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 57, 301–309.

17. Kalyuzhny, A. E. (2005) Chemistry and biology of the ELISPOT assay. Methods Mol Biol 302, 15–31. 18. Tanguay, S. and Killion J. J. (1994) Direct comparison of ELISPOT and ELISA-based assays for detection of individual cytokinesecreting cells. Lymphokine Cytokine Res 13, 259–263. 19. Kalyuzhny, A. and Stark S. (2001) A simple method to reduce the background and improve well-to-well reproducibility of staining in ELISPOT assays. J Immunol Methods 257, 93–97.

Chapter 8 ELISPOT Assay for Neuroscience Research: Studying TNFa Secretion from Microglial Cells Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny Abstract The major application of ELISPOT assays is to study secretion of cytokines and chemokines from immune system cells. We adapted this assay to study TNFA secretion from microglial BV2 cells, which are similar in physiology to microglia in the nervous system. Stimulation of BV2 cells with 1 Mg/mL LPS resulted in a robust secretion of TNFA. Unlike uniform round spots formed by TNFA secreted by immune system cells, BV2 cells produced spots with short zigzag “tails” indicating that BV2 cells were actively moving during the incubation. In spite of irregular shapes, spots could be easily counted using an ELISPOT reader. Our study has shown the feasibility of employing an ELISPOT assay as a tool for neuroscience research to study the mechanisms underlying protein secretion from microglial cells. In addition, due to its convenient format, ELISPOT can be used for high-throughput screening of the potency of novel drugs to stimulate or inhibit cytokine secretion by microglial cells in the brain. Key words: ELISPOT, Neuroscience, BV2 cells, Microglia, TNFA

1. Introduction The ELISPOT assay was originally designed to study the secretion of antibodies from B cells (1–3) and then was modiﬁed to study secretion of cytokines and chemokines from immune system cells (4). Due to its high sensitivity, ELISPOT remains the technique of choice to study the frequency of cytokine secreting immune system cells in cancer research (5–9), AIDS research (10, 11), and immunization and vaccine studies (12–14). ELISPOT assays are typically used to study cytokine secretion by cells from peripheral blood and lymphoid tissues. However, this technology can be used on other cell types, including microglial cells in the central and peripheral nervous systems. Microglial cells are the resident macrophages in brain and in spinal cord and provide Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_8, © Springer Science+Business Media, LLC 2012

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immune defense of the CNS: they resemble immune system cells in that they also secrete cytokines and chemokines (15, 16). For example, microglia may secrete pro-inﬂammatory cytokines, including IL-1B and TNFA, that can affect neuronal function (16–19), induce neurodegeneration (15, 20, 21), and are also implicated in pain (22–24). In our current study, we adapted the ELISPOT assay to study the frequency of TNFA secreting microglial BV2 cells (25), which are similar to naturally occurring microglial cells (20, 26). Our study has shown that, in addition to immunology studies, ELISPOT can also be used as a powerful tool for neuroscience research to study mechanisms underlying protein secretion from microglial cells. In addition, ELISPOT can be used for high-throughput screening of potent neuroprotective drugs.

2. Materials 2.1. Culture of BV2 Cells

1. Growth media: 1 L of DMEM with glucose (4.5 g/L) and L-glutamine (4 mM), 100 mL fetal calf serum, sodium bicarbonate (3.7 g/L), 1× Pen/Strep/Amphotericin (10 mL/L). 2. PBS/EDTA containing 0.1 M NaCl, 0.01 M NaH2PO4· monohydrate, and 0.04% EDTA-free acid (1.37 mM EDTA). Dissolve salts in water and adjust pH to 7.5. Store at room temperature. 3. 1× phosphate-buffered saline (PBS). 4. Centrifuge capable of spinning 50-mL culture tubes at 500 × g. 5. Humidiﬁed incubator. 6. Hemacytometer to count lymphocytes under the microscope. 7. Trypan blue dye. 8. Upright microscope equipped with bright-ﬁeld illumination and phase contrast condenser. 9. Culture ﬂasks of various sizes.

2.2. ELISPOT Assay

1. Commercially available ready-to-use ELISPOT assay kits (R&D Systems, Inc.) to study secretion of mouse TNF-A (EL410). Each kit includes a 96-well PVDF membrane-backed plate precoated with capture antibody, a concentrated solution of detection antibody, a concentrated solution of streptavidinconjugated alkaline phosphatase, BCIP/NBT chromogenic substrate, wash and dilution buffers. 2. Mitogens to stimulate release of cytokines from cultured BV2 cells: Lipopolysaccharides (LPS) from Escherichia coli. 3. Hand-held Nunc-Immuno™ 12-plate washer (Thermo Fisher Scientiﬁc, Rochester, NY).

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4. Membrane-removal device (MVS Paciﬁc, Minneapolis, MN). 5. ELISPOT plate reader QHub (MVS Paciﬁc, Minneapolis, MN).

3. Methods 3.1. Culture of BV2 Cells

Like many other types of cells, BV2 cells are stored frozen in liquid nitrogen (e.g., ﬁve million cells per vial) and before they can be plated into the ELISPOT plates they need to be thawed and transferred into fresh culture media. Procedures are performed at room temperature unless speciﬁed otherwise. 1. Remove a vial with frozen BV2 cells from liquid nitrogen storage tank and thaw it rapidly at 37°C (1 min/1 mL of cell suspension). 2. Add 1 mL of thawed cells to 15 mL growth media prewarmed at 37°C. 3. Spin cells in a centrifuge at 500 × g for 5 min at room temperature. 4. Remove supernatant and discard it. Add 10 mL of growth media. 5. Spin cells again in a centrifuge for 5 min. 6. Discard supernatant and resuspend cells in 15 mL of a fresh growth media in a culture ﬂask. 7. Culture cells until they are conﬂuent (approximately 48 h). 8. Prewarm fresh culture media at 37°C. 9. Discard the media from the culture ﬂask and wash adhered BV2 cells twice with 10 mL of PBS. 10. Add PBS/EDTA to the ﬂask and incubate at 37°C until cells begin to detach (10–15 min). 11. Shake cells, add 15 mL of growth media, and transfer cells into a 50-mL conical tube. 12. Centrifuge cells for 5 min at 500 × g. 13. Discard supernatant and resuspend cells in 10 mL of prewarmed growth media. 14. Mix cell sample (50 ML) 1:2 with trypan blue dye and pipette 10 ML of that mixture into each side of a hemacytometer under a coverslip. Count cells under the microscope using a 20× lens and phase contrast condenser (see Note 1).

3.2. ELISPOT Assay

1. Plate BV2 cells (100 ML/well) into the ELISPOT plates at cell concentrations of 1.2 r103 and 104 cells/mL (see Note 2). 2. Stimulate BV2 cells with LPS at 1 Mg added directly to cells in ELISPOT plates and incubated in a CO2 incubator at 37°C for 18 h (see Notes 3–5).

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3. After ﬁnishing the incubation, remove growth media from the plates with a multichannel pipette and wash the plate once with 1× PBS and then add 100 ML of PBS/EDTA to plate for 10 min to remove adherent cells. Wash the plate by rinsing wells four times with wash buffer (see Notes 6–8). 4. Make working solutions of the detection antibodies by mixing concentrated detection antibodies 1:120 with dilution buffer. 5. Add 100 ML of detection antibody working solution into each well and incubate ELISPOT plates overnight in the refrigerator or in the cold room at 2–8°C (see Note 9). 6. Wash plates three times with the wash buffer. 7. Prepare a working solution of streptavidin-alkaline phosphatase by mixing the concentrated stock solution 1:120 with the corresponding dilution buffer. 8. Add 100 ML of streptavidin-alkaline phosphatase working solution to each well and incubate for 2 h. 9. Wash plates three times with wash buffer. 10. Add 100 ML of ready-to-use BCIP/NBT substrate into each well and incubate for 30–60 min in a place protected from direct light. 11. Wash plates three times with distilled water and let them dry completely. 12. Quantify spots (Fig. 1, Table 1) using an automated ELISPOT reader (see Note 10).

Fig. 1. Typical ELISPOT image of TNFA secreted by microglial BV2 cells. BV2 cells were plated at 1,000 cells per well (a) and 120 cells per well (b). Note that, unlike uniform round spots formed by TNFA secreted by immune system cells, many spots formed by TNFA secreted from BV2 microglial cells have “zigzag tails” as a result of active movement of BV2 cells during the incubation.

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Table 1 Well-to-well reproducibility of TNFa ELISPOT on BV2 cells stimulated with 1 mg/mL LPS Number of spot forming cells (SFCs) Well #1

Well #2

Well #3

Well #4

Well #5

Well #6

120 cells per well

100

101

100

97

92

92

1,000 cells per well

486

448

403

485

555

508

Mean/well ± SD

97 ± 4.1

480 ± 51.8

4. Notes 1. Overﬁlling the hemacytometer with cell solution may result in inaccurate cell quantiﬁcation. While counting cells on a hemacytometer, ﬁrst ﬁnd the middle square which contains 25 smaller squares and count cells in 5 of them. Calculate the average and multiply by 25 (total number of squares in that area) and then multiply by 2 (cell dilution factor), multiply by 10,000 to determine the number of cells in 1 mL of original cell suspension. The resulting number should be used for calculating serial dilutions of cells. 2. Making serial dilutions of cells allows the user to avoid overdevelopment of the ELISPOT plate and to obtain a quantiﬁable number of spots that can be counted either manually or using an automated ELISPOT plate reader. 3. For better well-to-well reproducibility, cells need to be mixed thoroughly before adding them into the wells. This may require shaking the tube containing the cells after ﬁlling every four wells in the ELISPOT plate. 4. Plates can be wrapped in aluminum foil to provide even heat distribution across the bottom of the ELISPOT plates during their incubation. This helps to improve well-to-well spot consistency across the plate (27). Aluminum foil also helps to reduce background staining. This is a very simple procedure which can be done as follows: before plating the cells, the ELISPOT plate is placed onto 13 × 16-cm piece of aluminum foil (e.g., Reynolds Wrap Quality Aluminum Foil, Consumer Products Division of Reynolds Metal, Richmond, VA); the cells are then added into the wells and the plate is covered with the lid. The edges of the foil are shaped loosely around the edges of the plate to wrap it. After ﬁnishing the incubation of the cells, the foil can be removed and either discarded or saved and used on the next ELISPOT plate.

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Fig. 2. An example of background staining that can be caused by omitting EDTA from the PBS used to wash microglial cells off the plate in step 3 in Subheading 3.2.

5. Shelves in the CO2 incubator must be leveled to avoid moving cells toward one side of the well: this may produce under- and overdeveloped parts of the well and hinder quantiﬁcation of spots. It is also important to avoid disturbing cultured cells (e.g., by slamming the door of the incubator) during the incubation, which may cause the formation of weakly stained fuzzy spots. 6. Make sure that the height of the prongs in the hand held plate washer is properly adjusted, so prongs do not touch the membranes on the bottom of ELISPOT plates. PVDF membranes backing the ELISPOT plate are fragile and can get easily punctured by protruding prongs. 7. Between washes it is important to tap out the excess liquid in the wells onto a paper towel to prevent diluting the subsequent reagents added to the plate. 8. Addition of EDTA to PBS is highly recommended because it facilitates the removal of microglial cells sticking to the membranes backing ELISPOT plates. Unremoved cells induce a strong background staining (Fig. 2 below) that obscures TNFA formed spots and affects their quantiﬁcation. 9. To ensure accurate results, reagent addition should be continuous and completed within 15 min. 10. ELISPOT plates must be completely dry before analysis using an ELISPOT reader because wet membranes appear dark, obscuring the detection and quantiﬁcation of spots.

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References 1. Sedgwick, J. D., and Holt, P. G. (1983) A solidphase immunoenzymatic technique for the enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 57, 301–309. 2. Sedgwick, J. D. (2005) ELISPOT assay: a personal retrospective. Methods Mol Biol 302, 3–14. 3. Czerkinsky, C. C., Nilsson, L. A., Nygren, H., Ouchterlony, O., and Tarkowski, A. (1983) A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 65, 109–121. 4. Kalyuzhny, A. E. (2005) Chemistry and biology of the ELISPOT assay. Methods Mol Biol 302, 15–31. 5. Arlen, P., Tsang, K. Y., Marshall, J. L., Chen, A., Steinberg, S. M., Poole, D., et al.(2000) The use of a rapid ELISPOT assay to analyze peptidespeciﬁc immune responses in carcinoma patients to peptide vs. recombinant poxvirus vaccines. Cancer Immunol Immunother 49, 517–529. 6. Asai, T., Storkus, W. J., and Whiteside, T. L. (2000) Evaluation of the modiﬁed ELISPOT assay for gamma interferon production in cancer patients receiving antitumor vaccines. Clin Diagn Lab Immunol 7, 145–154. 7. Bienvenu, J., Monneret, G., Fabien, N., and Revillard, J. P. (2000) The clinical usefulness of the measurement of cytokines. Clin Chem Lab Med 38, 267–285. 8. Pass, H. A., Schwarz, S. L., Wunderlich, J. R., and Rosenberg, S. A. (1998) Immunization of patients with melanoma peptide vaccines: immunologic assessment using the ELISPOT assay. Cancer J Sci Am 4, 316–323. 9. Schmittel, A., Keilholz, U., Thiel, E., and Scheibenbogen, C. (2000) Quantiﬁcation of tumor-speciﬁc T lymphocytes with the ELISPOT assay. J Immunother 23, 289–295. 10. Howell, D. M., Feldman, S. B., Kloser, P., and Fitzgerald-Bocarsly, P. (1994) Decreased frequency of functional natural interferon-producing cells in peripheral blood of patients with the acquired immune deﬁciency syndrome. Clin Immunol Immunopathol 71, 223–230. 11. Chapman, A. L., Munkanta, M., Wilkinson, K. A., Pathan, A. A., Ewer, K., Ayles, H., et al. (2002) Rapid detection of active and latent tuberculosis infection in HIV-positive individuals by enumeration of Mycobacterium tuberculosis-speciﬁc T cells. Aids 16, 2285–2293. 12. Kamath, A. T., Groat, N. L., Bean, A. G., and Britton, W. J. (2000) Protective effect of DNA immunization against mycobacterial infection is associated with the early emergence of

interferon-gamma (IFN-gamma)-secreting lymphocytes. Clin Exp Immunol 120, 476–482. 13. Dolter, K. E., Evans, C. F., Ellefsen, B., Song, J., Boente-Carrera, M., Vittorino, R., et al. (2010) Immunogenicity, safety, biodistribution and persistence of ADVAX, a prophylactic DNA vaccine for HIV-1, delivered by in vivo electroporation. Vaccine. Epub 2010 Nov 18. 14. Salmon-Ceron, D., Durier, C., Desaint, C., Cuzin, L., Surenaud, M., Hamouda, N. B., et al. (2010) Immunogenicity and safety of an HIV-1 lipopeptide vaccine in healthy adults: a phase 2 placebo-controlled ANRS trial. AIDS 24, 2211–2223. 15. Tansey, M. G., and Goldberg, M. S. (2009) Neuroinﬂammation in Parkinson’s disease: its role in neuronal death and implications for therapeutic intervention. Neurobiol Dis 37, 510–518. Epub 2009 Nov 10. 16. Park, K. M., and Bowers, W. J. (2010) Tumor necrosis factor-alpha mediated signaling in neuronal homeostasis and dysfunction. Cell 22, 977–983. Epub 2010 Jan 21. 17. Cunningham, A. J., Murray, C. A., O’Neill, L. A., Lynch, M. A., and O’Connor, J. J. (1996) Interleukin-1 beta (IL-1 beta) and tumour necrosis factor (TNF) inhibit long-term potentiation in the rat dentate gyrus in vitro. Neurosci Lett 203, 17–20. 18. Pickering, M., Cumiskey, D., and O’Connor, J. J. (2005) Actions of TNF-alpha on glutamatergic synaptic transmission in the central nervous system. Exp Physiol 90, 663–670. Epub 2005 Jun 8. 19. Pickering, M., and O’Connor, J. J. (2007) Proinﬂammatory cytokines and their effects in the dentate gyrus. Prog Brain Res 163, 339–354. 20. Tran, T. A., McCoy, M. K., Sporn, M. B., and Tansey, M. G. (2008) The synthetic triterpenoid CDDO-methyl ester modulates microglial activities, inhibits TNF production, and provides dopaminergic neuroprotection. J Neuroinﬂammation 5, 14. 21. Chakraborty, S., Kaushik, D. K., Gupta, M., and Basu, A. (2010) Inﬂammasome signaling at the heart of central nervous system pathology. J Neurosci Res 88, 1615–1631. 22. Vallejo, R., Tilley, D. M., Vogel, L., and Benyamin, R. (2010) The role of glia and the immune system in the development and maintenance of neuropathic pain. Pain 10, 167–184. Epub 2010 Apr 5. 23. Spicarova, D., and Palecek, J. (2010) Tumor necrosis factor alpha sensitizes spinal cord TRPV1 receptors to the endogenous agonist N-oleoyldopamine J Neuroinﬂammation 7, 49.

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24. Austin, P. J., and Moalem-Taylor, G. (2010) The neuro-immune balance in neuropathic pain: Involvement of inﬂammatory immune cells, immune-like glial cells and cytokines. J Neuroimmunology 229, 26–50. Epub 2010 Sep 25. 25. Blasi, E., Barluzzi, R., Bocchini, V., Mazzolla, R., and Bistoni, F. (1990) Immortalization of murine microglial cells by a v-raf/v-myc

carrying retrovirus. J Neuroimmunol 27, 229–237. 26. Hald, A., and Lotharius, J. (2005) Oxidative stress and inﬂammation in Parkinson’s disease: is there a causal link? Exp Neurol 193, 279–90. 27. Kalyuzhny, A., and Stark, S. (2001) A simple method to reduce the background and improve well-to-well reproducibility of staining in ELISPOT assays J Immunol Methods 257, 93–7.

Chapter 9 ELISPOT Assay as a Tool to Study the Effects of Stem Cells on Cytokine Secretion Jun-Seop Shin and Chung-Gyu Park Abstract Mesenchymal stem cells (MSCs) have many regulatory effects (e.g., T-cell suppression) on various immune cells. As aberrant T-cell activation is a primary cause of many diseases, understanding the underlying mechanisms of how MSCs exert immunosuppression is an important issue with potential therapeutic implications. A group of cytokines was shown to be involved as soluble mediators in the immunosuppressive effects of MSCs. An enzyme-linked immunospot (ELISPOT) assay is an ideal method to ﬁnd potential mediators involved in the MSC-immunosupporessive pathway; additionally, the ELISPOT assay can measure changes in the full spectrum of cytokines produced during T-cell activation in the presence of MSCs. Here, we show that during a mixed lymphocyte reaction, interleukin-10 (IL-10)-secreting splenocytes increased in number in the presence of MSC-conditioned media; the increase in IL-10 levels in the supernatant was conﬁrmed by an independent enzyme-linked immunosorbent assay (ELISA). Key words: Mesenchymal stem cells, Immunosuppression, Cytokine, IL-10, Conditioned media

1. Introduction Mesenchymal stem cells (MSCs) are nonhematopoietic stem cells and are capable of differentiation into mesodermal lineages as well as endodermal and neuroectodermal lineages (1–3). In addition, recent studies indicate that these cells exhibit immunomodulatory effects on various immune cells, including dendritic cells (DCs), macrophages, and B and T cells, both in vitro and in vivo (4–6). The study of the immunosuppressive effect of MSCs on T cells may yield a potential cell therapy for diseases caused by aberrant T-cell activation, such as acute graft-versus-host disease (GVHD) (7). Several studies have demonstrated that the immunosuppressive effects of MSCs are mediated by either direct cell-to-cell

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contact or by soluble factors. The latter are either secreted from MSCs or upregulated in the immune cells by the presence of MSCs; the MSC-associated factors include interleukin-10 (IL-10), transforming growth factor-B1 (TGF-B1), hepatocyte growth factor (HGF), prostaglandin E2 (PGE2), nitric oxide (NO), and indoleamine 2,3-dioxygenase (IDO) (8–10). However, the mechanism by which MSCs mediate immunosuppression using soluble factors remains controversial. We report that the presence of conditioned media (CM) derived from MSCs in a mixed lymphocyte reaction (MLR) increased the number of IL-10-secreting splenocytes, as measured by an ELISPOT assay, and that the use of neutralizing antibodies to block the IL-10 signaling partially abrogated the immunosuppressive effect of MSC CM (11).

2. Materials 2.1. MSC and Fibroblast Culture

1. Complete media: High-glucose Dulbecco’s Modiﬁed Eagle’s Medium (HG-DMEM) (Gibco Laboratories Inc., Grand Island, NY, USA) supplemented with heat-inactivated 10% fetal bovine serum (FBS) (Gibco Laboratories Inc.), 5% horse serum (Gibco Laboratories Inc.), 50 Mg/ml gentamicin (Choogwae Pharmaceutical Co., Yong-In, Korea), 2 mM Glutamax (Invitrogen Corp., Carlsbad, CA, USA), 100 mM nonessential amino acids (Sigma, St. Louis, MO, USA), 10 mM HEPES, and 55 MM 2-mercaptoethanol. 2. TrypLE™ Express solution (Gibco Laboratories Inc.).

2.2. Preparation of Splenocytes

1. Microscope slides. 2. Red blood cell lysis buffer (Sigma–Aldrich, St. Louis, MO, USA). 3. 70-Mm-diameter cell strainer.

2.3. ELISPOT

1. 96-well ELISPOT plates (Millipore, Bedford, MA, USA). 2. IL-10 capture antibody (clone JES5-16E3, eBioscience, San Diego, CA, USA). 3. Biotinylated anti-IL-10 detection antibody (clone JES5-2A5, eBioscience). 4. Streptavidin–alkaline phosphatase (Promega, Madison, WI, USA). 5. NBT/BCIP stock solution (Roche, Indianapolis, IN, USA). 6. Alkaline phosphatase substrate buffer (100 mM Tris–Cl, pH 9.5, 100 mM NaCl, and 5 mM MgCl2 in distilled water).

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7. PBS supplemented with 0.05% Tween-20 (Sigma, St. Louis, MO, USA) (PBST). 8. PBST supplemented with 1% BSA (PBST–1% BSA). 9. 35% Ethanol solution (optional). 2.4. ELISA

1. 96-well Maxi-sorb plate (Nalge Nunc International, Rochester, NY, USA). 2. IL-10 capture antibody (clone JES5-16E3, eBioscience, San Diego, CA, USA). 3. Biotinylated anti-IL-10 detection antibody (clone JES5-2A5, eBioscience). 4. Blocking solution: 1× assay diluent (eBioscience, San Diego, CA, USA). 5. Avidin–horseradish peroxidase (HRP) (eBioscience, San Diego, CA, USA). 6. 3,3c,5,5c-tetramethylbenzidine (TMB) (eBioscience, San Diego, CA, USA).

substrate

solution

7. Stop solution: 2 N H2SO4. 8. PBS supplemented with 0.05% Tween-20 (PBST).

3. Methods Immunosuppressive effects of MSCs were mediated by either cellto-cell direct interaction or indirectly by soluble factors released from MSCs or effector immune cells. In an earlier study, we found that the immunosuppression activity of MSCs was largely mediated by soluble factors; therefore, we simpliﬁed the assay system by using CM obtained from actively growing MSCs. Other studies reported that the production of suppressive cytokines, such as TGF-B1 or IL-10, was promoted by MSCs (8). We understand that the main mechanism underlying this cytokine-mediated immunosuppression involves the downregulation of many cytokines required for T-cell proliferation or survival (12, 13). We found that the ELISPOT assay is well-suited for ﬁnding potential suppressive mediators as well as measuring changes in a full spectrum of cytokines produced during T-cell activation (in an MLR) in the presence of MSC CM. It is important to note that an independent ELISA should be done to conﬁrm the increase in cytokine concentration and exclude the possibility of contributing effects of the MSC CM itself. We used an ELISA assay to conﬁrm increased secretion of IL-10. Most successful results in ELISA and ELISPOT assays depend on good pairs of antibodies recognizing different epitopes: one for capturing

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target cytokines in the sample with high afﬁnity and the other for detecting the cytokines. Many good antibody pairs for mouse and human cytokines and chemokines are already commercially available. 3.1. Preparation of CM from MSCs and Fibroblasts

3.2. Preparation of Splenocytes

BALB/c MSCs or ﬁbroblasts (2 × 106 cells from each) are seeded in a 100-mm culture dish (Nalge Nunc International, Rochester, NY, USA) and are cultured for 72 h in complete media. When the plate reaches about 90% conﬂuence, culture supernatant is harvested and centrifuged at 1,200 × g at room temperature for 10 min. Ninety percent of the upper aqueous portion is carefully withdrawn, aliquoted in 1.5-mL microtubes, and kept at −70°C (see Notes 1 and 2). 1. 8–12-Week-old BALB/c or C57BL/6 mice are used. After sacriﬁce by cervical dislocation, fur on the left side is sterilized using 70% ethanol and a small incision is made along the left side of the animal. 2. The body cavity is cut open and the spleen is removed using forceps and placed into 10 mL of cold DMEM containing 50 Mg/mL gentamicin in a 100-mm culture dish (Nalge Nunc International Inc.). 3. Spleen is cut into 5–6 pieces using scissors. 4. Pieces of spleen are mechanically homogenized between the frosted slides. 5. Homogenized spleen is passed through a cell strainer mounted on a 50-mL conical tube. 6. Cell strainer is washed with 5 mL of complete media. 7. The conical tube is spun at 800 × g for 10 min. 8. The supernatant is discarded, and the pellet is resuspended in 1 mL of red blood cell lysis buffer and incubated at room temperature for 5–10 min. 9. Roughly, 9 mL of complete media is added to the conical tube and spun as in step 7. 10. The supernatant is discarded and the pellet is resuspended in 3 mL of complete media. 11. 10 ML of the cell suspension is diluted with trypan blue and counted using a hemocytometer to determine the total splenocyte cell count (see Note 3).

3.3. ELISPOT

1. The day prior to the ELISPOT experiment, each well of a 96-well ELISPOT plate (Millipore, Bedford, MA, USA) is soaked with 15 ML/well of 35% ethanol for 1 min and then washed twice with 200 ML of PBS per well before ethanol evaporation (see Note 4).

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2. The plate is coated with 100 ML of capture anti-IL-10 monoclonal antibody per well (the antibody is diluted 1:60, antibody to PBS, in sterile PBS) and incubated overnight at 4°C. The plate is sealed to prevent evaporation during the incubation period. 3. On the day of the experiment, the plate is washed once with 200 ML of PBST per well and blocked with 200 ML of complete media per well for 30 min at 37°C. 4. BALB/c splenocytes and G-irradiated C57BL/6 splenocytes (20 Gy) are used as responder and stimulator, respectively. Both cell types (5 × 105 cells of each in 100 ML complete media) are cultured with 100 ML of complete media (control), BALB/c MSC CM (MSC CM), or ﬁbroblast CM (Fibro. CM) for 72 h. The controls are as follows: irradiated C57BL/6 splenocytes only, BALB/c splenocytes only, and blank wells. 5. After 72 h, the cells are discarded and 200 ML of distilled water is added to each well. The plate is then placed on ice for 10–15 min (see Notes 5 and 6). 6. The plate is washed ﬁve times with 200 ML of PBST per well. 7. 100 ML of detection antibody, diluted 1:60 in PBS, is added to each well; the plate is then incubated for 1 h at 37°C (see Note 7). 8. The plate is washed ﬁve times with 200 ML of PBST per well, and 100 ML of streptavidin–alkaline phosphatase, diluted 1:2,000 in PBST–1% BSA, is added to each well and incubated for 1 h at 37°C. 9. The plate is washed four times with 200 ML of PBST per well and twice with 200 ML of PBS per well (see Note 8). 10. 50 ML of developing solution, composed of 20 ML/mL of NBT/BCIP stock solution in an alkaline phosphatase substrate buffer, is added to each well and color development is carefully monitored for 5–30 min. 11. When the experiment yields a reasonably high ratio of signal to noise, the plate is washed with tap water and dried by blotting with absorbent paper. 12. The resulting spots (Figs. 1 and 2) are counted and analyzed on a computer-assisted AID ELISPOT Reader System (AID, Straßberg, Germany). 3.4. ELISA

1. A 96-well Maxi-sorb plate is coated with 100 ML of capture antibody diluted 1:200, antibody to PBS, in PBS per well and stored overnight at 4°C. The plate is tightly sealed to prevent evaporation during the incubation period. 2. On the day of the experiment, the plate is washed ﬁve times with 200 ML of PBST per well and incubated with 200 ML of 1× assay diluent per well for 1 h at room temperature. 3. The plate is washed ﬁve times with 200 ML of PBST per well.

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a

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l

tro

n Co

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Fig. 1. Measurement of IL-10-secreting cells in MLR by ELISPOT assay. (a) G-irradiated C57BL/6 splenocytes (Irr. B) and BALB/c splenocytes (5 × 105 cells each) were mixed and incubated for 72 h in the presence of 100 ML of complete media (control), MSC CM, or ﬁbroblast CM (Firbo. CM). Additional wells containing stimulator (Irr. B only), responder (Balb/c only), or nothing except complete media (-) control were used as appropriate controls. Three independent experiments were done in triplicate and a representative image from one experiment is shown. (b) The number of the resulting spots was counted by a computer-assisted AID ELISPOT Reader System and the graph was drawn using SigmaPlot 10.0 program (Systat Software Inc., San Jose, CA, USA). *** and * denote p-value less than 0.01 and 0.05, respectively, compared with control.

4. 100 ML of standard mouse IL-10 is serially diluted in 1× assay diluent and combined with experimental samples from the MLR (cell-free supernatant) or the MLR with MSC CM; the solutions are then added to separate wells on the ELISA plate and incubated for 2 h at room temperature (see Note 9). 5. The plate is washed ﬁve times with 200 ML of PBST per well.

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M ly ly ol CM ol on c on ontr .C ntr / c Co MSC ibro rr. B b ) l + (I +F Ba

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M M ly ly ol ol ntr C C ro. C B on /c on ontr S c . b b i ) r M l r + ( I +F Ba

Co

Fig. 2. Proﬁling of cytokine secretion patterns in MLR by ELISPOT assay. G-irradiated C57BL/6 splenocytes (Irr. B) and BALB/c splenocytes (5 × 105 cells each) were mixed and incubated for 24 h (a, b) or 48 h (c, d) in the presence of 100 ML of complete media (control), MSC CM, or ﬁbroblast CM (Fibro. CM). Additional wells containing stimulator (Irr. B only), responder (Balb/c only), or nothing except complete media (-) control were used as appropriate controls. Number of the resulting spots was counted by a computer-assisted AID ELISPOT Reader System and the graph was drawn using SigmaPlot 10.0 program (Systat Software Inc., San Jose, CA, USA). *** and * denote p-value less than 0.01 and 0.05, respectively, compared with control.

6. 100 ML of detection antibody, diluted 1:60 in PBS, is added to each well; the plate is then incubated for 1 h at room temperature. 7. The plate is washed ﬁve times with 200 ML of PBST per well. 8. 100 ML of avidin–HRP, diluted in 1× assay diluent at 1:2,000, is added to each well and the plate is sealed and incubated for 30 min at room temperature. 9. The plate is washed seven times with 200 ML of PBST per well. 10. 100 ML of the substrate solution is added to each well and incubated for 15 min at room temperature. 11. 50 ML of stop solution is added to each well. 12. Absorbance is read at 450 nm with background subtraction at 590 nm (Fig. 3).

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IL-10 (pg/ml)

500

MLR +MSC CM

400

300

200

100

0 24

48

72

Reaction time (hr)

Fig. 3. Measurement of IL-10 levels by ELISA. G-irradiated C57BL/6 splenocytes and BALB/c splenocytes (5 × 105 cells each) were mixed and incubated for the indicated times in the presence of 100 ML of complete media (MLR) or MSC CM. IL-10 levels in the resulting culture supernatants were measured by the sandwich ELISA method. *** and * denote p-value less than 0.01 and 0.05, respectively, compared with MLR.

4. Notes 1. BALB/c MSCs were prepared by ﬂushing femurs and tibias with complete media; cells were cultivated in 25-cm2 ﬂasks at a concentration of 106 nucleated cells per mL in 5% CO2 at 37°C. After 72 h, nonadherent cells were removed and adherent cells were expanded in 15 passages. Before examining the immunosuppressive effects of MSCs, they were tested for their ability to differentiate into adipocytes and osteoblasts. Also, their immunophenotypes were characterized by FACS analysis; the cells were the following: CD106+, CD45−, CD14−, CD11c−, and CD31−. BALB/c ﬁbroblasts were prepared from mouse foreskin using a standard explant culture method (14). 2. MSC CM was prepared by seeding 2 million cells in a 100-mm culture dish and incubating for 48–72 h until the cells reached 90% conﬂuence (~6 million cells). With approximating average cell numbers to about 4 million per dish and total media at about 10 mL, 100 ML of MSC CM represents cell contents secreted from 4 × 104 actively growing MSCs. Therefore, in this protocol, the ratio of splenocyte to MSC would be 12.5:1 (5 × 105 splenocytes: 4 × 104 MSCs) if MSCs themselves were used instead of MSC CM. 3. Viability of splenocytes should be at least >90% by trypan blue test. After counting, both stimulator and responder cells were diluted to 107 cells/mL and stimulator cells were irradiated by gamma rays (20 Gy). Fifty microliters of cell suspension was

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dispensed into each well, making total cell counts 106 cells per well. Cells should always be kept at room temperature and processed within 3 h after isolation to sustain normal cytokine secretion. 4. The method for preactivation of the PVDF membrane using ethanol differs from protocol to protocol with ethanol concentrations ranging from 35 to 80% and incubation time from 1 to 10 min. Even in some protocols, the preactivation step is unnecessary. Although we have found that better results, such as strong spot intensity with low background levels, are consistently obtainable in the absence of the preactivation step, we wanted to keep the manufacturer’s recommendation in this protocol. 5. Incubation in distilled water leads to hypotonic lysis of residual cells and, thus, prevents nonspeciﬁc irregular spot formation. 6. Most of the cytokines produced from T cells are de novo synthesized with maximum cytokine synthesis persisting for only 4–6 h (15). IL-2 and IFN-G are part of this early group; therefore, the incubation time for these cytokines should be less than 24 h. For IL-4, TGF-B, and IL-10, a longer incubation time, up to 72 h, is needed. In our protocol, the incubation time is 24 h for IL-2 and IFN-G detection, 48 h for IL-4 and TGF-B, and 72 h for IL-10, at which time the most distinct spots are discernible. 7. Before adding the detection antibody, the antibody tube should be clariﬁed by centrifugation at 10,000 × g for 5 s and only the clear supernatant should be used. Failure to clarify antibody may result in nonspeciﬁc spot formation due to antibody aggregates. 8. Some reagents may leak through the membrane into the backside of the plate; additional washings of both sides of the membrane with PBS prevent a high background signal. 9. MLR was performed by incubating 5 × 105 BALB/c splenocytes and 5 × 105 G-irradiated C57BL/6 splenocytes in 100 ML complete media in the presence of 100 ML of complete media (control) or MSC CM for the indicated times. At the indicated time, the total media was thoroughly withdrawn by gentle pipetting several times and centrifuged at 1,200 × g for 10 min to remove cells and cell debris. Supernatant was carefully withdrawn and stored at −70°C until assay.

Acknowledgments This study was supported by a grant from the Korea Healthcare Technology R&D Project, Ministry for Health, Welfare & Family Affairs, Republic of Korea (Project No. A040004).

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References 1. Pittenger, M. F., Mackay, A. M., Beck, S. C., Jaiswal, R. K., Douglas, R., Mosca, J. D., et al. (1999) Multilineage potential of adult human mesenchymal stem cells. Science 284, 143–147. 2. Woodbury, D., Reynolds, K., and Black, I. B. (2002) Adult bone marrow stromal stem cells express germline, ectodermal, endodermal, and mesodermal genes prior to neurogenesis. J Neurosci Res 69, 908–917. 3. Munoz-Elias, G., Woodbury, D., and Black, I. B. (2003) Marrow stromal cells, mitosis, and neuronal differentiation: stem cell and precursor functions Stem Cells 21, 437–448. 4. Rasmusson, I. (2006) Immune modulation by mesenchymal stem cells. Exp Cell Res 312, 2169–2179. 5. Fibbe, W. E., Nauta, A. J., and Roelofs, H. (2007) Modulation of immune responses by mesenchymal stem cells. Ann N Y Acad Sci 1106, 272–278. 6. Lim, J. H., Kim, J. S., Yoon, I. H., Shin, J. S., Nam, H. Y., Yang, S. H., et al. (2010) Immonomodulation of delayed-type hypersensitivity responses by mesenchymal stem cells is associated with bystander T cell apoptosis in the draining lymph node. J Immunol 185, 4022–4029. 7. Le Blanc, K., Rasmusson, I., Sundberg, B., Gotherstrom, C., Hassan, M., Uzunel, M., et al. (2004) Treatment of severe acute graft-versushost disease with third party haploidentical mesenchymal stem cells. Lancet 363, 1439–1441. 8. Nasef, A., Chapel, A., Mazurier, C., Bouchet, S., Lopez, M., Mathieu, N., et al. (2007)

Identiﬁcation of IL-10 and TGF-beta transcripts involved in the inhibition of T-lymphocyte proliferation during cell contact with human mesenchymal stem cells Gene Expr 13, 217–226. 9. Meisel, R., Zibert, A., Laryea, M., Gobel, U., Daubener, W., and Dilloo, D. (2004) Human bone marrow stromal cells inhibit allogeneic T-cell responses by indoleamine 2,3-dioxygenasemediated tryptophan degradation Blood 103, 4619–4621. 10. Nasef, A., Ashammakhi, N., and Fouillard, L. (2008) Immunomodulatory effect of mesenchymal stromal cells: possible mechanisms. Regen Med 3, 531–546. 11. Yang, S. H., Park, M. J., Yoon, I. H., Kim, S. Y., Hong, S. H., Shin, J. Y., et al. (2009) Soluble mediators from mesenchymal stem cells suppress T cell proliferation by inducing IL-10. Exp Mol Med 41, 315–324. 12. Perrin, G. Q., Johnson, H. M., and Subramaniam, P. S. (1999) Mechanism of interleukin-10 inhibition of T-helper cell activation by superantigen at the level of the cell cycle Blood 93, 208–216. 13. Bright, J. J., Kerr, L. D., and Sriram, S. (1997) TGF-beta inhibits IL-2-induced tyrosine phosphorylation and activation of Jak-1 and Stat 5 in T lymphocytes. J Immunol 159, 175–183. 14. Takashima, A. (1998) Establishment of ﬁbroblast cultures. Curr Protoc Cell Biol 2.1.1–2.1.9. 15. Klinman, D. (2008) ELISPOT assay to detect cytokine-secreting murine and human cells. Curr Protoc Immunol 83: 6.19.1-6.19.9.

Chapter 10 Combining ELISPOT and ELISA to Measure Amounts of Cytokines Secreted by a Single Cell Jodi Hagen, Jeffrey P. Houchins, and Alexander E. Kalyuzhny Abstract Enzyme-linked immunospot (ELISPOT) assay allows for the determination of the frequency of cytokine-secreting cells, but does not answer the question of how much cytokine is secreted per cell. In our study, we combined ELISPOT and ELISA assays and developed a protocol to calculate the amount of IFN gamma secreted by each cell. A suspension of human peripheral blood mononuclear cells was split into two pools and cells from one pool were cultured in a regular ELISPOT plate, whereas cells from the other pool were cultured in an uncoated, “blank,” ELISPOT plate. After ﬁnishing the incubations, the amount of IFN gamma was measured by ELISA in culture media collected from both plates. The “blank” plate served to measure a total amount of secreted IFN gamma, whereas the ELISPOT plate served to measure the amount of unbound (UB) IFN gamma. Subtracting the amount of unbound IFN gamma from its total amount and dividing it by the number of spots in the ELISPOT plate allows for the calculation of the average amount of IFN gamma in a spot formed by a single cell. Key words: ELISPOT, ELISA, Peripheral blood mononuclear cells, PBMCs, IFN gamma, Measurement of spotted cytokines

1. Introduction Although the enzyme-linked immunospot (ELISPOT) assay is thought to be more sensitive than ELISA, allowing for the detection of a single cytokine-secreting cell out of a million (1), the major limitation of the ELISPOT assay is that, unlike ELISA, it does not quantitate the amount of secreted cytokines (2, 3). The output of an ELISPOT assay is a formation of colored spots which appear at the sites of cells releasing cytokines, and it appears that each individual spot represents a “footprint” of a single cytokinereleasing cell.

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ELISPOT allows detection of just a few cytokine-releasing cells out of tens of thousands, making this technique a method of choice for vaccine development (4–6), cancer research (7), AIDS research (8–10), allergy research (11), and autoimmune disease studies (12–14). We worked out a simple protocol for the determination of the average amount of IFN gamma secreted by a single cell. Our protocol can be easily adapted for measuring other cytokines in different species and utilized for both basic research and for developing diagnostic applications.

2. Materials 2.1. Isolation and Culture of Human PBMCs

1. Ficoll-Paque PLUS (GE Biosciences, St. Giles, UK). 2. 50 mM Phosphate-buffered saline (PBS), pH 7.2. 3. Red blood cells lysing solution: 155 mM NH4Cl, 10 mM NaHCO3, and 0.1 mM EDTA. 4. RPMI complete culture medium: RPMI1640 (1 L) (Gibco-BRL, Grand Island, NY) supplemented with 50 mL of heat-inactivated fetal calf serum, 1.19 g HEPES, 2 g of sodium bicarbonate, 3.5 ML of beta-mercaptoethanol, and 50 mg of Gentamicin Reagent Solution (see Notes 1 and 2). 5. Centrifuge capable of spinning 50-mL culture tubes at 500 × g. 6. Hemacytometer to count lymphocytes under the microscope. 7. Trypan blue dye. 8. Microscope equipped with bright-ﬁeld illumination and phasecontrast optics. 9. Uncoated, 96-well ELISPOT plates (Cat# MAIPN0B, Millipore).

2.2. ELISPOT Assay

1. Commercially available ready-to-use ELISPOT assay kit (R&D Systems, Inc.) to study secretion of human IFN-G (Cat # EL285). Each kit includes a 96-well, PVDF membrane-backed plate precoated with capture antibody, a concentrated solution of detection antibody, a concentrated solution of streptavidinconjugated alkaline phosphatase, BCIP/NBT chromogenic substrate, and wash and dilution buffers. 2. Blocking buffer to block membranes in plates uncoated with antiIFN gamma antibodies: PBS with 1% BSA and 5% sucrose. 3. Mitogens to stimulate release of cytokines from cultured peripheral blood mononuclear cells (PBMCs): Calcium ionomycin (CaI) and phorbol 12-myristate 13-acetate (PMA).

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4. Hand-held Nunc-Immuno™ 12-plate washer (Thermo Fisher Scientiﬁc). 5. Membrane-removal device (MVS Paciﬁc, Minneapolis, MN; http://www.mvspaciﬁc.com). 6. ELISPOT plate reader QHub (MVS Paciﬁc, Minneapolis, MN; http://www.mvspaciﬁc.com). 2.3. ELISA Assay

1. Commercially available ready-to-use Quantikine ELISA Kit (R&D Systems, Inc.) to study secretion of human IFN-G (Cat # DIF50). Each kit includes a 96-well plate precoated with capture antibody, a conjugate solution, standard, diluents to perform the assay, wash buffer, color reagents (substrate solution), and stop solution. 2. Hand-held Nunc-Immuno™ 12-plate washer (Thermo Fisher Scientiﬁc) (see Note 9). 3. Centrifuge capable of spinning 15-mL culture tubes at 500 × g. 4. ELISA microplate reader capable of measuring absorbance at 450 nm, with the correction wavelength set at 540 or 570 nm.

3. Methods 3.1. Isolation of Human PBMCs

1. Collect blood samples from healthy donors in standard citratephosphate-dextrose unit bags (Leukopack, Memorial Blood Centers of Minnesota). 2. Separate PBMCs using density centrifugation (500 × g for 30 min) by layering 25 mL of blood on 20 mL of 1.077 g/mL Ficoll-Paque Plus at 25°C (see Note 3). 3. Discard the upper plasma layer after centrifugation and transfer the blood cells into two sterile 50-mL tubes. 4. Resuspend the blood cells in 45 mL of sterile PBS and centrifuge for 5 min at 500 × g. 5. Discard the supernatant, and resuspend the pellet in 10 mL of red blood cell lysing solution and incubate for 5 min at room temperature. 6. After lysing, resuspend PBMCs by adding sterile PBS up to the 50-mL graduation mark on the tube. 7. Centrifuge the tubes for 5 min at 500 × g. 8. Discard supernatants and add 30–40 mL of RPMI complete medium to the tubes with PBMCs. 9. Mix cells 1:2 with Trypan blue dye and pipette 10 ML of that mixture into each side of a hemacytometer under a coverslip (see Note 4). Count the cells under the microscope using a 20× lens and a phase-contrast condenser.

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10. Isolated PBMCs are divided into two pools and cells from one pool are cultured in a regular ELISPOT plate, whereas cells from the other pool are cultured in an uncoated, blank, ELISPOT plate. 3.2. ELISPOT Assay

Cells are plated into both coated ELISPOT plates and plates not coated with capture anti-IFN gamma antibodies. 1. Prepare uncoated ELISPOT plates and culture PBMCs as follows: (a) Block ELISPOT plates by adding 100 ML of block buffer per well and incubate for 1.5–5 h at room temperature. (b) Discard block buffer and ﬁll each well with 200 ML of RPMI complete culture media. Allow to sit a minimum of 20 min and discard the culture media just before plating PBMCs. 2. Plate PBMCs (100 ML/well; six wells per group) into both uncoated and coated ELISPOT plates at cell concentrations of 104 and 105 cells/mL (see Notes 5 and 6). 3. Stimulate PBMCs with a mixture of CaI and PMA by adding them directly to the ELISPOT plate, so their ﬁnal concentrations reach 0.5 Mg/mL and 50 ng/mL, respectively. 4. Incubate in a CO2 incubator at 37°C for 18 h (see Notes 7 and 8). 5. After incubation, collect the supernatant mixture with a multichannel pipette from both uncoated and coated plates for subsequent ELISA analysis. (Uncoated plates can be discarded at this time, but coated plates are processed to develop spots.) Then, wash the plate by rinsing wells four times with wash buffer to remove PBMCs from the plate (see Notes 9 and 10). 6. Prepare working solutions of detection antibodies by mixing concentrated detection antibodies 1:120 with dilution buffer. 7. Add 100 ML of detection antibody working solution into each well and incubate ELISPOT plates overnight at 2–8°C. 8. Wash plates three times with the wash buffer. 9. Prepare a working solution of streptavidin–alkaline phosphatase by mixing the concentrated stock solution 1:120 with corresponding dilution buffer. 10. Add 100 ML of streptavidin–alkaline phosphatase working solution into each well and incubate for 2 h at room temperature. 11. Wash plates three times with wash buffer. 12. Add 100 ML of ready-to-use BCIP/NBT substrate into each well and incubate for 30–60 min at room temperature in a place protected from direct light. 13. Wash plates three times with distilled water and let them dry completely (see Note 11). 14. Quantify spots using an automated ELISPOT reader.

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1. To remove contaminating PBMCs, centrifuge supernatants collected from coated and uncoated ELISPOT plates at 500 × g. 2. Pipette 500 mL of Calibrator Diluent RD6-21 into polypropylene tubes. Use the kit standards stock solution to prepare a dilution series. Mix each tube thoroughly before the next transfer. The undiluted standard serves as the high standard (1,000 pg/mL). Calibrator Diluent RD6-21 serves as the zero standard (0 pg/mL). 3. Add 100 ML of Assay Diluent RD1-51 to each well. Add 100 ML of standard or sample to each well (see Note 12). Cover with an adhesive strip and incubate for 2 h at room temperature. 4. Aspirate and wash the plate four times with wash buffer (see Note 10). 5. Add 200 ML of conjugate to each well. Cover with an adhesive strip and incubate for 2 h at room temperature. 6. Aspirate and wash the plate four times with wash buffer (see Note 10). 7. Add 200 ML of substrate solution to each well. Incubate in the dark for 30 min at room temperature. 8. Add 50 ML of stop solution to each well. The color should change from blue to yellow (see Note 13). 9. Determine the optical density of each well within 30 min using a microplate reader set to 450 nm. If wavelength correction is available, set to 540 or 570 nm (see Note 14). If wavelength correction is not available, subtract readings at 540 or 570 nm from the readings at 450 nm. This subtraction corrects for optical imperfections in the plate. Readings made directly at 450 nm without correction may be higher and less accurate.

3.4. Calculation of the Amount of Cytokine per Spot (See Note 15) (Table 1)

1. Calculate the average amount of IFN gamma in the culture media collected from the uncoated plate and from the coated ELISPOT plate measured by ELISA. 2. Calculate the average number of spots counted with an ELISPOT reader. 3. Subtract the average amount of unbound (UB) IFN gamma from the total (T ) amount of IFN gamma per well (0.1mL) and divide it by the average number of spots (N) to calculate the average amount of IFN gamma per each spot (Cspot): C spot

T

UB r 0.1 . N

This simple protocol allows for the calculation of the average amount of IFN gamma secreted by a single cell that can be easily adapted for measuring other cytokines in a variety of species.

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Table 1 Example of calculating the average amount of IFN gamma in a single spot Replicate wells

1

2

3

Average

Amount of total (T) IFN gamma measured in culture media collected from uncoated ELISPOT plate (pg/mL)

40.8

45.2

51.8

45.9

Amount of unbound (UB) IFN gamma measured in culture media collected from coated ELISPOT plate (pg/mL)

11.5

13.7

12.1

12.4

Number of spots in coated ELISPOT plate

52

55

58

55

Cspot

(45.9 − 12.4) × 0.1/55 = 0.0609 pg/spot

Although this approach has some limitations, it can be quite useful for estimating the extent of cytokine production at a single-cell level. This protocol can be easily adapted for measuring other cytokines and used for both a basic research as well as for developing diagnostic applications.

4. Notes 1. Sterilize RPMI complete culture medium and reagents that are used to separate out the white blood cells by ﬁltering through 0.2-Mm sterile ﬁlter to allow their long-term storage. 2. When using fetal calf serum, it is important to heat inactivate the serum at 56°C for 30 min. After the heat inactivation, the serum should be ﬁltered. 3. When layering Ficoll, make sure that the blood does not mix with the Ficoll to gain the best separation and highest yield of PBMCs. 4. Overﬁlling the hemacytometer with cell solution may result in inaccurate cell quantiﬁcation. While counting cells on a hemacytometer, ﬁrst ﬁnd the middle square which contains 25 smaller squares and count cells in 5 of them. Calculate the average and multiply by 25 (total number of squares in that area), then multiply by 2 (cell dilution factor), and multiply by 10,000 to determine the number of cells in 1 mL of original cell suspension. The resulting number should be used for calculating serial dilutions of PBMCs. 5. Making serial dilutions of cells allows the user to avoid overdevelopment of the ELISPOT plate and to obtain a quantiﬁable number of spots that can be counted either manually or using an automated ELISPOT plate reader.

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6. For better well-to-well reproducibility, cells need to be mixed thoroughly before adding them into the wells. This may require shaking the tube containing the cells after ﬁlling every four wells in the ELISPOT plates. 7. Plates can be wrapped in aluminum foil to provide even heat distribution across the bottom of the ELISPOT plates during their incubation. This helps to improve well-to-well spot consistency across the plate (described by Kalyuzhny and Stark (15) and in the kit datasheet). Aluminum foil also helps to reduce a background staining. This is a very simple procedure which can be done as follows: before plating cells, the ELISPOT plate is placed onto 13 × 16-cm piece of aluminum foil (e.g., Reynolds Wrap Quality Aluminum Foil, Consumer Products Division of Reynolds Metal, Richmond, VA); the cells are then added into the wells, the plate is covered with the lid, and the edges of the foil are shaped loosely around the edges of the plate to wrap it. After incubating the cells, the foil can be removed and either discarded or saved and used on the next ELISPOT plate. 8. Shelves in the CO2 incubator must be leveled to avoid moving cells toward one side of the well: this may produce under- and overdeveloped parts of the well and hinder quantiﬁcation of spots. It is also important to avoid disturbing cultured cells (e.g., by slamming the door of the incubator) during the incubation which may cause developing weakly stained fuzzy spots. 9. Make sure that the height of the prongs in the handheld plate washer is properly adjusted so that prongs do not touch the membranes on bottom of the ELISPOT plates. PVDF membranes on the ELISPOT plate are fragile and are easily punctured by protruding prongs. 10. Between washes, it is important to tap out the excess liquid in the well onto a paper towel to prevent diluting the subsequent reagents added into the plate. 11. ELISPOT plates must be completely dried before analysis because wet membranes appear dark and obscure detection and quantiﬁcation of spots. 12. To ensure accurate results, reagent addition should be uninterrupted and completed within 15 min. 13. If the color in the well changes to green instead of yellow or does not appear to be uniform, gently tapping the plate ensures thorough mixing. 14. If wavelength correction is not available, subtract readings at 540 or 570 nm from the readings at 450 nm. This subtraction corrects for optical imperfections in the plate. Readings made directly at 450 nm without correction may be higher and less accurate.

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15. The amount of cytokine calculated per spot is affected by cells with strong secretory activity (producing large-size spots of high-intensity staining). Therefore, for better accuracy, it may be necessary to add to this equation both (1) sizes of spots and (2) their staining intensities. References 1. Tanguay, S., and Killion, J. J. (1994) Direct comparison of ELISPOT and ELISA-based assays for detection of individual cytokine-secreting cells Lymphokine. Cytokine Res 13, 259–263. 2. Kalyuzhny, A. E. (2005) Chemistry and biology of the ELISPOT assay. Methods Mol Biol 302, 15–31. 3. Kalyuzhny, A. E. (2009) ELISPOT assay on membrane microplates. Methods Mol Biol 536, 355–365. 4. Pass, H. A., Schwarz, S. L., Wunderlich, J. R., and Rosenberg, S. A. (1998) Immunization of patients with melanoma peptide vaccines: immunologic assessment using the ELISPOT assay. Cancer J Sci Am 4, 316–323. 5. Asai, T., Storkus, W. J., and Whiteside, T. L. (2000) Evaluation of the modiﬁed ELISPOT assay for gamma interferon production in cancer patients receiving antitumor vaccines. Clin Diagn Lab Immunol 7, 145–154. 6. Kamath, A. T., Groat, N. L., Bean, A. G., and Britton, W. J. (2000) Protective effect of DNA immunization against mycobacterial infection is associated with the early emergence of interferon-gamma (IFN-gamma)-secreting lymphocytes. Clin Exp Immunol 120, 476–482. 7. Schmittel, A., Keilholz, U., Thiel, E., and Scheibenbogen, C. (2000) Quantiﬁcation of tumor-speciﬁc T lymphocytes with the ELISPOT assay. J Immunother 23, 289–295. 8. Keane, N. M., Price, P., Stone, S. F., John, M., Murray, R. J., and French, M. A. (2000) Assessment of immune function by lymphoproliferation underestimates lymphocyte functional capacity in HIV patients treated with highly active antiretroviral therapy. AIDS Res Hum Retroviruses 16, 1991–1996.

9. Chapman, A. L., Munkanta, M., Wilkinson, K. A., Pathan, A. A., Ewer, K., Ayles, H.., et al. (2002) Rapid detection of active and latent tuberculosis infection in HIV-positive individuals by enumeration of Mycobacterium tuberculosis-speciﬁc T cells. AIDS 16, 2285–2293. 10. Eriksson, K., Nordstrom, I., Horal, P., Jeansson, S., Svennerholm, B., Vahlne, A., et al. (1992) Ampliﬁed ELISPOT assay for the detection of HIV-speciﬁc antibody-secreting cells in subhuman primates. J Immunol Methods 153, 107–113. 11. Jakobson, E., Masjedi, K., Ahlborg, N., Lundeberg, L., Karlberg, A. T., and Scheynius, A. (2002) Cytokine production in nickel-sensitized individuals analysed with enzyme-linked immunospot assay: possible implication for diagnosis. Br J Dermatol 147, 442–449. 12. Pelfrey, C. M., Cotleur, A. C., Lee, J. C. and Rudick, R. A. (2002) Sex differences in cytokine responses to myelin peptides in multiple sclerosis. J Neuroimmunol 130, 211–223. 13. Bienvenu, J., Monneret, G., Fabien, N., and Revillard, J. P. (2000) The clinical usefulness of the measurement of cytokines. Clin Chem Lab Med 38, 267–285. 14. Okamoto, Y., Gotoh, Y., Tokui, H., Mizuno, A., Kobayashi, Y. and Nishida, M. (2000) Characterization of the cytokine network at a single cell level in mice with collagen-induced arthritis using a dual color ELISPOT assay. J Interferon Cytokine Res 20, 55–61. 15. Kalyuzhny, A., and Stark, S. (2001) A simple method to reduce the background and improve well-to-well reproducibility of staining in ELISPOT assays J Immunol Methods 257, 93–97.

Part IV Image and Data Analysis

Chapter 11 How ELISPOT Morphology Reﬂects on the Productivity and Kinetics of Cells’ Secretory Activity Alexey Y. Karulin and Paul V. Lehmann Abstract Over the past decade, ELISPOT has become well-established as a mainstream technology for the study of immune responses in vivo mainly due to its unique ability to detect rare antigen-speciﬁc lymphocytes ex vivo. The primary readout for ELISPOT assays has traditionally been the measurement of the frequency of analyte-secreting cells within a test population. While it has been generally appreciated that ELISPOT is a high-information-content assay system in which spot morphologies provide additional valuable information on the amount of analyte secreted by individual cells as well as the kinetics of the secretory process, the precise relationships involved have not been fully characterized and the speciﬁc relevant information conveyed by spot morphologies has remained largely unexplored. In an attempt to bridge this gap, we formulated an in silico kinetic model for spot formation and derived a solution for the model in both a general and a numerical form. Both solutions suggested a logarithmic relationship between spot size and cell productivity. This chapter involves an in-depth analysis of the relationship between observed spot morphologies and cells’ secretory functions (as well as an examination of additional assay parameters), and predictions based on the mathematical model are veriﬁed under experimental assay conditions where possible. Key words: ELISPOT, ImmunoSpot®, Antibodies, Capture, Cytokines, Kinetic model, Binding, Afﬁnity, Avidity, Spot morphology, Spot size, Spot density, Spot formation, Spot density proﬁle, Spot size distribution, Cell productivity, Binding kinetics, Differential equation, Numerical solution, Diffusion, Image analysis

1. Introduction ELISPOT is the only assay available today that is suited to measure the secretory activity of individual cells. However, the exact relationship between spot morphology and the basic parameters of the secretory process remains unresolved. The ability to directly measure the amount of cytokine produced by individual T or B cells ex vivo may open a yet underutilized dimension for ELISPOT analysis and may contribute to a better understanding of these Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_11, © Springer Science+Business Media, LLC 2012

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cells’ functions. T-cell ELISPOT assays performed on PBMC invariably reveal a wide range of spot sizes and morphologies (see also Chapter 12 in this volume), and there are multiple lines of evidence which suggest that the variation in spot size/density is linked to a biological correlate (1). In this regard, the amount of cytokine secreted by individual antigen-speciﬁc T cells, rather than differences in their frequencies, was found to be one of the factors responsible for the immune deﬁciency in individuals with HIV (2). Cytokine productivity accommodated in a spot size-based model allowed us to explain the relapsing nature of the autoimmune disease, experimental allergic encephalomyelitis (EAE), while accounting for T-cell responsiveness to antigen with the use of a single parameter (spot size variability) (3). The functional avidity of T cells for antigen can be established by titration of the antigen. In such assays, the spot size and the strength of T-cell stimulation are positively correlated: increased by a higher antigen dose or by the addition of a costimulatory antibody (1, 4, 5). For different cell types producing the same cytokine, like CD4, CD8, or NK cells, we reported clearly different spot morphologies (6, 7). Spot morphologies also vary for different cytokines (IFN-G, IL-2, Granzyme B, IL-4, and IL-10) when produced by the same cell type (e.g., CD4 cells) (8, 9). Deﬁning the exact relationship between spot morphology and the kinetics of cytokine secretion may, therefore, add additional information about the biology of the secreting cells and their interaction with other cell types or reveal pathological conditions. Therefore, the development of a quantitative model for the analysis of spot morphology should be important for extracting highcontent information contained within ELISPOT results, adding valuable immune diagnostic information. On the practical end, thus far, the process of selecting antibodies suitable for ELISPOT assays was done purely on an empirical basis because antibodies performing well in ELISAs many times did not work at all in ELISPOT. An understanding of the relationship between spot morphologies (size, density, and diffuseness) and the binding properties of the antibodies can help facilitate the selection process. Although it is somewhat intuitive that both spot size and density should reﬂect a cell’s productivity, the challenge for establishing the exact relationship is that the dynamics involved are multiparametric and highly complex. These include the analyte secretion rate, the net amount produced, and its binding and lateral diffusion as deﬁned by the capture antibody’s afﬁnity for the analyte. In this chapter, these relationships are addressed using a mathematical model which is conﬁrmed by comparing the results obtained in silico with data generated in real ELISPOT assays. All what is described in the following for the classic enzymatic detection of plate-bound analyte by ELISPOT applies (with minor variations that are speciﬁed) for FLUOROSPOT assays as well (see Chapter 6 in this volume).

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2. Materials 1. All ELISPOT images were captured and analyzed using a CTL ImmunoSpot® Series 5 Analyzer (Cellular Technology Ltd., Cleveland, OH). ELISPOT image analysis, including studies of spot size distributions, was done with the ImmunoSpot® 5.0 Professional Software Suite. 2. The analytical solution of the ELISPOT kinetic model was based on the dual Laplace–Fourier transformation. 3. The numerical solution was done by using the Runge–Kutta algorithm. 4. The ImmunoSpot® Simulation Software 1.0 was written in Visual C++ .NET 2003 development environment.

3. Methods 3.1. Kinetic Model of ELISPOT

The proposed model assumes that the secreting cell (G) is located on the surface of an ELISPOT well membrane (Fig. 1) which is coated with antianalyte capture antibody. The membrane surface is two dimensional (XY ) and the space above the membrane is three dimensional (XYZ ), where dimension Z represents the distance from any point in the well to the membrane. In this model, the interaction of the analyte with the capture antibody occurs on the membrane surface, whereas diffusion of analyte in the half-space above the membrane. Relative to the size of the cell, the area of the membrane, and the liquid volume above, it can be considered inﬁnite. The diffusion of the secreted analyte is described by the equation: tC D $C , tt

(1)

Fig. 1. Macrokinetic model of ELISPOT. Analyte-secreting cell (G) is located on the surface of the membrane. Secreted analyte can be either bound by capture antibodies on the membrane surface or diffuse away from the cell into media above the membrane.

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t2 t2 t2 2 2 is the Laplace operator, D is the analyte 2 tx ty tz coefﬁcient of diffusion (m2/s), C is the concentration of the analyte in the solution (1/m3), and t is time in seconds. The interaction of the analyte with the capture antibodies on the surface of the membrane (at z = 0) is a reversible heterogenic reaction of second order and can be described by the standard equation: where $

¤ tN N ³ k ¥ 1 ´ C z 0 k N , tt N ¦ *µ

(2)

whereby N and N* are surface concentrations of the aanalyte bound and total antibodies (bound plus free) (in 1/m2), and k+ (in m/s) and k− (in 1/s) are kinetic constants of direct (binding) and reversed (dissociation) reactions on the surface, respectively. Please note that the association rate constant k+ is expressed in unusual units. This is because the concentration of capture antibodies on the membrane is expressed in units of surface concentration (1/m2) and that of the analyte as volume concentration (1/m3). Term N represents the fraction of free antibody-binding sites (not 1 N* occupied by analyte) on the membrane surface. At time zero (t = 0), the cell is not yet secreting, and the concentrations of the analyte and occupied antibody-binding sites are: C t 0 0, N

t 0

N

t 00

(3)

Diffusion of analyte away from the surface can be described as:

D

tC tz

z 0

¤ N ³ q k N k ¥ 1 ´ C z 0 . N ¦ *µ

(4)

It is directly proportional to the cell productivity – q (in 1/s) – and to the dissociation rate of the analyte from the surface and inversely proportional to the rate of analyte binding to the antibodies on the membrane. The source of the secretion (the cell itself) is localized in a circular region G on the surface z = 0. Outside of this region, the productivity of the source is q = 0 (in numbers of analyte molecules per unit of time). We also assume that far from the surface, the concentration of the analyte is equal to zero: C z lc 0.

(5)

The goal, then, is to deﬁne the distribution function for the concentration of bound analyte on the membrane surface around the cell (i.e., the distribution of the bound analyte within the spot), N (x , y , t ), and, from that, the function of the spot size (the region where the surface concentration of bound cytokine is different from zero for a ﬁxed value) for time and cell productivity. This ﬁxed

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threshold value Nl is the detection limit. Strictly speaking, spots do not have deﬁned sizes. Rather, at the periphery, the density of spots asymptotically reaches zero at the distance from the secreting cell N 0 x , y lc. 3.2. Function of Spot Area Relative to Cell Productivity

A system of differential equations with an initial condition and boundary conditions that are not linear cannot be solved in an analytical form (i.e., as a mathematical formula). Only numerical solution can be obtained (which is discussed later). To transform it into a linear system and to solve the problem in the analytical form, we have to make the additional assumption that the concentration of the capture antibody on the membrane is much higher than the concentration of the analyte. In this case, the membrane surface concentration of free antibody-binding sites is always equal to its N total number: N * (term 1 1). This condition is true for N* membranes that have high antibody-binding capacity (such as PVDF) and when the cell’s productivity is low. While this simpliﬁcation is applicable only to certain assay conditions, it is valuable for deﬁning the basic mathematical principles which describe ELISPOT. Therefore, Eq. 2 can take the linear form: tN k C z 0 k N tt

(6)

with simpliﬁed boundary condition:

D

tC tz

q k N k C z 0.

(7)

z 0

Leaving aside further mathematical details as outside of the scope of this chapter (these can be found on our Web site, at http://www. immunospot.com), we afﬁrm that the solution of the linear task (Eq. 1), (Eq. 6) with boundary condition (Eq. 7) can be obtained for the total amount of analyte bound in the spot area (N0): 4 t k Qt 3 pD N0 2 1 k t 3

(8)

and for the characteristic radius of the spot (R):

R 2 RC2

9 Dt 4 ¤ Dt ³ 1 5 RC2 3 ¥¦ RC2 ´µ Dt 1 2 RC

where RC is the diameter of the cell.

2

,

(9)

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The density proﬁle of the spot, i.e., the concentration of occupied antibody-binding sites (N) versus the distance from the center of the spot (r) can also be reconstructed if we approximate a bell-shaped spot density proﬁle with a normal distributed function. N N 0e

r2 R2

(10)

The result is a logarithmic function of spot radius versus cell productivity: r* ~ Ln(Q / N l ),

(11)

whereby r* represents the radius of the spot at a deﬁned time point, t; Q is the total productivity of the cell; and Nl is the spot detection limit. Since the normal distribution function (and real spot density) asymptotically reaches zero density with r, for practical reasons we deﬁne spots (with radius r*) as areas where density is equal to or higher than the threshold deﬁned by our detection limit. For the same type of assay (same analyte, capture antibody, and cell size, and with ﬁxed incubation time), the radius of the spot is proportional to the square root of the natural logarithm of cell productivity. In other words, the area of the spot is linearly proportional to the natural logarithm of cell productivity: S* ~ Ln(Q / N l ),

(12)

where S* is spot area. A more general, nonlinear case solution can be obtained only by computer modeling. In Fig. 2a, theoretical spot proﬁles are shown for different cell productivities simulating actual ELISPOT conditions. A numerical solution for the more general, nonlinear case also followed closely a logarithmic function (Eq. 12) (Fig. 2b), supporting the assumption we made about a normal density distribution of the spots. The close correlation between the two solutions supports our major conclusion about the area of the spot being linearly proportional to the natural logarithm of cell productivity (see Note 1). The typical range of spot size distributions seen in ELISPOT assays for different cytokines spans two to three orders of magnitude (1, 4, 5, 8, 9). Because of this logarithmic relationship between spot size and cell productivity, one can assume that the productivity of cells can span a range of several orders of magnitude. 3.3. Cell Productivity, Peak Density and Total Density of Spots

Another parameter which reﬂects the secreting cells’ productivity is the peak intensity of spots. It is intuitive to assume that the peak intensity in the center of the spot should be proportional to cell productivity, and indeed our linear model solution conﬁrms it. However, in reality, linear conditions often are not satisﬁed. For example, for cells that are strong producers, the secreted analyte saturates the capture antibodies close to the center of the spot, and

11

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Bound analyte (N*) m-2

4E+15 Q sec-1 3E7 1E7 3E6 1E6 3E5 1E5 3E4 1E4 3E3 1E3 3E2

3E+15

2E+15

1E+15

0

2.0

b

1.5

1.0

0.5 0.0 0.5 Spot radius (r*) mm

1.0

1.5

2.0

1.4

Spot area (S) mm2

1.2 1 1.5

0.8 1

0.6 0.5

0.4 0 1.E+03

0.2 0 0.E+00

1.E+07 Cell productivity (Q)

1.E+05

2.E+07

1.E+07

3.E+07

sec-1

Fig. 2. Spot size as a logarithmic function of cell productivity. Simulation is done by numerical solution of nonlinear system with following parameter values: N* = 3 x 1015 (1/m2), D = 3 x 10–12 (m2/s), k+ = 6 x 10–9 (m/s), k– = 10–4 (1/s), t = 8 h. (a) Spot density distribution for different cell productivities Q (1/s) as indicated in the legend. (b) Theoretical graphs of spot area S (mm2) from cell productivity Q. Smaller insert represents same data in logarithmic coordinates with best linear regression ﬁt.

in those areas further analyte cannot be bound. Subsequently, the spot proﬁles take on the appearance of a “Table Mount”, i.e., display an extended plateau in the center (Fig. 2a). Thus, peak spot intensity vs. cell productivity is linear only in the range of low productivities (when linear conditions are satisﬁed), but reaches a plateau when antibodies begin to be saturated by excess of analyte (Fig. 3). A total amount of bound analyte in the spot (N0) is also directly proportional to productivity (Q) when linear conditions are satisﬁed (equation 8). With the increase of Q it does not plateau (like peak intensity) but changes into a logarithmic function similar to one of spot area vs. productivity (see insert in the Fig. 3). In practice the amount of bound analyte can be measured only when it

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0 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08 Cell productivity (Q) sec-1

Fig. 3. Function of peak spot density N* (1/m2) from cell productivity Q (1/s) shows close to linear behavior at low productivities, but reaches plateau with increase of analyte production by cells. An insert shows a function of a total amount of bound analyte (number of molecules) in the spot area from Q in logarithmic coordinates. All parameters are same as in Fig. 2.

exceeds the sensitivity threshold resulting in nonlinear relation even for low cell productivity (i.e. in small spots big fraction of bound analyte stays undetected). Therefore spot size is the only parameter linked to cell productivity by a single known mathematical function in all cases regardless of the experimental conditions (see Note 2). 3.4. Spot Morphology and Kinetics of ELISPOT

Our mathematical model of ELISPOT formation also allows us to predict how different analyte secretion rates are reﬂected in different spot morphologies, which in turn are also inﬂuenced by certain assay parameters, namely the density of capture antibody on the surface of the well, its relative afﬁnity for the speciﬁc analyte, or, to be more precise, its association and dissociation kinetic constants (see Note 3). Since these relations are complex, we dissect them in the following using computer simulations. Figure 4 shows the time course of spot formation at different analyte productivity rates, the same rates as we used to build spot proﬁles in Fig. 2. For each productivity rate, the spot growth decreases with time. Also, there is a noticeable lag period for low producers during which spots are not yet detectable due to the set detection limit. These simulated spot formation kinetics are very close to the experimental data we reported in ref. 1. The spots continue growing as long as the cells keep secreting analyte, but what happens if the cells stop secreting while the incubation continues? Due to the reversible nature of the binding between analyte and capture antibody, the spot sizes continue to grow even after secretion stops while at the same time the spots’ peak densities decrease. The spots grow larger, but fainter (as seen in Fig.5a).

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1 1E6 0.9

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0.8

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Fig. 4. Kinetics of the ELISPOTs formation. Spot areas are plotted versus time for different cell productivities. Parameters used are the same as in Fig. 2.

This happens because, in the absence of replenishment through secretion, the concentration of free analyte near the membrane surface drops instantly. The decrease of free analyte concentration in turn shifts the equilibrium of the antibody–analyte binding reaction and leads to a dissociation of bound analyte. Some of the dissociated analytes diffuse laterally and are recaptured resulting in the continued growth of the spot size. Most of the dissociated analytes, however, diffuse away from the surface into the supernatant. If this process continues long enough, the spots disappear completely. Figure 5b shows how this dynamic process affects peak spot density and size. Overincubation of cells in culture is one of the reasons why spots can appear “diffuse.” To obtain quality spots, and for correct quantitative measurements of per cell productivity, it is important therefore to match the ELISPOT assay’s duration with the actual secretory activity of the cells (see Note 4). Shorter or longer assay periods underestimate the per cell productivity. 3.5. Properties of Capture Antibodies and Spot Morphology

Properly performing ELISPOT assays require carefully selected antibodies. The selection of antibody pairs that work well for ELISPOT assays has been a tedious empirical process – some, but not all, ELISPOT kit manufacturers have selected carefully. It is well-known that antibodies recommended for ELISA or intracytoplasmatic cytokine staining (ICS) often do not perform well for ELISPOT. At a closer look, most of the problems occur with the coating (primary) antibodies. There are three different reasons why these antibodies have a major impact on capturing analyte. First, there is the difference in afﬁnities of antibodies for the analyte; second, even for antibodies of the same afﬁnity, the association

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0.8 0.7 0.6 0.5 0.4 0.3

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Fig. 5. Overincubation in ELISPOT. (a) Spot density proﬁle evolution after the termination of analyte secretion by cell. Secretion was stopped after 8 h; total assay duration was 8 and 24 h as indicated on the legend inside the graph. (b) Dynamics of peak spot density (solid line) and spot size (dashed line) before and after analyte secretion is stopped. Cell productivity Q = 3 × 104 (1/s); all other parameters are same as in Fig. 2.

and dissociation kinetics are critical (see below); and third, the afﬁnity of the capture antibody for the membrane results in differential coating densities between different capture antibodies. With our model at hand, one can analyze in which way each of these three factors affects spot morphology. 3.6. The Effect of a Capture Antibody’s Afﬁnity for Analyte on Spot Morphology

Antibody afﬁnity (for monovalent binding) or avidity (for bi- and polyvalent binding) describes the antibody–antigen (here, analyte) equilibrium binding constant, and is deﬁned by the ratio between the association and dissociation rate. The effect of capture antibody afﬁnity on spot morphology is illustrated in Fig. 6. First, let us assume that the association rates are the same for two antibodies, and the difference in their afﬁnity results from different dissociation rates. The antibody with the slower dissociation rate constant

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4E+15 k-=1E-3 k-=1E-4 k-=1E-5

3E+15

2E+15

1E+15

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Fig. 6. Spot morphologies at different afﬁnities of capture antibodies. For all three spot proﬁles, productivity Q = 3 × 104 (1/s); association rate constants and surface density of capture antibodies were same as in Fig. 2. Dissociation rate constants (1/s) for each spot proﬁle are shown in the legend inside the ﬁgure.

(and therefore with the higher afﬁnity) produces larger and also brighter spots (see Note 5). 3.7. The Effect of Association/ Dissociation Rates on Spot Morphology Beyond Afﬁnity

Importantly, afﬁnity alone does not sufﬁce to describe spot formation. Antibodies with highly different association/dissociation rate constants can have an identical afﬁnity for the analyte as long the ratio of association/dissociation stays the same. Let us consider two antibodies with the same afﬁnity; for one of these antibodies, both the association and dissociation follow a fast kinetics; for the other antibody, association and dissociation follow a slow kinetics. Our model predicts that the two antibodies will produce highly different spots. For moderate to high secretion rates, the antibody with the fast association/dissociation rate produces spots with the same peak density, albeit larger in size as compared to the antibody with the “slow” kinetics (Fig. 7). This effect of binding kinetics on spot morphology results from the fact that antibodies with “fast” association kinetics have a greater chance of capturing the analyte before it diffuses away from the membrane. With “slow” kinetics, spots are reduced in size, but their peak value (which mostly depends on the equilibrium constant) stays basically the same due to high local concentration of analyte at close proximity to the cell. Thus, the kinetics of antibody binding mostly affects spot sizes and does not much alter their peak densities – spots do not tend to become more “diffuse” with “slow” kinetics. However, low analyte secretion rates combined with “slow” binding kinetics of the capture antibody can result in the analyte not binding at all and its secretion going undetected. Therefore, in addition to a high equilibrium constant (high afﬁnity) for the analyte, a capture antibody which displays ideal

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3E+15 k6E-10, k 1E-5 k6E-9, k 1E-4 k18E-9, k 3E-4

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Fig. 7. Antibodies with the same equilibrium binding constants (afﬁnity), but with different association/dissociation kinetics, produce spots with different morphology. Parameters are same as in Fig. 6.

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Fig. 8. Spot morphologies at different surface densities of capture antibodies. N* = 3 x 1014 (1/m2) and N* = 3 x 1015 (1/m2) (as indicated in the ﬁgure legend), Q = 3 × 104 (1/s); all other parameters are same as in Fig. 2.

properties for use in ELISPOT must also have a high association rate (see Note 6). For selection of such antibodies, direct kinetic measurements (e.g., using BIOCORE instruments) are recommended in addition to conventional afﬁnity measurements. 3.8. Density of Capture Antibodies and Spot Morphology

In addition to the capture antibody’s binding properties versus the analyte, its binding afﬁnity for the membrane is critical for its performance in ELISPOT assays. Our computer simulation shows that high surface density of capture antibodies on the membrane results in tight and bright spots, whereas low density leads to bigger spots which lack a dark center (“diffuse” spots) (Fig. 8) (see Note 7).

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Fig. 9. Experimental spot morphologies at low (a) and high (b) density of antihuman IFN-G capture antibodies on MAHA and PVDF membrane plates (Millipore). Underneath – 3D spot density plots are shown for corresponding wells (generated by CTL ImmunoSpot® 5.0 Professional software). In both cases, the same coating antibodies, cell samples, antigen, and development reagents were used.

Coating of plates with capture antibody relies on hydrophobicitydependent physical adsorption. For high-density coating, therefore, both the membrane and the antibody need to be hydrophobic. The results of the above simulation (Fig. 8) closely reproduce the experimental spot density distributions seen when ELISPOT assays are performed using PVDF (highly hydrophobic) or mixed cellulose ester membranes (MAHA, less hydrophobic, shown in Figs. 9 and 10, respectively). So far, PVDF membranes, which our group introduced for use in cytokine ELISPOT assays (10), provide the highest density of antibody coating compared to other plate/ membrane types. Coating PVDF membranes through physical adsorption results in a high enough density of capture antibodies for single- and dualcolor ELISPOT assays. However, our calculations and experimental data both suggest that for three and more color assays, the surface density of the individual capture antibodies drops below the optimal level, as the antibodies compete for the limited number of adsorption sites. New coating principles have to be developed for multiplexing ELISPOT assays beyond two analytes.

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Fig. 10. Experimental spot density distributions. With “good” capture antibodies, spots are much brighter; “bad” antibodies or low surface density of coating antibodies result in weak “diffuse” spots without prominent peaks. 2D intensity proﬁles for two spots of similar size are shown (generated by CTL ImmunoSpot® 5.0 software).

3.9. The Impact of Secondary Antibody and Additional Detection Reagents

In our considerations so far, we have taken into account only the concentration of the membrane-bound analyte (along with the contribution of the coating antibody) to build density distributions of spots. How do secondary antibodies and the subsequent detection steps affect spot morphology? Because there is no competition between analyte binding by antibodies and free analyte diffusion during the secondary detection step, the characteristics of secondary (or detection) antibodies do not affect spot morphology much (see Note 8). Similar to conventional ELISA assays, low afﬁnity of detection antibodies can be compensated by higher antibody concentrations and/or longer incubation times (see Note 9). Classically, indirect methods have been used to detect the membrane-bound analyte involving biotinylated secondary antibodies, followed by addition of enzymatically labeled streptavidin (tertiary reagent) and substrate. In most cases, the secondary antibody and the tertiary reagent are present in much higher concentrations than their corresponding afﬁnity constants, i.e., they are added in excess. In such cases, the ﬁnal signal detected is linearly proportional to the concentration of the bound analyte. The same is applicable to enzymatic detection steps. The substrate concentrations in ELISPOT assay are also much higher than the Michaelis–Menten constants for the enzymes used. In this setting, the speed of substrate conversion is linearly proportional to the bound enzyme. With some limitations discussed below, the enzymatic detection system therefore reveals the concentration of analyte bound on the membrane – the secondary antibodies and the subsequent detection steps do not signiﬁcantly affect the spot morphology.

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3.10. The Impact of Substrate Buildup

Substrate buildup can, however, obscure the exact optical measurement of the amount of precipitate in a spot. Most substrates used for ELISPOT assays are not transparent, and after a certain “buildup” they reach a maximal optical density (this would be a situation analogous to painting a car with multiple layers of nontransparent paint). Such substrate “buildup” causes a plateau in the middle of the spot similar to when an excess of analyte blocks all capture antibodies around the secreting cell (see above). The use of backlight can help to distinguish between the two types of plateaus, however. Flat spots generated by excess analyte stay ﬂat when backlit, but substrate buildup becomes visible when backlight is turned on. In general, the detection of ﬂat spots indicates that the maximum or total density of spots is not proportional to cells’ productivities and is not a good parameter to estimate cytokine secretion rates.

3.11. The ELISA Effect on ELISPOT

During our above analysis of ELISPOT formation, we did not mention the “ELISA effect.” This results when large quantities of analyte avoid being captured around the cells and diffuse into the supernatant. Eventually, this analyte is also captured on the membrane, but instead of spots a carpet-like coloration is seen. Lowafﬁnity, slow-binding kinetics and low surface density of capture antibodies create an ELISA effect, as does the overproduction of analyte, e.g., after mitogen stimulations (see Note 10). The coloration is proportional to the concentration of the analyte in solution and, therefore, to the number and productivity of the secreting cells in the well. It cannot be accurately accounted for in our single cellbased model. Extensive ELISA effects affect spot morphology measurements, obscuring detectable spot size and peak density of the spots. The automatic correction of camera exposure times, which is a feature of ImmunoSpot® Analyzers (i.e., scanning with “Autolight”), and the use of backlight, however, are sufﬁcient to obtain accurate spot counts even when major ELISA effects occur.

3.12. Asynchronous Analyte Production

In the above, we did not explicitly address the issue of asynchronous cell secretion. One cell can give a short burst of analyte release, whereas another cell may release slowly but steadily over a longer period of time. If the same total amount of analyte was secreted during a short period of time, cells which have a delayed but faster release will produce smaller and brighter spots (8- and 24-h proﬁles in Fig. 11). If one cell secretes faster, but stops secretion much earlier than the other, the residual spot (for the same total amount of the secreted analyte) will be bigger and less dense – “diffuse” (compare “24 h” and “stopped after 8 h” proﬁles in Fig. 11). We discussed the effect of “overincubation” in Subheading 3.4. Two distinct types of spots (normal vs. diffuse) detected with the same capture antibodies may point to different subpopulations of cells producing the same analyte, but with distinct activation/secretion kinetics (see Note 11). Asynchronous production needs also to be

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Fig. 11. Asynchronous secretion by different types of cells. For all three proﬁles, the net amount of the secreted analyte was the same. “Fast secretion” started after 16 h and stopped at 24 h. “Slow secretion” started at time zero and continued for the entire 24-h incubation period. “Fast stopped at 8 h” started at time zero and stopped after 8 h of incubation. Productivity of “fast” secretor Q = 3 × 104 (1/s) and “slow” secretor Q = 104 (1/s). All other parameters are the same as in Fig. 6.

considered when the production of different analytes is measured in two-color ELISPOT, as their kinetics may differ. For example, IL-2 and IFN-G production by human antigen-stimulated T cells peaks within 24 h, whereas the secretion of IL-4, IL-5, and IL-17 does not even begin by 24 h following stimulation and peaks at around +72 h. Such different secretion kinetics are not, however, an obstacle to detecting the numbers of the respective analyte-producing cells: for example, IFN-G- and IL-17-secreting cells can be readily detected in a double-color IFN-G/IL-17 assay of 72 h duration. However, when assessing spot morphologies, the IFN-G spots are fainter and larger than in a 24-h assay due to the diffusion that occurs after the IFN-G secretion has stopped. For accurate productivity measurements, analytes should be chosen whose secretion kinetics is similar, e.g., IL-2 and IFN-G or IL-4 and IL-5. 3.13. FLUOROSPOT Versus ELISPOT

As the ﬁeld progresses toward multiplex measurements, increasingly ﬂuorochrome-labeled detection antibodies are being used for the visualization of plate-bound analyte. Such FLUOROSPOT assays have principal advantages over the classic enzymatic variant. First, FLUOROSPOT can be multiplexed for the detection of at least six analytes in the same well. Second, the ﬂuorescence intensity is directly proportional to the amount of analyte and label bound in the spot area. There is no substrate conversion ampliﬁcation step. Unlike for traditional ELISPOT, no substrate buildup can occur leading to the underestimation of the signal intensity. For exact quantitative measurements of plate-bound analyte (cell productivities) therefore, ﬂuorescent visualization of spots is ideal,

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whereby all of the above considerations regarding analyte binding to the membrane apply (see Note 12). 3.14. Concluding Remarks

Despite the aforementioned simpliﬁcations, our kinetic model (which considers only the surface density and binding characteristics of capture antibodies) sufﬁces to provide a clear understanding of the spot morphologies seen in ELISPOT and FLUOROSPOT assays and to explain how these different morphologies are related to a cell’s productivity. More than a decade of experimental work with ELISPOT and FLUOROSPOT performed in our laboratory has provided conﬁrmatory data for this model. At the basic science level, a major contribution of this model is that it reveals the logarithmic nature of the relationship between spot size and productivity. Thus, it has become clear that the extent of variation in the cytokine secretion rate of T cells spans several orders of magnitude. Since most of the analytes which ELISPOT and FLUOROSPOT measure are bioactive molecules, one needs to assume that the biological signiﬁcance of T cells secreting very low or very high amount of such molecules might be fundamentally different. Thus, it is possible that the weak producers do not secrete enough cytokine to perform effector functions, and would be mistakenly classiﬁed as effector cells. On the other hand, the T cells with orders of magnitudes higher productivity rates might be the key effector populations. The former might act only when they release cytokine in a targeted fashion: in direct cell-to-cell interactions, functioning as helper cells or as regulatory cells. The latter might secrete sufﬁcient cytokine to generate effects in the wider surrounding tissues leading to local or systemic inﬂammation. ELISPOT and FLUOROSPOT are high-content assays that provide high-resolution information on individual cells’ secretory activity. By studying spot morphologies beyond mere spot counts, we can gain new insights into T-cell biology and T cell-mediated immunity, adding a new dimension to immune diagnostics.

4. Notes 1. The area (size) of spots in ELISPOT and FLUOROSPOT assay is a logarithmic function of cell productivity. Therefore, the range of cytokine produced by individual cells is much wider than it appears based on experimental spot size distributions and can cover a few orders of magnitude. 2. For practical purposes, both spot size and total spot density can be used for the quantitative assessment of analyte production by individual cells. However spot size remains the only parameter directly linked to the productivity by a single known mathematical function in all experimental conditions.

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3. Spot morphology is a function of the surface density and kinetic parameters of capture antibodies. 4. Incubation of cells in ELISPOT or FLUOROSPOT assays long after cells stop secreting results in big, fuzzy (diffuse) spots without well-deﬁned peaks and may eventually lead to complete spot disappearance. 5. High-afﬁnity capture antibodies produce bigger and brighter spots than low-afﬁnity ones. 6. Among capture antibodies with equal afﬁnities, antibodies with faster association rates produce bigger and brighter spots than antibodies with a low association rate – the latter may not enable spot formation at all. 7. High density of capture antibody results in dense, tight spots; low, suboptimal density results in big, fuzzy (diffuse) spots without a well-deﬁned peak. 8. Because there is no competition between analyte binding by antibodies and free analyte diffusion during the secondary detection step, secondary (or detection) antibodies do not much affect spot morphology. 9. Similar to conventional ELISA assays, low afﬁnity of detection antibodies can be compensated by higher concentrations and longer incubation times. 10. Low surface density, low afﬁnity, or slow association kinetics of capture antibodies result in strong ELISA effect, leading to low-contrast spots over a uniformly stained background. 11. Asynchronous analyte secretion by different cell types results in distinct spot morphologies. 12. Fluorescent spot detection with directly labeled secondary antibodies provides more direct measurement of spot densities as compared to enzymatic detection systems. References 1. Hesse M. D., Karulin A. Y., Boehm B. O., Lehmann P. V., and Tary-Lehmann M. (2001) A T cell clone’s avidity is a function of its activation state. J Immunol 167, 1353–1361. 2. Helms T., Boehm B. O., Asaad R. J., Trezza R. P., Lehmann P. V., and Tary-Lehmann M. (2000) Direct visualization of cytokine-producing recall antigen-speciﬁc CD4 memory T cells in healthy individuals and HIV patients. J Immunol 164, 3723–3732. 3. Targoni O. S., and Lehmann P. V. (1998) Endogenous myelin basic protein inactivates

the high avidity T cell repertoire. J Exp Med 187, 2055–2063. 4. Karulin A. Y., Hesse M. D., Tary-Lehmann M., and Lehmann P. V. (2000) Single-cytokineproducing CD4 memory cells predominate in type 1 and type 2 immunity. J Immunol 164, 1862–1872. 5. Hofstetter H. H., Targoni O. S., Karulin A.Y., Forsthuber T. G., Tary-Lehmann M., and Lehmann P. V. (2005) Does the frequency and avidity spectrum of the neuroantigen-speciﬁc T cells in the blood mirror the autoimmune

11 process in the central nervous system of mice undergoing experimental allergic encephalomyelitis? J Immunol 174, 4598–4605. 6. Schwander S. K., Torres M., Carranza C. C., Escobedo D., Tary-Lehmann M., Anderson P. et al. (2000) Pulmonary mononuclear cell responses to antigens of Mycobacterium tuberculosis in healthy household contacts of patients with active tuberculosis and healthy controls from the community. J Immunol 165, 1479–1485. 7. Quast S., Zhang W., Shive C., Kovalovski D., Ott P. A., Herzog B. A., et al. (2005) IL-2 absorption affects IFN-gamma and IL-5, but not IL-4 producing memory T cells in double color cytokine ELISPOT assays. Cell Immunol 237, 28–36.

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8. Guerkov R. E., Targoni O. S., Kreher C. R., Boehm B. O., Herrera M. T., Tary-Lehmann M., et al. (2003) Detection of low-frequency antigen-speciﬁc IL-10-producing CD4(+) T cells via ELISPOT in PBMC: cognate vs. nonspeciﬁc production of the cytokine. J Immunol Methods 279, 111–121. 9. Kleen T. O., Asaad R., Landry S. J., Boehm B. O., and Tary-Lehmann M. (2004) Tc1 effector diversity shows dissociated expression of granzyme B and interferon-gamma in HIV infection. AIDS 18, 383–392. 10. Forsthuber T., Yip H. C., and Lehmann P. V. (1996) Induction of TH1 and TH2 immunity in neonatal mice. Science 271, 1728–1730.

Chapter 12 Mathematical Algorithms for Automatic Search, Recognition, and Detection of Spots in ELISPOT Assay Sergey S. Zadorozhny and Nikolai N. Martynov Abstract Accuracy of spot detection and quantiﬁcation plays a critical role in the analysis of ELISPOT data. Differences in staining intensities of spots and their morphological variations make it difﬁcult developing a reliable software application. We have developed an image recognition method allowing for the automatic detection of round objects (spots) on ELISPOT images independently of the registration conditions. The emphasis is done on objects of elliptical shape which is typical for a wide range of spots that can be analyzed by both monochrome and a dual-color version of our software. The method of subdivision of objects into groups is also described which is based on color attributes of spots. Key words: ELISPOT, Image analysis, Spot recognition, Spot detection, Mathematical algorithm, Dual-color ELISPOT

1. Introduction Accuracy of spot detection and quantiﬁcation is of critical importance for the analysis of ELISPOT data. However, spots have different staining intensities and vary in morphology (see Chapter 11) which makes it challenging for software developers to design a software application. It is well-known that identical objects can look dramatically different depending on illumination conditions and optical characteristics of illuminated objects. This hinders the analysis and interpretation of the objects in the ﬁeld of view when image processing must be independent of its registration conditions. For solving such problems, morphological image analysis methods were designed and they proved their efﬁciency (1, 2). Mathematical notion of the form comprises the foundation of such methods. The form (e.g., proﬁle or shape of the spot) is the Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_12, © Springer Science+Business Media, LLC 2012

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maximum invariant of image transformations that take place under various registration conditions using different cameras and so on (1). That is why the form is deﬁned not only by the analyzed object and the scene it is on, but is also connected with the model of scene (or object) registration being the fundamental part of morphological analysis. In some practical cases, the proﬁles of objects are predeﬁned. For example, the spots in ELISPOT assay (including dual-color ELISPOT assays) are either round or have a concentric proﬁle. This makes it possible to successfully solve a variety of application problems related to detecting and classifying such objects.

2. Materials Standard IBM-PC compatible personal computer was used running under Microsoft Windows XP operating system. Microsoft C++ Compiler 6.0, standard edition, was used to compile the software described in this chapter. Test images, captured by color Unibrain Fire-i 785c video camera (http://www.unibrain.com), were kindly presented to us by MVS Paciﬁc, LLC (http://www.mvspaciﬁc.com).

3. Methods 3.1. Algorithm

1. Image form construction. Image form construction is a substantial part of the morphological analysis. The quality of form construction greatly inﬂuences the ultimate result of morphological analysis. For example, the round objects can be described in terms of a set of concentric circles, whereas the image composed of linear object can be described in terms of a set of narrow parallel bars and so on (see details in Subheading 3.4). 2. Comparison of objects on the basis of image form. Let us deﬁne the image form by deﬁning the sets of constant brightness in the view angle X. Let set V f be the set of all images with forms not more complicated than the form of image f. From this set to choose the image having the best approximation of image g, we need to solve the following extreme problem: Pf g g

2

inf

[f` g

f ` V f

]

(1)

where Pf operator is the orthogonal projector on the linear image space with forms not more complicated than the form of image f:

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Pf g £ i

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(2)

i

Here, ci (x , y ) is the indicating function of i-ﬁeld of image f which equals to 1 in inner i-ﬁeld dots and equals to 0 in other dots, Ci – the brightness of i-ﬁeld of the chosen image. So, P f g is the best approximation for image g by images with forms not more complicated than the form of image f. Consequently, the image P f g g represents all distinctions between image f and image g on the basis of the form and is called the “image of residual.” And functional P f g g could be used as the measure of difference between image g and image f on the basis of the form. However, this algorithm for low-contrast images is error-prone because any low-contrast image is similar to any other image (brightness levels Ci coincides). That is why the measure of the similarity by form is deﬁned as the following ratio: tf g

Pf g g P f g P0 g

,

(3)

where P0 is the projector of image g on constant image: P0 g

(c X , g ) cX

2

cX

(indicating function c X is equal to 1 in the whole view angle X). Let us state the obvious features of the functional Eq. 3. The less the value of the functional t f g , the more the similarity between the image g and the image f and the farther it is from constant. In real situations, if the image g is close to constant, then the denominator of the fraction is fairly small and is comparable to the nominator of the fraction. And if the image g is not more complicated than the image f by form and is not a constant, then the fraction equals 0. And, at last, if the image g is not similar to image f by form and differs from the constant, then the numerator and denominator of the fraction are approximately equal to each other. 3.2. Using Morphological Approach to Search for Objects

According to this theory, the problem of searching for objects can be formulated as follows: 1. Let g be the image with some objects inside. 2. Let f be the ideal image deﬁned on some subspace of the view angle X. 3. The problem is to ﬁnd positions of all objects on image g with forms close to ideal image f.

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Fig. 1. Effects of illumination conditions on the results of the automated search process. (a) Field of view in bottom of the ELISPOT well is evenly illuminated which results in almost a uniform background. (b) Illumination is uneven and as a result sharp round spots can be easily detected by the software, whereas blurry spots that have irregular form may remain undetected.

When objects are spots of a circular proﬁle, then we may choose an ideal image as decomposition of a view angle into rings with 1- to 2-pixel width. As a result, a search process can be reduced to the following procedure: 4. Deﬁne the window over the given image with dimensions equal to dimensions of the ideal image. This window scans the image progressively meanwhile processing the part of the given image under the window. 5. The fragment of the given image under the window is compared to the ideal image by form according to the proximity criterion deﬁned by functional (Eq. 3). 6. Local minimums are retrieved after calculating the value of the functional in each pixel inside the image. All points (pixels) with minimum values less than some threshold are associated with centers of the spots. Figure 1a b, shows the typical results of the search process based on described morphological method. Figure 1a illustrates an even illumination situation (background is almost uniform), whereas Fig. 1b shows the example of a nonuniform illumination. As it comes from these examples, sharp round spots can be easily detected, whereas blurred spots of uncertain shape remained undetected. 3.3. Color Classiﬁcation

There are situations when detecting the correct color of the spot is important for accurate diagnostics: in dual-color ELISPOT assays, the color of the spot not only provides means for recognizing spots, but also serves as a marker of the type of a secreted cytokine. The problem of color classiﬁcation is well-known and can be solved by

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cluster analysis (3) which subdivides sets of objects by associating them with some classes on the basis of the mathematical criterion of classiﬁcation quality. This criterion must reﬂect somehow the following nonformal demands: 1. Inside the groups, objects must be closely connected to each other. 2. Objects of different groups are quite distant from each other. The central point in cluster analysis is the choice of metrics (measure of proximity of objects to each other). The choice of metrics greatly inﬂuences the ultimate result of subdivision of objects into groups according to a given algorithm of the dividing process. This choice is closely correlated with main goals of the research in whole, with physical and statistical nature of utilized information, etc. Another important feature of cluster analysis is the measure of proximity between groups of objects (see Note 1). Let us go over the most popular proximity measures characterizing mutual disposition of groups of objects. Let wi – ith group of objects, N – the number of objects in the group wi, vector mi – arithmetic mean of objects in wi, and q(wn, wm) – the distance between groups wn and wm. The nearest neighbor distance is the distance between the closest objects of the clusters: q min wn , wm

min

xi wn , x j wm

d xi , x j

(4)

The farthest neighbor distance is the distance between the farthest objects of the clusters: q max wn , wm

max

xi wn , x j wm

d xi , x j

(5)

The centroid distance is the distance between the central points of the clusters: q w1 , wm d m1 , mm

(6)

The choice of the measure of the distance between the clusters basically affects the outline of the geometrical groups of objects generated by algorithms of cluster analysis in the feature space. Those algorithms based on the nearest neighbor distance are adequate in a speciﬁc case of groups which have complicated chain structure. On the other hand, the algorithms based on determining the farthest neighbor distance are suitable when dealing with speciﬁc case of groups that form spheroid clouds in a feature space. The centroid distance algorithms occupy the intermediate position and satisfy the speciﬁc case of groups of objects with ellipsoid shape.

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Fig. 2. Analysis of a dual-analyte ELISPOT assay. QuantiHub 4.1 is capable of recognizing and detecting spots that have different colors as well as different shades of the same color within a large dynamic range.

Problem of grouping spots based on their color features can be easily solved because typically it is only needed to deﬁne not more than two groups. First, the farthest spots from the whole set of detected spots are determined. If there are only two clusters, then these spots must belong to different clusters and proximity measure is deﬁned according to Eq. 5. Then, the iteration is done on all other spots and they are associated with either ﬁrst or a second cluster corresponding to their distance from the earlier-found farthest spots. Finally, Eqs. 4 and 6 may be used as a proximity criterion for clusters. If the distance calculated according to above formulas is less than some critical value, then the subdivision of two clusters is not valid: there are more than two clusters in the set of objects. The critical value is deﬁned on the basis of speciﬁc additional demands of how many colors in clusters must be different. Figure 2 illustrates a color subdivision of spots in dual-color ELISPOT images. 3.4. Automatic Construction of the Image Form

If a pure mathematical description of the object is difﬁcult to perform, then the problem of form construction (construction of regions of constant brightness) can be solved using the real image of such an object. Let us represent the image f for constructing form as follows: n

f (x , y ) £ Ci ci (x , y ) i 1

(7)

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where Ci – the brightness in ith region; ci (x , y ) – indicating function of ith region which is equal to 1 in region points and equals to 0 elsewhere (see Note 2). Let us use the following algorithm of optimum irregular division. First, we make regular uniform division and calculate the image P f f . This image has brightness levels C1i, i = 1,n. Then, we divide image f on intervals with mean values equal to C1i. After that, we repeat image calculation P f f and get brightness levels C2i. We can continue making such iterations until brightness levels stop changing. Such algorithm may be called the algorithm of optimal image approximation by step functions (Eq. 2). This algorithm leads to division of interval of image brightness levels on n intervals with no more than one region of constant brightness of image f. To speed up the algorithm convergence, we can choose nonuniform initial levels of division correlating with the histogram of the brightness levels of the image. In such a division, we have more levels which are more frequent in the histogram. 3.5. Practical Applications of the Method

The described method is a useful mathematical foundation of software application for counting spots in ELISPOT assays. However, a mathematical method is not sufﬁcient itself for obtaining accurate and reliable quantiﬁcation results (see Note 3). It has to be pointed out that the information about the mean value of spots dimensions and a mean value of their deviations is of critical importance and without it for computer software it is impossible to correctly interpret whether too small and too big spots are relevant or not (see Note 4). That is why many other systems need precise manual adjustments to compensate for different image capture conditions. Using algorithms described above, we have developed a QuantiHub software (http://www.mz-computers.com; distributed by MVS Paciﬁc LLC, http://www.mvspaciﬁc.com) which utilizes a different approach: adjustments for ﬁxed image capture conditions are done beforehand. QuantiHub software is capable of automatic processing of the images captured from the ELISPOT plates (including dual-analyte ELISPOT assays) in 1–2–3 predeﬁned spot-counting modes. QuantiHub version for single-color ELISPOT assays (Versions 3.6 or less) was released in 2002 and completely redeveloped in 2010 (Version 4.1) allowing quantiﬁcation of both single-analyte (single-color detection) and two-analyte (dual-color detection) ELISPOT assays. The system utilizes Unibrain Fire-i 785c camera (http://www.unibrain.com) and allows for accurate quantiﬁcation results without the necessity of manual adjustments of the system during image acquisition at different illumination conditions. QuantiHub Version 4.1 has two predeﬁned counting modes – Fast and Enhanced. The Fast mode is less time consuming and can be used on a vast majority of ELISPOT assays (see Fig. 3), whereas the

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Fig. 3. Typical dual-color ELISPOT detection results using the “Fast” counting mode in QuantiHub 4.1 software.

Fig. 4. Typical dual-color ELISPOT detection results using the “Enhanced” counting mode in QuantiHub 4.1 software.

Enhanced mode needs more time and is intended for rather difﬁcult situations, such as when there are too many spots and many of them almost merge with each other and have a low contrast (see Fig. 4). In addition to a desktop version, QuantiHub 4.1 is available as a Web service (http://www.mz-computers.com and http://www. mvspaciﬁc.com) to researchers who prefer to be ﬂexible in analyzing

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their ELISPOT images loaded from laboratories located in different places. Investigators, who wish to develop their own ELISPOT quantiﬁcation software, can utilize information provided in this chapter which can save them a software development time and help to design a robust spot recognition and quantiﬁcation desktop application.

4. Notes 1. Cluster analysis algorithms vary signiﬁcantly. For example, these may be algorithms targeting a linear search of either object combinations or random division of object set. The majority of such algorithms consist of two groups. The ﬁrst group is intended for initial (possibly, artiﬁcial or arbitrary) subdivision of objects set into classes and a certain criterion of the quality of automatic classiﬁcation is deﬁned. The second group of algorithms is intended for objects which are interchanged between classes until the value of criterion quits reﬁning. 2. Any image with the proﬁle which is not more complicated than f can be constructed by combination of brightness levels Ci. Therefore, the construction of image form is reduced to deﬁnition of indicating functions. For simplicity, the entire interval of images’ brightness may be divided into n equal intervals. However, such a division does not guarantee that each interval contains only one region of constant brightness of image f. 3. Regardless how good mathematical algorithms are, it is impossible to get uniformly good results with all possible sets of images of the wells in ELISPOT plates. This is observed, for example, on images with either inadequate (too dark) or excessive (too bright) illumination, when the differences in object– object and object–background brightness are not profound. 4. From the physical point of view, dimensions of spots depend on the focal distance of the lens and the distance from the lens to the object which can vary signiﬁcantly. References 1. Yu. P. Pyt’ev (1993) Morphological Image Analysis. Pattern Recognition and Image Analysis. 1, 19 – 28. 2. S. S. Zadorozhny, Yu. P. Pyt’ev, and Chulichkov, A. I. (2000) Morphological Methods in Automatic Recognition of Cars’ License Plates

from Their Video-Images. Pattern Recognition and Image Analysis. 2, 288–292. 3. Yu. I. Zhuravlev (1998) An Algebraic Approach to Recognition or Classiﬁcations Problems. Pattern Recognition and Image Analysis. 1, 59–100.

Chapter 13 Objective, User-Independent ELISPOT Data Analysis Based on Scientiﬁcally Validated Principles Wenji Zhang and Paul V. Lehmann Abstract ELISPOT results used to be evaluated visually which, however, is inevitably subjective, inaccurate, and cumbersome. Even when applying automated image analysis to this end, the results are highly variable if the counting parameters are set subjectively. Since objective, accurate, and reproducible measurements are fundamental to science, major efforts have been undertaken over the last decade at CTL to understand the scientiﬁc principles behind ELISPOT data and to develop “intelligent” image analysis algorithms based on these principles. Thus, a spot recognition and gating algorithm was developed to automatically recognize the signatures of deﬁned cell populations, such as T cells, discerning them from irrelevant cell types and noise. In this way, the science of ELISPOT data analysis has been introduced, permitting exact frequency measurement against background. As ELISPOT assays become a gold standard for monitoring antigenspeciﬁc T-cell immunity in clinical trials, the need has surfaced to make ELISPOT data transparent, reproducible, and tamper-proof, complying with Good Laboratory Practice (GLP) and Code for Federal Regulations (CFR) Part 11 guidelines. Flow cytometry-based and other immune monitoring assay platforms face the same challenge. In this chapter, we provide an overview of how CTL’s ImmunoSpot® platform for ELISPOT data analysis, management, and documentation meets these challenges. Key words: ELISPOT, T cells, Good Laboratory Practice, Code for Federal Regulations, ImmunoSpot®, SmartCount™, SpotMap™, AutoGate™, Data analysis, Data management, Data documentation, Spot morphology, Spot recognition

1. Introduction For the past few decades, classic T-cell assays, such as proliferation and killer assays, have been a form of art which could be performed successfully only by highly trained personnel (and those who, in addition, were blessed with a “green thumb”), but would not work reliably for most. Moreover, those assays had the reputation of being rather irreproducible, even by such investigators. The introduction of ELISPOT, intracellular cytokine staining (ICS), and Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_13, © Springer Science+Business Media, LLC 2012

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multimer analysis (e.g., tetramer and pentamer) for T-cell monitoring promised to move the ﬁeld closer to attaining reliable measurements on T-cell immunity. Recently, studies have been undertaken to investigate the interlaboratory variability of these assay platforms (1). Identical aliquots of cryo-preserved peripheral blood mononuclear cells (PBMCs) had been sent to the participating laboratories for testing, and the results showed up to 35-, 20-, and 100-fold differences, respectively, within data obtained for ELISPOT, ICS, and tetramer. The authors of this study concluded thus: “The high degree in variability makes the comparison between any two laboratories become a game of chance.” This disturbingly high variability of T-cell assay results obtained by the multicenter initiatives might have had many reasons – certainly a major one shared by all assay platforms being data analysis. Indeed, highly discrepant results were obtained even when a single raw ICS ﬁle (acquired on a single ﬂow cytometer) was sent to different experienced investigators for analysis (C. Britten, unpublished). In this setting, all assay- or instrument-related variables were removed, the only variable being data analysis. Alerted by the high variability of the results, the international Society of Biological Treatment of cancer (iSBTc) just initiated an “ICS Gating Panel” project when this chapter was written that invites scientists experienced in ICS to develop a gating harmonization strategy. Another study published concurrently (2) found the opposite to be the case for ELISPOT: ELISPOT data were highly reproducible among different laboratories, with the maximal variation being 0.42-fold (83-fold less than what was reported in ref. 1). Notably, in this latter study, the participants were all ELISPOT novices, and the results of their ﬁrst ever ELISPOT assays were obtained and reported. However, all participating laboratories followed the same protocol and the data analysis was done by a fully automated platform. Thus, for obtaining highly reproducible ELISPOT data, it is essential to eliminate subjectivity from spot counting and to replace it by fully automated, scientiﬁcally validated, and statistics-based principles for spot recognition and gating. In the following, we will outline how this is accomplished by the ImmunoSpot® Software. 1.1. Spot Morphology and Spot Recognition

One key piece of information to be gained from T-cell ELISPOT assays is the frequency of antigen-speciﬁc T cells within the entire sample cell population, as measured by the number of T cells engaged in cytokine production following antigen stimulation. This frequency reﬂects the clonal size of the antigen-speciﬁc T cells, and therefore, the magnitude of T cell immunity. Obviously then, one prerequisite for obtaining accurate frequency information is that both the assay and the image acquisition must be optimized for single-cell resolution.

1.1.1. Antigen Presentation

Each spot within an ELISPOT assay reﬂects on a single cell’s secretory activity. In an interferon gamma (IFN-G) ELISPOT assay, for example,

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IFN-G is captured by the membrane-bound anti-IFN-G antibody in the area directly surrounding the secreting cell with its size and density reﬂecting on the amount of cytokine produced by the cell within the assay’s entire duration (3) (see also Notes 1 and 2). Spot size and density are thus critical parameters for ELISPOT data analysis (see also Chapters 11 and 12). The kinetics of cytokine production is also reﬂected by the spot morphology, i.e., its density and general shape. For example, a rapid secretion rate will produce a large, fuzzy spot, whereas the slow but steady release of cytokine will result in a smaller, denser spot (see Note 3). In ELISPOT assays performed with PBMC, the individual antigen-speciﬁc T cell will interact by chance with different types of antigen-presenting cells (APC). Each of these APC has different co-stimulatory property. When a T cell becomes activated by an antigen-presenting B cell, it will produce less cytokine, and will do so in a delayed fashion as compared to antigen recognition on a dendritic cell – antigen presentation by a macrophage will provide an intermediate T-cell response (4). For this reason, the spots seen in T-cell assays involving PBMC are invariably heterogeneous in size and density. When it comes to analyzing such results, therefore, it is not the individual spot, but rather the distributions and population kinetics of all spots within an assay that need to be examined. The ImmunoSpot® software that is designed for user-independent analysis of ELISPOT data recognizes ﬁrst all spots, irrespective of size and density, and then subjects these spots to statistical evaluation to determine spot distributions. 1.1.2. Antigen Dose

In ELISPOT assays, the antigen dose also affects cytokine secretion rates of T cells and, hence, spots morphologies. Stimulation of a T-cell clone with a high dose of antigenic peptide induces stronger cytokine production in the individual T cells (that is, they produce larger and/or more dense spots) than does the stimulation of the same clone with low-dose peptide (3). When stimulated with a single antigen dose, as is frequently the case in ELISPOT assays, high-avidity T cells within the PBMC will produce larger spots than low-avidity T cells. Similarly, increased T-cell co-stimulation was shown to result in increased per-cell productivity (5).

1.1.3. Pathological Variations

In diseases such as HIV, the per-cell cytokine productivity can be signiﬁcantly reduced, resulting in smaller spots (6). One of the many advantages of ELISPOT over other assays which measure net cytokines in supernatant (ELISA, CBA/Luminex) or mRNA is the ELISPOT assay’s ability to determine whether a decreased net cytokine production is caused by a decreased number of cytokinesecreting T cells or by the reduced per-cell productivity of the same number of T cells. In order to account for physiological and pathological variations in per-cell productivity, ELISPOT data analysis software must therefore be highly versatile, with the ability to

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recognize and analyze all variants of spots, by automatically ﬁne tuning the counting parameters. Such ﬁne tuning can be done manually (which will be inherently subjective), or by ImmunoSpot® software automatically, and hence in a user-independent fashion. 1.1.4. Assay-Related and Physiological Variations

Spot morphology varies if different antibodies are used for cytokine detections. A capture antibody with low afﬁnity will produce fainter and more diffuse spots than a high-afﬁnity capture antibody. Furthermore, the spot morphology will vary when different concentrations of the same antibody are used for coating. The durations of the assay can also inﬂuence spot morphology signiﬁcantly. Spots grow in size and density when the assay duration is prolonged and the cells secrete continuously, as is the case for T-cell-derived IFN-G (3). The outcome is different, however, when there is an early burst of production that comes to a halt before the assay is terminated. In such cases, the spot size will continue to grow even after the production of the cytokine has stopped (due to lateral cytokine diffusion caused by the reversibility of its interaction with the membrane antibodies), but spot intensity will fade due to the dilution of the cytokine. The temperature during enzymatic substrate development and the nature of the substrate will also play a role in deﬁning the spot morphology. Red spots developed with horseradish peroxidase/amino-ethyl carbazole (HRP-AEC) differ fundamentally from the blue alkaline phosphatase-nitro-blue tetrazolium chloride/bromo-4-chloro-3c-indolyphosphate p-toluidine (ALPH-NBT/BCIP) spots, with the former being more pristine with a fainter background, while the latter more dramatic and fuzzy with a frequently more heavily stained background.

1.1.5. Background Variations

ELISPOT data analysis is further complicated by the fact that the spots occur over a variable background as is inherent to ELISPOT assays. While the analyte is captured around the secreting cells (resulting in the spots), some of it diffuses into the supernatant, and thereafter gets absorbed on the membrane producing a color carpet. This “ELISA effect” is more pronounced in areas of the well where densities of secreting cells are higher, many times at the edge of the well, resulting in a variable background even within a single well. Accurate ELISPOT data analysis therefore not only requires the precise recognition of various spot morphologies, but these must also be recognized over varying backgrounds over different wells or within a single well. Automatic background correction is crucial for accurate analysis of ELISPOT data and is a key feature of the ImmunoSpot® Software (see Notes 4 and 5). Recognizing spots of different morphologies over various backgrounds is a challenge for automated ELISPOT data analysis. While counting parameters can be manually ﬁne-tuned to accurately analyze spots on a well-to-well basis (in the same cumbersome and subjective way as it is done for ﬂow cytometry), the SmartCount™ module of the ImmunoSpot® software performs these adjustments

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fully automatically for walk-away analysis. The ImmunoSpot® software recognizes ﬁrst all spots, of all sizes and densities, correcting for background variations, and then subjects the spots to statistical evaluation of distributions to automatically set gates (see Fig. 1). Audit trails are produced throughout the process, with overlays of counted spots saved allowing researchers to review the accuracy of the automated process. 1.2. Gating

After the accurate recognition of spots of various size and morphologies over various backgrounds, the next challenge for ELISPOT analysis is accurate gating. As for the analysis of ﬂow cytometry data, also for ELISPOT data analysis, it is inconceivable to meaningfully “count spots” without proper gating (see Fig. 1). In that example provided in Fig. 1, the PBMC were isolated from a subject who was undergoing a cytokine storm. As a consequence, in the media control a high number of cells are seen spontaneously producing IFN-G – these are primarily NK cells and DC. When antigen is added, the antigen-speciﬁc T cells are triggered to secrete IFN-G – because T cells produce more IFN-G on a per-cell basis than cells of the innate immune system, a new “juicier” spot category appears over the background. Thus, the spot size and morphology allows researchers to distinguish cytokine production by different cell types within mixed cell populations. In general, T cells produce substantially more cytokine on a per-cell basis, resulting in larger and denser spots than cells of the innate immune system (see Notes 6 and 7). For example, when IL-10 production by PBMC is measured in ELISPOT assays, most of the “antigen-induced” spots are not T-cell derived (as would be expected), but rather are produced by macrophages in response to bacterial lipopolysaccharide (LPS) contamination of the antigen solution. However, such macrophage-derived IL-10 spots are considerably smaller than the IL-10 spots generated by antigen-speciﬁc T cells – by gating the latter can be identiﬁed (7). While the LPS-induced macrophagederived spots provide no information on speciﬁc immunity, the antigen-induced T-cell-derived IL-10 spots do, since they indicate the presence of regulatory T cells. In order to measure the accurate frequency of the T-cell-derived spots, background spots need to be excluded by setting appropriate “gates.” Similarly, small and faint IL-6 spots are produced by macrophages, while antigen-speciﬁc T cells produce larger and “juicier” spots that can be identiﬁed by gating (8). ELISPOT data analysis software must therefore be capable of distinguishing different populations of spots to determine the gates for the relevant information required for T-cell diagnostics (see Note 8). The gate settings will critically affect the number of spots counted. For this reason, one of the main goals of ELISPOT data analysis has been to establish objective criteria for gating, thereby exorcizing the “ghost of subjectivity” which has haunted ELISPOT data analysis and is haunting ﬂow cytometer data analysis even until today.

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Fig. 1. Statistics-based automated ELISPOT counting. Raw images of a medium control well (“Media”) and the corresponding antigen-stimulated well (“Antigen”) are shown in the top two panels (a). Underneath (b) are the spot counts obtained without gating, with each spot highlighted. (The ImmunoSpot® software establishes the spot recognition parameters automatically by learning spot morphologies, instead of requiring users to subjectively set a multitude of parameters inevitably leading to variability in counts.) The spots automatically recognized are highlighted by the software, creating complete transparency of the (ungated, intermediate) counting result. While all spots in this medium well are “real” (being produced either by the cells of the innate immune system or by antigen-stimulated T cells), and recognized precisely as spots by the software, for establishing the T-cell count this ungated result is completely wrong. The next step is gating. The ImmunoSpot® software automatically establishes size distributions for the media and the antigen-triggered wells and sets the gates automatically, based on the statistical analysis of the distributions as we established in J. Immunol. 2000 164:1862–72 and J. Immunol. 2001, 167:1353–61, among several other publications. After re-counting with these gates, the counts are now correct and objective (c). Thus, there is no human judgment involved in the analysis – anyone in the world analyzing these images with the ImmunoSpot® software would come up with exactly the same scientiﬁcally validated count.

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The simplest experimental models that were used to establish ELISPOT gating criteria involved the use of T-cell clones that produced IFN-G. These T cells were activated by the nominal peptide on a clonal population of APCs which cannot express IFN-G (3). In such experiments, conducted over a wide range of T-cell frequencies, the numbers of T cells plated per well closely matched the numbers of spots detected. Even though the T cells and APC were clonal, the spot sizes varied over a wide range. Closer analysis of the spot size distributions showed that they followed a log-normal distribution. When the peptide dose was lowered, the per-cell productivity decreased, but the size distribution of spots still followed a log-normal pattern. Similarly, when the assay duration was changed, the mean spot size varied, but the log-normal distribution remained. In all subsequent studies of human and murine cells, for clonal and bulk populations, for all cytokines measured (IL-2, IL-3, IL-4, IL-5, IL-6, IL-10, and IFN-G) and in ELISPOT assays measuring granzyme B, perforin, or TRAIL, this log-normal distribution of spots was observed (2–10). Therefore, by assessing morphologies of a multitude of individual spots, the statistical qualities of the distributions of the spots can be established, allowing the software to automatically set objective criteria for recognizing populations and set the gates, thereby identifying with a 99.7% conﬁdence the spots formed by a speciﬁc cell population. In addition, having established these distributional properties, clusters of spots can be recognized and the numbers of spots constituting these clusters can be calculated. 1.3. Hardware Requirements

One limiting factor in the accuracy of ELISPOT data analysis is the hardware used for image acquisition. There is a common misconception that the pixel resolution of the camera is the key factor in determining image quality; however, this is an overly simplistic view. A ﬁne-grain ﬁlm alone does not provide pristine photographs unless the optics, the illumination, and many other ﬁne details are optimized. Likewise, ELISPOT readers need to be high-end optical instruments to allow for the accurate analysis of ELISPOT data at single-cell resolution. In addition, such readers must feature precise robotic motion control and image centering algorithms, so as to accurately position and capture the membrane surface. Well identity is of regulatory concern and must be veriﬁed by slip-proof, encoder-controlled stages, and by faithfully recording the accurate well positions for each well during image acquisition. The illumination of a well will largely affect the performance of any ELISPOT analyser. An ELISPOT analyser should have a closed architecture to exclude the inﬂuence of ambient light. The light source must be optimized to provide even illumination of the well bottom without reﬂections caused by the wall of the well. A backlight, in addition to the top light, will allow for increased luminescence of the membrane, thus facilitating the separation of adjacent

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spots, and the discernment of discrete colors for dual color analysis. The light must be stabilized to permit constant performance over the operation period over a decade of use. Only industry grade illumination will provide constant readings. ImmunoSpot® Series Analyzers have been designed to meet these criteria and are equipped with a user friendly module that permits the user “at the click of a button” to verify the consistent performance of the machine. A further challenge is harmonizing the performance of different ELISPOT analysers, as desirable for multicenter studies, and as required for the comparability of data generated in different laboratories. No two cameras capture identical images, unless the cameras are ﬁne-tuned to the same calibration standards, along with standardizing lighting conditions and other variables, two ELISPOT readers of the same model from the same manufacturer might provide rather variable counts. Substantial effort has been invested by CTL to create fully harmonized readers that produce consistently identical results; starting with the ImmunoSpot® Series 6 analysers, these have now become available.

2. Materials 1. ImmunoSpot® Series 6 Analyzer (CTL, Shaker Heights, OH). 2. ImmunoSpot® 6.0 Software (CTL). 3. SpotMap™ 6.0 Software (CTL).

3. Methods 3.1. Scanning

In the ﬁrst step of ELISPOT analysis, an ImmunoSpot® Analyzer scans and saves image ﬁles of individual ELISPOT wells of a plate. The machine progresses automatically from well to well, using optical feedback to automatically center on each well, thus compensating for irregularities in the plate geometry. (ELISPOT plates are manufactured using a high-temperature molding process, and are prone to deform as they cool down.) Digital encoders keep track of the precise position of each well, thus helping to conﬁrm well identity and positioning. In addition, the software keeps track of the encoder information, the time stamp, and the identity of the operator for tracking of such information for regulatory purposes. The end point of the automated scanning process for an ELISPOT plate is a tamper-proof set of 96 image ﬁles, each representing a digital photograph of one well from the original 96-well plate. Scanning can also be done for 384-, 12-, 24-, and 6-well formats.

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The saved image ﬁles allow users to document and analyze ELISPOT assay results long after the original plates have decayed, and to reproduce the analysis results. While “live” analysis of images (i.e., without saving them to a disk ﬁle) is also possible, it is not recommended, because this obscures the transparency and reproducibility of the data, and thus violates good scientiﬁc and laboratory practice. For work that demands high-throughput scanning, a robotic arm and stacker can be used to automatically load up to 200 plates a time. The user can instruct the ImmunoSpot® software to automatically count and process all plates instead of tediously working with each plate individually. Grouping of plates together can expedite all phases of the work: counting, quality control, and data export. The Plate Manager module permits one to ﬂexibly group plates from different experiments and different locations for streamlined counting, quality control, viewing, printing, and exporting. 3.2. Analysis

The saved image ﬁles can then be processed on the analyzer itself, or on remote workstations equipped with the ImmunoSpot® software. The dissociation of scanning and analysis enables work to proceed more efﬁciently by permitting an indeﬁnite number of users to analyze images independently, without tying up the core machine.

3.2.1. Automated Analysis

The main steps of automated counting are as follows.

Loading Plate Images

Virtually any number of plates can be loaded at this stage, from a single folder (“Experiment”), individually, or as a group from any number of experiments, due to the ﬂexible software design.

Deﬁning Counting Parameters

Accurate counting requires informing the software about the nature of the spots to be counted. As discussed above, the spot characteristics can vary considerably, depending on the assay conditions and the cytokines under examination. For this reason, the ImmunoSpot® software has been designed to learn and auto-adjust the counting parameters using a simple two-stage process. Step 1: SmartCount™ for automatic spot recognition (Fig. 2). By clicking on wells that contain characteristic spots for a given assay, the software learns to recognize the cardinal features of the spots of interest. While establishing the appropriate counting parameters for the respective spot type which is fully automated (and therefore objective and reproducible), the parameters can be manually ﬁnetuned for spot morphology, sensitivity, and a multitude of other criteria. If such adjustments are needed, e.g., for atypical wells that contain artifacts, it is recommended to do these in the quality control step such that the objective counts and the subjective modiﬁcations are transparently documented.

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Fig. 2. Recognition of spot morphology. The ﬁrst step of the analysis process is to “teach” the software the morphology of the spots. This can be done by clicking on wells that contain spots typical of those which are to be analyzed. The software will analyze such spots and highlight those that are being recognized by outlining them. In the subsequent step, the distribution of the spots will be analyzed.

Step 2: AutoGate™ for automatic gating (Fig. 3). After the software is instructed to learn the spot morphology for accurate spot recognition, it needs to understand the distributional properties of the spots. Several wells containing typical spots need to be sampled to accumulate information for an accurate statistical analysis of the spot size distribution. Typically, this requires the sampling of 3–10 wells, a process which takes less than half a minute. At one click of a button, the software will automatically calculate and set the lower and upper gate values based on the spot’s distributional properties. The AutoGate™ feature thus allows objective, statistics-based criteria to be used in setting the minimum and maximum gate for spots to be included in the count. Spots smaller than the lower limit speciﬁed by the minimum gate are ignored; i.e., they are excluded from the ﬁnal spot count. Typically, these are spots secreted by innate immune system and should not be included into the frequency of antigen-speciﬁc T cells. Spots larger than the maximum gate value are counted as cell clusters by default: the software automatically estimates the number of cells in the clusters to be included into the count.

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Fig. 3. Establishing the minimal/maximal gates by AutoGate™. The distribution of the spots in the media control wells (columns 1–3 of the plate loaded) is captured in the left histogram (labeled “NEGATIVE”). The distribution of the spots in antigen testing wells (columns 4–6 of the plate loaded) is captured in the right histogram (labeled “POSITIVE”). The “AutoGate” feature uses the distributional properties of this cumulative data to calculate the minimum and maximum limits or “gates” (indicated by the vertical lines in the histograms). When the actual well shown is recounted with these limits in place, 627 spots are “gated out,” resulting in the spot count of 25 shown at the top. Automated Counting

Once the parameters have been established and assigned to the wells, the software automatically counts spots on any number of plates or sections thereof. Overlays of the raw image ﬁles and of the counting results are saved for each well, as are the counting parameters. The results of the counting process thus are transparent, documented, and easily reproduced for subsequent veriﬁcation in the quality control step.

3.2.2. Quality Control

As ELISPOT assays can be subjected to artifacts, contaminants, damaged or leaking membranes, etc., the ImmunoSpot® software was designed to permit corrections for each of the situations if needed, so that the valuable data could be remedied (see Notes 9–11). For a streamlined review, the ImmunoSpot® software allows the user to view image overlays that indicate which spots have actually been counted and to make corrections if needed (Fig. 4). In the example shown, an artifact that resulted from the clumping of cells by free DNA in a freeze–thawed PBMC sample) has been excised.

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Fig. 4. Quality control in ELISPOT analysis. The image on the left-hand side shows the counting results obtained in automated analysis mode on a well that contains an artifact (in this case, a cell clump caused the spot cluster in the upper left-hand quadrant). The cluster was treated as a group of individual spots, resulting in a spot count of 63. In quality control mode, this artifact-containing region can be outlined (in green) and excluded from the analysis, as shown on the right-hand side. The software then normalizes the spot count by correcting for the size of that region. In the example shown, 33 spots were actually detected, and this count was increased by 8 to compensate for the exclude area, resulting in an adjusted spot count of 41. (The asterisk beside the spot count of 41 indicates that this is a re-calculated value, rather than a direct measurement.) In keeping with Good Laboratory Practices, the software saves and annotates all such subjective adjustments made to the objective automated count. This same example also contains a spot near the center of the well that exceeds the upper gate threshold. This spot was automatically outlined in dark blue during automated analysis, indicating that it was treated as a cluster. In such cases, the software automatically calculates the number of spots required to generate such a cluster based on the average spot size and density distribution, and re-computes the spot count accordingly (the asterisk beside the spot count of 63 likewise indicates that this value was re-calculated, as does the automatically generated A11 annotation code).

The software has calculated how many spots would occupy the excised region if the artifact was not there, assuming an even distribution of spots in the well. To ensure Good Laboratory Practices (GLPs) compliance, all changes made in QC are recorded and annotated automatically by the ImmunoSpot® software. This allows the principal investigator or regulatory agency to determine at a glance whether the counting results have been changed relative to the automated count, and whether the changes made are accurate and appropriate. As part of this documentation, the software also automatically generates a set

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of plates and well image ﬁles which can be helpful in preparing presentations, publications, or discussion of the results. Direct PowerPoint Register Mark export function of the software also makes it convenient for the user to arrange groups of wells for direct comparison at desired magniﬁcations. 3.3. ELISPOT Data Management

ELISPOT assays are highly suited for high-throughput work. It is not uncommon for a single well-trained team to test each day hundreds of samples for reactivity to hundreds of antigens. However, even a small assay can contain a ﬂood of information. Just three 96-well plates, for example, require storing 864 image ﬁles – raw images, counting overlays, QC images, along with the records of counting parameters, the numbers of spots counted, and the spot size/density statistics for each well. This information needs to be linked to the assay information, i.e., to the source of the cell material tested (e.g., PBMC of donor “X”), to the number of cells per well (so that frequencies can be normalized “per million”), to the antigens tested and their concentrations, and to the cytokines measured. Thus, in even a small three-plate assay, there can be more than 4,320 sets of data which need to be linked together. The ImmunoSpot® software’s SpotMap™ module was speciﬁcally designed to manage all data automatically. For each well, and for each plate, the software documents the assay conditions: which cells were plated in which numbers, which antigens were used to challenge the cells and in which concentrations, and which cytokines were measured (Fig 5). Custom plate layouts can be generated in a streamlined fashion for multiplate experiments – the SpotMap™ software will even calculate the amounts of reagents needed for each assay. Once the counting results are available, they can be quickly linked, within the SpotMap™ software, to the other assay parameters: at the click of a button, even the most complex ELISPOT assay can be evaluated, the statistics calculated, and the requested information represented in virtually any desired format.

4. Notes 1. T cells can move around during the assay, causing the spots to develop “tails.” This is especially true when T cells have been preactivated in vivo or in vitro, as this makes the cells particularly mobile. 2. Small clusters can result from T cells migrating from one APC to an adjacent APC within the assay’s duration while they continue to secrete cytokines. At higher magniﬁcation, such clusters are linked by a cytokine trail. Such clusters should be – and are – counted by the ImmunoSpot® software as having

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Fig. 5. ELISPOT data management. The spot counts in 96-well format are linked to the plate layout. For each well, the antigen, the test subject, the cytokine, and the number of cells plated are speciﬁed. All these data are linked and processed for exporting into a database.

been derived from a single cell. Frequencies can be veriﬁed by serial dilution of cells. The number of cells plated and the spot counts are linear in the 100,000–800,000 PBMC per well range (2). 3. Occasionally, white dots can be seen in the middle of the spots. These result from the substrate peeling off, e.g., when the ﬂow rate of the plate washer is too high, or the plates are banged too hard while washing. The “Fill Holes” feature of the ImmunoSpot® software can be used to mask the white dots, allowing for the spots to be counted accurately. 4. In addition to ELISA effects, background coloration can be darker in some parts of the well due to leakage of the membrane or nonspeciﬁc protein precipitations. The ImmunoSpot® software will automatically correct for such variations in the background, but if additional adjustments are needed, in the quality control (QC) module, one can compensate by adjusting the “background balance” parameter. 5. The background coloration tends to be elevated in wells with high number of cytokine-producing cells (i.e., the number of

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spots) due to the “ELISA effect.” The “Auto-light” feature of the ImmunoSpot® Analyser compensates for the increased background coloration. However, for accurate counts in the high-frequency range, it is recommended that one perform serial dilutions of the cells to establish the number of spots below which cell numbers plated and spot counts are linear, and above which spot conﬂuence occurs. That cut-off number will depend on the analyte and on assay conditions used. It can be over 5,000 spots per well, e.g., for an IL-10 assay which produces small spots, and can be as low as 400 spots per well for big fuzzy spots, such as IL-2. Spot counts above the linearity cut-off number are best expressed as too numerous to count (TNTC). The precise value can be established by repeating the experiment with a lower number of cells plated per well. 6. Sometimes, the number of spots in the medium background is high for all samples, due to the stimulatory effects of serum. Even brief exposure to nontested serum, e.g., during washes or during freezing, can drive up the background intensity. The use of serum-free media for cryopreservation, thawing, and testing can eliminate this problem (2). 7. The number of spots in the medium control wells can be high for a single individual out of several tested. This is a common ﬁnding for individuals undergoing cytokine storms in vivo, e.g., due to a clinical or subclinical infection or other massive immune stimulation. In such cases, gating cannot distinguish between background and foreground spots (since both are T-cell derived), but statistics can (see also Chapters 14 and 15). 8. Some assays, such as IL-6, IL-10, and TNF, tend to give highbackground coloration in general, due to the activation of macrophages on the membrane of the ELISPOT plate. Such background spots are frequently smaller than the antigen-induced spots produced by T cells, and can be gated out automatically by the ImmunoSpot® software. 9. On occasion, the counting parameters established can recognize valid spots for most test subjects, but not for others. For example, spots that are either smaller or larger than usual can be seen with particularly low- (or high-) avidity T-cell responses, or if co-stimulation is decreased (or increased). This is one reason why, as part of any ELISPOT analysis, the researcher should have the option of viewing both the raw images and the counting results in QC mode. This allows the researcher to judge whether the counting parameters established do indeed apply to all subjects under examination. If re-counting of any given subject becomes necessary, the altered parameters are automatically annotated by ImmunoSpot® software, thus drawing attention to the atypical spot morphology or other image characteristics.

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10. The well images can contain artifacts due to membrane damage – for example, when the membrane is accidentally scratched with a pipette tip. The affected area can be excised in QC mode, and using the normalization algorithm, the spot count is re-computed. This re-normalization is performed by computing the number of spots required to ﬁll the excised area, using the average spot size and distribution density in the rest of the well. The same technique can also be used to correct for cell clumping. For example, if the testing was done in triplicate, and a clump is found in one of the wells, this clump can be excised and the spot count can be normalized. In both cases, these corrections are automatically recorded by the software in the form of annotations added to the well records. 11. Seeing is believing! Never blindly trust ELISPOT counts, whether from your own laboratory or from others! Overlays of both the raw images and the counting results are a simple and transparent way of understanding the assay results and judging the counting accuracy. Well surveys containing this information can be printed or exported into graphics ﬁles or PowerPoint presentations, allowing assessments of assay results to be performed at a glance. The direct side-by-side display of medium control and antigen wells can speak volumes about the quality of the assay and the appropriateness of spot counts.

Acknowledgments We would like to thank all those who worked at Cellular Technology Ltd., and at Case Western Reserve University under the direction of Prof. Paul V. Lehmann on establishing the scientiﬁc foundations of cytokine ELISPOT assays. At the postdoctoral level, these are (in alphabetical order): Drs. Don Anthony, Beate Berner, Thomas Forsthuber, Peter Heeger, Alexey Karulin Damian Kovalovsky, Stephanie Kuerten, Patrick Ott, Clara Pelfrey, Frauke Rininsland, Stephan Schwander, Tobias Schlingman, Oleg Targoni, and Magdalena Tary-Lehmann. Several graduate students at Case have also made major contributions in our ELISPOT efforts: Wolf Bartholomae, Jan Baus, Kamruz Darabi, Marcus Dittrich, Julia Eisenberg, Kristina Feldmann, Judith Gottwein, Robert Guerkov, Thomas Helms, Bernhard Herzog, Maike Hesse, Harald Hofstetter, Thomas Kleen, Christian Kreher, Haydar Kuekrek, Anke Lonsdorf, Kai Loevenbrueck, Stephan Quast, Tarvo Rajasalu, Britta Stern, and Hualin Yip. We are indebted to the hardware and software development efforts of Johannes Albrecht, Tameem Ansari, Istvan Becza, Dwaine Bensen, Andras Bakos, Georg Bezzeg, Richard Caspell, Carsten Lohrmann, Zoltan Megyesi, Brian Skinner, Akos Subucz, Dean Velasco, and Szabo Zsolt.

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References 1. Janetzki. S., Britten, C.M., Kalos, M., Levitsky, H.I., Maecker, H.T., Melief, C.J., et al., (2009) “MIATA”-minimal information about T cell assays. Immunity 31, 527–528. 2. Zhang, W., Caspell, R., Karulin, A.Y., Ahmad, M., Haicheur, N., Abdelsalam, A., et al. (2009) ELISPOT assays provide reproducible results among different laboratories for T-cell immune monitoring--even in hands of ELISPOTinexperienced investigators. J Immunotoxicol 6, 227–234. 3. Hesse, M.D., Karulin, A.Y., Boehm, B.O., Lehmann, P.V., and Tary-Lehmann, M. (2001) A T cell clone’s avidity is a function of its activation state. J Immunol 167, 1353–1361. 4. Ott, P.A., Tary-Lehmann, M., and Lehmann, P.V. (2007) The secretory IFN-G response of single CD4 memory cells after activation on different antigen presenting cell types. Clin Immunol 124, 267–276 5. Ott, P.A. Berner, B.R., Herzog, B.A., Guerkov, R., Yonkers, N.L., Boehm, et al. (2004) CD28 costimulation enhances the sensitivity of the ELISPOT assay for detection of antigen-speciﬁc memory effector CD4+ and CD8+ cell populations in human diseases. J Immunol Methods 285, 223–235.

6. Helms, T., Boehm, B.O., Assad, R.J, Trezza, R.T., Lehmann, P.V., and Tary-Lehmann, M. (2000) Direct visualization of cytokine-producing, recall antigen-speciﬁc CD4 memory T cells in healthy individuals and HIV patients. J Immunol 164, 3723–3732. 7. Guerkov, R.E., Targoni, O.S, Kreher, C.R., Boehm, B.O., Herrera, M.T., Tary-Lehmann, M., et al. (2003) Detection of low-frequency antigen-speciﬁc IL-10-producing CD4+ T cells via ELISPOT in PBMC: cognate vs. nonspeciﬁc production of the cytokine. J Immunol Methods 279, 111–121. 8. Hofstetter H.H., Karulin A., Forsthuber, T.G., Ott, P.A., Tary-Lehmann, M., and Lehmann P.V. (2005) The cytokine signature of MOGspeciﬁc CD4 cells in the EAE of C57BL/6 mice. J Neuroimmunol 170, 105–14. 9. T. M., Kuerten, S., Zhang, W., Shive, C.L., Kreher, C.R., Boehm, B.O., et al. (2007) Granzyme B production distinguishes recently activated CD8(+) memory cells from resting memory cells. Cell Immunol 247, 36–48. 10. , S., Kleen, T., Assad, R.J., Lehmann, P.V., and Tary-Lehmann, M. (2007) Dissociated production of perforin, granzyme B and IFN-G by HIVspeciﬁc CD8+ cells in HIV infection. AIDS Research and Human Retroviruses, 24, 62–71.

Chapter 14 Statistical Analysis of ELISPOT Assays Marcus Dittrich and Paul V. Lehmann Abstract Cytokine ELISPOT assays have emerged as a powerful tool for the detection of rare antigen-speciﬁc T cells in freshly isolated cell material, such as blood. While ELISPOT assays allow one to directly visualize and count extremely low frequencies of cytokine-secreting T cells among millions of nonsecreting bystander cells, the interpretation of ELISPOT data can become ambiguous when (a) spot numbers in antigen-containing wells are low, (b) spot counts in negative control wells are elevated, and particularly (c) when both of the above occur simultaneously. Thus, the primary task, even before statistics are employed, must be the optimization of the basic assay parameters and reagents such that the assay yields low background signal in the negative-control wells and the maximal number of antigen-induced spots in test wells, i.e., the signal-to-noise ratio is maximized. Furthermore, the use of proper spot-size gating parameters for data analysis is indispensable for screening out irrelevant background spots, and thus increasing the signal-to-noise ratio. The goal of most ELISPOT experiments is to identify positive T-cell responses as deﬁned by a signiﬁcantly elevated spot count in antigen-stimulated wells over the nonstimulated medium-control or negative-control antigen. In this chapter, we conclude that – with some limitations – the T-Test and related statistical methods which rely on the assumption of normal distribution are suitable for identifying positive ELISPOT results. Key words: ELISPOT, Statistical analysis, Signal to noise, Spot-size gating parameters, PBMC, ImmunoSpot, MATLAB, ANOVA, Wilcoxon Rank Sum Test, T-Test, Poisson distribution

1. Introduction Because ELISPOT typically aims for the detection of rare antigenspeciﬁc cells within a variable background, the notion of “fuzzy in, fuzzy out” applies to ELISPOT data – perhaps more so than for other immunoassays. Statistical analysis cannot itself substitute for experimental stringency in performing ELISPOT assays which provide clear, unambiguous results. Preceding the statistical analysis of ELISPOT data, therefore, great attention must be given to establishing spot counts (which are in part based on the statistical analysis of the spot-size distributions) before the counts obtained within an experiment are subject to further statistical analysis.

Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_14, © Springer Science+Business Media, LLC 2012

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1.1. “Precise in”: Optimizing Signal to-Noise 1.1.1. The Choice of Membrane

In cytokine ELISPOT assays, cytokine secretion from individual cells is measured. In typical experiments, 96-well PVDF membrane plates are precoated with a cytokine-speciﬁc capture antibody, e.g., a suitable anti-IFN-G antibody. Due to its fractal surface and high hydrophobicity, PVDF membranes have been found to outperform most other membranes which had been used previously for cytokine ELISPOT assays (1), and therefore the use of PVDF membranes is highly recommended for obtaining optimal results when performing these tests. Historically, cytokine ELISPOT assays had been very fragile and hard to reproduce before the introduction of PVDF membranes.

1.1.2. The Choice of Cell Numbers Plated

In the next step of the assay, the test cell material (e.g., human PBMC) is plated into the precoated wells, both with and without antigen. For T-cell assays, the test cells need to be dense enough to allow for optimal contact between T cells and antigen-presenting cells (APC), but the cells must not be overcrowded, as it is essential that each T cell sits directly on the membrane so that its secretory product can be effectively captured. For human PBMC, 100,000–800,000 cells can be plated per well into 96-well plates with the number of antigeninduced spots per number of PBMC plated following a linear relationship (2). (In contrast, when a monolayer of APC is provided, even single T cells can be plated per well and tested (3)). For the purpose of economizing cell utilization, ELISPOT assays are frequently performed with 100,000 PBMC per well. However, when low-frequency T cells are to be assayed, increasing cell numbers up to 800,000 per well increases the signal in a directly linear fashion without disproportionately introducing noise into the system. When ambiguous results are obtained through the testing of low cell numbers (e.g., 100,000 per well in 96-well plates), retesting these PBMCs with higher cell numbers can provide clear results. The cells can be readily retested, since protocols have been developed that permit an investigator to freeze PBMC such that their functionality in ELISPOT assays is retained and is effectively identical to fresh cells upon thawing (4). Also, 6-well PVDF membrane plates are being introduced that allow for increasing the sample size per well tenfold respective to 96-well plates, thus increasing 10× the signal-to-noise resolution of ELISPOT assays.

1.1.3. Establishing the Correct Spot Counts in Antigen-Stimulated Wells

Upon antigen stimulation, the antigen-speciﬁc T cells engage in the secretion of cytokine which is captured around the cell by the membrane-bound capture antibody. Overall, the size and density of the resulting cytokine “spot” is reﬂective of the quantity of cytokine produced by the cell, and the morphology of the spots reveals the secretion kinetics: i.e., fuzzy spots reﬂect a rapid secretion kinetics, whereas sharp spots are indicative of slow analyte release (5) (see also Chapter 11 in this volume). Invariably, T-cell ELISPOT assays provide a wide spectrum of spot sizes and morphologies, irrespective of whether T-cell clones or primary antigen-speciﬁc

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T-cell populations are studied, and of the analyte (3, 6). Thus, the question arises as to how to reliably establish spot counts, i.e., how to distinguish whether larger spots result from cell clustering, and how to determine the smallest spots which still are to be counted. Tremendous variations in counts can be seen in the absence of clear, unambiguous criteria for counting. Such criteria can be objectively established, however, by understanding the rules that underlie spot-size distributions. For T-cell clones, as well as primary T cells secreting various cytokines, it has been determined that the spot-size distribution always follows log-normal distribution (3, 6). Thus, proper analysis establishes the spot-size distribution for an assay and subjects the size distribution curve to statistical analysis, automatically setting the gates at 99.7% conﬁdence to establish upper and lower limits, respectively, for the largest and smallest spot sizes which still belong to that distribution. In this way, spots that exceed this size gate can be recognized as clusters, and spots that are smaller than the lower limit are gated out. Such statistical analysis permits objective, user-independent counting while spot counts established without such analysis are subjective, unreliable numbers contributing to “fuzzy in.” 1.1.4. Correct Spot Counts in Negative-Control Wells

ELISPOT assays aim at establishing frequencies of antigen-speciﬁc T cells, i.e., measuring antigen-induced spots over medium-control or irrelevant antigen. The negative-control wells, however, can also contain cytokine-secreting cells (which generate spots). Such background spots typically are produced by cells of the innate immune system which also secrete the cytokine in question. One of the primary challenges of ELISPOT analysis is to differentiate between such background spots and antigen-induced, T cell-derived spots. This frequently can be accomplished by size gating. T cells typically have a substantially higher cytokine secretion rate (resulting in lager spot-size distributions) than cells of the innate immune system (resulting in a smaller spot-size spectrum) (7). Thus, analogous to gating in ﬂow cytometry, the smaller spot-size distributions seen in the negative controls can be gated out, allowing for identiﬁcation of the larger, T cell-derived spots in the antigen-stimulated wells. ELISPOT gating can be done automatically (and thus, objectively) following statistical principles (for more on this topic, please refer to the Chapter 13 in this volume). Medium-control and antigenstimulated spot counts generated without such counting principles are literarily meaningless numbers (much like ungated ﬂow cytometry frequencies), and provide a “fuzzy in” for which subsequent statistical analysis cannot compensate.

1.1.5. Lowering the Medium Background: Thus Increasing Signal to Noise

ELISPOT assays, like most cellular assays, have traditionally been performed using culture media which contain serum. Serum, however, is a major assay variable. It contains cytokines that stimulate or suppress the test PBMC, many times resulting in an increased

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background spot production (triggering cytokine secretion by cells of the innate immune system) that is not necessarily accompanied by an increased antigen-speciﬁc response by T cells. Because the cytokines present in serum bind to high-afﬁnity receptors on PBMC, even brief exposure of PBMC to a stimulatory or suppressive serum during freezing/thawing or washing the PBMC can effectively ruin an ELISPOT assay. The use of serum-free media for freezing, washing, and testing is, therefore, highly recommended providing typically higher signal-to-noise ratios than even the “best” sera selected for this purpose (2). 1.2. Testing Antigenand Medium-Control Wells in Replicates

Because in PBMC, antigen-speciﬁc T cells typically occur in low frequencies, ELISPOT assays frequently need to detect small increases in antigen-induced spot counts versus the negative control. Therefore, to increase the statistical relevancy of the resulting spot counts, it has become convention to measure cytokine production in medium-control and antigen-containing wells in replicate wells, typically triplicates. After proper counting of replicate wells for each condition (assuring a “high resolution in” – see above), further analysis can be done to establish whether there are indeed more spots seen in the antigen-containing wells. Many times, the spot counts in replicate wells are consistent, and the differences between medium-control and antigen-stimulated wells are large – in such cases, the interpretation of the results is clear-cut. However, the interpretation of ELISPOT data can become ambiguous when (a) the spot numbers in antigen-induced wells are low, (b) spot counts in negative-control wells are elevated, and (c) when both of the former occur simultaneously. Here is where the grey area of ELISPOT data interpretation lies, with different solutions, mostly empirical, propagated to resolve the problem (8). Further, the choice of triplicates is entirely empirical, with its statistical foundation not having been sufﬁciently clearly delineated – more on this below.

1.3. Counting Apoptotic Cells in Addition to Live and Dead

Many times, immunizations cause only a moderate increase in the frequency of antigen-speciﬁc T cells. Such differences can be detected when comparing antigen-induced responses in preimmunization PBMC samples with PBMC obtained at various time points following immunization. Such longitudinal testing is mostly done with cells that have been stored/shipped for many hours, sometime days, before being frozen and/or tested. Protracted handling can damage the cells, resulting in increased numbers of dead as well as apoptotic cells. Standard Trypan Blue cell counting can discern between live and dead cells, but does not detect cells that have entered upon the irreversible path of apoptosis. Apoptotic cells are still alive, and are counted as such with Trypan Blue and other live/dead counting methods. Apoptotic T cells should be reckoned as effectively dead for functional assays, however, because they are refractive to antigen stimulation and will be dead before

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they could produce cytokine. ELISPOT assay harmonization panels have, therefore, repeatedly emphasized the need to count apoptotic cells, in addition to live and dead cells, and to correct the live count by subtracting the apoptotic count. Various dyes are available to stain apoptotic, live, and dead cells with different colors for visual counting using standard hemocytometers and UV microscopy or, more conveniently and precisely, by image analysis or ﬂow cytometry. 1.4. Empirical Versus Statistical Evaluation of Medium-Control Versus AntigenInduced Spot Numbers

A key issue in ELISPOT data evaluation is setting the threshold beyond which a response should be considered positive. Several different approaches are described in the literature which may be classiﬁed mainly into two different categories: empirical and statistical (8). Empirical approaches generally do not have sound theoretical justiﬁcations, but can rather be regarded as rules of thumb. Such commonly used approaches require an arbitrarily designated minimum difference between the mean spot counts for the antigencontaining wells and the negative-control wells, mostly medium. Other empirical rules demand the ratio of antigen-elicited spots to spot numbers in the medium control to exceed a certain value – here, often a twofold increase is considered to be the cutoff for a positive response. Various combinations of these rules have also been proposed, such as combining the minimum threshold R0 for spot count per 106 cells with a minimum threshold C0 ratio of antigen to control; responses exceeding these values are designated as positive, yet the two thresholds may be chosen in such a way as to obtain a false-positive rate <0.01 (9). The advantage of empirical rules is their simplicity. Their major drawback is their lack of reliability when it comes to detecting weak responses. Since they have no precise theoretical foundation, empirical rules have no claim to universality, since they do not rely on explicit model assumptions which could be tested for and thus ensure the general applicability of the proposed procedure. In contrast to empirical rules, statistical tests rely on a theoretical background. Here, the observed data (or a statistical result of the data, such as the mean) is tested for its compatibility with a (usually, purely random) null model (null hypothesis) to obtain a p-value. The p-value is consequently a measurement of the probability of the observed data under the chosen null model, where low p-values indicate evidence which is highly improbable to be observed under the null model and suggests a rejection of the null hypothesis. Thus, the p-value itself delivers a quantitative measurement of probability, and not a binary yes/no decision for the identiﬁcation of a positive response. A call for a positive response is then usually claimed when the p-value is below a certain threshold called the signiﬁcance level A, where values smaller than 0.05 or 0.01 are typically considered signiﬁcant. Hence, given the null hypothesis is

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true, the probability of Type I errors (false-positive calls) would be 5% (for p = 0.05) or 1% (for p = 0.01). Note that with this deﬁnition the p-value always needs to be interpreted in relation to the particular null model under consideration; in fact, it could also be regarded as a measurement of distance from a certain null model. Moreover, the null model often relies on speciﬁc assumptions, and the violation of this assumption leads to invalid results. Exactly this point is presently the bottleneck for scientiﬁcally validated statistical analyses of ELISPOT data, since no data or predictions are currently available regarding the distribution and inter-/independence of ELISPOT data points (spots). Since as of yet, distributional assumptions are not available for ELISPOT data evaluation, some researchers advocate the use of resampling-based statistical approaches that do not involve such assumptions (10). It allows to perform single or multiple testing corrections against a randomly permutated null distribution in one step. Simulation studies with data drawn from Poisson and Negative Binomial Distributions (which account for an overdispersion effect) have been conducted for various assays. The authors advocate the usage of permutation tests which require the generation of a background distribution using a large number (e.g., 10,000) of random permutations and comparison of the value of the observed test statistics from the real (unpermutated) data against this background distribution. An empirical p-value can then be computed as the fraction of test statistics with a value greater than or equal to the value from the permuted background dataset. To generate a sufﬁciently large background distribution (in the case of low replicate numbers), the permutations can be performed over all different antigens tested (10). A general problem with the global permutation approach is that the p-value of one antigen is dependent on the responses to other antigens, so the methods may fail to recognize a moderate response in the presence of a large response against one of the other antigens (11). Thus, the permutation of the controls and of each antigen separately has been proposed (8, 11). Generally, resampling-based techniques are rather computationally intensive (which, however, is not a great problem with modern computers) and often not as easily available to many experimental researchers as standard testing methods. Furthermore, when only a low number of replicates is available, as is typically the case in ELISPOT experiments, the p-value distribution can become “granular” and, due to the limited number of different permutations possible, it may become impossible to obtain p-values below a certain threshold. Empirical evaluation of ELIPSOT data or resampling-based statistics is a valid approach to ELISPOT analysis only when the distributional properties of spots are not known. Therefore, we set out to establish those experimentally, allowing for the introduction of assumption-based statistics.

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2. Materials For ELISPOT counting, and thus for generating the numeric data that can be subject to further statistical analysis, we recommend the ImmunoSpot Series 5 Instrumentation and Software by CTL, Shaker Hts, Ohio, because it offers user-independent, statisticsbased setting of counting parameters for spot recognition and gating. Serum-free media for freezing, thawing, and testing of PBMC, as well as PBMC reference samples with established antigenspeciﬁc response levels, are also available from CTL. 1. Regular T-Test statistics can be computed using standard spreadsheet software, such as Microsoft Excel which is included in the Microsoft Ofﬁce Suite. 2. For advanced statistical analysis of ELISPOT data (like ﬁtting linear models, testing distributional assumptions, or permutation tests), real statistical software packages are required. A prominent example is the freely available statistical software package R (12) which can be downloaded from http://cran.r-project.org/. This is a very powerful statistical computer language which is broadly extensible by a wide range of additional packages. To exploit the full power of R, some skills in computer programming are recommended. Similarly, the commercial MATLAB software (http://www.mathworks.com/) includes a statistics toolbox which may also be used for analyzing ELISPOT data. 3. Alternatively, other commercial statistical software packages featuring a graphical user interface are available, like GraphPad Prism® (http://www.graphpad.com/prism/prism.htm), SPSS (http://www.spss.com), SAS (http://www.sas.com), or Statistica (http://www.statsoft.com/).

3. Methods 3.1. Normal Distribution of ELISPOT Data for >30 Spots per Well

To address the distributional properties of ELISPOT data, we performed experiments with a transfected cell line that constitutively secretes IFN-G. Increasing numbers of such cells were plated into an IFN-G ELISPOT assay, with 192 replicate wells for each cell number, and the distribution of the spot counts was studied in detail. The results showed that, for lower number of cells (<20), the spot counts approximated the Poisson distribution closer than the normal distribution, whereas wells with spot counts higher than 30 gave an excellent ﬁt to the normal distribution (Fig. 1). Several statistical tests are available to verify whether the normality assumption holds. These include the Shapiro-Wilks, KolmogorovSmirnov, or the Anderson-Darling tests – the latter, in particular, is

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Fig. 1. Measured versus theoretical distribution of ELISPOT counts. IFN-G-transfected CHO cells were plated into an IFN-G ELISPOT assay at 15, 30, 60, and 120 cells per well, with 192 replicate wells per cell number, developed and counted with ImmonoSpot® Software. Distributions for spot counts are depicted for a Poisson distribution (dotted lines ) and a normal distribution (the solid line) with the means and a standard deviation corresponding to the square root of the means.

one of the most powerful tools for detecting most departures from normality (13). Applying the Anderson Darling test to the above data, we found no signiﬁcant deviation from the normal distribution for the set of wells with 30 (p-value 0.058), 60 (p-value 0.52), and 120 (p-value 0.23) cells per well. For the wells with 15 cytokinesecreting cells, we found only a slight deviation from the normal distribution (p-value 0.02). Therefore, we conclude that for ELISPOT data in the range of >15 spot counts per well a normal distribution of the data can safely be assumed, and statistical tests which require normally distributed data should be applicable. Thus, the T-Test is well-suited for the statistical analysis of such data, and also all T-Test-related statistics used in linear models and, in particular, the Analysis of Variance (ANOVA), should apply. 3.2. Poisson Distribution of ELISPOT Data with 15 Spots or Fewer per Well

Since optimized ELISPOT assays measure absolute numbers of secreting cells, they produce real counts of individual cells, and not semiquantitative data or ratios. Thus, one could expect ELISPOT data to follow a Poisson distribution. Indeed, analyzing the ELISPOT counts from our transfected cell line, we found no

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Fig. 2. Overdispersion of ELISPOT count data. Left panel: Simulated count data drawn from a Poisson distribution with mean parameter lambda for 15, 30, 60, and 120 cells per well (n = 192 each) exhibit a variance equal to the mean as expected. Right panel : Real spot count data obtained with the transfected cell lines display a classical overdispersion effect, that is, the variance increases with the mean.

signiﬁcant deviation from the Poisson distribution at low counts (15 spots) per well. In the low-count range (<15 spots), ELISPOT data should thus be assumed to have a Poisson distribution. In contrast to the normal distribution, the Poisson distribution supports only positive values and is skewed rightward in the range of low intensities, i.e., small mean values (especially in the range <5). For higher mean values, however, the Poisson distribution becomes more symmetric and approximates a normal distribution (Fig. 1). Furthermore, it should be noted that for Poisson-distributed data there is a coupling between the mean and the variance (with variance = mean) as the Poisson distribution is characterized by only a single parameter. In effect, this means that the variance increases proportionally as the mean increases, meaning that the ratio of the variance to the mean is the same for any response intensity (Fig. 2; left side). As is the case with real count data from many different areas, one can also observe an overly large variance with increasing mean values for ELISPOT count data, an effect called overdispersion (Fig. 2; right panel). The use of the Negative Binomial Distribution has been proposed to model count data for ELISPOT counts (10). This distribution can be regarded as an extension of the Poisson distribution. It has two parameters which allow modeling of both the variance and the mean, and it is thus able to handle the overdispersion problem. A common used remedy in cases of assumed deviations from the normal distributions is the usage of nonparametric tests, such as the well-known Wilcoxon Rank Sum Test. This might entail

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a loss of power in case of normally distributed data, but can provide an improved power over the T-Test in cases of considerable deviation from the normal distribution. A further concern is that data originating from two different distributions need to be compared when the medium background falls into the <20 spots category while the antigen-induced spots are >30 (see Note 1). 3.3. The Pragmatic Solution: The Applicability of the T-Test for ELISPOT Data Analysis

For pragmatic purposes, it is important to know how the Poisson distribution of low-count data affects the performance of the T-Test (which requires normally distributed data) in the low-count range. Our data from simulations indicate that even for Poissondistributed data in the low-count range, no increased number of false positives for a signiﬁcance level of 0.05 is to be expected. However, the T-Test may fail to identify weak responses. The extent to which the power of the T-Test is affected by a Poisson distribution in the low-signal range needs further investigation. Increasing the number of replicates in any case increases the probability of detecting weak responses. The triplicates typically used are certainly a minimum. The determination of a reasonable number of replicates, however, depends on several variables and often requires an advanced statistical power analysis for a particular experimental setup and expected effect size (see Note 2). In conclusion, the usage of empirical rules, such as using fold changes with a ﬁxed threshold, should in general be discouraged. Albeit enjoying some popularity, such empirical rules do not incorporate any information about variance, and thus provide no measurement of conﬁdence. Instead, the application of solid statistical tests is highly recommended. The question of which statistical tests are best suited for ELISPOT evaluation is an important one, and is certainly not completely resolved (see Note 3). Our results from transfected cell lines indeed suggest a Poisson distribution, but only for data in the low-count range. For spot counts >15, no signiﬁcant deviation from the normal distribution could be observed, indicating that spot counts in that range can be assumed to be normally distributed. This implies that most standard statistical tests, which are valid for normally distributed data, like the T-Test and ANOVA models, are applicable in this setting. In any case, more investigations (experimental data as well as computer simulations) are needed not only to assess potential effects of deviations from normality in the low spot-count range, but also to develop novel methods for the analysis of ELISPOT data.

4. Notes 1. Although being essentially a nonparametric test, the Wilcoxon Rank Sum Test requires the data to be independent and identically distributed (iid). While the ﬁrst assumption of independence

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is not affected, the second assumption of identical distributions is obviously violated. Whether this has practical implications for the statistical analysis of ELISPOT data has not yet been investigated, and still requires more in-depth research. 2. On a practical note, it is recommended to perform all tests using at least triplicates, and if the results fall along the borderline, to retest cryopreserved aliquots at higher cell numbers, and with more replicates. 3. An important aspect here is the understanding of the distribution of ELISPOT data. Since the assay provides counts of individual antigen-speciﬁc T cells that are randomly distributed during pipetting into the wells, on theoretical grounds, one might expect ELISPOT data to follow a Poisson distribution. References 1. Forsthuber, T., Yip, H. C., and Lehmann, P. V. (1996) Induction of TH1 and TH2 immunity in neonatal mice. Science 271, 1728–1730. 2. Zhang, W., Caspell, R., Karulin, A. Y., Ahmad, M., Haicheur, N., Abdelsalam, A., et al. (2009) ELISPOT assays provide reproducible results among different laboratories for T-cell immune monitoring – even in hands of ELISPOTinexperienced investigators. J Immunotoxicol 6, 227–234. 3. Hesse, M. D., Karulin, A. Y., Boehm, B. O., Lehmann, P. V., and Tary-Lehmann, M. (2001) A T cell clone’s avidity is a function of its activation state. J Immunol 167, 1353–1361. 4. Kreher, C. R., Dittrich, M. T., Guerkov, R., Boehm, B. O., and Tary-Lehmann, M. (2003) CD4+ and CD8+ cells in cryopreserved human PBMC maintain full functionality in cytokine ELISPOT assays, J Immunol Methods 278, 79–93. 5. Ott, P. A., Tary-Lehmann, M., and Lehmann, P. V. (2007) The secretory IFN-gamma response of single CD4 memory cells after activation on different antigen presenting cell types, Clin Immunol 124, 267–276. 6. Nowacki, T. M., Kuerten, S., Zhang, W., Shive, C. L., Kreher, C. R., Boehm, B. O., et al. (2007) Granzyme B production distinguishes recently activated CD8(+) memory cells from resting memory cells. Cell Immunol 247, 36–48. 7. Guerkov, R. E., Targoni, O. S., Kreher, C. R., Boehm, B. O., Herrera, M. T., Tary-Lehmann, M., et al. (2003) Detection of low-frequency

antigen-speciﬁc IL-10-producing CD4(+) T cells via ELISPOT in PBMC: cognate vs. nonspeciﬁc production of the cytokine. J Immunol Methods 279, 111–121. 8. Moodie, Z., Price, L., Gouttefangeas, C., Mander, A., Janetzki, S., Lower, M., et al. (2010) Response deﬁnition criteria for ELISPOT assays revisited. Cancer Immunol Immunother 59, 1489–1501. 9. Dubey, S., Clair, J., Fu, T. M., Guan, L., Long, R., Mogg, R., et al. (2007) Detection of HIV vaccine-induced cell-mediated immunity in HIV-seronegative clinical trial participants using an optimized and validated enzymelinked immunospot assay. J Acquir Immune Deﬁc Syndr 45, 20–27. 10. Hudgens, M. G., Self, S. G., Chiu, Y. L., Russell, N. D., Horton, H., and McElrath, M. J. (2004) Statistical considerations for the design and analysis of the ELISpot assay in HIV-1 vaccine trials. J Immunol Methods 288, 19–34. 11. Moodie, Z., Huang, Y., Gu, L., Hural, J., and Self, S. G. (2006) Statistical positivity criteria for the analysis of ELISpot assay data in HIV-1 vaccine trials. J Immunol Methods 315, 121–132. 12. R Development Core Team. (2009) R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. 13. Anderson, T. W., and Darling, D. A. (1952) Asymptotic Theory of Certain Goodness of Fit Criteria Based on Stochastic Processes. Ann Math Stat 23, 193–212.

Chapter 15 Response Determination Criteria for ELISPOT: Toward a Standard that Can Be Applied Across Laboratories Zoe Moodie, Leah Price, Sylvia Janetzki, and Cedrik M. Britten Abstract ELISPOT assay readout is often dichomized as positive or negative responses according to prespeciﬁed criteria. However, these criteria can vary widely across institutions. The adoption of a common response criterion is a key step toward cross-laboratory comparability. This chapter describes the two main approaches to response determination, identifying the strengths and limitations of each. Nonparametric statistical tests and consideration of data quality are recommended and instructions provided for their ready implementation by nonstatisticians and statisticians alike. Key words: ELISPOT assay, Positive response criteria, Harmonization, Replicate variation, Statistical hypothesis test

1. Introduction Since its introduction more than 20 years ago (1), the ELISPOT assay has been widely used to measure IFN-G production by individual antigen-speciﬁc T lymphocytes in a variety of disease settings, including cancer immunotherapy, HIV vaccine, infectious disease, and autoimmunity research (2–8). With such widespread use of the ELISPOT assay, it is often of interest to combine data across multiple laboratories or to compare these data formally or informally. This can be challenging on several levels as laboratories vary in their standard operating procedures resulting in different levels of variability across institutions (9–12). In addition, cellular assays are intrinsically complex and hence variability can be difﬁcult to control even within one center (13). The variability is inﬂuenced by several steps in the process, including (1) the cell material that

Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7_15, © Springer Science+Business Media, LLC 2012

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goes into the assay (14), (2) the materials, reagents, and protocol of the assay itself (15), (3) the hard- and software and chosen settings for spot acquisition (16, 17), and ﬁnally (4) the rules and/ or tests applied to analyze and interpret the raw data (15, 18, 19). This chapter focuses on the variation of the results due to the use of different approaches to determine whether a given set of raw data, i.e., the # of spot-forming cells (SFCs)/PBMCs, is considered a positive antigen-speciﬁc T-cell response. ELISPOT data are often ﬁrst summarized in terms of the number of positive responders, hence various criteria to establish a positive response have been proposed, typically by comparing the data in the antigen-stimulated wells to the data in the negative-control wells. However, the various proposed methods can differ widely in their resulting positivity calls and no commonly accepted standard exists (2, 3, 10, 19–22). Applying a common response determination criterion across laboratories would represent an important step toward reducing cross-laboratory variability. This chapter reviews many of the commonly used methods for ELISPOT response determination and motivates the use of nonparametric statistical tests based on example data (see also Chapters 13 and 14 in this volume). An explicit example of how to calculate responses with these methods using an existing Web tool is provided in the Subheading 4.

2. Materials The data example considered is based on data from three consecutive interlaboratory testing projects organized by the Cancer Immunotherapy Immunoguiding Program (CIP). In these studies, 11, 13, and 16 laboratories (phases I, II, and III, respectively) quantiﬁed the number of CD8+ T cells speciﬁc for two model antigens with PBMC samples that were centrally prepared and then distributed to the participating laboratories. All participants used their preferred ELISPOT protocol, and therefore the datasets generated in these studies can be considered representative of results generated by a wide range of different protocols commonly applied within Europe. Each laboratory was asked to test in triplicate 18 preselected donors (5 in the phase I, 8 in the phase II, and 5 in the phase III) with two synthetic peptides (HLA-A*0201 restricted epitopes of CMV and inﬂuenza) as well as PBMC in medium alone for background determination. The selection of the donors was such that 21 donor/antigen combinations (6 in the ﬁrst phase, 8 in the second phase, and 7 in the third phase) were expected to demonstrate a positive response with the remaining 15 donor/ antigen combinations not expected to demonstrate a positive response. Pretesting of potential donor samples for the proﬁciency

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panels was performed at two time points by two independent labs. Only samples from donors that had consistent results in all four performed experiments were selected for use in the proﬁciency panels.

3. Methods The two principal approaches of determining a positive response in ELISPOT assay data are empirical and statistical. 3.1. Empirical Approach

The empirical approach typically employs a threshold for the difference between the mean spot counts in the antigen-stimulated experimental wells and those in the negative-control wells and/or an x-fold change between the means of the experimental and negative-control wells. These thresholds are meant to represent biological signiﬁcance and may be laboratory-, procedure-, and operator-dependent. Ideally, empirical criteria should be based on an independent, blinded study of known positive and negative samples to determine operating characteristics, such as the true positive and true negative rates (i.e., sensitivity and speciﬁcity), as was done in Dubey et al. (20). It can be challenging to compare or combine responses across laboratories using different thresholds for positivity if the laboratories have diverse standards for establishing their respective criteria. Since empirical criteria are typically based on absolute or x-fold differences in mean responses, they therefore ignore the inherent variability in the data and also fail to detect extreme outliers. Mean responses that are derived from replicates that are highly variable (Scenario 1 in Table 1) provide much less-convincing evidence of a positive response than the means derived from replicates with very little variability (Scenario 2 in Table 1), although the empirical rule would declare both to be positive responses. Conversely, differences that fall just below the threshold for positivity may be very convincing if the variability is extremely small (Scenario 3 in Table 1), but would not be considered a positive response by the empirical criteria. An additional drawback to the empirical approach is that the operating characteristics depend on how closely the datasets used to deﬁne the thresholds resemble the data to which the thresholds are applied. If the standard operating procedures of the assay change over time or if there are subtle shifts in the background of the assay over time, the thresholds may need to be redeﬁned based on a new, blinded study of known positive and negative samples tested under the new conditions. Empirical criteria also do not offer uniform control of the false-positive rate when testing multiple antigens within the same sample, as is commonly the case.

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Table 1 Data examples to illustrate how empirical criteria ignore between-replicate variability Negative-control wells

Experimental wells

C1

C2

C3

C4

C5

C6

A1

A2

A3

Scenario 1

8

22

19

1

2

8

500

2

9

Scenario 2

8

9

10

11

10

12

170

172

168

Scenario 3

0

1

1

0

0

1

10

11

10

Scenario 4

1

0

0

1

0

0

6

6

6

In Scenarios 1 and 2, the mean difference of antigen-stimulated wells (A1–A3) and negative-control wells (C1–C6) is the same. Between-replicate variability is high in Scenario 1 and low in Scenario 2, yet data from both scenarios would be considered positive by many empirical criteria (mean difference = 160, fold difference = 17). Scenario 3 might fail empirical criteria (mean difference = 9.8, fold difference = 20.7), yet variability is very low and antigen-stimulated responses may appear convincing to many investigators. Scenario 4 might not be considered positive if the limit of detection of the assay is 6

3.2. Statistical Approaches

The statistical approach employs a hypothesis test to formally compare the antigen-stimulated and negative-control well replicates. The quantitative responses (i.e., # SFC/PBMC) are then evaluated to determine whether the statistically signiﬁcant differences are of scientiﬁc relevance, e.g., above the limit of detection of the assay. Various statistical tests for ELISPOT response determination have been recently proposed (e.g., 19, 21, 22). The t-test is also commonly used (2, 3, 23), as many investigators are familiar with the test despite its strong assumptions in this setting (19). Nonparametric methods are better suited to settings, such as ELISPOT, where the sample sizes are small (e.g., triplicate wells) and the distribution of SFCs is unknown. The distribution-free resampling (DFR) methods described in 22 and 19 are examples of nonparametric methods (see Note 1). The DFR(eq) criterion tests a null hypothesis of equal background, and experimental well means using a permutation test with Westfall-Young Stepdown max T adjustment for multiple testing across the different antigens or peptides considered (24). The DFR(2×) criterion tests a null hypothesis that the mean of the experimental wells is less than or equal to twice the mean of the background wells using a bootstrap test with the same multiplicity adjustment. As details of these methods can be found in the original references, this chapter focuses on the implementation of these methods and recommendations about which method is most applicable in a given setting. The DFR(eq) method is most appropriate in settings, where a signiﬁcant difference in experimental and background well means

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is believed to indicate a positive response and a false-positive rate of 5% is acceptable. In settings, where one wants to control the falsepositive rate at a lower level and a larger signiﬁcant mean difference is required to indicate a response (e.g., twofold over background), one might prefer DFR(2×). For example, a background-corrected mean of 20 per million PMBC is less striking when the mean background is 100 per million PBMC than when the mean background is 2 and the experimental mean is 22 per million PBMC. It is straightforward to modify the method to test for a different fold difference. The DFR methods are based on nonparametric testing at the antigen/peptide pool level, and hence require a minimum number of replicates to ensure adequate power: at least three triplicates for experimental and negative-control wells or at least two experimental and four negative-control wells. Power and sensitivity increase with the number of replicate wells: three experimental wells and six negative control wells or four and four are the recommended minimum number of replicates based on simulation studies (19, 21, 22). 3.3. Data Quality Considerations

While response determination is an important element in the analysis of ELISPOT data, the quality of the data prior to response determination is also important to consider. Variability of the data is taken into account by the statistical test, but should also be considered as an element of quality control at the laboratory level prior to determining positive responses. In addition, the minimum level of response or limit of detection of the assay should be understood to aid in interpreting the scientiﬁc relevance of samples deemed positive by the response criteria. To ensure adequate data quality, a variance ﬁlter for “extreme” outliers is suggested, whereby experimental replicates that do not pass the ﬁlter should be rerun or excluded. We recommend using the ratio of the variance to median + 1 as a measure of the variability of the spot counts within a replicate. The ﬁlter is designed to identify samples, where the majority of responses are low (hence, the median is small) but the variance is large due to an extreme outlier (see Note 2). Data (see Note 3) may pass the proposed variance ﬁlter, be statistically signiﬁcant yet the level of response be less than the lower limit of detection of the assay. For example, consider the data in Scenario 4 of Table 1. The variance ratio is 0 and the sample is considered a positive response by both DFR(eq) and DFR(2×) due to the clear signal in the experimental wells yet the level of response may be too low to be considered a positive response. Before claiming that a statistical signiﬁcant result is a positive response, the limit of detection of the ELISPOT assay should be taken into consideration. The international conference on harmonization of technical requirements for registration of pharmaceuticals for human use

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(ICH) produced a guideline, Q2R1, on the validation of analytic procedures (http://www.ich.org/LOB/media/MEDIA417.pdf). The limit of detection is deﬁned there as the lowest amount of analyte in a sample that can be detected but not necessarily quantiﬁed as an exact value. The guideline describes three methods to estimate the limit of detection: visual evaluation, signal to noise, and response based on standard deviation and slope. The signal-tonoise method best applies to the ELISPOT assay, where spot counts from the experimental wells are the signal and spot counts from the negative-control or background wells are the noise. A signal-to-noise ratio of 2:1 or 3:1 is generally considered acceptable for estimating the limit of detection. This guideline was applied to estimate the limit of detection across multiple laboratories following a broad range of representative ELISPOT protocols (19). As a typical ELISPOT protocol in these studies lead to a background of approximately 2 SFCs/100,000 PBMCs, the limit of detection for a laboratory showing average performance was estimated to be 4–6 SFCs/100,000 PBMCs. Hence, one might recommend that a response should not be considered positive if the mean of the experimental wells is less than the limit of detection even if the DFR methods declare a positive response. Laboratories may greatly differ in their limit of detection, however, and therefore the limit of detection should be established within each laboratory based on reliable datasets. This can then be used to guide the threshold for the minimum level of a positive response. To demonstrate the large differences in results when using different methods to determine a response, we applied various methods to ELISPOT data described in the Materials section. Ten commonly used empirical rules and three statistical methods were applied to these data and the response detection rate and falsepositive rates reported in Table 2. A positive response was expected in 282 donor/antigen combinations and a negative response was expected in 196 such combinations. As is clearly demonstrated in Table 2, the response detection rates and the false-positive rates can vary greatly depending on which response determination rule is used. With the empirical rules, the larger the required fold difference over background, the lower the overall response detection rate (74% for YE 2YC , 66% for YE 3YC , and 59% for YE 4YC ) but the lower the false positive rate (17% for YE 2YC , 9% for YE 3YC , and 7% for YE 4YC ). Similarly, the larger the required minimum antigen spot count, the lower the response detection rate but the lower the false positive rate. These response detection rates are from a large group of laboratories with heterogeneous ELISPOT protocols, and therefore there is no prior experience as to which empirical rule would be the most appropriate to use. Examining the different empirical rules, it is not immediately clear which rule should be applied for response determination.

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Table 2 Response detection and false-positive rates for each method based on data from 19 centers and three proﬁciency panel phases Overall (no ﬁlters)

Variance ratio and LLD ﬁlters applied

Response Response detection False-positive Response detection False-positive determination method rate N = 282 rate N = 196 rate N = 275 rate N = 196 YE 2YC

74%

17%

57%

1%

YE 2YC and

59%

3%

57%

1%

49%

1%

49%

1%

YE 3YC

66%

9%

52%

0%

YE 3YC and

54%

1%

52%

0%

45%

0%

46%

0%

YE 4YC

59%

7%

48%

0%

YE 4YC and

49%

1%

48%

0%

42%

0%

43%

0%

49%

1%

49%

0%

T-test

76%

10%

62%

3%

DFR(eq) (22)

75%

11%

60%

3%

DFR(2×) (19)

61%

2%

51%

0%

YE 5 / 100, 000 YE 2YC and YE 10 / 100, 000

YE 5 / 100, 000 YE 3YC and YE 10 / 100, 000

YE 5 / 100, 000 YE 4YC and YE 10 / 100, 000 YE q 4YC and YE q 5.5 / 100, 000

(20)

Empirical rules are listed in the ﬁrst ten rows, where YE denotes the mean number of spot-forming cells/100,000 PBMCs in the experimental wells and YC denotes the mean number of spot-forming cells/100,000 PBMCs in the negative-control wells (“background”). Statistical methods are listed in the last three rows. In 20 samples, a response determination could not be made with the DFR methods due to some laboratories having performed only duplicate experimental or negative-control conditions

All three of the statistical tests make a response determination using a p-value cutoff of 0.05. This implies that on average we would expect a 5% false-positive rate. In fact, the false-positive rate in this dataset is larger than 5% for both the t-test and DFR(eq) tests, but is smaller than 5% for the DFR(2×) test. When using the ﬁlters (last columns of Table 2), the false-positive rates for all methods are less than 5%. The table illustrates how results can differ dramatically depending on which method is used for response determination

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although greater agreement between methods is seen when the ﬁlters are applied. The analysis underlines the need for standardization of the response determination process. This chapter reviews objective methods to determine a positive response for ELISPOT assay data. The two main approaches, empirical and statistical, are reviewed and illustrated using an example dataset. The main advantages of empirical criteria are that they are generally intuitive and easy to implement. The main drawbacks are that they do not account for the variability inherent in the data and they do not control the overall false-positive rate when multiple antigens are used, as is common practice. To appropriately justify the thresholds selected for empirical criteria, a laboratory would need to determine the sensitivity and speciﬁcity of these for a given assay protocol based on large representative datasets with known responder status (known positive and negative donors). The main advantage of the statistical approach is that it can be applied with little prior knowledge of the operating characteristics of the assay protocol, therefore across a wide variety of settings, provided the assumptions of the statistical test are valid. In addition, statistical tests allow control of the false-positive rate as well as the overall falsepositive rate if multiple antigens are considered. The t-test requires parametric assumptions about the distribution of the data or a much larger sample size (rule of thumb: n > 30) than is practical for the number of replicate wells in the ELISPOT assay. A nonparametric test, such as used by the DFR(eq) or DFR(2×), is recommended. Both of these methods control the overall false-positive rate when testing multiple antigens and avoid parametric assumptions about the data. In both empirical and statistical approaches, responses are determined by comparing experimental and negative-control well data. The background values for spot production can differ between donors and across time both within and across donors. Increases in the background spot counts inﬂuence the sensitivity of the method. Adding more replicate wells for both control and experimental conditions increases the power of the test. If resources are limited, increasing the number of negative-control wells results in appreciable gains (21, 22), particularly when many antigens are tested since all antigens are compared to the same control wells within a donor. Six control wells are recommended whenever possible, with three experimental wells. Alternatively, four experimental and four control wells showed similar power to detect responses (21). Prior to applying a statistical test, it is recommended that experimental replicates with large variance ratio, deﬁned as the sample variance of the replicates divided by the median + 1, be excluded and/or rerun. The threshold for this should be based on laboratory experience. Replicates with a large variance ratio are likely to be errors and should not be considered reliable. If responses are expected to be large, a less-strict variance ratio may be used (19).

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Responses that are declared positive by the statistical methods should be further examined for their relevance. For example, responses deemed positive may have net differences (i.e., mean experimental − mean negative control) that fall below the limit of detection of the assay although they are statistically signiﬁcantly different. These should be interpreted with caution, thus some investigators might introduce a minimum threshold spot count below which results are considered negative. In summary, there is solid statistical and empirical evidence to support the use of statistical hypothesis testing methods to determine ELISPOT response. Hence, a nonparametric method is recommended, such as the DFR(eq) or DFR(2×). The DFR(eq) method is preferred in settings, where a 5% false-positive rate is acceptable and one wants to detect low to moderate response magnitudes regardless of the level of background. The DFR(2×) method is appropriate in settings, where one wants more stringent control of the false-positive rate, e.g., 1%, and/or more stringent evidence of a fold difference in the means of the experimental and negative-control wells is more of interest than mere equality. Several user-friendly options are available to implement the recommended DFR statistical tests. The original R code is available at http://www.scharp.org/zoe/runDFR for download along with a link to download the freely available R software. A Web tool is also available at that address to upload a data ﬁle and run the code directly from the Web site; instructions are provided in the Notes. In addition, an Excel macro has also been developed to implement the statistical methods with Excel, available upon request.

4. Notes 1. Response deﬁnition by the two DFR methods can be done by using the Web tool found at http://www.scharp.org/zoe/ runDFR. It is imperative that the data ﬁle be correctly formatted as speciﬁed on the Web site. Namely, column 1 must contain the numeric participant identiﬁer with no dashes (e.g., “id”), column 2 must specify the number of the visit (e.g., “day”) enter 1 if participant’s samples are from the same day, and column 3 must list the antigen name (be it peptide, peptide pool, protein, or gene) used to stimulate the cells. The next columns correspond to the numeric responses in each well. Missing data should be entered as “NA” or a blank cell. The data ﬁle should be saved as a comma-separated values (.csv) ﬁle. This can be done in Excel by saving the ﬁle as a .csv-type ﬁle (when saving the ﬁle, .csv is listed in the pull-down menu). Further instructions can be found in the Supplemental Materials in our previous publication (19).

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Table 3 Example data for web tool at http://www.scharp.org/zoe/run DFR ID

Day Antigen Well 1 Well 2 Well 3 Well 4 Well 5 Well 6

14552

1

Negctl

14552

1

14552

9

4

9

Tyr

27

25

21

1

Flu

14

5

9

14559

1

Negctl

5

4

9

14559

1

Tyr

31

33

32

14559

1

Flu

12

7

NA

2

6

7

7

8

5

2. One is added to the median in the denominator of the variance ﬁlter to avoid division by a zero median. Based on an analysis of large datasets from 19 laboratories in the CIP, 5% of replicates had a variance ratio >10. Each laboratory should determine an acceptable threshold for the variance ratio based on their data. Using 10 as the threshold for the variance ﬁlter, the experimental well responses (10, 10, 50) would fail as the variance-to-median ratio is 48. 3. An example of correctly formatted data is provided in Table 3. The “id” column is all numeric (no character or dashes), the “day” column is all numeric, “antigen” column contains consistently labeled antigen names, and the fourth through ninth columns contain the spot counts in the wells with missing data denoted as NA. Once the data have been saved as a .csv ﬁle, point the browser to http://www.scharp.org/zoe/runDFR/ and enter the relevant information: Number of antigens: 2. Number of experimental wells: 3. Number of control wells: 6. Name of negative control: negctl. Submit your .csv data ﬁle here (view an example): myﬁle.csv ﬁle created as described above. Click on Run Me icon and wait for output to print on screen. The results may also be downloaded as a .csv ﬁle. DFR(eq) positivity calls are listed in the “DFR(eq) response” column and DFR(2×) calls in the “DFR(2×) response” column. The corresponding p-values are listed in the respective adjusted p-value (adjp) columns.

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References 1. Czerkinsky, C., Andersson, G., Ekre, H.P., Nilsson, L.A., Klareskog, L., and Ouchterlony, O. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of gamma-interferon-secreting cells. J Immunol Methods 110:29–36 2. Herr, W., Schneider, J., Lohse, A.W., Meyer zum Buschenfelde, K., and Wolfel, T. (1996) Detection and quantiﬁcation of blood-derived CD8+ T lymphocytes secreting tumor necrosis factor a in response to HLA-A2.1-binding melanoma and viral peptide antigens. J Immunol Methods 191:131–142 3. Herr, W., Protzer, U., Lohse, A.W., Gerken, G., Meyer zum Buschenfelde, K.H., and Wolfel, T. (1998) Quantiﬁcation of CD8+ T lymphocytes responsive to human immunodeﬁciency virus (HIV) peptide antigens in HIV-infected patients and seronegative persons at high risk for recent HIV exposure. J Infect Dis 178:260–265 4. Smith, S.G., Joosten. S.A., Verscheure, V., Pathan, A.A., McShane, H., Ottenhoff, T.H., et al. (2009) Identiﬁcation of major factors inﬂuencing ELISpot-based monitoring of cellular responses to antigens from Mycobacterium tuberculosis. PLoS ONE 4(11): e7972. doi:10.1371/journal.pone.0007972 5. Schloot, N.C., Meierhoff, G., Karlsson, F.M., Ott, P., Putnam, A.,and Lehmann, P., et al. (2003) Comparison of cytokine ELISpot assay formats for the detection of islet antigen autoreactive T cells. Report of the third immunology of diabetes society T-cell workshop. J Autoimmun 21:365–376 6. Asai, T., Storkus, W.J. and Whiteside, T.L. (2000) Evaluation of the modiﬁed ELISPOT assay for gamma interferon production in cancer patients receiving antitumor vaccines. Clin Diagn Lab Immunol 7:145–154 7. Cox, J.H., Ferrari, G., and Janetzki, S. (2006) Measurement of cytokine release at the single cell level using the ELISPOT assay. Methods 38:274–282 8. Speiser, D.E., Pittet, M.J., Guillaume, P., Lubenow, N., Hoffman, E., Cerottini, J.C., et al. (2004) Ex vivo analysis of human antigen-speciﬁc CD8+ T-cell responses: quality assessment of ﬂuorescent HLA-A2 multimer and interferon-gamma ELISPOT assays for patient immune monitoring. J Immunother 27:298–308 9. Scheibenbogen, C., Romero, P., Rivoltini, L., Herr, W., Schmittel, A., Cerottini, J.C., et al. (2000) Quantitation of antigen-reactive T cells in peripheral blood by IFNgamma-ELISPOT

assay and chromium-release assay: a four-centre comparative trial. J Immunol Methods 20;244: 81–89 10. Janetzki, S., Panageas, K.S., Ben-Porat, L., Boyer, J., Britten, C.M., Clay, T.M., et al. (2008) Results and harmonization guidelines from two large-scale international Elispot proﬁciency panels conducted by the Cancer Vaccine Consortium (CVC/SVI). Cancer Immunol Immunother 57:303–315 11. Britten, C.M., Gouttefangeas, C., Welters, M.J., Pawelec, G., Koch, S., Ottensmeier, C., et al. (2008) The CIMT-monitoring panel: a two-step approach to harmonize the enumeration of antigen-speciﬁc CD8+ T lymphocytes by structural and functional assays. Cancer Immunol Immunother 57:289–302 12. Cox, J.H., Ferrari, G., Kalams, S.A., Lopaczynski, W., Oden, N., and D’Souza, M.P. (2005) Results of an ELISPOT proﬁciency panel conducted in 11 laboratories participating in international human immunodeﬁciency virus type 1 vaccine trials. AIDS Res Hum Retroviruses 21:68–81 13. Janetzki, S., Britten, C.M., Kalos, M., Levitsky, H.I., Maecker, H.T., Melief, C.J., et al. (2009) “MIATA”-minimal information about T cell assays. Immunity 31:527–528 14. Smith, J.G., Joseph, H.R., Green, T., Field, J.A., Wooters, M., Kaufhold, R.M., et al (2007) Establishing acceptance criteria for cellmediated-immunity assays using frozen peripheral blood mononuclear cells stored under optimal and suboptimal conditions. Clin Vaccine Immunol 14(5):527–37 15. Janetzki, S., Cox, J.H., Oden, N., Ferrari, G. (2005) Standardization and validation issues of the ELISPOT assay. Methods Mol Biol 302:51–86 16. Janetzki, S., Schaed, S., Blachere, N.E., BenPorat, L., Houghton, A.N., Panageas, K.S. (2004) Evaluation of Elispot assays: inﬂuence of method and operator on variability of results. J Immunol Methods 291(1–2): 175–83 17. Ryan, J.E., Ovsyannikova, I.G., Dhiman, N., Pinsky, N.A., Vierkant, R.A., Jacobson, R.M., et al. (2005) Inter-operator variation in ELISPOT analysis of measles virus-speciﬁc IFN-gamma-secreting T cells. Scand J Clin Lab Invest 65(8):681–9 18. Cox, J.H., Ferrari, G., Kalams, S.A., Lopaczynski, W., Oden, N. and D’Souza, M.P. (2005) Results of an ELISPOT proﬁciency panel conducted in 11 laboratories participating in international human immunodeﬁciency

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virus type 1 vaccine trials. AIDS Res Hum Retroviruses 21(1):68–81 19. Moodie, Z., Price, L., Gouttefangeas, C., Mander, A., Janetzki, S., Lower, M., et al. (2010) Response deﬁnition criteria for ELISPOT assays revisited. Cancer Immunol Immunother 59(10):1489–501 20. Dubey, S., Clair, J., Fu, T.M., Guan, L., Long, R., Mogg, R., et al. (2007) Detection of HIV vaccine-induced cell-mediated immunity in HIV-seronegative clinical trial participants using an optimized and validated enzymelinked immunospot assay. J Acquir Immune Deﬁc Syndr 45:20–27 21. Hudgens, M.G., Self, S.G., Chiu, Y.L., Russell, N.D., Horton, H. and McElrath, M.J. (2004) Statistical considerations for the design and

analysis of the ELISpot assay in HIV-1 vaccine trials. J Immunol Methods 288:19–34 22. Moodie, Z., Huang, Y., Gu, L., Hural, J. and Self, S.G. (2006) Statistical positivity criteria for the analysis of ELISpot assay data in HIV-1 vaccine trials. J Immunol Methods 315:121–132 23. Herr, W., Linn, B., Leister, N., Wandel, E., Meyer zum Buschenfelde, K., and Wolfel, T. (1997) The use of computer-assisted video image analysis for the quantiﬁcation of CD8+ T lymphocytes producing tumor necrosis factor spots in response to peptide antigens. J Immunol Methods 203:141–152 24. Westfall, P.H. and Young, S.S. (1993) Resampling-based multiple testing: examples and methods for p-value adjustment. John Wiley and Sons, New York

Part V ELISPOT Assay for Vaccine Development and Diagnostics

Chapter 16 Detection of Vaccinia Virus-Speciﬁc IFNg and IL-10 Secretion from Human PBMCs and CD8+ T Cells by ELISPOT Benjamin J. Umlauf, Norman A. Pinsky, Inna G. Ovsyannikova, and Gregory A. Poland Abstract High-throughput in vitro assays, which rapidly and succinctly assess the immune status of large cohorts of individuals, are essential tools for conducting population-based studies, including vaccine research. The enzyme-linked immunospot (ELISPOT) assay has emerged as a sensitive, reliable high-throughput tool to measure functional recall immunity by assessing the frequency of antigen-speciﬁc cytokine-secreting lymphocytes present in peripheral blood mononuclear cells (PBMCs). For the past 10 years, ELISPOT method has been the dominant platform and a standard for the cell-mediated immune (CMI) assays. ELISPOT assays are used extensively as a measure of CMI response to vaccines, including smallpox (vaccinia), following primary or secondary vaccination. Here, we present detailed methodology for using ELISPOT assays to detect the frequency of cytokine secreting vaccinia-speciﬁc lymphocytes including optimized protocols for growing, titrating, and inactivating vaccinia virus; isolating, cryopreserving, and thawing human PBMCs; and ﬁnally, detecting vaccinia-speciﬁc IL-10 and IFNG secreting lymphocytes, as well as CD8+ IFNG T cells following in vitro stimulation of PBMCs with vaccinia virus. The methods presented below, although optimized for vaccinia virus, emphasize principles that can be generally applied to create ELISPOT assays capable of assessing the immune status as well as antiviral CD8+ T cell response of individuals following primary or secondary vaccination with other licensed or novel vaccines. Key words: Vaccinia virus, IFNG ELISPOT, IL-10 ELISPOT, CD8+ ELISPOT, Smallpox vaccine, Secreted cytokine

1. Introduction Vaccinia virus is the prototypic orthopoxvirus that has been widely used in the twentieth and twenty-ﬁrst centuries to vaccinate individuals against smallpox (variola) as it induces cross-protective immunity against variola (1, 2). Although routine vaccination of individuals with vaccinia virus ceased during the mid-1970s in

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the USA, President George W. Bush initiated the US National Smallpox Vaccination Program in 2002 to vaccinate healthcare workers, ﬁrst responders, and mission-critical forces with smallpox vaccine due to the possible threat that smallpox or another orthopoxvirus could be weaponized and used as a bioterorrist weapon (3). The resurgence of individuals receiving smallpox vaccination as a result of the US National Smallpox Vaccinization program has fueled the development and implementation of high-throughput assays that are designed to rapidly and succinctly assess the immune status of large populations postsmallpox vaccination. Successful vaccination against smallpox requires both humoral and cell-mediated immune (CMI) responses (1, 4). The vacciniaspeciﬁc humoral response can be measured using several methods; however, a neutralizing antibody (Ab) assay is considered the gold standard by the greater scientiﬁc community to quantify circulating vaccinia-speciﬁc Ab titers in sera (5). While no single gold standard assay exists to measure vaccinia-speciﬁc CMI response, an enzyme-linked immunospot (ELISPOT) assay is a high-throughput assay that assesses vaccinia-speciﬁc CMI response following smallpox vaccination (2, 6, 7). ELISPOT assays can be used to functionally assess recall immunity (8), to determine the frequency of vaccinia-speciﬁc cytokine-secreting lymphocytes at a single cell level (9), and to determine the Th-type immune response proﬁle of a vaccinated individual (10). Mayo Clinic’s Vaccine Research Group has optimized the use of both IFNG and IL-10 ELISPOT assays to accurately assess both Th1-like and Th2-like cytokine proﬁles, respectively, for subjects previously vaccinated with vaccinia virus based on frequency of cytokine-secreting lymphocytes following in vitro stimulation with vaccinia virus. In addition, we also describe an ELISPOT assay to measure IFNG secretion of vaccinia-speciﬁc CD8+ T cells that does not require any expansion, presorting, or isolation of T cells. The antiviral CD8+ T cell-speciﬁc IFNG ELISPOT allows for a more precise measurement of vacciniaspeciﬁc IFNG response to vaccinia antigens by removing lymphocytes that are not speciﬁc to vaccinia, but still have the potential to secrete IFNG in response to vaccinia virus stimulation (e.g., NK cells). Moreover, a CD8+-speciﬁc IFNG ELISPOT allows for a measurement of vaccinia-speciﬁc cytotoxic T lymphocyte (CTL) response in individuals previously vaccinated with vaccinia virus. Most importantly, ELISPOT assays allow for a per cell measure of vaccinia-speciﬁc CMI response without in vitro expansion of the T-cell population or puriﬁcation of T-cell subsets; thus, ELISPOT assays are a rapid, reliable in vitro method to monitor immune response postsmallpox vaccination. Here, we describe in detail vaccinia-speciﬁc IL-10 ELISPOT, IFNG ELISPOT, and IFNG CD8+ T-cell ELISPOT assays as well as techniques that are essential for measuring human vaccinia virus-speciﬁc CMI response using ELISPOT assays.

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2. Materials 2.1. Infecting HeLa Cells with Vaccinia Virus

1. HeLa S3 Cells [American Type Culture Collection (ATCC), Manassas, VA, Number CCL-2.2]. 2. Approximately 500 mL of DMEM with high glucose and L-glutamine supplemented with 10% fetal calf serum (FCS) and 100 U/mL penicillin–100 Mg/mL streptomycin. 3. Hank’s balanced salt solution (HBSS), prewarmed to 37°C. 4. Vaccinia virus, New York City Board of Health (NYCBOH) strain (ATCC). 5. 75-cm2 Sterile tissue culture ﬂasks.

2.2. Harvesting Vaccinia Virus

1. Cell scrapers. 2. 16-mL 10 mM Tris–HCl, pH 9.0. 3. 4-mL 1 mM Tris–Cl, pH 9.0. 4. 50-mL Sterile polypropylene conical centrifuge tubes. 5. 17-mL 36% Sucrose. 6. 1.8-mL Cryogenic tubes.

2.3. Titrating Vaccinia Virus

1. Vero cells (ATCC, Number CCL-81). 2. MEM with L-glutamine and Earle’s salts supplemented with 10% FCS (Hyclone) and 100 U/mL penicillin–100 Mg/mL streptomycin. 3. 1× Sterile phosphate-buffered saline (PBS). 4. 1% Crystal violet in 70% methanol. 5. 12-Well sterile tissue culture plates. 6. 2.5% Trypsin in HBSS.

2.4. Inactivation of Vaccinia Virus

1. 1 mg/mL Psoralen diluted in sterile H2O. 2. 0.1% Bovine serum albumin in HBSS. 3. 35 mm Petri dishes. 4. DNA Cross-linker with 365-nm long-wave UV bulb.

2.5. Collecting and Isolating Peripheral Blood Mononuclear Cells

1. HISTOPAQUE-1077. 2. Accuspin™ tube (Sigma). 3. 1× Sterile PBS. 4. ACK lysis buffer (Invitrogen – Life Technologies, Carlsbad, CA). 5. Cell strainer. 6. Trypan blue. 7. Hemacytometer.

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8. RPMI freezing medium: RPMI 1640 with L-glutamine supplemented with 20% FCS (Hyclone) and 10% DMSO. 9. 1.8-mL Cryogenic freezing tubes. 2.6. Thawing Cryopreserved PBMCs

1. 15-mL Sterile conical centrifuge tubes. 2. RPMI culture medium supplemented with DNase [RPMI 1640 with L-glutamine supplemented with 10% FCS, 100 U/mL penicillin–100 Mg/mL streptomycin, 1 mM sodium pyruvate, and 10 Mg/mL DNase (Sigma)]. 3. RPMI culture medium [RPMI 1640 with L-glutamine supplemented with 5% FCS (Hyclone), 100 U/mL penicillin–100 Mg/ mL streptomycin, and 1 mM sodium pyruvate]. 4. Cell strainers. 5. 50-mL Sterile conical centrifuge tubes. 6. Trypan blue. 7. 1× Sterile PBS.

2.7. Resting PBMCs in the Presence of IL-2

1. 24-Well sterile tissue culture plate. 2. Recombinant IL-2 cytokine (Chiron). 3. RPMI culture medium: RPMI 1640 with L-glutamine supplemented with 5% FCS (Hyclone), 100 U/mL penicillin– 100 Mg/mL streptomycin, and 1 mM sodium pyruvate. 4. 0.25% Trypsin–EDTA (Gibco). 5. Trypan blue. 6. 1u Sterile PBS.

2.8. Infecting PBMCs with Vaccinia Virus for IL-10 Secretion

1. Human IL-10 ELISPOT kit (BD Pharmingen cat. no. 551018). 2. RPMI culture medium: RPMI 1640 with L-glutamine (Gibco) supplemented with 5% FCS (Hyclone), 100 U/mL penicillin–100 Mg/mL streptomycin, and 1 mM sodium pyruvate. 3. Inactivated vaccinia virus multiplicity of infection (MOI) 0.05. 4. Phytohemagglutinin-P (PHA-P) (Sigma). 5. Aluminum foil.

2.9. Infecting PBMCs with Vaccinia Virus for IFNg Secretion

1. Human IFNG ELISPOT kit (either total PBMCs or CD8+ T cells) (R&D cat. no. PEL285 and PEL3094, respectively). 2. RPMI culture medium: RPMI 1640 with L-glutamine (Gibco) supplemented with 5% FCS (Hyclone), 100 U/mL penicillin–100 Mg/mL streptomycin (Gibco), and 1 mM sodium pyruvate. 3. Inactivated vaccinia virus MOI = 5.0. 4. PHA-P (Sigma). 5. Aluminum foil.

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1. Human IL-10 ELISPOT kit (BD Pharmingen cat. no. 551018). 2. 3,3’, 5,5’-Tetramethylbenzidine (TMB) substrate (Moss Inc. cat. no. TMBH-500). 3. Stereomicroscope or automated ELISPOT reader.

2.11. Detection of IFNg Secreting Cells 2.12. Detection of IFNg CD8+ Secreting Cells

1. Human IFNG ELISPOT kit (R&D cat. no. PEL285). 2. Stereomicroscope or automated ELISPOT reader. 1. Human CD8+ IFNG ELISPOT kit (R&D cat. no. PEL3094). 2. Stereomicroscope or automated ELISPOT reader.

3. Methods The following methods describe the steps required to detect human IFNG and IL-10 secreting lymphocytes (1) infecting HeLa cells with vaccinia virus, (2) harvesting vaccinia virus, (3) titrating vaccinia virus, (4) inactivating vaccina virus, (5) collecting and isolating PBMCs, (6) thawing cryopreserved PBMCs, (7) resting PBMCs in the presence of IL-2, (8) infecting PBMCs with vaccinia virus for IL-10 secretion, (9) infecting PBMCs with vaccinia virus for IFNG secretion, (10) detecting IL-10 secreting cells, (11) detecting IFNG secreting cells, and (12) detecting CD8+ IFNG secreting T cells. 3.1. Infecting HeLa Cells with Vaccinia Virus

1. Seed 1 × 106 to 5 × 106 HeLa cells per T75 culture ﬂask. 2. Incubate ﬂasks at 37°C in a 5% CO2 humidiﬁed incubator until cells are 80–90% conﬂuent (approximately 2–3 days). 3. Dilute virus stock to MOI = 0.05–0.1 in 2 mL of medium per tissue culture ﬂask. 4. Aspirate all medium from tissue culture ﬂask using a sterile Pasteur pipette connected to a vacuum ﬂask. 5. Wash ﬂask once with HBSS and aspirate. 6. Infect each sterile T75 ﬂask containing HeLa cells with 2 mL of virus suspension. 7. Swirl ﬂask gently to ensure that virus suspension covers all cells. 8. Incubate for 2 h at 37°C in a 5% CO2 humidiﬁed incubator swirling ﬂask every 15 min. 9. After 2 h, add approximately 20 mL of complete medium to the ﬂask. 10. Incubate cells for 2–3 days checking for the formation of plaques [i.e., cytopathic effect (CPE)] daily until >90% CPE is observed.

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3.2. Harvesting Vaccinia Virus

The method below is a modiﬁed protocol for culturing and purifying vaccinia virus (11). HeLa cells should be harvested only after >90% CPE is observed. 1. Remove all medium from ﬂask and pipette into a 50-mL sterile polypropylene conical centrifuge tube. 2. Centrifuge medium at 500 u g for 10 min at room temperature, and aspirate supernate without disturbing the cell pellet. Save the cell pellet (see Note 1). 3. Using cell lifters, scrape the remaining cells from the ﬂasks into 2 mL of 10 mM Tris–Cl, pH 9.0. If harvesting multiple ﬂasks, scrape cells into 2 mL of 10 mM Tris–Cl, pH 9.0 per ﬂask. 4. Pool cells harvested from each ﬂask with the cell pellet collected in step 2 into a 50-mL sterile polypropylene conical centrifuge tube, and centrifuge the conical tube at 500 u g for 10 min at room temperature. 5. Aspirate supernate then resuspend the cell pellet in 14 mL of 10 mM Tris–Cl, pH 9.0. 6. Transfer cell/virus suspension to a 15-mL U bottom tube and place on ice. 7. Sonicate cell/virus suspension for 30 s at full power (wear proper protective equipment during sonication including a laboratory coat and eye protection). 8. Allow suspension to cool down for 1–2 min on ice, then resonicate cell/virus suspension for 30 s. 9. Layer sonicated cell/virus suspension onto 17 mL of 36% sucrose solution in a sterile ultra centrifuge tube. Place cell/ virus suspension on ice without disturbing the virus suspension-sucrose interface. 10. Centrifuge suspension at 15,800 rpm, 4°C for 80 min. Place ultra centrifuge tube on ice. 11. Aspirate supernatant and resuspend pellet in 4 mL of 1 mM Tris–Cl, pH 9.0, in a U bottom tube. 12. Sonicate suspension for 1 min as described in steps 7 and 8. 13. Store viral suspension at 4°C until the virus is titrated (see Note 2).

3.3. Titrating Vaccinia Virus

The method presented below is slightly modiﬁed from established protocols for determining the titer of vaccinia virus in pfu/mL (12, 13). Two separate aliquots of virus should be titrated in parallel to ensure that the observed titer is reﬂective of the complete viral stock. The titer of each aliquot should have a pfu/mL within 0.5 log of each other. If both titers are within 0.5 log, average the pfu/mL to determine titer of vaccinia viral stock. If the observed

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titers are greater than 0.5 log apart, repeat titration with a third aliquot. 1. Seed 12-well tissue culture (TC) plates with 3 × 105 Vero cells per well 1–3 days before titrating. Cells should be >90% conﬂuent before infection with virus (see Note 3). One 12-well TC plate is sufﬁcient for testing one dilution of the virus in quadruplicate along with the negative controls and standard reference virus. 2. Dilute 2.5% trypsin (10× trypsin) to 1× concentration (0.25%) with calcium and magnesium free 1× PBS. 3. Mix equal volumes (1:1) of 1× trypsin and virus stock in a microcentrifuge tube. Incubate mixture in a 37°C water bath for 30 min. 4. After incubation, perform a tenfold series of dilutions of the virus using medium. Generally, 10−3 to 10−11 dilutions are used for determining virus titers. 5. Dilute reference standard virus, obtained from ATCC or wellcharacterized vaccinia virus with known pfu, to 500 pfu/mL. 6. Remove medium from the wells of one TC plate leaving 50–100 ML of medium in each well. 7. Add 100 ML of diluted viral test samples to the top four wells of the 12-well TC plate. 8. Add 100 ML of standard reference virus to the middle four wells of the 12-well TC plate. 9. Add 100 ML of medium to the bottom four wells of the 12-well TC plate for negative controls (see Note 4). 10. Rock plate vigorously to ensure the inoculum covers the entire surface of each well. 11. Incubate plates at 37°C in a 5% CO2 humidiﬁed incubator for 1 h. Rock TC plates every 15 min during the incubation period. 12. After 1 h, add 1 mL of medium to each well. 13. Incubate plates at 37°C in a 5% CO2 humidiﬁed incubator for 72 h. 14. After 72 h of incubation, remove medium from the plate (see Note 5). 15. Gently add 1–2 mL of 1× PBS to each well of the TC plate. 16. Gently swirl PBS and then aspirate. 17. Add approximately 1 mL of 1% crystal violet in 70% methanol to each well. Allow the plate to incubate at room temperature for at least 20 min to ﬁx and stain the remaining Vero cells as well as inactivate the virus.

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18. Dump crystal violet solution into the sink. Wash wells in a gentle stream of tap water until the water runs clear. 19. Allow plates to dry. 20. Count plaques per well in the wells with the highest number of countable plaques (usually no more than 120 plaques) per well to calculate titer. 21. Titer in pfu/mL average plaques/well × dilution × 10 (100 ML added per well) × 2 (virus is diluted 1:1 with trypsin). 22. Count plaques in the next lower dilution and average the titers to get the overall titer in pfu/mL (see Note 6). 3.4. Inactivation of Vaccinia Virus

Inactivated virus protects laboratory workers from live virus exposure while not damaging the surface antigens that stimulate a host response. Importantly, inactivated virus has been shown to stimulate cells in assays measuring recall immunity comparable to live virus (10, 14). 1. Dilute virus stock to proper pfu/mL (1 × 108) with 0.1% bovine serum albumin (BSA) in HBSS. 2. Add 5.0 Mg/mL psoralen to the virus stock. 3. Place virus suspension in a 35-mm Petri dish. 4. Incubate at room temperature for 10 min. 5. After 10 min, place 35-mm Petri dish under cross-linker and UV irradiate for 60 s at 365 nm. 6. Remove virus suspension from cross-linker and aliquot 0.3 mL of inactivated virus suspension into prelabeled cryogenic freezing tubes. 7. Repeat titration assay (Subheading 3.4) to determine pfu/mL of inactivated virus. Inactivation should result in a 7–8 log reduction in titer. 8. Store aliquots at −80°C.

3.5. Collecting and Isolating PBMCs

The method below is used to separate PBMCs from whole blood that is collected in tubes treated with heparin or EDTA to prevent coagulation. This procedure is based on the manufacturer’s protocol for separating PBMCs in Accuspin™ tubes. Using cryopreserved PBMCs rather than fresh PBMCs to perform ELISPOT assays has several beneﬁts. Freezing isolated PBMCs allows ELISPOT assays to be performed at a later date postblood draw. This minimizes assay drift because subjects can be tested in consecutive tests and saves money because the entire microplate can be ﬁlled with samples. Furthermore, using cryopreserved PBMCs allows for the use of multiple recruitment sites to collect the subject’s specimens, but have ELISPOT assays performed at a single site thus minimizing variability. Importantly,

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cryopreserving human PBMCs does not affect the ability of lymphocytes to produce cytokines (15, 16). 1. Warm HISTOPAQUE-1077 to room temperature using a 37°C water bath. Keep HISTOPAQUE-1077 out of direct light. 2. Pipette 15 mL of HISTOPAQUE-1077 into the upper chamber of each Accuspin™ tube. 3. Centrifuge Accuspin tubes at 800 × g for 30 s to move HISTOPAQUE-1077 into the lower chamber of the Accuspin™ tube. 4. Gently pipette the whole blood from a tube treated with anticoagulant (Heparin or EDTA) into the upper chamber of the Accuspin™ tube (see Note 7). 5. Add sterile 1× PBS into the Accuspin™ tube up to the 45-mL mark. 6. Gently mix blood and PBS; do not force any blood below the frit. 7. Centrifuge Accuspin™ tubes at 1,000 × g for 15 min at 25°C with the brake OFF. 8. After centrifugation carefully remove approximately half of the plasma layer using a sterile Pasteur pipette. Do not disturb the white layer (buffy coat) of PBMCs directly above the frit. 9. Using a sterile Pasteur pipette, carefully remove the layer of PBMCs (white hued layer directly above the frit) and transfer it to a 15-mL sterile conical centrifuge tube. 10. Add 1× sterile PBS to PBMCs bringing the volume of liquid in the 15-mL conical centrifuge tube up to the 10-mL mark to wash cells. 11. Resuspend PBMCs by inverting the tube several times. 12. Centrifuge at 500 × g for 10 min at 25°C with brake ON. 13. Remove supernatant without disturbing the cell pellet. 14. Add 5 mL of ACK lysis buffer to the cell pellet. Resuspend cells by pipetting cell suspension up and down. 15. Allow cells to incubate at room temperature for 5 min in the ACK lysis buffer. 16. Add 1× sterile PBS to the cells + ACK lysis buffer, to bring the volume of liquid in the 15-mL conical centrifuge tube up to the 10-mL mark. 17. Centrifuge at 500 × g for 10 min at 25°C with brake ON. 18. Remove supernatant without disturbing the cell pellet then resuspend pellet in 5 mL of 1× sterile PBS. 19. Place a cell strainer on top of a 50-mL conical centrifuge tube. Transfer the cell suspension from the 15-mL conical centrifuge

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tube to the 50-mL conical centrifuge through the cell strainer (see Note 8). 20. To count the number of live and dead cells, place 200 ML of 1u PBS, 37.5 ML of Trypan blue, and 12.5 ML of cell suspension into a 5-mL falcon tube; mix well then ﬁll a hemacytometer with 10 ML of sample. Count and record the number of unstained (live) cells in the outer four quadrants of the hemocytometer. 21. Total number of cells Number of live cells/4 × 10,000 × 20 (dilution factor [250/12.5]) × total volume of cells (5 mL or pooled total). 22. Centrifuge cell suspension at 500 × g for 10 min at 25°C with brake ON. 23. Adjust cell concentration to 1 u 107 cells/mL with 4°C RPMI freezing medium (see Note 9). 24. Aliquot 1 mL of cell suspension into prelabeled cryogenic freezing tubes. 25. Place cryogenic freezing tubes into a −80°C freezer in a controlled-rate freezing container overnight. 26. Transfer cells to a liquid nitrogen storage tank for long-term storage (see Note 10). 3.6. Thawing Cryopreserved PBMCs

1. Warm RPMI culture medium supplemented with DNase in a 37°C water bath for a minimum of 15 min. 2. Add 100 ML of RPMI culture medium supplemented with DNase into a 15-mL conical centrifuge for each sample being thawed. 3. Remove one vial of PBMCs (cell concentration 1 × 107) for each sample from liquid nitrogen storage tank. 4. Rapidly thaw PBMCs stored in cryogenic freezing tubes using a 37°C water bath by swirling the vial in the water bath until a small amount of ice remains. 5. Quickly wipe the vial with 70% ethanol and place in a sterile tissue culture hood. 6. Pipette each sample from the cryogenic freezing tube into a 15-mL conical centrifuge tube containing 100 ML of RPMI culture medium supplemented with DNase (prepared in step 2). Do not pipette cells up and down. 7. Mix the cells and medium by gently shaking the 15-mL conical centrifuge tube. 8. Slowly add 500 ML of RPMI culture medium supplemented with DNase while swirling the tube gently to mix the cells and medium together.

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9. In 1 min add double the amount (1 mL) of RPMI culture medium supplemented with DNase to the cell suspension in the15-mL conical centrifuge tube. 10. Continue adding double the amount of RPMI culture medium supplemented with DNase every minute until the cell suspension reaches a ﬁnal volume of 10 mL. 11. Cap each conical tube and invert it ﬁve times to mix the cells; do not vortex cell suspension. 12. Centrifuge at 300 u g for 7 min at 25°C with brake ON. 13. Remove supernatant then resuspend cells in 10 mL of RPMI culture medium supplemented with DNase. 14. Cap each conical tube and invert it ﬁve times to mix the cells; do not vortex cell suspension. 15. Incubate cells at 37°C for 20 min by placing the 15-mL conical centrifuge tubes in a 37°C water bath. Invert tubes once 10 min into the incubation period. 16. After 20-min incubation, place cells on ice for 7 min. 17. Centrifuge cells at 300 u g for 7 min at 4°C with brake ON. 18. Carefully remove all supernatant and resuspend cells in 1 mL of RPMI culture medium supplemented with 5% FCS. 19. Place a cell strainer on top of a 50-mL conical centrifuge tube. Transfer the cell suspension from the 15-mL conical centrifuge tube to the 50-mL conical centrifuge through the cell strainer (see Note 11). 20. To count the number of live and dead cells, place 200 ML of 1u PBS, 37.5 ML of Trypan blue, and 12.5 ML of cell suspension into a falcon tube. Mix well and ﬁll a hemacytometer with 10 ML of sample. Count and record the number of unstained (live) cells in the outer four quadrants of the hemocytometer. 21. Total number of cells = Number of live cells/4 × 10,000 × 20 (dilution factor [250/12.5]) u total volume of cells (1 mL or pooled total). 22. Adjust the cell concentration to 2 u 106 cells/mL by adding RPMI culture medium supplemented with 5% FCS. 3.7. Resting PBMCs in the Presence of IL-2

This step is only required if PBMCs were damaged or stressed before or during the isolation procedure, for example, if blood has to be shipped overnight before isolation of PBMCs. IL-2 has been shown to increase T-cell survival and proliferation as well as prevent apoptosis (17–19). By resting PBMCs overnight in 50 IU/mL, then recounting and plating for ELISPOT assay, T-cell viability is maintained, thus increasing the precision and reproducibility of the assay. Resting PBMCs in IL-2 is cost and labor intensive and only needs to be performed if the background (negative controls) is

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above the acceptable thresholds or if PBMCs have low-viability postthawing procedure. If PBMCs were isolated and frozen from a blood sample within 12 h, it is unnecessary to perform an IL-2 resting phase (20). 1. Add 2-mL of cell suspension from Subheading 3.7, step 22, into each well of a 24-well sterile tissue culture plate (ﬁnal concentration 4 u 106 cells/well) (see Note 12). 2. Add 1 ML of IL-2 (concentration 1 × 105 IU/mL) per well such that the ﬁnal concentration of IL-2 in each well is 50 IU/mL. 3. Incubate plate at 37°C in a 5% CO2 humidiﬁed incubator for 18 h. 4. After 18 h, remove medium from wells and pool all medium from one subject into a single 15-mL conical centrifuge tube. 5. Add 0.5 mL of prewarmed (see Note 13) 0.25% Trypsin– EDTA to each well. 6. Place plate back in a 37°C in a 5% CO2 humidiﬁed incubator until cells detach (approximately 10 min). Conﬁrm detachment with a microscope. 7. Remove cells/trypsin suspension from each well and add to the corresponding 15-mL conical centrifuge tube which contains medium harvested from the same wells in step 4. 8. Add another 0.5 mL of prewarmed 0.25% Trypsin–EDTA to each well (see Note 14), and incubate plate for 10 min in a 37°C in a 5% CO2 humidiﬁed incubator. 9. Add 0.5 mL of RPMI culture medium supplemented with 5% FCS to each well and mix by pipetting up and down. Pool suspension from each well with the corresponding 15-mL conical centrifuge tube which contains cells harvested from that well in steps 4 and 7. 10. Bring the volume of each 15-mL conical centrifuge tube up to 10 mL by adding RPMI culture medium supplemented with 5% FCS. 11. Centrifuge cells at 300 × g for 7 min at 4°C with brake ON. 12. Remove supernatant without disturbing the cell pellet then resuspend cell pellet in 0.5 mL of RPMI culture medium supplemented with 5% FCS. Keep cells on ice once resuspended. 13. Count the number of live and dead cells by placing 200 ML of 1× PBS, 37.5 ML of Trypan blue, and 12.5 ML of cell suspension into a falcon tube. Mix well and ﬁll a hemacytometer with 10 ML of sample. Count and record the number of unstained (live) cells in the outer four quadrants of the hemocytometer. 14. Total number of cells Number of live cells/4 u 10,000 u 20 (dilution factor [250/12.5]) × total volume of cells (0.5 mL).

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15. Adjust the cells to the desired concentration by adding RPMI culture medium supplemented with 5% FCS (see Note 15). 16. Keep cells on ice until they are ready to be plated (see Note 16). 3.8. Infecting PBMCs with Vaccinia Virus for IL-10 Secretion

The method below is used for a kit that does not come with precoated microplates. These microplates must be coated with capture antibody (puriﬁed antihuman IL-10) before use. The protocols below do not use HeLa cell lysate as a negative control; rather, culture medium was used as a negative control for ELISPOT assays. Using HeLa cell lysate as a negative control could be beneﬁcial because only one variable is altered (absence of vaccinia virus in the culture). However, the presence of HeLa cell lysate could alter the cytokine secretion patterns of isolated PBMCs and as such it is not a true negative control. In addition, manufacturing and storing HeLa cell lysate costs personnel time, money, and freezer space; thus, using culture medium rather than HeLa cell lysate as a negative control is a scientiﬁcally as well as economically attractive option (see Note 17). 1. One day prior to plating PBMCs, coat the PVDF (polyvinylidene diﬂuoride)-backed microplate with capture antibody (puriﬁed antihuman IL-10) by adding 100 ML of dilute capture antibody (1:200 in 1× sterile PBS) per well in a sterile culture hood (see Note 18). 2. Cover microplate with the lid and allow it to incubate overnight at 4°C. 3. Two hours before plating cells in the microplate, remove microplate from 4°C refrigerator and discard the contents of the microplate wells inside a sterile culture hood by ﬂicking. Invert plate and blot dry on paper toweling. 4. Wash wells once with RPMI culture medium supplemented with 10% FCS. Discard the contents of the microplate wells inside a sterile culture hood by ﬂicking then invert microplate and blot dry on paper toweling. 5. Block microplate by adding 200 ML/well of RPMI culture medium supplemented with 10% FCS to each well and incubate at room temperature for 2 h. 6. Remove and discard all medium from the microplate inside a sterile culture hood by ﬂicking; invert microplate and blot dry on a paper toweling. 7. Remove vaccinia virus aliquots from −80°C freezer and thaw under a cold stream of water. 8. Dilute vaccinia virus stock aliquots to MOI = 0.05 with 4°C RPMI culture medium supplemented with 5% FCS. 9. Add 100 ML of cell suspension (adjusted to 1 × 106 cells/mL) to each well (ﬁnal concentration 1 u 105 cells/well).

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10. Add 100 ML of RPMI culture medium supplemented with 5% FCS to columns 1–3 of each row (see Note 19). 11. Add 100 ML of vaccinia virus MOI 0.05 to columns 4–6 of each row. 12. Add 100 ML of PHA-P (concentration 5 Mg/mL) to column 7 of each row. 13. Wrap microplate with aluminum foil and incubate for exactly 24 h at 37°C in a 5% CO2 humidiﬁed incubator (see Note 20). 3.9. Infecting PBMCs with Vaccinia Virus for IFNg Secretion

The method below describes the detection and visualization of IFNG secreting lymphocytes (and speciﬁc lymphocyte subsets) using PVDF microplates that are precoated with human anti-IFNG capture Ab from the manufacturer. 1. Thirty minutes prior to plating, remove a microplate precoated with human anti-IFNG Ab from 4°C storage. 2. Block microplate by adding 200 ML of RPMI culture medium supplemented with 5% FCS per well and incubate at room temperature for 20 min. 3. Remove and discard medium from all wells inside a sterile culture hood by ﬂicking; invert microplate and blot dry on paper toweling. 4. Remove vaccinia virus aliquots from −80°C freezer and thaw under a cold stream of water. 5. Dilute vaccinia virus stock aliquots to MOI = 5.0 with 4°C RPMI culture medium supplemented with 5% FCS. 6. Add 50 ML of cell suspension (for CD8+ IFNG [cell] 1 u 107, for total IFNG [cell] 4 u 106) to each well (ﬁnal concentration CD8+ 5 u 105 cells/well; total IFNG = 2 u 105 cells/well). 7. Add 50 ML of RPMI culture medium supplemented with 5% FCS to columns 1–3 of each row (see Note 21). 8. Add 50 ML of vaccinia virus (MOI = 5.0) to columns 4–6 of each row. 9. Add 50 ML of PHA-P (concentration 5 Mg/mL) to column 7 of each row. 10. Cover microplate with aluminum foil and incubate for exactly 24 h for total IFNG or 6 h for CD8+ IFNG at 37°C in a 5% CO2 humidiﬁed incubator.

3.10. Detection of IL-10 Secreting Cells

1. After PVDF microplate has incubated 24 h, discard medium from the microplate into a sink by ﬂicking. Invert microplate and blot dry on paper towels. 2. Wash the microplate two times with 200 ML/well of deionized (DI) water. Allow wells to soak for 3–5 min each wash.

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3. After DI wash, wash wells three times with 200 ML/well with wash buffer (1× PBS supplemented with 0.05% Tween 20), discarding wash buffer in the sink after each wash. 4. Dilute detection Ab (biotinylated antihuman IL-10) 1:250 (2 Mg/mL) in 1× PBS supplemented with 10% FCS (see Note 22). 5. Add 100 ML of detection Ab suspension per well and incubate with lid covering the microplate for 2 h at room temperature. 6. After 2-h incubation, ﬂick Ab suspension into a sink, invert microplate, and blot dry on paper towels. 7. Wash wells three times with 200 ML/well with wash buffer (1u PBS supplemented with 0.05% Tween 20). Allow wells to soak 1–2 min each wash. 8. Add 100 ML per well of dilute enzyme conjugate [(StreptavidinHRP) 1:100 in 1× PBS containing 10% FCS] (see Note 23). 9. Cover microplate and incubate it for 1 h at room temperature. 10. After 1 h of incubation, discard dilute enzyme conjugate into the sink by ﬂicking. 11. Wash wells four times with 200 ML/well of wash buffer (1u PBS supplemented with 0.05% Tween 20). Allow wells to soak 1–2 min each wash. 12. Wash wells two times with 200 ML/well of 1u PBS. After ﬁnal wash invert microplate and blot dry on paper toweling. 13. Add 100 ML of prewarmed to room temperature 3,3c,5,5c-tetramethylbenzidine (TMB) to each well and incubate microplates in the dark for 30 min (see Note 24). 14. Discard TMB into the sink by ﬂicking. 15. Remove plastic backing from the microplate and discard. Stop substrate reaction by rinsing both the back and front of the microplate in DI water three times. 16. Invert microplate and blot dry on paper toweling; wipe the bottom of the microplate dry with paper toweling. 17. Allow microplate to air-dry overnight. 18. Once the microplate is completely dry, the spots per well can be counted using an automated ELISPOT reader such as an ImmunoSpot® reader from Cellular Technology Ltd. (CTL, Shaker Heights, OH) or manually using a stereomicroscope. 3.11. Detection of IFNg Secreting Cells

1. After PVDF microplate has incubated 24 h, discard medium from the microplate into a sink by ﬂicking. Invert microplate and blot dry on paper towels. 2. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot until dry. To make wash buffer, add 50 mL of wash buffer concentrate (provided in the kit) to 450 mL of H2O.

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3. Immediately prior to use, prepare detection antibody (biotinylated antihuman IFNG) by mixing 100 ML of detection Ab concentrate with Dilution Buffer 1. Mix thoroughly, then add 100 ML of Ab suspension per well. 4. Incubate microplate overnight at 4°C. 5. The following day, discard detection Ab suspension from the microplate into a sink by ﬂicking; invert microplate and blot dry. 6. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot dry. 7. Directly prior to use, prepare Streptavidin-AP by adding 100 ML of Streptavidin-AP concentrate A to bottle of Dilution Buffer 2. Mix thoroughly, then add 100 ML of Streptavidin-AP suspension per well. 8. Incubate microplate for 2 h at room temperature. 9. Prewarm BCIP/NBT Chromogen substrate to room temperature using a 37°C water bath. 10. After the microplate has incubated for 2 h, discard Streptavidin-AP solution from wells into a sink by ﬂicking. 11. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot dry. 12. Add 100 ML of BCIP/NBT Chromogen substrate per well. 13. Incubate microplate in the dark for 30 min at room temperature. 14. After the microplate has incubated for 30 min in the dark, discard BCIP/NBT Chromogen substrate from wells into a sink by ﬂicking; invert microplate and blot dry. 15. Remove plastic backing from the microplate and discard. Stop substrate reaction by rinsing both the back and front of the microplate in DI water three times. 16. Invert microplate and blot dry on paper toweling; wipe the bottom of the microplate dry with paper toweling. 17. Allow microplate to air-dry overnight. 18. Once the microplate is completely dried, the spots per well can be counted using an automated ELISPOT reader such as an ImmunoSpot® reader from CTL or manually using a stereomicroscope. 3.12. Detection of IFNg CD8+ Secreting T Cells

1. After the microplate has incubated for 6 h in 37°C in a 5% CO2 humidiﬁed incubator, discard the contents of the microplate wells into a container containing bleach within a sterile environment by ﬂicking. Invert microplate and blot dry on paper toweling. 2. Remove any unbound cells by washing microplate with 250 ML of 1× sterile PBS three times. After each wash, invert microplate and blot dry.

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3. After three washes, add 100 ML of RPMI culture medium supplemented with 5% FCS to each well. 4. Wrap microplate in aluminum foil and incubate microplate for 18 additional hours (24 h total) in a 5% CO2 humidiﬁed incubator. 5. After the microplate has incubated for 18 h, discard medium from wells into a sink by ﬂicking. 6. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot dry. To make wash buffer, add 50 mL of wash buffer concentrate (provided in the kit) to 450 mL of H2O. 7. Immediately prior to use, prepare detection antibody (biotinylated antihuman IFNG) by mixing 100 ML of detection Ab concentrate with Dilution Buffer 1. Mix thoroughly, then add 100 ML of Ab suspension per well. 8. Incubate microplate overnight at 4°C. 9. The following day, discard the content of the microplate wells into a sink by ﬂicking; invert microplate and blot dry. 10. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot dry. 11. Directly prior to use, prepare Streptavidin-AP by adding 100 ML of Streptavidin-AP concentrate A to bottle of Dilution Buffer 2. Mix thoroughly, then add 100 ML of Streptavidin-AP suspension per well. 12. Incubate microplate for 2 h at room temperature. 13. Prewarm BCIP/NBT Chromogen substrate to room temperature using a 37°C water bath. 14. After the microplate has incubated for 2 h, discard Streptavidin-AP solution from wells into a sink by ﬂicking. 15. Wash microplate four times using wash buffer provided with the kit. After each wash, invert microplate and blot dry. 16. Add 100 ML of BCIP/NBT Chromogen substrate per well. 17. Incubate microplate in the dark for 30 min at room temperature. 18. After the microplate has incubated for 30 min in the dark, discard BCIP/NBT Chromogen substrate from wells into a sink by ﬂicking; invert microplate and blot dry. 19. Remove plastic backing from the microplate and discard. Stop substrate reaction by rinsing both the back and front of the microplate in DI water three times. 20. Invert microplate and blot dry on paper toweling; wipe the bottom of the microplate dry with paper toweling. 21. Allow microplate to air-dry overnight.

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22. Once the microplate is completely dry, the spots per well can be counted using an automated ELISPOT reader such as an ImmunoSpot® reader from CTL or manually using a stereomicroscope.

4. Notes 1. The tissue culture medium contains 103–10 4 pfu/mL of vaccinia virus and should be disposed properly in a hazardous waste container. 2. Vaccinia virus is stable at 4°C for several months. 3. Vero cells used for this assay should not be passaged more than 14 times before performing this assay. 4. Normally, one whole plate can be inoculated without the risk of cells drying out. Minimize cell drying by using the plate lid to cover the plate whenever possible and occasionally shake the plate vigorously to spread the residual medium equally across the wells. 5. Dispose medium as infectious waste. 6. Generally, acceptable vaccinia virus stock has a titer of >108 pfu/mL. 7. For optimal separation, do not add more than 20 mL of whole blood into the Accuspin™ tube. 8. At this point if you have multiple tubes for one subject, then they should be pooled into one 50-mL conical centrifuge tube before cell counting. 9. Required volume (mL) freezing medium 1 u 107/total number of cells. 10. When transferring cells keep on dry ice to prevent thawing. 11. At this point if you have multiple tubes for one subject, then they should be pooled into one 50-mL conical centrifuge tube before cell counting. 12. If any cells are remain after the 2 mL/well addition, then add remaining cells to a new well and bring the volume of the well up to 2 mL with RPMI culture medium supplemented with 5% FCS. 13. Warm 0.25% Trypsin–EDTA in a 37°C water bath for a minimum of 15 min prior to use. 14. Trypsinize wells twice to maximize recovery of PBMCs. 15. The desired concentration of PBMCs is dependent on the type of ELISPOT assay being performed. The required PBMC concentration by ELISPOT assays are as follows: IL-10

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ELISPOT = 1 × 106 cells/mL, IFNG ELISPOT = 4 × 106 cells/mL, and CD8+ IFNG ELISPOT = 1 × 107 cells/mL. 16. To minimize cell death, cells should be plated within 30 min after they have been counted. 17. HeLa cell lysate is manufactured by identical methods of infecting HeLa cells and harvesting HeLa cells (Subheadings 3.1 and 3.2, respectively) except cell cultures are not inoculated with vaccinia virus. 18. For one microplate, using BD Pharmingen IL-10 ELISPOT kit cat. no. 551018, add 50 ML of puriﬁed antihuman IL-10 Ab to 9.95 mL of sterile 1× PBS. 19. Using the microplate vertically will allow for 12 subjects to be tested on one microplate in triplicate as well as a positive control. 20. Wrapping microplate in aluminum foil during incubation reduces well-to-well variability (21). 21. See Note 19. 22. For one microplate, add 40 ML of detection Ab stock to 9.96 mL of 1× PBS supplemented with 10% FCS. 23. For one microplate, add 100 ML of Streptavidin-HRP to 9.9 mL of PBS containing 10% FCS. 24. If the background hue interferes with detecting spots, then decrease the development time.

Acknowledgments We would like to thank the entire Mayo Clinic Vaccine Research Group for their invaluable technical assistance and discussion during the development and execution of these assays. This work was supported by NIH contract AI40065. References 1. Kennedy, R.B., Ovsyannikova, I.G., Jacobson, R.M., and Poland, G.A. (2009)The immunology of smallpox vaccines. Curr Opin Immunol 21, 314–320. 2. Weltzin, R,. Liu, J., Pugachev, K.V., Myers, G.A., Coughlin, B., Blum, P.S., et al. (2003) Clonal vaccinia virus grown in cell culture as a new smallpox vaccine. Nat Med 9, 1125–1130. 3. Poland, G.A., Grabenstein, J.D., Neff, J.M. (2005) The US smallpox vaccination program: a review of a large modern era smallpox

vaccination implementation program. Vaccine 23, 2078–2081. 4. Damon, I.K., Davidson, W.B., Hughes, C.M., Olson, V.A., Smith, S.K., Holman, R.C., et al. (2009) Evaluation of smallpox vaccines using variola neutralization. J Gen Virol 90, 1962–1966. 5. Kennedy, R.B., Pankratz, V.S., Swanson, E., Watson, D., Golding, H., and Poland, G.A. (2009) Statistical approach to estimate vaccinia- speciﬁc neutralizing antibody titers using a high throughput assay. Clin Vaccine Immunol 16, 1105–1112.

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6. Frey, S.E., Newman, F.K., Cruz, J., et al. (2002) Dose-related effects of smallpox vaccine. N Engl J Med 346, 1275–80. 7. Kim, S.H., Yeo, S.G., Cho, J.H., et al. (2006) Cell-mediated immune responses to smallpox vaccination. Clin Vaccine Immunol 13, 1172–1174. 8. Helms, T., Boehm, B.O., Asaad, R.J., Trezza, R.P., Lehmann, P.V., and Tary-Lehmann, M. (2000) Direct visualization of cytokineproducing recall antigen-speciﬁc CD4 memory T cells in healthy individuals and HIV patients. J Immunol 164, 3723–3732. 9. Czerkinsky, C., Andersson, G., Ekre, H.P., Nilsson, L.A., Klareskog, L., and Ouchterlony, O. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of gamma-interferon-secreting cells. J Immunol Methods 110, 29–36. 10. Ryan, J.E., Dhiman, N., Ovsyannikova, I.G., Vierkant, R.A., Pankratz, V.S., and Poland, G.A. (2009) Response surface methodology to determine optimal cytokine responses in human peripheral blood mononuclear cells after smallpox vaccination. J Immunol Methods 341, 97–105. 11. Earl, P.L., Moss, B., Wyatt, L.S., and Carroll, M.W. (2001) Generation of recombinant vaccinia viruses. Curr Protoc Mol Biol Chapter 16, Unit 16. 12. Earl, P.L., Cooper, N., Wyatt, L.S., Moss, B., and Carroll, M.W. (2001) Preparation of cell cultures and vaccinia virus stocks. Curr Protoc Mol Biol Chapter 16, Unit 16. 13. Newman, F.K., Frey, S.E., Blevins, T.P., Mandava, M., Bonifacio, A. Jr., Yan, L., et al. (2003) Improved assay to detect neutralizing antibody following vaccination with diluted or undiluted vaccinia (Dryvax) vaccine. J Clin Microbiol 41, 3154–3157. 14. Tsung, K., Yim, J.H., Marti, W., Buller, R.M., and Norton, J.A. (1996) Gene expression and

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cytopathic effect of vaccinia virus inactivated by psoralen and long-wave UV light. J Virol 70, 165–171. Kreher, C.R., Dittrich, M.T., Guerkov, R., Boehm, B.O., and Tary-Lehmann, M. (2003) CD4+ and CD8+ cells in cryopreserved human PBMC maintain full functionality in cytokine ELISPOT assays. J Immunol Methods 2003 278, 79–93. Smith, J.G., Liu, X., Kaufhold, R.M., Clair, J., and Caulﬁeld, M.J. (2001) Development and validation of a gamma interferon ELISPOT assay for quantitation of cellular immune responses to varicella-zoster virus. Clin Diagn Lab Immunol 8, 871–879. Salomoni, P., Perrotti, D., Martinez, R., Franceschi, C., and Calabretta, B. (1997) Resistance to apoptosis in CTLL-2 cells constitutively expressing c-Myb is associated with induction of BCL-2 expression and Mybdependent regulation of bcl-2 promoter activity. Proc Natl Acad Sci U S A 94, 3296–3301. Stern, J.B., and Smith, K.A. (1986) Interleukin-2 induction of T-cell G1 progression and c-myb expression. Science 233, 203–206. Letourneau, S., Krieg, C., Pantaleo, G., and Boyman, O. (2009) IL-2- and CD25dependent immunoregulatory mechanisms in the homeostasis of T-cell subsets. J Allergy Clin Immunol 123, 758–762. Kierstead, L.S., Dubey, S., Meyer, B., Tobery, T.W., Mogg, R., Fernandez, V.R., et al. (2007) Enhanced rates and magnitude of immune responses detected against an HIV vaccine: effect of using an optimized process for isolating PBMC. AIDS Res Hum Retroviruses 23, 86–92. Kalyuzhny, A., and Stark, S. (2001) A simple method to reduce the background and improve well-to-well reproducibility of staining in ELISPOT assays. J Immunol Methods 257, 93–97.

Chapter 17 ELISPOT Assays to Enumerate Bovine IFN-g-Secreting Cells for the Development of Novel Vaccines Against Bovine Tuberculosis Martin Vordermeier and Adam O. Whelan Abstract Enumeration of antigen-speciﬁc cells after vaccination is one of the prime immunological parameters determined when developing vaccines. Due to their exquisite sensitivity (limits of detection can be below 1/100,000 cells), ELISPOT assays are therefore an important tool in vaccine development programs. This is particularly the case for vaccines against diseases that require protective cell-mediated immunity, such as tuberculosis. This chapter describes ELISPOT assays detecting bovine IFN-G. Key words: ELISPOT, Cytokines, IFN-G, Effector and memory T-cell responses, Cultured ELISPOT

1. Introduction We are engaged in developing novel vaccines against bovine tuberculosis which is caused by infection with Mycobacterium bovis. In this disease, Th1 responses, and in particular IFN-G production, are considered to be major contributors to protective immunity (1). Therefore, this chapter describes ELISPOT assays detecting bovine IFN-G (2–4). An example of ELISPOT results observed in cattle after subunit vaccination with a mycobacterial antigen, delivered as DNA vaccine or as pool of synthetic peptides, is shown in Fig. 1 (4). The same assay principles apply to the detection of other cytokines or chemokines. However, due to the lack of bovinespeciﬁc reagents, important cytokines assessed in human systems, like interleukin-2 (IL-2), cannot be probed by ELISPOT in cattle at present. Cytokine ELISPOT assays in their basic form detect

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Fig. 1. Example of ex vivo ELISPOT assays used to assess vaccine-induced cellular immunity: in vitro cellular immune responses after vaccination. PBMCs were stimulated in vitro peptide pool containing the complete set of 14 overlapping peptides covering the Rv3019c sequence. (a) IFN responses measured by ELISPOT and expressed as mean SFC/106 PBMC ± S.E.M. per group (n = 8 calves/group). (b) Proliferative responses expressed as mean stimulation index (SI) (cpm with peptides/cpm of medium control) ± S.E.M per group (n = 8 calves/group). Symbols: circles, peptide vaccination; diamonds, DNA/peptide vaccination; triangles, DNA vaccination; squares, saline control group. *p < 0.05; **p < 0.003. Arrows indicate time of vaccinations. From ref. 4.

Fig. 2. Flow chart of ex vivo and cultured ELISPOT assay.

effector T-cell responses since assays are initiated directly using ex vivo peripheral blood mononuclear cells (PBMCs), see Fig. 1. However, assessment of memory responses is also a vital parameter to be assessed following vaccination. This can also be addressed using a modiﬁcation of the ex vivo ELISPOT, the so-called cultured ELISPOT system (Fig. 2). The principle of the cultured ELISPOT is that the IFN-G ELISPOT is not performed on ex vivo PBMC, but on cells that have been expanded and differentiated in vivo for around 2 weeks by initial stimulation with antigen and then regular feeds of IL-2. The majority of effector cells that are initially stimulated by this approach die of IFN-G-induced cell

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Fig. 3. Example of cultured ELISPOT application: Correlation of memory responses with protection. Memory cell responses were determined by cultured IFN-G ELISPOT assay. Results of ELISPOT analysis are expressed as mean spot-forming cells (SFCs)/million cells. Cultured ELISPOT assays were performed before Mycobacterium bovis infection 14 weeks post-BCG vaccination and compared to outcome of infection with M. bovis at week 28 post-vaccination (animals were challenged with M. bovis at week 14 postvaccination). Protection has been determined by bacterial load (log CFU/g tissue). Shown is the correlation of mean cultured ELISPOT responses and mean bacterial loads using data from unvaccinated cattle as well as cattle vaccinated with three different vaccines that induced various degrees of protection (from ref. 2).

apoptosis during this culture and expansion step, whereas memory cells differentiate into secondary effector cells that can be enumerated at the end of this culture step by IFN-G ELISPOT. This assay was ﬁrst applied in the human system and applied to diseases, such as malaria (5, 6). A study by Godkin et al. (7) has demonstrated that the CD4+ T-cell memory population probed with this assays consisted mainly of central memory cells expressing the chemokine receptor CCR7. We have adapted this system for use in cattle (8– 11), and its protocol is also described in this chapter. Interestingly, in our hands, cultured ELISPOT responses were a direct predictor of vaccine efﬁcacy against M. bovis challenge in cattle (10, 11), as illustrated in Fig. 3.

2. Materials 2.1. Cell Culture

1. Nonessential amino acids (Sigma–Aldrich, Poole, UK). 2. Penicillin–streptomycin solution (penicillin 10,000 U/mL, streptomycin 10 Mg/mL). 3. Tissue culture medium (TCM): Add to 500 mL RPMI1640 (with Glutamax-1 and 25 mM HEPES buffer): 50 mL heat-activated fetal bovine serum (FBS), 5 mL penicillin– streptomycin solution, 5 mL nonessential amino acids, 0.5 mL 2-mercaptoethanol (from 50 mM stock solution).

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4. Human IL-2 (Sigma–Aldrich) (see Note 1). 5. Antigens as required for system: Antigens can be recombinant proteins or synthetic peptides used in our hands generally at 2–5 Mg/mL diluted in TCM (recombinant proteins) or 5–10 Mg/mL (synthetic peptides) (see Note 2). 6. Positive controls: Staphylococcal enterotoxin B (SEB) diluted to 2 Mg/mL in TCM (see Note 3). 7. Hank’s balanced salt solution (HBSS). 8. 24-well tissue culture plates. 2.2. ELISPOT

1. Coating buffer: 0.05 M carbonate/bicarbonate buffer, pH 9.6, to be prepared using buffer tablets. Stored at −20°C, ﬁlter sterilize before use through 0.2-Mm ﬁlter. 2. MABTECH Bovine/Ovine/Equine IFN-G ELISPOT kit (ALP) which includes the anti-bovine IFN-G-coating monoclonal antibodies (mAbs) bIFNG-I, biotinylated PAN IFN-G detection mAb (PAN-biotin), and Streptavidin–Horseradish Peroxidase (MABTECH, Stockholm, Sweden, www.mabtech. com: Product Code: 3115-2H) (see Note 4). 3. PBS-Tween (PBST); 0.5 mL Tween 20 to 1 L of PBS. 4. PBST/bovine serum albumin (BSA); 100 mg BSA to 1 L PBST, ﬁlter sterilize through 0.2-Mm ﬁlter. 5. 3-Amino-9-ethylcarbazole (AEC) staining kit (Sigma–Aldrich) (see Note 5).

2.3. Equipment

1. ELISPOT plates, Millipore MAIPS4510 (Millipore, Watford, UK) (see Note 6). 2. Humidiﬁed CO2 incubator. 3. Vortex mixer. 4. Orbital plate shaker. 5. Cell centrifuge. 6. ELISPOT plate reader (e.g. from AID GmbH, Strassberg, Germany; http://www.aid-diagnostika.com).

3. Method 3.1. ELISPOT Plate Preparation

1. Coat ELISPOT plates with 100 Ml/well capture antibody (bIFNG-1, at 7.5 Mg/mL) diluted in coating buffer. Wrap plates in cling ﬁlm and store in refrigerator; plates can be kept up to 3 days in refrigerator until use (see Note 7).

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Note: Steps 1–4 need to be performed in class 2 biosafety cabinet. 1. Discard coating antibody solution and wash plates twice with 200 Ml/well RPMI-1640 using a multi-channel pipette to add wash ﬂuid. Flick off ﬂuid between and after washes. Then, block wells for 45–60 min with 200 Ml/well of TCM at 37°C. 2. Flick off blocking solution and add 100 Ml/well antigens in duplicate or triplicate diluted in TCM according to requirements of experiment (see Note 2). 3. Add 100 Ml/well of PBMC suspended in TCM at 1–4 × 106/mL. Incubate for 20–24 h at 37°C and 5% CO2. Ensure that plates are level so that cells are evenly distributed (see Note 8). 4. After this incubation period, shake plates on plate shaker (400– 500 rpm, 5–10 s) and ﬂick off cells into container in a biosafety class 2 cabinet. Wash plates twice with H2Odd and then three times with PBST by adding 200 Ml/well wash ﬂuids using multi-channel pipette. Place on shaker each time for 10 s before ﬂicking off wash ﬂuid. Remove as much wash ﬂuid as possible by vigorously tapping plate on paper towels. 5. Add 100 Ml/well detection mAb (PAN-biotin, 0.25 Mg/mL, diluted in PBST–BSA) and incubate for 2 h at room temperature (above 20°C). 6. Wash three to four times with PBST as in step 4. Remove as much wash ﬂuid as possible by tapping plate on paper towels. 7. Add 100 Ml/well streptavidin–peroxidase conjugate (diluted 1:100 in PBST/BSA) and incubate for 1 h at room temperature. 8. Wash plates six times with PBST and then add 100 Ml/well AEC substrate prepared as per kit instructions (see Note 5). 9. Once spots have developed (ca. 10 min, but this should be closely monitored to avoid over-development), ﬂick off substrate and wash with copious amounts of tap water (under tap will do), remove plastic cover, and wash back of wells. Allow plates to dry either in air or using a drying oven set at 30°C. 10. Keep plates in dark before counting using an automated ELISPOT reader. Plates can also be read manually using a dissecting microscope. 11. Result presentation: Conventionally, responses are expressed as mean spot-forming cell (SFC)/106 cells of duplicate or triplicate wells.

3.3. Cultured ELISPOT

1. PBMCs are stimulated in 24-well plates (2 × 106 PBMC/mL, 1-mL aliquot in TCM) with antigens (e.g. recombinant proteins at 2–10 Mg/mL, see Note 2). PBMC should preferentially be

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freshly prepared, but can also be prepared from cryo-preserved stocks. Incubate in 37°C humidiﬁed CO2 incubator. Set up at least two wells per cell line (see Note 9). 2. PBMC cultures to be fed on days 3 and 7 with recombinant human IL-2 to a ﬁnal concentration of 10 U/mL in the following way; on day 3, add 0.5 mL of IL-2 (30 U/mL in TCM) directly to each 1 mL culture; on day 7, carefully remove and discard 0.5 mL of culture supernatant without disturbing cell layer at bottom of wells and replace with 0.5 mL of IL-2 (30 U/mL in TCM) (see Note 10). 3. On day 10, carefully remove half of the supernatant and replace with equal volume of TCM without IL-2. Do not disturb cell layer. 4. On day 12, remove 1–1.25 mL medium from each well, and replace with same volume of fresh TCM without IL-2. 5. The day before the assay (day 12), prepare ELISPOT plates by coating them overnight at 4°C with a bovine IFN-G-speciﬁc mAb ELISPOT as described above in Subheading 3.1. 6. On the day of assay (day 13), prepare fresh PBMC to be used as antigen-presenting cells (APCs) and re-suspend PBMC at 2–4 × 106/mL in TCM. 7. To the pre-blocked ELISPOT plates (see step 1 in Subheading 3.2 above), add APC (2 × 105 PBMC/well) to required wells (see Note 11). 8. Incubate for 90–120 min at 37°C. 9. In the meantime, warm up TCM aliquot to 37°C. 10. After the 90–120-min incubation step (step 8), shake up plates vigorously (plate shaker) and ﬂick off cells into container in cabinet. 11. Wash plates with warm TCM by addition of 200 Ml TCM/ well, shake plates on orbital plate shaker (100–200 rpm, 10 s), and discard medium in container in cabinet. Repeat this step twice more. 12. After last wash, add antigen solutions in TCM in 100 Ml/well in duplicate or triplicate and return plates to 37°C humidiﬁed CO2 incubator. Add 100 Ml TCM to “no antigen” control wells and add mitogen solution to positive control wells. Antigens can be recombinant proteins (assay concentrations 2–5 Mg/mL) or synthetic peptides (assay concentrations 2–10 Mg/mL) (see Note 2); positive control can be SEB (1–2 Mg/mL) or other mitogens (see Note 3). 13. Pool cells from 24-well T-cell line culture plates and wash cultured cells four times with HBSS solution by centrifugation (300 × g, room temperature), count viable cells, and re-suspend at required cell titres (see next step).

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14. Add between 5 × 103 and 2 × 104 cells to wells of ELISPOT plates containing the antigen solutions and APCs (see Note 11). 15. After 24 h of incubation in the presence of cells, develop spots as described for ex vivo ELISPOT (Subheading 3.2, steps 4–10). 16. Tabulation of results and interpretation criteria: There are a number of ways to tabulate the results. We advise to normalize results obtained (SFC/well) to the number of input cells (from step 1) by using the following formula: SFC/106 input cells = [S × (N/E)]/IR (see Note 12). Input cell ratio (IR): Number of input cells (step 1) divided by 106; N: output cell number (total viable cells counted at step 13); E: number of cells/well during ELISPOT (step 14); S: SFC/well (step 15).

4. Notes 1. Human IL-2 works effectivly to expand bovine T cells and is easier to source than bovine IL-2. 2. The protein and peptide concentrations given should be viewed as guidelines based on our experiences with the mycobacterial antigens. However, antigen concentrations should always be optimized with the antigens to be investigated and in the system they are applied to. Please also note that the concentrations given are ﬁnal assay concentrations and that the actual antigen solutions to be plated out should be of twice the ﬁnal assay concentrations. 3. Other positive control stimuli can also be used, such as the mitogens Pokeweed mitogen (PWM) or phytohaemagglutinin (PHA). Both mitogens can be used at 5 Mg/mL ﬁnal assay concentration. 4. Alternative bovine IFN-G mAb sources can also be used (e.g. see: AbD Serotec, http://www.abdserotec.com). 5. Tetramethylbenzidine (TMB, Sigma–Alrdrich) is an alternative substrate and can also be used with horseradish peroxidasebased conjugates. Alternatively, it is possible to substitute horseradish peroxidase conjugates with alkaline phosphatase-based reagents to be used in combination with substrates, such as Fast Red or 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium (BCIP/NBT, Sigma–Aldrich). We have had excellent results using BCIP/NBT which appears to provide not only higher sensitivity, but also increased background staining. 6. Alternative sources of plates suitable for ELISPOT applications are available, although their use would require careful

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optimization. Our experience has found that polyvinylidene ﬂuoride (PVDF)-based membranes provided improved spot formation compared with nitrocellulose or nylon membranes and that Millipore MAIPS4510 plates were found to be optimal for our application. 7. Pre-wetting of plates: Some sources recommend pre-wetting of membranes with 35–70% ethanol prior to mAb coating (http://www.mabtech.com). In our hands, this is not necessary. However, should a pre-wetting step be included, it is imperative to remove the ethanol by repeated washes of membranes with coating buffer or distilled water prior to the coating step. The volume of ethanol used and the incubation period need to be optimized depending on the source of ELISPOT plates used. 8. These cell titres are for guidance only and have to be optimized for respective application and experiment. Instead of PBMC, more deﬁned T-cell populations can also be probed, although this requires the addition of APCs, such as magnetically sorted CD14+ monocytes, dendritic cells, or adherent cell populations, as described for the cultured ELISPOT assay. APCs also need to be titrated to optimize their concentration for the antigens and experimental systems under investigation. To ensure equal temperature distribution during the incubation step, and hence improved uniformity of spot quality, ELISPOT plates can be wrapped in tin foil. They should also not be stacked up in the incubator. Further, it is best avoided to place them in an incubator that is subject to vibrations, such as being sited on the same bench as a large centrifuge, since the cells’ agitation and movement could result in enlarged and/or diffuse spots. 9. In addition to PBMC, sorted cell populations can also be used, such as CD4+ T cells or different memory cell populations. These cultures need to be supplemented with APC, such as CD14+ monocytes. 10. Alternatively, lines can be fed with IL-2 at days 5 and 8. 11. When peptides are used as antigens, it is also possible to perform these assays without addition of APC. This not only reduces the number of spot visualized in the medium control wells, but also reduces the overall number of peptide-speciﬁc SFCs. When not using APCs, we recommend that at least 2 × 104 cultured cells/well are used during the ELISPOT. 12. Worked example: (a) Input cells: 4 × 106 PBMC (step 1); therefore, IR = 4 (b) Recovered cells at the end of culture step (step 13): N = 106 viable cells

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(c) Cell concentration used in ELISPOT (step 14): E = 104 cells/well (d) ELISPOT (step 15): S = 50 SFC/well SFC/106 input cells = (50 × (106/104))/4 = 1,250.

Acknowledgements The authors’ work was supported by the Department for Environment, Food and Rural Affairs, the UK. References 1. Vordermeier, H.M., Chambers, M.A., Buddle, B.M., Pollock, J.M. and Hewinson, R.G. (2006) Progress in the development of vaccines and diagnostic reagents to control tuberculosis in cattle. Vet J 171, 229–244. 2. Vordermeier, H.M., Chambers, M.A., Cockle, P.J., Whelan, A.O., Simmons, J., and Hewinson, R.G. (2002) Correlation of ESAT6-speciﬁc gamma interferon production with pathology in cattle following Mycobacterium bovis BCG vaccination against experimental bovine tuberculosis. Infect Immun 70, 3026–3032. 3. Vordermeier, H.M., Rhodes, S.G., Dean, G., Goonetilleke, N., Huygen, K., Hill, A.V., et al. (2004) Cellular immune responses induced in cattle by heterologous prime-boost vaccination using recombinant viruses and bacille CalmetteGuerin. Immunology 112, 461–470. 4. Vordermeier H.M., Pontarollo R., Karvonen B., Cockle P., Hecker R., Singh, M., et al. (2005) Synthetic peptide vaccination in cattle: induction of strong cellular immune responses against peptides derived from the Mycobacterium bovis antigen Rv3019c. Vaccine 23, 4375–4384. 5. Keating S.M., Bejon P., Berthoud T., Vuola J.M., Todryk S., Webster, D.P., et al. (2005) Durable human memory T cells quantiﬁable by cultured enzyme-linked immunospot assays are induced by heterologous prime boost immunization and correlate with protection against malaria. J Immunol 175, 5675–5680.

6. Todryk S.M., Bejon P., Mwangi T., Plebanski M., Urban B., Marsh, K., et al. (2008) Correlation of memory T cell responses against TRAP with protection from clinical malaria, and CD4 CD25 high T cells with susceptibility in Kenyans. PLoS ONE 3, e2027. 7. Godkin, A.J., Thomas, H.C. and Openshaw, P.J. (2002) Evolution of epitope-speciﬁc memory CD4(+) T cells after clearance of hepatitis C virus. J Immunol 169, 2210–2214. 8. Vordermeier H.M., Dean G.S., Rosenkrands I., Agger E.M., Andersen P., Kaveh, D.A., et al. (2009) Adjuvants induce distinct immunological phenotypes in a bovine tuberculosis vaccine model. Clin Vaccine Immunol 16, 1443–1448. 9. Vordermeier, H.M., Huygen, K., Singh, M., Hewinson, R.G. and Xing, Z. (2006) Immune responses induced in cattle by vaccination with a recombinant adenovirus expressing Mycobacterial antigen 85A and Mycobacterium bovis BCG. Infect Immun 74, 1416–1418. 10. Vordermeier H.M., Villarreal-Ramos B., Cockle P.J., McAulay M., Rhodes S.G., Thacker, T., et al. (2009) Viral booster vaccines improve Mycobacterium bovis BCG-induced protection against bovine tuberculosis. Infect Immun 77, 3364–3373. 11. Waters W.R., Palmer M.V., Nonnecke B.J., Thacker T.C., Scherer C.F., Estes, D.M., et al. (2009) Efﬁcacy and immunogenicity of Mycobacterium bovis DeltaRD1 against aerosol M. bovis infection in neonatal calves. Vaccine 27, 1201–1209.

Chapter 18 IL-7 Addition Increases Spot Size and Number as Measured by T-SPOT.TB ® Marsha L. Feske, Miguel Medina, Edward A. Graviss, and Dorothy E. Lewis Abstract The interferon-gamma (IFN-J) release assay (IGRA) is an in vitro extension of the century-old in vivo tuberculin skin test, better known as the TST. Shortcomings to the TST are multifactorial and include limitations in sensitivity and speciﬁcity. IGRAs improve diagnostic speciﬁcity by using antigens not found in the Bacille Calmette-Guérin, a vaccine given in most countries. IGRAs capture the IFN-J produced by T cells in response to antigen stimulation. The ELISPOT immediately captures IFN-J produced directly from each cell, resulting in the generation of a cellular “footprint.” The dimensions and intensity of the generated footprint indicate the avidity of the secreting cell. We show a further improvement in IGRAs by addition of interleukin-7 (IL-7). IL-7 reduces T-cell apoptosis and stabilizes IFN-J message. In addition to increasing the number of spots in the ELISPOT T-SPOT.TB platform, IL-7 increased IFN-J production per cell as measured by an increase in spot size with no change in spot distribution. Key words: Interferon-gamma, Tuberculosis, Interleukin-7, T-SPOT.TB, IGRA, ELISPOT, TB, IFN-J, IL-7

1. Introduction The interferon-gamma (IFN-J) release assay (IGRA) is an in vitro extension of the century-old in vivo tuberculin skin test, better known as the TST. The TST was one of the ﬁrst diagnostics (1) and is still used today. The need for a diagnostic replacement for the TST had been long recognized, but awaited scientiﬁc advancement. Shortcomings include limitations in speciﬁcity (2, 3) and sensitivity (4–6). Reduced speciﬁcity correlates with the use of the Bacille Calmette-Guérin (BCG) (7), a vaccine given in most countries.

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TST sensitivity is reduced in persons with immunosuppression, a characteristic that has become more pronounced with the worldwide HIV epidemic (8, 9). IGRAs employ scientiﬁc achievements in assay development and cytokine detection using either the ELISA or ELISPOT platform to capture IFN-J produced in response to antigen stimulation within 16–24 h. The ﬁrst IGRA prototypes, like the TST, relied on cell-mediated responses to puriﬁed protein derivative (PPD), a puriﬁed fraction of Mycobacterium tuberculosis (MTB) proteins (5). Sequencing of the MTB genome revealed a region of difference (RD-1) that was not present in BCG (10). This ﬁnding led to the replacement of the IGRA PPD peptide pool with two speciﬁc RD-1 MTB peptides (ESAT-6 and CFP10) and recently TB 7.7 (11). The use of RD-1 peptides increased predictive values of IGRAs and further increased their viability as diagnostics (4, 12, 13). IGRAs also offer improved diagnostic feasibility compared to the TST because there is no requirement for a follow-up visit reducing the cost for the provider, and barriers to care, such as transportation, scheduling, and cost for the subject. The ELISPOT platform requires isolation of peripheral blood mononuclear cells (PBMCs) from whole blood, a step that requires more time and training. The isolation of PBMCs, however, is essential to both the speciﬁcity and sensitivity of the assay and serves a dual purpose. First, it ensures that the same number of PBMCs are stimulated, allowing the assay to be less dependent on the subjects’ T-cell number or underlying T-cell repertoire. Secondly, isolation of PBMCs allows spot visualization of individual effector cells. Spot size and density can be used to deﬁne the measured kinetics of the secreting cell. This function may prove to be valuable as the speciﬁcity of spot readers improves or as scientiﬁc knowledge concerning TB immunology advances (14). Most effector memory T cells secrete their products within 4–6 h of antigen presentation (15). In the ELISA assay, the secreted IFN-J may be diluted, degraded, or utilized in proportion to the timing of the antigen-presenting cell (APC) presentation and the stop point in the assay. The ELISPOT immediately captures IFN-J produced directly from each cell, resulting in the generation of a cellular “footprint” and the reduction of dilution. For a T cell, this footprint has a characteristic dark center with a halo effect that is the visual result of the degradation of the initial IFN-J produced upon activation (16). The dimensions and intensity of the generated footprint are indicators of the avidity of the secreting cell (17). ELISA-based QuantiFERON-TB Gold® (QFT-G) and QuantiFERON-TB Gold in Tube® (QFT-GIT) (Cellestis, Victoria Australia) and the ELISPOT-based T-SPOT.TB® (Oxford Immunotec, Oxford, England) have gained endorsements from medical entities, such as the American Thoracic Society (ATS) and Center for Disease Control and Prevention (CDC) (18), and

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approval as a latent TB infection (LTBI) diagnostic from regulatory agencies in many countries, including the US FDA, European CE Mark, and Chinese FDA. Since approval of the ﬁrst TB IGRA in 2001, and the ﬁrst M. tuberculosis-speciﬁc IGRA in 2004, extensive research has focused on the positive and negative predictive values of each platform (4, 7, 19–22). This type of scrutiny is not common for a diagnostic, but is expected because of the global burden of LTBI and the lack of a gold-standard for comparison. Without a gold-standard, those interested in reporting assay performance must explain the meaning of discordance with the TST and the discordance of serial assays. Overall, the use of IFN-J production as a diagnostic indicator has proven to be useful; but mechanisms that cause variability are imperative for understanding IGRA results in the context of disease progression and coinfection. Comparatively little research has focused on the cellular mechanisms involved in IGRA variability. The measurement of IFN-J responses to TB-speciﬁc antigens is dependent not only on the platform, but more importantly on the frequency of IFN-Jproducing memory cells (23), the time since exposure (14), interleukin-2 (IL-2) (24) and IL-12 availability (25), effective antigen presentation, which can vary depending on the APC type in vivo (26), and whether the T cell has been recently activated (17). We reasoned that since IL-7 is a cytokine known to cause immune activation (27, 28), affect dendritic cell maturation (29), and enhance antigen-speciﬁc responses (30) and T-cell survival (24), it might improve memory T-cell responses in vitro. We stimulated isolated PBMCs in both IGRA platforms, and measured antigen-speciﬁc responses with and without IL-7 in IGRA-positive subjects (ESTAT-6 and CFP10 peptides). We also measured responses in both control and IGRA-positive subjects to tetanus, viral peptide pools from inﬂuenza virus, cytomegalovirus, Epstein-Barr virus (CEF) and cytomegalovirus (IE-1), negativecontrol peptides, and a positive-control mitogen, phytohemagglutinin (PHA). In both diagnostic platforms, IL-7 augments memory-speciﬁc responses. In vitro, IL-7 increases the number of cells producing IFN-J as measured by the TSPOT.TB ® assay (31). In ELISA, the cell-to-volume ratio of 250,000 cells in 100 Pl increased the difference between IL-7-treated and -untreated samples (31). In addition to more spots, some IL-7-augmented responses have an increased production of IFN-J per cell or “spotforming unit” as measured by spot size comparisons to antigen stimulation without IL-7 (Figs. 1 and 2). The spots produced by IFN-J-producing cells vary in size (32). Clonal T-cell pools activated by a single type of APC demonstrate that although the spot sizes of a T cell vary, they follow a lognormal distribution (33). Software on the automated CTL ELISPOT reader (S4) (Cellular Technology Limited Ohio, USA) allows the comparison of histograms of the footprints in each well

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Fig. 1. Spot sizes (log mm2) resulting from viral antigen stimulation of control PBMCs (n = 5). In this example, PBMCs were stimulated with viral peptides CEF, IE-1, or tetanus antigen for 20 h, the TSPOT.TB ® performed, and the plate counted by CTL plate reader using S4 software to get a distribution of the counted spot sizes. This clearly shows that IL-7 addition to viral antigen-stimulated PBMCs from healthy controls causes a right shift (increase size) in spot size distribution. The mean difference in spot size with IL-7 was signiﬁcant (p < 0.05) at −2.20 log mm2 and −1.80 log mm2 (* = p < 0.05).

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3.00 2.00

1.00 0.00

3.00 2.80 2.60 2.40 2.20 2.00 1.80 1.60 1.40 1.20 1.00 0.80 0.60 Category Boundaries (log sq mm)

Fig. 2. Spot sizes (log mm2) resulting from RD-1 peptide stimulation of IGRA-positive PBMCs (n = 3). In this example, PBMCs were stimulated with RD-1 peptide pools A and B for 20 h, the TSPOT.TB ® performed, and the plate counted by CTL plate reader using S4 software to determine the distribution of the counted spot sizes. This clearly shows that IL-7 addition to RD-1 peptide-stimulated PBMCs from IGRA-positive subjects causes a right shift (increase size) in spot size distribution. The spot size boundary for NK cells is well below the mean spot size of −2.00 log mm2 for which a signiﬁcant change was seen (p < 0.05) in PBMCs when IL-7 was added to RD-1 stimulation (* = p < 0.05).

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or in groups of wells. The kinetics of the IFN-J-secreting cell detected by the ELISPOT membrane is measured by the rate at which IFN-J is released (spot density) and the length of time that the cell secretes (spot size). Histogram comparisons of the category boundaries (the log of the spot size in square millimeters) measured by the spot reader show that the addition of IL-7 increased the length of time that the cell secreted (Figs. 1 and 2), indicating that APC contact time was increased (34). There are two likely cellular sources of IFN-J production in the IGRA assays: NK cells and T cells. Because IL-7 increased the number of spots, it was important to test whether the spots resulted from secreting NK cells. We, therefore, depleted PBMC samples (Miltinyi Biotech Bergisch Gladback, Germany) so that the only remaining cell type was NK cells. We also depleted PBMC samples to study isolated T-cell subsets. We found that the footprints made by NK cells were much smaller than those made by T cells (Figs. 1–3). The use of a plate reader (such as CTL) allows for spot gating based on size which can exclude NK cells when measuring T cell-speciﬁc responses. Fig. 3 shows the results of stimulating 250,000 PBMCs or NK cells with the mitogen control. The graphs in Figs. 1–3 show

Fig. 3. Examples of spot sizes (log mm2) of PBMC or NK cells stimulated with PHA mitogen or RD-1 antigen. In this example, PBMCs or PBMCs depleted of all cells, but NK cells, were stimulated with PHA or RD-1 antigens (T-SPOT.TB ® panel A and B) for 20 h, T-SPOT.TB ® performed, and the spot size determined. This data shows that NK cells leave spot “footprints” when stimulated with PHA and antigen. This data emphasizes the importance of using plate reader gating to ensure that the mitogen-stimulated positive control is indeed a non-NK cell response. This is achievable because the NK cells’ spot size (−3.0 log mm2) is at the tail of the normal distribution of secreting T cells and can be used as a gating parameter during spot counting.

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that the difference seen with the addition of IL-7 is greatest in spots with size ranges between −2.6 log mm2 and −1.0 log mm2 which is above the size (−3.0 log mm2) measured for NK cells (Fig. 3).

2. Materials 1. T-SPOT.TB kit® (Oxford Immunotec, Oxford, England). 2. Lithium heparin blood collection tubes. 3. Ficoll-Paque Plus/Histopaque-1077 or alternative density gradient. 4. Pasteur pipette for underlying of density gradient. 5. Conical tubes for blood centrifugation. 6. BSA supplemented Aim-V® media (Invitrogen Carlsbad, CA). 7. 1× tissue culture phosphate-buffered saline (PBS). 8. Distilled or deionized water. 9. Centrifuge and buckets for 15 or 50-mL conical tubes. 10. Pipette (0–2, 0–20, 20–200 Pl) and sterile pipette tips. 11. Reservoirs for multichannel pipette. 12. Trypan blue, hemocytometer, and microscope or hematology analyzer for cell counts. 13. 5% CO2 humidiﬁed incubator. 14. Plastic squirt bottle or plate washer. 15. Inverted microscope or an ELISPOT reader. 16. Recombinant human (rh) IL-7. 17. Antigens of interest (whole proteins or peptide pools).

3. Methods 1. Prior to starting, read the entire protocol. Familiarize yourself with technique (Fig. 4, Table 1) and the science behind the assay (see Note 1). 2. Obtain 8 mL or more of human peripheral blood using lithium heparin vacutainers. 3. Within 8 h of blood collection, place blood into appropriatesize conical tube (see Note 2). 4. Add an equal amount of PBS to dilute blood (e.g., to 8 mL of blood, add 8 mL of PBS). 5. Underlay blood/PBS mixture with a density gradient measuring one-fourth amount of total volume of blood/PBS mixture

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Fig. 4. Diagram of process.

Table 1 Template of assay set-up 1

2

Nil Control

100 ªl of cell sample

B

Panel A

100 ªl of cell sample + antigen 1

C

Panel B

100 ªl of cell sample + antigen 2

D

Positive Control 100 ªl of cell sample + positive control

E

IL-7 Control

A

100 ªl of cell sample + 1ul of IL-7 [1ng/ªl]

(e.g., 20 mL of blood + 20 mL of PBS + 10 mL of density gradient). 6. Immediately centrifuge for 30 min at 25°C and 1,000 × g with low acceleration and the brake off. 7. Carefully remove sample and collect white cloudy layer of PBMCs (Fig. 5). 8. As a washing step, add AIM-V® to the collected sample to equal the amount of the starting blood volume. 9. Centrifuge sample again at 1,000 × g and 25°C for 10 min (Fig. 6).

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Fig. 5. Characteristic PBMC layer after density gradient separation.

Fig. 6. Visible cell pellet after washing.

10. Remove the supernatant and resuspend the cells in a volume of AIM-V equal to one-ﬁfth the original blood volume (e.g., if original blood volume is 20 mL, resuspend in 4 mL AIM-V®). 11. Perform cell count and bring PBMC concentration to 2.5 × 106 per mL (2.5 × 105 per 100 Pl) using supplemented AIM-V® media (see Notes 3 and 4). 12. After calculating the total numbers of PBMCs, decide if the test can be run in duplicate or triplicate based on PBMC volume (see Note 5).

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Fig. 7. Example of T-SPOT.TB ® wells.

13. Take the T-SPOT.TB® kit from storage (2–8°C) and remove the appropriate number of T-SPOT.TB® precoated eight-well strips from the packaging (Fig. 7). Clip strips into plate holder and allow product equilibration to room temperature (see Note 6). 14. Mix cells thoroughly and place 100 Pl of cell sample into each well (see Notes 7 and 8). 15. Add reagents (antigens and mitogens of choice) immediately to wells. If using the T-SPOT.TB® antigen panels A, B, and positive control, 50 Pl of each stimulating product is added to each well. If other whole proteins or peptide pools are used as stimulating antigen(s), the respective concentrations should be titrated to ﬁnd the effective dose used as described by the manufacturer or as the literature recommends. In our experience with tetanus toxoid (TT) and keyhole limpet hemocyanin (KLH) protein, we found 1 Pg/mL to be an ideal concentration (see Note 9). 16. Add 1 ng of rh IL-7 per well to IL-7 costimulated test wells (in plate above, wells E, F, G, and H). 17. Cover plate with plate lid. Immediately and carefully transfer plate to 37°C incubator and incubate for 20 h (see Note 10). 18. After a 20-h incubation at 37°C, discard cells from plate by swiftly shaking the plate over sink. 19. Wash the plate by ﬁlling each well with 200 Pl PBS per well. Shake PBS from plate, and repeat wash three times. When ﬁnished, shake the plate and tap on paper towel to remove excess PBS (see Note 11).

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20. Reconstitute the conjugate (mouse monoclonal antibody to alkaline phosphatase-conjugated IFN-J) according to manufacturer’s protocol. Set out substrate solution (BCIP/NBTplus) to allow equilibration to room temperature. 21. Using a multichannel pipette and reservoir, add 50 Pl of PBSdiluted conjugate to each well and incubate in the dark at 2–8°C for 1 h. 22. Discard conjugate from the plate by shaking. Follow by four washes with PBS. 23. Add 50 Pl of substrate to each well using a multichannel pipette and incubate at room temperature for 7 min. 24. Stop the reaction step by ﬁlling the plate with deionized/distilled water. Allow to dry overnight (see Note 12). 25. Read spots with an automated reader and verify with blinded stereoscope counts. 26. Once an ELISPOT assay has been standardized (as is the case with using the T-SPOT.TB® platform), the same counting parameters can be used to count all assays for objective, standardized comparisons of results across a diverse population of individuals (see Note 13).

4. Notes 1. We recommend using the standardized/optimized T-SPOT.TB® kit for measurement of IFN-J production. This platform minimizes problems with coating antibody concentration and nonspeciﬁc absorption of unrelated proteins: two of the ﬁve known variables in ELISPOT platform. We found that the use of T-SPOT.TB® plates was an ideal strategy to ensure the consistency of our results regardless of the antigen used to stimulate IFN-J from PBMCs and various cell subsets. In addition, we were able to forgo the laborious tasks of ﬁnding the ideal combination of coating and detection antibodies and the veriﬁcation of each antibody lot. 2. Ensure that isolated PBMCs are stimulated within 8 h of the blood draw time and within 2–3 h of PBMC isolation. When longer time periods are used between PBMC isolation and stimulation, no spots or less-distinct spots result (unpublished observation). 3. The concentration and number of cells are important and have a direct impact on the amount of cytokine detected (31).

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4. The cell counting step is extremely important as it is one of the steps, where interassay variability can be reduced. All counts are based on the sample from which they were taken. Additionally, counting via the hemocytometer or cell counter is a sampling of the sample. To improve statistical validity, we suggest that a minimum of three samples be counted and the average used to determine the concentration. 5. We recommend, at minimum, one negative-control well of cells in media alone, one positive-control well of mitogenstimulated cells, and all test wells in duplicate (though triplicate is preferred). 6. If using T-SPOT.TB®, plan experiment and plate layout through the use of a template to minimize waste. 7. Because well-to-well results are compared based on the antigens and substrates added, it is essential to mix the sample well prior to plating to ensure homogeneity of each sample. 8. When adding reagents and samples, be careful not to damage ELISPOT membranes with pipette tips. 9. If performing test wells in duplicate or triplicate, we suggest that cell stimulations are done ﬁrst in the total volume of cells to be tested for that parameter. For instance, if stimulating with tetanus toxoid in triplicate: (a) Pipette 650 Pl of cell sample into a 1-mL Eppendorf tube and add 0.65 Pg of TT. (b) Mix well and pipette 100 Pl each into the three respective antigen-stimulated wells. (c) Add 3.5 ng of IL-7 to mixture in Eppendorf tube. (d) Mix well and pipette 100 Pl each into the three respective antigen + IL-7-stimulated wells. 10. Placing a paper towel dampened with ﬁlter-puriﬁed water under the plate reduces background (unpublished observation). 11. Be careful not to tear the plate membrane when removing excess PBS from plate. 12. Although spot counts can be assessed any time after the completion of the assay, spots are the darkest and most distinct after the plate is completely dry. 13. If using a validated assay, such as the T-SPOT.TB®, and a plate counter, the same parameters can be used to assess all plate counts (32). For our experiments, we used a sensitivity of 100, background of 100, the maximum and minimum cell size parameters, and a cell separation value of 3 as programmed on the CTL Immunospot S4 software®.

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References 1. Lebedeva, Z. A. (1977) [Early Diagnosis of Tuberculosis (on the Centenary of the Birth of Charles Mantoux)]. Med Sestra 36, 51–52. 2. Lalvani, A., and Pareek, M. (2010) A 100 Year Update on Diagnosis of Tuberculosis Infection. Br Med Bull 93, 69–84. 3. Okada, K., Mao, T. E., Mori, T., Miura, T., Sugiyama, T., Yoshiyama, T., et al., (2008) Performance of an Interferon-Gamma Release Assay for Diagnosing Latent Tuberculosis Infection in Children. Epidemiol Infect 136, 1179–1187. 4. Diel, R., Loddenkemper, R., and Nienhaus, A. (2010) Evidence-Based Comparison of Commercial Interferon-J Release Assays for Detecting Active TB. Chest 137, 952–968. 5. Lodha, R., and Kabra, S. K. (2004) Newer Diagnostic Modalities for Tuberculosis. Indian J Pediatr 71, 221–227. 6. Pai, M., Zwerling, A., and Menzies, D. (2008) Systematic Review: T-Cell--Based Assays for the Diagnosis of Latent Tuberculosis Infection: An Update. Ann Intern Med 149, 177–184. 7. Dheda, K., van, Z. S., Badri, M., and Pai, M. (2009) T-Cell Interferon-Gamma Release Assays for the Rapid Immunodiagnosis of Tuberculosis: Clinical Utility in High-Burden Vs. Low-Burden Settings. Curr Opin Pulm Med 15, 188–200. 8. Cattamanchi, A., Ssewenyana, I., Davis, J. L., Huang, L., Worodria, W., den Boon, S., et al., (2010) Role of Interferon-Gamma Release Assays in the Diagnosis of Pulmonary Tuberculosis in Patients with Advanced HIV Infection. BMC Infect Dis 10, 75–75. 9. Mazurek, G. H., Jereb, J., Lobue, P., Iademarco, M. F., Metchock, B., and Vernon, A. (2005) Guidelines for using the QuantiFERON-TB Gold Test for Detecting Mycobacterium Tuberculosis Infection, United States. MMWR Recomm Rep 54, 49–55. 10. Prabha, C., Karthic, S., Das, S. D., Swaminathan, S., Subramaniam, S., and Sukumar, B. (2005) Impact of Tuberculosis on Serum Leptin Levels in Patients with HIV Infection. Horm Res 63, 228–233. 11. Cellestis. QuantiFERON-TB Gold Package Insert. Cellestis. 12. Keeler, E., Perkins, M. D., Small, P., Hanson, C., Reed, S., Cunningham, J., et al., (2006) Reducing the Global Burden of Tuberculosis: The Contribution of Improved Diagnostics. Nature 444 Suppl 1, 49–57. 13. Pai, M., Minion, J., Sohn, H., Zwerling, A., and Perkins, M. D. (2009) Novel and Improved

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Technologies for Tuberculosis Diagnosis: Progress and Challenges. Clin Chest Med 30, 701. Ewer, K., Millington, K. A., Deeks, J. J., Alvarez, L., Bryant, G., and Lalvani, A. (2006) Dynamic Antigen-Speciﬁc T-Cell Responses After Point-Source Exposure to Mycobacterium Tuberculosis. Am J Respir Crit Care Med 174, 831–839. Klinman, D. (2008) ELISPOT Assay to Detect Cytokine-Secreting Murine and Human Cells. Curr Protoc Immunol Chapter 6, 6.19-6.19. Kalyuzhny, A. E. (2005) Handbook of ELISPOT: Methods and Protocols, in Methods in molecular biology, 302 Totowa, N.J.: Humana Press, c2005. Schlingmann, T. R., Shive, C. L., Targoni, O. S., Tary-Lehmann, M., and Lehmann, P. V. (2009) Increased Per Cell IFN-J Productivity Indicates Recent in Vivo Activation of T Cells. Cell Immunol 258, 131–137. CDC. (2000) Targeted Tuberculin Testing and Treatment of Latent Tuberculosis Treatment. CDC MMWR. Pai, M., Riley, L. W., and Colford, John M, Jr. (2004) Interferon-Gamma Assays in the Immunodiagnosis of Tuberculosis: A Systematic Review. Lancet Infect Dis 4, 761–776. Orlando, G., Merli, S., Cordier, L., Mazza, F., Casazza, G., Villa, A. M., et al., (2010) Interferon-Gamma Releasing Assay Versus Tuberculin Skin Testing for Latent Tuberculosis Infection in Targeted Screening Programs for High Risk Immigrants. Infection 38, 195–204. Leyten, E. M. S., Arend, S. M., Prins, C., Cobelens, F. G. J., Ottenhoff, T. H. M., and van Dissel, J. T. (2007) Discrepancy between Mycobacterium Tuberculosis-Speciﬁc Gamma Interferon Release Assays using Short and Prolonged in Vitro Incubation. Clin Vaccine Immunol 14, 880–885. Grimes, C. Z., Hwang, L., Williams, M. L., Austin, C. M., and Graviss, E. A. (2007) Tuberculosis Infection in Drug Users: Interferon-Gamma Release Assay Performance. Int J Tuberc Lung Dis 11, 1183–1189. Helms, T., Boehm, B. O., Asaad, R. J., Trezza, R. P., Lehmann, P. V., and Tary-Lehmann, M. (2000) Direct Visualization of CytokineProducing Recall Antigen-Speciﬁc CD4 Memory T Cells in Healthy Individuals and HIV Patients. J Immunol 164, 3723–3732. Marrack, P., and Kappler, J. (2004) Control of T Cell Viability. Annu Rev Immunol 22, 765–787.

18 25. Flynn, J. L., and Chan, J. (2001) Immunology of Tuberculosis. Annu Rev Immunol 19, 93. 26. Ott, P. A., Tary-Lehmann, M., and Lehmann, P. V. (2007) The Secretory IFN-J Response of Single CD4 Memory Cells After Activation on Different Antigen Presenting Cell Types. Clinical Immunology 124, 267–276. 27. Rosenberg, S. A., Sportès, C., Ahmadzadeh, M., Fry, T. J., Ngo, L. T., Schwarz, S. L., et al., (2006) IL-7 Administration to Humans Leads to Expansion of CD8+ and CD4+ Cells but a Relative Decrease of CD4+ T-Regulatory Cells. J Immunother 29, 313–319. 28. Fry, T. J., and Mackall, C. L. (2005) The Many Faces of IL-7: From Lymphopoiesis to Peripheral T Cell Maintenance. J Immunol 174, 6571–6576. 29. Li, Masucci, Levitsky, and Levitsky, V. (2000) Effect of Interleukin-7 on the in Vitro Development and Maturation of Monocyte Derived Human Dendritic Cells. Scand. J Immunol 51, 361–371.

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30. Carreno, B. M., Becker-Hapak, M., and Linette, G. P. (2009) CD40 Regulates Human Dendritic Cell-Derived IL-7 Production that, in Turn, Contributes to CD8(+) T-Cell Antigen-Speciﬁc Expansion. Immunol Cell Biol 87, 167–177. 31. Feske, M., Nudelman, R. J., Medina, M., Lew, J., Singh, M., Couturier, J., et al., (2008) Enhancement of Human Antigen-Speciﬁc Memory T-Cell Responses by Interleukin-7 may Improve Accuracy in Diagnosing Tuberculosis. Clin Vaccine Immunol 15, 1616–1622. 32. Lehmann, P. V. (2005) Image Analysis and Data Management of ELISPOT Assay Results. Methods Mol Biol 302, 117–132. 33. Hesse, M. D., Karulin, A. Y., Boehm, B. O., Lehmann, P. V., and Tary-Lehmann, M. (2001) A T Cell Clone’s Avidity is a Function of its Activation State. J Immunol 167, 1353–1361. 34. Slifka, M. K., and Rodriguez, F. (1999) Rapid on/off Cycling of Cytokine Production by Virus-Speciﬁc CD8+ T Cells. Nature 401, 76.

Chapter 19 Overview of Membranes and Membrane Plates Used in Research and Diagnostic ELISPOT Assays Alan J. Weiss Abstract Polyvinylidene ﬂuoride (PVDF) membrane-bottomed, 96-well plates and 8-well strips constitute the formats in which the overwhelming majority of ELISPOT assays used in research and diagnostic applications are performed. PVDF is well suited for ELISPOT because it has a high antibody-binding capacity and because its white color provides an excellent backdrop for ELISPOT enumeration. Nitrocellulose (NC) and PVDF membranes and 96-well plates containing those membranes used in ELISPOT assays were initially commercialized for ﬁltration applications and later optimized for a range of different protein analytical applications. An overview of the development and biotechnology applications of PVDF membrane is provided. Characteristics and attributes of the membrane that are relevant to ELISPOT are summarized. Enhancements in PVDF membrane performance and optimization of devices for automation compatible and diagnostic ELISPOT applications are presented. Key words: ELISPOT, Polyvinylidene ﬂuoride membrane, Filter plate, Diagnostics

1. A Chronology of Relevant Developments

There are two different types of membranes (in 96-well plate formats) that are used in ELISPOT applications: Nitrocellulose (NC) and polyvinylidene ﬂuoride (PVDF) with PVDF becoming increasingly dominant over the past 10 years (although some features and guidance on how the use of NC plates will be provided, the emphasis in this chapter will be on PVDF and the use of PVDFbased devices in ELISPOT). The reason that NC and PVDF are the principal membrane types used in ELISPOT is based largely on their fortuitous suitability in a range of non-ELISPOT applications rather than on the development of optimized assay substrates to address the speciﬁc needs of the ELISPOT assay. The chronology of relevant membrane and application developments is outlined in Fig. 1.

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A.J. Weiss 1954: NC membranes become commercially available. Principal use is in removing bacteria from aqueous samples (sterile filtration) 1975: 0.45μm NC membrane used in DNA hybridization (Southern Blotting) assay (1) 1975: PVDF membranes become commercially available. Initially, principal use is in removing bacteria from aqueous samples. PVDF is more durable and more solvent resistant than NC and as such is more suited to large-scale filtration applications. 1979: 0.45μm NC membrane used in immunodetection of electro-blotted proteins (Western Blotting assay; see reference 2 ). 1983: ELISPOT assay developed on plastic, 96-well plates (3,4 ). 1985: 96-well plate with NC membrane becomes commercially available. Intended application is dot blotting of nucleic acids using vacuum transfer (instead of capillary transfer). 1986: 0.45μm PVDF membrane used in Western Blotting Assay (8,9 ). 1988: 96-well plate with NC membrane used in ELISPOT (5 ). 1992: 96-well plate with PVDF membrane becomes commercially available. Intended application is dot blotting of proteins using vacuum transfer (instead of electro-blotting). Millipore Corporation (Bedford, MA) reports in its 1994/5 Catalog that this plate can be used in ELISPOT 1995: Articles citing improved ELISPOT results using PVDF plates are published (6 ). 2002: Automation compatible 96-well plates become commercially available making it possible to use robotic liquid handlers to perform many of the reagent additions and plate washing steps associated with high throughput screening (HTS) 2003: Automation compatible, 96-well plates designed specifically for ELISPOT becomes commercially available 2003: Membrane-bottomed, 8-well strip plates become commercially available for diagnostic and research ELISPOT applications 2005: PVDF membranes are made with low background fluorescence making it much more possible to perform fluorescent ELISPOT assays 2008: Patented (7 ) T-Spot ELISPOT assay receives FDA PMA approval as a clinical diagnostic test for Tuberculosis

Fig. 1. Timeline of membrane, applications, and ELISPOT developments.

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As can be seen in Fig. 1, NC and then PVDF membranes were ﬁrst developed to serve the needs of sterile ﬁltration applications. In ways that were never anticipated by membrane manufacturers, the porosity and binding properties of these membranes enabled them to be used in two extremely important and burgeoning research applications; nucleic acid hybridization assays and Western blotting. Eventually, to serve the speciﬁc requirements of molecular biology and protein chemistry applications, NC and PVDF membrane-bottomed 96-well plates were developed and made commercially available. Independently and separately, ELISPOT assays were developed on 96-well plastic plates and took advantage of enzyme-linked immunosorbent assay (ELISA) techniques that had been perfected in that format. Since the immunodetection component of ELISPOT assays and Western blotting is essentially identical, it was only a matter of time until the overwhelming majority of ELISPOT assays were performed on membrane-bottomed, 96-well plates.

2. PVDF Fluoride Membrane Development and Applications

PVDF membranes were developed in part to overcome major limitations of NC, including poor chemical compatibility, shedding of particulates, and brittleness. Many of the same companies that were able to produce NC also developed the ability to make PVDF membranes over a range of different pore sizes. The PVDF polymer itself is highly resistant to chemical degradation – except in the presence of strong alkali (pH greater than 12) – and membranes made from PVDF are sufﬁciently elastic to withstand a broad range of fabrication conditions (including sonic welding and pleating) and high-pressure ﬁltration applications. Millipore, an early provider of PVDF membranes, created the trademark, “Durapore®” to call attention to these attributes. PVDF, like NC, is intrinsically hydrophobic. Whereas NC is only marginally hydrophobic and can be made water wettable by adding a surfactant (e.g., glycerin) or detergent (e.g., Triton®-X 100) to the membrane, PVDF is extremely hydrophobic and requires signiﬁcant surface modiﬁcation to make it compatible with aqueous solutions (see Note 1). A considerable amount of chemistry and patented technology was subsequently developed to make PVDF membranes hydrophilic. The important point to note is that even though the polymer itself is very hydrophobic, membranes made from PVDF can be either hydrophilic or hydrophobic depending on whether the manufacturer has modiﬁed or covered over the polymer surface with a secondary chemical treatment that is water compatible. The discussion of PVDF is relevant to ELISPOT in that the hydrophobic version of the 0.45 Mm (8) membrane was found to

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Fig. 2. A scanning electron micrograph of a 0.45 Mm PVDF membrane.

H

F

C

C

H

F

n

Fig. 3. Chemical structure of polyvinylidene ﬂuoride.

be an excellent Western blotting substrate. A scanning electron micrograph of a 0.45 Mm PVDF membrane appears in Fig. 2. PVDF binds proteins by hydrophobic interactions (van der Waal’s forces). This applies, of course, only to hydrophobic PVDF membranes. Most types of hydrophilic PVDF will not bind proteins to any appreciable degree. Interestingly, hydrophobic PVDF will bind single-stranded DNA and RNA, but will not bind double-stranded DNA. The chemical structure of PVDF is illustrated in Fig. 3. The use of PVDF membranes in Western blotting type applications grew rapidly. Unlike NC membranes, PVDF membranes could stand up to automated protein sequencing chemistries and were better able to retain low molecular weight proteins and peptides. PVDF was also better suited to a wider range of detection techniques, including ﬂuorescence and chemiluminescence. One signiﬁcant drawback of PVDF is the need to pre-wet the membrane

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in an alcohol solution prior to using it in Western blotting and most other applications that include immunodetection. The requirement to pre-wet with alcohol – which is completely necessary in Western blotting applications – is not universally applicable in ELISPOT. Differences in ELISPOT protocols with regard to the pre-wetting step are signiﬁcant and are likely to have an impact on the performance of the assay. This topic is discussed in greater detail later in this chapter (see Note 2).

3. 96-Well Filter Plate Development and Applications The rationale behind the development and commercialization of NC and PVDF is clear from the perspective of ﬁltration applications. As has been pointed out already, the use of each of these membrane types in molecular biology and protein chemistry applications was based on a fortuitous combination of membrane properties; high permeability (due to high porosity), and high DNA/ RNA and protein binding. The development of 96-well PVDF and NC bottomed plates was primarily driven by the secondary (i.e., biochemistry) applications. One of the attributes of both Southern and Western blotting is that (DNA) hybridization and antibody binding are diffusion limited reactions. In other words, the reactant in solution (complimentary DNA or antibody) must diffuse to the surface of the membrane before it can couple with the immobilized reactant (DNA or protein). In these types of solid phase reactions, the times required to reach equilibrium binding are much longer as compared to reactions in which both reactants are in solution. Typically, DNA hybridization (Southern blotting) and immunodetection (Western blotting) require from 2 to 24 h. Membrane-bottomed, 96-well plates, an example of which is seen in Fig. 4, made it possible to reduce the time requirements associated with solid phase binding. (Earlier, simpler versions of these plates, called “Dot Blot” or “Slot Blot” apparatuses provided the ﬁrst opportunity to exploit the beneﬁts of ﬁltration in these applications). Filtration of the reactant in solution through the membrane brings it into intimate contact with the reactant immobilized on the membrane surface. So long as the ﬁltration rate is kept low enough for hybridization or binding to take place, the time required to achieve efﬁcient capture can be dramatically reduced. Additionally, all wash steps in between various reactions can be accomplished using ﬁltration. The development of membrane-bottomed plates in conjunction with compatible vacuum manifolds made it possible to carry out from 1 to 96 different hybridization or immunodetection assays using less reagents and requiring less time as compared to standard methodologies.

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Fig. 4. 96-well ﬁlter-bottomed plate used for ELISPOT and HTS applications.

In addition to the membranes and other features that were useful for these applications, the plates were also designed to allow for discrete liquid transfer from the top (membrane-containing) plate to a (standard, plastic, 96-well) receiver plate. The ﬁlterplate components that allow for this to occur have the potential to interfere with ELISPOT applications in at least three different ways. 1. If alcohol is used to pre-wet membrane (this applies only to the use of PVDF membrane plates), and becomes trapped under the membrane, the alcohol can suppress or completely inhibit cytokine release (see Note 2). 2. If biotinylated antibody or avidin-enzyme conjugate becomes trapped under the membrane, high background will likely result (see Note 2). 3. If the membrane cannot be removed because of these components, it may be difﬁcult to analyze and essentially impossible to archive ELISPOT results. Preventing these types of problems during the ELISPOT assay and optimizing the different parts of the protocol are reviewed elsewhere. There are other plate attributes that may have an impact on ELISPOT assay performance or analysis. The membrane inside each well needs to be planar within a fraction of a millimeter

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(e.g., ±0.1 mm) in order to provide the best assay results. Lack of membrane ﬂatness may contribute to difﬁculties in imaging depending on the type of microscopy being used. Additionally, if the membrane is bowed, cells may tend to settle unevenly or roll to the relative low points (often either the center or the periphery) during incubation. Consequently, spots may become very unevenly distributed and difﬁcult to enumerate accurately especially if there is any spot conﬂuence. The plate itself should also be ﬂat (within a tolerance of perhaps 1 mm corner to corner) to assure compatibility with plate washers and most of the imaging software that supports automated image acquisition and analysis. There is one other feature of 96-well plates that has the potential to introduce variability into the ELISPOT assay. The 96-well plate is arrayed as 8 rows of 12 wells. Wells at the periphery (columns 1 and 12, rows A and F) of the plate are fundamentally different from “interior” wells insofar as they are in the most direct contact with the plate surroundings. Depending on incubation conditions and other protocol steps, this physical distinction may have some impact on one or more parts of the ELISPOT assay (see Note 3).

4. 96-Well Filter Plates in ELISPOT As evidenced by the chronology laid out in Fig. 1, ELISPOT assays were ﬁrst developed on plastic, 96-well plates. Shortly after NC-bottomed ﬁlter plates became available, the majority of ELISPOT assays were carried out in those plates. When PVDF ﬁlter plates were introduced, some investigators chose to use PVDF plates and some continued to use NC. The reasons for choosing one plate (membrane) over the other are highly varied and will not be addressed in detail here although over the past 10 years, the percentage of ELISPOT assays performed on PVDF plates has steadily increased. The fact that some laboratories and individual researchers feel strongly that one membrane is superior to the other runs contrary to the large body of Western blotting experience: Despite some clear-cut differences in how each of the membranes can be used, there is essentially no reported difference in terms of detection sensitivity or signal to noise on NC versus PVDF in the Western blotting application. This having been said, it is clear that the two membranes and their properties are quite different. Subheading 5 will be devoted to reviewing these properties which are summarized in Table 1, and highlighting the differences – especially as they might pertain to ELISPOT applications.

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Table 1 Comparison of PVDF and NC membranes

a

Attribute or characteristic

NC (used in ELISPOT) [nominal or average values]

PVDF (used in ELISPOT) [nominal or average values]

Pore sizea

0.45 Mm

0.45 Mm

Porosityb

70–75%

65–70%

Thickness

150 Mm

135 Mm

2

BET surface area (9)

6.5 m /g

6 m2/g

Surface area ratioc

250

350

Saturation binding capacity (IgG)

250 Mg/cm2

350 Mg/cm2

Binding capacity of top 1 Mm (IgG)

2 Mg

3 Mg

Wettability

Not wettable without prior addition of surfactants or detergents

Not directly wettable in water. Must be pre-wet with alcohol and then exchanged with water

Additives

Glycerin

None

Solvent compatibility

Not compatible with methanol or ethanol

Broadly compatible with a wide range of aqueous and organic solvents. Avoid prolonged exposure to strong alkali (e.g., pH > 12)

Mechanism of binding

Electrostatic

Hydrophobic

Things which will interfere with or destabilize binding of anti-cytokine antibodies

Chaotropes (e.g., Tween-20, Triton-X 100, etc.). Water (if never dried), Proteins, especially larger molecular weight proteins

Detergents (e.g., SDS), low polarity solvents (e.g., dimethyl formamide, etc.)

Compatibility with different detection modes

Ú Colorimetric Ù Fluorescence Ú Chemiluminescence

Ú Colorimetric Ú Fluorescence (marginal) Ú Chemiluminescence

Pore size is nominal and corresponds to the diameter of the largest particle that can pass through the membrane structure. A 0.45 Mm pore size membrane is expected to retain 100% of particles whose diameter exceeds 0.45 Mm b Porosity is the portion of the membrane volume that is occupied by air (not occupied by polymer). 1 cm2 of membrane whose thickness is 140 Mm (i.e., 0.014 cm) will have a volume of 0.014 cm3 (14 ML). If the membrane is 70% porous, it will contain approximately 10 ML of void space c Surface area ratio is the ratio of internal to frontal surface area. A surface area ratio of 250 means that in a 1 cm2 piece of membrane, the polymer surface area is 250 cm2

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5. Properties of PVDF Membrane that Affect Its Performance in ELISPOT

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Like NC, and for the same reasons, the PVDF membrane in plates used for ELISPOT has a nominal pore size of 0.45 Mm. PVDF is nominally 135 Mm thick and about 65–70% porous. The BET surface area is about the same as NCs and its surface area ratio is somewhat higher – around 350 (see Note 4). Consequently, PVDF can bind upward of 350 Mg/cm2 of IgG or in excess of 100 Mg per well of a 96-well plate. As with NC, blocking of PVDF should occur within a few hours (or less) of antibody coating. Failure to do so may result in a rapid and signiﬁcant loss of antibody activity. Once antibody has been coated and the membranes have been blocked (and washed using deionized water or very low molarity buffer), plates can be stored (desiccated and at room temperature) for weeks or even months (see Note 5). PVDF that has been coated with protein (e.g., as a consequence of antibody coating and blocking) will rewet spontaneously upon the addition of aqueous media. The major difference between NC and PVDF in ELISPOT applications is related to their mechanisms of binding and associated differences in handling or pretreatment. PVDF is very hydrophobic (its surface energy is approximately 21 dyn/cm) and will not wet out in water. In Western blotting applications, PVDF is always pre-wet in alcohol (typically 50–100% methanol), then normally exchanged in water, and ultimately equilibrated in a (transfer) buffer solution before applying the membrane to the polyacrylamide gel for electrophoresis. The fact that hydrophobic PVDF membranes will not wet out spontaneously in water – unless coated by (blotted) proteins – is even exploited in a Western blotting application called “Transillumination” (10). The overwhelming majority of literature in Western blotting references the pre-wetting step, so it is not a surprise that many ELISPOT protocols also include an alcohol pre-wet step. What is surprising is that some ELISPOT protocols do not include a pre-wet step. At Millipore (Danvers, MA), experiments were performed to determine the relative performance PVDF 96-well plates (catalogue number: MSIPS4510) that were either pre-wet with 15 MmL of 70% v/v methanol in water and rinsed, or not pretreated at all prior to antibody coating. Brieﬂy, following the alcohol pretreatment (or no pretreatment), plates were coated with 1 Mg of anti-human interferon-gamma (Mabtech, Stockholm, Sweden), and blocked for 2 h in tissue culture media (RPMI, Invitrogen, Carlsbad, CA) containing 10% fetal bovine serum (Invitrogen). 50,000 peripheral blood mononuclear cells (see Note 6) were added per well to 16 wells per plate, stimulated with 0.5 Mg phytohemagglutinin

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Table 2 The effect of alcohol pretreatment of PVDF on ELISPOT assay performance

a

Experiment number

Pre-wet spot number (Mean ± SDa)

Non pre-wet spot number (Mean ± SDa)

Non pre-wet as a percent of pre-wet

1

606 ± 46

413 ± 37

68%

2

577 ± 37

416 ± 34

72%

3

604 ± 35

440 ± 42

73%

4

609 ± 40

391 ± 28

64%

n = 12–16

(PHA-L, Sigma, St. Louis MO) and the plates were incubated overnight in a humidiﬁed, 37°C, 5% CO2 tissue culture incubator. ELISPOTs were visualized using biotinylated anti-human interferon-gamma (Mabtech), conjugated avidin-alkaline phosphatase (Mabtech) and BCIP/NBT Plus (Moss, Inc., Pasadena, CA) and then enumerated using an automated microscope (KS Elispot, Zeiss, Thornwood, New York) and its associated software. The results of four different experiments are summarized in Table 2. In these experiments, the cells in the untreated (nonpre-wet) plates produced approximately 30% fewer detectible spots. However, the consistency well to well and plate to plate was equivalent. Spot quality (intensity, uniformity, and size) and overall assay background were comparable in both plate types. Considering that half these results were obtained without pre-wetting, the comparable, side-by-side performance is quite remarkable. It would appear that the determination to pre-wet with alcohol or not can be made by individual laboratories based on their reagent selections and particular assay requirements. PVDF, like NC, is fully compatible with ELISA detection involving precipitating, color-forming substrates and chemiluminescent substrates. Although the ﬂuorescence background of PVDF is also high, due principally again to light scattering, ELISPOT assays have been developed on PVDF that are based on the use of ﬂuorescently labeled antibodies (11). The ability to develop ﬂuorescent immunoassays on PVDF membranes – particularly useful for applications in which more than one antigen is being simultaneously detected – was significantly enhanced with the commercialization of a specialized PVDF membrane (Immobilon-FL™; Millipore, Billerica, MA, USA in 2003) that was speciﬁcally developed for ﬂuorescence detection.

19

6. 96-Well Plates and 8-Well Strip Plates Speciﬁcally Developed for ELISPOT and Diagnostic ELISPOT Applications

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As was stated earlier in the chapter, 96-well plates were initially designed to enable sample ﬁltration and discrete transfer of ﬂuids. These design elements, which are not useful for ELISPOT, can actually interfere with the ELISPOT assay if the user is not careful. Consequently, in 2003, a 96-well plate with PVDF was commercialized that was designed speciﬁcally for ELISPOT applications (Fig. 5). There is no underdrain to facilitate discrete ﬂuid transfer in these ELISPOT-speciﬁc plates. Consequently, problems associated with alcohol pre-wetting and membrane removal after ELISPOT development are virtually eliminated. One of the issues with 96-well plates of all types is that many ELISPOT assays do not end up requiring the use of all 96-wells and unused wells essentially get wasted. This is especially true in diagnostic applications in which only one or two patients may be tested at a time. For these types of low-throughput applications, there are now 8-well strip plates available (Fig. 6). Individual (8-well) strips can be antibody coated, blocked, and used in an assay without any impact on the remaining strips. This particular format was ﬁrst used in a diagnostic

Fig. 5. 96-well ﬁlter-bottomed plate used speciﬁcally for ELISPOT.

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Fig. 6. Membrane-bottomed 8-well strip plate used speciﬁcally for ELISPOT (especially diagnostic applications).

test approved by the FDA in 2008 for tuberculosis infection and is now also commercially available for research and development applications

7. Final Remarks

Although with proper optimization, it is likely that comparable ELISPOT performance can be achieved using either type of membrane, it is unlikely that the same protocol will work equally well in both cases. Neither PVDF nor NC is a drop-in for the other (see Note 7). Fundamental differences in the two membranes, especially with regard to their mechanisms of binding and their ability to wet directly in water will affect their behavior in ELISPOT. Modiﬁcations that have been made in the design of 96-well plates to make them compatible with automation – including stricter dimensional speciﬁcations and rigid sidewalls (to allow handling by laboratory robotics and provide space for bar codes) have also beneﬁted ELISPOT applications. These plates are now fully compatible with standard plate washers as well as with imaging equipment and image analysis software. It is reasonable to believe, based on the importance of ELISPOT and the impressive growth in the number of assays being performed, that membranes and membrane-based plates may someday be optimized speciﬁcally for this application.

19

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8. Notes 1. PVDF in 96-well plates (for example, Millipore catalogue number MAIPS4510) used for ELISPOT is extremely hydrophobic. It will not wet out spontaneously upon the addition of water. The behavior of PVDF in this regard is signiﬁcantly different from that of NC. 96-well plates containing hydrophilic PVDF (for example, Millipore catalogue number MAHVS4510) will not work at all in the ELISPOT application. 2. As required or desired, the hydrophobic PVDF membrane should be pre-wet by adding 15 ML of 70% methanol to each well. Within 1 or 2 min (or less) of adding the methanol solution, the membrane should be rinsed by adding 100 ML of water or coating buffer to the well and aspirating or decanting immediately thereafter. The rinse step may be repeated once more. The antibody solution should be added immediately after rinsing the membrane. Adding larger (e.g., 50 ML) or more concentrated (e.g., 100%) volumes of methanol creates the risk of liquid collecting under the membrane. This liquid cannot be washed out effectively and may create serious problems later on in the ELISPOT assay. 3. 96-well plates containing NC (for example, Millipore catalogue number MAHAS4510) may contain some wells, especially at the periphery of the plate, that will not wet out immediately upon addition of aqueous solution (e.g., antibody coating buffer). This may be the result of storage and handling conditions. In any event, wells that do not wet out immediately may produce spurious results and should not be used in the ELISPOT assay. 4. The binding capacity of both NC and PVDF (q75 Mg/well) far exceeds the amount of speciﬁc antibody (typically a 5 Mg/well) that is used to coat the membrane. Whereas this is beneﬁcial insofar as it results in the localization of the antibody at or very near the membrane’s top surface, it makes it absolutely necessary to block the membrane to prevent high levels of background due to nonspeciﬁc binding. Membrane should be blocked within a few hours of antibody coating to prevent the loss of antibody activity. 5. Once membranes have been coated and blocked, they may be rinsed in water or low molarity buffer (e.g., 10 mM phosphate, pH 7.4) and stored desiccated for weeks or months. 6. The membrane frontal surface area in a typical 96-well plate is approximately 0.3 cm2. If the responding T-cell is assumed to be round and estimated to have a nominal diameter of 10–15 Mm, approximately 150,000 cells would constitute a

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monolayer. Adding more than approximately 100,000 cells creates the risk of some of these cells not being in intimate contact with the membrane. If the cell secreting cytokine is not in direct contact with the antibody-coated membrane, it is possible that the shape and intensity of its corresponding ELISPOT may be so irregular as to disqualify it from being enumerated. In instances when the response rate to antigen is anticipated to be so low that it is advisable to stimulate 500,000 or 1,000,000 cells to get a signiﬁcant response above background, it might be best to add 100,000 cells per well to 5 or 10 wells and determine the aggregate response £10 wells , not the average response. 7. It is nearly certain that substituting a PVDF plate into a protocol that has been optimized using an NC plate will produce satisfactory results. The opposite is also true. The ELISPOT assay, especially the antibody coating, blocking, and color development steps should be optimized for whichever plate is going to be used. References 1. Southern, E.M. (1975) Detection of speciﬁc sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol. 98, 503–517 2. Towbin, H., Staehelin, T., and Gordon, J. (1979) Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc. Natl. Acad. Sci. U.S.A. 76, 4350–4354 3. Czerkinsky, C.C., Nilsson, L.A., Nygren, H., Ouchterlony, O., and Tarkowski, A. (1983) A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of speciﬁc antibody-secreting cells. J Immunol Methods 65, 109–121 4. Sedgwick, J.D. and Holt, P.G. (1983) A solidphase immunoenzymatic technique for the enumeration of speciﬁc antibody-secreting cells. J Immunol. Methods 57, 301–309 5. Czerkinsky, C., Andersson, G., Ekre, H.-P., Nilsson, L.-A., Klareskog, L., and Ouchterlony, O. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of gamma-interferon-secreting cells. J Immunol Methods 110, 29–36 6. Schielen, P., van Rodijnen, W., Tekstra, J., Albers, R., and Seinen, W. (1995) Quantiﬁcation of natural antibody producing B cells in rats by

an improved ELISPOT technique using the polyvinylidene diﬂuoride membrane as the solid support. J Immunol Methods 188, 33–41 7. Lalvani; Ajit, and Brookes; Roger Hamilton. “Assay method for peptide speciﬁc T-cells.” US Patent 7,575,870. August 18, 2009 8. Pluskal, M.F., Przekop, M.B., Kavonian, M.R., Vecoli, C. and Hicks, D.A. (1986) Immobilon PVDF transfer membrane: a new membrane substrate for Western blotting of proteins. BioTechniques 4, 272–282 [Japanese reference to the use of 0.2 Mm membrane (GVHP) in protein blotting] 9. Brunauer, S., Emmett, P.H., and Teller, E. (1938) Adsorption of Gases in Multimolecular Layers. J. Am. Chem. Soc. 60, 309–319 10. Reig, J.A. and Klein, D.C. (1988) Submicron quantities of unstained proteins are visualized on polyvinylidene ﬂuoride membranes by transillumination. Applied and Theoretical Electrophoresis 1, 59–60. 11. Gazagne, A., Claret, E., Wijdenes, J., Yssel, H., Bousquet, F., Levy, E., Vielh, P., Scotte, F., Goupil, T.L., Fridman, W.H., Tartour, E. (2003) A Fluorospot assay to detect single T lymphocytes simultaneously producing multiple cytokines. J Immunol Methods. 283, 91–98.

INDEX A Adeno-associated virus (AAV) ....................................65–72 Anesthesia reagents acepromazine ...............................................................67 atropine........................................................................67 glycopyrrolate ..............................................................67 lidocaine hydrochloride ...............................................67 propofol .......................................................................67 Antigens FIVBang ................................................................... 51, 57 FIV IWV ...............................................................51, 57 FIVPet ........................................................................... 57 FIV p24 recombinant protein ................................51, 57 HIV–1HXB2 .............................................................51, 57 HIV–1LAV p24..............................................................57 HIV–1 recombinant protein ..................................51, 57 Association for Cancer Immunotherapy (CIMT) .............30

B BALB/c ................................................................... 108–113 Bioterorrist weapon .........................................................200 Blood draw ............................................................... 13, 18, 238 shipping .......................................................................18 storage ........................................................... 18, 93, 120

C Calcium ionomycin (CaI) .................40–42, 91, 92, 116, 118 Cancer Immunotherapy Consortium of the Cancer Research Institute (CIC/CRI) ...................29–31 Cat specific-pathogen-free ...........................................50, 55 C57BL/6 ................................................................. 108–113 CD cells isolation Dynabeads® ................................................................. 50 Ficoll............................................................................ 50 MyOne™ ....................................................................50 Untouched™ ...............................................................50 CEF.................................................... 7, 14, 27, 29, 231, 232 Cells antigen-presenting cells (APCs) ................10, 13, 14, 20, 65, 157, 161, 167, 174, 224–226, 230, 231, 233

BV2 ..................................................................... 98–101 CD14................................................................. 112, 226 CD31......................................................................... 112 CD45......................................................................... 112 CD106....................................................................... 112 CD11c ....................................................................... 112 dendritic cells (DC) .....................................20, 105, 157, 159, 226, 231 fibroblast .............................................106, 108–110, 112 HeLa .......................................... 201, 203, 204, 211, 217 mesenchymal stem cells (MSC) ........................ 105–113 microglial ............................................................. 97–102 productivity.........................................125–142, 157, 161 splenocytes ................................................. 106, 108–113 T cell line ..................................10, 21, 27, 179–182, 224 Cellular immune response ....................4, 65, 66, 68, 69, 220 CMV-canine factor IX (cFIX) ..........................................66 Concanavalin A (Con A)......................40–42, 51, 53, 57, 58 Crystal violet ................................................... 201, 205, 206 CTL-AntiAggregate™........................................................7 CTL-CryoABC™...............................................................7 CTL-Test™ .........................................................................7 Cytokine bead array (CBA) ............................... 8, 9, 13, 157 Cytokines granzyme B.......................................... 4, 8, 26, 126, 161 IFN-G ......................... 4, 8, 10, 11, 14, 18, 26, 27, 30, 50, 77, 79–84, 88, 89, 91–93, 113, 116, 117, 126, 137, 140, 156–159, 161, 174, 179, 180, 185, 219–227, 229–231, 233, 238 IL–2......................... 4, 11, 18, 21, 22, 47–52, 54, 72, 77, 79–84, 88, 89, 91–93, 113, 126, 140, 161, 169, 202, 203, 209–210, 219, 220, 222, 224–226, 231 IL–3........................................................................... 161 IL–4................................... 10, 17, 18, 39–44, 84, 88–93, 113, 126, 140, 161 IL–5................................ 17, 18, 88, 89, 91–93, 140, 161 IL–6............................................................159, 161, 169 IL–7................................................................... 229–239 IL–8............................................................88, 89, 91–93 IL–10 ...... 79, 80, 106, 107, 109, 110, 112, 113, 126, 159, 161, 169, 199–217 IL–12 ................................................................... 49, 231 IL–17 ........................................ 4, 10, 17, 18, 21, 22, 140

Alexander E. Kalyuzhny (ed.), Handbook of ELISPOT: Methods and Protocols, Methods in Molecular Biology, vol. 792, DOI 10.1007/978-1-61779-325-7, © Springer Science+Business Media, LLC 2012

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HANDBOOK OF ELISPOT 258 Index Cytokines (Continued) perforin .............................................4, 8, 17, 26, 47, 161 TNF-A ............. 4, 8, 47, 77, 88, 89, 91–93, 97–102, 129 TRAIL .............................................................. 4, 8, 161 Cytomegalovirus (CMV) ................... 7, 11, 27, 66, 186, 231

D Determinant mapping ...........................................10, 12–13 Dulbecco’s phosphate-buffered saline................................67

E ELISA Assay Diluent RD1–51 .............................................119 Calibrator Diluent RD6–21 ......................................117 microplate reader ...............................................117, 119 Quantikine.................................................................117 ELISPOT antibodies affinity ..........................126, 133–136, 138, 142, 158 anti-CD3 ...............................................................84 anti-CD28 .......................................................81, 83 anti-IFN-G-FITC ............................................81, 84 anti-IL–2-biotin ..............................................81, 84 association rate............................. 128, 134–136, 142 avidity ........8, 12–15, 17, 21, 126, 134, 157, 169, 230 7-B6–1 ................................................................... 80 binding....83, 126, 128–130, 133–136, 138, 142, 250, 255 coating ..................26, 80, 81, 83, 137, 138, 223, 238, 251, 255, 256 9D7........................................................................ 80 dissociation rate ................................... 128, 134–136 1-D1K ...................................................................80 12G8......................................................................80 JES5–2A5 .................................................... 106, 107 JES5–16E3 ..................................................106, 107 asynchronous analyte production .......................139–140 background .....................7, 10, 19–21, 26, 34, 44, 53, 61, 72, 94, 101, 102, 121, 142, 148, 153, 158–159, 168, 169, 173, 175–178, 182, 186–190, 192, 193, 209, 225, 244, 252, 255, 256 BCIP ..................................40, 42, 51, 60, 71, 91, 93, 98, 100, 106, 109, 116, 118, 158, 214, 215, 225, 238, 252 bovine ..................................... 50, 81, 201, 206, 219–227 ex vivo ............................... 10–12, 15, 125, 220, 223, 225 fluorescent Cy3 ............................................................ 78–82, 84 Cy5 ..................................................................79–82 DAPI .....................................................................80 enhancer ..........................................................80, 82 FITC .........................................................78–82, 84 fluorospot............................16, 77–84, 126, 140–142

harmonization ..................15, 25–34, 156, 162, 177, 189 horse .............................................................. 40, 42, 106 image analysis algorithm ....................................15, 49, 61, 145–153 AutoGate™ ................................................. 164, 165 automatic construction ................................ 150–151 brightness .............................146, 147, 150, 151, 153 cluster analysis algorithms ...........................149, 153 Code for Federal Regulations (CFR) Part 11 guidelines .......................................................155 color classification........................................148–150 data analysis ....................8, 12, 15, 19, 155–170, 182 data documentation .............................................166 data management.........................................167, 168 differential equation .............................................129 Good Laboratory Practice (GLP) ..... 12, 15, 21, 166 illumination ................40, 91, 98, 116, 145, 148, 151, 153, 161, 162 image form construction ......................................146 ImmunoSpot® ......................7, 16, 21, 127, 137–139, 156–160, 162, 163, 165–169, 213, 214, 216 iSpot spectrum .......................................................80 local minimums ...................................................148 logarithmic function ............................ 130, 131, 141 morphological approach ..............................147–148 numerical solution ............................... 127, 129–131 pixels ............................................................148, 161 QuantiHub ............................................ 41, 150–152 SmartCount™ .............................................158, 163 SpotMap™ ..................................................162, 167 spot morphology ......................... 125, 126, 132–139, 142, 156–159, 163, 164, 169 spot recognition ...............12, 16, 145–153, 156–160, 163, 164, 179 international Society of Biological Treatment of cancer (iSBTc) ....................................................15, 156 negative control ......... 57, 60, 69, 71, 175–177, 186–190, 192–194, 205, 209, 211, 239 neuroscience research...........................................97–102 nitro blue tetrazolium (NBT) ..............40, 42, 51, 60, 71, 91, 93, 98, 100, 106, 109, 116, 118, 158, 214, 215, 225, 238, 252 optimization ................................................... 31–33, 83, 226, 254 pre-validation.........................................................31, 32 proficiency panel .................................... 27–34, 187, 191 spots automatic search ..........................................145–153 classifying ............................................................146 concentric profile .................................................146 density .................. 130–134, 137, 138, 141, 142, 233 density profile ..............................................130, 134 detection ...................................... 130, 142, 145–153 diffusion ...............................................................158

HANDBOOK OF ELISPOT 259 Index distribution kinetics .....................................127–129 formation .................. 26, 84, 113, 132, 135, 142, 226 fuzzy .........................44, 94, 102, 121, 142, 157, 158, 169, 174, 175 kinetic model ....................................... 127–129, 141 mathematical model ....................................126, 132 morphology .........................125, 126, 132–139, 142, 156–159, 163, 164, 169 numerical solution ............................... 127, 129–131 profile .................................................. 130–132, 135 recognition .......................12, 16, 145–153, 156–160, 163, 164, 179 shape .............................121, 130, 145, 148, 149, 157 size ......................... 21, 126–128, 130–135, 139, 141, 157–159, 161, 164, 166, 167, 170, 173–175, 229–239 spot forming cells (SFC)......................... 42, 43, 101, 186, 188, 190, 220, 221, 223, 225–227 spot forming units (SFU) .............. 53, 54, 56, 59–61 spot size distribution ....................127, 130, 141, 161, 164, 173, 175, 232 too numerous to count (TNTC)..........................169 well-to-well spot consistency ................... 44, 94, 121 standardization Cancer Immunotherapy Immunoguiding Program (CIP) ............................................... 30, 186, 194 empirical approach............................... 177, 187–188 false positive rates ........................ 177, 187, 189–193 HLA-A*0201 restricted epitopes of CMV ..........186 Influenza ..............................................................186 mean responses ....................................................187 negative control ...................57, 60, 69, 71, 175–177, 186–190, 192–194, 205, 209, 211, 239 Q2R1 guideline ...................................................190 signal-to-noise ratio .....................................176, 190 true negative rates ................................................187 true positive rates .................................................187 statistical analysis analysis of variance (ANOVA).....................180, 182 Anderson-Darling test .........................................179 distribution-free resampling (DFR).............188–194 GraphPad Prism® .................................................179 Kolmogorov-Smirnov test ...................................179 MATLAB ...........................................................179 negative binomial distribution .....................178, 181 Poisson distribution .....................................179–183 SAS .....................................................................179 Shapiro-Wilks test ...............................................179 software package R ..............................................179 spot-size gating parameters ................. 159–161, 233 SPSS ....................................................................179 Statistica .............................................................. 179 T-test ...................... 21, 179, 180, 182, 188, 191, 192 type I errors ......................................................... 177

Westfall-Young Stepdown max T adjustment......188 Wilcoxon Rank Sum Test ............................181, 182 streptavidin-AP (Streptavidin conjugated to alkaline phosphatase) .........................51, 59, 71, 214, 215 substrate build-up .............................................. 139, 140 validation .............................................13, 15–16, 26, 27, 31–33, 155–170 ELISPOT suppliers AID Diagnostika .........................................................80 CTL ......................... 7, 12, 15, 16, 18–21, 48, 49, 52, 55, 57, 66, 68, 127, 137, 138, 162, 179, 200, 213, 214, 216, 231–233, 239 Mabtech ................................................ 51, 80, 222, 252 Millipore ...........................50, 68, 80, 106, 108, 116, 222, 226, 244, 245, 251, 252, 255 MVS Pacific ...................................91, 99, 117, 146, 151 R&D Systems....................... 40, 50, 51, 91, 98, 116, 117 Epitope mapping ................................................... 13, 47–61 Epstein Barr virus (EBV) ................................ 7, 27, 79, 231

F FACS......................................................................... 52, 112 Feline immunodeficiency virus (FIV)......................... 47–49, 51–55, 57–59 Feline immunodeficiency virus (FIV) vaccine dual-subtype .................................................... 49, 52, 53 FD–1 adjuvant .............................................................49 Fel-O-Vax®FIV ................................................ 49, 53, 55 FIVPet ..................................................................... 49, 57 FIVShi ........................................................................... 49 IWV .....................................................49, 51–55, 57–59 FIV vaccine. See Feline immunodeficiency virus (FIV) vaccine Frequency measurements ................................8, 9, 12, 15, 20

H Hepatocyte growth factor (HGF) ...................................106 High throughput T cell testing.............................. 12, 13, 66 HiTrap heparin column .....................................................67 HIV vaccine ........................................ 4, 29, 48, 49, 51, 52, 55

I Immune monitoring ..................... 6, 7, 15–16, 26, 28, 33, 34 Immunosuppression ................................ 105–107, 112, 230 Indoleamine 2,3-dioxygenase (IDO)............................... 106

K Keyhole limpet hemocyanin (KLH) ................................237

L Lipopolysaccharide (LPS) ......................57, 98, 99, 101, 159 Luminex .......................................................8, 9, 13, 16, 157

HANDBOOK OF ELISPOT 260 Index M Major histocompatibility complex (MHC) .............7, 12, 13, 48, 49, 55, 65, 66 Media AIM-V® ........................................... 50, 51, 59, 234–236 amphotericin................................................................98 beta-mercaptoethanol .................................... 40, 88, 116 bovine serum albumin (BSA) ................50, 81, 201, 206, 222, 234 conditioned ........................................................106–113 DMEM ............................................... 98, 106, 108, 201 DMSO .......................................................... 67, 69, 202 Eagle’s ........................................................................ 106 fetal bovine serum (FBS) ........................... 106, 221, 251 fetal calf serum (FCS)..........................40, 43, 80, 81, 83, 88, 93, 98, 116, 120, 201, 202, 209–213, 215–217 gentamicin ......................................40, 88, 106, 108, 116 Hanks’ balanced salt solution (HBSS) .................. 50, 91, 201, 222 heat inactivated dog serum ..........................................68 HEPES ....................................40, 80, 88, 106, 116, 221 HIV-seronegative human sera .....................................50 horse serum................................................................106 ISCOVE................................................................67, 69 MEM ........................................................................ 201 penicillin ...................................68, 69, 80, 201, 202, 221 RPMI .................................................. 40, 41, 43, 80, 88, 91–93, 116–118, 120, 202, 208–212, 215, 216, 221, 223, 251 serum free ............................. 7, 18, 20, 30, 169, 176, 179 specific-pathogen-free (SPF) .................................50, 55 Waymouth .............................................................67, 69 Membrane-removal device ............................ 41, 91, 99, 117 MHC. See Major histocompatibility complex (MHC) MIATA ............................................................................. 31 Mixed cellulose ester membranes .................................... 137 Mixed lymphocyte reaction (MLR) .........106, 107, 110–113 Multiplexing .....................................10, 12, 16–17, 137, 140 Myelin basic protein (MBP)..............................................14

N Nitric oxide (NO) ............................................................ 106

P PBST................................................107, 109–111, 222, 223 Peptide library ....................................................... 13, 66, 69 Peripheral blood mononuclear cell (PBMC) apoptotic ...................................................... 19, 176–177 canine .......................................................................... 39 cat ................................................................ 7, 48, 52–57 CD3....................................................................... 60, 84 CD4............................................ 18, 48, 50, 55, 126, 226 CD8.........................4, 18, 48, 50, 55, 126, 186, 199–217

counting ............................ 18, 20, 94, 120, 176, 179, 209 cytotoxic T lymphocyte (CTL) ............7, 15, 48, 66, 200 dog ......................................................................... 66, 68 effector and memory T cell responses ................ 220, 230 equine .................................................................... 40–42 feline ................................................................ 39, 50, 59 freezing ..........................7, 10, 18, 19, 176, 179, 206, 208 HLA-typed ............................................................. 7, 21 horse ............................................................................ 42 infecting with vaccinia virus ...............199–203, 211–212 NK cells ..................................................... 159, 232–234 oxidative stress ............................................89, 90, 92, 93 reference samples ........................................7, 21, 27, 179 resting ..................................... 18–19, 202, 203, 209–210 T cells .................................10, 11, 14, 20, 27, 48, 50, 52, 157, 174, 176, 199–217 Th1 ........................................................................ 48, 93 Th2 ...........................................................78, 88, 93, 200 Th17 ............................................................................ 78 thawing ........7, 18, 19, 174, 176, 179, 202, 203, 208–210 Tregs ............................................................................ 78 trypan blue ........................ 40, 91, 92, 116, 117, 201, 202 Phorbol 12-myristate 13-acetate (PMA) ............. 40–42, 91, 92, 116, 118, 244 Phosphate buffered saline (PBS) .........18, 40, 50, 57, 59, 60, 67, 68, 71, 80–83, 88, 92, 98–100, 102, 107–109, 111, 113, 116, 117, 201, 202, 205, 207–211, 213, 214, 217, 222, 234, 235, 237–239 Plates aluminum foil .......................... 44, 81, 94, 101, 121, 202, 212, 215, 217 autofluorescence.....................................................80, 83 ethanol ..............................................57, 81, 83, 226, 250 IPFL ............................................................................ 80 membranes alcohol pretreatment .................................... 251, 252 blocking ................................................. 83, 116, 251 brittleness.............................................................245 characteristic ........................................................ 250 chemical compatibility .........................................245 chemiluminescence .............................. 246, 250, 252 compatibility with different detection modes ......250 Dot Blot ...................................................... 244, 247 Durapore® ............................................................ 245 flatness ................................................................. 249 fluorescence ......................................................... 250 fluorescence background .............................. 244, 252 Immobilon-FLTM ..............................................252 MAHAS4510......................................................255 MAIPS4510 .........................................222, 226, 255 mechanism of binding ..........................250, 251, 254 methanol ............................... 201, 205, 250, 251, 255 nitrocellulose (NC) ..............226, 243–247, 249–252, 254–256

HANDBOOK OF ELISPOT 261 Index particulates ...........................................................245 polyvinylidene fluoride (PVDF) ............ 50, 226, 243 pore size ............................................... 245, 250, 251 pre-wetting ...................................226, 247, 251–253 protein sequencing ............................................... 246 saturation binding capacity .................................. 250 shedding .............................................................. 245 Slot Blot .............................................................. 247 solvent compatibility ............................................250 surface area ratio ..........................................250, 251 thickness ..............................................................250 wettability ............................................................250 Nunc-Immuno™ washer ......................... 41, 91, 98, 117 washing ................................................ 84, 113, 168, 244 Pokeweed mitogen (PWM).............................................225 Prostaglandin E2 (PGE2) ...............................................106

R Red blood cells (RBC) lysing solution.......................92, 116

S Single cell analysis secretion ......... 3, 6–8, 115–122, 156, 200 Smallpox vaccine .............................................................200 Standardized operating procedures (SOPs) .......... 26, 27, 29, 30, 32, 34 Staphylococcal enterotoxin B (SEB) .......................222, 224

T T cell affinity .............................................................13, 176 T cell avidity ......................... 8, 12–15, 17, 21, 126, 157, 169

T cell mediated immunity ................................. 12, 107, 141 T cell receptor (TCR).................................. 9, 13, 49, 65, 66 Tetanus toxoid (TT) ................................................237, 239 Transforming growth factor-B1 (TGF-B1) ..... 106, 107, 113 Tris ...................................................................106, 201, 204 TrypLE™.......................................................................... 106 Tuberculosis Bacille Calmette-Guérin (BCG) vaccine..................221, 229, 230 bovine ................................................................ 219–227 CFP10 peptides ................................................. 230, 231 ESAT–6..................................................................... 230 human.........................................................219, 221, 234 interferon-gamma (IFN-G) release assay (IGRA) ............ 229–233 Mycobacterium tuberculosis (MTB) ............................. 230 QuantiFERON-TB Gold® (QFT-G)........................ 230 QuantiFERON-TB Gold in Tube® (QFT-GIT) ...... 230 region of difference (RD–1) .......................230, 232, 233 TB 7.7 ....................................................................... 230 T-SPOT.TB® ..................................................... 229–239 tuberculin skin test (TST) ................................. 229–231 Tween 20 ...................................... 68, 71, 107, 213, 222, 250

U Unibrain Fire-i ........................................................ 146, 151 US National Smallpox Vaccinization Program ................ 200

V Vaccinia ................................................................... 199–217

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