Measuring Immunity: Basic Biology and Clinical Assessment Edited by Michael T Lotze and Angus W Thomson AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD PARIS • SAN DIEGO • SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Measuring Immunity: Basic Biology and Clinical Assessment To the Institute and Departmental leaders at the University of Pittsburgh: Richard Simmons, Thomas Starzl, Timothy Billiar, Joseph Glorioso, Ronald Herbman and Arthur Levine who have all supported our work both in the laboratory and the clinic This book is printed on acid-free paper Copyright © 2005, Elsevier Ltd All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (ϩ44) 1865 843830, fax: (ϩ44) 1865 853333, e-mail: permissions@elsevier.co.uk You may also complete your request on-line via the Elsevier homepage (http://www.elsevier.com), by selecting ‘Customer Support’ and then ‘Obtaining Permissions’ Elsevier Academic Press 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.elsevier.com Elsevier Academic Press 84 Theobald’s Road, London WC1X 8RR, UK http://www.elsevier.com British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2004116778 ISBN 0-12-455900-X For information on all Elsevier Academic Press publications visit our website at http://www.books.elsevier.com Typeset by Newgen Imaging Systems (P) Ltd., Chennai, India Printed and bound in Great Britain 05 06 07 08 Working together to grow libraries in developing countries www.elsevier.com | www.bookaid.org | www.sabre.org Contents Foreword Jeffrey A Bluestone and Vicki Seyfert-Margolis ix Preface Michael T Lotze and Angus W Thomson xiii Contributors xv Section I Fundamentals of the Immune Response MHC Class I Russell D Salter MHC Class II Amy Y Chow, Julia J Unternaehrer and Ira Mellman Cytokine receptor heterogeneity David H McDermott Genetic diversity at human cytokine loci in health and disease Grant Gallagher, Joyce Eskdale and Jeff L Bidwell Signaling molecules affecting immune response Paul J Hertzog, Jennifer E Fenner and Ashley Mansell Toll-like receptors in innate immunity Thomas R Hawn and David M Underhill DNA sequence-specific transcription factors Philip E Auron Genetic diversity in NK and NKT cells Rachel Allen and Anne Cooke Section II Serologic Assays Handling sera and obtaining fluid from different compartments Dmitriy W Gutkin, Diana Metes and Michael R Shurin 10 Acute-phase proteins and inflammation Chau-Ching Liu and Joseph M Ahearn 11 Complement in health and disease Chau-Ching Liu and Joseph M Ahearn 12 Immunoglobulin titers and immunoglobulin subtypes Popovic Petar, Diane Dubois, Bruce S Rabin and Michael R Shurin 13 Human antiglobulin responses Lorin K Roskos, Sirid-Aimée Kellermann and Kenneth A Foon 14 Rheumatoid factors Martin A.F.J van de Laar 15 Autoantibodies Ezio Bonifacio and Vito Lampasona 16 Antibody affinity using fluorescence Sergey Y Tetin and Theodore L Hazlett 17 SLE-associated tests Maureen McMahon and Kenneth Kalunian 18 Multiplexed serum assays Anna Lokshin 12 23 35 62 80 91 110 119 121 131 144 158 172 187 193 201 210 221 vi Section III Contents Cellular Enumeration and Phenotyping 231 19 Handling and storage of cells and sera: practical considerations Stephen E Winikoff, Herbert J Zeh, Richard DeMarco and Michael T Lotze 233 20 Phenotypic and functional measurements on circulating immune cells and their subsets Albert D Donnenberg and Vera S Donnenberg 237 21 Natural killer cells Bice Perussia and Matthew J Loza 257 22 Tetramer analysis Peter P Lee 268 23 Peripheral blood naive and memory B cells Jean-Pierre Vendrell 277 24 Dendritic cells Kenneth Field, Slavica Vuckovic and Derek N.J Hart 290 25 Monocytes and macrophages Salvador Nares and Sharon M Wahl 299 26 Tumor cells Hans Loibner, Gottfried Himmler, Andreas Obwaller and Patricia Paukovits 312 27 Regulatory T cells Zoltán Fehérvari and Shimon Sakaguchi 322 28 Intracellular cytokine assays Amy C Hobeika, Michael A Morse, Timothy M Clay, Takuya Osada, Paul J Mosca and H Kim Lyerly 336 Section IV 341 Cellular Function and Physiology 29 Cytolytic assays Stephen E Winikoff, Herbert J Zeh, Richard DeMarco and Michael T Lotze 343 30 Mixed leukocyte reactions Stella C Knight, Penelope A Bedford and Andrew J Stagg 350 31 Antigen/mitogen-stimulated lymphocyte proliferation Theresa L Whiteside 361 32 Monitoring cell death Deborah Braun and Matthew L Albert 369 33 Cytokine enzyme linked immunosorbent spot (ELISPOT) assay Donald D Anthony, Donald E Hricik and Peter S Heeger 380 34 Testing natural killer cells Nikola L Vujanovic 396 Section V 405 Provocative Assays in vivo 35 Delayed type hypersensitivity responses William J Burlingham, Ewa Jankowska-Gan, Anne M VanBuskirk, Ronald P Pelletier and Charles G Orosz 407 36 Rebuck windows: granulocyte function Daniel R Ambruso 419 37 The vascular and coagulation systems Franklin A Bontempo 428 38 Sentinel node assays Galina V Yamshchikov and Craig L Slingluff, Jr 434 39 Imaging inflammation N Scott Mason, Brian J Lopresti and Chester A Mathis 445 Contents Section VI Assays in Acute and Chronic Diseases vii 463 40 Cancer – solid tumors Mary L Disis and the Immunologic Monitoring Consortium 465 41 Cancer – hematologic disorders Edward D Ball and Peter R Holman 473 42 Autoimmunity – rheumatoid arthritis Peter C Taylor 481 43 Autoimmunity – type diabetes Patrizia Luppi and Massimo Trucco 494 44 Autoimmunity – systemic lupus erythematosus Sharon Chambers and David A Isenberg 505 45 Autoimmunity – multiple sclerosis Beau M Ances, Nancy J Newman and Laura J Balcer 515 46 Autoimmunity – inflammatory bowel disease Scott E Plevy and Miguel Reguiero 525 47 Autoimmunity – endocrine Michael T Stang and John H Yim 543 48 Autoimmunity – vasculitis Jan Willem Cohen Tervaert and Jan Damoiseaux 560 49 Transplantation Darshana Dadhania, Choli Hartono and Manikkam Suthanthiran 569 50 Viral responses – HIV-1 Bonnie A Colleton, Paolo Piazza and Charles R Rinaldo Jr 578 51 Viral responses – epstein-barr virus David Rowe 587 52 Viral responses – hepatitis Tatsuya Kanto 598 53 Dermatology Clemens Esche 610 54 Arteriosclerosis Beatriz Garcia Alvarez and Manuel Matas Docampo 620 55 Primary immunodeficiencies Robertson Parkman 630 56 Asthma and allergy Lanny J Rosenwasser and Jillian A Poole 639 Section VII 647 New Technologies 57 Serum proteomic profiling and analysis Richard Pelikan, Michael T Lotze, James Lyons-Weiler, David Malehorn and Milos Hauskrecht 649 58 Imaging cytometry Michael T Lotze, Lina Lu and D Lansing Taylor 660 59 Cancer biometrics Monica C Panelli and Francesco M Marincola 666 60 Genomics and microarrays Minnie Sarwal and Farzad Alemi 697 61 Image informatics Andres Kriete 707 Index 713 Christopher Gibson (Publishing Director, Elsevier), Victoria Lebedeva (Developmental Editor, Elsevier), Angus W Thomson (Editor), Tessa Picknett (Senior Publisher, Elsevier) and Michael T Lotze (Editor) Foreword THE BEDSIDE IS THE BENCH Jeffrey A Bluestone1 and Vicki Seyfert-Margolis2 Director, Immune Tolerance Network, Director and Professor, UCSF Diabetes Center and the Department of Medicine, University of California, San Francisco, San Francisco, CA; 2Executive Director, Tolerance Assay Group, Immune Tolerance Network and Assistant Professor, UCSF Diabetes Center and the Department of Medicine, University of California, San Francisco, San Francisco, CA, USA A young woman confronted with a diagnosis of systemic lupus erythematosus (SLE) can expect lifelong complications arising from the disease itself, as well as the therapies used to treat this condition About 50–70 per cent of SLE patients experience inflammation of the kidneys As such, the young woman can expect to be treated with high doses of corticosteroids, often accompanied by the alkylating agent cyclophosphamide Unfortunately, the prednisone and cyclophosphamide treatment often results in an initial improvement, but more than 50 per cent of SLE patients will experience a disease flare again within years Moreover, serious complications of high-dose corticosteroid and cytoxan therapy in SLE patients include osteoporosis, aseptic necrosis, hypertension, diabetes, opportunistic infection, and cataracts as well as gonadal failure, hemorrhagic cystitis and cancer Clearly, safer and more effective therapies are needed for SLE Most importantly, there is no way to predict the flares or remission using immunological analyses in affected patients Practically speaking, treatment of SLE and other autoimmune diseases remains similar to the therapies used 10 years ago However, years of elegant work studying immunity and immune-mediated diseases in animal models combined with recent advances in human immunology and genomics offers an unprecedented opportunity to develop new therapies There is, arguably, no more important concern in moving forward in the development of new immunotherapies than the measurement and quantification of the human immune response Indeed, with the observed increase in immune-mediated disease and an ever-growing stable of immunomodulatory agents reaching clinical stages of development, the need for reliable indicators of the state of the human immune system has never been greater The editors of this guide should therefore be congratulated for assembling a highly relevant, and indeed, very timely portrait of our current abilities and future prospects in this respect Importantly, if perhaps not unexpectedly, we have come to discover that the human immune system differs in many significant ways from the preclinical animal models used as justification for pursuing new therapies in human studies A growing body of literature detailing the many examples of therapies that work well in mice but fail to generate similar efficacy in humans (Mestas and Hughes, 2004) underscores the divide between our respective understanding of mouse and human immunology The scarcity of hard human data on immune mechanisms is truly the Achilles heel of immune-based therapeutic development Typically, immune-based diseases are diagnosed by measuring a pathological process that has already taken place This means that the destruction by the immune system is already well underway Effective monitoring and early detection of these diseases is challenging at many levels, unlike preclinical efforts which can sample the immune response at the site of immune attack (e.g graft, draining lymph node or inflamed tissue); human sampling is relegated often to the peripheral blood far away from where the action is and rarely before the immune response is already damaging to the target tissue x Foreword Take for example, the case of organ transplantation, where the key clinical challenges are to combat both acute and chronic rejection At present, the gold standard for diagnosis of organ dysfunction is biopsy, which while accurate, provides its diagnosis only after significant organ damage has occurred Immunological methods that detect events occurring upstream of the pathology would provide a welcome window of opportunity for earlier intervention A related issue in organ transplantation is that of clinical tolerance induction New potential tolerogenic strategies are now entering the clinic, many with the goal of complete immunosuppressive therapy withdrawal Immunosuppressive withdrawal, however, is more than just the objective of these studies; rather it has been elevated to the status of an endpoint for these trials Until have a clear description of the immunological properties of tolerance in humans, we are left with only an operational, rather than mechanistic definition of tolerance in humans Achieving a therapeutic benefit is the goal of all phase II and III trials and is currently measured using clinical endpoints Clinical indicators, as currently measured, often not offer objective quantitative markers for assessments of drug actions Thus clinical endpoints will greatly benefit from the addition of studies designed to measure human immunity qualitatively and quantitatively There is a pressing need for new surrogate markers for measuring changes in the immune system A case demonstrating the problems associated with relying on clinical endpoints can be made by looking at the history of immunologic therapies for HIV infection Antiretroviral therapy has effectively reduced the rate of progression of HIV-infected patients to AIDS to ~2 per cent per year Thus, trials of additional therapies require large patient populations and/or many years of treatment in order to obtain statistically significant proof of improved efficacy Furthermore, studies of early HIV infection are virtually impossible without some alternative marker for disease progression because of the long time it takes (up to 10 years or more) for many patients to get sick Similarly, in the case of cancer, current therapeutic inventions rely on clinical endpoints such as disease progression and death to determine efficacy These endpoints, although a fair assessment of the clinical efficacy of the therapy, not provide insights in the immune manifestations of therapy Is the immune system activated by the therapy, is the tumor resistant to the therapy or does it escape immune surveillance by mutating target antigens? But perhaps the clinical settings that most appropriately illustrate the need for new technologies and data that allow us to characterize the human immune system are the autoimmune diseases The diagnosis of specific autoimmune diseases is often problematic due to overlapping pathologies and a lack of clearly distinguishable clinical features between the various diseases American College of Rheumatology (ACR) diagnostic guidelines rely upon primarily pathologic criteria that, similar to the diagnosis of allograft rejection, present well into disease development – features such as clinical and radiological evidence of tissue damage The prognostication of specific autoimmune diseases presents an even greater challenge, given that the etiology of many of these diseases remains unclear In fact, one of the most fundamental questions in autoimmunity remains unanswered: what are the immunological characteristics that distinguish a healthy patient from one with an underlying autoimmune disorder? At present, there are no reliable laboratorybased immunologic methods that are capable of discriminating between a rheumatoid arthritis patient from a healthy control and a multiple sclerosis patient from the same This ‘readout’ problem is so severe that in diseases such as type diabetes, current therapeutic interventions rely on clinical endpoints such as hemoglobin A1c to determine efficacy This metabolic parameter can be influenced by the rigor of glucose control, diet and environmental factors not the quintessential immunology of autoimmune disease If we have no measurable description of the immunological hallmarks of the disease itself, how then can we begin to assess the efficacy of one therapy over another? Clearly, our potential for success in the clinic is now limited by our inability to assess the immunological impact of our interventions Throughout the field of immunology, it is therefore imperative that we develop new biological assays that allow precise and reliable measures of human immunity The benefits will be enormous: surrogate markers for clinical efficacy providing more relevant, accurate and ethically justified means of assessing new therapeutics; new diagnostic tools that would permit earlier intervention and perhaps even preventative therapies; the ability to move beyond ‘one size fits all’ medicine towards more individualized therapy; and a wealth of new, direct knowledge of the human clinical experience that will pave the way for improved, second generation therapies Much of the research elegantly summarized in this book reflects the growing efforts to identify specialized markers that can be used in individual disease settings to distinguish the patient from normal individuals, the responder from the non-responders Thus, the papers presented within this volume are a testament to the grand opportunity that lies before us They serve not only to highlight the progress already achieved towards this goal, but present us with a series of difficult challenges as we move forward Together they suggest that we have moved into a new phase of development in measuring immunity, one where old approaches might be best discarded in favor of a new paradigm for assay development In fact, this new paradigm may be best summed up by the multiple efforts emerging in the academic community, with the primary goal to develop robust standardized assays for measuring human immunity These efforts include various workshops, as well as the emergence of several large clinical trials consortiums such as the Control PBMC DC2 DC1 103 HLADR PC5 SSc log 24% 102 101 100 68% 100 CD4 PC7 CD3,14,19 ECD 101 102 103 1000 1000 103 13% 102 101 Ascites 100 75% 100 101 102 103 1000 1023 65% 102 SSc log LN CD123 PE 103 101 100 28% 100 101 102 103 CD11c FITC FSc Figure 20.1 Detection of DC1 and DC2 DCs in the peripheral blood of a healthy subject are contrasted with ascitic fluid and peritoneal lymph node (LN) from a patient with newly diagnosed untreated ovarian cancer Cell suspensions were stained with antibodies CD11c-FITC, CD123-PE, a cocktail of CD3, CD14 and CD19-PE, HLADR-PC5 and CD4-PC7 Cells were acquired on a Beckman-Coulter FC500 cytometer In this analysis, lymphoid DCs have been color-evented red and myeloid DCs have been colored blue The differences in scatter properties of these two populations are shown in the last column In ovarian cancer, myeloid DCs are larger in ascites, compared to control peripheral blood In LN, where lymphoid DCs predominate, the myeloid DC evidence a population with low forward and side scatter, consistent with apoptosis 10 10 10 CD4 ECD FSC 102 • 101 V-beta 20 10 10 103 V-beta V-beta 17 102 101 104 V-beta 16 101 102 103 V-beta FITC V-beta FITC 104 103 H 102 101 20 40 60 80100 120 FSC 60 50 40 30 20 10 0 102 V-beta 5.1 103 20 30 40 Time (arbitrary units) 50 60 2.0 5.1 14.0 17.0 7.1 3.0 13.1 8.0 22 21.3 9.0 4.0 1.0 20.0 13.6 13.2 12.0 16.0 11.0 23.0 5.3 5.2 18.0 7.2 V-beta 18 101 Percent CD8+ T cells Flu TMer APC 102 60 50 40 30 20 10 103 D 10 103 101 E V-beta PE SSC FSC Width CD8 PC7 C G F 104 G 104 F 104 A V-beta PE B 120 B 100 80 60 40 20 0 20 40 60 80100 120 20 40 60 80100120 FSC FSC A 120 100 80 60 40 20 V-beta Figure 20.4 Rare event detection of TCR V-beta usage among Flu tetramerϩ CD8ϩ T cells In panel A, a gate is created on forward scatter pulse height and forward scatter pulse width, excluding doublets (high pulse width for a given pulse height) In panel B the analysis is limited to events displaying lymphoid forward and side light scatter properties Panel C (dump gate) is used both to positively identify CD8ϩ T cells and to eliminate CD4ϩ T cells (and anything else appearing positive in the ECD channel) Panel D is the live gate on tetramerϩ events Only tetramerϩ events falling within this gate were saved in the listmode file, greatly reducing the size of the datafile and increasing the speed of analysis Forward scatter is shown versus Time (panel E), a calculated parameter that is used to identify and eliminate any spurious event bursts (none were detected here) Finally, panels F and G (from separately stained tubes) show the results of the application of this compound gating strategy Panel F shows the usage of V-beta 18 (0.04 per cent), 5.1 (0.0 per cent) and 20 (1.9 per cent) among Flu tetramerϩ/CD8ϩ/CD4Ϫ cells Panel G shows that the majority of Flu tetramerϩ T cells (51.4 per cent) are V-beta 16ϩ Panel H shows the final result, the V-beta repertoire of flu tetramerϪ and flu tetramerϩ CD8ϩ T cells Data from tetramerϩ events were collected for 24 V-beta specificities Data were acquired separately for determination of V-beta usage in tetramer negative T cells (without the use of the live gate D) The results are plotted in the order of frequency of V-beta usage in tetramer negative CD8ϩ T cells (blue bars) Common specificities such as V-beta 2, 5.1, 14 and 17 were very underrepresented in flu tetramerϩ T cells (red bars), whereas V-beta 16, which comprised only 1.2 per cent of total CD8ϩ T cells, accounted for 51.4 per cent of flu tetramerϩ T cells These results are consistent with the clonality (or pauci-clonality) of the majority of influenza matrix peptide specific T cells in this sample Number SSc log CD45RA PE Rhodamine 123 CD8 ECD LOAD EFFLUX Z42412 15 30 Time (minutes) • Caspase+ prop to Anx+ • CaspaseINT annexin+ • Early Annexin V • Caspase- annexin+ • Intermediate • Late ΦΦLux 2 Annexin V ΦΦLux 1 3 Forward scatter Z43254 Forward scatter Figure 20.5 R123 load and efflux in CD45RA negative central memory/effector memory peripheral CD8ϩ T cells Peripheral blood mononuclear cells were obtained from a normal healthy volunteer Cells were stained with antibodies CD45RA-PE, CD8-ECD and CD4 plus CD3 PE-Cy5 Cells were acquired on a Beckman-Coulter XL cytometer Cells were gated on CD3ϩ events (not shown) Low side scatter CD8ϩ, CD45RA- T cells were identified using a compound gate (top panels) The lower panels show R123 fluorescence as a function of acquisition time During the first of acquisition and prior to addition of R123, baseline autofluorescence was determined R123 LOAD: Acquisition of R123 unstained sample was paused, R123 was added (0.13 ␮M final concentration) and acquisition was immediately resumed; cells were acquired continuously for an additional 15 R123 EFFLUX: Loaded cells were chilled and washed times with 10 ml of ice cold PBS, resuspended in culture medium at 37ЊC and returned to the cytometer Cells were maintained at 37ЊC and data acquired continuously for 30 R123 loading and efflux were also determined for CD3ϩ CD8ϩ CD45RAϩ cells and for the analogous CD4ϩ T-cell subsets (not shown) Figure 20.6 Early events in T cell apoptosis Ficoll/Hypaque separated mononuclear cells recovered from the ascitic fluid of an untreated ovarian cancer patient were stained with antibodies CD45RA-ECD, CD27-PC5 and CD4-PC5 Cells were washed and incubated with Phi Phi Lux-G1D2, washed again, resuspended in high calcium buffer and incubated with Annexin V-PE Cells were acquired immediately on a Beckman-Coulter FC500 cytometer CD4ϩ effector memory cells were identified by gating on CD4ϩ, low and intermediate side scatter, CD27Ϫ and CD45RAϪ events (not shown) Color eventing was used to identify three populations of cells based on caspase activity and annexin V binding In the first population (blue), caspase activity and annexin V binding increase coordinately, forming a diagonal in the bivariate scatter plot In the second population (yellow) annexin V binding remains high, but caspase activity, while still present, is reduced In the third population (red), annexin binding is positive and somewhat decreased, but at this point caspase activity is lost The temporal sequence of apoptotic events was confirmed by backgating on Phi Phi Lux versus forward light scatter, using color eventing to identify the three populations Here, it can be seen that the first population (blue) forms an arc as it gains and then loses caspase activity as it collapses in forward scatter The second population already has low light scatter and further loses caspase activity The red population, the last in the sequence, consists of small annexin V low, caspase negative cells Note that a few red colored events have normal forward scatter characteristic These represent annexin V- cells that were misclassified when the red color event gate was created in the top right panel SSc log SSc log Live CD4 DIP d5 CD4+ 1989.LMD FSc CD4 PC7 A 51% 33% 20% CD27 PE 42% C C 3% B 8% CD25 PC5 A B 40% D D CD45RA ECD CD71 FITC 11% Figure 20.7 Antigen-driven upregulation of CD25 and CD71 in CD4ϩ T cell subsets Peripheral blood mononuclear cells were obtained from a healthy subject and cultured for days in the presence of diphtheria toxoid (2 ␮g/ml) Cultured cells were harvested and stained with antibodies CD71-FITC, CD27-PE, CD45RA-ECD, CD25-PC5 and CD4-PC7 Cells were acquired on a Beckman-Coulter FC500 cytometer In the top left panel, responding cells can be visualized as CD4ϩ T cells with high log side scatter CD4ϩ cells were filtered through a scatter gate (top right panel) used to eliminate late apoptotic cells, if present and subsetted on the basis of CD45RA and CD27 expression (A, B, C, D) The outcome parameters, CD71 and CD25 were determined in each subset CD27 PE SSc log CD25 PC5 CD4 Gated CD71 FITC FSc CD45RA ECD Figure 20.8 The use of color-eventing simultaneously to view outcome and classification parameters The data are the same as those displayed in Figure 20.7 Here the outcome parameters CD71 and CD25 have been divided into quadrants and each quadrant used as a color-evented gate For example, CD71/CD25 double positive cells were colored gold These events are then displayed in bivariate scatter plots of forward by log side scatter and CD45RA by CD71 expression Proliferating cells (gold) can clearly be visualized as CD45RA negative, CD27 intermediate cells with high forward scatter and high log side scatter SSc log A FSc CD8 APC FSc SSc log CD27 PC5 CD4 PC7 CD45RO A405 B CD45RA ECD CD62L PE R10 R11 R12 CD45RA ECD CCR5 APC-C7 CCR5 APC-C7 CD27 PC5 R9 CCR7 FITC CD45RO A405 CCR7 FITC CD62L PE Figure 20.9 Polychromatic visualization of differentiation markers on CD4ϩ and CD8ϩ T cells The gating strategy for this analysis begins in A in the same way as in Figure 20.7, this time with CD4ϩ and CD8ϩ populations identified and subsetted on CD45RA and CD27 in parallel From here the subpopulations each give rise to two bivariate plots (CD62L versus CD45RO and CCR7 versus CCR5) The data for the CD8ϩ population is shown in 9B Note that CD8ϩ naive cells comprise a homogeneous population of CD45RAϩ, CD45ROϪ, CCr7ϩ, CCD5Ϫ cells, the majority of which are CD62Lϩ A plasma Crosslinking to red blood cells and unwanted cells enriched B cells Ficoll-Hypaque Ficoll-Hypaque B CD40L-transfected CDw32L cells CD40 L a CD40 + IL-2 + IL-10 spin Red blood cells and rosetted cells Ig-SCs: two-color ELISPOT assay IgG/IgA IgG/IgM b Day Memory B cells Specific ASCs: Ig-sub class ELISPOT assay IgG IgA IgM c Ig-SCs Day Specific antibody ELISA in supernatants d Day 10 e Immunoblot Day 10 Figure 23.2 General procedure for detection and enumeration of circulating Ag-specific memory B cells (A): Isolation of the derived peripheral B cells by eliminating unwanted cells and red blood cells using the tetrameric antibody complex reagent and gradient (Ficoll-Hypaque) centrifugation (Ba): The purified B cells are cultured with CD40L-transfected CDW32L mouse fibroblasts and IL-2 plus IL-10 (Bb): Ig-SCs are enumerated by the two-color ELISPOT assay at day (Bc): Inducible specific antibody-secreting cells are enumerated at day by an ELISPOT (Bd): Specific antibodies are detected in cell-free supernatants at day10 by ELISA (Be): Antibody specificities are determined by immunoblotting Ig-SCs, immunoglobulin secreting cells; ASCs, antibody secreting cells C 103 102 PC5-CD27 Fs-scatter PC5-CD27 CD 19+ 101 100 101 102 103 100 102 101 103 100 CD38+ 85.1% 101 100 PE-CD38 102 PC7-CD19 103 102 103 CD38+ 91.5% 101 FITC-IgD 102 103 101 102 103 FITC-IgD 103 CD38++ 78.1% CD38++ 0.5% 102 CD38+ 79.2% 101 100 E4 3.2% 100 100 H 103 CD38++ 5.5% 101 101 FITC-IgD 102 100 D4 11.7% 101 CD27+ IgD+ IgM+ memory B cells 35% 100 Naive B cells 48.3% G 103 CD38 ++ 3.4% PE-CD38 101 F 103 101 102 PC7-CD19 E 100 102 100 PC7-CD19 10 103 PE-CD38 100 D 103 Memory B cells 49.6% PE-IgM B 102 CD38+ 23.1% 101 100 H4 20.5% 100 PE-CD38 A 101 102 PC5-CD27 103 100 101 102 103 PC5-CD27 Figure 23.3 Circulating B cell subsets characterized according to membrane IgD, IgM, CD27, and CD38 receptor expression After washing in RPMI, the blood is divided into two samples: one is incubated with a mixture of phycoerythrin-cyanin (PC7) conjugated anti-CD19, phycoerythrin-cyanin (PC5) conjugated anti-CD27, phycoerythrin (PE) conjugated anti-IgM and fluorescein isiothiocyanate (FITC) conjugated anti-IgD antibodies The second is incubated with a mixture of PC7 conjugated anti-CD19, FITC conjugated anti-IgD, PC5 conjugated anti-CD27 and PE conjugated anti-CD38 (A) Memory B cells and naive B cells are enumerated on the basis of size and CD19 expression (B) CD19ϩ CD27ϩ total memory B cells (C) CD19ϩ IgDϩCD27Ϫ naive B cells (D) CD19ϩ CD27ϩ IgMϩ IgDϩ memory B cell subset Using the second sample, subpopulations of circulating B cells are analyzed on the basis of their CD38 expression: (E) CD19ϩ CD38ϩ; (F) naive B cells CD38ϩ or CD38ϩϩ; (G) memory B cells CD38ϩ or CD38ϩϩ and (H) activated memory B cells CD38ϩ or CD38ϩ ϩ The horizontal line separates the CD38ϩ negative and positive subpopulations defined using isotypic controls The upper quadrant is divided into two subpopulations of CD19ϩ cells expressing high CD38ϩϩ or low CD38ϩ expression after CD40-CD40L B-cell activation in vitro The number of B cells in each population is determined by using calibrated fluorospheres Kit (Flow Count™) according to the manufacturer’s instructions (Beckman-Coulter) Figure 25.1 Monocyte and macrophage antigen expression in bone marrow, blood or tissues Promonocytes exit the bone marrow, mature into monocytes and express receptors important in phagocytosis and microbial recognition Monocytes leave the circulation and take up residence in tissues where they differentiate into morphologically, phenotypically and functionally heterogeneous effector cells such as macrophages or dendritic cells Macrophages become activated if exposed to antigens, pathogens and to substances such as lipopolysaccharide (LPS) via TLR and interferon-␥ (IFN-␥) (classically activated) or IL-4, IL-13 (alternative activation) Antigens expressed on activated macrophages are denoted with asterisks (*) Fluorescence intensity A 80 60 40 20 0 10 20 30 60 Time (minutes) B C 10 60 Figure 25.4 (A) Phagocytosis of FITC-labeled zymosan A particles by monocytes isolated by counterflow centrifugal elutriation and analyzed by FACS A 20:1 zymosan A:monocyte ratio was used and samples harvested and analyzed by FACS at 10–60 after the addition of particles Photomicrographs of monocytes after a 10-min (B) and 60-min (C) incubation with FITC zymosan Bone marrow Blood Plasma Depletion of erythrocytes Enrichment of DTCs by Positve selection of antigen expressing cells Depletion of hematopoietic cells Density centrifugation Immunocytology PCR techniques Immunostaining DNA/RNA extraction Microscopic analysis PCR/RT-PCR Detection of amplified target molecules Enzymatically Flurescent double stained DTCs stained DTCs (Cytokeratin/red) (Cytokeratin/red EpCAM/green; nucleus/blue) Ethidium bromide gel DNA ladder Positive control Positive sample Figure 26.1 Illustration of methods for the detection of disseminated tumor cells in blood and bone marrow Figure 41.2 H&E section of a follicular center cell non-Hodgkin’s lymphoma 985 920 850 785 720 650 585 520 450 Figure 43.2 Photograph of the computer screen displaying a gel of peripheral blood lymphocytes from a type diabetic child subjected to TCR V␤-specific amplification The fluorescent blue PCR products corresponding to V␤ gene families 1-24 were electrophoresed on a 16-cm, per cent polyacrylamide non-denaturing gel for h on an ABI 377 DNA Sequencer and automatically analyzed by GeneScan software As internal control, a portion of the C␣ region of the TCR is also amplified by using primers labeled with a different dye (6-TET), which originate green bands The relative abundance of T cells carrying each V␤ chain transcript is estimated by calculating a ratio of V␤ fluorescent and C␣ fluorescent areas for each V␤ gene family Figure 53.1 Skin is composed of epidermis, dermis and subcutaneous tissue (Courtesy of Drs P.M Manolson and T.L Barrett, Johns Hopkins.) A B C D Figure 53.2 (A) Melanocytes (arrowed) (C) Langerhans cells express CD1a protein (D) Immunofluorescence staining for HLA-DR in an epidermal sheet (Courtesy of Drs P.M Manolson and T.L Barrett, Johns Hopkins (A–C) and N Romani, University of Innsbruck, Austria (D).) A B C D E F Figure 53.3 Pemphigus vulgaris (A) Bullae occur intraepidermally and therefore rupture easily, leaving denuded erosions (B) Suprabasilar blistering (C) Direct immunofluorescence demonstrates IgG in the intracellular regions of the epidermis Bullous pemphigoid (D) Bullae occur on erythematous patches and urticarial plaques (E) Subepidermal blistering (F) Direct immunofluorescence staining shows linear deposit of IgG and complement at the dermal-epidermal junction (Courtesy of Dr G.J Anhalt, Johns Hopkins.) A B C Figure 53.4 Patch testing (A) Test materials are applied to the skin under occlusive patches (B) that are removed after 48 h (C) ϩ2 reaction Normal cells Apoptotic cells Figure 58.5 Multiple apoptosis analysis F-actin appears in green, nuclei in blue and mitochondria in red Figure 54.1 Unstable carotid plaque 100 80 60 40 20 % of cells with dye 120 Live cell dye Dead cell dye Wells Figure 58.6 Cell viability assay by ArrayScan II Figure 54.2 Cells in an unstable carotid plaque: macrophages and T lymphocytes Mitotic index: MDCK + Vinblastine (100 nM, 12 hrs) 70 50 40 30 20 10 A B C D E Wells Figure 58.7 Mitotic index to quantify curacin A antimitotic effect F G H Mitotic index (%) 60 Protein G p50 FRB (a) (b) (d) (e) (a) DNA Primer Antibody Enzyme Generic DNA Circle (b) Target (c) Signal-generating “decorator” (c) (d) (e) mm Figure 59.1 Detecting protein-protein interactions on glass slides (a) Slide probed with BODIPY-FL-IgG (0.5 ␮g/ml) (b) Slide probed with Cy3-IkB␣ (0.1 ␮g/ml) (c) Slide probed with Cy5-FKBP12(0.5 ␮g/ml) and no rapamycin (d) Slide probed with Cys-FKBP12 (0.5 ␮g/ml) and no rapamycin (e) Slide probed with BODIPY-FL-IgG(0.5 ␮g/ml)) Cy3-IkB␣ (0.1 ␮g/ml) Cy5-FKBP12 (0.5 ␮g/ml), and 100 nM rapamycin In all panels, BODIPY-FL, Cy3, and Cy5 fluorescence were false-colored blue, green, and red, respectively (a) Figure 59.4 Schematic representation of immunoassays with RCA signal amplification (a) In the adaptation of RCA used for protein signal amplification, the end of an oligonucleotide primer is attached to an antibody (b) The antibody-DNA conjugate binds to its specific target molecule; in the multiplexed microarray immunoassay, the targets are biotinylated secondary antibodies and the conjugate is an anti-biotin antibody (c) A circular DNA molecule hybridizes to its complementary primer on the conjugate, and in the presence of DNA polymerase and nucleotides, rolling-circle replication occurs (d) A long single DNA molecule that represents a concatamer of complements of the circle DNA sequence is generated that remains attached to the antibody (e) This RCA product is detected by hybridization of multiple fluorescent, complementary oligonucleotide probes RCA product fluorescence is measured with a conventional microarry scanning device The amount of florescence at each spot is directly proportional to the amount of specific protein in the original sample (b) Trends in biotechnology Figure 59.3 An illustration of the presentation of immobilized proteins in an array (a) An idealized illustration of the presentation of immobilized proteins in an array The proteins are all iniformly oriented, properly folded and optimally spaced to allow protein-protein interactions (b) Current technologies present proteins in a range of orientations, with varying degrees of denaturation and with the presence of nonspecifically absorbed proteins (a) DNA/gelatin (b) (c) Slide Lipid Cells Assay (f) Mean signal intensty (arbitrary units) (d) (e) 30 Cy3 GFP Merged 25 20 15 10 10 20 30 40 GEP cDNA (pg) 50 Figure 59.5 Well-less transfection of plasmid DNAs in defined areas of a lawn of mammalian cells (a) Protocol for making microarrays of transfected cells (b) Laser scan image of a GFP-expressing microarray made from a slide printed in a 14 10 pattern with a GFP expression construct (c) Higher magnification image obtained with fluorescence microscopy of the cell cluster boxed in b Scale bar, 100 ␮m (d) Expression levels of cell clusters in a microarray are proportional, over a fourfold range, to the amount of plasmid DNA printed on the slide Indicated amounts of the GFP construct assume a 1-nl printing volume The graph shows the mean s.d of the fluorescence intensities of the cell clusters (n = 6) The fluorescent image is from a representative expirement (e) CO-transfection is possible with transfected cell microarrays Arrays with elements containing expression constructs for HA-GST, GFP of both were transfected and processed for immunofluorescence and imaged with a laser scanner Cy3, cell clusters expressing HA-GST; GFP, cell clusters expressing GFP; merged, superimposition of Cy3 and GFP signals Yellow colour indicates co-expression Scale bar, 100 ␮m (f) Enlarged view of boxed area of scan image from (e) SELDI Biological proteinchipTM array method Preactivated proteinchip Analyte capture Bind ‘capture’ molecule Wash Block unused sites Add EAM Laser pulses Laser ionisation/desorption Figure 59.6 SELDI Biological ProteinChip™ Array Method Direct mass detection Reference mRNA Sample mRNA Prepare microarray Reverse transcription fluorescent labeling Hybridize to microarray Analysis Experimental samples Genes Identification of differentially-expressed genes; functionally-related genes; disease classificaton Figure 60.1 The generation of a microarray experiment begins with the labeling and reverse transcription of sample and reference mRNA After competitive hybridization to a pre-formed cDNA or oligonucleotide microarray, data are scanned and subsequently analyzed according to a variety of protocols Among the most popular is ‘clustering’ of data, or organizing genes and experimental samples according to their expression patterns and relatedness (adapted from Chua and Sarwal, 2002) B-and T-cell genes MHC class I and II STAT signaling Structural genes transporters Transcription factors Apoptosis genes AR-1 AR-2 AR-3 >6 >3 1:1