- Email: [email protected]
Measuring Ag-specific immune responses: understanding immunopathogenesis and improving diagnostics in infectious disease, autoimmunity and cancer
Review
TRENDS in Immunology
Vol.26 No.9 September 2005
Measuring Ag-specific immune responses: understanding immunopathogenesis and improving diagnostics in infectious disease, autoimmunity and cancer Florian Kern1, Giuseppina LiPira2, Jan W. Gratama3, Fabrizio Manca2 and Mario Roederer4 1
Institut fu¨r Medizinische Immunologie, Charite´ – Universita¨tsmedizin Berlin, Campus Mitte, 10098 Berlin, Germany Laboratory of Clinical and Experimental Immunology, G. Gaslini Institute, 16148 Genoa, Italy 3 Laboratory for Clinical and Tumor Immunology, Department of Medical Oncology, Erasmus MC – Daniel den Hoed, 3075 EA, Rotterdam, the Netherlands 4 Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892-3015, USA 2
Characterization of antigen-specific immune responses at the single-cell level has been made possible by recent advancements in reagent and technology development, combined with increasing knowledge of molecular mechanisms. Fluorescently labelled MHC–peptide multimers and antigens identify directly specific T and B cells, respectively, whereas dynamic assays exploit mediator production or secretion, or the changes in surface expression of other proteins, to identify specific lymphocytes – some techniques enabling the recovery of viable cells. Meanwhile, multiparameter flow cytometry has emerged as the most versatile platform for integrating most of these methods. As the complexity of experimental data increases, so does the level of technical sophistication required for analysis and interpretation, both in terms of basic research and modern medicine, with new applications for infectious diseases, autoimmunity and cancer. Introduction Cellular immune responses have an important role in numerous conditions, including infection, malignancy, autoimmune disease and transplantation. Recent technologies for the identification, enumeration and characterization of antigen (Ag)-specific lymphocytes have great potential, not only for basic research but also for understanding disease pathogenesis and the application to medicine. The long-used bulk assays applied to quantify Ag-specific immune functions are now being replaced by far more informative single-cell assays. Apart from the great potential for analyzing vaccine efficacy, these assays provide unprecedented detail about disease progression or remission, and the effects of therapeutic interventions. The complex technology used for single-cell assays can Corresponding author: Kern, F. ([email protected]). Available online 20 July 2005
address a wide array of issues, including Ag processing and presentation, epitope definition, subset differentiation, activation markers and effector functions. This review describes the state-of-the art for this field, both in terms of technology and application, as covered in the current literature and during the first conference on the measurement of Ag-specific immune responses (MASIR; www.masir.org), which was held in Courmayeur (Italy) in January 2005. From tuberculin skin test to peptide–MHC multimer studies The following section highlights the most important technical developments in the area of antigen-specific lymphocyte detection over the last 20 years. Principles of Ag-specific assays: evolution from bulk to single cell The first assay to measure cellular immunity was the tuberculin skin test (TST) a century ago, however, its biological significance was only understood when T cells and their functions became known [1]. Failure to respond to TST or similar skin tests might originate both in the afferent or efferent arms of the underlying immune response, involving Ag delivery, uptake, transport and presentation, and T-cell activation, proliferation, migration and effector function. The first assays to measure Ag-specific immune responses were those studying proliferation by measuring the incorporation of 3H-thymidine into the DNA of proliferating cells [2]. This method is still in use and lends itself well to large-scale screening. The first T-cell effector function to be tested in vitro was cytotoxicity, by labelling ‘target’ cells with 51Cr, incubating them with cytolytic ‘effector’ cells and measuring the
www.sciencedirect.com 1471-4906/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.it.2005.07.005
478
Review
TRENDS in Immunology
release of radioactivity [3]. In many laboratories, this assay is still the gold standard for measuring cytotoxic T-lymphocyte (CTL) function, although the use of nonradioactive labelling dyes is a viable alternative [4]. In vivo CTL assays can be performed in laboratory animals by infusing peptide-pulsed and unpulsed target cells (identifiable by different intensity fluorochrome labelling) into immunized and non-immunized animals. After the incubation period, specific killing can be determined by analysing tissue samples in immunized and non-immunized animals with regard to the ratio of pulsed:unpulsed target cells [5]. Another way to measure cellular activation is through the quantification of secreted mediators (e.g. cytokines) by specific ligand assays (e.g. ELISA). Importantly, the number or type of effector cells in these assays is not known: only the outcome of cellular activation is measured. They can be referred to as ‘bulk’ assays. The ELISpot technology marked the transition from bulk to single-cell assays because it enables the enumeration of mediator-secreting cells [6]. Using ELISpot, areas (‘spots’) of specific mediator secretion (‘footprints’ of Ag-activated lymphocytes) in a well are detected by a ligand assay. A more recent method, often referred to as intracellular cytokine staining (ICS), can ‘visualize’ secreted mediators in Ag-activated cells directly. Short ex vivo stimulation induces T cells to produce a variety of mediators. Because of secretion inhibition, fixation and permeabilization of cells, these can be detected intracellularly by specific, fluorochrome-labelled antibodies [7]. This assay, which identifies Ag-specific T cells in a multiparametric fashion [8], revolutionized Ag-specific immunology. More recent assays combine single-cell detection with live-cell recovery. The cytokine capture assay, for example, permits the detection, analysis and isolation of viable cytokine-secreting T cells. While a bi-specific antibody binds CD45 on the leukocyte surface and catches the secreted cytokine, a second phycoerythrin (PE)-conjugated antibody identifies the ‘trapped’ cytokine. Anti-PE antibody coated paramagnetic microbeads can be used to enrich these cells in a magnetic column for further experimentation [9]. A variation of this technology, selective for tumour necrosis factor-a (TNF-a), takes advantage of the unique secretion pathway of this cytokine: an inhibitor of the TNF-a-converting enzyme (TACE) prevents shedding of newly synthesized and surface expressed TNF-a, which can thus be detected without permeabilization [10,11]. Two very recent assays measure CD4 or CD8 T-cell functions, which broadly correlate with helper and cytotoxic activities, respectively, rather than cytokines or other mediators. Upon helper cell activation, de novo synthesized CD154 is expressed transiently on the cell surface, where it activates Ag-presenting cells (APCs) through CD40. AntiCD154 antibodies present during stimulation bind to, and are internalized with, the receptor [12]; alternatively, antibodies blocking the CD154–CD40 interaction can be included to inhibit signal-induced CD154 internalization, thus enabling its staining on the surface [13]. www.sciencedirect.com
Vol.26 No.9 September 2005
For cytotoxicity, a similar assay measures the mobilization of CD107a/b to the cell surface [14]. When the cytolytic cell is stimulated to release perforin or granzyme, the granule-associated integral membrane proteins, CD107a/b, are exposed transiently on the cell surface. At this time, fluorescent anti-CD107a/b antibodies present in the culture media will bind and be internalized, thus identifying cells that have degranulated. Note that, although cytolytic activity is dependent on degranulation, such that CD107 mobilization can correlate highly with cytolytic activity [15], not all degranulating cells are cytolytic [16]. It is a particular advantage that both the CD154 and the CD107a/b assays can be used to identify and isolate viable cells that have a broad functional spectrum. The analysis of cellular proliferation has been revolutionized by the advent of single-cell assays that permit the tracking of cell divisions. Instead of 3H-thymidine, cells can, for example, be labelled with bromo-deoxy-uridine (BrdU), which is also incorporated into the DNA of dividing cells [17]. Alternatively, cellular proteins can be labelled with the non-toxic, fluorescein-related dye, carboxyfluoroscein diacetate succinimidyl ester (CFDA-SE, or CFSE in its intracellular, reduced form). On Ag activation, proliferating cells can thus be detected by either increased fluorescence (BrdU) or loss of staining as a result of equal division of stained proteins between daughter cells at each mitosis (CFSE) [18]. BrdU can also be used in pulse-chase experiments to follow in vivo division and measure cellular turnover [19,20]. Finally, MHC–peptide multimers are a class of reagents that can bind directly to the Ag receptor of the cognate epitope-specific T cell. Unlike the assays listed earlier, this one is not dependent on any specific T-cell function, only on the ability of the T-cell receptor (TCR) to recognize the peptide–MHC complex. To raise the avidity, such that binding is stable during the assay, multimerization is required. Formats for MHC I molecules include dimers (Ig scaffolding) [21], tetramers (biotinylated MHC complexes, streptavidin backbone) [22], streptamers (biotinylated MHC complexes, ‘strep-tactin’ backbone) [23], pentamers (MHC complexes multimerized by a selfassembling coil–coil domain) [24] and dextramers (MHC complexes attached to a dextran backbone). Unlike other formats, streptamers can be dissociated by adding free biotin to the assay, resulting in the dissociation of the now monomeric MHC–peptide complexes from the TCR [23]; this might be an advantage for functional analyses following cell isolation or adoptive transfer. The advent of MHC II multimers was announced in the late 1990s; however, only a limited number of these reagents are available to date [25,26]. MHC II molecules are heterodimers of a and b chains, which together form the binding groove. This is why the production of these reagents is much more difficult. However, studies using MHC II tetramers carrying, for example, HIV, influenza or melanoma peptides, have been reported [27–29]. Nevertheless, class II allele distribution, epitope identification and Ag-specific T-cell frequencies are still responsible for the gap in clinical applications of MHC II versus MHC I multimers.
Review
TRENDS in Immunology
Of note, identification of Ag-specific B cells at the single-cell level was accomplished nearly 10 years before the development of MHC multimers. The first proof-ofprinciple study used the fluorescent protein phycoerythrin as an immunogen and subsequent tool to identify B cells that expressed phycoerythrin-specific surface Ig [30]. Since then, others have used fluorochrome-conjugated Ags to identify B cells specific for haptens [31], hen egg lysozyme [32] and even peptides [33]. ELISpot-assays were first developed to enumerate B cells and were used to track B-cell memory development [34]. Most recently, Baumgarth reported the enumeration and characterization of influenza-specific B cells following infectious challenge in the mouse model [35]. These technologies are of great interest because B-cell responses are considered paramount in the development of any new vaccine. The basic principles, advantages and disadvantages of these assays are summarized in Table 1. Figure 1 shows a schematic of the Ag-specific immune response and explains which sections are exploited in the various assays described. Use and combinations of methods A comprehensive analysis of T-cell responses to Ags must encompass a broad variety of parameters: (i) The magnitude of the response: how many (or what percent of) memory T cells are specific for the Ag? (ii) The breadth of the response: how many different epitopes from a given Ag are recognized by the T cells in an individual? (iii) The clonality of the response: how many different T-cell clones (receptors) are generated for each epitope? (iv) The effectiveness of the response: what is the avidity with which T cells recognize the epitopes?
Vol.26 No.9 September 2005
479
(v) Response mechanisms: what are the functions of the Ag-specific T cells (e.g. proliferation, cytotoxicity and the cytokine and chemokine profile)? Fortunately, many of the technologies listed earlier might be combined to correlate distinct functional measurements (Table 2). Table 3 shows important technical requirements and specific features of these assays. A complete description of the response of an individual to an Ag requires the determination of all of these parameters. For example, in the evaluation of a vaccine response, it is hypothesized that the best vaccines will induce: (i) the greatest number of specific T cells; (ii) responses to multiple different epitopes for the immunogen; (iii) a broad clonality of response; (iv) a response with high functional avidity; and (v) a broad functional repertoire, including proliferation, cytotoxic activity and Th1 cytokine production. We expect that ‘correlates of protection’ or ‘correlates of pathogenesis’ will be determined within the next 5–10 years and are likely to arise from a subset of these variables. Importantly, many of the methods listed earlier are compatible with other technologies that provide different information. For example, using flow cytometric sorting, T cells that recognize specific peptide–MHC complexes can be isolated for the purpose of gene-chip array analysis (to quantify the expression of genetic programs), to determine the rate of viral infection, such as of Ag-specific CD4 T cells by HIV [36], or other biochemical characterizations. Notably, the assays that require fixation are typically not compatible with RNA-based assays (gene chips); for this purpose, viable cells must be isolated.
Applications The most common applications include the definition of T-cell specificities, that is, the determination of T-cell
Table 1. Flow cytometrya-based assays for T cells Method MHC–peptide multimers
Main features (time required) Probe for specific TCR (1 h)
Advantages Precise, accurate
Anti-TCR-idiotype antibody
Antibody-specific for the unique TCR (1 h) Upregulation of, for example, CD25, CD69 on Ag-activated cells (5–18 h) Detection of secretion-inhibited cytokine (or other mediator)producing cells (5–18 h) Detection and isolation of cytokine-secreting cells (5–18 h) Stabilization of TNF-a at the cell surface (5–18 h) Stabilization of de novo synthesized CD154 at the CD4 T-cell surface (5–18 h) Staining of CD107a/b at the cell surface during exocytosis (5–18 h) Equal division of dye between daughter cells 3–7 days Detection of BrdU incorporated into DNA (3–7 days)
Simple
Surface activation markers
Intracytoplasmic cytokine staining assay Cytokine capture assay TACE-inhibition assay CD154 assay
CD107 assay
CFSE-dilution assay BrdU-incorporation assay
Viable assay, positive or negative sorting
Limitations Availability of recombinant MHC alleles; knowledge of epitopes Impractical; only mouse data available No clear-cut definition of specific cells
Information on cell functions (cytokine profile)
Cells non-viable
Analysis can precede purification
Carry-over of irrelevant cells
Simultaneous measurement of secreted mediators Independent of cytokine profile
Selection by TNF-a production
Close correlate of CTL activity
Limited to degranulating cells
Repeated cell divisions can be tracked Identification of proliferating cells
Limited to dividing cells
Not applicable to CD8 T cells
Limited to dividing cells
a Flow-cytometry is the only platform that can combine the detection of Ag specific T cells with multi-parametric analysis of these cells. Such analysis might include phenotypic markers, intracellular molecules (e.g. cytokines, enzymes, receptors), function (degranulation) and proliferation. The unique advantage of being able to include all these parameters simultaneously is implied each time that flow cytometry is mentioned here.
www.sciencedirect.com
Review
480
TRENDS in Immunology
Vol.26 No.9 September 2005
Static assays
Dynamic assays
T-cell repertoire
Time scale (hrs)
ELISA
0.2
T cell
ELIspot Capture assay ICS, TACE
Cytokines
T cell T cell T celll
APC
*
MHC–peptide multimer
CTL function, 51Cr
**
Anti-id Ab
2.0
Activation markers
CTL function, CD107 Helper function Helper function, CD154 n atio lifer Pro
20.0
3H-thym
uptake
BrdU CFSE dilution
200.0 MHC multimer
Specific T cell
CD4 helper function
Bulk assays
Antigenic peptide
Activation markers
CD8 CTL function
Single cell assays
*
MHC–peptide multimer
Secreted cytokines
MHC–peptide
Retained cytokines TRENDS in Immunology
Figure 1. T-cell function assays (left) are defined as static because no cellular function is required for identification. They are based on reagents that directly and specifically bind to the TCR on the relevant cell. Anti-idiotypic antibodies are clone-specific. Multimers, by contrast, often recognize several clones because different TCRs might recognize the same MHC–peptide complex. Monoclonal antibodies for phenotyping can be used in static assays to characterize specific T cells. The readout functions of dynamic assays (right) are T-cell functions. Early dynamic assays measure Ag-induced expression of activation markers (e.g. CD25, CD69), synthesis of cytokines, other mediators or receptors or degranulation. Late dynamic assays estimate Ag-induced cell proliferation. Most assays can be used both for CD4 and CD8 T cells. Bulk assays can be quantified as a ratio of the measured functions in either the presence or absence of Ag. Single-cell assays can be quantified as frequencies of Ag-responsive cells.
Table 2. T-cell assay compatibility matrix ICS Cytokine-capture assay CD154 CD107 TACE CFSEa ELISpot
MHC–multimer binding Yes Yes Yes Yes Yes Yes No
ICS
Cytokine-capture assay
CD154
CD107
TACE
CFSE
No Yes Yes No Yes No
Yes Yes Yes Yesa No
Yes Yes Yes No
Yes Yes No
Yes No
No
Table 3. Specific assay requirements Assay
Multimers ICS Cytokine capture CD154 CD107 TACE CFSE ELISpot a
Cell preparationa
WB, PBMCs WB, PBMCs WB, PBMCs PBMCs PBMCs WB, PBMCs PBMCs PBMCs
Stimulation requirements AgCAPC Nf Yf Y Y Y Y Y Y
Co-stimulation N Of O O O O O O
b
Golgi inhibitors N Y N N Y N N N
c
Other reagents N N N Y Y Y N N
Time (hrs)
Recovery of viable cellse
0 5–18 5–18 5–18 5–18 5–18 72–144 5–18
C K C C C C C K
d
Whole blood (WB) requires subsequent lysis. The PBMC number required for analytical assays ranges from 1 to 5!105 cells, depending on the expected frequency of positive cells. For preparative assays, the PBMC number should be scaled up to 107–109. b Co-stimulation antibodies, usually CD28 and CD49d, are required typically for stimulation for (of) !12 h. c Typically, the Golgi inhibitors, such as Brefeldin A or Monensin, are used, whereas the TACE assay requires metalloproteinase inhibitors (MMPIs). d Other reagents needed during stimulation, for example, anti-CD107, anti-CD154 or anti-CD40. e Indicates whether live cells can be isolated based on the assay measurement. f Y represents yes, N represents no and O represents optional. www.sciencedirect.com
Review
TRENDS in Immunology
epitopes and the analysis of the phenotypic differentiation and function of antigen-specific T cells. Defining specificities Several technological advances have been presented recently to facilitate the large-scale analysis of epitopes, to visualize peptide–MHC interactions and to identify and select Ag-responsive T cells for more detailed characterization. Epitope identification frequently requires large numbers of peptides to be synthesized, which is costly and laborious. Therefore, many groups use algorithms to predict which peptides from a selected protein would bind a given MHC [37–39]. However, although useful, these algorithms are still imperfect. To provide multiple peptides at low cost for empirical testing, advanced highly parallel technologies for peptide synthesis have been developed, such as Spot or Multi-Pin synthesis [40,41]. Combinations of these technologies with ICS or ELISpot have only been reported recently. These include the large-scale screening of a comprehensive library of 66 000 Spot peptides, covering all possible 9 amino acid peptides in the whole cytomegalovirus (CMV) proteome (F. Kern, unpublished), and, screening O200 CMV protein-specific pools of overlapping Multi-pin peptides to identify numerous previously unidentified T-cell targets (L. Picker, unpublished). Currently, little is known about the APCs that present a given peptide in vivo; however, technologies are now being devised to identify and track these cells. Kunkel et al. [42] recently reported an amplification system, based on magneto-fluorescent liposomes, to visualize directly by flow cytometry as few as 100 peptide–MHC complexes per APC, and to evaluate functional T-cell responses in vivo. Cells that initiate immune responses can be tracked in vivo following Ag uptake through the mucosal route. This technology will help us understand the trafficking of APCs between the mucosa and the central immune organs, identifying the routes, kinetics and other parameters of the important innate processes that initiate Ag-specific immune responses, either immunogenic or tolerogenic. Following Ag presentation, activation of T cells can be measured as an early or late event. Phospho-specific antibodies enable the flow cytometric monitoring of protein phosphorylation of unique residues at the single-cell level [43]. However, because T-cell stimulation following interaction with the APC occurs asynchronously in the cell population in a time frame of hours, whereas phosphorylation and dephosphorylation occur within minutes, this method requires further refinements if it is to identify Ag-activated T cells. Nevertheless, this method has enormous potential in that it might identify all responding cells (by virtue of the initiation of signalling cascades); the simultaneous and independent measurement of different signalling pathways might enable the discrimination of differential cellular response programs. Recently, several groups have mapped the fine interaction of the TCR with peptide–MHC by sequencing the TCRs from all T cells that respond to a single peptide. This nascent field has already yielded interesting results. For example, in B6 mice, after primary and secondary www.sciencedirect.com
Vol.26 No.9 September 2005
481
influenza virus challenge, a proportion of high-frequency TCRb express ‘public’ complementarity-determining region 3b (CDR3b) sequences that are detected in every mouse and represent a ‘best fit’, whereas others are invariably not present. ‘Public’ CDR3b sequences originate from up to 10 different nucleic acid sequences, whereas unique (‘private’) sequences are always specified by a single nucleotype [44]. Douek and Price demonstrated that different types of TCR responses could dramatically impact on the ability of SIV to escape CTL control. Specifically, an immunodominant peptide, which generates a monomorphic TCR response (probably because of structural constraints), is quickly mutated during acute SIV infection to render the specific CTL response ineffective; a different immunodominant peptide from SIV generates a much broader TCR response and the virus is unable to escape CTL control of that response (because mutant peptides are still recognized efficiently) [45]. These studies will influence not only our understanding of how the structural interaction of TCR and peptide–MHC complexes drive immune responses, they will probably instruct us on how to modify vaccination techniques, for example, to best avoid the generation of rapid escape mutations in HIV. Phenotype differentiation and function The ability to detect Ag-specific T cells by flow cytometry has vastly extended our abilities to analyze the phenotypes and functional characteristics of Ag-specific T cells. Not surprisingly, in most published reports, Ag-specific T cells were analyzed with regards to those phenotypic markers of T-cell differentiation previously established using fairly limited but well known models or bulk peripheral blood mononuclear cells (PBMCs) [46–48]. A popular paradigm [46,49] postulates the existence of differentiation ‘compartments’ termed ‘central’ and ‘effector’ memory (TCM and TEM), which can be characterized by the expression levels of CD45RA and CCR7 and are associated with certain migration patterns and functions. However, T-cell differentiation does not follow the same rules for all Ags and pathogens: certain characteristics, such as the ability to respond to Ag without co-stimulation, degranulation or specific migration patterns are frequently associated with specific phenotypes, although it is becoming increasingly obvious that these phenotypes are not the same for T cells of different specificities [50,51]. In addition, the phenotypic markers used by different groups are not fully congruous, which makes it more difficult to compare data. For example, some groups have consistently classified CMV-specific CD8 T cells into four subsets, defined by the expression of CD45RA and CD27 [52,53]; however, others have preferentially used combinations of the following markers: CD45RO, CD27, CD28 or CD62L [54,55]. These, or similar divisions, intend to define essentially the same subsets, that is, ‘naı¨ve’, ‘early Ag experienced’, ‘late Ag experienced’ and ‘terminally differentiated’. Recent work illustrates that these models might in fact over-simplify the situation. When using many such parameters in combination (rather than two at a time), the number of subsets and different functions
482
Review
TRENDS in Immunology
increases dramatically because there is extensive overlap between the subsets defined on the basis of only two markers. The development of flow cytometric technology that can quantify simultaneously the expression of as many as 17 different molecules per cell [56] will enable the detailed analysis of which of these functions or phenotypes is most likely to be relevant to disease processes. For example, recent data (M. Betts, pers. commun.) illustrates that it might be necessary to measure five different functions on each CD8 T cell to identify functional differences between HIV non-progressors and progressors. With this technology comes a commensurate level of complexity, in the experimental design, data analysis and presentation. Overcoming these hurdles is an active field of development [56–58]; new tools to assist these efforts are urgently needed. The analysis of isolated single Ag-specific T cells, for example, with respect to cytokine mRNA expression [59] or TCR CDR3 sequences [60], after identification of these cells by tetramer staining and following multiparameter based sorting, is an elegant combination of technologies. Clinical usefulness Currently, the clinical applications of Ag-specific measurements are focused on two major topics: monitoring of responses to vaccines and correlating ‘natural’ T-cell responses with clinical outcomes. Among all vaccines, those against tumours and HIV are the most frequently investigated, whereas, with respect to natural responses, responses against CMVand HIV dominate current research. The induction of antitumour responses by peptidebased vaccines has been addressed recently [61,62]. A paradox of this form of immunotherapy is that substantial proportions of vaccinated patients develop measurable tumour-specific T-cell responses but only a small proportion of these responses appear to be effective (tumour regression). Stuge et al. recently applied MHC multimer-based sorting, the CD107 degranulation assay and the classic chromium release assay to determining the recognition efficiency of vaccine-elicited responses and endogenous anti-melanoma responses. By combining technologies, they revealed that the recognition efficiency of vaccine-induced T cells was predominantly low, whereas that of endogenous tumour Ag-induced T cells was usually high. These results show that looking at several parameters by combining methods, rather than relying on single readouts, will be crucial in making use of the currently available tools. With respect to HIV-specific T cells, functional and phenotypical differences between CD4 and CD8 T cells responding to viral Ags in HIV vaccinees versus naturally infected individuals were evaluated and correlated with the clinical course of HIV infection. Using 12-coloranalysis, Koup et al. found that vaccine-induced T-cell responses were distinct from those found in naturally infected subjects, either progressors or long-term nonprogressors (LTNPs). Interestingly, vaccine-induced responses matured during the first year and became more polyfunctional: a state that is more congruous with the functionality of HIV-specific T cells in LTNPs (R. Koup, pers. commun.). Betts et al. were able to follow www.sciencedirect.com
Vol.26 No.9 September 2005
an HIV-Gag vaccinee who actually became infected with HIV: ICS, MHC multimer binding and the CD107 degranulation assay were used to assess the T-cell response during the clinical course that rapidly developed the typical features of chronic HIV infection [63]. In terms of monitoring of HIV-specific T cells induced by natural infection, the numbers of CD4 and CD8 T cells responding to viral Ags in HIV-infected individuals were recently evaluated and correlated with viral load and disease progression. Thus, it was revealed that persistently low viral replication (!10 000 copies mlK1) during antiretroviral therapy is associated with higher frequencies of HIV-specific CD4 and CD8 T cells than complete suppression of replication or therapy failure [64]. Along the same lines, ICS was used recently to study the T-cell response to two important human CMV proteins, pp65 and IE-1, in heart-transplant recipients. The levels of CD8 T cells specific for the non-structural IE-1, rather than for pp65, correlate with protection from CMV-disease [65]. A common theme seems to be emerging: ‘effective’ antiviral T-cell responses, for example, those against CMV, Epstein–Barr virus (EBV) and HIV in non-progressors, constitute ‘polyfunctional’ T cells, that is, T cells that exhibit at least four or five different functions simultaneously. HIV progressors, by contrast, show a more restricted functional profile [66]. With respect to HIV, this was in fact suspected previously: rather than just quantity or breadth, the quality (functional profile) of the CD8 T-cell response might be of paramount importance [67]. Outlook The constantly growing array of tools for the analysis of cellular parameters persistently adds to the complexity of the information that we have on the key factors in Ag-specific immune responses. To generate biologically and clinically useful results from this ever-increasing wealth of information, we will eventually have to (re)define the most relevant elements using a minimum of technology, so as to enable widespread application. For example, the use of 17 fluorescence channels in flow cytometric lymphocyte analysis might lead to the identification of crucial subsets and functions that can then be monitored using considerably fewer channels. Importantly, flow cytometry-based assays are highly amenable to standardization [68]. Such standardized use should greatly facilitate the generation of meaningful and comparable information across the globe, pushing towards understanding Ag-specific immune responses. Acknowledgements This work was supported in part by grants from ISS (40F.50, 45F.26), CIPE, Ministry of Health PF, MIUR (RBNE01RB9B003), EU (QLK21999–01040) to FM and GLP.
References 1 Pearmain, G. et al. (1963) Tuberculin-induced mitosis in peripheral blood leucocytes. Lancet 1, 637–638 2 Bloom, B.R. (1971) In vitro methods in cell-mediated immunity in man. N. Engl. J. Med. 284, 1212–1213
Review
TRENDS in Immunology
3 Brunner, K.T. et al. (1968) Quantitative assay of the lytic action of immune lymphoid cells on 51Cr-labelled allogeneic target cells in vitro; inhibition by isoantibody and by drugs. Immunology 14, 181–196 4 Roden, M.M. et al. (1999) A novel cytolysis assay using fluorescent labeling and quantitative fluorescent scanning technology. J. Immunol. Methods 226, 29–41 5 Barber, D.L. et al. (2003) Cutting edge: rapid in vivo killing by memory CD8 T cells. J. Immunol. 171, 27–31 6 Czerkinsky, C. et al. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of g-interferon-secreting cells. J. Immunol. Methods 110, 29–36 7 Picker, L.J. et al. (1995) Direct demonstration of cytokine synthesis heterogeneity among human memory/effector T cells by flow cytometry. Blood 86, 1408–1419 8 Waldrop, S.L. et al. (1997) Determination of antigen-specific memory/ effector CD4CT cell frequencies by flow cytometry: evidence for a novel, antigen-specific homeostatic mechanism in HIV-associated immunodeficiency. J. Clin. Invest. 99, 1739–1750 9 Manz, R. et al. (1995) Analysis and sorting of live cells according to secreted molecules, relocated to a cell-surface affinity matrix. Proc. Natl. Acad. Sci. U. S. A. 92, 1921–1925 10 Bueno, C. et al. (2002) A new method for detecting TNF-a-secreting cells using direct-immunofluorescence surface membrane stainings. J. Immunol. Methods 264, 77–87 11 Rodriguez-Caballero, A. et al. (2004) A new simple whole blood flow cytometry-based method for simultaneous identification of activated cells and quantitative evaluation of cytokines released during activation. Lab. Invest. 84, 1387–1398 12 Chattopadhyay, P.K. et al. A live-cell assay to detect antigen-specific CD4 T cells with diverse cytokine profiles. Nat. Med. (in press) 13 Frentsch, M. et al. Direct access to CD4CT-cells specific for defined antigens according to CD154 expression. Nat. Med. (in press) 14 Betts, M.R. and Koup, R.A. (2004) Detection of T-cell degranulation: CD107a and b. Methods Cell Biol. 75, 497–512 15 Rubio, V. et al. (2003) Ex vivo identification, isolation and analysis of tumor-cytolytic T cells. Nat. Med. 9, 1377–1382 16 Catalfamo, M. et al. (2004) Human CD8CT cells store RANTES in a unique secretory compartment and release it rapidly after TcR stimulation. Immunity 20, 219–230 17 Houck, D.W. and Loken, M.R. (1985) Simultaneous analysis of cell surface antigens, bromodeoxyuridine incorporation and DNA content. Cytometry 6, 531–538 18 Lyons, A.B. (2000) Analysing cell division in vivo and in vitro using flow cytometric measurement of CFSE dye dilution. J. Immunol. Methods 243, 147–154 19 Tough, D.F. and Sprent, J. (1994) Turnover of naı¨ve- and memoryphenotype T cells. J. Exp. Med. 179, 1127–1135 20 Flynn, K.J. et al. (1999) In vivo proliferation of naı¨ve and memory influenza-specific CD8CT cells. Proc. Natl. Acad. Sci. U. S. A. 96, 8597–8602 21 Greten, T.F. et al. (1998) Direct visualization of antigen-specific T cells: HTLV-1 Tax11-19- specific CD8CT cells are activated in peripheral blood and accumulate in cerebrospinal fluid from HAM/TSP patients. Proc. Natl. Acad. Sci. U. S. A. 95, 7568–7573 22 Altman, J.D. et al. (1996) Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94–96 23 Knabel, M. et al. (2002) Reversible MHC multimer staining for functional isolation of T-cell populations and effective adoptive transfer. Nat. Med. 8, 631–637 24 Borg, N.A. et al. (2005) The CDR3 regions of an immunodominant T cell receptor dictate the ‘energetic landscape’ of peptide–MHC recognition. Nat. Immunol. 6, 171–180 25 Novak, E.J. et al. (1999) MHC class II tetramers identify peptidespecific human CD4CT cells proliferating in response to influenza A antigen. J. Clin. Invest. 104, R63–R67 26 McMichael, A.J. and Kelleher, A. (1999) The arrival of HLA class II tetramers. J. Clin. Invest. 104, 1669–1670 27 Scriba, T.J. et al. (2005) HIV-1-specific CD4CT lymphocyte turnover and activation increase upon viral rebound. J. Clin. Invest. 115, 443–450 28 Walker, M.R. et al. (2005) De novo generation of antigen-specific CD4CCD25C regulatory T cells from human CD4CCD25K cells. Proc. Natl. Acad. Sci. U. S. A. 102, 4103–4108 www.sciencedirect.com
Vol.26 No.9 September 2005
483
29 Zhang, Y. et al. (2005) A polyclonal anti-vaccine CD4 T cell response detected with HLA-DP4 multimers in a melanoma patient vaccinated with MAGE-3.DP4-peptide-pulsed dendritic cells. Eur. J. Immunol. 35, 1066–1075 30 Hayakawa, K. et al. (1987) Isolation of high-affinity memory B cells: phycoerythrin as a probe for antigen-binding cells. Proc. Natl. Acad. Sci. U. S. A. 84, 1379–1383 31 Lalor, P.A. et al. (1992) Functional and molecular characterization of single, (4-hydroxy-3-nitrophenyl)acetyl (NP)-specific, IgG1CB cells from antibody-secreting and memory B cell pathways in the C57BL/6 immune response to NP. Eur. J. Immunol. 22, 3001–3011 32 Townsend, S.E. et al. (2001) Single epitope multiple staining to detect ultralow frequency B cells. J. Immunol. Methods 249, 137–146 33 Newman, J. et al. (2003) Identification of an antigen-specific B cell population. J. Immunol. Methods 272, 177–187 34 Crotty, S. et al. (2004) Tracking human antigen-specific memory B cells: a sensitive and generalized ELISPOT system. J. Immunol. Methods 286, 111–122 35 Doucett, V.H. et al. Enumeration and characterization of virus-specific B cells by multicolor flow cytometry. J. Immuno. Meth. (in press) 36 Douek, D.C. et al. (2002) HIV preferentially infects HIV-specific CD4CT cells. Nature 417, 95–98 37 Rammensee, H.G. et al. (1997) MHC ligands and binding motifs, Landes Bioscience 38 Scheibenbogen, C. et al. (2002) Identification of known and novel immunogenic T-cell epitopes from tumor antigens recognized by peripheral blood T cells from patients responding to IL-2-based treatment. Int. J. Cancer 98, 409–414 39 Elkington, R. et al. (2003) Ex vivo profiling of CD8C-T-cell responses to human cytomegalovirus reveals broad and multispecific reactivities in healthy virus carriers. J. Virol. 77, 5226–5240 40 Maeji, N.J. et al. (1990) Multi-pin peptide synthesis strategy for T cell determinant analysis. J. Immunol. Methods 134, 23–33 41 Frank, R. (2002) The SPOT-synthesis technique. Synthetic peptide arrays on membrane supports–principles and applications. J. Immunol. Methods 267, 13–26 42 Kunkel, D. et al. (2003) Visualization of peptide presentation following oral application of antigen in normal and Peyer’s patches-deficient mice. Eur. J. Immunol. 33, 1292–1301 43 Krutzik, P.O. and Nolan, G.P. (2003) Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55, 61–70 44 Kedzierska, K. et al. (2004) Conserved T cell receptor usage in primary and recall responses to an immunodominant influenza virus nucleoprotein epitope. Proc. Natl. Acad. Sci. U. S. A. 101, 4942–4947 45 Price, D.A. et al. (2004) T cell receptor recognition motifs govern immune escape patterns in acute SIV infection. Immunity 21, 796–803 46 Sallusto, F. et al. (1999) Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 47 Picker, L.J. et al. (1993) Control of lymphocyte recirculation in man. I. Differential regulation of the peripheral lymph node homing receptor L-selectin on T cells during the virgin to memory cell transition. J. Immunol. 150, 1105–1121 48 Kern, F. et al. (1994) Discordant expression of LFA-1, VLA-4a, VLA-b1, CD45RO and CD28 on T-cell subsets: evidence for multiple subsets of ‘memory’ T cells. Int. Arch. Allergy Immunol. 104, 17–26 49 Sallusto, F. et al. (2004) Central memory and effector memory T cell subsets: function, generation, and maintenance. Annu. Rev. Immunol. 22, 745–763 50 Appay, V. et al. (2002) Memory CD8CT cells vary in differentiation phenotype in different persistent virus infections. Nat. Med. 8, 379–385 51 van Leeuwen, E.M. et al. (2004) Emergence of a CD4CCD28K granzyme BC, cytomegalovirus-specific T cell subset after recovery of primary cytomegalovirus infection. J. Immunol. 173, 1834–1841 52 Hamann, D. et al. (1997) Phenotypic and functional separation of memory and effector human CD8CT cells. J. Exp. Med. 186, 1407–1418 53 Hamann, D. et al. (1999) Faces and phases of human CD8 T-cell development. Immunol. Today 20, 177–180 54 Tsegaye, A. et al. (2003) Immunophenotyping of blood lymphocytes at birth, during childhood, and during adulthood in HIV-1-uninfected Ethiopians. Clin. Immunol. 109, 338–346
484
Review
TRENDS in Immunology
55 Wills, M.R. et al. (2002) Identification of naive or antigen-experienced human CD8CT cells by expression of costimulation and chemokine receptors: analysis of the human cytomegalovirus-specific CD8CT cell response. J. Immunol. 168, 5455–5464 56 Perfetto, S.P. et al. (2004) Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 57 De Rosa, S.C. et al. (2003) Beyond six colors: a new era in flow cytometry. Nat. Med. 9, 112–117 58 Baumgarth, N. and Roederer, M. (2000) A practical approach to multicolor flow cytometry for immunophenotyping. J. Immunol. Methods 243, 77–97 59 Johnson, B.J. et al. (2003) Single-cell perforin and granzyme expression reveals the anatomical localization of effector CD8C T cells in influenza virus-infected mice. Proc. Natl. Acad. Sci. U. S. A. 100, 2657–2662 60 Panus, J.F. et al. (2000) Antigen-specific T helper cell function: differential cytokine expression in primary and memory responses. J. Exp. Med. 192, 1301–1316 61 Valmori, D. et al. (2002) Circulating tumor-reactive CD8CT cells in melanoma patients contain a CD45RACCCR7K effector subset exerting ex vivo tumor-specific cytolytic activity. Cancer Res. 62, 1743–1750
Vol.26 No.9 September 2005
62 Pittet, M.J. et al. (2001) Ex vivo IFN-g secretion by circulating CD8 T lymphocytes: implications of a novel approach for T cell monitoring in infectious and malignant diseases. J. Immunol. 166, 7634–7640 63 Betts, M.R. et al. (2005) Characterization of functional and phenotypic changes in anti-Gag vaccine-induced T cell responses and their role in protection after HIV-1 infection. Proc. Natl. Acad. Sci. U. S. A. 102, 4512–4517 64 Alatrakchi, N. et al. (2005) Persistent low viral load on antiretroviral therapy is associated with T cell-mediated control of HIV replication. AIDS 19, 25–33 65 Bunde, T. et al. (2005) Protection from cytomegalovirus after transplantation is correlated with immediate early 1-specific CD8 T cells. J. Exp. Med. 201, 1031–1036 66 Pantaleo, G. and Koup, R.A. (2004) Correlates of immune protection in HIV-1 infection: what we know, what we don’t know, what we should know. Nat. Med. 10, 806–810 67 Rowland-Jones, S.L. et al. (2001) How important is the ‘quality’ of the cytotoxic T lymphocyte (CTL) response in protection against HIV infection? Immunol. Lett. 79, 15–20 68 Landay, A.L. et al. (2004) Performance of single cell immune response assays; proposed guideline. NCCLS, 23 (www.clsi.org/)
Five things you might not know about Elsevier 1. Elsevier is a founder member of the WHO’s HINARI and AGORA initiatives, which enable the world’s poorest countries to gain free access to scientific literature. More than 1000 journals, including the Trends and Current Opinion collections, will be available for free or at significantly reduced prices. 2. The online archive of Elsevier’s premier Cell Press journal collection will become freely available from January 2005. Free access to the recent archive, including Cell, Neuron, Immunity and Current Biology, will be available on both ScienceDirect and the Cell Press journal sites 12 months after articles are first published. 3. Have you contributed to an Elsevier journal, book or series? Did you know that all our authors are entitled to a 30% discount on books and stand-alone CDs when ordered directly from us? For more information, call our sales offices: +1 800 782 4927 (US) or +1 800 460 3110 (Canada, South & Central America) or +44 1865 474 010 (rest of the world) 4. Elsevier has a long tradition of liberal copyright policies and for many years has permitted both the posting of preprints on public servers and the posting of final papers on internal servers. Now, Elsevier has extended its author posting policy to allow authors to freely post the final text version of their papers on both their personal websites and institutional repositories or websites. 5. The Elsevier Foundation is a knowledge-centered foundation making grants and contributions throughout the world. A reflection of our culturally rich global organization, the Foundation has funded, for example, the setting up of a video library to educate for children in Philadelphia, provided storybooks to children in Cape Town, sponsored the creation of the Stanley L. Robbins Visiting Professorship at Brigham and Women’s Hospital and given funding to the 3rd International Conference on Children’s Health and the Environment. www.sciencedirect.com
TRENDS in Immunology
Vol.26 No.9 September 2005
Measuring Ag-specific immune responses: understanding immunopathogenesis and improving diagnostics in infectious disease, autoimmunity and cancer Florian Kern1, Giuseppina LiPira2, Jan W. Gratama3, Fabrizio Manca2 and Mario Roederer4 1
Institut fu¨r Medizinische Immunologie, Charite´ – Universita¨tsmedizin Berlin, Campus Mitte, 10098 Berlin, Germany Laboratory of Clinical and Experimental Immunology, G. Gaslini Institute, 16148 Genoa, Italy 3 Laboratory for Clinical and Tumor Immunology, Department of Medical Oncology, Erasmus MC – Daniel den Hoed, 3075 EA, Rotterdam, the Netherlands 4 Vaccine Research Center, NIAID, NIH, Bethesda, MD 20892-3015, USA 2
Characterization of antigen-specific immune responses at the single-cell level has been made possible by recent advancements in reagent and technology development, combined with increasing knowledge of molecular mechanisms. Fluorescently labelled MHC–peptide multimers and antigens identify directly specific T and B cells, respectively, whereas dynamic assays exploit mediator production or secretion, or the changes in surface expression of other proteins, to identify specific lymphocytes – some techniques enabling the recovery of viable cells. Meanwhile, multiparameter flow cytometry has emerged as the most versatile platform for integrating most of these methods. As the complexity of experimental data increases, so does the level of technical sophistication required for analysis and interpretation, both in terms of basic research and modern medicine, with new applications for infectious diseases, autoimmunity and cancer. Introduction Cellular immune responses have an important role in numerous conditions, including infection, malignancy, autoimmune disease and transplantation. Recent technologies for the identification, enumeration and characterization of antigen (Ag)-specific lymphocytes have great potential, not only for basic research but also for understanding disease pathogenesis and the application to medicine. The long-used bulk assays applied to quantify Ag-specific immune functions are now being replaced by far more informative single-cell assays. Apart from the great potential for analyzing vaccine efficacy, these assays provide unprecedented detail about disease progression or remission, and the effects of therapeutic interventions. The complex technology used for single-cell assays can Corresponding author: Kern, F. ([email protected]). Available online 20 July 2005
address a wide array of issues, including Ag processing and presentation, epitope definition, subset differentiation, activation markers and effector functions. This review describes the state-of-the art for this field, both in terms of technology and application, as covered in the current literature and during the first conference on the measurement of Ag-specific immune responses (MASIR; www.masir.org), which was held in Courmayeur (Italy) in January 2005. From tuberculin skin test to peptide–MHC multimer studies The following section highlights the most important technical developments in the area of antigen-specific lymphocyte detection over the last 20 years. Principles of Ag-specific assays: evolution from bulk to single cell The first assay to measure cellular immunity was the tuberculin skin test (TST) a century ago, however, its biological significance was only understood when T cells and their functions became known [1]. Failure to respond to TST or similar skin tests might originate both in the afferent or efferent arms of the underlying immune response, involving Ag delivery, uptake, transport and presentation, and T-cell activation, proliferation, migration and effector function. The first assays to measure Ag-specific immune responses were those studying proliferation by measuring the incorporation of 3H-thymidine into the DNA of proliferating cells [2]. This method is still in use and lends itself well to large-scale screening. The first T-cell effector function to be tested in vitro was cytotoxicity, by labelling ‘target’ cells with 51Cr, incubating them with cytolytic ‘effector’ cells and measuring the
www.sciencedirect.com 1471-4906/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.it.2005.07.005
478
Review
TRENDS in Immunology
release of radioactivity [3]. In many laboratories, this assay is still the gold standard for measuring cytotoxic T-lymphocyte (CTL) function, although the use of nonradioactive labelling dyes is a viable alternative [4]. In vivo CTL assays can be performed in laboratory animals by infusing peptide-pulsed and unpulsed target cells (identifiable by different intensity fluorochrome labelling) into immunized and non-immunized animals. After the incubation period, specific killing can be determined by analysing tissue samples in immunized and non-immunized animals with regard to the ratio of pulsed:unpulsed target cells [5]. Another way to measure cellular activation is through the quantification of secreted mediators (e.g. cytokines) by specific ligand assays (e.g. ELISA). Importantly, the number or type of effector cells in these assays is not known: only the outcome of cellular activation is measured. They can be referred to as ‘bulk’ assays. The ELISpot technology marked the transition from bulk to single-cell assays because it enables the enumeration of mediator-secreting cells [6]. Using ELISpot, areas (‘spots’) of specific mediator secretion (‘footprints’ of Ag-activated lymphocytes) in a well are detected by a ligand assay. A more recent method, often referred to as intracellular cytokine staining (ICS), can ‘visualize’ secreted mediators in Ag-activated cells directly. Short ex vivo stimulation induces T cells to produce a variety of mediators. Because of secretion inhibition, fixation and permeabilization of cells, these can be detected intracellularly by specific, fluorochrome-labelled antibodies [7]. This assay, which identifies Ag-specific T cells in a multiparametric fashion [8], revolutionized Ag-specific immunology. More recent assays combine single-cell detection with live-cell recovery. The cytokine capture assay, for example, permits the detection, analysis and isolation of viable cytokine-secreting T cells. While a bi-specific antibody binds CD45 on the leukocyte surface and catches the secreted cytokine, a second phycoerythrin (PE)-conjugated antibody identifies the ‘trapped’ cytokine. Anti-PE antibody coated paramagnetic microbeads can be used to enrich these cells in a magnetic column for further experimentation [9]. A variation of this technology, selective for tumour necrosis factor-a (TNF-a), takes advantage of the unique secretion pathway of this cytokine: an inhibitor of the TNF-a-converting enzyme (TACE) prevents shedding of newly synthesized and surface expressed TNF-a, which can thus be detected without permeabilization [10,11]. Two very recent assays measure CD4 or CD8 T-cell functions, which broadly correlate with helper and cytotoxic activities, respectively, rather than cytokines or other mediators. Upon helper cell activation, de novo synthesized CD154 is expressed transiently on the cell surface, where it activates Ag-presenting cells (APCs) through CD40. AntiCD154 antibodies present during stimulation bind to, and are internalized with, the receptor [12]; alternatively, antibodies blocking the CD154–CD40 interaction can be included to inhibit signal-induced CD154 internalization, thus enabling its staining on the surface [13]. www.sciencedirect.com
Vol.26 No.9 September 2005
For cytotoxicity, a similar assay measures the mobilization of CD107a/b to the cell surface [14]. When the cytolytic cell is stimulated to release perforin or granzyme, the granule-associated integral membrane proteins, CD107a/b, are exposed transiently on the cell surface. At this time, fluorescent anti-CD107a/b antibodies present in the culture media will bind and be internalized, thus identifying cells that have degranulated. Note that, although cytolytic activity is dependent on degranulation, such that CD107 mobilization can correlate highly with cytolytic activity [15], not all degranulating cells are cytolytic [16]. It is a particular advantage that both the CD154 and the CD107a/b assays can be used to identify and isolate viable cells that have a broad functional spectrum. The analysis of cellular proliferation has been revolutionized by the advent of single-cell assays that permit the tracking of cell divisions. Instead of 3H-thymidine, cells can, for example, be labelled with bromo-deoxy-uridine (BrdU), which is also incorporated into the DNA of dividing cells [17]. Alternatively, cellular proteins can be labelled with the non-toxic, fluorescein-related dye, carboxyfluoroscein diacetate succinimidyl ester (CFDA-SE, or CFSE in its intracellular, reduced form). On Ag activation, proliferating cells can thus be detected by either increased fluorescence (BrdU) or loss of staining as a result of equal division of stained proteins between daughter cells at each mitosis (CFSE) [18]. BrdU can also be used in pulse-chase experiments to follow in vivo division and measure cellular turnover [19,20]. Finally, MHC–peptide multimers are a class of reagents that can bind directly to the Ag receptor of the cognate epitope-specific T cell. Unlike the assays listed earlier, this one is not dependent on any specific T-cell function, only on the ability of the T-cell receptor (TCR) to recognize the peptide–MHC complex. To raise the avidity, such that binding is stable during the assay, multimerization is required. Formats for MHC I molecules include dimers (Ig scaffolding) [21], tetramers (biotinylated MHC complexes, streptavidin backbone) [22], streptamers (biotinylated MHC complexes, ‘strep-tactin’ backbone) [23], pentamers (MHC complexes multimerized by a selfassembling coil–coil domain) [24] and dextramers (MHC complexes attached to a dextran backbone). Unlike other formats, streptamers can be dissociated by adding free biotin to the assay, resulting in the dissociation of the now monomeric MHC–peptide complexes from the TCR [23]; this might be an advantage for functional analyses following cell isolation or adoptive transfer. The advent of MHC II multimers was announced in the late 1990s; however, only a limited number of these reagents are available to date [25,26]. MHC II molecules are heterodimers of a and b chains, which together form the binding groove. This is why the production of these reagents is much more difficult. However, studies using MHC II tetramers carrying, for example, HIV, influenza or melanoma peptides, have been reported [27–29]. Nevertheless, class II allele distribution, epitope identification and Ag-specific T-cell frequencies are still responsible for the gap in clinical applications of MHC II versus MHC I multimers.
Review
TRENDS in Immunology
Of note, identification of Ag-specific B cells at the single-cell level was accomplished nearly 10 years before the development of MHC multimers. The first proof-ofprinciple study used the fluorescent protein phycoerythrin as an immunogen and subsequent tool to identify B cells that expressed phycoerythrin-specific surface Ig [30]. Since then, others have used fluorochrome-conjugated Ags to identify B cells specific for haptens [31], hen egg lysozyme [32] and even peptides [33]. ELISpot-assays were first developed to enumerate B cells and were used to track B-cell memory development [34]. Most recently, Baumgarth reported the enumeration and characterization of influenza-specific B cells following infectious challenge in the mouse model [35]. These technologies are of great interest because B-cell responses are considered paramount in the development of any new vaccine. The basic principles, advantages and disadvantages of these assays are summarized in Table 1. Figure 1 shows a schematic of the Ag-specific immune response and explains which sections are exploited in the various assays described. Use and combinations of methods A comprehensive analysis of T-cell responses to Ags must encompass a broad variety of parameters: (i) The magnitude of the response: how many (or what percent of) memory T cells are specific for the Ag? (ii) The breadth of the response: how many different epitopes from a given Ag are recognized by the T cells in an individual? (iii) The clonality of the response: how many different T-cell clones (receptors) are generated for each epitope? (iv) The effectiveness of the response: what is the avidity with which T cells recognize the epitopes?
Vol.26 No.9 September 2005
479
(v) Response mechanisms: what are the functions of the Ag-specific T cells (e.g. proliferation, cytotoxicity and the cytokine and chemokine profile)? Fortunately, many of the technologies listed earlier might be combined to correlate distinct functional measurements (Table 2). Table 3 shows important technical requirements and specific features of these assays. A complete description of the response of an individual to an Ag requires the determination of all of these parameters. For example, in the evaluation of a vaccine response, it is hypothesized that the best vaccines will induce: (i) the greatest number of specific T cells; (ii) responses to multiple different epitopes for the immunogen; (iii) a broad clonality of response; (iv) a response with high functional avidity; and (v) a broad functional repertoire, including proliferation, cytotoxic activity and Th1 cytokine production. We expect that ‘correlates of protection’ or ‘correlates of pathogenesis’ will be determined within the next 5–10 years and are likely to arise from a subset of these variables. Importantly, many of the methods listed earlier are compatible with other technologies that provide different information. For example, using flow cytometric sorting, T cells that recognize specific peptide–MHC complexes can be isolated for the purpose of gene-chip array analysis (to quantify the expression of genetic programs), to determine the rate of viral infection, such as of Ag-specific CD4 T cells by HIV [36], or other biochemical characterizations. Notably, the assays that require fixation are typically not compatible with RNA-based assays (gene chips); for this purpose, viable cells must be isolated.
Applications The most common applications include the definition of T-cell specificities, that is, the determination of T-cell
Table 1. Flow cytometrya-based assays for T cells Method MHC–peptide multimers
Main features (time required) Probe for specific TCR (1 h)
Advantages Precise, accurate
Anti-TCR-idiotype antibody
Antibody-specific for the unique TCR (1 h) Upregulation of, for example, CD25, CD69 on Ag-activated cells (5–18 h) Detection of secretion-inhibited cytokine (or other mediator)producing cells (5–18 h) Detection and isolation of cytokine-secreting cells (5–18 h) Stabilization of TNF-a at the cell surface (5–18 h) Stabilization of de novo synthesized CD154 at the CD4 T-cell surface (5–18 h) Staining of CD107a/b at the cell surface during exocytosis (5–18 h) Equal division of dye between daughter cells 3–7 days Detection of BrdU incorporated into DNA (3–7 days)
Simple
Surface activation markers
Intracytoplasmic cytokine staining assay Cytokine capture assay TACE-inhibition assay CD154 assay
CD107 assay
CFSE-dilution assay BrdU-incorporation assay
Viable assay, positive or negative sorting
Limitations Availability of recombinant MHC alleles; knowledge of epitopes Impractical; only mouse data available No clear-cut definition of specific cells
Information on cell functions (cytokine profile)
Cells non-viable
Analysis can precede purification
Carry-over of irrelevant cells
Simultaneous measurement of secreted mediators Independent of cytokine profile
Selection by TNF-a production
Close correlate of CTL activity
Limited to degranulating cells
Repeated cell divisions can be tracked Identification of proliferating cells
Limited to dividing cells
Not applicable to CD8 T cells
Limited to dividing cells
a Flow-cytometry is the only platform that can combine the detection of Ag specific T cells with multi-parametric analysis of these cells. Such analysis might include phenotypic markers, intracellular molecules (e.g. cytokines, enzymes, receptors), function (degranulation) and proliferation. The unique advantage of being able to include all these parameters simultaneously is implied each time that flow cytometry is mentioned here.
www.sciencedirect.com
Review
480
TRENDS in Immunology
Vol.26 No.9 September 2005
Static assays
Dynamic assays
T-cell repertoire
Time scale (hrs)
ELISA
0.2
T cell
ELIspot Capture assay ICS, TACE
Cytokines
T cell T cell T celll
APC
*
MHC–peptide multimer
CTL function, 51Cr
**
Anti-id Ab
2.0
Activation markers
CTL function, CD107 Helper function Helper function, CD154 n atio lifer Pro
20.0
3H-thym
uptake
BrdU CFSE dilution
200.0 MHC multimer
Specific T cell
CD4 helper function
Bulk assays
Antigenic peptide
Activation markers
CD8 CTL function
Single cell assays
*
MHC–peptide multimer
Secreted cytokines
MHC–peptide
Retained cytokines TRENDS in Immunology
Figure 1. T-cell function assays (left) are defined as static because no cellular function is required for identification. They are based on reagents that directly and specifically bind to the TCR on the relevant cell. Anti-idiotypic antibodies are clone-specific. Multimers, by contrast, often recognize several clones because different TCRs might recognize the same MHC–peptide complex. Monoclonal antibodies for phenotyping can be used in static assays to characterize specific T cells. The readout functions of dynamic assays (right) are T-cell functions. Early dynamic assays measure Ag-induced expression of activation markers (e.g. CD25, CD69), synthesis of cytokines, other mediators or receptors or degranulation. Late dynamic assays estimate Ag-induced cell proliferation. Most assays can be used both for CD4 and CD8 T cells. Bulk assays can be quantified as a ratio of the measured functions in either the presence or absence of Ag. Single-cell assays can be quantified as frequencies of Ag-responsive cells.
Table 2. T-cell assay compatibility matrix ICS Cytokine-capture assay CD154 CD107 TACE CFSEa ELISpot
MHC–multimer binding Yes Yes Yes Yes Yes Yes No
ICS
Cytokine-capture assay
CD154
CD107
TACE
CFSE
No Yes Yes No Yes No
Yes Yes Yes Yesa No
Yes Yes Yes No
Yes Yes No
Yes No
No
Table 3. Specific assay requirements Assay
Multimers ICS Cytokine capture CD154 CD107 TACE CFSE ELISpot a
Cell preparationa
WB, PBMCs WB, PBMCs WB, PBMCs PBMCs PBMCs WB, PBMCs PBMCs PBMCs
Stimulation requirements AgCAPC Nf Yf Y Y Y Y Y Y
Co-stimulation N Of O O O O O O
b
Golgi inhibitors N Y N N Y N N N
c
Other reagents N N N Y Y Y N N
Time (hrs)
Recovery of viable cellse
0 5–18 5–18 5–18 5–18 5–18 72–144 5–18
C K C C C C C K
d
Whole blood (WB) requires subsequent lysis. The PBMC number required for analytical assays ranges from 1 to 5!105 cells, depending on the expected frequency of positive cells. For preparative assays, the PBMC number should be scaled up to 107–109. b Co-stimulation antibodies, usually CD28 and CD49d, are required typically for stimulation for (of) !12 h. c Typically, the Golgi inhibitors, such as Brefeldin A or Monensin, are used, whereas the TACE assay requires metalloproteinase inhibitors (MMPIs). d Other reagents needed during stimulation, for example, anti-CD107, anti-CD154 or anti-CD40. e Indicates whether live cells can be isolated based on the assay measurement. f Y represents yes, N represents no and O represents optional. www.sciencedirect.com
Review
TRENDS in Immunology
epitopes and the analysis of the phenotypic differentiation and function of antigen-specific T cells. Defining specificities Several technological advances have been presented recently to facilitate the large-scale analysis of epitopes, to visualize peptide–MHC interactions and to identify and select Ag-responsive T cells for more detailed characterization. Epitope identification frequently requires large numbers of peptides to be synthesized, which is costly and laborious. Therefore, many groups use algorithms to predict which peptides from a selected protein would bind a given MHC [37–39]. However, although useful, these algorithms are still imperfect. To provide multiple peptides at low cost for empirical testing, advanced highly parallel technologies for peptide synthesis have been developed, such as Spot or Multi-Pin synthesis [40,41]. Combinations of these technologies with ICS or ELISpot have only been reported recently. These include the large-scale screening of a comprehensive library of 66 000 Spot peptides, covering all possible 9 amino acid peptides in the whole cytomegalovirus (CMV) proteome (F. Kern, unpublished), and, screening O200 CMV protein-specific pools of overlapping Multi-pin peptides to identify numerous previously unidentified T-cell targets (L. Picker, unpublished). Currently, little is known about the APCs that present a given peptide in vivo; however, technologies are now being devised to identify and track these cells. Kunkel et al. [42] recently reported an amplification system, based on magneto-fluorescent liposomes, to visualize directly by flow cytometry as few as 100 peptide–MHC complexes per APC, and to evaluate functional T-cell responses in vivo. Cells that initiate immune responses can be tracked in vivo following Ag uptake through the mucosal route. This technology will help us understand the trafficking of APCs between the mucosa and the central immune organs, identifying the routes, kinetics and other parameters of the important innate processes that initiate Ag-specific immune responses, either immunogenic or tolerogenic. Following Ag presentation, activation of T cells can be measured as an early or late event. Phospho-specific antibodies enable the flow cytometric monitoring of protein phosphorylation of unique residues at the single-cell level [43]. However, because T-cell stimulation following interaction with the APC occurs asynchronously in the cell population in a time frame of hours, whereas phosphorylation and dephosphorylation occur within minutes, this method requires further refinements if it is to identify Ag-activated T cells. Nevertheless, this method has enormous potential in that it might identify all responding cells (by virtue of the initiation of signalling cascades); the simultaneous and independent measurement of different signalling pathways might enable the discrimination of differential cellular response programs. Recently, several groups have mapped the fine interaction of the TCR with peptide–MHC by sequencing the TCRs from all T cells that respond to a single peptide. This nascent field has already yielded interesting results. For example, in B6 mice, after primary and secondary www.sciencedirect.com
Vol.26 No.9 September 2005
481
influenza virus challenge, a proportion of high-frequency TCRb express ‘public’ complementarity-determining region 3b (CDR3b) sequences that are detected in every mouse and represent a ‘best fit’, whereas others are invariably not present. ‘Public’ CDR3b sequences originate from up to 10 different nucleic acid sequences, whereas unique (‘private’) sequences are always specified by a single nucleotype [44]. Douek and Price demonstrated that different types of TCR responses could dramatically impact on the ability of SIV to escape CTL control. Specifically, an immunodominant peptide, which generates a monomorphic TCR response (probably because of structural constraints), is quickly mutated during acute SIV infection to render the specific CTL response ineffective; a different immunodominant peptide from SIV generates a much broader TCR response and the virus is unable to escape CTL control of that response (because mutant peptides are still recognized efficiently) [45]. These studies will influence not only our understanding of how the structural interaction of TCR and peptide–MHC complexes drive immune responses, they will probably instruct us on how to modify vaccination techniques, for example, to best avoid the generation of rapid escape mutations in HIV. Phenotype differentiation and function The ability to detect Ag-specific T cells by flow cytometry has vastly extended our abilities to analyze the phenotypes and functional characteristics of Ag-specific T cells. Not surprisingly, in most published reports, Ag-specific T cells were analyzed with regards to those phenotypic markers of T-cell differentiation previously established using fairly limited but well known models or bulk peripheral blood mononuclear cells (PBMCs) [46–48]. A popular paradigm [46,49] postulates the existence of differentiation ‘compartments’ termed ‘central’ and ‘effector’ memory (TCM and TEM), which can be characterized by the expression levels of CD45RA and CCR7 and are associated with certain migration patterns and functions. However, T-cell differentiation does not follow the same rules for all Ags and pathogens: certain characteristics, such as the ability to respond to Ag without co-stimulation, degranulation or specific migration patterns are frequently associated with specific phenotypes, although it is becoming increasingly obvious that these phenotypes are not the same for T cells of different specificities [50,51]. In addition, the phenotypic markers used by different groups are not fully congruous, which makes it more difficult to compare data. For example, some groups have consistently classified CMV-specific CD8 T cells into four subsets, defined by the expression of CD45RA and CD27 [52,53]; however, others have preferentially used combinations of the following markers: CD45RO, CD27, CD28 or CD62L [54,55]. These, or similar divisions, intend to define essentially the same subsets, that is, ‘naı¨ve’, ‘early Ag experienced’, ‘late Ag experienced’ and ‘terminally differentiated’. Recent work illustrates that these models might in fact over-simplify the situation. When using many such parameters in combination (rather than two at a time), the number of subsets and different functions
482
Review
TRENDS in Immunology
increases dramatically because there is extensive overlap between the subsets defined on the basis of only two markers. The development of flow cytometric technology that can quantify simultaneously the expression of as many as 17 different molecules per cell [56] will enable the detailed analysis of which of these functions or phenotypes is most likely to be relevant to disease processes. For example, recent data (M. Betts, pers. commun.) illustrates that it might be necessary to measure five different functions on each CD8 T cell to identify functional differences between HIV non-progressors and progressors. With this technology comes a commensurate level of complexity, in the experimental design, data analysis and presentation. Overcoming these hurdles is an active field of development [56–58]; new tools to assist these efforts are urgently needed. The analysis of isolated single Ag-specific T cells, for example, with respect to cytokine mRNA expression [59] or TCR CDR3 sequences [60], after identification of these cells by tetramer staining and following multiparameter based sorting, is an elegant combination of technologies. Clinical usefulness Currently, the clinical applications of Ag-specific measurements are focused on two major topics: monitoring of responses to vaccines and correlating ‘natural’ T-cell responses with clinical outcomes. Among all vaccines, those against tumours and HIV are the most frequently investigated, whereas, with respect to natural responses, responses against CMVand HIV dominate current research. The induction of antitumour responses by peptidebased vaccines has been addressed recently [61,62]. A paradox of this form of immunotherapy is that substantial proportions of vaccinated patients develop measurable tumour-specific T-cell responses but only a small proportion of these responses appear to be effective (tumour regression). Stuge et al. recently applied MHC multimer-based sorting, the CD107 degranulation assay and the classic chromium release assay to determining the recognition efficiency of vaccine-elicited responses and endogenous anti-melanoma responses. By combining technologies, they revealed that the recognition efficiency of vaccine-induced T cells was predominantly low, whereas that of endogenous tumour Ag-induced T cells was usually high. These results show that looking at several parameters by combining methods, rather than relying on single readouts, will be crucial in making use of the currently available tools. With respect to HIV-specific T cells, functional and phenotypical differences between CD4 and CD8 T cells responding to viral Ags in HIV vaccinees versus naturally infected individuals were evaluated and correlated with the clinical course of HIV infection. Using 12-coloranalysis, Koup et al. found that vaccine-induced T-cell responses were distinct from those found in naturally infected subjects, either progressors or long-term nonprogressors (LTNPs). Interestingly, vaccine-induced responses matured during the first year and became more polyfunctional: a state that is more congruous with the functionality of HIV-specific T cells in LTNPs (R. Koup, pers. commun.). Betts et al. were able to follow www.sciencedirect.com
Vol.26 No.9 September 2005
an HIV-Gag vaccinee who actually became infected with HIV: ICS, MHC multimer binding and the CD107 degranulation assay were used to assess the T-cell response during the clinical course that rapidly developed the typical features of chronic HIV infection [63]. In terms of monitoring of HIV-specific T cells induced by natural infection, the numbers of CD4 and CD8 T cells responding to viral Ags in HIV-infected individuals were recently evaluated and correlated with viral load and disease progression. Thus, it was revealed that persistently low viral replication (!10 000 copies mlK1) during antiretroviral therapy is associated with higher frequencies of HIV-specific CD4 and CD8 T cells than complete suppression of replication or therapy failure [64]. Along the same lines, ICS was used recently to study the T-cell response to two important human CMV proteins, pp65 and IE-1, in heart-transplant recipients. The levels of CD8 T cells specific for the non-structural IE-1, rather than for pp65, correlate with protection from CMV-disease [65]. A common theme seems to be emerging: ‘effective’ antiviral T-cell responses, for example, those against CMV, Epstein–Barr virus (EBV) and HIV in non-progressors, constitute ‘polyfunctional’ T cells, that is, T cells that exhibit at least four or five different functions simultaneously. HIV progressors, by contrast, show a more restricted functional profile [66]. With respect to HIV, this was in fact suspected previously: rather than just quantity or breadth, the quality (functional profile) of the CD8 T-cell response might be of paramount importance [67]. Outlook The constantly growing array of tools for the analysis of cellular parameters persistently adds to the complexity of the information that we have on the key factors in Ag-specific immune responses. To generate biologically and clinically useful results from this ever-increasing wealth of information, we will eventually have to (re)define the most relevant elements using a minimum of technology, so as to enable widespread application. For example, the use of 17 fluorescence channels in flow cytometric lymphocyte analysis might lead to the identification of crucial subsets and functions that can then be monitored using considerably fewer channels. Importantly, flow cytometry-based assays are highly amenable to standardization [68]. Such standardized use should greatly facilitate the generation of meaningful and comparable information across the globe, pushing towards understanding Ag-specific immune responses. Acknowledgements This work was supported in part by grants from ISS (40F.50, 45F.26), CIPE, Ministry of Health PF, MIUR (RBNE01RB9B003), EU (QLK21999–01040) to FM and GLP.
References 1 Pearmain, G. et al. (1963) Tuberculin-induced mitosis in peripheral blood leucocytes. Lancet 1, 637–638 2 Bloom, B.R. (1971) In vitro methods in cell-mediated immunity in man. N. Engl. J. Med. 284, 1212–1213
Review
TRENDS in Immunology
3 Brunner, K.T. et al. (1968) Quantitative assay of the lytic action of immune lymphoid cells on 51Cr-labelled allogeneic target cells in vitro; inhibition by isoantibody and by drugs. Immunology 14, 181–196 4 Roden, M.M. et al. (1999) A novel cytolysis assay using fluorescent labeling and quantitative fluorescent scanning technology. J. Immunol. Methods 226, 29–41 5 Barber, D.L. et al. (2003) Cutting edge: rapid in vivo killing by memory CD8 T cells. J. Immunol. 171, 27–31 6 Czerkinsky, C. et al. (1988) Reverse ELISPOT assay for clonal analysis of cytokine production. I. Enumeration of g-interferon-secreting cells. J. Immunol. Methods 110, 29–36 7 Picker, L.J. et al. (1995) Direct demonstration of cytokine synthesis heterogeneity among human memory/effector T cells by flow cytometry. Blood 86, 1408–1419 8 Waldrop, S.L. et al. (1997) Determination of antigen-specific memory/ effector CD4CT cell frequencies by flow cytometry: evidence for a novel, antigen-specific homeostatic mechanism in HIV-associated immunodeficiency. J. Clin. Invest. 99, 1739–1750 9 Manz, R. et al. (1995) Analysis and sorting of live cells according to secreted molecules, relocated to a cell-surface affinity matrix. Proc. Natl. Acad. Sci. U. S. A. 92, 1921–1925 10 Bueno, C. et al. (2002) A new method for detecting TNF-a-secreting cells using direct-immunofluorescence surface membrane stainings. J. Immunol. Methods 264, 77–87 11 Rodriguez-Caballero, A. et al. (2004) A new simple whole blood flow cytometry-based method for simultaneous identification of activated cells and quantitative evaluation of cytokines released during activation. Lab. Invest. 84, 1387–1398 12 Chattopadhyay, P.K. et al. A live-cell assay to detect antigen-specific CD4 T cells with diverse cytokine profiles. Nat. Med. (in press) 13 Frentsch, M. et al. Direct access to CD4CT-cells specific for defined antigens according to CD154 expression. Nat. Med. (in press) 14 Betts, M.R. and Koup, R.A. (2004) Detection of T-cell degranulation: CD107a and b. Methods Cell Biol. 75, 497–512 15 Rubio, V. et al. (2003) Ex vivo identification, isolation and analysis of tumor-cytolytic T cells. Nat. Med. 9, 1377–1382 16 Catalfamo, M. et al. (2004) Human CD8CT cells store RANTES in a unique secretory compartment and release it rapidly after TcR stimulation. Immunity 20, 219–230 17 Houck, D.W. and Loken, M.R. (1985) Simultaneous analysis of cell surface antigens, bromodeoxyuridine incorporation and DNA content. Cytometry 6, 531–538 18 Lyons, A.B. (2000) Analysing cell division in vivo and in vitro using flow cytometric measurement of CFSE dye dilution. J. Immunol. Methods 243, 147–154 19 Tough, D.F. and Sprent, J. (1994) Turnover of naı¨ve- and memoryphenotype T cells. J. Exp. Med. 179, 1127–1135 20 Flynn, K.J. et al. (1999) In vivo proliferation of naı¨ve and memory influenza-specific CD8CT cells. Proc. Natl. Acad. Sci. U. S. A. 96, 8597–8602 21 Greten, T.F. et al. (1998) Direct visualization of antigen-specific T cells: HTLV-1 Tax11-19- specific CD8CT cells are activated in peripheral blood and accumulate in cerebrospinal fluid from HAM/TSP patients. Proc. Natl. Acad. Sci. U. S. A. 95, 7568–7573 22 Altman, J.D. et al. (1996) Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94–96 23 Knabel, M. et al. (2002) Reversible MHC multimer staining for functional isolation of T-cell populations and effective adoptive transfer. Nat. Med. 8, 631–637 24 Borg, N.A. et al. (2005) The CDR3 regions of an immunodominant T cell receptor dictate the ‘energetic landscape’ of peptide–MHC recognition. Nat. Immunol. 6, 171–180 25 Novak, E.J. et al. (1999) MHC class II tetramers identify peptidespecific human CD4CT cells proliferating in response to influenza A antigen. J. Clin. Invest. 104, R63–R67 26 McMichael, A.J. and Kelleher, A. (1999) The arrival of HLA class II tetramers. J. Clin. Invest. 104, 1669–1670 27 Scriba, T.J. et al. (2005) HIV-1-specific CD4CT lymphocyte turnover and activation increase upon viral rebound. J. Clin. Invest. 115, 443–450 28 Walker, M.R. et al. (2005) De novo generation of antigen-specific CD4CCD25C regulatory T cells from human CD4CCD25K cells. Proc. Natl. Acad. Sci. U. S. A. 102, 4103–4108 www.sciencedirect.com
Vol.26 No.9 September 2005
483
29 Zhang, Y. et al. (2005) A polyclonal anti-vaccine CD4 T cell response detected with HLA-DP4 multimers in a melanoma patient vaccinated with MAGE-3.DP4-peptide-pulsed dendritic cells. Eur. J. Immunol. 35, 1066–1075 30 Hayakawa, K. et al. (1987) Isolation of high-affinity memory B cells: phycoerythrin as a probe for antigen-binding cells. Proc. Natl. Acad. Sci. U. S. A. 84, 1379–1383 31 Lalor, P.A. et al. (1992) Functional and molecular characterization of single, (4-hydroxy-3-nitrophenyl)acetyl (NP)-specific, IgG1CB cells from antibody-secreting and memory B cell pathways in the C57BL/6 immune response to NP. Eur. J. Immunol. 22, 3001–3011 32 Townsend, S.E. et al. (2001) Single epitope multiple staining to detect ultralow frequency B cells. J. Immunol. Methods 249, 137–146 33 Newman, J. et al. (2003) Identification of an antigen-specific B cell population. J. Immunol. Methods 272, 177–187 34 Crotty, S. et al. (2004) Tracking human antigen-specific memory B cells: a sensitive and generalized ELISPOT system. J. Immunol. Methods 286, 111–122 35 Doucett, V.H. et al. Enumeration and characterization of virus-specific B cells by multicolor flow cytometry. J. Immuno. Meth. (in press) 36 Douek, D.C. et al. (2002) HIV preferentially infects HIV-specific CD4CT cells. Nature 417, 95–98 37 Rammensee, H.G. et al. (1997) MHC ligands and binding motifs, Landes Bioscience 38 Scheibenbogen, C. et al. (2002) Identification of known and novel immunogenic T-cell epitopes from tumor antigens recognized by peripheral blood T cells from patients responding to IL-2-based treatment. Int. J. Cancer 98, 409–414 39 Elkington, R. et al. (2003) Ex vivo profiling of CD8C-T-cell responses to human cytomegalovirus reveals broad and multispecific reactivities in healthy virus carriers. J. Virol. 77, 5226–5240 40 Maeji, N.J. et al. (1990) Multi-pin peptide synthesis strategy for T cell determinant analysis. J. Immunol. Methods 134, 23–33 41 Frank, R. (2002) The SPOT-synthesis technique. Synthetic peptide arrays on membrane supports–principles and applications. J. Immunol. Methods 267, 13–26 42 Kunkel, D. et al. (2003) Visualization of peptide presentation following oral application of antigen in normal and Peyer’s patches-deficient mice. Eur. J. Immunol. 33, 1292–1301 43 Krutzik, P.O. and Nolan, G.P. (2003) Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55, 61–70 44 Kedzierska, K. et al. (2004) Conserved T cell receptor usage in primary and recall responses to an immunodominant influenza virus nucleoprotein epitope. Proc. Natl. Acad. Sci. U. S. A. 101, 4942–4947 45 Price, D.A. et al. (2004) T cell receptor recognition motifs govern immune escape patterns in acute SIV infection. Immunity 21, 796–803 46 Sallusto, F. et al. (1999) Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 47 Picker, L.J. et al. (1993) Control of lymphocyte recirculation in man. I. Differential regulation of the peripheral lymph node homing receptor L-selectin on T cells during the virgin to memory cell transition. J. Immunol. 150, 1105–1121 48 Kern, F. et al. (1994) Discordant expression of LFA-1, VLA-4a, VLA-b1, CD45RO and CD28 on T-cell subsets: evidence for multiple subsets of ‘memory’ T cells. Int. Arch. Allergy Immunol. 104, 17–26 49 Sallusto, F. et al. (2004) Central memory and effector memory T cell subsets: function, generation, and maintenance. Annu. Rev. Immunol. 22, 745–763 50 Appay, V. et al. (2002) Memory CD8CT cells vary in differentiation phenotype in different persistent virus infections. Nat. Med. 8, 379–385 51 van Leeuwen, E.M. et al. (2004) Emergence of a CD4CCD28K granzyme BC, cytomegalovirus-specific T cell subset after recovery of primary cytomegalovirus infection. J. Immunol. 173, 1834–1841 52 Hamann, D. et al. (1997) Phenotypic and functional separation of memory and effector human CD8CT cells. J. Exp. Med. 186, 1407–1418 53 Hamann, D. et al. (1999) Faces and phases of human CD8 T-cell development. Immunol. Today 20, 177–180 54 Tsegaye, A. et al. (2003) Immunophenotyping of blood lymphocytes at birth, during childhood, and during adulthood in HIV-1-uninfected Ethiopians. Clin. Immunol. 109, 338–346
484
Review
TRENDS in Immunology
55 Wills, M.R. et al. (2002) Identification of naive or antigen-experienced human CD8CT cells by expression of costimulation and chemokine receptors: analysis of the human cytomegalovirus-specific CD8CT cell response. J. Immunol. 168, 5455–5464 56 Perfetto, S.P. et al. (2004) Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 57 De Rosa, S.C. et al. (2003) Beyond six colors: a new era in flow cytometry. Nat. Med. 9, 112–117 58 Baumgarth, N. and Roederer, M. (2000) A practical approach to multicolor flow cytometry for immunophenotyping. J. Immunol. Methods 243, 77–97 59 Johnson, B.J. et al. (2003) Single-cell perforin and granzyme expression reveals the anatomical localization of effector CD8C T cells in influenza virus-infected mice. Proc. Natl. Acad. Sci. U. S. A. 100, 2657–2662 60 Panus, J.F. et al. (2000) Antigen-specific T helper cell function: differential cytokine expression in primary and memory responses. J. Exp. Med. 192, 1301–1316 61 Valmori, D. et al. (2002) Circulating tumor-reactive CD8CT cells in melanoma patients contain a CD45RACCCR7K effector subset exerting ex vivo tumor-specific cytolytic activity. Cancer Res. 62, 1743–1750
Vol.26 No.9 September 2005
62 Pittet, M.J. et al. (2001) Ex vivo IFN-g secretion by circulating CD8 T lymphocytes: implications of a novel approach for T cell monitoring in infectious and malignant diseases. J. Immunol. 166, 7634–7640 63 Betts, M.R. et al. (2005) Characterization of functional and phenotypic changes in anti-Gag vaccine-induced T cell responses and their role in protection after HIV-1 infection. Proc. Natl. Acad. Sci. U. S. A. 102, 4512–4517 64 Alatrakchi, N. et al. (2005) Persistent low viral load on antiretroviral therapy is associated with T cell-mediated control of HIV replication. AIDS 19, 25–33 65 Bunde, T. et al. (2005) Protection from cytomegalovirus after transplantation is correlated with immediate early 1-specific CD8 T cells. J. Exp. Med. 201, 1031–1036 66 Pantaleo, G. and Koup, R.A. (2004) Correlates of immune protection in HIV-1 infection: what we know, what we don’t know, what we should know. Nat. Med. 10, 806–810 67 Rowland-Jones, S.L. et al. (2001) How important is the ‘quality’ of the cytotoxic T lymphocyte (CTL) response in protection against HIV infection? Immunol. Lett. 79, 15–20 68 Landay, A.L. et al. (2004) Performance of single cell immune response assays; proposed guideline. NCCLS, 23 (www.clsi.org/)
Five things you might not know about Elsevier 1. Elsevier is a founder member of the WHO’s HINARI and AGORA initiatives, which enable the world’s poorest countries to gain free access to scientific literature. More than 1000 journals, including the Trends and Current Opinion collections, will be available for free or at significantly reduced prices. 2. The online archive of Elsevier’s premier Cell Press journal collection will become freely available from January 2005. Free access to the recent archive, including Cell, Neuron, Immunity and Current Biology, will be available on both ScienceDirect and the Cell Press journal sites 12 months after articles are first published. 3. Have you contributed to an Elsevier journal, book or series? Did you know that all our authors are entitled to a 30% discount on books and stand-alone CDs when ordered directly from us? For more information, call our sales offices: +1 800 782 4927 (US) or +1 800 460 3110 (Canada, South & Central America) or +44 1865 474 010 (rest of the world) 4. Elsevier has a long tradition of liberal copyright policies and for many years has permitted both the posting of preprints on public servers and the posting of final papers on internal servers. Now, Elsevier has extended its author posting policy to allow authors to freely post the final text version of their papers on both their personal websites and institutional repositories or websites. 5. The Elsevier Foundation is a knowledge-centered foundation making grants and contributions throughout the world. A reflection of our culturally rich global organization, the Foundation has funded, for example, the setting up of a video library to educate for children in Philadelphia, provided storybooks to children in Cape Town, sponsored the creation of the Stanley L. Robbins Visiting Professorship at Brigham and Women’s Hospital and given funding to the 3rd International Conference on Children’s Health and the Environment. www.sciencedirect.com