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. Author manuscript; available in PMC: 2014 May 1.
Published in final edited form as: Arthritis Rheum. 2013 May;65(5):1148–1156. doi: 10.1002/art.37847

Multi-parameter flow cytometry for discovery of disease mechanisms in rheumatic diseases

Mark J Soloski 1, Francis J Chrest 1
PMCID: PMC3636160  NIHMSID: NIHMS433628  PMID: 23334929

Abstract

Flow cytometry has emerged as an essential tool for investigators in the study of the complexity of the immune system and the examination of its role in human health and disease. This technology has developed to the point where one can readily generate a large descriptive data set that details the levels of important immune cell subsets and defines an individual immune cell signature or “Immune-cellome”. This immune cell signature would clearly display individual variation but also would change in a manner reflective of disease state. Analysis of the “immune-cellome” may provide novel insight into disease pathophysiology, provide new biomarkers of disease activity and perhaps identify therapeutic targets. In this brief review we will cover current advances in complex flow cytometry and suggest ways this may be applied to the study of rheumatic diseases.

1. Introduction and Overview

In the past decades, our understanding of the various cellular components that function in the immune response has expanded at a rapid pace. The stage was set for the dissection of the immune system with the ground breaking work of Henry Claman who defined separate lineages of cooperating bone marrow and thymic derived immune cells and studies by Cantor and Boyce who were the first to use surface markers to define functional T cell subsets (1, 2). Now, it is recognized that there are multiple lineages of bone marrow derived cells and a vast array of lineage unique subsets that play key roles in the immune process either as direct effector cells or having an immunoregulatory role.

Flow cytometry has emerged as an essential tool for investigators in the study of the complexity of the immune system and the examination of its role in health and disease. The power of this technique lies in its ability to interrogate individual cells and simultaneously measure multiple parameters (up to 33 have been reported to date!) on each individual cell. This “interrogation” occurs at a high rate (1000+ cells/sec on up) and allows the investigator to precisely identify, quantify and characterize multiple subsets of immune cells in complex cell mixtures isolated from whole blood or tissues. This ability to generate large data sets from a single sample is particularly valuable for those investigating human diseases where samples can be small and limiting.

In the last decade there has been an explosion in the range of reagents and new applications that take advantage of flow cytometry to dissect the immune system and monitor its dynamics. This includes the development of libraries of antibody reagents that recognize unique surface, intracellular and secreted proteins, a large catalogue of laser excitable fluorescent compounds, new approaches toward coupling these compounds to proteins or other molecular species, reagents that identify antigen-specific lymphocytes and dyes that monitor cell replication and physiological changes within cells. Collectively, these approaches have allowed investigators to make remarkable progress in dissecting the complexity of the immune system, understanding function and addressing how this may vary in human disease.

As a result of these advances, polychromatic flow cytometry has become a powerful analytical tool that can generate insightful data relevant to rheumatic diseases. For example, the use of a specific marker panel (see below) can allow one to track the levels of one or more immune cell subsets in the blood of normal and disease subjects. If the study is longitudinal and/or includes a large cohort, then data relevant to disease severity or progression can be obtained. Such an approach determined that CXCR5+/ICOShi CD4 cells are expanded in the blood of a subset of patients with systemic lupus erythematous (3). This has generated great interest because such cells play a key role in regulating antibody formation. In addition to tracking a specific immune cell subset, one can devise several complex panels of reagents that detect a wide range of lymphoid and myeloid immune cells. This latter approach offers the opportunity of generating an individual yet comprehensive profile of immune cell subsets in patients that can be referred to as their immune cell signature or “immune-cellome”. The immune-cellome would be a snapshot of the immune status of an individual that can be compared to matched controls and monitored for changes during disease progression and/or treatment. Studies designed as such would provide valuable data sets that would identify new biomarkers of value in diagnosis, monitoring of disease progression and response to therapy as well as provide insights into the pathophysiological mechanisms that drive rheumatic diseases and ultimately hold the potential to identify new therapeutic targets.

In this brief review we will provide a general overview of the application of flow cytometry based complex immunophenotyping in rheumatic diseases. The interested reader is referred to several excellent review articles that have been published for additional details on sample handling, developing a panel of reagents, standardization of data acquisition and approaches toward data analysis (4–7).

2. Flow Cytometry: The Basics

A flow cytometer consists of three major components or systems (8) (Figure 1). First there is a fluidic system that has the capability of sampling a suspension of cells and delivering individual cells within a fluid stream so they can be interrogated by the second component, the laser/optical system. Lasers are a focused source of monochromatic light and a simple flow cytometer is equipped with one laser but more complex instruments can have four or more. When the laser strikes the cell two things can happen, the laser light can be reflected (or scattered) by the cell or absorbed by molecular species found on or in the cell. In the former case, scattered laser light can be of two types, forward scatter (FSC), which provides information on cell size or side scatter (SSC) which is informative for cellular complexity (i.e. does the cell have an irregular nucleus, lot’s of granules, etc.). Forward and side scattered laser light can be optically separated using a series of mirrors and filters and then directed to separate photo-detectors that absorb the photons and convert them into an electronic signal that is then stored (the electronic system).

Figure 1. A Basic Flow Cytometer.

Figure 1

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Illustrated are the basic elements of a simple flow cytometer. This includes a laser light source used to interrogate the cells delivered via a stream of fluid. Reflected laser light or fluorescence emissions are collected using a series of mirrors (blue rectangles) and lenses (blue ovals) to detectors that convert light signals to electronic signatures that can be processed and stored.

Laser light that is absorbed by the cell has a different fate. If the cell has molecular species with fluorescent properties bound to it, then these fluorescent compounds can absorb the energy of the light and emit it in the form of light at a higher wavelength. The simple physics behind it is that the energy of the laser beam absorbed by the cell bound fluorescent compound causes its electrons to move to a higher state. This state is unstable so the electrons move to a lower more stable state and in doing so releases energy some of which is in the form of light but at a higher (less energetic) wavelength than what was absorbed. The particular wavelength of light emitted is a property of the fluorescence compound. For example the well-known dye fluorescein isothiocyanate (FITC) efficiently absorbs light from a blue (488nm) laser and produces a green (525nm) emission while phycoerythrin (PE, from red algae) also absorbs light from a blue (488nm) laser but produces an orange (575nm) emission. Different fluorescent emissions can be optically separated by a series of mirrors and filters allowing one to determine if a range of fluorescent markers are present or absent from a single cell (Figure 1).

Data is collected in what is termed “list mode” where each cell (or event) interrogated is time coded and tabulated. Thus for each cell one has a “list” of data on all the signals generated including FSC, SSC and the range of chosen fluorescent emissions. In this manner a detailed description of individual cells can be generated and since flow cytometers can analyze individual cells at a fast rate (1–5000/sec) one can generate robust data sets on a large number of cells (105 and up) in a short period of time that can then be stored for subsequent analysis.

Displayed in Figure 2 is a flow cytometric analysis of human peripheral blood mononuclear cells (PBMCs) that were reacted with dye tagged monoclonal antibodies specific for the markers CD3 (FITC labeled) and the chemokine receptor CXCR5 (PE labeled). Analysis of both the scattered laser light parameters (FSC vs. SSC, Figure 2A) allows one to distinguish major PBMC cell types including lymphocytes (round nucleus, little cytoplasm), monocytes (larger with irregular nucleus and more cytoplasm) and granulocytes (large with cytoplasmic granules). This clear separation of cell types allows one to draw gates so that the fluorescent properties of only one cell type are considered. Figure 2B shows such an analysis and single parameter histograms of the individual markers on lymphocytes are displayed. This analysis demonstrates that CD3 is expressed uniformly by the majority of cells (CD3pos) and is clearly distinguished from those that do not (CD3neg). The expression pattern of the CXCR5 marker is more complex with at least three populations (negative, intermediate and high) being detected. While this is important information, the analysis of these two markers separately does not reveal the relationship between expression of the two markers CD3 and CXCR5. This relationship can be revealed when the two markers are co-analyzed in the dual parameter dot plot shown in Figure 2C. This analysis shows that the CXCR5int cells define a subset of CD3+ (T) cells while those that are CXCR5hi are CD3neg (9).

Figure 2. A Flow Cytometric Analysis of Human PBMCs.

Figure 2

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Human whole blood was first treated to remove RBCs, reacted with fluorescent-tagged markers for CD3 (FITC) and CXCR5 (PE) and analyzed by flow cytometry. Displayed in panel A is a two (dual) parameter dot plot displaying the FSC and SSC signals on a linear scale. Each dot represents single cell (or event) and by displaying both signals relative to each other one debris, lymphocytes, monocytes and granulocytes can be easily resolved. Panel B displays the distribution of the markers CD3 and CXCR5 on the “gated” lymphocytes as separate single color histograms that clearly define positive and negative lymphocyte populations. Panel C displays the same data viewed as a dual (two) parameter polychromatic plots were color is used to define high (red) and low (blue) density of events. This plot illustrates the relationship between CD3 and CXCR5 expression on human lymphocytes.

3. The Immune System – yes it is Complex – some examples!

We now understand the immune system to be composed of a wide variety of dynamically interacting cell subsets. The application of polychromatic flow cytometry has played a central role in the identification, isolation and characterization of functionally distinct human immune cell subsets. As more molecular markers become available, additional layers of complexity emerge and more and more immune subsets are defined. Despite the appearance that there is no real end in sight for immune subset determination, there seems to be a consensus emerging that the major subsets of immune cells can be defined by interrogating cells for the expression of specific cell surface structures. The expression patterns of these molecules on cells help identify specific immune cell subsets and the levels and distribution of the immune cell subsets in an individual would define a unique immune cell signature (or immune-cellome) that is reflective of the immune status of an individual. This immune-cellome would vary among individuals and be influenced by a range of factors including genetic variation, gender, age, exposure to environmental triggers and disease state. What follows below is a discussion of the major defined immune cell subsets that would be of general interest to those involved in the study of immune-mediated inflammatory disorders.

The varying display of cell surface markers has allowed the definition of CD4 and CD8 T cells into distinct subsets reflecting their activation and/or maturational state (See Table I). Differential expression of the isoforms CD45RA or CD45RO can define naïve and memory T cells respectively. The use of CD45RA versus CD45RO has been extensively discussed and while they define largely mutually exclusive subsets (see Figure 1) intermediate phenotypes can be clearly identified, perhaps defining transition states (7). Nevertheless, despite this complexity, CD45 isoforms together with CCR7 define naïve (C45RO−/RA+CCR7+), central memory (C45RO+/RA−CCR7+) and effector memory (C45RO+/RA−CCR7− subsets (7, 10). Selective chemokine receptor expression has been used to identify immune cell subsets with specific effector functions. Examples include, interferon-γ producing CD4+Th1or CD8+Tc1 subsets (CXCR3+ or CCR5+), CD4+Th2 or CD8+Tc2 subsets (CRTH2 or CXCR3−/CCR6−), CD4+ Th17 (CCR4+/CCR6+) and CD4 T follicular helper cells (CXCR5+) (9, 11–13). In addition Natural Tregs can be defined as CD4+CD137lo CD25+FoxP3+ (14).

Table 1.

Selective Phenotypic Markers for Relevant T cell Subsets

Functional
Immune Cell
Subset		 Specific Markers References
Naive CD4+ or CD8+ T cells CD45RA+CCR7+ or CD45RO−CCR7+ (7, 10)
Central Memory CD4+ or CD8+ T cells CD45RA−CCR7+ or CD45RO+CCR7+
Effector Memory CD4+ or CD8+ T cells CD45RA−CCR7− or CD45RO+CCR7+
CD4+ Th1 CCR5+ or CXCR3+ (11)
CD4+ Th2 CRTH2+ or CXCR3−CCR6− (12)
CD4+ Tfh CXCR5+ (9)
CD4+ Th17 CCR6+CCR4+ (13)
CD4+ Natural Tregs CD137loCD25+/FoxP3+ (14)
NKT Cells CD4+CD56+ (23)
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Selective marker expression can also be used to monitor the activation state of T cell subsets. These include markers that are rapidly up regulated upon activation and lost (CD137, CD154), and markers that are up regulated and remain (CD69, CD25, HLA-DR) (15). CD107a has also been included as a marker that is highly correlative for T cells with cytotoxic activity (16). Recently, the co-expression of CD38 and HLA-DR has been used as a marker of antigen activated CD4 and CD8 T cells and may be of particular interest in the detection and characterization of antigen-driven self-reactive T cells in rheumatic disease (4, 17)

A number of rheumatologic disorders display skin lesions, hence the examination of the levels of T cells that express chemokine receptors associated with skin homing may be of value. These include CXCR3, CCR3 and CCR5 that are found on cells migrating into inflamed sites and CCR4 and CCR10 expressed on cutaneous T cells (18–20). It is possible that the levels of such cells may vary depending on the degree of skin related inflammation.

There is significant interest in the roles of innate immune lymphocytes in rheumatic diseases. These include NKT, NK and TCR γ/δ subsets that have been proposed to either have direct effector function in tissue injury or play an immunoregulatory role (21, 22). NKT cells can be identified as CD56+/TCRαβ cells but iNKT cells are a subset of this population that uniquely expresses Vα24 and Vβ11. This subset can be detected either by co-staining with anti-Vα24 and Vβ11 reagents or with CD1d tetramer (23). This would enable one to detect and monitor the levels of iNKT (CD56+/ Vα24/Vβ11+, group I NKTs) and non-iNKT (CD56+/ Vα24/Vβ11−, group II NKT cells). TCR γ/δ subsets expressing Vδ1 have been shown to accumulate within inflamed synovia in Lyme and rheumatoid arthritis (24). One can readily identify and quantify total TCR γ/δ T cells using a pan TCR γ/δ reagent and define subsets differentially expressing CD8, CD56 and CD57. CD56 and CD57 have been used to define subsets of γ/δ T cells with potent cytolytic activity (25). NK cells can be identified as TCRneg/CD56+ cells and also divide them into functionally relevant subsets using CD57 and CD16. CD16−/lo CD56+ NK cells produce no IFN-γ, and have low cytolytic while CD16hi CD56+ NK cells produce IFN-γ and have potent cytotoxic activity (26). Several markers have been defined that distinguish activated and resting NK cells including CD62L, CD69, CD70 and CD96 (27, 28).

B cells, primarily as producers of self-reactive antibodies, clearly play a role in the progression of rheumatic disease and have been a target for immunotherapy (29, 30). B cells can be readily identified using the lineage-specific markers CD19 and CD20 (Table 2). The addition of more markers allows the identification of naïve B cells (IgM+/IgD+/CD19+/CD27−), plasmablasts (CD19lo/CD20−/CD27hi), early memory B cells (IgM+/IgD+/CD19+/CD27+), late memory B cell (IgM−/IgD−/CD19+/CD27+) and B1 cells (CD5+/IgM+/CD19+). In addition one can also monitor the activation/differentiation state for all subsets using CD69 and levels of CD23 and CD24 with the latter defining a B cell subset that may be altered in SLE (31).

Table 2.

Selective Phenotypic Markers for B cell Subsets

B Cell Subset Specific Markers Reference
Naive B cells CD19+/CD27−/IgD+ (4, 52)
Early Memory B cells CD19+/CD27+/IgD+
Late Memory B cells CD19+/CD27+/IgD−
Plasmablast CD19+CD20−CD27+CD38hi
B1 Cells CD5+/IgM+/IgDlo/CD19+−
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While the above information certainly illustrates the complexity of the immune system the question remains as to how does one investigate this complexity in the setting of rheumatic disease? The first issue to be grappled with is the source of immune cells. Because of the ease in sampling, the vast majority of studies examine PBMCs that are either analyzed on the day of collection or cryopreserved for later large batch analysis. Although the blood is not often the site of immune mediated tissue injury, a number of studies have successfully utilized PBMCs to investigate the responding immune system in the setting of infection, vaccination and during immune-mediated inflammatory disease thus validating its use (17, 32, 33). Frequently in rheumatic diseases there are therapeutic or diagnostic indications that allow access to cells at the site of injury such as synovial fluid in the various forms of inflammatory arthritis or bronchoalveolar lavage in the diagnosis of rheumatic diseases involving interstitial lung disease. In both cases immune cells from these sources can be readily examined by polychromatic flow cytometry and compared to PBMCs (34).

Once a cellular source is decided on, one then needs to devise a relevant panel of reagents that can detect the immune cell subsets of interest. The design of this panel(s) is of course dependent on how narrow or broad the question to be addressed is. Fortunately considerable effort has been put forward to designing optimal panels of reagents that allow one to reproducibly identify specific human immune cell subsets. For example, using an empiric approach, a panel of reagents was devised that optimally detected major human T cell subsets and allowed for cytokine responses to be measured (6). In this study different monoclonal antibody clones and fluorochrome combinations were tested so as to optimally identify CD4 and CD8 T cells with naïve, central memory or effector memory phenotypes.

Recently an effort has been put forward to generate “Optimized Multicolor Immunofluorescence Panels” (OMIPs) which are published on a regular basis (35). These OMIPs provide detailed information on cell handling, optimal reagent selection, gating strategies and data reporting. For example, recent publications have detailed OMIPs for the characterization of human antigen-specific T-cells or T regulatory cells, cell populations of considerable relevance to those interested in the rheumatic diseases (36, 37). Collectively, the application of such standardized panels not only saves the investigator the effort of panel design, which can take months, but this approach toward standardization will make for easier comparison of data sets from multiple labs and disease states.

Recently a well defined set of five reagent panels have been developed that clearly identify the major subsets of human immune cells and determine their activation state (4). This effort was an outgrowth of the recognized need for standardization of reagent panels as the interest grows in understanding the variation and complexity of the immune system in the normal and perturbed (diseased) state (the “Human Immunology Project”) (38). These well defined reagent panels can identify over 50+ immune cell subsets and have been proposed as a standard for the complex immunophenotyping that is a necessary part of the Human Immunology Project.

A great benefit of using multiple defined panels that identify distinct immune cell subsets is that one can gain novel insights into the complex relationships between the cells of the immune system. As an example, a set of 8 standardized multicolor panels were used to comprehensively analyze a broad range of immune cell subsets including, naïve and memory B and T cells, monocyte subsets, myeloid and plasmacytoid dendritic cells, innate immune cells, eosinophil’s and neutrophils (33). In total, over 100 distinct immune cell subsets could be detected using only 10 ml of heparinized whole blood. This approach was then used to examine samples from normal donors and patients with ankylosing spondylitis and led to the identification of a unique immune cell signature in the disease state. The power of such a comprehensive and unbiased approach is that unanticipated associations can be identified, as was the case in this study with elevated levels of unique subsets of B, NK and CD8 T cells associated with the disease state. While the contribution of these cells to disease has yet to be determined, we believe this illustrates the value of such an approach.

In another example, the levels of CXCR5 expressing CD4 T cells and B cell subset distribution was examined within the same PBMC samples from patients with juvenile dermatomyositis (9). This led to the observation that the frequency of circulating plasmablasts (CD19+CD20−CD27+CD38hi) had a positive correlation with the levels of CXCR5 expressing CD4 T cells displaying phenotypic markers associated with Th2 function (CCR6+CXCR3− and CCR6−/CXCR3−) and a negative correlation with CXCR5 expressing CD4 T cells displaying phenotypic markers associated with Th1 function (CCR6−CXCR3+). Importantly, this association was only seen in a subset of patients with severe disease potentially providing novel insights into disease pathogenesis and identifying a potential therapeutic target.

4. Data Analysis

The application of polychromatic flow cytometry to the study of human disease can yield enormous data sets, the analysis of which can be very daunting especially if a large number of patient samples are to be analyzed and compared. As a result there needs to be a well thought out data handling and analysis plan in place. This includes consistent sample identifiers, a data analysis strategy and plans for data archiving and storage. The practice of reproducible research is a key component of any large-scale study that has high data output and the lack of reproducibility has lead to major difficulties in previous high-throughput and integrative research (39, 40). Therefore, a large-scale analysis of a patient cohort should conform to published standards of reproducible research (40).

At the heart of the application of polychromatic flow cytometry to the analysis of immune cell subsets in patient cohorts is data analysis. There are available a number of commercially available (FlowJo and FSC express), free open source (Bioconductor’s flowCore package (41)) and other free software (see PUCL website for information, www.cyto.purdue.edu/flowcyt/software.htm) that can be applied. In most cases the software allows one to establish a defined gating strategy to identify the subsets of interest using an index sample data file. This gating strategy can then be applied to the larger set of samples. The software can derive a wide range of data including subset frequencies, mean fluorescence intensity, etc. for each sample and the data can be readily exported into a spreadsheet for further statistical analysis and determination of subset variation associated with disease state. The data can be exported as well to programs such as “Simplified Presentation of Incredibly Complex Evaluations” (SPICE) that allows for data exploration and statistical analysis of complex data sets (42). SPICE is freely available and supported by the NIAID/NIH (http://exon.niaid.nih.gov/spice/).

The software packages mentioned above all require manual gating where one defines the gates and makes alterations to accommodate sample variation. The major issue with this approach is the potential for subjective bias as to the placement of gates. This can be resolved either by having centralized data analysis or by having highly trained personnel in place to cross validate the analysis. In either case this manual gating approach can be labor intensive. Recently there has been progress in the development of software that attempts to circumvent these issues. Two such programs include flow analysis with automated multivariate estimation (FLAME) and density based merging (DBM) which use two distinct approaches for analyzing large highly complex flow cytometry data sets (43, 44). A third program, SPADE (spanning tree progression analysis of density normalized events), has been developed which bypasses normal gating and organizes data into predetermined clusters (45). All of these approaches show promise and such programs are in a state of refinement as high dimensional flow cytometry data set are accumulated across patients and disease states (32, 46)

5. Flow Cytometry Technology: New Advances offer even more possibilities!

There have been a number of advances of late that have made flow cytometry a readily available technology for investigators with an interest in immune mediated diseases. There are now available a wide range of easy to use table top instruments that can provide for the routine analysis of 5–8 fluorescent parameters. While at one time flow cytometers had been so costly they were only part of dedicated core facilities, there are now available analytical flow cytometers that are more modestly priced so that it is not uncommon that an individual lab can marshal resources for a dedicated instrument. While this has certainly allowed for more accessibility to flow cytometry such lower cost instruments have the drawback that they usually have a fixed laser and detector configuration so that only a limited number of specific flurochromes can be used. There do exist more sophisticated instruments that can accommodate multiple lasers and detectors having the capability of detecting 17 or more fluorescent parameters (5). While the use of these more complex instruments requires the involvement of trained expertise usually found only in a central shared core facility, software has been developed that allows for automatic configuration and standards of performance have been developed all of which make the use of this technology less intimidating (4, 47).

Recently a new flow cytometry technology has emerged, CyTOF, that offers the potential for the collection of even more multidimensional data (46, 48). This technology utilizes antibodies coupled to metal ions (vs. fluorescent compounds) and binding of these reagents to cells is detected by time-of flight mass spectroscopy of individual cells. The use of heavy metal labeling eliminates the need for complex compensation matrices and to date 31 different antibody reagents have been used simultaneously to interrogate immune cells with the potential for using up to 100 stable isotopes in a single sample. (46). This new technology has the potential to revolutionize the analysis of immune cells complexity and has been predicted by some to, at some point, replace fluorescence based multi-parameter flow cytometry (49).

Summary

It is becoming clear that technological advancements are allowing investigators the opportunity to probe ever deeper into the complexity of the immune system, understand how these components vary in the normal individual and how they are altered in the disease state (see recent review, (49)). These technologies include advances in flow cytometry which can define the “immune-cellome” as well as probe cell specific signaling pathways, new “deep sequencing” approaches to identify patterns of gene expression, high-throughput sequencing to profile antigen receptor usage and the development of sensitive multiplexed proteomic approaches to measure immune mediators in biological samples (46, 50, 51). If such powerful high dimensional data approaches are coupled to the study of robust and rigorously characterized rheumatic disease patient cohorts, there is the clear potential for a renaissance in our understanding of the complex interrelationship between components of the immune system in health and disease and unraveling the pathways that drive the pathophysiology of immune-mediated diseases. Such multidimensional approaches would pinpoint new therapeutic targets for treatment development and identify molecular and cellular biomarkers that will aid the clinician in diagnosis, the choice of therapy and the prediction of disease outcome. In the past decade we have witnessed the development of new immunobiologics that have had a significant impact on the treatment of many rheumatic disease. With the recent advances in the analysis of immune complexity and how this may vary in disease and treatment settings, we feel confident in saying the “best is yet to come!

Figure 3. Expression of CD45RA and CD45RO on CD4+ or CD8+ T Cells.

Figure 3

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Human PBMCs were purified using Ficoll Paque and stained with fluorescent-tagged markers for CD3 (Pac-Orange), CD4 (Pac-Blue), CD8 (APC-H7), CD45RA (PE-Texas Red) and CD45RO (FITC). Displayed are the levels of CD45RA vs. CD45RO on gated CD3+/CD4+ (left) or CD3+/CD8+ T cells. While CD45RA+/RO− (group I) and CD45RA−/RO+ (group III) subsets can be identified, there is also a significant population of cell with intermediate phenotype (group II).

Acknowledgements

We gratefully recognize support from the NIH, P30-AR053503 (MJS and FJC). Many thanks to Matt Presby for critical reading of this manuscript.

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