Elsevier

Current Opinion in Immunology

Volume 49, December 2017, Pages 27-36
Current Opinion in Immunology

Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis

https://doi.org/10.1016/j.coi.2017.08.005Get rights and content

Highlights

  • Single cell immunoprofiling reveals extensive heterogeneity among CD4+ T cells.

  • Multidimensional analyses identify novel CD4+ T cell populations associated with rheumatoid arthritis.

  • Single cell disease association studies require careful attention to study design to avoid confounding technical effects.

  • New analysis methods are emerging to take full advantage of complex single cell datasets.

CD4+ T cells have been long known to play an important role in the pathogenesis of rheumatoid arthritis (RA), but the specific cell populations and states that drive the disease have been challenging to identify with low dimensional single cell data and bulk assays. The advent of high dimensional single cell technologies  like single cell RNA-seq or mass cytometry  has offered promise to defining key populations, but brings new methodological and statistical challenges. Recent single cell profiling studies have revealed a broad diversity of cell types among CD4+ T cells, identifying novel populations that are expanded or altered in RA. Here, we will review recent findings on CD4+ T cell heterogeneity and RA that have come from single cell profiling studies and discuss the best practices for conducting these studies.

Introduction

Rheumatoid arthritis (RA) is a common autoimmune disorder characterized by chronic inflammation of the synovial tissues, leading to joint damage, disability, and increased mortality [1, 2]. The pathophysiology of RA involves a complex interplay between multiple cell types, including leukocyte populations, synovial fibroblasts, chondrocytes, osteoclasts and others [3]. Multiple lines of evidence drawn from genetic, histologic, and clinical observations point to key role for CD4+ T cells in directing the autoimmune response in RA. Genome-wide association studies (GWAS) have highlighted the major histocompatibility complex (MHC) as by far the strongest contributor to disease heritability, driven by variants in HLA-DRB1, HLA-DPB1, and HLA-B [4, 5]. HLA-DRB1 and HLA-DPB1 are components of the MHC class II molecule, which antigen presenting cells use to present antigens to CD4+ T cells. We have further demonstrated that genetic risk alleles outside of the MHC locus also point to a role for CD4+ T cells, as genes associated with these loci are preferentially expressed in effector memory CD4+ T cells [6, 7, 8]. In addition, CD4+ T cells are frequently found infiltrating the synovium in RA, often in dense lymphocyte aggregates [9, 10]. Importantly, interfering with T cell activation by blocking costimulatory signals with abatacept (CTLA4-Ig) is an effective therapy for clinical RA [3].

While it is clear that T cells play an important role in promoting RA pathology, pinpointing the specific T cell phenotypes or functions that are most relevant in this disease has been challenging. CD4+ T cells are typically categorized by the level of expression of surface and intracellular proteins that reflect functionally distinct cell types [11, 12]. However, T cells are highly heterogeneous, displaying diverse combinations of surface markers and effector functions. This heterogeneity makes it difficult to describe T cell infiltrates as bulk populations and has highlighted the value of single cell analyses to resolve this heterogeneity.

Single cell analyses by flow cytometry have contributed major insights into T cell abnormalities in RA [13, 14], yet flow cytometry analyses have been hampered the limited number of parameters that can be detected simultaneously, which are often insufficient to adequately assess a diverse T cell population. The recent rapid expansion of single cell technologies has led to a dramatic advance in the ability to study complex populations in large-scale with high dimensionality (Figure 1). This high-dimensional single cell profiling may lead to the identification of specific T cell populations or states that are mechanistically linked to disease and ideal for therapeutic targeting. In this review, we discuss recent advances in single cell immunoprofiling and describe their early application in RA. We will then discuss methodological and bioinformatic considerations to maximize the potential of single cell technologies in its application to define mechanisms of immune-mediated diseases.

Section snippets

Low-dimensional single cell analysis of T cells in RA

Single cell assays have a long history in the field of autoimmunity, beginning in 1969 with the initial use of fluorescent assays to label and sort immune cell populations [15, 16, 17, 18]. Cytometry has been thoroughly exploited in the exploration of lymphocyte heterogeneity in RA [19, 20, 21, 22, 23]. Subsequent improvements in flow cytometry technology have steadily increased the number of parameters that can be measured for each cell, provided access to cytoplasmic and nuclear protein

High-dimensional analyses reveal an expanded view of CD4+ T cell heterogeneity

The recent development of mass cytometry  a fusion of mass spectrometry and flow cytometry that is capable of the simultaneous acquisition of over 40 parameters on a single cell level  has further extended the dimensionality of single cell cytometric assays [43]. Mass cytometry relies upon staining cells with the same target-specific antibodies that are commonly used in flow cytometry to tag markers of interest; however, in mass cytometry antibodies are labeled with pure, non-radioactive rare

Early high-dimensional analyses of T cells in RA

These same technologies are already being used in RA tissue and blood to define key features of pathogenic CD4+ T cell populations in RA. We have recently applied mass cytometry to evaluate the heterogeneity of CD4+ T cells that infiltrate RA synovium [69]. With this high-dimensional analysis, we identified a T ‘peripheral helper’ (TPH) cell population that is markedly expanded in RA synovium, constituting ∼25% of synovial CD4+ T cells. TPH cells, characterized as PD-1hi CXCR5 CD4+, display a

Identifying biomarkers through cell phenotyping

As the diversity, precision, and cost of therapeutics for RA has increased, the importance of being able to determine the option best-suited for a given patient up front has become increasingly clear. There is now a major need for biomarkers to predict response to therapies with distinct mechanisms of action; however, efforts using multiplexed cytokine profiling and genetic variation have not yet led to clinically applicable tools [77, 78]. The increased resolution of single cell assays is an

The future of single cell immunoprofiling

Recent advances in availability and throughput have made single cell technologies a practical choice for conducting immunoprofiling studies to understand mechanisms of disease and define predictive biomarkers. The application of these methods in RA include the profiling of blood, as many studies that we refer to above already have done, but also performing immunoprofiling in human tissue. We and others are pursuing these goals in Accelerating Medical Partnerships Rheumatoid Arthritis/Systemic

Conclusions

The advent of single cell technologies has the potential to revolutionize the study of RA by offering an unbiased approach to detecting and characterizing cell heterogeneity in blood and tissue. High-dimensional single cell analyses of RA synovium have revealed novel lymphocyte and stromal cell populations that are pathologically expanded in the joints of RA patients. These cell populations may now be evaluated as potential therapeutic targets. Single cell transcriptomics and TCR repertoire

Conflicts of interest

None.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

This work is supported in part by funding from the National Institutes of Health (UH2AR067677, 1U01HG009088, and 1R01AR063759 (SR)), and the Doris Duke Charitable Foundation Grant #2013097. D.A.R. is supported by Rheumatology Research Foundation Tobe and Stephen Malawista, MD Endowment in Academic Rheumatology.

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