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Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by pathologic T cell-B cell interactions and autoantibody production. Defining the T cell populations that drive B cell responses in SLE may enable design of therapies that specifically target pathologic cell subsets. Here, we evaluated the phenotypes of CD4+ T cells in the circulation of 52 SLE patients drawn from multiple cohorts and identified a highly expanded PD-1hiCXCR5-CD4+ T cell population. Cytometric, transcriptomic, and functional assays demonstrated that PD-1hiCXCR5-CD4+ T cells from SLE patients are T peripheral helper (Tph) cells, a CXCR5- T cell population that stimulates B cell responses via IL-21. The frequency of Tph cells, but not T follicular helper (Tfh) cells, correlated with both clinical disease activity and the frequency of CD11c+ B cells in SLE patients. PD-1hiCD4+ T cells were found within lupus nephritis kidneys and correlated with B cell numbers in the kidney. Both IL-21 neutralization and CRISPR-mediated deletion of MAF abrogated the ability of Tph cells to induce memory B cell differentiation into plasmablasts in vitro. These findings identify Tph cells as a highly expanded T cell population in SLE and suggest a key role for Tph cells in stimulating pathologic B cell responses.

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)+HLA-DRAhi sublining fibroblasts, IL1B+ pro-inflammatory monocytes, ITGAX+TBX21+ autoimmune-associated B cells and PDCD1+ peripheral helper T (TPH) cells and follicular helper T (TFH) cells. We defined distinct subsets of CD8+ T cells characterized by GZMK+, GZMB+, and GNLY+ phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1+HLA-DRAhi fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

Author(s): Deepak A. Rao (corresponding author) [1]; Michael F. Gurish [1]; Jennifer L. Marshall [2]; Kamil Slowikowski [1, 3, 4, 5, 6]; Chamith Y. Fonseka [1, 3, 4, 6, 7]; [...]

Background: Despite the report of an imbalance between CD4+ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results. Objectives: To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results. Methods: Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intranuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC). Results: Conventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and antiinflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2+ T-bet+) subset and an unconventional but pro-inflammatory IL-17+ T-bet+ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10-3; odds ratio=9.7, p=1.5x10-3, respectively). In contrast, a FoxP3+ IL-2+ HLA-DR+ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10-7). Conclusion: Taking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4+ T cell subset imbalances in RA blood. [ABSTRACT FROM AUTHOR]

Mixed-effects regression of single-cell data accounts for confounding variation and reveals an expanded CD4+ T cell population in rheumatoid arthritis. Pinpointing culprits in single-cell data: New techniques analyzing single cells are being applied to clinical samples. These techniques generate large datasets, and it can be challenging to identify rare cell types associated with disease. Fonseka et al. developed a simple statistical method to do just that while simultaneously controlling for confounding biological and technical variation. Using their method on single-cell mass and flow cytometry data revealed an expanded CD4+ T cell subset in the blood of rheumatoid arthritis patients that was later seen to contract upon treatment. The authors' method performed better than the current gold standard and could be a potentially widely applied tool for the analysis of high-dimensional single-cell data. High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27− HLA-DR+ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10−3). The frequency of CD27− HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27− HLA-DR+ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27− HLA-DR+ cells were associated with RA (meta-analysis P = 2.3 × 10−4). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27− HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. CD27− HLA-DR+ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (TH1)– and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data. [ABSTRACT FROM AUTHOR]

Background: Despite the report of an imbalance between CD4 + T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results., Objectives: To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results., Methods: Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC)., Results: Conventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2 + T-bet + ) subset and an unconventional but pro-inflammatory IL-17 + T-bet + subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10 -3 ; odds ratio=9.7, p=1.5x10 -3 , respectively). In contrast, a FoxP3 + IL-2 + HLA-DR + Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10 -7 )., Conclusion: Taking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4 + T cell subset imbalances in RA blood., Competing Interests: Authors CF and SH are currently employed by the companies eGenesis and BioNTech, respectively. They were not employed by these companies during the conception of the study or the generation of the experimental data. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mulhearn, Marshall, Sutcliffe, Hannes, Fonseka, Hussell, Raychaudhuri, Barton and Viatte.)