Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis.

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.

Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis.

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.