Abstract
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.