Abstract:

High dimensional single-cell analyses have dramatically 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 due to technical and inter-individual variation. Here we present Mixed effects modeling of Associations of Single Cells (MASC), a novel 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 confounds and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from 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 (OR = 1.7; p = 0.0011). 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 respond to immunosuppressive therapy. Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained ~5-fold higher frequencies of CD27- HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. We find that CD27- HLA-DR+ cells are abundant producers of IFN-γ and also express perforin and granzyme A at elevated levels. Thus MASC identified the expansion of a unique Th1 skewed effector T cell population with cytotoxic capacity in RA. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single cell data.

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