Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis Academic Article uri icon


MeSH Major

  • Arthritis, Rheumatoid


  • Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. 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-g (IFN-g) 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.

publication date

  • October 17, 2018



  • Academic Article



  • eng

Digital Object Identifier (DOI)

  • 10.1126/scitranslmed.aaq0305

PubMed ID

  • 30333237

Additional Document Info


  • 10


  • 463