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Sci Transl Med. 2018 Oct 17;10(463). pii: eaaq0305. doi: 10.1126/scitranslmed.aaq0305.

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

Author information

1
Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
2
Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
3
Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
4
Department of Biomedical Informatics, Harvard University, Cambridge, MA 02138, USA.
5
Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.
6
Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY 10021, USA.
7
David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY 10021, USA.
8
Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10021, USA.
9
Division of Rheumatology, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA.
10
Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
11
Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. soumya@broadinstitute.org.
12
Institute of Inflammation and Repair, University of Manchester, Manchester, UK.

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.

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