Article
Mass cytometry combined with computational data mining reveals a polymorphous immune cell signature of active rheumatoid arthritis
Search Medline for
Authors
Published: | October 8, 2019 |
---|
Outline
Text
Background: Innate and adaptive immune mechanisms drive the pathogenesis of rheumatoid arthritis (RA) and are targets of approved therapies. However, not all patients can be appropriately treated, which defines the need for additional therapeutic concepts combined with personalized treatment. At the same time, a systematic assessment of immune cell dysregulation in the patients’ blood that may provide insight into common and individual immune pathology features is lacking.
Methods: We here employed 44-dimensional mass cytometry (CyTOF technology) to deeply profile PBMC in 35 patients with active RA vs. 31 age/gender-matched controls, permitting the automated identification of 60 global PBMC, 80 T cell, and 50 B cell populations by a nested FlowSOM clustering/hierarchical gating approach.
Results: Active RA was characterized by diminished frequencies of MAIT and gd T cell, IgA+ and IgM+ memory B cell and plasmablast clusters, while the frequency of a CD14highCD16low monocyte subset was significantly increased (MWU test, BH-adjusted p-values, p<0.05). While MAIT and gd T cells frequencies were inversely correlated with serum Crp (r=-.55 and -.56, p<0.001), IgA+ memory B cells inversely correlated with DAS28 values (r=-0.34, p=0.04), suggesting that at least some components of the RA immune signature are associated with disease activity. Notably, the vast majority of differentially abundant T and B cell clusters were memory or effector cells, underpinning the impact of antigen-dependent lymphocyte differentiation for the immunological fingerprint of active RA. Furthermore, computational data mining by Citrus and CellCNN consistently revealed significantly lower detection of the inflammatory chemokine receptor CXCR3 in RA patients across different T, B and NK cell subsets.
Conclusion: In this study, we established a multi-component immune cell fingerprint of active RA featuring aberrations of innate and adaptive immune cells. This immune cell reference map of RA will serve for comparison with data from other autoimmune diseases and longitudinal profiling of patients during therapy.