gms | German Medical Science

47. Kongress der Deutschen Gesellschaft für Rheumatologie (DGRh), 33. Jahrestagung der Deutschen Gesellschaft für Orthopädische Rheumatologie (DGORh), 29. Jahrestagung der Gesellschaft für Kinder- und Jugendrheumatologie (GKJR)

04.09. - 07.09.2019, Dresden

Mass cytometry combined with computational data mining reveals a polymorphous immune cell signature of active rheumatoid arthritis

Meeting Abstract

  • Axel Schulz - Deutsches Rheuma-Forschungzentrum Berlin (DRFZ), Massenzytometrie, Berlin
  • Tyler Burns - Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), Leibniz-Institut, Mass Cytometry, Berlin
  • Silke Stanislawiak - DRFZ Berlin, Berlin
  • Sabine Baumgart - Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), Leibniz-Institut, Immunmonitoring, Berlin
  • Vera Bockhorn - DRFZ Berlin, Berlin
  • Julia Patermann - Immanuel Krankenhaus, Innere-Rheumatologie, Berlin
  • Sandra Burger - Immanuel Krankenhaus Berlin, Berlin
  • Andreas Krause - Immanuel Krankenhaus Berlin, Klinik für Innere Medizin, Abteilung Rheumatologie und Klinische Immunologie, Berlin
  • Andreas Grützkau - Deutsches Rheuma-Forschungszentrum (DRFZ), Berlin
  • Henrik Mei - Deutsches Rheuma-Forschungszentrum (DRFZ), Charité - Universitätsmedizin Berlin, Medizinische Klinik mit Schwerpunkt Rheumatologie und klinische Immunologie, Berlin

Deutsche Gesellschaft für Rheumatologie. Deutsche Gesellschaft für Orthopädische Rheumatologie. Gesellschaft für Kinder- und Jugendrheumatologie. 47. Kongress der Deutschen Gesellschaft für Rheumatologie (DGRh), 33. Jahrestagung der Deutschen Gesellschaft für Orthopädische Rheumatologie (DGORh), 29. Jahrestagung der Gesellschaft für Kinder- und Jugendrheumatologie (GKJR). Dresden, 04.-07.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocRA.48

doi: 10.3205/19dgrh220, urn:nbn:de:0183-19dgrh2206

Published: October 8, 2019

© 2019 Schulz et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

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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.