Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis
Jun Inamo, … , Deepak A. Rao, Fan Zhang
Jun Inamo, … , Deepak A. Rao, Fan Zhang
Published March 17, 2025
Citation Information: J Clin Invest. 2025;135(6):e185217. https://doi.org/10.1172/JCI185217.
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Research Article Autoimmunity Immunology

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune disease currently with no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in at-risk populations prior to clinical onset is crucial to establishing effective prevention strategies. Here, we applied multimodal single-cell technologies (mass cytometry and CITE-Seq) to characterize the immunophenotypes in blood from at-risk individuals (ARIs) identified through the presence of serum antibodies against citrullinated protein antigens (ACPAs) and/or first-degree relative (FDR) status, as compared with patients with established RA and people in a healthy control group. We identified significant cell expansions in ARIs compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5lo naive B cell population was expanded in ACPA-positive FDRs. Further, we uncovered the molecular phenotype of the CCR2+CD4+ T cells, expressing high levels of Th17- and Th22-related signature transcripts including CCR6, IL23R, KLRB1, CD96, and IL22. Our integrated study provides a promising approach to identify targets to improve prevention strategy development for RA.

Authors

Jun Inamo, Joshua Keegan, Alec Griffith, Tusharkanti Ghosh, Alice Horisberger, Kaitlyn Howard, John F. Pulford, Ekaterina Murzin, Brandon Hancock, Salina T. Dominguez, Miranda G. Gurra, Siddarth Gurajala, Anna Helena Jonsson, Jennifer A. Seifert, Marie L. Feser, Jill M. Norris, Ye Cao, William Apruzzese, S. Louis Bridges, Vivian P. Bykerk, Susan Goodman, Laura T. Donlin, Gary S. Firestein, Joan M. Bathon, Laura B. Hughes, Andrew Filer, Costantino Pitzalis, Jennifer H. Anolik, Larry Moreland, Nir Hacohen, Joel M. Guthridge, Judith A. James, Carla M. Cuda, Harris Perlman, Michael B. Brenner, Soumya Raychaudhuri, Jeffrey A. Sparks, The Accelerating Medicines Partnership RA/SLE Network, V. Michael Holers, Kevin D. Deane, James Lederer, Deepak A. Rao, Fan Zhang

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Figure 5

Identification of different B cell and NK cell populations that were associated with ARIs.

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Identification of different B cell and NK cell populations that were ass...
(A) Distribution of frequencies of cell types for B cell subsets that were identified as ARI-related cell types in B. *P < 0.05, **P < 0.01 by 2-sided Wilcoxon’s test. (B) Associations of B cell neighborhoods with ARIs versus controls (left), distributions of cell neighborhood correlations (middle), and odds ratios (right) are shown. (C) Expression of selected surface proteins within B cells. (D) Distributions of activation marker (CD21 and CD23) antibody staining in the conventional naive B cell cluster (B-0) and the PAX5lo naive B cell cluster (B-6). Low expression of CD21 and/or CD23 indicates activated B cells. (E) Distribution of frequencies of cell types for NK cell subsets that were identified as ARI-related cell types in F. **P < 0.01, ****P < 0.0001 by 2-sided Wilcoxon’s test. (F) Associations of NK cell neighborhoods with ARIs versus controls (left), distributions of cell neighborhood correlations (middle), and odds ratios (right) are shown. (G) Expression of selected surface proteins within NK cells. All the statistical association tests were adjusted for age and sex. For all CNA-based association results, cells in UMAP are colored in red (expansion) or blue (depletion), and P value is shown as well. Error bars for odds ratios represent 95% confidence intervals.