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.
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
Usage data is cumulative from March 2025 through March 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 1,262 | 0 |
318 | 0 | |
Figure | 229 | 0 |
Supplemental data | 303 | 0 |
Citation downloads | 12 | 0 |
Totals | 2,124 | 0 |
Total Views | 2,124 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.