To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA-seq and flow cytometry to T cells, B cells, monocytes and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis. Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics together revealed cell states expanded in RA synovia: THY1(CD90) + HLA-DRA hi sublining fibroblasts, IL1B + pro-inflammatory monocytes, ITGAX + TBX21 + autoimmune-associated B cells and PDCD1 + T peripheral helper (Tph) and T follicular helper (Tfh). We defined distinct subsets of CD8 + T cells characterized by a GZMK + , GZMB + and GNLY + phenotype. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1 + HLA-DRA hi fibroblasts, and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

Wei, Slowikowski, Fonseka, Rao et al A single cell map of the RA joint Abstract 78 To define the cell populations in rheumatoid arthritis (RA) driving joint inflammation, we applied 79 single-cell RNA-seq (scRNA-seq), mass cytometry, bulk RNA-seq, and flow cytometry to sorted 80 T cells, B cells, monocytes, and fibroblasts from 51 synovial tissue RA and osteoarthritis (OA) 81 patient samples. Utilizing an integrated computational strategy based on canonical correlation 82 analysis to 5,452 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass 83 cytometry and transcriptomics together revealed cell states expanded in RA synovia: 84 THY1 + HLA high sublining fibroblasts (OR=33.8), IL1B + pro-inflammatory monocytes (OR=7.8), 85 CD11c + T-bet + autoimmune-associated B cells (OR=5.7), and PD-1 + Tph/Tfh (OR=3.0). We also 86 defined CD8 + T cell subsets characterized by GZMK + , GZMB + , and GNLY + expression. Using 87 bulk and single-cell data, we mapped inflammatory mediators to source cell populations, for 88 example attributing IL6 production to THY1 + HLA high fibroblasts and naïve B cells, and IL1B to 89 pro-inflammatory monocytes. These populations are potentially key mediators of RA 90 pathogenesis. 91 92 93 94 95 96 97 98 99 100

ObjectiveTo establish in a global setting the relationships between countries’ socioeconomic status (SES), measured biological disease modifying antirheumatic drug (bDMARD)-usage and disease outcomes. To assess if prescription and reimbursement rules and generic access to medication relates to a countries’ bDMARD-usage.MethodsData on disease activity and drug use from countries that had contributed at least 100 patients were extracted from the METEOR database. Mean disease outcomes of all available patients at the final visit were calculated on a per-country basis. A questionnaire was sent to at least two rheumatologists per country inquiring about DMARD-prices, access to treatment and valid regulations for prescription and reimbursement.ResultsData from 20 379 patients living in 12 different countries showed that countries’ SES was positively associated with measured disease activity (meanDAS28), but not always with physical functioning (HAQ-score). A lower country’s SES, stricter rules for prescription and reimbursement of bDMARDs as well as worse affordability of bDMARDs were associated with lower bDMARD-usage. bDMARD-usage was negatively associated with disease activity (although not with physical functioning), but the association was moderate at best.ConclusionsDisease activity in patients with rheumatoid arthritis as well as bDMARD-usage varies across countries worldwide. The (negative) relationship between countries’ bDMARD-usage and level of disease activity is complex and under the influence of many factors, including—but not limited to—countries’ SES, affordability of bDMARDs and valid prescription and reimbursement rules for bDMARDs.

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