Raw data for article "Phenotypes of synovial fluid Treg cells in checkpoint blockade-related inflammatory arthritis"

Regulatory T (Treg) cells may become dysregulated in Checkpoint blockade-related inflammatory arthritis (CBIA), and we aimed to profile phenotypes and cytokine-secreting patterns of Tregs in CBIA. Using a 77-protein panel, we here profiled and compared single-cell membrane proteomics of Treg cells in synovial fluid (SF) in 15 patients with active CBIA onset, 12 patients with active rheumatoid arthritis (RA), and 9 CBI-treated cancer patients with non-autoimmune inflammatory knee swelling. Microbead-sorted Treg subsets from CBIA patients underwent 32-cytokine panel secretome analysis. Peripheral blood (PB) Tregs from 7 CBIA and 6 RA patients were similarly analyzed. Findings were correlated with modified Clinical Disease Activity Index (mCDAI) in CBIA patients. Unsupervised clustering revealed two atypical immune-activating Treg cell clusters common to both CBIA and RA patients, in which a immuno-activating (featuring ICOS+CD134+CD137+) cluster was distinct to CBIA patients. This immuno-activating cluster was found to have a positive correlation to the mCDAI in CBIA patients. In single-cell secreting proteomics of SF-derived Treg cells in CBIA patients, we found that clusters distinct to the immuno-activating cell group featured inflammatory cytokine secretion of mainly MCP1 and MCP4, which was validated by Peripheral CBIA secreting proteomics (vs. RA, which preferentially secreted CCL11/CXCL10). Other non-immuno-activating cells mainly secreted immune-modulatory cytokines of IL10, IL4, and TGFB1. Consistently, MCP1/MCP4+polysecreting cluster proportion was also positively correlated with mCDAI. At single-cell proteomic level, an atypical, MCP1/MCP4+polysecreting immuno-activating Treg cell type is found to have a strong relation to clinical disease activity of CBIA.This dataset contains three datasets of single-cell membrane /secreting proteomics data (located within three folders respectively), and each folder contains the original values of protein expression (.csv files), analysis codes (.Rdata, .txt files), and grouping information (.xlsx files).