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Rheumatoid arthritis (RA) is an autoimmune disease involving antigen-specific T and B cells. Here, we perform single-cell RNA and repertoire sequencing on paired synovial tissue and blood samples from 12 seropositive RA patients. We identify clonally expanded CD4 + T cells, including CCL5+ cells and T peripheral helper (Tph) cells, which show a prominent transcriptomic signature of recent activation and effector function. CD8 + T cells show higher oligoclonality than CD4 + T cells, with the largest synovial clones enriched in GZMK+ cells. CD8 + T cells with possibly virus-reactive TCRs are distributed across transcriptomic clusters. In the B cell compartment, NR4A1+ activated B cells, and plasma cells are enriched in the synovium and demonstrate substantial clonal expansion. We identify synovial plasma cells that share BCRs with synovial ABC, memory, and activated B cells. Receptor-ligand analysis predicted IFNG and TNFRSF members as mediators of synovial Tph-B cell interactions. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate the synovium of patients with RA. Activated B cells and T cells accumulate within joints of patients with rheumatoid arthritis. Here, the authors use single-cell transcriptome and repertoire profiling to identify clonally expanded synovial B cells and T cells and define their phenotypes and predicted cell-cell interactions. [ABSTRACT FROM AUTHOR]

Synovial tissue inflammation is a hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. Here, we examine genome-wide open chromatin at single-cell resolution in 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identify 24 chromatin classes and predict their associated transcription factors, including a CD8 + GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating with an RA tissue transcriptional atlas, we propose that these chromatin classes represent 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrate the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance, as well as the nomination of classes mediating the effects of putatively causal RA genetic variants. The epigenetic changes underlying the heterogeneity of RA disease presentation have been the subject of intense scrutiny. In this study, the authors use multiple single-cell sequencing datasets to define 'chromatin superstates' in patients with RA, which associate with distinct transcription factors and disease phenotypes. [ABSTRACT FROM AUTHOR]

Objective: Recent studies have uncovered diverse cell types and states in the rheumatoid arthritis (RA) synovium; however, limited data exist correlating these findings with patient‐level clinical information. Using the largest cohort to date with clinical and multicell data, we determined associations between RA clinical factors with cell types and states in the RA synovium. Methods: The Accelerated Medicines Partnership Rheumatoid Arthritis study recruited patients with active RA who were not receiving disease‐modifying antirheumatic drugs (DMARDs) or who had an inadequate response to methotrexate (MTX) or tumor necrosis factor inhibitors. RA clinical factors were systematically collected. Biopsies were performed on an inflamed joint, and tissue were disaggregated and processed with a cellular indexing of transcriptomes and epitopes sequencing pipeline from which the following cell type percentages and cell type abundance phenotypes (CTAPs) were derived: endothelial, fibroblast, and myeloid (EFM); fibroblasts; myeloid; T and B cells; T cells and fibroblasts (TF); and T and myeloid cells. Correlations were measured between RA clinical factors, cell type percentage, and CTAPs. Results: We studied 72 patients (mean age 57 years, 75% women, 83% seropositive, mean RA duration 6.6 years, mean Disease Activity Score‐28 C‐reactive Protein 3 [DAS28‐CRP3] score 4.8). Higher DAS28‐CRP3 correlated with a higher T cell percentage (P < 0.01). Those receiving MTX and not a biologic DMARD (bDMARD) had a higher percentage of B cells versus those receiving no DMARDs (P < 0.01). Most of those receiving bDMARDs were categorized as EFM (57%), whereas none were TF. No significant difference was observed across CTAPs for age, sex, RA disease duration, or DAS28‐CRP3. Conclusion: In this comprehensive screen of clinical factors, we observed differential associations between DMARDs and cell phenotypes, suggesting that RA therapies, more than other clinical factors, may impact cell type/state in the synovium and ultimately influence response to subsequent therapies. [ABSTRACT FROM AUTHOR]

AbstractTranslating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. We developed a new non-parametric statistical method, SCENT (Single-Cell ENhancer Target gene mapping) which models association between enhancer chromatin accessibility and gene expression in single-cell multimodal RNA-seq and ATAC-seq data. We applied SCENT to 9 multimodal datasets including > 120,000 single cells and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in eQTLs and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases. In addition, we were able to link somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining non-coding variant function., Accepted Manuscript

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation, and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here, we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B, and T cells. For example, a T cell HLA-DQA1 eQTL (rs3104371) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses., Accepted Manuscript