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ImmunologybioRxiv
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Spatial transcriptomics reveals distinct inflammatory and adaptive immune properties of rheumatoid arthritis synovial fibroblasts

Camilla Ribeiro Lima MachadoiD, Mina Yao, David L. BoyleiD, Robert J. BenschopiD, James T. Parker, Wei Wang, Gary S. FiresteiniD

Abstract

ObjectiveRheumatoid arthritis (RA) synovium displays cellular heterogeneity, with gene expression driving pathogenesis. Prior transcriptomic studies relied on disaggregated tissue, which causes cell loss and induction. We applied spatial transcriptomics to investigate synovial lining and sublining fibroblasts and macrophages from RA and osteoarthritis (OA) patients. MethodsFresh frozen synovial tissues from 7 RA and 8 OA patients were analyzed using the NanoString GeoMX DSP Whole Transcriptome Assay. Lining and sublining regions were segmented into fibroblasts and macrophages. Principal component analysis separated samples by cell type and disease. Differentially expressed genes (DEG) were analyzed by linear mixed models (p-value0.5) and Reactome pathway (FDR<0.02) analysis. ResultsDEG analysis of RA compared to OA revealed distinct gene signatures across regions and cell types. RA lining fibroblasts exhibited a strong pro-inflammatory and matrix-destructive signature, while RA sublining fibroblasts showed an unexpected role in antigen presentation and adaptive immunity. RA lining macrophages exhibited enrichment for interleukin signaling, extracellular matrix organization, and translation-related pathways. In contrast, sublining macrophages showed minimal transcriptional differences between RA and OA, suggesting limited pathogenic involvement. Comparison between lining and sublining within RA showed that lining fibroblasts display a higher activated phenotype than sublining cells. ConclusionSpatial transcriptomic analysis uncovers distinct region- and cell-type-specific transcriptional profiles in RA synovium. Lining fibroblasts are highly activated and destructive, and sublining fibroblasts contribute surprisingly to adaptive immunity. This data provides clues to region-cell-type-specific functions that could be exploited to identify novel therapeutic targets.

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02 /Specification & Classification

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