DEMORA: DEep spatial characterization of synovial MacrOphages in Rheumatoid Arthritis

Lead Research Organisation: Queen Mary University of London
Department Name: William Harvey Research Institute

Abstract

Rheumatoid Arthritis (RA) is the most common autoimmune disease affecting the joints. The primary site of the disease process is the synovial tissue (ST), where macrophages play a central role in driving inflammation. In physiologic conditions, the synovial membrane consists of a thin lining and a relatively acellular sublining containing a few resident macrophages, but in RA, the ST changes considerably. Three different "pathotypes" have been described depending on the "quantity/quality" of the cellular infiltrate: lympho-myeloid, diffuse-myeloid, and pauci-immune. Although in different amounts, synovial tissue macrophages (STM) are present in all three pathotypes. In the last five years, single-cell molecular profiling of STM has revealed the existence of phenotypically and functionally distinct clusters, leading to the definition of a new STM taxonomy. Here, I hypothesize that different STM clusters shape the synovial tissue micro-environment in RA and influence tissue pathology and response to treatment. By applying to an existing unique bioresource of >800 ST, cutting-edge technology such as digital spatial profiling integrated with single-cell RNA-seq and in silico deconvolution, I aim to: determine the location/topographical distribution of STM clusters and the existence of specific "niche" (e.g., intra/peri-ectopic lymphoid structures, peri-vascular or nerve); identify STM clusters associated with each synovial pathotype and driving pathotypes transition; assess if specific STM clusters predict clinical response to anti-rheumatic drugs, how they change post-treatment, and which subsets emerge in patients refractory to multiple medications. This in-depth STM characterization in RA ST will enhance our understanding of the mechanisms sustaining chronic arthritis and non-response to treatments, integrate and improve predictive algorithms based on ST cellular/molecular signatures, and may suggest new therapeutic targets.

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