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Tough, D.

Publications and source records attributed to Tough, D..

2 recordsLinked to original sources

The ontogeny of myeloid-stromal synovial tissue niches in rheumatoid arthritis.

Recent single-cell multi-omic and spatial analyses of synovial biopsies have transformed our understanding of myeloid cell-driven mechanisms underlying human joint pathology and tissue homeostasis in Rheumatoid arthritis (RA). However, the developmental trajectories of synovial tissue macrophage (STM) subsets in humans remain poorly understood, due in part to the lack of models that faithfully replicate synovial tissue niches. This hinders the exploration of the therapeutic potential of targeting specific synovial myeloid cell clusters. Using multi-omics analyses of synovial tissue from an allogeneic bone marrow transplant recipient, we show that joint-specific tissue-resident STM subsets, including both health- and disease-associated clusters, can derive from peripheral blood monocytes. Analysis of embryonic synovial joints revealed that macrophage localization and maturation in the joints are preceded by local stromal niche specialisation, indicating that synovial fibroblasts (FLS) provide tissue-specific instructive cues to STM precursors. To elucidate human STM developmental trajectories, we established a SNP-based fate-tracking human synovial organoid system by embedding distinct blood-derived myeloid precursors, together with FLS clusters from RA synovial biopsies and endothelial cells, into 3D structures. These organoids reproduced key synovial tissue features, including lining and sublining architecture and stromal-myeloid cell cluster composition. Importantly, they supported differentiation of all resident STM subsets: homeostatic lining TREM2pos macrophages, their pathogenic TREM2lowSPP1pos counterparts that characterize the RA hyperplastic lining, and both homeostatic and RA-associated perivascular LYVE1pos STM clusters, all traced to monocytic precursors. In summary, we show that development of STM subsets is driven by fibroblast-conditioned spatial niches. We have established a novel, tractable ex vivo platform to dissect the niche-specific cues driving homeostatic versus pathogenic phenotypic clusters. One Sentence SummaryHuman tissue-resident STMs develop from monocytes under the guidance of cues from FLS within discrete spatial locations.

immunology↗

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.

genomics↗