eLifeSystems BiologyPeer-reviewed journal article
Systems Level Identification Of A Matrisome-Associated Macrophage Polarization State In Multi-Organ Fibrosis
Kevin Y. HuangiD, Kunal MishraiD, Harry ParkiD, Xie Yi, John F. OuyangiD, Enrico G. PetrettoiD, Jacques V. BehmoarasiD
Abstract
Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unraveled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggest that tissue resident macrophages, expressing osteopontin (SPP1), associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues, and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 13 single cell RNA-sequencing datasets to profile 225,985 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodeling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarization state within SPP1+ macrophages. Importantly, the MAM polarization state follows a differentiation trajectory from SPP1+ macrophages, which was conserved across all fibrotic tissues and driven by NFATC1 and HIVEP3 regulons. Unlike SPP1+ macrophages, the MAM polarization state shows a positive association with ageing in mice and humans, and across multiple tissues during homeostasis. These results suggest an advanced, age-dependent polarization state of SPP1+ macrophages in fibrotic tissues as a result of prolonged inflammatory cues within each tissue microenvironment.
- organism: human
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: trajectory inference
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 62
- FWCI
- 12.18
- Field percentile
- top 1%
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