Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

生物材料纤维化模型中的迁移学习可识别体内衰老异质性以及对跨物种和不同病理的血管化和基质产生的贡献

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作者:Christopher Cherry, James I Andorko, Kavita Krishnan, Joscelyn C Mejías, Helen Hieu Nguyen, Katlin B Stivers, Elise F Gray-Gaillard, Anna Ruta, Jin Han, Naomi Hamada, Masakazu Hamada, Ines Sturmlechner, Shawn Trewartha, John H Michel, Locke Davenport Huyer, Matthew T Wolf, Ada J Tam, Alexis N Peña, 

Abstract

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells' (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo-derived senescence signature (SenSig) using a foreign body response-driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and "cartilage-like" fibroblasts as senescent and defined cell type-specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.

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