Citations 1
Transfer learning identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies
Stivers, Katlin B. , Gray-Gaillard, Elise , Ruta, Anna , Hamada, Naomi, Hamada, Masakazu , Trewartha, Shawn , Wolf, Matthew, Keerthivasan, Shilpa , Saux, Claude Le, Housseau, Franck , Deursen, Jan M. van , Pardoll, Drew, Elisseeff, Jennifer H. , Cherry, Christopher, Andorko, James I. , Krishnan, Kavita , Mejías, Joscelyn C. , Nguyen, Helen Hieu , Han, Jin , Hamada, Naomi, Hamada, Masakazu, Sturmlechner, Ines , Michel, John H. , Huyer, Locke Davenport , Tam, Ada J , Peña, Alexis N. , Fertig, Elana Judith , Baker, Darren J.
No container title available | September 20, 2022 | Volume: No volume available | Issue: No issue available | Pages: No page available
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’ (SnC) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivoderived senescence signature using a foreign body response (FBR) fibrosis model in a SnC reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell typespecific senescence associated secretory phenotypes (SASP). 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 RNAseq (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34- CSF1R-TGFßR 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 transcriptional profiles and SASP factors produced by SnCs that regulate tissue structure and pathology.