Transfer learning of an in vivo-derived senescence signature identifies conserved tissue-specific senescence across species and diverse pathologies [bulk RNA-seq]

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite their pathological role and therapeutic interest, senescent cells' (SnCs) phenotype in vivo remains unclear. Here, we developed an in vivo-derived senescence signature using a foreign body response (FBR) fibrosis model in a SnC reporter mouse. We identified stromal cells as the primary SnC in the FBR and produced a SnC transcriptomic signature. Transfer learning identified SnCs in diverse murine and human data single cell RNAseq (scRNAseq) sets. Further, we found both conserved and tissue-specific SnC and secretory phenotypes. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFbR signaling axis, contributing to angiogenic and fibrotic responses. Overall, our study identifies a conserved transcriptional profile of SnCs and transfer learning approach that may be broadly applied to identify and understand senescence in vivo. Overall design: Bulk RNAseq comparing sorted p16+ and p16- stromal cells from foreign body fibrosis model