Unveiling the cell-type-specific landscape of cellular senescence through single-cell transcriptomics using SenePy

ABSTRACT Senescent cells accumulate in tissues with organismal age and contribute causally to multiple chronic diseases. In vivo senescent cell phenotypes are heterogeneous because cellular context and stressors vary by cell type and tissue. Due to the variability of senescence programs, there is no universal method to identify senescent cells and even widely used markers, such as CDKN2A , are not ubiquitous. Therefore, we interrogated the Tabula Muris Senis mouse single-cell aging atlas and an array of single-cell datasets from human donors that spanned many ages to find cell-specific signatures of cellular senescence. We derived 75 mouse and 65 human senescence signatures from individual cell populations. CDKN2A and other markers of senescence were overrepresented in these signatures but there were many novel senescence genes present at higher rates. Within individual cell populations, we observed multiple programs of senescence with distinct temporal and transcriptional characteristics. We packaged the signatures along with a single-cell scoring method into an open-source package: SenePy. SenePy signatures better recapitulate cellular senescence than available methods when tested on multiple in vivo RNA-seq datasets and a p16 ink4a reporter single-cell dataset. We used SenePy to map the kinetics of senescent cell accumulation across 97 cell types from humans and mice. SenePy also generalizes to disease-associate senescence and we used it to identify an increased burden of senescent cells in COVID-19 and myocardial infarction. This work provides a significant advancement towards our ability to identify and characterize in vivo cellular senescence.