SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types

SUMMARY There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize ‘senotypes’ and senescent cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed ‘SenCat’. We found that, while senescent cells from all primary tissue types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Machine learning analysis of the SenCat transcriptomic and proteomic datasets successfully identified independent sets of senescent human cells, and senescent-like cells in mouse lung and kidney. In sum, SenCat represents a much-needed resource to identify senescent cells across tissues in the body. HIGHLIGHTS Identifying senescent cells in organisms in vivo remains a challenge We created SenCat: a catalog transcriptomes and proteomes of senescent primary cells Machine learning (ML) analysis of SenCat identified robust senescence scores ML-derived senescence scores uncovered senescent-like cell dynamics in vivo