SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types.

There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize 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 cell types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Moreover, machine-learning-refined SenCat signatures enabled senescence scoring and identification across multiple human and mouse datasets, both at bulk and single-cell levels. In sum, SenCat represents a much-needed resource to identify senescence across multiple cell types and tissues in the body.