SenCat: Cataloging human cell aging by analyzing multiple types of aged primary cells using different molecular data

SenCat maps senescence signatures across many primary human cell types rather than pointing to one universal marker.

This resource study profiled transcriptomes and proteomes from 14 primary human cell types across more than 30 senescence paradigms and refined signatures with machine learning across human and mouse bulk and single-cell datasets.

The abstract reports no single shared unique senescence marker, so identification depends on context-specific signatures rather than a universal label.