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0This research introduces SenCat, a comprehensive multi-omic atlas designed to map cellular senescence across 14 human primary cell types and over 30 experimental models. By analyzing transcriptomes and proteomes, the researchers discovered that while senescent cells lack a single universal marker, they share conserved metabolic and damage-response pathways. To overcome the challenge of cellular heterogeneity, the study utilized machine learning to develop robust senescence scoring systems that outperform traditional indicators like p16 or p21. These ML-derived signatures were successfully validated in vivo, accurately identifying senescent cell accumulation in both aging and chemotherapy-treated mouse tissues. Ultimately, SenCat offers a versatile framework for quantifying senescence across different species and organs, providing a critical resource for future geriatric and therapeutic research.
References:
Anerillas C, Altes G, Gresova K, et al. SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types[J]. bioRxiv, 2026: 2026.02. 05.703986.
