SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types
6 affiliations
- 1Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program (NIA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
- 2Translational Gerontology Branch, NIA IRP, NIH, Baltimore, MD, USA
- 3Center for Alzheimer's and Related Dementias, NIA IRP and National Institute of Neurological Disorders and Stroke (NINDS) IRP, NIH, Bethesda, MD, USA
- 4Center for Alzheimer's and Related Dementias, NIA IRP and National Institute of Neurological Disorders and Stroke (NINDS) IRP, NIH, Bethesda, MD, USA; DataTecnica LLC, Washington, DC 20812, USA
- 5Translational Gerontology Branch, NIA IRP, NIH, Baltimore, MD, USA. Electronic address: [email protected]
- 6Laboratory of Genetics and Genomics, National Institute on Aging Intramural Research Program (NIA IRP), National Institutes of Health (NIH), Baltimore, MD, USA. Electronic address: [email protected]
Abstract
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
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BibTeX
@article{anerillas2026sencat,
title = {{SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types}},
author = {Carlos Anerillas and Gisela Altés and Katarína Grešová and Dimitrios Tsitsipatis and Krystyna Mazan-Mamczarz and Reema Banarjee and Ana S.G. Cunningham and Martín Salamini-Montemurri and Jen‐Hao Yang and Rachel B. Munk and Martina Rossi and Yulan Piao and Bradley Olinger and Quinn Strassheim and Jennifer L. Martindale and Jinshui Fan and Chang‐Yi Cui and Supriyo De and Delaney V. Rutherford and Ying Hao and Zhirong Li and Jessica P. Roberts and Yue Andy Qi and Kotb Abdelmohsen and Rafael de Cabo and Allison B. Herman and Manolis Maragkakis and Nathan Basisty and Myriam Gorospe},
journal = {Molecular Cell},
year = {2026},
month = jun,
doi = {10.1016/j.molcel.2026.05.017},
url = {https://doi.org/10.1016/j.molcel.2026.05.017},
note = {CC BY},
}
RIS
TY - JOUR
TI - SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types
AU - Carlos Anerillas
AU - Gisela Altés
AU - Katarína Grešová
AU - Dimitrios Tsitsipatis
AU - Krystyna Mazan-Mamczarz
AU - Reema Banarjee
AU - Ana S.G. Cunningham
AU - Martín Salamini-Montemurri
AU - Jen‐Hao Yang
AU - Rachel B. Munk
AU - Martina Rossi
AU - Yulan Piao
AU - Bradley Olinger
AU - Quinn Strassheim
AU - Jennifer L. Martindale
AU - Jinshui Fan
AU - Chang‐Yi Cui
AU - Supriyo De
AU - Delaney V. Rutherford
AU - Ying Hao
AU - Zhirong Li
AU - Jessica P. Roberts
AU - Yue Andy Qi
AU - Kotb Abdelmohsen
AU - Rafael de Cabo
AU - Allison B. Herman
AU - Manolis Maragkakis
AU - Nathan Basisty
AU - Myriam Gorospe
PY - 2026
DA - 2026/06/11
JO - Molecular Cell
DO - 10.1016/j.molcel.2026.05.017
UR - https://doi.org/10.1016/j.molcel.2026.05.017
AB - 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
SN - 1097-2765
SN - 1097-4164
ER -