ArticleMolecular cell2026
SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types.
Article in Molecular cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- A compendium of circulating biomarkers of senescence in humans: Insights on mechanistic impact across health domains and modulation by therapeutic interventions.Ageing research reviews · 2026Review
- Autologous cytokine-induced NK cells as candidate cellular senolytics: evidence, obstacles, and the experiments still needed.GeroScience · 2026Review
- UHRF1 overexpression generates distinct senescent states with different Tp53 dependencies.EMBO reports · 2026Article
- Review
- Inhibiting cyclin D1-CDK6 suppresses senescence-associated inflammatory gene expression and age-related functional decline.Nature aging · 2026Article
- Circulating cell type senescence signatures track distinct dimensions of health status and trajectories in human longitudinal cohorts.Cell reports · 2026Article
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Authors and funding
29 authors.
Funding
Abstract
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.
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Registered trials
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