Demonstrates improved identification of senescent cells in tissues, suggesting its utility in understanding aging and injury.
Identifying senescent cells via single-cell transcriptome profiling data remains challenging due to cellular heterogeneity and overlap with other cellular states. Here, we present SenFlag, a streamlined gene signature for enhanced identification of senescent cells based on integration of core gene expression features. SenFlag was derived through systematic assessment of bulk and single-cell RNA-sequencing datasets across multiple senescence models. It captures a conserved transcriptional program characterized by reduced expression of proliferation-associated genes and chromatin-associated genes ( HMGB1/2, HMGN2 ), combined with upregulation of cell-cycle inhibitors ( CDKN1A/CDKN2A ) and of CCND1 . Additionally, SenFlag incorporates lysosomal features, including increased expression of V-ATPase subunits and cathepsins. SenFlag identifies a rare but progressively accumulating population of senescent cells across tissues in both mice and humans in vivo, with enrichment in epithelial and endothelial compartments. SenFlag-positive cells increase with age and following tissue injury, and are reduced in datasets involving senescence-targeting interventions, supporting its specificity in vivo. Together, SenFlag provides a robust and interpretable signature for identifying senescent cells in single-cell datasets and facilitates the study of senescence across physiological and pathological contexts.
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Altulea et al. (2026) studied this question.
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