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<p>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 (<i>HMGB1/2, HMGN2</i>), combined with upregulation of cell-cycle inhibitors (<i>CDKN1A/CDKN2A</i>) and of <i>CCND1</i>. 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.</p>

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SenFlag gene signature identifies senescent cells in mouse and human tissues through a conserved core transcriptional program

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Abdullah Altulea,
  • Sebastian Mackedenski,
  • Jamil Nehme,
  • Marco Demaria

摘要

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.