ICE: An imputation framework for detecting cellular senescence using weak single-cell signatures

BackgroundCellular senescence represents a stable cell cycle arrest state that plays critical roles in tissue aging and age-related pathologies. Although single-cell RNA sequencing (scRNA-seq) enables comprehensive profiling of millions of cells in aged tissues, reliably identifying senescent cells remains challenging due to the weak and non-specific expression of their marker genes. Existing computational methods can detect active cell states through gene set scoring, but their performance for senescence genes has not been rigorously evaluated. ResultsIn this study, we show that senescence genes display weak, non-specific expression patterns across different tissues and are susceptible to dropout events in scRNA-seq. Current scoring approaches suffer intrinsic limitations in single-cell detection using weakly expressed markers. Simply expanding the gene set of weak markers failed to improve detection accuracy. To overcome these limitations, we developed ICE (Imputation-based Cell Enrichment), a computational framework that combines expression imputation with iterative marker refinement. ICE substantially improved detection precision in pancreatic cells with weakly expressed markers, increasing it from 56% to 98%. Using consensus markers, ICE identified senescent fibro-adipogenic progenitors (FAPs) in muscle tissue, characterized by elevated expression of senescence-associated secretory phenotype (SASP) genes. When applied to individual senescence markers (CDKN1A/p21, CDKN2A/p16, ATF3, and MX1), ICE successfully identified marker-specific cell populations. These included stressed {beta} cells in aging and type-I interferon-responsive microglia in Alzheimers disease (AD). ConclusionsOur study introduces ICE, a rigorous framework for detecting cell states defined by weakly expressed markers at the single-cell level. This tool enables the reliable identification of senescence-associated populations, facilitating a deeper characterization of their heterogeneity and temporal dynamics across diverse human tissues and disease contexts.