Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators

• Published in: Cell metabolism Vol. 36; no. 5; pp. 1126 - 1143.e5
• Main Authors: Tao, Wanyu, Yu, Zhengqing, Han, Jing-Dong J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 07.05.2024
• Subjects: Aging; Cellular Senescence; computational tool; COVID-19; Humans; Machine Learning; SARS-CoV-2 - metabolism; senescence; senescence identification; senescence quantification; senescence regulators; single cell; Single-Cell Analysis; trajectory; Transcriptome; senescence; computational tool; single cell; senescence identification; trajectory; senescence quantification; senescence regulators
• ISSN: 1550-4131; 1932-7420; 1932-7420
• DOI: 10.1016/j.cmet.2024.03.009

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells. [Display omitted] •The SenCID program is developed to identify senescent cells from bulk or scRNA-seq•SenCID finds six modes of senescence among cell types with different characteristics•SenCID finds varied trajectories in aging and disease to common senescent endpoint•SenCID identifies genome-wide senescence modulators and their hierarchies Tao et al. developed a computational program that identifies and tracks 6 types of senescent cells based on single-cell transcriptomes from tens of thousands of cells.