Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China.
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China; Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
Cell Metab. 2024 May 7;36(5):1126-1143.e5. doi: 10.1016/j.cmet.2024.03.009. Epub 2024 Apr 10.
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.
细胞衰老是许多与衰老相关病理的基础，但它的异质性给研究和靶向衰老细胞带来了挑战。我们在这里提出了一个机器学习程序 Senescent Cell Identification（SenCID），它可以准确地识别批量和单细胞转录组中的衰老细胞。在跨越 30 种细胞类型的 52 个衰老转录组数据集的 602 个样本上进行训练后，SenCID 可以识别出六种主要的衰老身份（SIDs）。不同的 SIDs 表现出不同的衰老基线、干性、基因功能以及对 senolytics 的反应。SenCID 可以在正常衰老、慢性疾病和 COVID-19 下重建衰老轨迹。此外，当应用于单细胞 Perturb-seq 数据时，SenCID 有助于揭示衰老调节剂的层次结构。总的来说，SenCID 是精确单细胞分析细胞衰老的重要工具，能够针对衰老细胞进行靶向干预。
