单细胞 z-bio 2025-03-12 277

　　Construction and Application Process of Aging Cell Identification Model

　　With the support of the National Natural Science Foundation of China's "Digital Decoding of Immunity" major research project (approval number: 92374207), Professor Han Jingdong's team from the Center for Quantitative Biology/Joint Center for Life Sciences at Peking University has made breakthroughs in the study of cellular aging trajectories and regulatory mechanisms. The research results, titled "Single cell aging identification reveals aging heterogeneity, trajectories, and modulators," were published on April 10, 2024 in the journal Cell Metabolism. The paper link is: https://doi.org/10.1016/j.cmet.2024.03.009 .

　　Cellular aging plays a crucial role in many diseases, but current research on cellular aging lacks universal aging cell markers and traditional identification methods have limitations in detection. In response to the above challenges, the research team has developed a machine learning based SenCID (Senecent Cell Identification) algorithm to accurately identify aging cells and evaluate the degree of aging from human single-cell transcriptome data. SenCID divides cells into six types of "senescence IDs" (SIDs), and different SIDs cells have significant differences in aging baseline, cell stemness, gene function, and response to aging lysis; SenCID combined with trajectory reconstruction algorithm reconstructed cell aging trajectories under different physiological and pathological conditions such as normal individual aging, chronic disease and COVID-19 infection on the basis of single cell data of human tissues; SenCID has also been applied to transcriptome data of single-cell gene perturbation technology to identify gene perturbation factors that can promote or inhibit cellular aging; To lay the foundation for promoting research on the mechanism of cellular aging and cellular aging intervention.