Xie Sijia, Luo Xinwei, Hong Feitong, Wei Yijie, Hao Yuduo, Xie Xueqin, Li Xiaolong, Xie Guangbo, Dao Fuying, Lyu Hao
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Aging Dis. 2025 Mar 17. doi: 10.14336/AD.2025.0218.
Individual aging is a complex biological process involving multiple levels, with molecular changes existing in heterogeneity across different cell types and tissues, being regulated by both internal and external factors. Traditional senescence markers, including p16, cell morphological changes, and cell cycle arrest, can only partially reflect the complexity of senescence. Single-cell omics technology facilitates the integration of multi-faceted data, including gene expression profiles, spatial dynamics, chromatin accessibility and metabolic pathways. This comprehensive approach enhances the development of biomarkers, granting us a more profound insight into the heterogeneity inherent within senescent cell populations. In this review, we summarize the application of single cell multi-omics approaches in analyzing senescence mechanisms and potential intervention targets from the perspectives of transcriptomics, epigenetics, metabolomics, and proteomics, explore the potential of developing new senescence markers at the cellular level using machine learning algorithms and artificial intelligence in bioinformatics analysis. Finally, we further discuss the challenges and prospective trajectories within this research domain to provide a more comprehensive perspective on dissecting the regulatory networks of senescence cells.
个体衰老乃是一个涉及多个层面的复杂生物学过程，分子变化在不同细胞类型和组织中存在异质性，并受内部和外部因素调控。传统的衰老标志物，包括p16、细胞形态变化和细胞周期停滞，只能部分反映衰老的复杂性。单细胞组学技术有助于整合多方面的数据，包括基因表达谱、空间动态、染色质可及性和代谢途径。这种综合方法促进了生物标志物的开发，使我们能够更深入地洞察衰老细胞群体中固有的异质性。在本综述中，我们从转录组学、表观遗传学、代谢组学和蛋白质组学的角度总结了单细胞多组学方法在分析衰老机制和潜在干预靶点方面的应用，探讨了在生物信息学分析中使用机器学习算法和人工智能在细胞水平开发新的衰老标志物的潜力。最后，我们进一步讨论了该研究领域内的挑战和未来发展轨迹，以便更全面地剖析衰老细胞的调控网络。
