Sun Shuhui, Jiang Mengmeng, Ma Shuai, Ren Jie, Liu Guang-Hui
Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing 100029, China.
Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China.
Trends Endocrinol Metab. 2025 Feb;36(2):133-146. doi: 10.1016/j.tem.2024.07.009. Epub 2024 Aug 23.
Our limited understanding of metabolic aging poses major challenges to comprehending the diverse cellular alterations that contribute to age-related decline, and to devising targeted interventions. This review provides insights into the heterogeneous nature of cellular metabolism during aging and its response to interventions, with a specific focus on cellular heterogeneity and its implications. By synthesizing recent findings using single-cell approaches, we explored the vulnerabilities of distinct cell types and key metabolic pathways. Delving into the cell type-specific alterations underlying the efficacy of systemic interventions, we also discuss the complexity of integrating single-cell data and advocate for leveraging computational tools and artificial intelligence to harness the full potential of these data, develop effective strategies against metabolic aging, and promote healthy aging.
我们对代谢性衰老的有限理解给理解导致与年龄相关衰退的各种细胞变化以及设计针对性干预措施带来了重大挑战。本综述深入探讨了衰老过程中细胞代谢的异质性本质及其对干预措施的反应,特别关注细胞异质性及其影响。通过综合使用单细胞方法的最新研究结果,我们探索了不同细胞类型和关键代谢途径的脆弱性。深入研究全身干预有效性背后的细胞类型特异性变化,我们还讨论了整合单细胞数据的复杂性,并提倡利用计算工具和人工智能来充分发挥这些数据的潜力,制定对抗代谢性衰老的有效策略,并促进健康衰老。