衰老
细胞衰老
生物
功能(生物学)
计算生物学
表型
细胞生物学
遗传学
基因
作者
Aditi U. Gurkar,Akos A. Gerencser,Ana L. Mora,Andrew C. Nelson,Anru R. Zhang,Anthony B. Lagnado,Archibald Enninful,Christopher C. Benz,David Furman,Delphine Beaulieu,Diana Jurk,Elizabeth L. Thompson,Fei Wu,Fernanda Rodriguez,G. Barthel,Hao Chen,Hemali Phatnani,Indra Heckenbach,Jeffrey H. Chuang,Jeremy Horrell
出处
期刊:Nature Aging
日期:2023-07-03
卷期号:3 (7): 776-790
被引量:100
标识
DOI:10.1038/s43587-023-00446-6
摘要
Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells. SenNet Consortium members review current and emerging methodologies for spatially resolved mapping of senescent cells. They discuss their limitations and challenges involved in the aim of creating a comprehensive atlas of senescent cells during aging.
科研通智能强力驱动
Strongly Powered by AbleSci AI