Sheng Li, Paula A. Agudelo Garcia, Constantin Aliferis, Michael J. Becich, Jazmin Calyeca, Benjamin D. Cosgrove, Jennifer Elisseeff, Negin Farzad, Elana J. Fertig, Carolyn Glass, Liangcai Gu, Qianjiang Hu, Zhicheng Ji, Melanie Königshoff, Nathan K. LeBrasseur, Dongmei Li, Anjun Ma, Qin Ma, Vilas Menon, Jacob T. Mitchell, Ana L. Mora, Sushma Nagaraj, Andrew C. Nelson, Laura J. Niedernhofer, Mauricio Rojas, Hash Brown Taha, Jinhua Wang, Siyuan Wang, Pei-Hsun Wu, Jichun Xie, Ming Xu, Miao Yu, Xu Zhang, Yue Zhao, Peter D. Adams, Cristina Aguayo-Mazzucato, Darren J. Baker, Christopher Benz, David A. Bernlohr, Marta Bueno, Jin Chen, Bennett G. Childs, Jeffrey H. Chuang, Dongjun Chung, Mythili Dileepan, Li Ding, Mingze Dong, Francesca Duncan, Archibald Enninful, William F. Flynn, Ana Catarina Franco, David Furman, Vesna Garovic, Stephanie Halene, Allison B. Herman, Ann V. Hertzel, Kanako Iwasaki, Hyeongseon Jeon, Jeon Woong Kang, Shilpita Karmakar, James L. Kirkland, Ron Korstanje, Erich Kummerfeld, Jun Hee Lee, Yang Liu, Yao Lu, Jose Lugo-Martinez, Helene Martini, Simon Melov, Nicolas Musi, João F. Passos, Samuel T. Peters, Irfan Rahman, Ramalakshmi Ramasamy, Alexandra N. Rindone, Paul D. Robbins, Paul Robson, Jhonny Rodriguez-Lopez, Lorena Rosas, Nadia Rosenthal, Marissa J. Schafer, Birgit Schilling, Elizabeth L. Schmidt, Kevin Schneider, Kaustav Sengupta, Jian Shu, Peter T. C. So, Li Sun, Tamar Tchkonia, Marcos G. Teneche, Natalia Vanegas, Julia Wang, Juan Xie, Shanshan Yin, Ke Zhang, Quan Zhu, Rong Fan
{"title":"用计算多组学推进对细胞衰老的生物学理解","authors":"Sheng Li, Paula A. Agudelo Garcia, Constantin Aliferis, Michael J. Becich, Jazmin Calyeca, Benjamin D. Cosgrove, Jennifer Elisseeff, Negin Farzad, Elana J. Fertig, Carolyn Glass, Liangcai Gu, Qianjiang Hu, Zhicheng Ji, Melanie Königshoff, Nathan K. LeBrasseur, Dongmei Li, Anjun Ma, Qin Ma, Vilas Menon, Jacob T. Mitchell, Ana L. Mora, Sushma Nagaraj, Andrew C. Nelson, Laura J. Niedernhofer, Mauricio Rojas, Hash Brown Taha, Jinhua Wang, Siyuan Wang, Pei-Hsun Wu, Jichun Xie, Ming Xu, Miao Yu, Xu Zhang, Yue Zhao, Peter D. Adams, Cristina Aguayo-Mazzucato, Darren J. Baker, Christopher Benz, David A. Bernlohr, Marta Bueno, Jin Chen, Bennett G. Childs, Jeffrey H. Chuang, Dongjun Chung, Mythili Dileepan, Li Ding, Mingze Dong, Francesca Duncan, Archibald Enninful, William F. Flynn, Ana Catarina Franco, David Furman, Vesna Garovic, Stephanie Halene, Allison B. Herman, Ann V. Hertzel, Kanako Iwasaki, Hyeongseon Jeon, Jeon Woong Kang, Shilpita Karmakar, James L. Kirkland, Ron Korstanje, Erich Kummerfeld, Jun Hee Lee, Yang Liu, Yao Lu, Jose Lugo-Martinez, Helene Martini, Simon Melov, Nicolas Musi, João F. Passos, Samuel T. Peters, Irfan Rahman, Ramalakshmi Ramasamy, Alexandra N. Rindone, Paul D. Robbins, Paul Robson, Jhonny Rodriguez-Lopez, Lorena Rosas, Nadia Rosenthal, Marissa J. Schafer, Birgit Schilling, Elizabeth L. Schmidt, Kevin Schneider, Kaustav Sengupta, Jian Shu, Peter T. C. So, Li Sun, Tamar Tchkonia, Marcos G. Teneche, Natalia Vanegas, Julia Wang, Juan Xie, Shanshan Yin, Ke Zhang, Quan Zhu, Rong Fan","doi":"10.1038/s41588-025-02314-y","DOIUrl":null,"url":null,"abstract":"<p>Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features. This Review provides a comprehensive overview of multiomic approaches for the characterization and biological understanding of cellular senescence. The technical capability and challenges of each approach are discussed, and practical guidelines are provided for selecting tools for identifying, characterizing and spatially mapping senescent cells. The importance of computational analyses in multiomics research, including senescent cell identification, signature detection and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. Finally, future directions and the potential impact of multiomic approaches on the biological understanding of cellular senescence are discussed.</p>","PeriodicalId":18985,"journal":{"name":"Nature genetics","volume":"36 1","pages":""},"PeriodicalIF":29.0000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing biological understanding of cellular senescence with computational multiomics\",\"authors\":\"Sheng Li, Paula A. Agudelo Garcia, Constantin Aliferis, Michael J. Becich, Jazmin Calyeca, Benjamin D. Cosgrove, Jennifer Elisseeff, Negin Farzad, Elana J. Fertig, Carolyn Glass, Liangcai Gu, Qianjiang Hu, Zhicheng Ji, Melanie Königshoff, Nathan K. LeBrasseur, Dongmei Li, Anjun Ma, Qin Ma, Vilas Menon, Jacob T. Mitchell, Ana L. Mora, Sushma Nagaraj, Andrew C. Nelson, Laura J. Niedernhofer, Mauricio Rojas, Hash Brown Taha, Jinhua Wang, Siyuan Wang, Pei-Hsun Wu, Jichun Xie, Ming Xu, Miao Yu, Xu Zhang, Yue Zhao, Peter D. Adams, Cristina Aguayo-Mazzucato, Darren J. Baker, Christopher Benz, David A. Bernlohr, Marta Bueno, Jin Chen, Bennett G. Childs, Jeffrey H. Chuang, Dongjun Chung, Mythili Dileepan, Li Ding, Mingze Dong, Francesca Duncan, Archibald Enninful, William F. Flynn, Ana Catarina Franco, David Furman, Vesna Garovic, Stephanie Halene, Allison B. Herman, Ann V. Hertzel, Kanako Iwasaki, Hyeongseon Jeon, Jeon Woong Kang, Shilpita Karmakar, James L. Kirkland, Ron Korstanje, Erich Kummerfeld, Jun Hee Lee, Yang Liu, Yao Lu, Jose Lugo-Martinez, Helene Martini, Simon Melov, Nicolas Musi, João F. Passos, Samuel T. Peters, Irfan Rahman, Ramalakshmi Ramasamy, Alexandra N. Rindone, Paul D. Robbins, Paul Robson, Jhonny Rodriguez-Lopez, Lorena Rosas, Nadia Rosenthal, Marissa J. Schafer, Birgit Schilling, Elizabeth L. 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The importance of computational analyses in multiomics research, including senescent cell identification, signature detection and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. 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Advancing biological understanding of cellular senescence with computational multiomics
Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features. This Review provides a comprehensive overview of multiomic approaches for the characterization and biological understanding of cellular senescence. The technical capability and challenges of each approach are discussed, and practical guidelines are provided for selecting tools for identifying, characterizing and spatially mapping senescent cells. The importance of computational analyses in multiomics research, including senescent cell identification, signature detection and interactions of senescent cells with microenvironments, is highlighted. Moreover, tissue-specific case studies and experimental design considerations for individual organs are presented. Finally, future directions and the potential impact of multiomic approaches on the biological understanding of cellular senescence are discussed.
期刊介绍：
Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation.
Integrative genetic topics comprise, but are not limited to:
-Genes in the pathology of human disease
-Molecular analysis of simple and complex genetic traits
-Cancer genetics
-Agricultural genomics
-Developmental genetics
-Regulatory variation in gene expression
-Strategies and technologies for extracting function from genomic data
-Pharmacological genomics
-Genome evolution
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