Jing Wang, Xiaolan Zhou, Peng Yu, Jun Yao, Pengfei Guo, Qiushi Xu, Yuqi Zhao, Guanlong Wang, Qianru Li, Xiaofeng Zhu, Gang Wei, Weixu Wang, Ting Ni
{"title":"基于转录组的人类普遍衰老指数(hUSI)在各种条件下可靠地预测细胞衰老。","authors":"Jing Wang, Xiaolan Zhou, Peng Yu, Jun Yao, Pengfei Guo, Qiushi Xu, Yuqi Zhao, Guanlong Wang, Qianru Li, Xiaofeng Zhu, Gang Wei, Weixu Wang, Ting Ni","doi":"10.1038/s43587-025-00886-2","DOIUrl":null,"url":null,"abstract":"<p><p>Despite the manifestation and contribution of cellular senescence to aging and various diseases, accurate identification of heterogeneous senescent cells remains challenging. Current senescence evaluation methods rely mainly on limited markers or homogeneous samples, which might fail to capture universal senescence features, limiting their generalizability. Here we developed the human universal senescence index (hUSI), an accurate and robust senescence evaluation method for diverse cells and samples. Based on features learned from the most comprehensive cellular senescence-associated transcriptome data so far, hUSI demonstrated its convincing connections with senescence phenotypes and superior robustness in predicting senescence state. Using hUSI, we discovered potential senescence regulators and mapped senescent cell accumulation across cell types in coronavirus disease 2019 (COVID-19). The method also facilitates decoding heterogeneous senescence states in melanoma tumors, identifying prognosis-associated signaling pathways. Overall, hUSI demonstrates its utility in characterizing cellular senescence across biological contexts, with broad applications in aging research and clinical practice.</p>","PeriodicalId":94150,"journal":{"name":"Nature aging","volume":" ","pages":"1159-1175"},"PeriodicalIF":17.0000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12176644/pdf/","citationCount":"0","resultStr":"{\"title\":\"A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.\",\"authors\":\"Jing Wang, Xiaolan Zhou, Peng Yu, Jun Yao, Pengfei Guo, Qiushi Xu, Yuqi Zhao, Guanlong Wang, Qianru Li, Xiaofeng Zhu, Gang Wei, Weixu Wang, Ting Ni\",\"doi\":\"10.1038/s43587-025-00886-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Despite the manifestation and contribution of cellular senescence to aging and various diseases, accurate identification of heterogeneous senescent cells remains challenging. Current senescence evaluation methods rely mainly on limited markers or homogeneous samples, which might fail to capture universal senescence features, limiting their generalizability. Here we developed the human universal senescence index (hUSI), an accurate and robust senescence evaluation method for diverse cells and samples. Based on features learned from the most comprehensive cellular senescence-associated transcriptome data so far, hUSI demonstrated its convincing connections with senescence phenotypes and superior robustness in predicting senescence state. Using hUSI, we discovered potential senescence regulators and mapped senescent cell accumulation across cell types in coronavirus disease 2019 (COVID-19). The method also facilitates decoding heterogeneous senescence states in melanoma tumors, identifying prognosis-associated signaling pathways. Overall, hUSI demonstrates its utility in characterizing cellular senescence across biological contexts, with broad applications in aging research and clinical practice.</p>\",\"PeriodicalId\":94150,\"journal\":{\"name\":\"Nature aging\",\"volume\":\" \",\"pages\":\"1159-1175\"},\"PeriodicalIF\":17.0000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12176644/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature aging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s43587-025-00886-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature aging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43587-025-00886-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/29 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.
Despite the manifestation and contribution of cellular senescence to aging and various diseases, accurate identification of heterogeneous senescent cells remains challenging. Current senescence evaluation methods rely mainly on limited markers or homogeneous samples, which might fail to capture universal senescence features, limiting their generalizability. Here we developed the human universal senescence index (hUSI), an accurate and robust senescence evaluation method for diverse cells and samples. Based on features learned from the most comprehensive cellular senescence-associated transcriptome data so far, hUSI demonstrated its convincing connections with senescence phenotypes and superior robustness in predicting senescence state. Using hUSI, we discovered potential senescence regulators and mapped senescent cell accumulation across cell types in coronavirus disease 2019 (COVID-19). The method also facilitates decoding heterogeneous senescence states in melanoma tumors, identifying prognosis-associated signaling pathways. Overall, hUSI demonstrates its utility in characterizing cellular senescence across biological contexts, with broad applications in aging research and clinical practice.