{"title":"机器学习提取肿瘤细胞抗辐射特异性EdU荧光模式。","authors":"Masae Ikura, Tsuyoshi Ikura, Kanji Furuya","doi":"10.1111/gtc.70050","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The thymidine analog EdU (5-ethynyl-2-deoxyuridine) is incorporated into DNA during replication and has traditionally been used as a marker of S-phase cells. In this study, we discovered that EdU fluorescence images display substantial cell-to-cell variability, which could be classified into multiple clusters by unsupervised machine learning. This suggests that seemingly random EdU patterns contain reproducible, computationally recognizable features. Building on our observation that distinct patterns emerged in response to radiation stress, we investigated whether radioresistant cancer cells exhibit specific EdU signatures. Analysis of PLK1-overexpressing cells, which acquire radioresistance through altered DNA replication, revealed radiation-induced EdU patterns distinct from control cells. Prompted by the observation that these cells displayed markedly enlarged and intensified γ-H2AX foci, a marker of DNA damage, we employed a supervised machine learning model based on γ-H2AX patterns to isolate the radioresistant cell subpopulation. We then extracted the EdU signals from these isolated cells and, through further unsupervised machine learning, successfully identified a characteristic pattern specific to radioresistance. This establishes a machine learning framework capable of extracting universal rules from the dynamic networks that vary among individual cells, which provides a novel platform for a screening system to identify molecules involved in radioresistance, focusing on cancer heterogeneity.</p>\n </div>","PeriodicalId":12742,"journal":{"name":"Genes to Cells","volume":"30 5","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Extracts Radiation Resistant-Specific EdU Fluorescence Pattern in Cancer Cells\",\"authors\":\"Masae Ikura, Tsuyoshi Ikura, Kanji Furuya\",\"doi\":\"10.1111/gtc.70050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>The thymidine analog EdU (5-ethynyl-2-deoxyuridine) is incorporated into DNA during replication and has traditionally been used as a marker of S-phase cells. In this study, we discovered that EdU fluorescence images display substantial cell-to-cell variability, which could be classified into multiple clusters by unsupervised machine learning. This suggests that seemingly random EdU patterns contain reproducible, computationally recognizable features. Building on our observation that distinct patterns emerged in response to radiation stress, we investigated whether radioresistant cancer cells exhibit specific EdU signatures. Analysis of PLK1-overexpressing cells, which acquire radioresistance through altered DNA replication, revealed radiation-induced EdU patterns distinct from control cells. Prompted by the observation that these cells displayed markedly enlarged and intensified γ-H2AX foci, a marker of DNA damage, we employed a supervised machine learning model based on γ-H2AX patterns to isolate the radioresistant cell subpopulation. We then extracted the EdU signals from these isolated cells and, through further unsupervised machine learning, successfully identified a characteristic pattern specific to radioresistance. This establishes a machine learning framework capable of extracting universal rules from the dynamic networks that vary among individual cells, which provides a novel platform for a screening system to identify molecules involved in radioresistance, focusing on cancer heterogeneity.</p>\\n </div>\",\"PeriodicalId\":12742,\"journal\":{\"name\":\"Genes to Cells\",\"volume\":\"30 5\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genes to Cells\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/gtc.70050\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genes to Cells","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/gtc.70050","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Machine Learning Extracts Radiation Resistant-Specific EdU Fluorescence Pattern in Cancer Cells
The thymidine analog EdU (5-ethynyl-2-deoxyuridine) is incorporated into DNA during replication and has traditionally been used as a marker of S-phase cells. In this study, we discovered that EdU fluorescence images display substantial cell-to-cell variability, which could be classified into multiple clusters by unsupervised machine learning. This suggests that seemingly random EdU patterns contain reproducible, computationally recognizable features. Building on our observation that distinct patterns emerged in response to radiation stress, we investigated whether radioresistant cancer cells exhibit specific EdU signatures. Analysis of PLK1-overexpressing cells, which acquire radioresistance through altered DNA replication, revealed radiation-induced EdU patterns distinct from control cells. Prompted by the observation that these cells displayed markedly enlarged and intensified γ-H2AX foci, a marker of DNA damage, we employed a supervised machine learning model based on γ-H2AX patterns to isolate the radioresistant cell subpopulation. We then extracted the EdU signals from these isolated cells and, through further unsupervised machine learning, successfully identified a characteristic pattern specific to radioresistance. This establishes a machine learning framework capable of extracting universal rules from the dynamic networks that vary among individual cells, which provides a novel platform for a screening system to identify molecules involved in radioresistance, focusing on cancer heterogeneity.
期刊介绍:
Genes to Cells provides an international forum for the publication of papers describing important aspects of molecular and cellular biology. The journal aims to present papers that provide conceptual advance in the relevant field. Particular emphasis will be placed on work aimed at understanding the basic mechanisms underlying biological events.