机器学习提取肿瘤细胞抗辐射特异性EdU荧光模式。

IF 1.3 4区 生物学 Q4 CELL BIOLOGY
Genes to Cells Pub Date : 2025-09-16 DOI:10.1111/gtc.70050
Masae Ikura, Tsuyoshi Ikura, Kanji Furuya
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引用次数: 0

摘要

胸腺嘧啶类似物EdU(5-乙基-2-脱氧尿嘧啶)在复制过程中被纳入DNA,传统上被用作s期细胞的标记物。在这项研究中,我们发现EdU荧光图像显示出大量细胞间的可变性,可以通过无监督机器学习将其分类为多个簇。这表明,看似随机的EdU模式包含可重复的、计算上可识别的特征。基于我们对辐射应激反应中出现的不同模式的观察,我们研究了耐辐射癌细胞是否表现出特定的EdU特征。通过改变DNA复制获得辐射抗性的plk1过表达细胞的分析显示,辐射诱导的EdU模式与对照细胞不同。由于观察到这些细胞显示出明显扩大和增强的γ-H2AX焦点(DNA损伤的标记),我们采用基于γ-H2AX模式的监督机器学习模型来分离辐射抗性细胞亚群。然后,我们从这些分离的细胞中提取EdU信号,并通过进一步的无监督机器学习,成功地确定了特定于辐射抗性的特征模式。这建立了一个机器学习框架,能够从个体细胞之间变化的动态网络中提取通用规则,这为筛选系统提供了一个新的平台,以识别与辐射抗性有关的分子,重点关注癌症异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine Learning Extracts Radiation Resistant-Specific EdU Fluorescence Pattern in Cancer Cells

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.

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来源期刊
Genes to Cells
Genes to Cells 生物-细胞生物学
CiteScore
3.40
自引率
0.00%
发文量
71
审稿时长
3 months
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.
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