Scholarlink

<p>In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous human diseases, the labour-intensive methods employed to study DNA replication have hindered large-scale analyses of its roles in pathological processes. In this study, we employ two distinct methodologies. We first apply supervised machine learning, successfully classifying S-phase patterns in wild-type mouse embryonic stem cells (mESCs), while additionally identifying altered replication dynamics in <i>Rif1</i>-deficient mESCs. Given the constraints imposed by a classification-based approach, we then develop an unsupervised method for large-scale detection of aberrant S-phase cells. Such a method, which does not aim to classify patterns based on pre-defined categories but rather detects differences autonomously, closely recapitulates expected differences across genotypes. We therefore extend our approach to a well-characterised cellular model of inducible deregulated origin firing, involving cyclin E overexpression. Through parallel EdU- and PCNA-based analyses, we demonstrate the potential applicability of our method to patient samples, offering a means to identify the contribution of deregulated DNA replication to a plethora of pathogenic processes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月26日

查看全文
  • Julian Ng-Kee-Kwong,
  • Ben Philps,
  • Fiona N. C. Smith,
  • Aleksandra Sobieska,
  • Naiming Chen,
  • Constance Alabert,
  • Hakan Bilen,
  • Sara C. B. Buonomo

摘要

In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous human diseases, the labour-intensive methods employed to study DNA replication have hindered large-scale analyses of its roles in pathological processes. In this study, we employ two distinct methodologies. We first apply supervised machine learning, successfully classifying S-phase patterns in wild-type mouse embryonic stem cells (mESCs), while additionally identifying altered replication dynamics in Rif1-deficient mESCs. Given the constraints imposed by a classification-based approach, we then develop an unsupervised method for large-scale detection of aberrant S-phase cells. Such a method, which does not aim to classify patterns based on pre-defined categories but rather detects differences autonomously, closely recapitulates expected differences across genotypes. We therefore extend our approach to a well-characterised cellular model of inducible deregulated origin firing, involving cyclin E overexpression. Through parallel EdU- and PCNA-based analyses, we demonstrate the potential applicability of our method to patient samples, offering a means to identify the contribution of deregulated DNA replication to a plethora of pathogenic processes.