Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics
Abstract 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...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07744-2 |
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| author | Julian Ng-Kee-Kwong Ben Philps Fiona N. C. Smith Aleksandra Sobieska Naiming Chen Constance Alabert Hakan Bilen Sara C. B. Buonomo |
| author_facet | Julian Ng-Kee-Kwong Ben Philps Fiona N. C. Smith Aleksandra Sobieska Naiming Chen Constance Alabert Hakan Bilen Sara C. B. Buonomo |
| author_sort | Julian Ng-Kee-Kwong |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a3f6cf3e4f3a4e3981e8d69348293c46 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-a3f6cf3e4f3a4e3981e8d69348293c462025-08-20T02:01:39ZengNature PortfolioCommunications Biology2399-36422025-02-018111210.1038/s42003-025-07744-2Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamicsJulian Ng-Kee-Kwong0Ben Philps1Fiona N. C. Smith2Aleksandra Sobieska3Naiming Chen4Constance Alabert5Hakan Bilen6Sara C. B. Buonomo7Institute of Cell Biology, School of Biological Sciences, University of EdinburghSchool of Informatics, University of EdinburghSchool of Informatics, University of EdinburghSchool of Informatics, University of EdinburghInstitute of Cell Biology, School of Biological Sciences, University of EdinburghDivision of Molecular, Cell & Developmental Biology, School of Life Sciences, University of DundeeSchool of Informatics, University of EdinburghInstitute of Cell Biology, School of Biological Sciences, University of EdinburghAbstract 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.https://doi.org/10.1038/s42003-025-07744-2 |
| spellingShingle | Julian Ng-Kee-Kwong Ben Philps Fiona N. C. Smith Aleksandra Sobieska Naiming Chen Constance Alabert Hakan Bilen Sara C. B. Buonomo Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics Communications Biology |
| title | Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics |
| title_full | Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics |
| title_fullStr | Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics |
| title_full_unstemmed | Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics |
| title_short | Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics |
| title_sort | supervised and unsupervised deep learning based approaches for studying dna replication spatiotemporal dynamics |
| url | https://doi.org/10.1038/s42003-025-07744-2 |
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