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

TL;DR

This paper introduces deep learning methods to study DNA replication patterns in cells, enabling large-scale analysis of replication dynamics and potential applications in disease research.

Contribution

The novel contribution is the development of supervised and unsupervised deep learning approaches to detect and classify DNA replication dynamics in mouse embryonic stem cells.

Findings

Supervised machine learning successfully classified S-phase patterns in wild-type mouse embryonic stem cells.

An unsupervised method detected altered replication dynamics in Rif1-deficient cells and cyclin E overexpression models.

The methods showed potential applicability to patient samples for studying pathogenic processes.

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.

When tested on well-characterised cellular models, supervised and unsupervised deep learning closely recapitulate expected differences in DNA replication dynamics across genotypes, holding promise for identifying aberrant DNA replication at scale.

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