{"title":"Epithelial-mesenchymal transition couples with cell cycle arrest at various stages.","authors":"Sophia Hu, Yong Lu, Gaohan Yu, Zhiqian Zheng, Weikang Wang, Ke Ni, Amitava Giri, Jingyu Zhang, Yan Zhang, Kazuhide Watanabe, Guang Yao, Jianhua Xing","doi":"10.1101/2025.02.24.639880","DOIUrl":null,"url":null,"abstract":"<p><p>Numerous computational approaches have been developed to infer cell state transition trajectories from snapshot single-cell data. Most approaches first require projecting high-dimensional data onto a low-dimensional representation, raising the question of whether the dynamics of the system become distorted. Using epithelial-to-mesenchymal transition (EMT) as a test system, we show that both biology-guided low-dimensional representations and stochastic trajectory simulations in high-dimensional state space, not representations obtained with <i>brute force</i> dimension-reduction methods, reveal multiple distinct paths of TGF-β-induced EMT. The paths arise from coupling between EMT and cell cycle arrest at either the G1/S, G2/M or M checkpoints, contributing to cell-cycle related EMT heterogeneity. The present study emphasizes that caution should be taken when inferring transition dynamics from snapshot single-cell data in two- or three-dimensional representations, and that incorporating dynamical information can improve prediction accuracy.</p>","PeriodicalId":519960,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888286/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv : the preprint server for biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2025.02.24.639880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Numerous computational approaches have been developed to infer cell state transition trajectories from snapshot single-cell data. Most approaches first require projecting high-dimensional data onto a low-dimensional representation, raising the question of whether the dynamics of the system become distorted. Using epithelial-to-mesenchymal transition (EMT) as a test system, we show that both biology-guided low-dimensional representations and stochastic trajectory simulations in high-dimensional state space, not representations obtained with brute force dimension-reduction methods, reveal multiple distinct paths of TGF-β-induced EMT. The paths arise from coupling between EMT and cell cycle arrest at either the G1/S, G2/M or M checkpoints, contributing to cell-cycle related EMT heterogeneity. The present study emphasizes that caution should be taken when inferring transition dynamics from snapshot single-cell data in two- or three-dimensional representations, and that incorporating dynamical information can improve prediction accuracy.