Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data.

ABSTRACT: Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed singl

SUBMITTER: Sha Y 

PROVIDER: S-EPMC7515733 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature