Dataset Information

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 single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

SUBMITTER: Sha Y

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Sha Yutong Y Wang Shuxiong S Zhou Peijie P Nie Qing Q

Nucleic acids research 20200901 17

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 in ...[more]

PMID: 32870263

Similar Datasets

Project description:DCLK1 and Lgr5 have recently been identified as markers of quiescent and cycling stem cells in the small intestinal crypts, respectively. Epithelial-mesenchymal transition (EMT) is a key development program that is often activated during cancer invasion and metastasis, and also imparts a self-renewal capability to disseminating cancer cells. Utilizing the Citrobacter rodentium (CR)-induced transmissible murine colonic hyperplasia (TMCH) model, we observed a relative decrease in DCLK1 expression in the colonic crypts, with significant shift towards stromal staining at peak (12 days post infection) hyperplasia, whereas staining for Lgr5 and Msi-1 increased several fold. When hyperplasia was regressing (days 20-34), an expansion of DCLK1+ve cells in the CR-infected crypts compared with that seen in uninfected control was recorded. Purified colonic crypt cells exhibiting epigenetic modulation of the transforming growth factor-? (TGF?), Wnt and Notch pathways on 12 or 34 days post infection formed monolayers in vitro, and underwent trans-differentiation into fibroblast-like cells that stained positive for vimentin, fibronectin and DCLK1. These cells when trypsinized and regrown in soft agar, formed colonospheres/organoids that developed into crypt-like structures (colonoids) in Matrigel and stained positive for DCLK1. Mice exhibiting 12 or 34 days of TMCH were given azoxymethane once for 8?h (Gp1) or weekly for 3 weeks (Gp2), and subjected to crypt isolation. Crypt cells from Gp1 animals formed monolayers as well as colonospheres in soft agar and nodules/tumors in nude mice. Crypt cells isolated from Gp2 animals failed to form the monolayers, but developed into colonospheres in soft agar and nodules/tumors in nude mice. Thus, both hyperplasia and increased presence of DCLK1+ve cells promote cellular transformation in response to a second hit. The TMCH model, therefore, provides an excellent template to study how alterations in intestinal stem cells promote trans-differentiation, crypt regeneration or colon carcinogenesis following bacterial infection.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets