ArticleNucleic acids research2020
Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 70 citations in OpenAlex.
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Cell State Transitions Drive the Evolution of Disease Progression in B-Lymphoblastic Leukemia.Cancer research communications · 2026Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression.National science review · 2025Article
- Engineering CAR-T Therapeutics for Enhanced Solid Tumor Targeting.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.NPJ systems biology and applications · 2025Article
- Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling.iScience · 2024Review
- Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.Communications biology · 2024Article
- bHLH transcription factors cooperate with chromatin remodelers to regulate cell fate decisions during Arabidopsis stomatal development.PLoS biology · 2024Article
- Single-Cell Informatics for Tumor Microenvironment and Immunotherapy.International journal of molecular sciences · 2024Review
- Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition.Genesis (New York, N.Y. : 2000) · 2024Review
- Topological and geometric analysis of cell states in single-cell transcriptomic data.Briefings in bioinformatics · 2024Article
- Single-cell RNA-seq reveals keratinocyte and fibroblast heterogeneity and their crosstalk via epithelial-mesenchymal transition in psoriasis.Cell death & disease · 2024Article
- Data-driven energy landscape reveals critical genes in cancer progression.NPJ systems biology and applications · 2024Article
- Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.Nature machine intelligence · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Application of single-cell RNA sequencing in head and neck squamous cell carcinoma.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023Article
- Single-cell CaJournal of the Royal Society, Interface · 2023Article
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.