ArticleeLife2017
Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Allostery in Biomolecular Condensates.Journal of molecular biology · 2026Review
- Controllability of the gene regulatory network in zebrafish embryogenesis.Scientific reports · 2025Article
- mRNA turnover dynamics are affected by cell differentiation and loss of the cytosine methyltransferase Nsun2.Nucleic acids research · 2025Article
- Regulation of PKCi-mediated pluripotency and gene expression by polycomb complex 1 in mouse embryonic stem cells.American journal of translational research · 2025Article
- Single cell spatial biology over developmental time can decipher pediatric brain pathologies.Neurobiology of disease · 2024Review
- Physical modeling of embryonic transcriptomes identifies collective modes of gene expression.bioRxiv : the preprint server for biology · 2024Article
- Evolution of hierarchy and irreversibility in theoretical cell differentiation model.PNAS nexus · 2024Article
- Collective signalling drives rapid jumping between cell states.Development (Cambridge, England) · 2023Article
- Population-level antagonism between FGF and BMP signaling steers mesoderm differentiation in embryonic stem cells.Biology open · 2023Article
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- Design and Fabrication of Artificial Stem Cell Microenvironments.Bioengineering (Basel, Switzerland) · 2022Article
- Modulating mesendoderm competence during human germ layer differentiation.Cell reports · 2021Article
- Phenotype-tissue expression and exploration (PTEE) resource facilitates the choice of tissue for RNA-seq-based clinical genetics studies.BMC genomics · 2021Article
- RVAgene: generative modeling of gene expression time series data.Bioinformatics (Oxford, England) · 2021Article
- Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation.Nucleic acids research · 2020Article
- Article
- scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.PLoS computational biology · 2020Article
- Interpretable factor models of single-cell RNA-seq via variational autoencoders.Bioinformatics (Oxford, England) · 2020Article
- Landscape inferred from gene expression data governs pluripotency in embryonic stem cells.Computational and structural biotechnology journal · 2020Article
- Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition.Journal of clinical medicine · 2019Article
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12 authors.
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Abstract
The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.
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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.