ArticleCell2021
A single-embryo, single-cell time-resolved model for mouse gastrulation.
Article in Cell, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.
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Who cites it
115 citing papers in PubMed.
- A systematic single-cell analysis of enhancer RNAs across human and mouse.Nucleic acids research · 2026Article
- New visual culture of science: analysis of graphical abstracts in the life science journal "Cell".History and philosophy of the life sciences · 2026Article
- Mitochondrial stress activates ELT-2-dependent lysosomal proteostasis to extend lifespan inScience advances · 2026Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
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- AXIN1 and AXIN2 regulate the WNT-signaling landscape to promote distinct mesoderm programs.Developmental cell · 2026Article
- Neighborhood-informed positional information for precise cell identity specification.Molecular systems biology · 2026Article
- Developmental chronology of mouse embryo from 2-cell stage through birth.Nature cell biology · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development.Genes & development · 2026Article
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- MiTo: tracing the phenotypic evolution of somatic cell lineages via mitochondrial single-cell multi-omics.Nature communications · 2026Article
- YAP1 and QSER1 are key modulators of embryonic signaling pathways in the mammalian epiblast.EMBO reports · 2026Article
- Preventing Differentiation Towards Primitive Macrophages in Stem Cells With Down Syndrome.Immunology · 2026Article
- Constructing the spatiotemporal atlas of single-cell lineage trajectories in stereotypic biological structures.iScience · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- InCURA: integrative gene clustering based on transcription factor binding sites.Nucleic acids research · 2025Article
- IceQream: Quantitative chromosome accessibility analysis using physical TF models.Nature communications · 2025Article
- Myocardium and endocardium of the early mammalian heart tube arise from independent multipotent lineages specified at the primitive streak.Developmental cell · 2025Article
55 more citing papers are in PubMed but not listed here.
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15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.
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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.