FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling
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Le résumé fourni par la source
Gastrulation, a critical developmental stage involving germ layer specification and axes formation, is a major point of failure in human development, contributing to pregnancy loss and congenital malformations. However, due to ethical constraints and anatomical differences in animal models, the failure modes underlying human gastrulation remain poorly understood. To elucidate these failure modes, we introduce FATE-MAP (Failure Analysis and Trajectory Evaluation via Mechanistic-AI Prediction), an integrated platform that combines high-throughput perturbations of human 2D gastruloids with quantitative phenotypic mapping, predictive deep learning, and mechanistic morphogen modeling. Analyzing over 2000 drug-treated human 2D gastruloids, we mapped a phenotypic morphospace that separates canonical patterning, in which primitive-streak fates are correctly specified and radially organized, from failure modes, defined as departures from this organization and marked by a loss of a required fate and/or radial symmetry. To predict and interpret patterning outcomes, FATE-MAP combines a transformer linking chemical structure to phenotype with PDE simulations of morphogen transport and cell fate specification, and projects both outputs onto the experimentally defined morphospace. Applying this framework, we flagged two clinical molecules as potential teratogens and identified two parameters, cell density and SOX2 stability, that form orthogonal morphospace axes along which canonically patterned gastruloids systematically vary. FATE-MAP thus provides a roadmap for decoding human developmental trajectories and accelerating safe therapeutic discovery.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling
- Date Crossref
- 19/02/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of California Center for BioEngineering pays non établi dans la noticeUniversité ou école supérieure
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Coherus BioSciences (United States) pays non établi dans la noticeEntreprise
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Broad Institute Infectious Disease and Microbiome Program pays non établi dans la noticeOrganisation à but non lucratif
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Massachusetts Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
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Inc. Integrated Biosciences pays non établi dans la noticeEntreprise
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Institute for Medical Engineering and Science and Department of Biological Engineering pays non établi dans la noticeStructure de recherche
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Harvard University Wyss Institute for Biologically Inspired Engineering pays non établi dans la noticeUniversité ou école supérieure
Center for BioEngineering — University of California, Coherus BioSciences (United States) et Infectious Disease and Microbiome Program — Broad Institute, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.