Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map

During morphogenesis, embryonic tissues display fluid-like behavior with fluctuating strain rates. Digital cell lineages reconstructed from 4D images of developing zebrafish embryos are used to infer representative tissue deformation patterns and their association with developmental events. Finite deformation analysis along cell trajectories and unsupervised machine learning are applied to obtain reduced-order models condensing the collective cell motions, delineating tissue domains with distinct 4D biomechanical behavior. This reduced-order kinematic description is reproducible across specimens and matches fate maps of the zebrafish brain in wild-type and nodal pathway mutants (<i>zoep<sup>tz57/tz57</sup></i> ), shedding light into the morphogenetic defects causing these mutants' cyclopia. Furthermore, the inferred kinematic maps also match expression maps of the gene transcription factor <i>goosecoid</i> (<i>gsc</i>). In summary, this work introduces an objective analytical framework to systematically unravel the complex spatiotemporal patterns of embryonic tissue deformations and couple them with cell fate and gene expression maps.

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Publication Details

Journal
iScience
Published
2025-01-09
DOI
https://doi.org/10.1016/j.isci.2025.111753
Citations
2
Primary Topic
Morphological variations and asymmetry
Type
article
Field-Weighted Citation Impact
8.18

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article

Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map

Adeline Boyreau, René Doursat, María J. Ledesma‐Carbayo, A. Santos et al.
2 citations
iScience
Morphological variations and asymmetry
8.18
article

Unsupervised spatiotemporal classification of deformation patterns of embryonic tissues matches their fate map

Adeline Boyreau, René Doursat, María J. Ledesma‐Carbayo, A. Santos, Thierry Savy, Nadine Peyriéras, Juan C. del Álamo, José María Goicolea Ruigómez, David Pastor-Escuredo, Paul Bourgine, Benoît Lombardot
article en
2 citations

Abstract

During morphogenesis, embryonic tissues display fluid-like behavior with fluctuating strain rates. Digital cell lineages reconstructed from 4D images of developing zebrafish embryos are used to infer representative tissue deformation patterns and their association with developmental events. Finite deformation analysis along cell trajectories and unsupervised machine learning are applied to obtain reduced-order models condensing the collective cell motions, delineating tissue domains with distinct 4D biomechanical behavior. This reduced-order kinematic description is reproducible across specimens and matches fate maps of the zebrafish brain in wild-type and nodal pathway mutants (<i>zoep<sup>tz57/tz57</sup></i> ), shedding light into the morphogenetic defects causing these mutants' cyclopia. Furthermore, the inferred kinematic maps also match expression maps of the gene transcription factor <i>goosecoid</i> (<i>gsc</i>). In summary, this work introduces an objective analytical framework to systematically unravel the complex spatiotemporal patterns of embryonic tissue deformations and couple them with cell fate and gene expression maps.

iScienceVol. 28(3)
Centre National de la Recherche Scientifique (FR), University of Washington (US), Université Paris Cité (FR), Université Paris-Saclay (FR), University of California San Diego (US), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES), Laboratoire Matière et Systèmes Complexes (FR), Universidad Politécnica de Madrid (ES)
National Science Foundation, University of Washington, Comunidad de Madrid, Agence Nationale de la Recherche, Universidad Politécnica de Madrid, Centre National de la Recherche Scientifique, National Institutes of Health, European Regional Development Fund, National Institute of Allergy and Infectious Diseases, Division of Chemical, Bioengineering, Environmental, and Transport Systems
Openalex Percentile: Top 4%
Morphological variations and asymmetry
8.18
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