Learning developmental mode dynamics from single-cell trajectories
- Massachusetts Institute of Technology, United States
Abstract
Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at various stages of embryonic development. This rapid experimental progress poses the theoretical challenge of translating high-dimensional imaging data into predictive low-dimensional models that capture the essential ordering principles governing developmental cell migration in complex geometries. Here, we combine mode decomposition ideas that have proved successful in condensed matter physics and turbulence theory with recent advances in sparse dynamical systems inference to realize a computational framework for learning quantitative continuum models from single-cell imaging data. Considering pan-embryo cell migration during early gastrulation in zebrafish as a widely studied example, we show how cell trajectory data on a curved surface can be coarse-grained and compressed with suitable harmonic basis functions. The resulting low-dimensional representation of the collective cell dynamics enables a compact characterization of developmental symmetry breaking and the direct inference of an interpretable hydrodynamic model, which reveals similarities between pan-embryo cell migration and active Brownian particle dynamics on curved surfaces. Due to its generic conceptual foundation, we expect that mode-based model learning can help advance the quantitative biophysical understanding of a wide range of developmental structure formation processes.
Data availability
Raw data used in this study can be obtained athttps://doi.org/10.1038/s41467-019-13625-0https://imb-dev.gitlab.io/cell-flow-navigator/
Article and author information
Author details
Alasdair Hastewell
Funding
European Molecular Biology Organization (ALTF 528-2019)
- Alexander Mietke
Deutsche Forschungsgemeinschaft (431144836)
- Alexander Mietke
James S. McDonnell Foundation
- Jörn Dunkel
Alfred P. Sloan Foundation (G-2021-16758)
- Jörn Dunkel
MathWorks
- Nicolas Romeo
MathWorks
- Alasdair Hastewell
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Raymond E Goldstein, University of Cambridge, United Kingdom
Publication history
- Received: March 23, 2021
- Accepted: December 24, 2021
- Accepted Manuscript published: December 29, 2021 (version 1)
- Version of Record published: February 24, 2022 (version 2)
Copyright
© 2021, Romeo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Learning developmental mode dynamics from single-cell trajectories
eLife 10:e68679.
https://doi.org/10.7554/eLife.68679
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