Learning developmental mode dynamics from single-cell trajectories

  1. Nicolas Romeo
  2. Alasdair Hastewell
  3. Alexander Mietke  Is a corresponding author
  4. Jörn Dunkel  Is a corresponding author
  1. 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/

The following previously published data sets were used

Article and author information

Author details

  1. Nicolas Romeo

    Department of Physics, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6926-5371
  2. Alasdair Hastewell

    Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2633-380X
  3. Alexander Mietke

    Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    amietke@mit.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1170-2406
  4. Jörn Dunkel

    Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    dunkel@math.mit.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8865-2369

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

  1. Raymond E Goldstein, University of Cambridge, United Kingdom

Version history

  1. Received: March 23, 2021
  2. Accepted: December 24, 2021
  3. Accepted Manuscript published: December 29, 2021 (version 1)
  4. 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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  1. Nicolas Romeo
  2. Alasdair Hastewell
  3. Alexander Mietke
  4. Jörn Dunkel
(2021)
Learning developmental mode dynamics from single-cell trajectories
eLife 10:e68679.
https://doi.org/10.7554/eLife.68679

Share this article

https://doi.org/10.7554/eLife.68679

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