Learning developmental mode dynamics from single-cell trajectories

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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

1. G Shah
2. et al
(2019) Data from: Multi-scale imaging and analysis identify pan-embryo cell dynamics 394 of germlayer formation in zebrash, Nat. Commun. 10, 5753 (2019).
https://doi.org/10.1038/s41467-019-13625-0.

https://imb-dev.gitlab.io/cell-flow-navigator/

Article and author information

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Nicolas Romeo

Department of Physics, Massachusetts Institute of Technology, Cambridge, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6926-5371
Alasdair Hastewell

Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2633-380X
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.

"This ORCID iD identifies the author of this article:" 0000-0003-1170-2406
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.

"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.

Copyright

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.