Learning developmental mode dynamics from single-cell trajectories
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 develop...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2021-12-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/68679 |
| _version_ | 1828165881812746240 |
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| author | Nicolas Romeo Alasdair Hastewell Alexander Mietke Jörn Dunkel |
| author_facet | Nicolas Romeo Alasdair Hastewell Alexander Mietke Jörn Dunkel |
| author_sort | Nicolas Romeo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T01:52:24Z |
| format | Article |
| id | doaj.art-258f19f209144a13a16a89094bfe7e88 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T01:52:24Z |
| publishDate | 2021-12-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-258f19f209144a13a16a89094bfe7e882022-12-22T03:52:54ZengeLife Sciences Publications LtdeLife2050-084X2021-12-011010.7554/eLife.68679Learning developmental mode dynamics from single-cell trajectoriesNicolas Romeo0https://orcid.org/0000-0001-6926-5371Alasdair Hastewell1https://orcid.org/0000-0003-2633-380XAlexander Mietke2https://orcid.org/0000-0003-1170-2406Jörn Dunkel3https://orcid.org/0000-0001-8865-2369Department of Mathematics, Massachusetts Institute of Technology, Cambridge, United States; Department of Physics, Massachusetts Institute of Technology, Cambridge, United StatesDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, United StatesDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, United StatesDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, United StatesEmbryogenesis 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.https://elifesciences.org/articles/68679embryocell migrationspectral representationcontinuum model |
| spellingShingle | Nicolas Romeo Alasdair Hastewell Alexander Mietke Jörn Dunkel Learning developmental mode dynamics from single-cell trajectories eLife embryo cell migration spectral representation continuum model |
| title | Learning developmental mode dynamics from single-cell trajectories |
| title_full | Learning developmental mode dynamics from single-cell trajectories |
| title_fullStr | Learning developmental mode dynamics from single-cell trajectories |
| title_full_unstemmed | Learning developmental mode dynamics from single-cell trajectories |
| title_short | Learning developmental mode dynamics from single-cell trajectories |
| title_sort | learning developmental mode dynamics from single cell trajectories |
| topic | embryo cell migration spectral representation continuum model |
| url | https://elifesciences.org/articles/68679 |
| work_keys_str_mv | AT nicolasromeo learningdevelopmentalmodedynamicsfromsinglecelltrajectories AT alasdairhastewell learningdevelopmentalmodedynamicsfromsinglecelltrajectories AT alexandermietke learningdevelopmentalmodedynamicsfromsinglecelltrajectories AT jorndunkel learningdevelopmentalmodedynamicsfromsinglecelltrajectories |