Learning developmental mode dynamics from single-cell trajectories

TL;DR

This paper introduces a novel computational framework that combines mode decomposition and sparse dynamical systems inference to learn low-dimensional, interpretable models of collective cell migration during embryogenesis from high-resolution imaging data.

Contribution

It develops a generic mode-based model learning approach that captures developmental symmetry breaking in embryonic cell migration, bridging physics-inspired methods with biological data.

Findings

Successfully inferred hydrodynamic models from zebrafish embryo data

Revealed similarities between cell migration and active Brownian particles

Provided a low-dimensional representation of complex cell dynamics

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.