Abstract
Living tissues undergo deformation during morphogenesis. In this process, cells generate mechanical forces that drive the coordinated cell motion and shape changes. Recent advances in experimental and theoretical techniques have enabled in situ measurement of the mechanical forces, but the characterization of mechanical properties that determine how these forces quantitatively affect tissue deformation remains challenging, and this represents a major obstacle for the complete understanding of morphogenesis. Here, we proposed a non-invasive reverse-engineering approach for the estimation of the mechanical properties, by combining tissue mechanics modeling and statistical machine learning. Our strategy is to model the tissue as a continuum mechanical system and to use passive observations of spontaneous tissue deformation and force fields to statistically estimate the model parameters. This method was applied to the analysis of the collective migration of Madin-Darby canine kidney cells, and the tissue flow and force were simultaneously observed by the phase contrast imaging and traction force microscopy. We found that our monolayer elastic model, whose elastic moduli were reverse-engineered, enabled a long-term forecast of the traction force fields when given the tissue flow fields, indicating that the elasticity contributes to the evolution of the tissue stress. Furthermore, we investigated the tissues in which myosin was inhibited by blebbistatin treatment, and observed a several-fold reduction in the elastic moduli. The obtained results validate our framework, which paves the way to the estimation of mechanical properties of living tissues during morphogenesis.
Highlights
The body of multicellular organisms must be properly shaped in order to exert its functions, and this proper formation is based on the orchestration of cellular behaviors, such as cell division, differentiation, migration, and other
We propose a reverse-engineering method to identify the mechanical properties, which is based on the combination of tissue mechanics modeling and statistical machine learning
Previous works suggested that anisotropic cell division can contribute the tissue mechanics [22, 23], we modeled the cell growth as isotropic and homogeneous expansion with the rate Dg, which is partially supported by a previous report that cell division in the monolayer shows no particular orientation [24]
Summary
The body of multicellular organisms must be properly shaped in order to exert its functions, and this proper formation is based on the orchestration of cellular behaviors, such as cell division, differentiation, migration, and other. Remarkable progress has been made in the development of the technologies allowing the measurements of the generated forces and stress in the living tissues [3], which represents a crucial step towards linking the underlying molecular activities with the morphogenesis. Epithelial tissues represent important model systems for the understanding of the force dynamics during morphogenesis, because their two-dimensional sheet structure facilitates the observation of the processes that occur in these tissues and analysis. The simple flat-sheet structure of the monolayer allows us to apply the same technique to observe a spatio-temporal profile of the cell traction force in a wide field of view [8], and to determine where and how the force and stress are generated [9, 10]
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