The Geometric Basis of Epithelial Convergent Extension

Abstract

Shape changes of epithelia during animal development, such as convergent extension, are achieved through concerted mechanical activity of individual cells. While much is known about the corresponding large scale tissue flow and its genetic drivers, the question of cell-scale coordination remains open. We propose that this coordination can be understood in terms of mechanical interactions and instantaneous force balance within the tissue. Using whole embryo imaging data forDrosophilagastrulation, we exploit the relation between balance of local cortical tension forces and cell geometry. This unveils how local positive feedback on active tension and passive global deformations account for coordinated cell rearrangements. We develop a model that bridges the cell and tissue scale dynamics and predicts the dependence of total tissue extension on initial anisotropy and hexagonal order of the cell packing. Our study provides general insight into the encoding of global tissue shape in local cell-scale activity.

Signatures of structural disorder in developing epithelial tissues

“…This suggests that cell rearrangement promotes disorder and that this disorder then might hinder further rearrangement. This is consistent with ideas from recent findings that disordered packings of cells extend more slowly than more ordered packings in some contexts [40,41]. Future studies will be needed to fully address how tissue disorder impacts tissue remodeling and flow.…”

Learning dynamical models of single and collective cell migration: a review

“…One avenue to circumvent this problem is to use a basis of modes that uses the geometry of the epithelial layer more directly [314,317]. This approach applies in a regime that is dominated by tension in the cell edges, and that exhibits a time-scale separation between the tissue dynamics and the relaxation of individual cell edges, implying that the vertices are in force balance.…”

“…Under these assumptions, the tissue dynamics decomposes into two independent contributions: the tensions determine the dynamics of the angles (at fixed areas), while isogonal modes predict the evolution of areas (at fixed angles) [314]. Due to force balance at each vertex, the tensions can then be directly inferred from the experimentally observed angles at each vertex [317]. Thus, in this decomposition, the tensions are no longer (hidden) parameters that have to be fitted indirectly, but can be measured directly from the images.…”

“…In a further step, a machine learning approach was proposed to infer the coupled dynamics of the key molecular components (myosin and E-cadherin) and the morphogenetic flow [353]. To understand how these tissue-level processes are orchestrated at the cellular scale, recent work has proposed an approach to infer the active tensions driving tissue flow from the observed cell geometry based on the assumption of force balance [314,317]. This allows a mode decomposition of the tissue dynamics into the dynamics of junctional angles (determined by tension) and cell areas (isogonal modes) (also refer to more general discussion in section 7.1).…”

“…This allows a mode decomposition of the tissue dynamics into the dynamics of junctional angles (determined by tension) and cell areas (isogonal modes) (also refer to more general discussion in section 7.1). This also allowed disentangling active vs passive T1 transitions in the tissue, which have been shown to enable convergence extension-movement in large-scale tissue deformation processes such as during gastrulation [315][316][317]. In general, a key challenge to test how well these models are constrained and their predictive power is whether one can for instance predict mutants, generalise the findings to other organisms, or make predictions for new experiments.…”