Glassy dynamics in three-dimensional embryonic tissues

Abstract

Many biological tissues are viscoelastic, behaving as elastic solids on short timescales and fluids on long timescales. This collective mechanical behaviour enables and helps to guide pattern formation and tissue layering. Here, we investigate the mechanical properties of three-dimensional tissue explants from zebrafish embryos by analysing individual cell tracks and macroscopic mechanical response. We find that the cell dynamics inside the tissue exhibit features of supercooled fluids, including subdiffusive trajectories and signatures of caging behaviour. We develop a minimal, three-parameter mechanical model for these dynamics, which we calibrate using only information about cell tracks. This model generates predictions about the macroscopic bulk response of the tissue (with no fit parameters) that are verified experimentally, providing a strong validation of the model. The best-fit model parameters indicate that although the tissue is fluid-like, it is close to a glass transition, suggesting that small changes to single-cell parameters could generate a significant change in the viscoelastic properties of the tissue. These results provide a robust framework for quantifying and modelling mechanically driven pattern formation in tissues.

Sphingosine 1-phosphate receptor 1 regulates the directional migration of lymphatic endothelial cells in response to fluid shear stress

“…such as elastic, solid-like behaviour on short timescales and fluid-like cell rearrangements at longer timescales [41][42][43][44][45][46][47][48]. Consistent with this description, confluent epithelial and endothelial sheets show spontaneous stress fluctuations that can propagate many cell lengths across cell monolayers [44,46].…”

A density-independent rigidity transition in biological tissues

“…To model cells with a cortical network of actomyosin and adhesive molecules on their surfaces, particles interact as repulsive disks or spheres, sometimes with an additional short-range attraction [8,9]. These models generically exhibit a glass transition at a critical packing density of particles, φ c , where φ c < 1 [1,8,10,11], and near the transition point they exhibit collective motion [8] that is very similar to that seen in experiments [12].…”

“…Recent experiments suggest that when cells are packed ever more densely, they start to exhibit collective motion [1][2][3] traditionally seen in non-living disordered systems such as colloids, granular matter or foams [4][5][6]. These collective behaviors exhibit growing timescales and lengthscales associated with rigidity transitions.…”

“…To model cells with a cortical network of actomyosin and adhesive molecules on their surfaces, particles interact as repulsive disks or spheres, sometimes with an additional short-range attraction [8,9]. These models generically exhibit a glass transition at a critical packing density of particles, φ c , where φ c < 1 [1,8,10,11], and near the transition point they exhibit collective motion [8] that is very similar to that seen in experiments [12].An important open question is whether the density-driven glass transition in SPP models explains the glassy behavior observed in non-proliferating confluent biological tissues, where there are no gaps between cells and the packing fraction φ is fixed at precisely unity. For example, zebrafish embryonic explants are confluent three-dimensional tissues where the cells divide slowly and therefore the number of cells per unit volume remains nearly constant.…”

Growth of smaller grain attached on larger one: Algorithm to overcome unphysical overlap between grains

“…Simulation of grains interaction can be used to observe cell swap in caging effect [1], biological cell sorting [2], and tissue growth [3], where the latest is similar to colony growth as observed in [4]. One of the problems in simulating this kind of interaction during growing process of a grain is that the produced overlap between grains is increasing faster than the changing position of the interacting grains, which can introduce computation failure.…”