Forces driving cell sorting inHydra

Abstract

Summary statementHydra regenerates after dissociation into single cells. We show how physical mechanisms can explain the first step of regeneration, whereby ectodermal and endodermal cells sort out to form distinct tissue layers. AbstractCell sorting, whereby a heterogeneous cell mixture organizes into distinct tissues, is a fundamental patterning process in development. So far, most studies of cell sorting have relied either on 2-dimensional cellular aggregates, in vitro situations that do not have a direct counterpart in vivo, or were focused on the properties of single cells. Here, we report the first multiscale experimental study on 3-dimensional regenerating Hydra aggregates, capable of reforming a full animal. By quantifying the kinematics of single cell and whole aggregate behaviors, we show that no differences in cell motility exist among cell types and that sorting dynamics follow a power law. Moreover, we measure the physical properties of separated tissues and determine their viscosities and surface tensions. Based on our experimental results and numerical simulations, we conclude that tissue interfacial tensions are sufficient to explain Hydra cell sorting. Doing so, we illustrate D'Arcy Thompson's central idea that biological organization can be understood through physical principles, an idea which is currently re-shaping the field of developmental biology.

Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime

“…More recently, Nakajima et al [ 9 ] reported a power-law decay, with an exponent which depends on the proportion of the two cell types: from n = 1/3 for even mixture to n = 1/4 for uneven mixture. Cochet-Escartin et al [ 8 ] also reported power-law decays, but with significantly higher exponents: n ∈ [0.49, 0.74] from experiments, while n ∈ [0.55, 0.59] from simulations.…”

Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime

“…These results suggest that in our simulations cell sorting is achieved through diffusionand-coalescence mechanism and that on long times the kinetics is limited by the diffusion of (rounded) clusters, both for even and uneven mixture ratios. Actually, this should come as no surprise, since cluster diffusion has a slower kinetics than cluster rounding, which generally is characterized by an exponential rather than algebraic law [7,8] although Nakajima and Ishihara attribute the n = 1/3 power law they observed for even mixture to the cluster rounding process [9].…”

“…In addition to boundary conditions, initial geometry of the aggregate and dimension of space also affect the kinetics of cell sorting, as pointed out by Cochet-Escartin et al [8]. In their study, cells were initially placed on a sheet in a 3D space.…”

“…The DAH postulates that the surface tension of tissue arises from cell adhesion, and that cell configurations are determined by minimization of the surface energy of cell aggregates in a manner similar to phase separation processes, although surface energy minimization is an active process for cell sorting: cellular activity is necessary to untrap from the many local minima of the rough energy landscape of a cellular system. Since Steinberg's hypothesis statement, several numerical studies have proven that the DAH could reproduce cell sorting phenomena similar to those observed experimentally [4][5][6][7][8]. However, no clear consensus has emerged from these studies on the kinetics law of DAH-driven cell sorting : in their seminal papers [4,5], Graner and Glazier found -on a somewhat smaller system -that the boundary length, defined as the number of mismatched cell contacts, decays logarithmically.…”

Sorting Things Out: Cell Sorting during Hydra Regeneration

“…Cochet-Escartin et al (8) set out to address these ambiguities using a methodical combination of in vivo experiments (e.g., two-photon imaging of regenerating Hydra aggregates), and computational models (e.g., 3D cellular Potts models). By tracking cell sorting and the migration trajectories of individual cells in real-time from the moment of reaggregation up through cellular sorting, the authors were able to establish complete trajectories of the cells involved in the process.…”