Biomechanics and the Thermotolerance of Development

Abstract

Successful completion of development requires coordination of patterning events with morphogenetic movements. Environmental variability challenges this coordination. For example, developing organisms encounter varying environmental temperatures that can strongly influence developmental rates. We hypothesized that the mechanics of morphogenesis would have to be finely adjusted to allow for normal morphogenesis across a wide range of developmental rates. We formulated our hypothesis as a simple model incorporating time-dependent application of force to a viscoelastic tissue. This model suggested that the capacity to maintain normal morphogenesis across a range of temperatures would depend on how both tissue viscoelasticity and the forces that drive deformation vary with temperature. To test this model we investigated how the mechanical behavior of embryonic tissue (Xenopus laevis) changed with temperature; we used a combination of micropipette aspiration to measure viscoelasticity, electrically induced contractions to measure cellular force generation, and confocal microscopy to measure endogenous contractility. Contrary to expectations, the viscoelasticity of the tissues and peak contractile tension proved invariant with temperature even as rates of force generation and gastrulation movements varied three-fold. Furthermore, the relative rates of different gastrulation movements varied with temperature: the speed of blastopore closure increased more slowly with temperature than the speed of the dorsal-to-ventral progression of involution. The changes in the relative rates of different tissue movements can be explained by the viscoelastic deformation model given observed viscoelastic properties, but only if morphogenetic forces increase slowly rather than all at once.

Emergent mechanics of actomyosin drive punctuated contractions and shape network morphology in the cell cortex

“…To compare dynamic remodeling of model filament-motor arrays with in vivo data collected from time-lapse sequences we generated time-lapse sequences of models mimicking the linear additive fluorescence signal observed in confocal time-lapse sequences. Computed synthetic time-lapse sequences allowed us to analyze aster formation with image-based tools commonly applied to live-cell F-actin dynamics [ 11 , 17 , 63 ]. As an illustration, we track the normalized mean intensity of filaments within a fixed circular region of interest (ROI), to observe intensity increases over the first second as the network begins to form a ring structure, but drops after 2 seconds ( Fig 3B ) as the ring forms outside of the ROI.…”

“…The normalized mean intensity then maintains a high level reflecting compaction of the ring into the aster together with filament turnover since filaments are randomly depolymerized and relocated randomly to places within the hexagon, including regions outside of the ROI. A kymograph provides another method of quantifying the aster emergence ( Fig 3C , upper panel) and also reveals movement of filaments into the aster similar to that seen in live-cell time-lapses of F-actin in Xenopus cells [ 11 , 17 ]. For comparison, we include a kymograph from a simulation with high filament turnover (p 2 , 5 s -1 ) where asters do not assemble ( Fig 3C , lower panel).…”

“…In many cases actomyosin contractions observed in cells can be stable (e.g. asters in Xenopus at sites of bundled extracellular matrix, or in 'nodes' late in gastrulation) but often contractions are transient [ 11 , 17 ]. To fully recapitulate the in vivo dynamics of punctuated actomyosin contractions, asters would need to dissipate after formation.…”

“…Aster-like structures are often observed in these time-lapse sequences. Such asters form and persist for a few minutes before dissipating [ 11 , 12 , 17 – 20 ] ( Fig 1A ; S1 Video ). The central cores of actin asters are enriched with active myosin II which appears to lag the assembly of the aster ( Fig 1B ; S2 Video ).…”

Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach

“…Indeed, for explants with similar average change in area over time (ΔA/Δt), simulations with a parameter set that resulted in a small error for a similarly sized explant in the test set. Thus we would expect that the trends we discovered would be the same if we analyzed more explants in the future, taking into account embryo to embryo variation in stiffness [58], clutch to clutch variation [59], and possible environmental variations such as room temperature that might affect rates and deformation maps of tissue spreading [60].…”