Cortical Tension Allocates the First Inner Cells of the Mammalian Embryo

Every cell in our body originates from the pluripotent inner mass of the embryo, yet it is unknown how biomechanical forces allocate inner cells in vivo. Here we discover subcellular heterogeneities in tensile forces, generated by actomyosin cortical networks, which drive apical constriction to position the first inner cells of living mouse embryos. Myosin II accumulates specifically around constricting cells, and its disruption dysregulates constriction and cell fate. Laser ablations of actomyosin networks reveal that constricting cells have higher cortical tension, generate tension anisotropies and morphological changes in adjacent regions of neighboring cells, and require their neighbors to coordinate their own changes in shape. Thus, tensile forces determine the first spatial segregation of cells during mammalian development. We propose that, unlike more cohesive tissues, the early embryo dissipates tensile forces required by constricting cells via their neighbors, thereby allowing confined cell repositioning without jeopardizing global architecture.Fil: Samarage, Chaminda R.. Monash University; AustraliaFil: White, Melanie D.. Monash University; AustraliaFil: Alvarez, Yanina Daniela. Monash University; Australia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fierro González, Juan Carlos. Monash University; AustraliaFil: Henon, Yann. Monash University; AustraliaFil: Jesudason, Edwin C.. National Health Service Scotland; Reino UnidoFil: Bissiere, Stephanie. Monash University; Australia. Institute of Molecular and Cell Biology; SingapurFil: Fouras, Andreas. Monash University; AustraliaFil: Plachta, Nicolas. Monash University; Australia. Institute of Molecular and Cell Biology; Singapu

Maximum shear rate measurements within the ventricle corresponding to the co-ordinate system described in Figure 8.

<p>Highest shear occurs at 0° (VB valve) and as the fish matures a more uniform shear environment is developed. Measurements are the average for each age group.</p

<i>In Vivo</i> Wall Shear Measurements within the Developing Zebrafish Heart

<div><p>Physical forces can influence the embryonic development of many tissues. Within the cardiovascular system shear forces resulting from blood flow are known to be one of the regulatory signals that shape the developing heart. A key challenge in investigating the role of shear forces in cardiac development is the ability to obtain shear force measurements <i>in vivo</i>. Utilising the zebrafish model system we have developed a methodology that allows the shear force within the developing embryonic heart to be determined. Accurate wall shear measurement requires two essential pieces of information; high-resolution velocity measurements near the heart wall and the location and orientation of the heart wall itself. We have applied high-speed brightfield imaging to capture time-lapse series of blood flow within the beating heart between 3 and 6 days post-fertilization. Cardiac-phase filtering is applied to these time-lapse images to remove the heart wall and other slow moving structures leaving only the red blood cell movement. Using particle image velocimetry to calculate the velocity of red blood cells in different regions within the heart, and using the signal-to-noise ratio of the cardiac-phase filtered images to determine the boundary of blood flow, and therefore the position of the heart wall, we have been able to generate the necessary information to measure wall shear <i>in vivo</i>. We describe the methodology required to measure shear <i>in vivo</i> and the application of this technique to the developing zebrafish heart. We identify a reduction in shear at the ventricular-bulbar valve between 3 and 6 days post-fertilization and demonstrate that the shear environment of the ventricle during systole is constantly developing towards a more uniform level.</p></div

Brightfield images of a 4dpf zebrafish ventricle (a) before and (b) after cardiac-phase filtering.

<p>(c) Map of the local signal-to-noise ratio (SNR) after processing (b).</p

The local signal-to-noise ratio can be used to define the boundary, allowing calculation of the slope of the wall.

<p>(a) Binary mask created by thresholding the SNR map. Colourmap of the slope at the wall in (b) the horizontal direction and (c) the vertical direction after the heart wall identification process.</p

Calculation of the wall shear rate (s ⁻¹) from the velocity and the slope of the wall.

<p>(a) Brightfield images of a 3dpf zebrafish ventricle during systole with overlaid with velocity vectors calculated using PIV. Contours of (b) dv/dx, (c) du/dy and (d) the calculated wall shear rate (s⁻¹) also overlaid with velocity vectors. Shear is concentrated in the region of the ventricular bulbar valve with the majority of the remainder of the heart experiencing comparatively low shear.</p

Wall shear rate during peak systole for (a)–(d) 3dpf, (e)–(h) 4dpf, (i)–(l) 5dpf and (m)–(p) 6dpf embryonic zebrafish used in this study.

<p>Vectors indicate the magnitude and direction of velocity while colourmap provides the wall shear rate. For clarity only every second vector is shown in the horizontal direction and every fifth in the vertical direction.</p

Uniformity measurements of the ventricle during peak systole.

<p>A clear trend towards a more uniform shear environment can be seen.</p

A reconstructed 3D volume (11 mm×11 mm×55 mm), based on images captured with one camera.

<p>Two velocity components (u and v) were solved for each camera. At this stage, the velocity along the z-axis (w) was still unsolved. The z-component velocity was acquired from another camera where a full 3D3C vector field can be created at the overlapped volume with reconstruction algorithm.</p

Two holographic images H₁ and H₂, at two successive time points, t₁ and t₂ for a range of z.

<p><i>P</i>_i represents the inline hologram due to slice <i>z</i>_i at <i>t</i> = <i>t</i>₁, and Q_i is similarly defined at <i>t</i> = <i>t</i>₂. Under the assumption of weak scattering by each slab, and neglecting both interference between adjacent particles and an irrelevant additive constant, H₁ = ∑_i<i>P</i>_i and H₂ = ∑_i<i>Q</i>_i.</p