A new endosomal tethering mechanism involving a mechanochemical cycle of the dimeric coiled-coil protein EEA1 regulated by Rab5:GTP binding and GTP hydrolysis. Within cells, Rab GTPase proteins recruit tethering effectors that allow transport vesicles to dock and fuse with their target membranes. This raises the question of how the long, rod-like tethering molecules such as the protein EEA1 can capture vesicles, yet allow the reduction of the distance between the membranes for fusion. Marino Zerial and colleagues address this question by reconstituting an endosomal tethering machinery consisting of EEA1, membranes and vesicles carrying Rab5. They find that, in a new role, Rab5:GTP induces a change in EEA1 conformation from extended to flexible, allowing it to bend. In addition, they show that membrane tethering by EEA1:Rab5:GTP generates a force that also helps to bring two membranes together. An early step in intracellular transport is the selective recognition of a vesicle by its appropriate target membrane, a process regulated by Rab GTPases via the recruitment of tethering effectors1,2,3,4. Membrane tethering confers higher selectivity and efficiency to membrane fusion than the pairing of SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) alone5,6,7. Here we address the mechanism whereby a tethered vesicle comes closer towards its target membrane for fusion by reconstituting an endosomal asymmetric tethering machinery consisting of the dimeric coiled-coil protein EEA1 (refs 6, 7) recruited to phosphatidylinositol 3-phosphate membranes and binding vesicles harbouring Rab5. Surprisingly, structural analysis reveals that Rab5:GTP induces an allosteric conformational change in EEA1, from extended to flexible and collapsed. Through dynamic analysis by optical tweezers, we confirm that EEA1 captures a vesicle at a distance corresponding to its extended conformation, and directly measure its flexibility and the forces induced during the tethering reaction. Expression of engineered EEA1 variants defective in the conformational change induce prominent clusters of tethered vesicles in vivo. Our results suggest a new mechanism in which Rab5 induces a change in flexibility of EEA1, generating an entropic collapse force that pulls the captured vesicle towards the target membrane to initiate docking and fusion.

A prerequisite for the systems biology analysis of tissues is an accurate digital three-dimensional reconstruction of tissue structure based on images of markers covering multiple scales. Here, we designed a flexible pipeline for the multi-scale reconstruction and quantitative morphological analysis of tissue architecture from microscopy images. Our pipeline includes newly developed algorithms that address specific challenges of thick dense tissue reconstruction. Our implementation allows for a flexible workflow, scalable to high-throughput analysis and applicable to various mammalian tissues. We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy. Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization. The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

Bile, the central metabolic product of the liver, is transported by the bile canaliculi network. The impairment of bile flow in cholestatic liver diseases has urged a demand for insights into its regulation. Here, we developed a predictive 3D multi-scale model that simulates fluid dynamic properties successively from the subcellular to the tissue level. The model integrates the structure of the bile canalicular network in the mouse liver lobule, as determined by high-resolution confocal and serial block-face scanning electron microscopy, with measurements of bile transport by intravital microscopy. The combined experiment-theory approach revealed spatial heterogeneities of biliary geometry and hepatocyte transport activity. Based on this, our model predicts gradients of bile velocity and pressure in the liver lobule. Validation of the model predictions by pharmacological inhibition of Rho kinase demonstrated a requirement of canaliculi contractility for bile flow in vivo. Our model can be applied to functionally characterize liver diseases and quantitatively estimate biliary transport upon drug-induced liver injury.

Functional tissue architecture originates by self-assembly of distinct cell types, following tissue-specific rules of cell-cell interactions. In the liver, a structural model of the lobule was pioneered by Elias in 1949. This model, however, is in contrast with the apparent random 3D arrangement of hepatocytes. Since then, no significant progress has been made to derive the organizing principles of liver tissue. To solve this outstanding problem, we computationally reconstructed 3D tissue geometry from microscopy images of mouse liver tissue and analyzed it applying soft-condensed-matter-physics concepts. Surprisingly, analysis of the spatial organization of cell polarity revealed that hepatocytes are not randomly oriented but follow a long-range liquid-crystal order. This does not depend exclusively on hepatocytes receiving instructive signals by endothelial cells, since silencing Integrin-β1 disrupted both liquid-crystal order and organization of the sinusoidal network. Our results suggest that bi-directional communication between hepatocytes and sinusoids underlies the self-organization of liver tissue.

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.