Uncovering developmental time and tempo using deep learning.

During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

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.

Apical bulkheads accumulate as adaptive response to impaired bile flow in liver disease.

Hepatocytes form bile canaliculi that dynamically respond to the signalling activity of bile acids and bile flow. Little is known about their responses to intraluminal pressure. During embryonic development, hepatocytes assemble apical bulkheads that increase the canalicular resistance to intraluminal pressure. Here, we investigate whether they also protect bile canaliculi against elevated pressure upon impaired bile flow in adult liver. Apical bulkheads accumulate upon bile flow obstruction in mouse models and patients with primary sclerosing cholangitis (PSC). Their loss under these conditions leads to abnormally dilated canaliculi, resembling liver cell rosettes described in other hepatic diseases. 3D reconstruction reveals that these structures are sections of cysts and tubes formed by hepatocytes. Mathematical modelling establishes that they positively correlate with canalicular pressure and occur in early PSC stages. Using primary hepatocytes and 3D organoids, we demonstrate that excessive canalicular pressure causes the loss of apical bulkheads and formation of rosettes. Our results suggest that apical bulkheads are a protective mechanism of hepatocytes against impaired bile flow, highlighting the role of canalicular pressure in liver diseases.

Correction: Quantification of nematic cell polarity in three-dimensional tissues.

[This corrects the article DOI: 10.1371/journal.pcbi.1008412.].

An endosomal tether undergoes an entropic collapse to bring vesicles 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 effectors. Membrane tethering confers higher selectivity and efficiency to membrane fusion than the pairing of SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) alone. 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.

Bile canaliculi remodeling activates YAP via the actin cytoskeleton during liver regeneration.

The mechanisms of organ size control remain poorly understood. A key question is how cells collectively sense the overall status of a tissue. We addressed this problem focusing on mouse liver regeneration. Using digital tissue reconstruction and quantitative image analysis, we found that the apical surface of hepatocytes forming the bile canalicular network expands concomitant with an increase in F-actin and phospho-myosin, to compensate an overload of bile acids. These changes are sensed by the Hippo transcriptional co-activator YAP, which localizes to apical F-actin-rich regions and translocates to the nucleus in dependence of the integrity of the actin cytoskeleton. This mechanism tolerates moderate bile acid fluctuations under tissue homeostasis, but activates YAP in response to sustained bile acid overload. Using an integrated biophysical-biochemical model of bile pressure and Hippo signaling, we explained this behavior by the existence of a mechano-sensory mechanism that activates YAP in a switch-like manner. We propose that the apical surface of hepatocytes acts as a self-regulatory mechano-sensory system that responds to critical levels of bile acids as readout of tissue status.

Resilience of three-dimensional sinusoidal networks in liver tissue.

Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow-a feature known for hierarchical networks, but unexpected for plexus-like networks. We quantify network resilience in terms of a permeability-at-risk, i.e., permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver.

Quantification of nematic cell polarity in three-dimensional tissues.

How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology. Here, we characterize a unique type of polarity found in liver tissue, nematic cell polarity, which is different from vectorial cell polarity in simple, sheet-like epithelia. We propose a conceptual and algorithmic framework to characterize complex patterns of polarity proteins on the surface of a cell in terms of a multipole expansion. To rigorously quantify previously observed tissue-level patterns of nematic cell polarity (Morales-Navarrete et al., eLife 2019), we introduce the concept of co-orientational order parameters, which generalize the known biaxial order parameters of the theory of liquid crystals. Applying these concepts to three-dimensional reconstructions of single cells from high-resolution imaging data of mouse liver tissue, we show that the axes of nematic cell polarity of hepatocytes exhibit local coordination and are aligned with the biaxially anisotropic sinusoidal network for blood transport. Our study characterizes liver tissue as a biological example of a biaxial liquid crystal. The general methodology developed here could be applied to other tissues and in-vitro organoids.

The High Mobility Group A1 (HMGA1) Chromatin Architectural Factor Modulates Nuclear Stiffness in Breast Cancer Cells.

Plasticity is an essential condition for cancer cells to invade surrounding tissues. The nucleus is the most rigid cellular organelle and it undergoes substantial deformations to get through environmental constrictions. Nuclear stiffness mostly depends on the nuclear lamina and chromatin, which in turn might be affected by nuclear architectural proteins. Among these is the HMGA1 (High Mobility Group A1) protein, a factor that plays a causal role in neoplastic transformation and that is able to disentangle heterochromatic domains by H1 displacement. Here we made use of atomic force microscopy to analyze the stiffness of breast cancer cellular models in which we modulated HMGA1 expression to investigate its role in regulating nuclear plasticity. Since histone H1 is the main modulator of chromatin structure and HMGA1 is a well-established histone H1 competitor, we correlated HMGA1 expression and cellular stiffness with histone H1 expression level, post-translational modifications, and nuclear distribution. Our results showed that HMGA1 expression level correlates with nuclear stiffness, is associated to histone H1 phosphorylation status, and alters both histone H1 chromatin distribution and expression. These data suggest that HMGA1 might promote chromatin relaxation through a histone H1-mediated mechanism strongly impacting on the invasiveness of cancer cells.

Virtual tissue microstructure reconstruction across species using generative deep learning.

Analyzing tissue microstructure is essential for understanding complex biological systems in different species. Tissue functions largely depend on their intrinsic tissue architecture. Therefore, studying the three-dimensional (3D) microstructure of tissues, such as the liver, is particularly fascinating due to its conserved essential roles in metabolic processes and detoxification. Here, we present TiMiGNet, a novel deep learning approach for virtual 3D tissue microstructure reconstruction using Generative Adversarial Networks and fluorescence microscopy. TiMiGNet overcomes challenges such as poor antibody penetration and time-intensive procedures by generating accurate, high-resolution predictions of tissue components across large volumes without the need of paired images as input. We applied TiMiGNet to analyze tissue microstructure in mouse and human liver tissue. TiMiGNet shows high performance in predicting structures like bile canaliculi, sinusoids, and Kupffer cell shapes from actin meshwork images. Remarkably, using TiMiGNet we were able to computationally reconstruct tissue structures that cannot be directly imaged due experimental limitations in deep dense tissues, a significant advancement in deep tissue imaging. Our open-source virtual prediction tool facilitates accessible and efficient multi-species tissue microstructure analysis, accommodating researchers with varying expertise levels. Overall, our method represents a powerful approach for studying tissue microstructure, with far-reaching applications in diverse biological contexts and species.

Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas.

Three-dimensional (3D) geometrical models are potent tools for quantifying complex tissue features and exploring structure-function relationships. However, these models are generally incomplete due to experimental limitations in acquiring multiple (> 4) fluorescent channels in thick tissue sections simultaneously. Indeed, predictive geometrical and functional models of the liver have been restricted to few tissue and cellular components, excluding important cellular populations such as hepatic stellate cells (HSCs) and Kupffer cells (KCs). Here, we combined deep-tissue immunostaining, multiphoton microscopy, deep-learning techniques, and 3D image processing to computationally expand the number of simultaneously reconstructed tissue structures. We then generated a spatial single-cell atlas of hepatic architecture (Hep3D), including all main tissue and cellular components at different stages of post-natal development in mice. We used Hep3D to quantitatively study 1) hepatic morphodynamics from early post-natal development to adulthood, and 2) the effect on the liver's overall structure when changing the hepatic environment after removing KCs. In addition to a complete description of bile canaliculi and sinusoidal network remodeling, our analysis uncovered unexpected spatiotemporal patterns of non-parenchymal cells and hepatocytes differing in size, number of nuclei, and DNA content. Surprisingly, we found that the specific depletion of KCs results in morphological changes in hepatocytes and HSCs. These findings reveal novel characteristics of liver heterogeneity and have important implications for both the structural organization of liver tissue and its function. Our next-gen 3D single-cell atlas is a powerful tool to understand liver tissue architecture, opening up avenues for in-depth investigations into tissue structure across both normal and pathological conditions.

Gastrulation: Nodal signaling controls ordered collective cell migration.

By combining classical embryological experiments, advanced imaging, and active-particle simulations, a new study reveals how the coupling of signaling and active mechanical forces triggers the collective ordered internalization of mesendoderm cells during gastrulation in zebrafish embryos.

Regulation of Nodal signaling propagation by receptor interactions and positive feedback.

During vertebrate embryogenesis, the germ layers are patterned by secreted Nodal signals. In the classical model, Nodals elicit signaling by binding to a complex comprising Type I/II Activin receptors (Acvr) and the co-receptor Tdgf1. However, it is currently unclear whether receptor binding can also affect the distribution of Nodals themselves through the embryo, and it is unknown which of the putative Acvr paralogs mediate Nodal signaling in zebrafish. Here, we characterize three Type I (Acvr1) and four Type II (Acvr2) homologs and show that - except for Acvr1c - all receptor-encoding transcripts are maternally deposited and present during zebrafish embryogenesis. We generated mutants and used them together with combinatorial morpholino knockdown and CRISPR F0 knockout (KO) approaches to assess compound loss-of-function phenotypes. We discovered that the Acvr2 homologs function partly redundantly and partially independently of Nodal to pattern the early zebrafish embryo, whereas the Type I receptors Acvr1b-a and Acvr1b-b redundantly act as major mediators of Nodal signaling. By combining quantitative analyses with expression manipulations, we found that feedback-regulated Type I receptors and co-receptors can directly influence the diffusion and distribution of Nodals, providing a mechanism for the spatial restriction of Nodal signaling during germ layer patterning.

Building a body is complicated. Cells must organise themselves head-to-tail, belly-to-back, and inside-to-outside. They do this by laying down a chemical map, which is made up of gradients of molecular signals, high in some places and lower in others. The amount of signal each cell receives helps to decide which part of the body it will become. One of the essential signals in developing vertebrates is Nodal. It helps cells to tell inside from outside and left from right. Cells detect Nodal using an activin receptor and co-receptor complex, which catch hold of passing Nodal proteins and transmit developmental signals into cells. An important model to study Nodal signals is the zebrafish embryo, but the identity of the activin receptors and their exact role in this organism has been unclear. To find out more, Preiß, Kögler, Mörsdorf et al. studied the activin receptors Acvr1 and Acvr2 in zebrafish embryos. The experiments revealed that two putative Acvr1 and four Acvr2 receptors were present during early development. To better understand their roles, Preiß et al. eliminated them one at a time, and in combination. Losing single activin receptors had no effect. But losing both Acvr1 receptors together stopped Nodal signalling and changed the distribution of the Nodal gradient. Loss of all Acvr2 receptors also caused developmental problems, but they were partly independent of Nodal. This suggests that Acvr1s seem to be able to transmit signals and to shape the Nodal gradient, and that Acvr2s might have another, so far unknown, role. Nodal signals guide the development of all vertebrates. Understanding how they work in a model species like zebrafish could shed light on their role in other species, including humans. A clearer picture could help to uncover what happens at a molecular level when development goes wrong.

Anisotropic expansion of hepatocyte lumina enforced by apical bulkheads.

Lumen morphogenesis results from the interplay between molecular pathways and mechanical forces. In several organs, epithelial cells share their apical surfaces to form a tubular lumen. In the liver, however, hepatocytes share the apical surface only between adjacent cells and form narrow lumina that grow anisotropically, generating a 3D network of bile canaliculi (BC). Here, by studying lumenogenesis in differentiating mouse hepatoblasts in vitro, we discovered that adjacent hepatocytes assemble a pattern of specific extensions of the apical membrane traversing the lumen and ensuring its anisotropic expansion. These previously unrecognized structures form a pattern, reminiscent of the bulkheads of boats, also present in the developing and adult liver. Silencing of Rab35 resulted in loss of apical bulkheads and lumen anisotropy, leading to cyst formation. Strikingly, we could reengineer hepatocyte polarity in embryonic liver tissue, converting BC into epithelial tubes. Our results suggest that apical bulkheads are cell-intrinsic anisotropic mechanical elements that determine the elongation of BC during liver tissue morphogenesis.

Liquid-crystal organization of liver tissue.

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-ß1 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.

Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression.

Early disease diagnosis is key to the effective treatment of diseases. Histopathological analysis of human biopsies is the gold standard to diagnose tissue alterations. However, this approach has low resolution and overlooks 3D (three-dimensional) structural changes resulting from functional alterations. Here, we applied multiphoton imaging, 3D digital reconstructions and computational simulations to generate spatially resolved geometrical and functional models of human liver tissue at different stages of non-alcoholic fatty liver disease (NAFLD). We identified a set of morphometric cellular and tissue parameters correlated with disease progression, and discover profound topological defects in the 3D bile canalicular (BC) network. Personalized biliary fluid dynamic simulations predicted an increased pericentral biliary pressure and micro-cholestasis, consistent with elevated cholestatic biomarkers in patients' sera. Our spatially resolved models of human liver tissue can contribute to high-definition medicine by identifying quantitative multiparametric cellular and tissue signatures to define disease progression and provide new insights into NAFLD pathophysiology.

A Predictive 3D Multi-Scale Model of Biliary Fluid Dynamics in the Liver Lobule.

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.

A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture.

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.