Embryo-uterine interaction coordinates mouse embryogenesis during implantation.

Embryo implantation into the uterus marks a key transition in mammalian development. In mice, implantation is mediated by the trophoblast and is accompanied by a morphological transition from the blastocyst to the egg cylinder. However, the roles of trophoblast-uterine interactions in embryo morphogenesis during implantation are poorly understood due to inaccessibility in utero and the remaining challenges to recapitulate it ex vivo from the blastocyst. Here, we engineer a uterus-like microenvironment to recapitulate peri-implantation development of the whole mouse embryo ex vivo and reveal essential roles of the physical embryo-uterine interaction. We demonstrate that adhesion between the trophoblast and the uterine matrix is required for in utero-like transition of the blastocyst to the egg cylinder. Modeling the implanting embryo as a wetting droplet links embryo shape dynamics to the underlying changes in trophoblast adhesion and suggests that the adhesion-mediated tension release facilitates egg cylinder formation. Light-sheet live imaging and the experimental control of the engineered uterine geometry and trophoblast velocity uncovers the coordination between trophoblast motility and embryo growth, where the trophoblast delineates space for embryo morphogenesis.

An ex vivo system to study cellular dynamics underlying mouse peri-implantation development.

Upon implantation, mammalian embryos undergo major morphogenesis and key developmental processes such as body axis specification and gastrulation. However, limited accessibility obscures the study of these crucial processes. Here, we develop an ex vivo Matrigel-collagen-based culture to recapitulate mouse development from E4.5 to E6.0. Our system not only recapitulates embryonic growth, axis initiation, and overall 3D architecture in 49% of the cases, but its compatibility with light-sheet microscopy also enables the study of cellular dynamics through automatic cell segmentation. We find that, upon implantation, release of the increasing tension in the polar trophectoderm is necessary for its constriction and invagination. The resulting extra-embryonic ectoderm plays a key role in growth, morphogenesis, and patterning of the neighboring epiblast, which subsequently gives rise to all embryonic tissues. This 3D ex vivo system thus offers unprecedented access to peri-implantation development for in toto monitoring, measurement, and spatiotemporally controlled perturbation, revealing a mechano-chemical interplay between extra-embryonic and embryonic tissues.

Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana.

Lateral root formation determines to a large extent the ability of plants to forage their environment and thus their growth. In Arabidopsis thaliana and other angiosperms, lateral root initiation requires radial cell expansion and several rounds of anticlinal cell divisions that give rise to a central core of small cells, which express different markers than the larger surrounding cells. These small central cells then switch their plane of divisions to periclinal and give rise to seemingly morphologically similar daughter cells that have different identities and establish the different cell types of the new root. Although the execution of these anticlinal and periclinal divisions is tightly regulated and essential for the correct development of the lateral root, we know little about their geometrical features. Here, we generate a four-dimensional reconstruction of the first stages of lateral root formation and analyze the geometric features of the anticlinal and periclinal divisions. We identify that the periclinal divisions of the small central cells are morphologically dissimilar and asymmetric. We show that mother cell volume is different when looking at anticlinal vs. periclinal divisions and the repeated anticlinal divisions do not lead to reduction in cell volume, although cells are shorter. Finally, we show that cells undergoing a periclinal division are characterized by a strong cell expansion. Our results indicate that cells integrate growth and division to precisely partition their volume upon division during the first two stages of lateral root formation.

A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule.

A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana, enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera.

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA, and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS-CoV-2 serological assays. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.

Accurate and versatile 3D segmentation of plant tissues at cellular resolution.

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

ilastik: interactive machine learning for (bio)image analysis.

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.