We present a microscopy technique that enables long-term time-lapse microscopy at single-cell resolution in moving and feeding Caenorhabditis elegans larvae. Time-lapse microscopy of C. elegans post-embryonic development is challenging, as larvae are highly motile. Moreover, immobilization generally leads to rapid developmental arrest. Instead, we confine larval movement to microchambers that contain bacteria as food, and use fast image acquisition and image analysis to follow the dynamics of cells inside individual larvae, as they move within each microchamber. This allows us to perform fluorescence microscopy of 10–20 animals in parallel with 20 min time resolution. We demonstrate the power of our approach by analysing the dynamics of cell division, cell migration and gene expression over the full ∼48 h of development from larva to adult. Our approach now makes it possible to study the behaviour of individual cells inside the body of a feeding and growing animal.

Biological systems are subject to inherent stochasticity. Nevertheless, development is remarkably robust, ensuring the consistency of key phenotypic traits such as correct cell numbers in a certain tissue. It is currently unclear which genes modulate phenotypic variability, what their relationship is to core components of developmental gene networks, and what is the developmental basis of variable phenotypes. Here, we start addressing these questions using the robust number of Caenorhabditis elegans epidermal stem cells, known as seam cells, as a readout. We employ genetics, cell lineage tracing, and single molecule imaging to show that mutations in lin-22, a Hes-related basic helix-loop-helix (bHLH) transcription factor, increase seam cell number variability. We show that the increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisions to asymmetric and vice versa during development, which affect the terminal seam cell number in opposing directions. We demonstrate that LIN-22 acts within the epidermal gene network to antagonise the Wnt signalling pathway. However, lin-22 mutants exhibit cell-to-cell variability in Wnt pathway activation, which correlates with and may drive phenotypic variability. Our study demonstrates the feasibility to study phenotypic trait variance in tractable model organisms using unbiased mutagenesis screens.

During embryonic development, epithelial cell blocks called somites are periodically formed according to the segmentation clock, becoming the foundation for the segmental pattern of the vertebral column. The process of somitogenesis has recently been recapitulated with murine and human pluripotent stem cells. However, an in vitro model for human somitogenesis coupled with the segmentation clock and epithelialization is still missing. Here, we report the generation of human somitoids, organoids that periodically form pairs of epithelial somite-like structures. Somitoids display clear oscillations of the segmentation clock that coincide with the segmentation of the presomitic mesoderm. The resulting somites show anterior-posterior and apical-basal polarities. Matrigel is essential for epithelialization but dispensable for the differentiation into somite cells. The size of somites is rather constant, irrespective of the initial cell number. The amount of WNT signaling instructs the proportion of mesodermal lineages in somitoids. Somitoids provide a novel platform to study human somitogenesis.

Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.

Digitalization is a challenging topic for manufacturing SMEs. Besides improving their technological base, they have to keep the business offer attractive, e.g. by bundling their products with smart services. In addition to limited resources, often the necessary knowledge is missing to select the right technologies and develop attractive services based on their functionality. Due to the resulting high risks, SMEs neglect the chances of digitization and lose competitiveness. To start with, they need a maturity assessment model that can help them understand their level of readiness to implement aspects, such as Industry 4.0. In order to identify and fit such a model for SMEs, already existing assessment methods were analyzed according to a series of selected dimensions linked to SMEs features. The evaluation result illustrates requirements that have to be addressed by a new maturity model suitable for SMEs attempting digitalization.