Background: Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration, and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangiocarcinoma organoids in parallel using light sheet and bright-field microscopy for up to 7 days.Results: We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation, and degeneration and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation, and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright-field microscopy analysis of entire cultures.Conclusion: Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.

Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91 %) whereas the other methods each failed for at least one dataset (F-measure ≤ 69 %). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.

This novel system for the long-term fluorescence imaging of live three-dimensional cultures provides minimal photodamage, control of temperature, CO₂, pH, and media flow.

Self-organised three-dimensional (3D) cell cultures, collectively called 3D-oids, include spheroids, organoids and other co-culture models. Systematic evaluation of these models forms a critical new generation of high-content screening (HCS) systems for patient-specific drug analysis and cancer research. However, the standardisation of working with 3D-oids remains challenging and lacks convincing implementation. This study develops and tests HCS-3DX, a next-generation system for HCS analysis in 3D imaging and image evaluation. HCS-3DX is based on three main components: an automated Artificial Intelligence (AI)-driven micromanipulator for 3D-oid selection, an HCS foil multiwell plate for optimised imaging, and image-based AI software for single-cell data analysis. We validated HCS-3DX directly on 3D tumour models, including tumour-stroma co-cultures. Our data demonstrate that HCS-3DX achieves a resolution that overcomes the limitations of current systems and reliably and effectively performs 3D HCS at the single-cell level. Its application will enhance the accuracy and efficiency of drug screening processes, support personalised medicine approaches, and facilitate more detailed investigations into cellular behaviour within 3D structures.

In three-dimensional light microscopy, the heterogeneity of the optical density in a specimen ultimately limits the achievable penetration depth and hence the three-dimensional resolution. The most direct approach to reduce aberrations, improve the contrast and achieve an optimal resolution is to minimise the impact of changes of the refractive index along an optical path. Many implementations of light sheet fluorescence microscopy operate with a large chamber filled with an aqueous immersion medium and a further inner container with the specimen embedded in a possibly entirely different non-aqueous medium. In order to minimise the impact of the latter on the optical quality of the images, we use multi-facetted cuvettes fabricated from vacuum-formed ultra-thin fluorocarbon (FEP) foils. The ultra-thin FEP-foil cuvettes have a wall thickness of about 10–12 µm. They are impermeable to liquids, but not to gases, inert, durable, mechanically stable and flexible. Importantly, the usually fragile specimen can remain in the same cuvette from seeding to fixation, clearing and observation, without the need to remove or remount it during any of these steps. We confirm the improved imaging performance of ultra-thin FEP-foil cuvettes with excellent quality images of whole organs such us mouse oocytes, of thick tissue sections from mouse brain and kidney as well as of dense pancreas and liver organoid clusters. Our ultra-thin FEP-foil cuvettes outperform many other sample-mounting techniques in terms of a full separation of the specimen from the immersion medium, compatibility with aqueous and organic clearing media, quick specimen mounting without hydrogel embedding and their applicability for multiple-view imaging and automated image segmentation. Additionally, we show that ultra-thin FEP foil cuvettes are suitable for seeding and growing organoids over a time period of at least ten days. The new cuvettes allow the fixation and staining of specimens inside the holder, preserving the delicate morphology of e.g. fragile, mono-layered three-dimensional organoids.