Automated high-speed 3D imaging of organoid cultures with multi-scale phenotypic quantification.

Current imaging approaches limit the ability to perform multi-scale characterization of three-dimensional (3D) organotypic cultures (organoids) in large numbers. Here, we present an automated multi-scale 3D imaging platform synergizing high-density organoid cultures with rapid and live 3D single-objective light-sheet imaging. It is composed of disposable microfabricated organoid culture chips, termed JeWells, with embedded optical components and a laser beam-steering unit coupled to a commercial inverted microscope. It permits streamlining organoid culture and high-content 3D imaging on a single user-friendly instrument with minimal manipulations and a throughput of 300 organoids per hour. We demonstrate that the large number of 3D stacks that can be collected via our platform allows training deep learning-based algorithms to quantify morphogenetic organizations of organoids at multi-scales, ranging from the subcellular scale to the whole organoid level. We validated the versatility and robustness of our approach on intestine, hepatic, neuroectoderm organoids and oncospheres.

Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.

Introduction: The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans. Materials and Methods: A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebra and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. The results were validated on an external, independent group of CT scans. Results: The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset, whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets. Conclusions: Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.

Plk4 Regulates Centriole Asymmetry and Spindle Orientation in Neural Stem Cells.

Defects in mitotic spindle orientation (MSO) disrupt the organization of stem cell niches impacting tissue morphogenesis and homeostasis. Mutations in centrosome genes reduce MSO fidelity, leading to tissue dysplasia and causing several diseases such as microcephaly, dwarfism, and cancer. Whether these mutations perturb spindle orientation solely by affecting astral microtubule nucleation or whether centrosome proteins have more direct functions in regulating MSO is unknown. To investigate this question, we analyzed the consequences of deregulating Plk4 (the master centriole duplication kinase) activity in Drosophila asymmetrically dividing neural stem cells. We found that Plk4 functions upstream of MSO control, orchestrating centriole symmetry breaking and consequently centrosome positioning. Mechanistically, we show that Plk4 acts through Spd2 phosphorylation, which induces centriole release from the apical cortex. Overall, this work not only reveals a role for Plk4 in regulating centrosome function but also links the centrosome biogenesis machinery with the MSO apparatus.