Abstract
Organoids provide an accessible in-vitro system to mimic the dynamics of tissue regeneration and development. However, long-term live-imaging of organoids remains challenging. Here we present an experimental and image-processing framework capable of turning long-term light-sheet imaging of intestinal organoids into digital organoids. The framework combines specific imaging optimization combined with data processing via deep learning techniques to segment single organoids, their lumen, cells and nuclei in 3D over long periods of time. By linking lineage trees with corresponding 3D segmentation meshes for each organoid, the extracted information is visualized using a web-based “Digital Organoid Viewer” tool allowing unique understanding of the multivariate and multiscale data. We also show backtracking of cells of interest, providing detailed information about their history within entire organoid contexts. Furthermore, we show cytokinesis failure of regenerative cells and that these cells never reside in the intestinal crypt, hinting at a tissue scale control on cellular fidelity.
Competing Interest Statement
A.B. and P.S. are co-founders of Viventis Microscopy Sarl that commercializes the light-sheet microscope used in this study.
Footnotes
We have updated the work and added: 1) comparison of the LSTree prediction methods with existing networks (Stardist, Elephant) 2) added information on the role of Lats1 and Limk1 on the influence of binucleated cell appearance 3) added extra information on backtracking of stained cells in Supplementary 3) improved readability of the text and added detailed documentation in our Supplementary and in the LSTree repository with example data 4) cross-checked text and corrected typos, added complementary information, etc.
