Multiscale light-sheet organoid imaging framework

Abstract

We present an imaging framework capable of turning long term light-sheet imaging of organoids into digital organoids. The framework takes advantage of deep learning techniques to faithfully segment single organoids, their lumen, cells and nuclei in 3D and over long periods of time. In parallel, large lineage trees for each organoid are predicted and corrected to iteratively improve the tracking and segmentation performances over time. To visualize the extracted information, we developed a web-based 'Digital Organoid Viewer' that allows a unique understanding of the multivariate and multiscale data by linking 2D lineage trees with the corresponding 3D segmentation meshes. We also backtracked single cells of interest after fixation obtaining detailed information about their history within the entire organoid context. Furthermore, we show nuclei merging events that arise from cytokinesis failure and that these polyploid never reside in the intestinal crypt, hinting at a tissue scale control and feedback on cellular fidelity. Molecularly, these cytokinesis failures depend on a regenerative state of the organoids and are regulated by Lats1 and RXR and we propose a model of tissue integrity by multi-scale check points. This discovery sheds light on the robustness of a regenerative YAP cellular state, questioning the role of polyploidy in intestinal regeneration.

Tissue confinement regulates cell growth and size in epithelia

“…Interestingly, the volumes of epithelial cells cultured in 3D more closely matched those in vivo , with a mean of 1020±520 μm 3 (Fig. 1B, diamonds) (Elamin et al, 2012; Engelberg et al, 2011; Goldspink et al, 2017; Karve et al, 2017; Medeiros et al, 2021; Morizane et al, 2015; Schutgens et al, 2019). These data suggest that epithelial cell volume is strongly influenced by the tissue environment.…”

“…1A) (3D cell viewer -Allen Cell Explorer.) and published cell volume measurements (Cadart et al, 2018;Elamin et al, 2012;Engelberg et al, 2011;Goldspink et al, 2017;Guo et al, 2017;Karve et al, 2017;Leung et al, 2014;Medeiros et al, 2021;Morizane et al, 2015;Padovan-Merhar et al, 2015;Park et al, 2010;Perez-Gonzalez et al, 2019;Schutgens et al, 2019;Viana et al, 2021;Yao et al, 2020;Zhao et al, 2008). When cell volume measurements weren't available, we estimated cell volume by identifying cells perpendicular to the tissue slice and then measuring their length and width.…”

Tracking by Weakly-Supervised Learning and Graph Optimization for Whole-Embryo C. elegans lineages

“…A number of automated cell tracking approaches have been developed to tackle reduced SNR as well as frame rates on the order of minutes. Such methods have enabled a range of studies on a variety of organisms, where it wouldn't have been feasible to do tracking manually [12,18,2,16,27,5]. The Cell Tracking Challenge (CTC) [25], an extensive benchmark that contains 2d+time and 3d+time datasets of different organisms recorded with a variety of microscopes, allows for a quantitative comparison of automated cell tracking methods.…”

Homeostasis, injury and recovery dynamics at multiple scales in a self-organizing intestinal crypt

“…cells that can duplicate their genome in the absence of intervening mitosis) leads to the formation of stable nonproliferating giant cells, whereas the same treatment triggers apoptosis in cells that are not developmentally programmed to endoreduplicate (59). Although endoreduplication is not expected in crypt cells, enlarged polynucleated cells have been reported to remain in the epithelium without dying in a recent light-sheet organoid imaging study tracking the progeny of a cell after cytokinesis failure, which was induced by the inhibition of LATS1 (60), which is a phosphorylated by CDK1 during mitosis (61). Thus, we chose to replicate this phenotype to show the capacity of our model to predict possible complex responses in the intestine.…”