Multiscale light-sheet organoid imaging framework
Posted on: 29 August 2021 , updated on: 1 September 2021
Preprint posted on 12 May 2021
Article now published in Nature Communications at http://dx.doi.org/10.1038/s41467-022-32465-z
Categories: biochemistry, bioengineering, cell biology, developmental biology
Background
Organoids, multicellular in vitro models of their corresponding organs, are an excellent resource for understanding the cellular and developmental mechanisms that regulate tissue and organ growth. In recent years, organoids have relieved some of the difficulties and complexities of working with organs in vivo. Intestinal organoids are a great example of a reproducible system capable of recapitulating not only major morphological features (e.g., crypt formation) [1] but also one of the major intestine capabilities — its regenerative response [2].
One advantage of working with organoids is that their development can be precisely followed under a microscope, potentially allowing the description of their cells’ genealogical tree or lineage. However, imaging and analyzing organoid growth remains challenging, as it requires long-term imaging microscopy techniques coupled with cell-tracking and dedicated analysis pipelines to recover morphological or cell lineage information.
In this exciting new preprint, Liberali and colleagues leverage their extensive organoid-imaging knowledge [3] to present LSTree (an acronym for Light-Sheet Tree), a light-sheet imaging framework to analyze and digitally visualize organoids. LST goes from image pre-processing, cell lineage tracking, and segmentation to analytical tools to extract morphological and microscopy features. The authors used this light-sheet microscopy-based imaging framework to analyze and reconstruct, at an unprecedented spatiotemporal resolution, the cell growing dynamics and cell lineage relationships during intestinal organoid development.
About the preprint
Imaging framework
Medeiros et al. took advantage of the abundance of knowledge on intestinal organoids and used it to provide quantitative information on intestinal organoid growth dynamics using light-sheet and spinning disk microscopy.
The authors based their initial work on previously published protocols for imaging organoids, using cultured mouse intestinal crypt organoids and single-cell dissociation, followed by fluorescent-activated cell sorting (FACS) and embedding into a Matrigel drop. Hence, organoid-derived single cells were cultured into a sample holder specially designed for light-sheet imaging using a dual-illumination inverted light-sheet microscope. In addition, the authors developed a position-dependent illumination alignment step to obtain the best image quality possible. Aiming to reduce the storage needs, the authors compressed and cropped the images. The low signal-to-noise ratio, due to the very low laser intensity to avoid phototoxicity, was improved by denoising and deconvoluting the images.
To track and record the cell and cell lineages, Medeiros et al. worked with intestinal organoid-derived cells expressing the nuclear Histone 2B and mem9 membrane peptide, each tagged with fluorescent proteins (Fig. 1). Then, the authors recorded the time-lapse growth of the intestinal organoids every 10 min for about four days to study the individual cell dynamics by semi-automated tracking and single-cell segmentation. Owing to the 3D spatiotemporal dissection of the intestinal cellular dynamics, the authors were able to extract 3D information and plot the data over this time period, shedding light on cell division, cell and nucleus volume, and density, as well as cell changes induced by the daily media changes. Remarkably, they found nuclei volume decreased with each cell locating more distantly to the organoid lumen with each LSTree generation.
Fig. 1. Light-sheet high-resolution imaging framework. Modified from Medeiros et al. 2021.
Deep learning 4D organoid segmentation and multiscale digital viewer
With their protocol established, the authors added their image processing and data analyses into LSTree (for details visit: https://github.com/fmi-basel/LSTree), which was implemented as a Luigi (i.e., a workflow engine) (https://github.com/spotify/luigi). The next challenge was to perform organoid segmentation with their framework. The authors introduced a number of different segmentation strategies for organoid segmentation (cells and nuclei) that use convolutional neural networks and a trained deep learning model. Using this strategy, they extracted several distinct features from the organoid cells. Continuing with the framework development, Medeiros et al. trained and refined a deep learning model together with developing a segmentation-tracking approach to map the association between nuclei in two consecutive frames in 4D. With this achieved, the authors created a digital organoid viewer to facilitate data exploration. Remarkably, the viewer combines lineage cell trees and cell meshes, representing a multiscale organoid digital web-viewer (Fig. 2).
Fig. 2. Highlights of the organoid digital viewer developed by the authors.
Functional imaging through fixation and backtracking
Aiming to address the end state of the organoid cells and to overcome several problems involved with imaging multiple fluorescent organoid lines (i.e., reporter lines), the authors included fixation and immunolabelling as the last step after the recording. Following live recording, they integrated fixation and immunofluorescence to detect the cells expressing their proteins of interest. Hence, post-fixation staining of DLL1 (also known as Delta-like ligand 1 precursor, a marker for secretory lineage cells) and Lysozyme (a marker for differentiated Paneth cells) allowed the authors to ask about symmetry breaking of the intestinal organoid. Of note, the authors also calculated that on average an organoid cell has 5 closest neighbors. Thus, the authors were able to image and backtrack the stained cells with the light-sheet live recordings, enhancing the discovery capabilities of LSTree.
Polyploidy events during early gut organoid development
A challenging observation was that some cells went through a few rounds of failed cell division, meaning that two sister nuclei divided into two instead of four nuclei at the end of their cell cycle. The authors showed that the cause of these failed division events was an issue during cytokinesis. Interestingly, the organoids seem to exclude these cytokinesis failure events from the crypt, but a high proportion of the “failed” cell progeny localized to the villus. Thus, the authors proposed that the organoid system possesses an intrinsic self-preserving mechanism by avoiding cell division failure or damaged cells within the crypt – something they speculate could also occur in the gut. To finish, Medeiros et al. showed that LATS1 and YAP participate in regulating successful mitosis and cytokinesis, whereas perturbations of these resulted in inefficient mitosis and cell division.
What I liked about this preprint
This impressive multivariate and multiscale analytical framework was applied from a single cell to hundreds of cells and encompassed image pre-processing, lineage tracking, and segmentation coupled with analytical tools. Therefore, LSTree allowed the researchers to extract single-cell morphological features in 4D. In addition to developing a light-sheet microscopy pipeline combined with a web-based “digital organoid viewer”, the authors proposed a model of tissue development and integrity maintenance that challenges the concept of polyploidy in tissue homeostasis and regeneration. This remarkable approach will open new avenues in our understanding of organoid development and might allow further multi-parametric functional characterization of different organoid growth.
Future directions and questions to the authors
- Is there any potential for combining your LSTree approach with spatial transcriptomics?
- Have you applied this light-sheet imaging strategy and LSTree to study other kinds of organoids? I wonder whether other tissue-specific organoids might represent hurdles and challenges to further test LSTree applicability and feasibility.
- What was the most challenging aspect of creating LSTree?
- Have you explored the roles of YAP-mediated mechanotransduction or ECM/nuclear mechanotransduction of YAP as regulators of successful cell division/cytokinesis? Any thoughts if not explored yet?
Acknowledgments
Osvaldo Contreras acknowledges Irepan Salvador for initial discussions of the Medeiros et al. preprint and Helen Robertson for valuable comments on this PreLight.
References
[1] Serra, D., Mayr, U., Boni, A. et al. Self-organization and symmetry breaking in intestinal organoid development. Nature 569, 66–72 (2019). https://doi.org/10.1038/s41586-019-1146-y
[2] Lukonin, I., Serra, D., Challet Meylan, L. et al. Phenotypic landscape of intestinal organoid regeneration. Nature 586, 275–280 (2020). https://doi.org/10.1038/s41586-020-2776-9
[3] Yang, Q., Xue, SL., Chan, C.J. et al. Cell fate coordinates mechano-osmotic forces in intestinal crypt formation. Nat Cell Biol 23, 733–744 (2021). https://doi.org/10.1038/s41556-021-00700-2
doi: https://doi.org/10.1242/prelights.30401
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3 years
Gustavo de Medeiros
Dear Osvaldo,
thank you for selecting our work for this issue of preLights!
Regarding your questions:
1) One of the things we really like about LSTree is the power of combining different data representations (lineage trees and segmented cells) within the same visualization tool, and this is one aspect that could be translated to spatial transcriptomics: to have a visualization of the RNA-seq data – e.g. via TSNE or UMAP – side by side with the corresponding imaging data. This way one could e.g. select particular cells / regions of interest from the imaging data and the corresponding points on the RNA-seq data would be highlighted, with all other properties of the cells embedded as a list of features on the RNA-seq plot, thus giving a more complete understanding of the information at hand.
2) Right now we are applying our LSTree method to other already acquired 3D+T imaging data in order to properly benchmark it and test its limits. As the recording strategy and the LSTree framework are not directly coupled, one can apply 4D datasets coming from different microscope types into the framework. The main care here is then to have good training sets for the neural networks.
3) Although tracking is one of the most basic features needed in live recorded data, most tracking approaches rely on utilizing many partial tracks in order to create lineage trees that in turn resemble an average of all of the observed systems. In our case, we needed to have trees that are 100% correct, and for that we tried to get lineage tree predictions that can avoid us having too much time on the correction / extension of these initial tracks. Turns out this fine tuning can be quite delicate depending on the data at hand, and one still needs some training sets to have a good enough network.
To facilitate this, we are now looking at ways to combine other lineage tree prediction methods to the following steps from the LSTree framewrok, making the framework more modular and open.
4) This is an interesting point, as YAP-mediated mechanotransduction or ECM/nuclear mechanotransduction of YAP may have interesting roles on the systematic appearance of mitotic defects during early organoid growth from single cells.
From looking at the recordings, during the moments where we see mitotic errors the intestinal organoids usually seem quite “malleable”, i.e. cells do not seem very stiff overall. One then could speculate that the very variability in YAP, necessary from a molecular perspective for proper symmetry breaking, might lead some cells to change their biomechanical properties to a point where cytokinesis cannot be completed. Definitely worth checking further!
Kind regards,
Gustavo