Multiscale light-sheet organoid imaging framework.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
18 08 2022
18 08 2022
Historique:
received:
24
05
2022
accepted:
02
08
2022
entrez:
18
8
2022
pubmed:
19
8
2022
medline:
23
8
2022
Statut:
epublish
Résumé
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 combined 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.
Identifiants
pubmed: 35982061
doi: 10.1038/s41467-022-32465-z
pii: 10.1038/s41467-022-32465-z
pmc: PMC9388485
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4864Informations de copyright
© 2022. The Author(s).
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