DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.
Drosophila
Cell division
Convolutional neural network
Deep learning
Epithelia
Extrusion
Live imaging
SOPs
Journal
Development (Cambridge, England)
ISSN: 1477-9129
Titre abrégé: Development
Pays: England
ID NLM: 8701744
Informations de publication
Date de publication:
01 Jul 2023
01 Jul 2023
Historique:
received:
03
03
2023
accepted:
28
05
2023
medline:
3
7
2023
pubmed:
7
6
2023
entrez:
7
6
2023
Statut:
ppublish
Résumé
Accurately counting and localising cellular events from movies is an important bottleneck of high-content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning that allows automatic detection of cellular events and their precise xyt localisation on live fluorescent imaging movies without segmentation. We focused on the detection of cell extrusion, the expulsion of dying cells from the epithelial layer, and devised DeXtrusion: a pipeline based on recurrent neural networks for automatic detection of cell extrusion/cell death events in large movies of epithelia marked with cell contour. The pipeline, initially trained on movies of the Drosophila pupal notum marked with fluorescent E-cadherin, is easily trainable, provides fast and accurate extrusion predictions in a large range of imaging conditions, and can also detect other cellular events, such as cell division or cell differentiation. It also performs well on other epithelial tissues with reasonable re-training. Our methodology could easily be applied for other cellular events detected by live fluorescent microscopy and could help to democratise the use of deep learning for automatic event detections in developing tissues.
Identifiants
pubmed: 37283069
pii: 316593
doi: 10.1242/dev.201747
pmc: PMC10323232
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : European Research Council
ID : Competition for Space in Development and Disease, grant number 758457),
Pays : International
Organisme : European Research Council
ID : 758457
Pays : International
Informations de copyright
© 2023. Published by The Company of Biologists Ltd.
Déclaration de conflit d'intérêts
Competing interests The authors declare no competing or financial interests.
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