Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
20
12
2021
accepted:
12
07
2022
pubmed:
7
9
2022
medline:
21
1
2023
entrez:
6
9
2022
Statut:
ppublish
Résumé
We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.
Identifiants
pubmed: 36065022
doi: 10.1038/s41587-022-01427-7
pii: 10.1038/s41587-022-01427-7
pmc: PMC7614077
mid: EMS155316
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
44-49Subventions
Organisme : Medical Research Council
ID : MC_UP_1201/23
Pays : United Kingdom
Organisme : Howard Hughes Medical Institute
Pays : United States
Informations de copyright
© 2022. The Author(s).
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