Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations

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...

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Autores principales: Malin-Mayor, Caroline, Hirsch, Peter, Guignard, Leo, McDole, Katie, Wan, Yinan, Lemon, William C., Kainmueller, Dagmar, Keller, Philipp J., Preibisch, Stephan, Funke, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614077/
https://www.ncbi.nlm.nih.gov/pubmed/36065022
http://dx.doi.org/10.1038/s41587-022-01427-7
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author Malin-Mayor, Caroline
Hirsch, Peter
Guignard, Leo
McDole, Katie
Wan, Yinan
Lemon, William C.
Kainmueller, Dagmar
Keller, Philipp J.
Preibisch, Stephan
Funke, Jan
author_facet Malin-Mayor, Caroline
Hirsch, Peter
Guignard, Leo
McDole, Katie
Wan, Yinan
Lemon, William C.
Kainmueller, Dagmar
Keller, Philipp J.
Preibisch, Stephan
Funke, Jan
author_sort Malin-Mayor, Caroline
collection PubMed
description 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.
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spelling pubmed-76140772023-01-18 Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations Malin-Mayor, Caroline Hirsch, Peter Guignard, Leo McDole, Katie Wan, Yinan Lemon, William C. Kainmueller, Dagmar Keller, Philipp J. Preibisch, Stephan Funke, Jan Nat Biotechnol Brief Communication 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. Nature Publishing Group US 2022-09-05 2023 /pmc/articles/PMC7614077/ /pubmed/36065022 http://dx.doi.org/10.1038/s41587-022-01427-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Brief Communication
Malin-Mayor, Caroline
Hirsch, Peter
Guignard, Leo
McDole, Katie
Wan, Yinan
Lemon, William C.
Kainmueller, Dagmar
Keller, Philipp J.
Preibisch, Stephan
Funke, Jan
Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title_full Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title_fullStr Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title_full_unstemmed Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title_short Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
title_sort automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614077/
https://www.ncbi.nlm.nih.gov/pubmed/36065022
http://dx.doi.org/10.1038/s41587-022-01427-7
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