Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations]
The zebrafish (Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wellcome
2023-04-01
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| Series: | Wellcome Open Research |
| Subjects: | |
| Online Access: | https://wellcomeopenresearch.org/articles/7-275/v3 |
| _version_ | 1797739747109502976 |
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| author | James C. Smith Rebecca A. Jones David J. Barry Matthew J. Renshaw |
| author_facet | James C. Smith Rebecca A. Jones David J. Barry Matthew J. Renshaw |
| author_sort | James C. Smith |
| collection | DOAJ |
| description | The zebrafish (Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype, and accurate characterization of such delays is imperative. Despite this, the only way at present to identify and quantify these delays is through manual observation, which is both time-consuming and subjective. Machine learning approaches in biology are rapidly becoming part of the toolkit used by researchers to address complex questions. In this work, we introduce a machine learning-based classifier that has been trained to detect temporal developmental differences across groups of zebrafish embryos. Our classifier is capable of rapidly analyzing thousands of images, allowing comparisons of developmental temporal rates to be assessed across and between experimental groups of embryos. Finally, as our classifier uses images obtained from a standard live-imaging widefield microscope and camera set-up, we envisage it will be readily accessible to the zebrafish community, and prove to be a valuable resource. |
| first_indexed | 2024-03-12T14:02:35Z |
| format | Article |
| id | doaj.art-41011216162047fba55a7d969f633b9c |
| institution | Directory Open Access Journal |
| issn | 2398-502X |
| language | English |
| last_indexed | 2024-03-12T14:02:35Z |
| publishDate | 2023-04-01 |
| publisher | Wellcome |
| record_format | Article |
| series | Wellcome Open Research |
| spelling | doaj.art-41011216162047fba55a7d969f633b9c2023-08-22T01:00:00ZengWellcomeWellcome Open Research2398-502X2023-04-01721485Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations]James C. Smith0https://orcid.org/0000-0003-2413-9392Rebecca A. Jones1https://orcid.org/0000-0002-1530-5400David J. Barry2Matthew J. Renshaw3https://orcid.org/0000-0001-5238-9191Developmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UKDevelopmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UKCrick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UKCrick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UKThe zebrafish (Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype, and accurate characterization of such delays is imperative. Despite this, the only way at present to identify and quantify these delays is through manual observation, which is both time-consuming and subjective. Machine learning approaches in biology are rapidly becoming part of the toolkit used by researchers to address complex questions. In this work, we introduce a machine learning-based classifier that has been trained to detect temporal developmental differences across groups of zebrafish embryos. Our classifier is capable of rapidly analyzing thousands of images, allowing comparisons of developmental temporal rates to be assessed across and between experimental groups of embryos. Finally, as our classifier uses images obtained from a standard live-imaging widefield microscope and camera set-up, we envisage it will be readily accessible to the zebrafish community, and prove to be a valuable resource.https://wellcomeopenresearch.org/articles/7-275/v3Zebrafish development machine learning staging developmental delay classifiereng |
| spellingShingle | James C. Smith Rebecca A. Jones David J. Barry Matthew J. Renshaw Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] Wellcome Open Research Zebrafish development machine learning staging developmental delay classifier eng |
| title | Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_full | Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_short | Automated staging of zebrafish embryos using machine learning [version 3; peer review: 1 approved, 2 approved with reservations] |
| title_sort | automated staging of zebrafish embryos using machine learning version 3 peer review 1 approved 2 approved with reservations |
| topic | Zebrafish development machine learning staging developmental delay classifier eng |
| url | https://wellcomeopenresearch.org/articles/7-275/v3 |
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