EmbryoNet : using deep learning to link embryonic phenotypes to signaling pathways

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2023
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Safroshkin, Matvey
Arutyunov, Grigory
Kurzbach, Anica
Bihler, Johanna
Hagauer, Julia
Jordan, Ben
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European Union (EU): 863952
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Nature Methods. Springer. 2023, 20, pp. 815-823. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-01873-4
Zusammenfassung

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.

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ISO 690CAPEK, Daniel, Matvey SAFROSHKIN, Hernán MORALES-NAVARRETE, Nikan TOULANY, Grigory ARUTYUNOV, Anica KURZBACH, Johanna BIHLER, Julia HAGAUER, Ben JORDAN, Patrick MÜLLER, 2023. EmbryoNet : using deep learning to link embryonic phenotypes to signaling pathways. In: Nature Methods. Springer. 2023, 20, pp. 815-823. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-01873-4
BibTex
@article{Capek2023-05-08Embry-67064,
  year={2023},
  doi={10.1038/s41592-023-01873-4},
  title={EmbryoNet : using deep learning to link embryonic phenotypes to signaling pathways},
  volume={20},
  issn={1548-7091},
  journal={Nature Methods},
  pages={815--823},
  author={Capek, Daniel and Safroshkin, Matvey and Morales-Navarrete, Hernán and Toulany, Nikan and Arutyunov, Grigory and Kurzbach, Anica and Bihler, Johanna and Hagauer, Julia and Jordan, Ben and Müller, Patrick}
}
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