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Uncovering developmental time and tempo using deep learning

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2023

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Nature Methods. Springer. 2023, 20(12), pp. 2000-2010. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-02083-8

Zusammenfassung

During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.

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570 Biowissenschaften, Biologie

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ISO 690TOULANY, Nikan, Hernán MORALES-NAVARRETE, Daniel CAPEK, Jannis GRATHWOHL, Murat ÜNALAN, Patrick MÜLLER, 2023. Uncovering developmental time and tempo using deep learning. In: Nature Methods. Springer. 2023, 20(12), pp. 2000-2010. ISSN 1548-7091. eISSN 1548-7105. Available under: doi: 10.1038/s41592-023-02083-8
BibTex
@article{Toulany2023Uncov-69445,
  year={2023},
  doi={10.1038/s41592-023-02083-8},
  title={Uncovering developmental time and tempo using deep learning},
  number={12},
  volume={20},
  issn={1548-7091},
  journal={Nature Methods},
  pages={2000--2010},
  author={Toulany, Nikan and Morales-Navarrete, Hernán and Capek, Daniel and Grathwohl, Jannis and Ünalan, Murat and Müller, Patrick}
}
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