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Uncovering developmental time and tempo using deep learning
Nikan Toulany1,2,3, Hernán Morales-Navarrete1,4, Daniel Čapek1
1Systems Biology of Development, University of Konstanz, Konstanz, Germany.
Nature Methods
|November 23, 2023
Summary
We developed a deep learning method to analyze embryo development. This approach objectively quantifies developmental time and tempo, enabling accurate staging and analysis of evolutionary changes.
Area of Science:
- Developmental Biology
- Evolutionary Biology
- Computational Biology
Background:
- Embryonic development involves complex morphological changes.
- Differences in developmental tempo are key drivers of evolutionary novelty.
- Accurately describing these developmental processes is challenging.
Purpose of the Study:
- To present an automated, unbiased deep learning approach for analyzing embryo similarity across timepoints.
- To enable objective quantification of developmental time and tempo.
- To provide a standardized method for analyzing early embryogenesis.
Main Methods:
- Utilized deep learning for automated analysis of embryo morphology.
- Calculated similarities between embryos at different developmental stages.
- Developed an unsupervised approach for deriving staging atlases.
Main Results:
- Generated complex phenotypic fingerprints reflecting developmental time and tempo.
- Accurately staged embryos and quantified temperature-dependent developmental rates.
- Detected natural and induced alterations in developmental progression.
- Created de novo staging atlases for multiple species.
Conclusions:
- The deep learning approach offers objective quantification of developmental time and tempo.
- This method provides a standardized framework for analyzing embryogenesis.
- Enables deeper insights into evolutionary novelty driven by developmental tempo differences.
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