新颖性
生物
发育生物学
胚胎
发育阶段
人工智能
计算机科学
深度学习
表型
进化发育生物学
计算生物学
机器学习
进化生物学
心理学
遗传学
发展心理学
基因
社会心理学
作者
Nikan Toulany,Hernán Morales‐Navarrete,Daniel Čapek,Jannis Grathwohl,Murat Ünalan,Patrick Müller
出处
期刊:Nature Methods
[Springer Nature]
日期:2023-11-23
卷期号:20 (12): 2000-2010
被引量:3
标识
DOI:10.1038/s41592-023-02083-8
摘要
Abstract 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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