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XB-ART-58603
Development November 1, 2021; 148 (21):
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Deep learning is widely applicable to phenotyping embryonic development and disease.

Naert T , Çiçek Ö , Ogar P , Bürgi M , Shaidani NI , Kaminski MM , Xu Y , Grand K , Vujanovic M , Prata D , Hildebrandt F , Brox T , Ronneberger O , Voigt FF , Helmchen F , Loffing J , Horb ME , Willsey HR , Lienkamp SS .


Abstract
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader''s own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated ''The people behind the papers'' interview.

PubMed ID: 34739029
PMC ID: PMC8602947
Article link: Development
Grant support: [+]

Species referenced: Xenopus tropicalis
Genes referenced: atp1a1 col2a1 dct dyrk1a dyrk1a.2 hopx pcna pkd1 pkd2 psmd6 six1 slc12a3 tbx18 tyr
gRNAs referenced: pkd2 gRNA1 pkd2 gRNA2

Disease Ontology terms: autism spectrum disorder [+]
OMIMs: AUTISM

Article Images: [+] show captions
References [+] :
Akerberg, Deep learning enables automated volumetric assessments of cardiac function in zebrafish. 2020, Pubmed