Publication Date:
2022-05-27
Description:
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Naert, T., Çiçek, Ö., Ogar, P., Bürgi, M., Shaidani, N.-I., Kaminski, M. M., Xu, Y., Grand, K., Vujanovic, M., Prata, D., Hildebrandt, F., Brox, T., Ronneberger, O., Voigt, F. F., Helmchen, F., Loffing, J., Horb, M. E., Willsey, H. R., & Lienkamp, S. S. Deep learning is widely applicable to phenotyping embryonic development and disease. Development, 148(21), (2021): dev199664, https://doi.org/10.1242/dev.199664.
Description:
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.
Description:
T.N. received funding from H2020 Marie Skłodowska-Curie Actions (xenCAKUT - 891127). M.M.K. is supported by the Emmy Noether Programme of the Deutsche Forschungsgemeinschaft (KA5060/1-1). F.H. is the William E. Harmon Professor of Pediatrics. This research is supported by grants from the National Institutes of Health to F.H. (DK-076683-13 and RC2-DK122397) and M.E.H. (OD-010997, OD-030008 and HD-084409). H.R.W. is supported by a gift from the Overlook International Foundation and by grant support from the National Institutes of Mental Health Convergent Neuroscience Initiative and by the Psychiatric Cell Map Initiative (pcmi.ucsf.edu, 1U01MH115747-01A1) to Matthew State. S.S.L. is supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_189102), the Swiss National Centre of Competence in Research Kidney Control of Homeostasis and the European Union's Horizon 2020 Framework Programme (ERC-StrG DiRECT - 804474).
Keywords:
U-Net
;
Xenopus
;
Light-sheet microscopy
;
Deep learning
;
Cystic kidney disease
;
Craniofacial dysmorphia
Repository Name:
Woods Hole Open Access Server
Type:
Article
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