Published November 5, 2021 | Version v1
Journal article Open

Deep learning is widely applicable to phenotyping embryonic development and disease

Creators

  • 1. Institute of Anatomy, University of Zurich, Zurich 8057, Switzerland; Swiss National Centre of Competence in Research (NCCR) Kidney Control of Homeostasis (Kidney.CH), Zurich 8057, Switzerland.
  • 2. Department of Computer Science, Albert-Ludwigs-University, Freiburg 79100, Germany.
  • 3. National Xenopus Resource and Eugene Bell Center for Regenerative Biology and Tissue Engineering, Marine Biological Laboratory, Woods Hole, MA 02543, USA.
  • 4. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin 10115, Germany.
  • 5. Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA.
  • 6. Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115,USA.
  • 7. Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich 8057, Switzerland; Neuroscience Center Zurich, Zurich 8057, Switzerland.

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.

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Is published in
Journal article: 10.1242/dev.199664 (DOI)

Funding

European Commission
xenCAKUT – CRISPR/Cas9 based kidney disease modeling to elucidate novel genetic drivers and therapeutic targets in X. tropicalis 891127
European Commission
DiRECT – Directly reprogrammed renal cells for targeted medicine 804474