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Animal Models

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Selected Grantee Publications

Whole-Organism 3D Quantitative Characterization of Zebrafish Melanin by Silver Deposition Micro-CT

Katz et al., eLife. 2021.

https://www.biorxiv.org/content/10.1101/2021.03.11.434673v1

This research team combined micro-computed tomography (CT) with a novel application of ionic silver staining to characterize melanin distribution in whole zebrafish larvae. The resulting images enabled whole-body, computational analyses of regional melanin content and morphology. Normalized micro-CT reconstructions of silver-stained fish consistently reproduced pigment patterns seen by light microscopy and allowed direct quantitative comparisons of melanin content. Silver staining of melanin for micro-CT provides proof-of-principle for whole-body, 3D computational phenomic analysis of a specific cell type at cellular resolution. Advances such as this in whole-organism, high-resolution phenotyping provide superior context for studying the phenotypic effects of genetic, disease, and environmental variables. Supported by ORIP (R24OD018559).

Deep Learning-Based Framework for Cardiac Function Assessment in Embryonic Zebrafish from Heart Beating Videos

Naderi et al., Computers in Biology and Medicine. 2021.

https://www.sciencedirect.com/science/article/pii/S0010482521003590

Zebrafish is a powerful model system for a host of biological investigations, cardiovascular studies, and genetic screening. However, the current methods for quantifying and monitoring zebrafish cardiac functions involve tedious manual work and inconsistent estimations. Naderi et al. developed a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. The framework could be widely applicable with any laboratory resources, and the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity. Supported by ORIP (R44OD024874)