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Updated: Aug 22, 2025

Analyzing Craniofacial Morphogenesis in Zebrafish Using 4D Confocal Microscopy
Published on: January 30, 2014
High-throughput detection of craniofacial defects in fluorescent zebrafish
Joshua L Everson1,2, Yung-Chia Tseng1, Johann K Eberhart1,2
1Department of Molecular Biosciences, School of Natural Sciences, University of Texas at Austin, Austin, Texas, USA.
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Losses and malformations of cranial neural crest cell (cNCC) derivatives are a hallmark of several common brain and face malformations. Nevertheless, the etiology of these cNCC defects remains unknown for many cases, suggesting a complex basis involving interactions between genetic and/or environmental factors. However, the sheer number of possible factors (thousands of genes and hundreds of thousands of toxicants) has hindered identification of specific interactions. Here, we develop a high-throughput analysis that will enable faster identification of multifactorial interactions in the genesis of craniofacial defects. Zebrafish embryos expressing a fluorescent marker of cNCCs (fli1:EGFP) were exposed to a pathway inhibitor standard or environmental toxicant, and resulting changes in fluorescence were measured in high-throughput using a fluorescent microplate reader to approximate cNCC losses. Embryos exposed to the environmental Hedgehog pathway inhibitor piperonyl butoxide (PBO), a Hedgehog pathway inhibitor standard, or alcohol (ethanol) exhibited reduced fli1:EGFP fluorescence at one day post fertilization, which corresponded with craniofacial defects at five days post fertilization. Combining PBO and alcohol in a co-exposure paradigm synergistically reduced fluorescence, demonstrating a multifactorial interaction. Using pathway reporter transgenics, we show that the plate reader assay is sensitive at detecting alterations in Hedgehog signaling, a critical regulator of craniofacial development. We go on to demonstrate that this technique readily detects defects in other important cell types, namely neurons. Together, these findings demonstrate this novel in vivo platform can predict developmental abnormalities and multifactorial interactions in high-throughput.

