Data from: Automated deep-phenotyping of the vertebrate brain

Here we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex.

本研究介绍了一套适用于斑马鱼突变品系大规模深度表型分析的自动化平台，该平台借助光学投影断层成像（Optical Projection Tomography），以细胞分辨率快速获取三维脑特异性基因表达模式的成像数据。随后通过配准算法（registration algorithms）与相关性分析（correlation analysis）比对三维表达模式，自动检测突变体中所有具有统计学意义的表达异常，并将这些异常映射至脑图谱（brain atlas）。对主转录调控因子fezf2的突变开展自动化深度表型分析，不仅成功检出全部已知表型，还发现了既往研究中被忽视的重要新型神经功能缺陷。在端脑（telencephalon）中，我们首次证实fezf2突变斑马鱼存在显著的脑结构模式构建缺陷，尤其在谷氨酸能神经元群中表现突出。本研究结果揭示了斑马鱼与小鼠中fezf2功能的意外保守同源性：两类物种的fezf2突变均会引发端脑衍生的新皮层（neocortex）谷氨酸能神经元的功能缺陷。