Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

中文翻译：

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具有单神经元分辨率的果蝇 GAL4 驱动表达模式的可搜索图像资源

通过 GAL4/UAS 和相关方法对特定神经元进行精确、可重复的遗传访问是果蝇的一个关键优势神经科学。神经元靶向通常使用完整 GAL4 表达模式的光学显微镜记录，这通常缺乏可靠的细胞类型识别所需的单细胞分辨率。在这里，我们使用随机 GAL4 标记和 MultiColor FlpOut 方法来生成大规模的细胞分辨率共聚焦图像。我们正在发布 74,000 个此类成人中枢神经系统的对齐图像。该资源的预期用途是弥合电子或光学显微镜识别的神经元之间的差距。识别构成每个 GAL4 表达模式的单个神经元可以改进针对特定神经元的分裂 GAL4 组合的预测。为此，我们在 NeuronBridge 网站上提供了可搜索的图像。