Drosophila GAL4 brain imagery

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.

借助GAL4/UAS系统（GAL4/UAS）及相关技术，可实现对特定神经元的精准、可重复的遗传操控，这是果蝇神经科学研究的核心优势之一。当前，神经元靶向实验通常通过光学显微镜观测完整GAL4表达模式来记录，但此类图谱普遍缺乏可靠鉴定细胞类型所需的单细胞分辨率。本研究采用基于MultiColor FlpOut（MultiColor FlpOut）技术的随机GAL4标记方法，大规模生成具备单细胞分辨率的共聚焦成像数据。我们公开了74000份此类成年果蝇中枢神经系统的配准成像数据集。该数据集的预期应用场景之一，是填补电子显微镜与光学显微镜所识别神经元之间的信息鸿沟。解析构成每一个GAL4表达模式的单个神经元，可提升针对特定神经元的split-GAL4组合（split-GAL4）靶向方案的预测精度。为此，我们将这些成像数据在NeuronBridge网站（NeuronBridge）上线并支持检索。本研究验证了NeuronBridge平台的应用潜力：其可基于神经元形态特征，在不同成像模态与数据集间快速且高效地完成神经元匹配。