A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution.
A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution.
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DOI：
10.7554/elife.80660
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发表时间：
2023-02-23
期刊：
影响因子：
7.7
通讯作者：
FlyLight Project Team
中科院分区：
文献类型：
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作者：
Meissner GW;Nern A;Dorman Z;DePasquale GM;Forster K;Gibney T;Hausenfluck JH;He Y;Iyer NA;Jeter J;Johnson L;Johnston RM;Lee K;Melton B;Yarbrough B;Zugates CT;Clements J;Goina C;Otsuna H;Rokicki K;Svirskas RR;Aso Y;Card GM;Dickson BJ;Ehrhardt E;Goldammer J;Ito M;Kainmueller D;Korff W;Mais L;Minegishi R;Namiki S;Rubin GM;Sterne GR;Wolff T;Malkesman O;FlyLight Project Team
通过GAL 4/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神经科学的一个关键优势。神经元靶向通常使用全GAL 4表达模式的光学显微镜记录，其通常缺乏可靠的细胞类型鉴定所需的单细胞分辨率。在这里，我们使用随机GAL 4标记与多色FlpOut方法生成细胞分辨率共聚焦图像在大规模。我们正在发布74，000个这样的成人中枢神经系统的对齐图像。这种资源的预期用途是弥合电子或光学显微镜鉴定的神经元之间的差距。识别构成每个GAL 4表达模式的单个神经元改善了靶向特定神经元的分裂GAL 4组合的预测。为此，我们在NeuronBridge网站上提供了可搜索的图像。我们证明了NeuronBridge在成像模式和数据集上基于形态学快速有效地识别神经元匹配的潜力。
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.