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GAL4 驱动表达模式的可搜索图像资源,具有单个神经元分辨率。

A searchable image resource of GAL4 driver expression patterns with single neuron resolution.

机构信息

Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.

Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.

出版信息

Elife. 2023 Feb 23;12:e80660. doi: 10.7554/eLife.80660.

Abstract

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of 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.

摘要

通过 GAL4/UAS 及相关方法实现对特定神经元的精确、可重复的基因操作,是神经科学的一个主要优势。神经元靶向通常通过对全 GAL4 表达模式进行光学显微镜检测来记录,而这种方法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 GAL4 标记与多色 FlpOut 方法相结合,以生成大规模的细胞分辨率共聚焦图像。我们正在发布 74000 个这样的成年中枢神经系统的对齐图像。预计此资源的一个用途是弥合通过电子显微镜或光学显微镜鉴定的神经元之间的差距。鉴定构成每个 GAL4 表达模式的单个神经元可以提高对特定神经元进行靶向的分裂 GAL4 组合的预测能力。为此,我们已经在 NeuronBridge 网站上对这些图像进行了搜索。我们展示了 NeuronBridge 在基于形态学跨成像模式和数据集快速有效地识别神经元匹配方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/379b/10030108/53ec73d38176/elife-80660-fig1.jpg

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