Precise, repeatable genetic access to specific neurons via the GAL4/UAS system and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which mostly 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 27,000 such adult central nervous systems.An anticipated use of this resource is to bridge the gap between electron microscopyidentified neurons and light microscopy-based intersectional genetic approaches such as the split-GAL4 system. Identifying the individual neurons that make up each GAL4 expression pattern improves the prediction of which GAL4 enhancer fragments best combine via split-GAL4 to target neurons of interest. To this end we have developed the NeuronBridge search tool, which matches these light microscope neuronal images to neurons in the recently published FlyEM hemibrain. This work thus provides a resource and search tool that will significantly enhance both the efficiency and efficacy of split-GAL4 targeting of EM-identified neurons and further advance Drosophila neuroscience.Meissner, et al., 2020Gen1 MCFO Phase 1 release
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.
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