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

Abstract

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.

New genetic tools for mushroom body output neurons in Drosophila

“…We generated split-GAL4 genetic driver lines corresponding to MBON cell type using well-established methods (Dionne et al, 2018; Luan et al, 2006; Pfeiffer et al, 2010; Tirian and Dickson, 2017). The morphologies of the MBONs, produced by electron microscopic reconstruction, were used to search databases of light microscopic images to identify enhancers whose expression patterns might yield clean driver lines for that MBON when intersected (Mais et al, 2021; Meissner et al, 2023). We took advantage of an expanded set of starting reagents that were not available when Aso et al (2014a) generated the original set of split-GAL4 drivers for the MB cell types; in addition to the ∼7,000 GAL4 expression patterns described in (Jenett et al, 2012), we had access to an additional ∼9,000 GAL4 expression patterns (Tirian and Dickson, 2017).…”

Connectomics and the neural basis of behaviour

“…The first step requires matching morphologies between EM and existing lower-resolution light-level (LM) data. Methods to semi-automate this process have been developed and are starting to be used more widely, taking advantage of extensive LM driver lines or multi-colour flip-out libraries [27][28][29][30][31]. The success of this process is however strongly correlated to the amount of data available for a particular morphology, as the range of observed natural variability in both EM and LM data, as well as potential developmental abnormalities, need to be considered, not only to map individual neurons but to define cell types [17,32], the reproducible and recognisable sets of one or more neurons with similar morphology and connectivity between hemispheres and individuals.…”

“…• Neuroglancer (https://github.com/google/neuroglancer): WebGL-based viewer for volumetric data, often used for EM connectomics projects. • NeuronBridge (https://neuronbridge.janelia.org/) [27]: resource allowing similarity matching between MCFO LM images and EM morphologies. • natverse (https://natverse.org/) [37]: collection of R packages for neuroanatomical analysis, including LM and EM connectomics data.…”

Neural mechanisms to exploit positional geometry for collision avoidance

“…Major downstream neuron types of LPLC1 were identified in the hemibrain v1.1 dataset (Scheffer et al, 2020) through the neuprint website (Clements et al, 2020). Since there were no preexisting selective Gal4 drives to label PLP219 and PVLP112/113, we created a new split Gal4 lines by screening for hemidrivers targeting these cell types using color depth maximum intensity projection search (Otsuna et al, 2018) running on multi-color flip out image library (Meissner et al, 2020) on the NeuronBridge website (Clements et al, 2020).…”