@article {68799, title = {Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster}, journal = {eLife}, year = {2025}, month = {2025 May 06}, abstract = {

To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.

}, doi = {10.7554/eLife.106548.1}, url = {https://doi.org/10.7554/eLife.106548.1}, author = {Erica Ehrhardt and Samuel C Whitehead and Shigehiro Namiki and Ryo Minegishi and Igor Siwanowicz and Kai Feng and Hideo Otsuna and FlyLight Project Team and Geoffrey W Meissner and David Stern and Jim Truman and David Shepherd and Michael H. Dickinson and Kei Ito and Barry J Dickson and Itai Cohen and Gwyneth M Card and Wyatt Korff} } @article {68258, title = {NeuronBridge: an intuitive web application for neuronal morphology search across large data sets}, journal = {BMC Bioinformatics}, volume = {25}, year = {2024}, month = {2024 Mar 15}, pages = {114}, abstract = {

Background

Neuroscience research in\ Drosophila\ is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome{\textquoteright}s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.

Results

Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.

Conclusions

NeuronBridge fills a critical gap in the\ Drosophila\ research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at\ http://neuronbridge.janelia.org.

}, doi = {10.1186/s12859-024-05732-7}, url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05732-7}, author = {Jody Clements and Cristian Goina and Philip M. Hubbard and Takashi Kawase and Donald J. Olbris and Hideo Otsuna and Robert Svirskas and Konrad Rokicki} } @article {69329, title = {A split-GAL4 driver line resource for Drosophila CNS cell types}, journal = {bioRxiv}, year = {2024}, month = {2024 Jan 10}, abstract = {

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila central nervous system and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

}, doi = {10.1101/2024.01.09.574419}, url = {https://www.biorxiv.org/content/early/2024/04/01/2024.01.09.574419}, author = {Geoffrey W Meissner and Allison Vannan and Jennifer Jeter and Kari Close and Gina M DePasquale and Zachary Dorman and Kaitlyn Forster and Jaye Anne Beringer and Theresa V Gibney and Joanna H Hausenfluck and Yisheng He and Kristin Henderson and Lauren Johnson and Rebecca M Johnston and Gudrun Ihrke and Nirmala Iyer and Rachel Lazarus and Kelley Lee and Hsing-Hsi Li and Hua-Peng Liaw and Brian Melton and Scott Miller and Reeham Motaher and Alexandra Novak and Omotara Ogundeyi and Alyson Petruncio and Jacquelyn Price and Sophia Protopapas and Susana Tae and Jennifer Taylor and Rebecca Vorimo and Brianna Yarbrough and Kevin Xiankun Zeng and Christopher T Zugates and Heather Dionne and Claire Angstadt and Kelly Ashley and Amanda Cavallaro and Tam Dang and Guillermo A Gonzalez III and Karen L Hibbard and Cuizhen Huang and Jui-Chun Kao and Todd Laverty and Monti Mercer and Brenda Perez and Scarlett Pitts and Danielle Ruiz and Viruthika Vallanadu and Grace Zhiyu Zheng and Cristian Goina and Hideo Otsuna and Konrad Rokicki and Robert R Svirskas and Han SJ Cheong and Michael-John Dolan and Erica Ehrhardt and Kai Feng and Basel El Galfi and Jens Goldammer and Stephen J Huston and Nan Hu and Masayoshi Ito and Claire McKellar and Ryo Minegishi and Shigehiro Namiki and Aljoscha Nern and Catherine E Schretter and Gabriella R Sterne and Lalanti Venkatasubramanian and Kaiyu Wang and Tanya Wolff and Ming Wu and Reed George and Oz Malkesman and Yoshinori Aso and Gwyneth M Card and Barry J Dickson and Wyatt Korff and Kei Ito and James W Truman and Marta Zlatic and Gerald M Rubin and FlyLight Project Team} } @article {68571, title = {Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement}, journal = {eLife}, year = {2023}, month = {2023 Sep 18}, abstract = {

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After training, disinhibition from the appetitive-memory MBONs enhances the response of UpWiNs to reward-predicting odors. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was initiated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

}, doi = {10.7554/eLife.85756}, url = {https://doi.org/10.7554/eLife.85756}, author = {Yoshinori Aso and Daichi Yamada and Daniel Bushey and Karen Hibbard and Megan Sammons and Hideo Otsuna and Yichun Shuai and Toshihide Hige} } @article {67849, title = {Rapid reconstruction of neural circuits using tissue expansion and lattice light sheet microscopy}, journal = {eLife}, year = {2022}, month = {2022 Oct 26}, abstract = {

Electron microscopy (EM) allows for the reconstruction of dense neuronal connectomes but suffers from low throughput, limiting its application to small numbers of reference specimens. We developed a protocol and analysis pipeline using tissue expansion and lattice light-sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many samples with single synapse resolution and molecular contrast. We validate this approach in\ Drosophila, demonstrating that it yields synaptic counts similar to those obtained by EM, can be used to compare counts across sex and experience, and to correlate structural connectivity with functional connectivity. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

}, doi = {10.7554/eLife.81248}, url = {https://doi.org/10.7554/eLife.81248}, author = {Joshua L. Lillvis and Hideo Otsuna and Xiaoyu Ding and Igor Pisarev and Takashi Kawase and Jennifer Colonell and Konrad Rokicki and Cristian Goina and Ruixuan Gao and Amy Hu and Kaiyu Wang and John Bogovic and Daniel E. Milkie and Edward S. Boyden and Stephan Saalfeld and Paul W. Tillberg and Barry J. Dickson} }