@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 {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} } @article {67243, title = {Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization}, journal = {bioRxiv}, year = {2021}, month = {2021 Mar 8}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed\ Expansion-Assisted Iterative\ Fluorescence\ In\ Situ\ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 {\textmu}m) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, doi = {10.1101/2021.03.08.434304}, url = {https://doi.org/10.1101/2021.03.08.434304}, author = {Yuhan Wang and Mark Eddison and Greg Fleishman and Martin Weigert and Shengjin Xu and Frederick E. Henry and Tim Wang and Andrew L. Lemire and Uwe Schmidt and Hui Yang and Konrad Rokicki and Cristian Goina and Karel Svoboda and Eugene W. Myers and Stephan Saalfeld and Wyatt Korff and Scott M. Sternson and Paul W. Tillberg} }