@article {69373, title = {Connectome-driven neural inventory of a complete visual system}, journal = {Nature}, year = {2024}, month = {2025 Mar 26}, abstract = {

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain{\textquoteright}s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly{\textquoteright}s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the 53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

}, doi = {10.1038/s41586-025-08746-0}, url = {https://doi.org/10.1038/s41586-025-08746-0}, author = {Aljoscha Nern and Frank Loesche and Shin-ya Takemura and Laura E Burnett and Marisa Dreher and Eyal Gruntman and Judith Hoeller and Gary B Huang and Michal Januszewski and Nathan C Klapoetke and Sanna Koskela and Kit D Longden and Zhiyuan Lu and Stephan Preibisch and Wei Qiu and Edward M Rogers and Pavithraa Seenivasan and Arthur Zhao and John Bogovic and Brandon S Canino and Jody Clements and Michael Cook and Samantha Finley-May and Miriam A Flynn and Imran Hameed and Kenneth J Hayworth and Gary Patrick Hopkins and Philip M Hubbard and William T Katz and Julie Kovalyak and Shirley A Lauchie and Meghan Leonard and Alanna Lohff and Charli A Maldonado and Caroline Mooney and Nneoma Okeoma and Donald J Olbris and Christopher Ordish and Tyler Paterson and Emily M Phillips and Tobias Pietzsch and Jennifer Rivas Salinas and Patricia K Rivlin and Ashley L Scott and Louis A Scuderi and Satoko Takemura and Iris Talebi and Alexander Thomson and Eric T Trautman and Lowell Umayam and Claire Walsh and John J Walsh and C Shan Xu and Emily A Yakal and Tansy Yang and Ting Zhao and Jan Funke and Reed George and Harald F Hess and Gregory S X E Jefferis and Christopher Knecht and Wyatt Korff and Stephen M Plaza and Sandro Romani and Stephan Saalfeld and Louis K Scheffer and Stuart Berg and Gerald M Rubin and Michael B Reiser} } @article {69403, title = {Connectomic Analysis of Mitochondria in the Central Brain of Drosophila}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 25}, abstract = {

Mitochondria are an integral part of the metabolism of a neuron. EM images of fly brain volumes, taken for connectomics, contain mitochondria as well as the cells and synapses that have already been reported. Here, from the Drosophila hemibrain dataset, we extract, classify, and measure approximately 6 million mitochondria among roughly 21 thousand neurons of more than 5500 cell types. Each mitochondrion is classified by its appearance - dark and dense, light and sparse, or intermediate - and the location, orientation, and size (in voxels) are annotated. These mitochondria are added to our publicly available data portal, and each synapse is linked to its closest mitochondrion. Using this data, we show quantitative evidence that mitochodrial trafficing extends to the smallest dimensions in neurons. The most basic characteristics of mitochondria - volume, distance from synapses, and color - vary considerably between cell types, and between neurons with different neurotransmitters. We find that polyadic synapses with more post-synaptic densities (PSDs) have closer and larger mitochondria on the pre-synaptic side, but smaller and more distant mitochondria on the PSD side. We note that this relationship breaks down for synapses with only one PSD, suggesting a different role for such synapses.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.04.21.590464}, url = {https://www.biorxiv.org/content/early/2024/04/25/2024.04.21.590464}, author = {Patricia K Rivlin and Michal Januszewski and Kit D Longden and Erika Neace and Louis K Scheffer and Christopher Ordish and Jody Clements and Elliott Phillips and Natalie Smith and Satoko Takemura and Lowell Umayam and Claire Walsh and Emily A Yakal and Stephen M Plaza and Stuart Berg} } @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 {68800, title = {A Connectome of the Male Drosophila Ventral Nerve Cord}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 06}, abstract = {

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the\ Drosophila\ central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

}, doi = {10.1101/2023.06.05.543757}, url = {https://doi.org/10.1101/2023.06.05.543757}, author = {Shin-ya Takemura and Kenneth J Hayworth and Gary B Huang and Michal Januszewski and Zhiyuan Lu and Elizabeth C Marin and Stephan Preibisch and C Shan Xu and John Bogovic and Andrew S Champion and Han S J Cheong and Marta Costa and Katharina Eichler and William Katz and Christopher Knecht and Feng Li and Billy J Morris and Christopher Ordish and Patricia K Rivlin and Philipp Schlegel and Kazunori Shinomiya and Tomke Sturner and Ting Zhao and Griffin Badalamente and Dennis Bailey and Paul Brooks and Brandon S Canino and Jody Clements and Michael Cook and Octave Duclos and Christopher R Dunne and Kelli Fairbanks and Siqi Fang and Samantha Finley-May and Audrey Francis and Reed George and Marina Gkantia and Kyle Harrington and Gary Patrick Hopkins and Joseph Hsu and Philip M Hubbard and Alexandre Javier and Dagmar Kainmueller and Wyatt Korff and Julie Kovalyak and Dominik Krzeminski and Shirley A Lauchie and Alanna Lohff and Charli Maldonado and Emily A Manley and Caroline Mooney and Erika Neace and Matthew Nichols and Omotara Ogundeyi and Nneoma Okeoma and Tyler Paterson and Elliott Phillips and Emily M Phillips and Caitlin Ribeiro and Sean M Ryan and Jon Thomson Rymer and Anne K Scott and Ashley L Scott and David Shepherd and Aya Shinomiya and Claire Smith and Alia Suleiman and Satoko Takemura and Iris Talebi and Imaan F M Tamimi and Eric T Trautman and Lowell Umayam and John J Walsh and Tansy Yang and Gerald M Rubin and Louis K Scheffer and Jan Funke and Stephan Saalfeld and Harald F Hess and Stephen M Plaza and Gwyneth M Card and Gregory S X E Jefferis and Stuart Berg} } @article {68716, title = {PEELing: an integrated and user-centric platform for cell-surface proteomics analysis}, journal = {bioRxiv}, year = {2023}, month = {2023 Apr 25}, abstract = {

Proteins localized at the cellular interface mediate cell-cell communication and thus control many aspects of physiology in multicellular organisms. Cell-surface proteomics allows biologists to comprehensively identify proteins on the cell surface and survey their dynamics in physiological and pathological conditions. PEELing provides an integrated package and user-centric web service for analyzing cell-surface proteomics data. With a streamlined and automated workflow, PEELing evaluates data quality using curated references, performs cutoff analysis to remove contaminants, connects to databases for functional annotation, and generates data visualizations. Together with chemical and transgenic tools, PEELing completes a pipeline making cell-surface proteomics analysis handy for every lab.

}, doi = {10.1101/2023.04.21.537871}, url = {https://doi.org/10.1101/2023.04.21.537871}, author = {Xi Peng and Jody Clements and Zuzhi Jiang and Stephan Preibisch and Jiefu Li} }