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4 Janelia Publications

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    03/15/24 | NeuronBridge: an intuitive web application for neuronal morphology search across large data sets
    Jody Clements , Cristian Goina , Philip M. Hubbard , Takashi Kawase , Donald J. Olbris , Hideo Otsuna , Robert Svirskas , Konrad Rokicki
    BMC Bioinformatics. 2024-03-15;25:114. doi: 10.1186/s12859-024-05732-7

    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’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.

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    02/26/24 | Nested neural circuits generate distinct acoustic signals during Drosophila courtship
    Joshua L. Lillvis , Kaiyu Wang , Hiroshi M. Shiozaki , Min Xu , David L. Stern , Barry J. Dickson
    Current Biology. 2024 Feb 26;34(4):808-24. doi: 10.1016/j.cub.2024.01.015

    Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings towards females to produce two different song modes – pulse and sine song – which signal species identity and male quality. Using cell-type specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song. Our data reveal that the song circuit is organized into two nested feed-forward pathways, with extensive reciprocal and feed-back connections. The larger network produces pulse song, the more complex and ancestral song form. A subset of this network produces sine song, the simpler and more recent form. Such nested organization may be a common feature of motor control circuits in which evolution has layered increasing flexibility on to a basic movement pattern.

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    09/01/23 | The Neural Basis of Drosophila Courtship Song
    Joshua L. Lillvis , Kaiyu Wang , Hiroshi M. Shiozaki , Min Xu , David L. Stern , Barry J. Dickson
    bioRxiv. 2023 Sep 01:. doi: 10.1101/2023.08.30.555537

    Animal sounds are produced by patterned vibrations of specific organs, but the neural circuits that drive these vibrations are not well defined in any animal. Here we provide a functional and synaptic map of most of the neurons in the Drosophila male ventral nerve cord (the analog of the vertebrate spinal cord) that drive complex, patterned song during courtship. Male Drosophila vibrate their wings toward females during courtship to produce two distinct song modes – pulse and sine song – with characteristic features that signal species identity and male quality. We identified song-producing neural circuits by optogenetically activating and inhibiting identified cell types in the ventral nerve cord (VNC) and by tracing their patterns of synaptic connectivity in the male VNC connectome. The core song circuit consists of at least eight cell types organized into overlapping circuits, where all neurons are required for pulse song and a subset are required for sine song. The pulse and sine circuits each include a feed-forward pathway from brain descending neurons to wing motor neurons, with extensive reciprocal and feed-back connections. We also identify specific neurons that shape the individual features of each song mode. These results reveal commonalities amongst diverse animals in the neural mechanisms that generate diverse motor patterns from a single set of muscles.

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    07/26/17 | Recent progress in the 3D reconstruction of Drosophila neural circuits.
    Shinomiya K, Ito M
    Decoding Neural Circuit Structure and Function:63-89. doi: 10.1007/978-3-319-57363-2_3

    The brain of fruit fly Drosophila melanogaster has been used as a model system for functional analysis of neuronal circuits, including connectomics research, due to its modest size (~700 μm) and availability of abundant molecular genetics tools for visualizing neurons. Three-dimensional (3D) reconstruction of high-resolution images of neurons or circuits visualized with appropriate methods is a critical step for obtaining information such as morphology and connectivity patterns of neuronal circuits. In this chapter, we introduce methods for generating 3D reconstructed images with data acquired from confocal laser scanning microscopy (CLSM) or electron microscopy (EM) to analyze neuronal circuits found in the central nervous system (CNS) of the fruit fly. Comparisons of different algorithms and strategies for reconstructing neuronal circuits, using actual studies as references, will be discussed within this chapter.

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