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51 Publications

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    05/16/24 | Hue selectivity from recurrent circuitry in Drosophila
    Christenson MP, Sanz Diez A, Heath SL, Saavedra-Weisenhaus M, Adachi A, Nern A, Abbott LF, Behnia R
    Nat Neurosci. 2024 May 16:. doi: 10.1038/s41593-024-01640-4

    In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.

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    04/25/24 | Connectomic Analysis of Mitochondria in the Central Brain of Drosophila
    Patricia K Rivlin , Michal Januszewski , Kit D Longden , Erika Neace , Louis K Scheffer , Christopher Ordish , Jody Clements , Elliott Phillips , Natalie Smith , Satoko Takemura , Lowell Umayam , Claire Walsh , Emily A Yakal , Stephen M Plaza , Stuart Berg
    bioRxiv. 2024 Apr 25:. doi: 10.1101/2024.04.21.590464

    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.

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    04/18/24 | Connectome-driven neural inventory of a complete visual system
    Aljoscha Nern , Frank Loesche , Shin-ya Takemura , Laura E Burnett , Marisa Dreher , Eyal Gruntman , Judith Hoeller , Gary B Huang , Michal Januszewski , Nathan C Klapoetke , Sanna Koskela , Kit D Longden , Zhiyuan Lu , Stephan Preibisch , Wei Qiu , Edward M Rogers , Pavithraa Seenivasan , Arthur Zhao , John Bogovic , Brandon S Canino , Jody Clements , Michael Cook , Samantha Finley-May , Miriam A Flynn , Imran Hameed , Kenneth J Hayworth , Gary Patrick Hopkins , Philip M Hubbard , William T Katz , Julie Kovalyak , Shirley A Lauchie , Meghan Leonard , Alanna Lohff , Charli A Maldonado , Caroline Mooney , Nneoma Okeoma , Donald J Olbris , Christopher Ordish , Tyler Paterson , Emily M Phillips , Tobias Pietzsch , Jennifer Rivas Salinas , Patricia K Rivlin , Ashley L Scott , Louis A Scuderi , Satoko Takemura , Iris Talebi , Alexander Thomson , Eric T Trautman , Lowell Umayam , Claire Walsh , John J Walsh , C Shan Xu , Emily A Yakal , Tansy Yang , Ting Zhao , Jan Funke , Reed George , Harald F Hess , Gregory S X E Jefferis , Christopher Knecht , Wyatt Korff , Stephen M Plaza , Sandro Romani , Stephan Saalfeld , Louis K Scheffer , Stuart Berg , Gerald M Rubin , Michael B Reiser
    bioRxiv. 2024 Apr 18:. doi: 10.1101/2024.04.16.589741

    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’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’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.

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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 Mar 15;25:114. doi: 10.1186/s12859-024-05732-7


    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.


    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.


    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

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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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    Card LabFlyEM
    06/07/23 | Transforming descending input into behavior: The organization of premotor circuits in the Drosophila Male Adult Nerve Cord connectome
    H. S. J. Cheong , K. Eichler , T. Stuerner , S. K. Asinof , A. S. Champion , E. C. Marin , T. B. Oram , M. Sumathipala , L. Venkatasubramanian , S. Namiki , I. Siwanowicz , M. Costa , S. Berg , Janelia FlyEM Project Team , G. S. X. E. Jefferis , G. M. Card
    bioRxiv. 2023 Jun 07:. doi: 10.1101/2023.06.07.543976

    In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal’s head to motor neurons (MNs) in the nerve cord of the animal’s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly, Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC, Takemura et al., 2023), including cell type and developmental lineage annotation (Marin et al., 2023), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the Drosophila ventral nerve cord in richer mechanistic detail.

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    06/06/23 | A Connectome of the Male Drosophila Ventral Nerve Cord
    Shin-ya Takemura , Kenneth J Hayworth , Gary B Huang , Michal Januszewski , Zhiyuan Lu , Elizabeth C Marin , Stephan Preibisch , C Shan Xu , John Bogovic , Andrew S Champion , Han S J Cheong , Marta Costa , Katharina Eichler , William Katz , Christopher Knecht , Feng Li , Billy J Morris , Christopher Ordish , Patricia K Rivlin , Philipp Schlegel , Kazunori Shinomiya , Tomke Sturner , Ting Zhao , Griffin Badalamente , Dennis Bailey , Paul Brooks , Brandon S Canino , Jody Clements , Michael Cook , Octave Duclos , Christopher R Dunne , Kelli Fairbanks , Siqi Fang , Samantha Finley-May , Audrey Francis , Reed George , Marina Gkantia , Kyle Harrington , Gary Patrick Hopkins , Joseph Hsu , Philip M Hubbard , Alexandre Javier , Dagmar Kainmueller , Wyatt Korff , Julie Kovalyak , Dominik Krzeminski , Shirley A Lauchie , Alanna Lohff , Charli Maldonado , Emily A Manley , Caroline Mooney , Erika Neace , Matthew Nichols , Omotara Ogundeyi , Nneoma Okeoma , Tyler Paterson , Elliott Phillips , Emily M Phillips , Caitlin Ribeiro , Sean M Ryan , Jon Thomson Rymer , Anne K Scott , Ashley L Scott , David Shepherd , Aya Shinomiya , Claire Smith , Alia Suleiman , Satoko Takemura , Iris Talebi , Imaan F M Tamimi , Eric T Trautman , Lowell Umayam , John J Walsh , Tansy Yang , Gerald M Rubin , Louis K Scheffer , Jan Funke , Stephan Saalfeld , Harald F Hess , Stephen M Plaza , Gwyneth M Card , Gregory S X E Jefferis , Stuart Berg
    bioRxiv. 2023 Jun 06:. doi: 10.1101/2023.06.05.543757

    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.

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    Card LabFlyEM
    06/06/23 | Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation
    Elizabeth C Marin , Billy J Morris , Tomke Stuerner , Andrew S Champion , Dominik Krzeminski , Griffin Badalamente , Marina Gkantia , Imaan F M Tamimi , Siqi Fang , Sung Soo Moon , Han S J Cheong , Feng Li , Philipp Schlegel , Stuart Berg , FlyEM Project Team , Gwyneth M Card , Marta Costa , David Shepherd , Gregory S X E Jefferis
    bioRxiv. 2023 Jun 06:. doi: 10.1101/2023.06.05.543407

    Our companion paper (Takemura et al., 2023) introduces the first completely proofread connectome of the nerve cord of an animal that can walk or fly. The base connectome consists of neuronal morphologies and the connections between them. However, in order to efficiently navigate and understand this connectome, it is crucial to have a system of annotations that systematically categorises and names neurons, linking them to the existing literature. In this paper we describe the comprehensive annotation of the VNC connectome, first by a system of hierarchical coarse annotations, then by grouping left-right and serially homologous neurons and eventually by defining systematic cell types for the intrinsic interneurons and sensory neurons of the VNC; descending and motor neurons are typed in (Cheong et al., 2023). We assign a sensory modality to over 5000 sensory neurons, cluster them by connectivity, and identify serially homologous cell types and a layered organisation likely corresponding to peripheral topography. We identify the developmental neuroblast of origin of the large majority of VNC neurons and confirm that (in most cases) all secondary neurons of each hemilineage express a single neurotransmitter. Neuroblast hemilineages are serially repeated along the segments of the nerve cord and generally exhibit consistent hemilineage-to-hemilineage connectivity across neuromeres, supporting the idea that hemilineages are a major organisational feature of the VNC. We also find that more than a third of individual neurons belong to serially homologous cell types, which were crucial for identifying motor neurons and sensory neurons across leg neuropils. Categorising interneurons by their neuropil innervation patterns provides an additional organisation axis. Over half of the intrinsic neurons of the VNC appear dedicated to the legs, with the majority restricted to single leg neuropils; in contrast, inhibitory interneurons connecting different leg neuropils, especially those crossing the midline, appear rarer than anticipated by standard models of locomotor circuitry. Our annotations are being released as part of the web application and also serve as the basis of programmatic analysis of the connectome through dedicated tools that we describe in this paper.

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    12/22/20 | Latent Feature Representation via Unsupervised Learning for Pattern Discovery in Massive Electron Microscopy Image Volumes
    Gary B Huang , Huei-Fang Yang , Shin-ya Takemura , Pat Rivlin , Stephen M Plaza
    arXiv. 2020 Dec 22:. doi: 10.48550/arXiv.2012.12175

    We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is to use data augmentations that preserve semantic meaning to generate synthetic examples of elements whose feature representations should be close to one another.
    We demonstrate the utility of our method applied to nano-scale electron microscopy data, where even relatively small portions of animal brains can require terabytes of image data. Although supervised methods can be used to predict and identify known patterns of interest, the scale of the data makes it difficult to mine and analyze patterns that are not known a priori. We show the ability of our learned representation to enable query by example, so that if a scientist notices an interesting pattern in the data, they can be presented with other locations with matching patterns. We also demonstrate that clustering of data in the learned space correlates with biologically-meaningful distinctions. Finally, we introduce a visualization tool and software ecosystem to facilitate user-friendly interactive analysis and uncover interesting biological patterns. In short, our work opens possible new avenues in understanding of and discovery in large data sets, arising in domains such as EM analysis.

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