Main Menu (Mobile)- Block

Main Menu - Block

janelia7_blocks-janelia7_fake_breadcrumb | block
Flyem / Publications
general_search_page-panel_pane_1 | views_panes

9 Publications

Showing 1-9 of 9 results
Your Criteria:
    10/06/21 | An open-access volume electron microscopy atlas of whole cells and tissues.
    Xu CS, Pang S, Shtengel G, Müller A, Ritter AT, Hoffman HK, Takemura S, Lu Z, Pasolli HA, Iyer N, Chung J, Bennett D, Weigel AV, Freeman M, Van Engelenburg SB, Walther TC, Farese RV, Lippincott-Schwartz J, Mellman I, Solimena M, Hess HF
    Nature. 2021 Oct 06:. doi: 10.1038/s41586-021-03992-4

    Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structures with nanometre resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations in that they visualize only a single slice or a relatively small volume of the cell, respectively. Focused ion beam-scanning electron microscopy (FIB-SEM) has demonstrated the ability to image small volumes of cellular samples with 4-nm isotropic voxels. Owing to advances in the precision and stability of FIB milling, together with enhanced signal detection and faster SEM scanning, we have increased the volume that can be imaged with 4-nm voxels by two orders of magnitude. Here we present a volume EM atlas at such resolution comprising ten three-dimensional datasets for whole cells and tissues, including cancer cells, immune cells, mouse pancreatic islets and Drosophila neural tissues. These open access data (via OpenOrganelle) represent the foundation of a field of high-resolution whole-cell volume EM and subsequent analyses, and we invite researchers to explore this atlas and pose questions.

    View Publication Page
    09/03/20 | A connectome of the adult drosophila central brain.
    Xu CS, Januszewski M, Lu Z, Takemura S, Hayworth KJ, Huang G, Shinomiya K, Maitin-Shepard J, Ackerman D, Berg S, Blakely T, Bogovic J, Clements J, Dolafi T, Hubbard P, Kainmueller D, Katz W, Kawase T, Khairy KA, Leavitt L, Li PH, Lindsey L, Neubarth N, Olbris DJ, Otsuna H, Troutman ET, Umayam L, Zhao T, Ito M, Goldammer J, Wolff T, Svirskas R, Schlegel P, Neace ER, Knecht CJ, Alvarado CX, Bailey DA, Ballinger S, Borycz JA, Canino BS
    eLife. 2020 Sep 03:. doi:

    The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.

    Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

    View Publication Page
    01/09/19 | Comparisons between the ON- and OFF-edge motion pathways in the brain.
    Shinomiya K, Huang G, Lu Z, Parag T, Xu CS, Aniceto R, Ansari N, Cheatham N, Lauchie S, Neace E, Ogundeyi O, Ordish C, Peel D, Shinomiya A, Smith C, Takemura S, Talebi I, Rivlin PK, Nern A, Scheffer LK, Plaza SM, Meinertzhagen IA
    eLife. 2019 Jan 09;8:. doi: 10.7554/eLife.40025

    Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

    View Publication Page
    11/01/18 | A resource for the antennal lobe provided by the connectome of glomerulus VA1v.
    Horne JA, Langille C, McLin S, Wiederman M, Lu Z, Xu CS, Plaza SM, Scheffer LK, Hess HF, Meinertzhagen IA
    eLife. 2018 Nov 01;7:. doi: 10.7554/eLife.37550

    Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in . Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The -positive, sexually dimorphic VA1v included >11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of >150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having 50 connections; these form 88% of the glomerulus' pre/postsynaptic sites.

    View Publication Page
    07/18/17 | A connectome of a learning and memory center in the adult Drosophila brain.
    Takemura S, Aso Y, Hige T, Wong AM, Lu Z, Xu CS, Rivlin PK, Hess HF, Zhao T, Parag T, Berg S, Huang G, Katz WT, Olbris DJ, Plaza SM, Umayam LA, Aniceto R, Chang L, Lauchie S, et al
    eLife. 2017 Jul 18;6:e26975. doi: 10.7554/eLife.26975

    Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall.

    View Publication Page
    11/03/15 | Synaptic circuits and their variations within different columns in the visual system of Drosophila.
    Takemura S, Xu CS, Lu Z, Rivlin PK, Parag T, Olbris DJ, Plaza S, Zhao T, Katz WT, Umayam L, Weaver C, Hess HF, Horne JA, Nunez-Iglesias J, Aniceto R, Chang L, Lauchie S, Nasca A, Ogundeyi O, Sigmund C, Takemura S, Tran J, Langille C, Le Lacheur K, McLin S, Shinomiya A, Chklovskii DB, Meinertzhagen IA, Scheffer LK
    Proceedings of the National Academy of Sciences of the United States of America. 2015 Nov 3;112(44):13711-6. doi: 10.1073/pnas.1509820112

    We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form ≥20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

    View Publication Page
    Hess LabFetter LabFlyEM
    02/16/15 | Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.
    Hayworth KJ, Xu CS, Lu Z, Knott GW, Fetter RD, Tapia JC, Lichtman JW, Hess HF
    Nature Methods. 2015 Feb 16;12(4):319-22. doi: 10.1038/nmeth.3292

    Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm × 10 nm × 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then 'volume stitched' back together, producing a final three-dimensional data set suitable for connectome tracing.

    View Publication Page
    10/01/12 | Super-resolution using sparse representations over learned dictionaries: reconstruction of brain structure using electron microscopy.
    Hu T, Nunez-Iglesias J, Vitaladevuni S, Scheffer L, Xu S, Bolorizadeh M, Hess H, Fetter R, Chklovskii D . 2012 Oct:

    A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

    View Publication Page
    01/01/10 | Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction.
    Veeraraghavan A, Genkin AV, Vitaladevuni S, Scheffer L, Xu C, Hess H, Fetter R, Cantoni M, Knott G, Chklovskii DB
    Computer Vision and Pattern Recognition (CVPR). 2010:1767-74. doi: 10.1109/CVPR.2010.5539846