1
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Meschi E, Duquenoy L, Otto N, Dempsey G, Waddell S. Compensatory enhancement of input maintains aversive dopaminergic reinforcement in hungry Drosophila. Neuron 2024; 112:2315-2332.e8. [PMID: 38795709 DOI: 10.1016/j.neuron.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/12/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Hungry animals need compensatory mechanisms to maintain flexible brain function, while modulation reconfigures circuits to prioritize resource seeking. In Drosophila, hunger inhibits aversively reinforcing dopaminergic neurons (DANs) to permit the expression of food-seeking memories. Multitasking the reinforcement system for motivation potentially undermines aversive learning. We find that chronic hunger mildly enhances aversive learning and that satiated-baseline and hunger-enhanced learning require endocrine adipokinetic hormone (AKH) signaling. Circulating AKH influences aversive learning via its receptor in four neurons in the ventral brain, two of which are octopaminergic. Connectomics revealed AKH receptor-expressing neurons to be upstream of several classes of ascending neurons, many of which are presynaptic to aversively reinforcing DANs. Octopaminergic modulation of and output from at least one of these ascending pathways is required for shock- and bitter-taste-reinforced aversive learning. We propose that coordinated enhancement of input compensates for hunger-directed inhibition of aversive DANs to preserve reinforcement when required.
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Affiliation(s)
- Eleonora Meschi
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Lucille Duquenoy
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Nils Otto
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Georgia Dempsey
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Scott Waddell
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK.
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2
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. Nat Commun 2024; 15:5698. [PMID: 38972924 PMCID: PMC11228034 DOI: 10.1038/s41467-024-49616-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.
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Affiliation(s)
- Ishani Ganguly
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.
| | - Rudy Behnia
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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3
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Xu C, Li Z, Lyu C, Hu Y, McLaughlin CN, Wong KKL, Xie Q, Luginbuhl DJ, Li H, Udeshi ND, Svinkina T, Mani DR, Han S, Li T, Li Y, Guajardo R, Ting AY, Carr SA, Li J, Luo L. Molecular and cellular mechanisms of teneurin signaling in synaptic partner matching. Cell 2024:S0092-8674(24)00696-2. [PMID: 38996528 DOI: 10.1016/j.cell.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 05/20/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024]
Abstract
In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionarily conserved teneurins are transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins' functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of a teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching.
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Affiliation(s)
- Chuanyun Xu
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Biology Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Zhuoran Li
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Biology Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Cheng Lyu
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Yixin Hu
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Biology Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Colleen N McLaughlin
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Kenneth Kin Lam Wong
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Qijing Xie
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - David J Luginbuhl
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Hongjie Li
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Namrata D Udeshi
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tanya Svinkina
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shuo Han
- Departments of Genetics, Biology, and Chemistry, Chan Zuckerberg Biohub, Stanford University, Stanford, CA 94305, USA
| | - Tongchao Li
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Yang Li
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Biology Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Ricardo Guajardo
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Alice Y Ting
- Departments of Genetics, Biology, and Chemistry, Chan Zuckerberg Biohub, Stanford University, Stanford, CA 94305, USA
| | - Steven A Carr
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jiefu Li
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Biology Graduate Program, Stanford University, Stanford, CA 94305, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Liqun Luo
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
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4
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Lee CT, Bell M, Bonilla-Quintana M, Rangamani P. Biophysical Modeling of Synaptic Plasticity. Annu Rev Biophys 2024; 53:397-426. [PMID: 38382115 DOI: 10.1146/annurev-biophys-072123-124954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Dendritic spines are small, bulbous compartments that function as postsynaptic sites and undergo intense biochemical and biophysical activity. The role of the myriad signaling pathways that are implicated in synaptic plasticity is well studied. A recent abundance of quantitative experimental data has made the events associated with synaptic plasticity amenable to quantitative biophysical modeling. Spines are also fascinating biophysical computational units because spine geometry, signal transduction, and mechanics work in a complex feedback loop to tune synaptic plasticity. In this sense, ideas from modeling cell motility can inspire us to develop multiscale approaches for predictive modeling of synaptic plasticity. In this article, we review the key steps in postsynaptic plasticity with a specific focus on the impact of spine geometry on signaling, cytoskeleton rearrangement, and membrane mechanics. We summarize the main experimental observations and highlight how theory and computation can aid our understanding of these complex processes.
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Affiliation(s)
- Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA;
| | - Miriam Bell
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA;
| | - Mayte Bonilla-Quintana
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA;
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA;
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5
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Yeh LH, Ivanov IE, Chandler T, Byrum JR, Chhun BB, Guo SM, Foltz C, Hashemi E, Perez-Bermejo JA, Wang H, Yu Y, Kazansky PG, Conklin BR, Han MH, Mehta SB. Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution. Nat Methods 2024; 21:1257-1274. [PMID: 38890427 PMCID: PMC11239526 DOI: 10.1038/s41592-024-02291-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/24/2024] [Indexed: 06/20/2024]
Abstract
The dry mass and the orientation of biomolecules can be imaged without a label by measuring their permittivity tensor (PT), which describes how biomolecules affect the phase and polarization of light. Three-dimensional (3D) imaging of PT has been challenging. We present a label-free computational microscopy technique, PT imaging (PTI), for the 3D measurement of PT. PTI encodes the invisible PT into images using oblique illumination, polarization-sensitive detection and volumetric sampling. PT is decoded from the data with a vectorial imaging model and a multi-channel inverse algorithm, assuming uniaxial symmetry in each voxel. We demonstrate high-resolution imaging of PT of isotropic beads, anisotropic glass targets, mouse brain tissue, infected cells and histology slides. PTI outperforms previous label-free imaging techniques such as vector tomography, ptychography and light-field imaging in resolving the 3D orientation and symmetry of organelles, cells and tissue. We provide open-source software and modular hardware to enable the adoption of the method.
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Affiliation(s)
- Li-Hao Yeh
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- ASML, San Jose, CA, USA
| | | | | | - Janie R Byrum
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- California's Stem Cell Agency, South San Francisco, CA, USA
| | - Bryant B Chhun
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Eikon Therapeutics, Hayward, CA, USA
| | - Syuan-Ming Guo
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Insitro, South San Francisco, CA, USA
| | - Cameron Foltz
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Quantinuum, Broomfield, CO, USA
| | | | - Juan A Perez-Bermejo
- Gladstone Institutes, San Francisco, CA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Yanhao Yu
- University of Southampton, Southampton, UK
| | | | - Bruce R Conklin
- Gladstone Institutes, San Francisco, CA, USA
- University of California San Francisco, San Francisco, CA, USA
| | - May H Han
- Stanford University, Palo Alto, CA, USA
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6
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Thiyagarajan VV, Sheridan A, Harris KM, Manor U. A deep learning-based strategy for producing dense 3D segmentations from sparsely annotated 2D images. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.599135. [PMID: 38915491 PMCID: PMC11195258 DOI: 10.1101/2024.06.14.599135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths.
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Affiliation(s)
- Vijay Venu Thiyagarajan
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712
| | - Arlo Sheridan
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, 92037
| | - Kristen M. Harris
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin Texas, 78712
| | - Uri Manor
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, 92037
- Department of Cell & Developmental Biology, School of Biological Sciences, University of California, San Diego, CA 92093
- Halıcıoğlu Data Science Institute, University of California, San Diego, CA 92093
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7
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Smolin N, Dombrovski M, Hina BW, Moreno-Sanchez A, Gossart R, Carmona CR, Rehan A, Hussein RH, Mirshahidi P, Ausborn J, Kurmangaliyev YZ, von Reyn CR. Neuronal identity control at the resolution of a single transcription factor isoform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598883. [PMID: 38915533 PMCID: PMC11195191 DOI: 10.1101/2024.06.14.598883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain exhibits remarkable neuronal diversity which is critical for its functional integrity. From the sheer number of cell types emerging from extensive transcriptional, morphological, and connectome datasets, the question arises of how the brain is capable of generating so many unique identities. 'Terminal selectors' are transcription factors hypothesized to determine the final identity characteristics in post-mitotic cells. Which transcription factors function as terminal selectors and the level of control they exert over different terminal characteristics are not well defined. Here, we establish a novel role for the transcription factor broad as a terminal selector in Drosophila melanogaster. We capitalize on existing large sequencing and connectomics datasets and employ a comprehensive characterization of terminal characteristics including Perturb-seq and whole-cell electrophysiology. We find a single isoform broad-z4 serves as the switch between the identity of two visual projection neurons LPLC1 and LPLC2. Broad-z4 is natively expressed in LPLC1, and is capable of transforming the transcriptome, morphology, and functional connectivity of LPLC2 cells into LPLC1 cells when perturbed. Our comprehensive work establishes a single isoform as the smallest unit underlying an identity switch, which may serve as a conserved strategy replicated across developmental programs.
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Affiliation(s)
- Natalie Smolin
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Mark Dombrovski
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | - Ryan Gossart
- Brandeis University, Department of Biology, Waltham, MA
| | | | - Aadil Rehan
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Roni H. Hussein
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Parmis Mirshahidi
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | | | - Catherine R. von Reyn
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
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8
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Boto T, Tomchik SM. Functional Imaging of Learning-Induced Plasticity in the Central Nervous System with Genetically Encoded Reporters in Drosophila. Cold Spring Harb Protoc 2024; 2024:pdb.top107799. [PMID: 37197830 DOI: 10.1101/pdb.top107799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Learning and memory allow animals to adjust their behavior based on the predictive value of their past experiences. Memories often exist in complex representations, spread across numerous cells and synapses in the brain. Studying relatively simple forms of memory provides insights into the fundamental processes that underlie multiple forms of memory. Associative learning occurs when an animal learns the relationship between two previously unrelated sensory stimuli, such as when a hungry animal learns that a particular odor is followed by a tasty reward. Drosophila is a particularly powerful model to study how this type of memory works. The fundamental principles are widely shared among animals, and there is a wide range of genetic tools available to study circuit function in flies. In addition, the olfactory structures that mediate associative learning in flies, such as the mushroom body and its associated neurons, are anatomically organized, relatively well-characterized, and readily accessible to imaging. Here, we review the olfactory anatomy and physiology of the olfactory system, describe how plasticity in the olfactory pathway mediates learning and memory, and explain the general principles underlying calcium imaging approaches.
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Affiliation(s)
- Tamara Boto
- Department of Physiology, Trinity College Dublin, Dublin 2, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Seth M Tomchik
- Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
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9
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Kramer TS, Flavell SW. Building and integrating brain-wide maps of nervous system function in invertebrates. Curr Opin Neurobiol 2024; 86:102868. [PMID: 38569231 DOI: 10.1016/j.conb.2024.102868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/13/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.
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Affiliation(s)
- Talya S Kramer
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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10
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Christenson MP, Sanz Diez A, Heath SL, Saavedra-Weisenhaus M, Adachi A, Nern A, Abbott LF, Behnia R. Hue selectivity from recurrent circuitry in Drosophila. Nat Neurosci 2024; 27:1137-1147. [PMID: 38755272 DOI: 10.1038/s41593-024-01640-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/04/2024] [Indexed: 05/18/2024]
Abstract
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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Affiliation(s)
- Matthias P Christenson
- Zuckerman Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Alvaro Sanz Diez
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Sarah L Heath
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Maia Saavedra-Weisenhaus
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Atsuko Adachi
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - L F Abbott
- Zuckerman Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Rudy Behnia
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA.
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11
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Lu C, Chen K, Qiu H, Chen X, Chen G, Qi X, Jiang H. Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy. Nat Commun 2024; 15:4677. [PMID: 38824146 PMCID: PMC11144272 DOI: 10.1038/s41467-024-49125-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 05/20/2024] [Indexed: 06/03/2024] Open
Abstract
Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here, we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions' reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.
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Affiliation(s)
- Chixiang Lu
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Kai Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Heng Qiu
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Xiaojun Chen
- School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Gu Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - Xiaojuan Qi
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
| | - Haibo Jiang
- Department of Chemistry, The University of Hong Kong, Hong Kong, China.
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12
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Nern A, Loesche F, Takemura SY, Burnett LE, Dreher M, Gruntman E, Hoeller J, Huang GB, Januszewski M, Klapoetke NC, Koskela S, Longden KD, Lu Z, Preibisch S, Qiu W, Rogers EM, Seenivasan P, Zhao A, Bogovic J, Canino BS, Clements J, Cook M, Finley-May S, Flynn MA, Hameed I, Fragniere AMC, Hayworth KJ, Hopkins GP, Hubbard PM, Katz WT, Kovalyak J, Lauchie SA, Leonard M, Lohff A, Maldonado CA, Mooney C, Okeoma N, Olbris DJ, Ordish C, Paterson T, Phillips EM, Pietzsch T, Salinas JR, Rivlin PK, Schlegel P, Scott AL, Scuderi LA, Takemura S, Talebi I, Thomson A, Trautman ET, Umayam L, Walsh C, Walsh JJ, Xu CS, Yakal EA, Yang T, Zhao T, Funke J, George R, Hess HF, Jefferis GSXE, Knecht C, Korff W, Plaza SM, Romani S, Saalfeld S, Scheffer LK, Berg S, Rubin GM, Reiser MB. Connectome-driven neural inventory of a complete visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589741. [PMID: 38659887 PMCID: PMC11042306 DOI: 10.1101/2024.04.16.589741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
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'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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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alexandra MC Fragniere
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Philipp Schlegel
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Gregory SXE Jefferis
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
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13
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Beiran M, Litwin-Kumar A. Prediction of neural activity in connectome-constrained recurrent networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581667. [PMID: 38854115 PMCID: PMC11160579 DOI: 10.1101/2024.02.22.581667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.
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Affiliation(s)
- Manuel Beiran
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Ashok Litwin-Kumar
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
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14
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Donovan EJ, Agrawal A, Liberman N, Kalai JI, Adler AJ, Lamper AM, Wang HQ, Chua NJ, Koslover EF, Barnhart EL. Dendrite architecture determines mitochondrial distribution patterns in vivo. Cell Rep 2024; 43:114190. [PMID: 38717903 DOI: 10.1016/j.celrep.2024.114190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/08/2024] [Accepted: 04/17/2024] [Indexed: 06/01/2024] Open
Abstract
Neuronal morphology influences synaptic connectivity and neuronal signal processing. However, it remains unclear how neuronal shape affects steady-state distributions of organelles like mitochondria. In this work, we investigated the link between mitochondrial transport and dendrite branching patterns by combining mathematical modeling with in vivo measurements of dendrite architecture, mitochondrial motility, and mitochondrial localization patterns in Drosophila HS (horizontal system) neurons. In our model, different forms of morphological and transport scaling rules-which set the relative thicknesses of parent and daughter branches at each junction in the dendritic arbor and link mitochondrial motility to branch thickness-predict dramatically different global mitochondrial localization patterns. We show that HS dendrites obey the specific subset of scaling rules that, in our model, lead to realistic mitochondrial distributions. Moreover, we demonstrate that neuronal activity does not affect mitochondrial transport or localization, indicating that steady-state mitochondrial distributions are hard-wired by the architecture of the neuron.
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Affiliation(s)
- Eavan J Donovan
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Anamika Agrawal
- Department of Physics, University of California, San Diego, La Jolla, CA 92092, USA
| | - Nicole Liberman
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Jordan I Kalai
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Avi J Adler
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Adam M Lamper
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Hailey Q Wang
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Nicholas J Chua
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Elena F Koslover
- Department of Physics, University of California, San Diego, La Jolla, CA 92092, USA
| | - Erin L Barnhart
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
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15
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Brezovec BE, Berger AB, Hao YA, Lin A, Ahmed OM, Pacheco DA, Thiberge SY, Murthy M, Clandinin TR. BIFROST: a method for registering diverse imaging datasets of the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.09.544408. [PMID: 37333105 PMCID: PMC10274908 DOI: 10.1101/2023.06.09.544408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments. Significance Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.
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16
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Shapson-Coe A, Januszewski M, Berger DR, Pope A, Wu Y, Blakely T, Schalek RL, Li PH, Wang S, Maitin-Shepard J, Karlupia N, Dorkenwald S, Sjostedt E, Leavitt L, Lee D, Troidl J, Collman F, Bailey L, Fitzmaurice A, Kar R, Field B, Wu H, Wagner-Carena J, Aley D, Lau J, Lin Z, Wei D, Pfister H, Peleg A, Jain V, Lichtman JW. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 2024; 384:eadk4858. [PMID: 38723085 DOI: 10.1126/science.adk4858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 03/27/2024] [Indexed: 05/31/2024]
Abstract
To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.
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Affiliation(s)
- Alexander Shapson-Coe
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Queen Mary, University of London, London E1 4NS, UK
| | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Art Pope
- Google Research, Mountain View, CA 94043, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Richard L Schalek
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter H Li
- Google Research, Mountain View, CA 94043, USA
| | - Shuohong Wang
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Neha Karlupia
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Sven Dorkenwald
- Google Research, Mountain View, CA 94043, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
- Computer Science Department, Princeton University, Princeton, NJ 08540, USA
| | - Evelina Sjostedt
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Dongil Lee
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | | | - Luke Bailey
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Angerica Fitzmaurice
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Rohin Kar
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Benjamin Field
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Hank Wu
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Julian Wagner-Carena
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - David Aley
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Joanna Lau
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA 02467, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Adi Peleg
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Google, Cambridge, MA 02142, USA
| | - Viren Jain
- Google Research, Mountain View, CA 94043, USA
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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17
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Lee SYJ, Dallmann CJ, Cook AP, Tuthill JC, Agrawal S. Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590808. [PMID: 38712128 PMCID: PMC11071415 DOI: 10.1101/2024.04.23.590808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly's femur-tibia joint and exteroceptive sensing of substrate vibrations, but it remains unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to auditory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. They also demonstrate how analyzing patterns of synaptic connectivity can distill organizing principles from complex sensorimotor circuits.
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18
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Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P, Lu AKY, Rymer T, Finley-May S, Paterson T, Parekh R, Dorkenwald S, Matsliah A, Yu SC, McKellar C, Sterling A, Eichler K, Costa M, Seung S, Murthy M, Hartenstein V, Jefferis GSXE, Funke J. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 2024; 187:2574-2594.e23. [PMID: 38729112 PMCID: PMC11106717 DOI: 10.1016/j.cell.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 10/04/2023] [Accepted: 03/13/2024] [Indexed: 05/12/2024]
Abstract
High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.
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Affiliation(s)
- Nils Eckstein
- HHMI Janelia Research Campus, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK; Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Michelle Du
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK.
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA.
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19
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Zhou FY, Yapp C, Shang Z, Daetwyler S, Marin Z, Islam MT, Nanes B, Jenkins E, Gihana GM, Chang BJ, Weems A, Dustin M, Morrison S, Fiolka R, Dean K, Jamieson A, Sorger PK, Danuser G. A general algorithm for consensus 3D cell segmentation from 2D segmented stacks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592249. [PMID: 38766074 PMCID: PMC11100681 DOI: 10.1101/2024.05.03.592249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.
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Affiliation(s)
- Felix Y. Zhou
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiguo Shang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Stephan Daetwyler
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zach Marin
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Md Torikul Islam
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin Nanes
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward Jenkins
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Gabriel M. Gihana
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bo-Jui Chang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Weems
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dustin
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Sean Morrison
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Reto Fiolka
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kevin Dean
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Jamieson
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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20
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Schmidt M, Motta A, Sievers M, Helmstaedter M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods 2024; 21:908-913. [PMID: 38514779 PMCID: PMC11093750 DOI: 10.1038/s41592-024-02226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
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Affiliation(s)
- Martin Schmidt
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
- Faculty of Science, Radboud University, Nijmegen, the Netherlands
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
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21
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Pribbenow C, Owald D. Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila. Learn Mem 2024; 31:a053919. [PMID: 38876487 PMCID: PMC11199954 DOI: 10.1101/lm.053919.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/26/2024] [Indexed: 06/16/2024]
Abstract
Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.
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Affiliation(s)
- Carlotta Pribbenow
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - David Owald
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany
- NeuroCure, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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22
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Cowley BR, Calhoun AJ, Rangarajan N, Ireland E, Turner MH, Pillow JW, Murthy M. Mapping model units to visual neurons reveals population code for social behaviour. Nature 2024; 629:1100-1108. [PMID: 38778103 PMCID: PMC11136655 DOI: 10.1038/s41586-024-07451-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1-5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8-11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Adam J Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Elise Ireland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Maxwell H Turner
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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23
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Hofbauer B, Zandawala M, Reinhard N, Rieger D, Werner C, Evers JF, Wegener C. The neuropeptide pigment-dispersing factor signals independently of Bruchpilot-labelled active zones in daily remodelled terminals of Drosophila clock neurons. Eur J Neurosci 2024; 59:2665-2685. [PMID: 38414155 DOI: 10.1111/ejn.16294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/29/2024]
Abstract
The small ventrolateral neurons (sLNvs) are key components of the central clock in the Drosophila brain. They signal via the neuropeptide pigment-dispersing factor (PDF) to align the molecular clockwork of different central clock neurons and to modulate downstream circuits. The dorsal terminals of the sLNvs undergo daily morphological changes that affect presynaptic sites organised by the active zone protein Bruchpilot (BRP), a homolog of mammalian ELKS proteins. However, the role of these presynaptic sites for PDF release is ill-defined. Here, we combined expansion microscopy with labelling of active zones by endogenously tagged BRP to examine the spatial correlation between PDF-containing dense-core vesicles and BRP-labelled active zones. We found that the number of BRP-labelled puncta in the sLNv terminals was similar while their density differed between Zeitgeber time (ZT) 2 and 14. The relative distance between BRP- and PDF-labelled puncta was increased in the morning, around the reported time of PDF release. Spontaneous dense-core vesicle release profiles of sLNvs in a publicly available ssTEM dataset (FAFB) consistently lacked spatial correlation to BRP-organised active zones. RNAi-mediated downregulation of brp and other active zone proteins expressed by the sLNvs did not affect PDF-dependent locomotor rhythmicity. In contrast, down-regulation of genes encoding proteins of the canonical vesicle release machinery, the dense-core vesicle-related protein CADPS, as well as PDF impaired locomotor rhythmicity. Taken together, our study suggests that PDF release from the sLNvs is independent of BRP-organised active zones, while BRP may be redistributed to active zones in a time-dependent manner.
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Affiliation(s)
- Benedikt Hofbauer
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Meet Zandawala
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- Department of Biochemistry and Molecular Biology, University of Nevada Reno, Reno, NV, USA
| | - Nils Reinhard
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Dirk Rieger
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Christian Werner
- Biocenter, Theodor-Boveri-Institute, Department of Biotechnology and Biophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Jan Felix Evers
- Centre for organismal studies COS, Universität Heidelberg, Heidelberg, Germany
- Cairn GmbH, Heidelberg, Germany
| | - Christian Wegener
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
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24
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Fiala A, Kaun KR. What do the mushroom bodies do for the insect brain? Twenty-five years of progress. Learn Mem 2024; 31:a053827. [PMID: 38862175 PMCID: PMC11199942 DOI: 10.1101/lm.053827.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 06/13/2024]
Abstract
In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.
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Affiliation(s)
- André Fiala
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Göttingen 37077, Germany
| | - Karla R Kaun
- Department of Neuroscience, Brown University, Providence, Rhode Island 02806, USA
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25
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Chan ICW, Chen N, Hernandez J, Meltzer H, Park A, Stahl A. Future avenues in Drosophila mushroom body research. Learn Mem 2024; 31:a053863. [PMID: 38862172 PMCID: PMC11199946 DOI: 10.1101/lm.053863.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/27/2024] [Indexed: 06/13/2024]
Abstract
How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.
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Affiliation(s)
- Ivy Chi Wai Chan
- Dynamics of Neuronal Circuits Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Developmental Biology, RWTH Aachen University, Aachen, Germany
| | - Nannan Chen
- School of Life Science and Technology, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing 210096, China
| | - John Hernandez
- Neuroscience Department, Brown University, Providence, Rhode Island 02906, USA
| | - Hagar Meltzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Annie Park
- Department of Physiology, Anatomy and Genetics, Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Aaron Stahl
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
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26
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Selcho M. Octopamine in the mushroom body circuitry for learning and memory. Learn Mem 2024; 31:a053839. [PMID: 38862169 PMCID: PMC11199948 DOI: 10.1101/lm.053839.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/20/2024] [Indexed: 06/13/2024]
Abstract
Octopamine, the functional analog of noradrenaline, modulates many different behaviors and physiological processes in invertebrates. In the central nervous system, a few octopaminergic neurons project throughout the brain and innervate almost all neuropils. The center of memory formation in insects, the mushroom bodies, receive octopaminergic innervations in all insects investigated so far. Different octopamine receptors, either increasing or decreasing cAMP or calcium levels in the cell, are localized in Kenyon cells, further supporting the release of octopamine in the mushroom bodies. In addition, different mushroom body (MB) output neurons, projection neurons, and dopaminergic PAM cells are targets of octopaminergic neurons, enabling the modulation of learning circuits at different neural sites. For some years, the theory persisted that octopamine mediates rewarding stimuli, whereas dopamine (DA) represents aversive stimuli. This simple picture has been challenged by the finding that DA is required for both appetitive and aversive learning. Furthermore, octopamine is also involved in aversive learning and a rather complex interaction between these biogenic amines seems to modulate learning and memory. This review summarizes the role of octopamine in MB function, focusing on the anatomical principles and the role of the biogenic amine in learning and memory.
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Affiliation(s)
- Mareike Selcho
- Department of Animal Physiology, Institute of Biology, Leipzig University, 04103 Leipzig, Germany
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27
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Sen E, El-Keredy A, Jacob N, Mancini N, Asnaz G, Widmann A, Gerber B, Thoener J. Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning. Learn Mem 2024; 31:a053726. [PMID: 38862170 PMCID: PMC11199949 DOI: 10.1101/lm.053726.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/18/2024] [Indexed: 06/13/2024]
Abstract
Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.
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Affiliation(s)
- Edanur Sen
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Amira El-Keredy
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Department of Genetics, Faculty of Agriculture, Tanta University, 31111 Tanta, Egypt
| | - Nina Jacob
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Nino Mancini
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Gülüm Asnaz
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Annekathrin Widmann
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Bertram Gerber
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Otto von Guericke University Magdeburg, Institute of Biology, 39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
| | - Juliane Thoener
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
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28
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Moreno-Sanchez A, Vasserman AN, Jang H, Hina BW, von Reyn CR, Ausborn J. Morphology and synapse topography optimize linear encoding of synapse numbers in Drosophila looming responsive descending neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591016. [PMID: 38712267 PMCID: PMC11071487 DOI: 10.1101/2024.04.24.591016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Synapses are often precisely organized on dendritic arbors, yet the role of synaptic topography in dendritic integration remains poorly understood. Utilizing electron microscopy (EM) connectomics we investigate synaptic topography in Drosophila melanogaster looming circuits, focusing on retinotopically tuned visual projection neurons (VPNs) that synapse onto descending neurons (DNs). Synapses of a given VPN type project to non-overlapping regions on DN dendrites. Within these spatially constrained clusters, synapses are not retinotopically organized, but instead adopt near random distributions. To investigate how this organization strategy impacts DN integration, we developed multicompartment models of DNs fitted to experimental data and using precise EM morphologies and synapse locations. We find that DN dendrite morphologies normalize EPSP amplitudes of individual synaptic inputs and that near random distributions of synapses ensure linear encoding of synapse numbers from individual VPNs. These findings illuminate how synaptic topography influences dendritic integration and suggest that linear encoding of synapse numbers may be a default strategy established through connectivity and passive neuron properties, upon which active properties and plasticity can then tune as needed.
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Affiliation(s)
- Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - Alexander N. Vasserman
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - HyoJong Jang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Catherine R. von Reyn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
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29
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Sakeena Syed D, Mark B, Kuroda S, Sustar A, Moussa A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Macrina T, Dickinson M, Lee WCA, Tuthill JC. Synaptic architecture of leg and wing premotor control networks in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.30.542725. [PMID: 37398440 PMCID: PMC10312524 DOI: 10.1101/2023.05.30.542725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
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Affiliation(s)
- Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony W. Azevedo
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Chris J. Dallmann
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Su-Yee J. Lee
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Switzerland
| | | | | | - Kisuk Lee
- Zetta AI, LLC, USA
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ, USA
- Computer Science Department, Princeton University, NJ, USA
| | | | | | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, MA, USA
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, WA, USA
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30
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. eLife 2024; 12:RP87602. [PMID: 38634460 PMCID: PMC11026096 DOI: 10.7554/elife.87602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton CollegeNorthfieldUnited States
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
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31
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Matsliah A, Yu SC, Kruk K, Bland D, Burke A, Gager J, Hebditch J, Silverman B, Willie K, Willie RW, Sorek M, Sterling AR, Kind E, Garner D, Sancer G, Wernet MF, Kim SS, Murthy M, Seung HS. Neuronal "parts list" and wiring diagram for a visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.12.562119. [PMID: 37873160 PMCID: PMC10592826 DOI: 10.1101/2023.10.12.562119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) at least double in number of types. We introduce a new Sm interneuron family, which contains more types than any other, and three new families of cross-neuropil types. Self-consistency of cell types is demonstrated through automatic assignment of cells to types by distance in high-dimensional feature space, and further validation is provided by algorithms that select small subsets of discriminative features. Cell types with similar connectivity patterns divide into clusters that are interpretable in terms of motion, object, and color vision. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.
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Affiliation(s)
| | - Szi-Chieh Yu
- Neuroscience Institute, Princeton University, USA
| | | | - Doug Bland
- Neuroscience Institute, Princeton University, USA
| | - Austin Burke
- Neuroscience Institute, Princeton University, USA
| | - Jay Gager
- Neuroscience Institute, Princeton University, USA
| | | | | | - Kyle Willie
- Neuroscience Institute, Princeton University, USA
| | | | | | | | - Emil Kind
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Dustin Garner
- Molecular, Cellular, and Developmental Biology, Univ. C alifornia Santa Barbara, USA
| | - Gizem Sancer
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Mathias F Wernet
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Sung Soo Kim
- Molecular, Cellular, and Developmental Biology, Univ. C alifornia Santa Barbara, USA
| | - Mala Murthy
- Neuroscience Institute, Princeton University, USA
| | - H Sebastian Seung
- Neuroscience Institute, Princeton University, USA
- Computer Science Department, Princeton University, U SA
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32
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Arntsen C, Guillemin J, Audette K, Stanley M. Tastant-receptor interactions: insights from the fruit fly. Front Nutr 2024; 11:1394697. [PMID: 38665300 PMCID: PMC11043608 DOI: 10.3389/fnut.2024.1394697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
Across species, taste provides important chemical information about potential food sources and the surrounding environment. As details about the chemicals and receptors responsible for gustation are discovered, a complex view of the taste system is emerging with significant contributions from research using the fruit fly, Drosophila melanogaster, as a model organism. In this brief review, we summarize recent advances in Drosophila gustation and their relevance to taste research more broadly. Our goal is to highlight the molecular mechanisms underlying the first step of gustatory circuits: ligand-receptor interactions in primary taste cells. After an introduction to the Drosophila taste system and how it encodes the canonical taste modalities sweet, bitter, and salty, we describe recent insights into the complex nature of carboxylic acid and amino acid detection in the context of sour and umami taste, respectively. Our analysis extends to non-canonical taste modalities including metals, fatty acids, and bacterial components, and highlights unexpected receptors and signaling pathways that have recently been identified in Drosophila taste cells. Comparing the intricate molecular and cellular underpinnings of how ligands are detected in vivo in fruit flies reveals both specific and promiscuous receptor selectivity for taste encoding. Throughout this review, we compare and contextualize these Drosophila findings with mammalian research to not only emphasize the conservation of these chemosensory systems, but to demonstrate the power of this model organism in elucidating the neurobiology of taste and feeding.
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Affiliation(s)
| | | | | | - Molly Stanley
- Department of Biology, University of Vermont, Burlington, VT, United States
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33
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Son R, Yamazawa K, Oguchi A, Suga M, Tamura M, Yanagita M, Murakawa Y, Kume S. Morphomics via next-generation electron microscopy. J Mol Cell Biol 2024; 15:mjad081. [PMID: 38148118 PMCID: PMC11167312 DOI: 10.1093/jmcb/mjad081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/02/2022] [Accepted: 12/23/2023] [Indexed: 12/28/2023] Open
Abstract
The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.
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Affiliation(s)
- Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kenji Yamazawa
- Advanced Manufacturing Support Team, RIKEN Center for Advanced Photonics, Wako 351-0198, Japan
| | - Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mitsuo Suga
- Multimodal Microstructure Analysis Unit, RIKEN–JEOL Collaboration Center, Kobe 650-0047, Japan
| | - Masaru Tamura
- Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Research Center, Tsukuba 305-0074, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
- IFOM—The FIRC Institute of Molecular Oncology, Milan 20139, Italy
| | - Satoshi Kume
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan
- Center for Health Science Innovation, Osaka City University, Osaka 530-0011, Japan
- Osaka Electro-Communication University, Neyagawa 572-8530, Japan
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34
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Eisenstein M. A milestone map of mouse-brain connectivity reveals challenging new terrain for scientists. Nature 2024; 628:677-679. [PMID: 38622253 DOI: 10.1038/d41586-024-01096-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
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35
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Gowda SBM, Banu A, Hussain S, Mohammad F. Neuronal mechanisms regulating locomotion in adult Drosophila. J Neurosci Res 2024; 102:e25332. [PMID: 38646942 DOI: 10.1002/jnr.25332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
The coordinated action of multiple leg joints and muscles is required even for the simplest movements. Understanding the neuronal circuits and mechanisms that generate precise movements is essential for comprehending the neuronal basis of the locomotion and to infer the neuronal mechanisms underlying several locomotor-related diseases. Drosophila melanogaster provides an excellent model system for investigating the neuronal circuits underlying motor behaviors due to its simple nervous system and genetic accessibility. This review discusses current genetic methods for studying locomotor circuits and their function in adult Drosophila. We highlight recently identified neuronal pathways that modulate distinct forward and backward locomotion and describe the underlying neuronal control of leg swing and stance phases in freely moving flies. We also report various automated leg tracking methods to measure leg motion parameters and define inter-leg coordination, gait and locomotor speed of freely moving adult flies. Finally, we emphasize the role of leg proprioceptive signals to central motor circuits in leg coordination. Together, this review highlights the utility of adult Drosophila as a model to uncover underlying motor circuitry and the functional organization of the leg motor system that governs correct movement.
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Affiliation(s)
- Swetha B M Gowda
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Ayesha Banu
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Sadam Hussain
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Farhan Mohammad
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
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36
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Gorko B, Siwanowicz I, Close K, Christoforou C, Hibbard KL, Kabra M, Lee A, Park JY, Li SY, Chen AB, Namiki S, Chen C, Tuthill JC, Bock DD, Rouault H, Branson K, Ihrke G, Huston SJ. Motor neurons generate pose-targeted movements via proprioceptive sculpting. Nature 2024; 628:596-603. [PMID: 38509371 DOI: 10.1038/s41586-024-07222-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/22/2024] [Indexed: 03/22/2024]
Abstract
Motor neurons are the final common pathway1 through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2 suppress a single class of proprioceptive neuron3 that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.
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Affiliation(s)
- Benjamin Gorko
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Igor Siwanowicz
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kari Close
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Mayank Kabra
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Allen Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jin-Yong Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Si Ying Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Alex B Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA
| | - Shigehiro Namiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Chenghao Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Neurological Sciences, University of Vermont, Burlington, VT, USA
| | - Hervé Rouault
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Turing Centre for Living systems, Aix-Marseille University, Université de Toulon, CNRS, CPT (UMR 7332), Marseille, France
| | - Kristin Branson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gudrun Ihrke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephen J Huston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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Sanfilippo P, Kim AJ, Bhukel A, Yoo J, Mirshahidi PS, Pandey V, Bevir H, Yuen A, Mirshahidi PS, Guo P, Li HS, Wohlschlegel JA, Aso Y, Zipursky SL. Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome. Neuron 2024; 112:942-958.e13. [PMID: 38262414 PMCID: PMC10957333 DOI: 10.1016/j.neuron.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/03/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.
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Affiliation(s)
- Piero Sanfilippo
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alexander J Kim
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Anuradha Bhukel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Juyoun Yoo
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pegah S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vijaya Pandey
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harry Bevir
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ashley Yuen
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Parmis S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Peiyi Guo
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Hong-Sheng Li
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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38
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Schretter CE, Sten TH, Klapoetke N, Shao M, Nern A, Dreher M, Bushey D, Robie AA, Taylor AL, Branson KM, Otopalik A, Ruta V, Rubin GM. Social state gates vision using three circuit mechanisms in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585289. [PMID: 38559111 PMCID: PMC10979952 DOI: 10.1101/2024.03.15.585289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Animals are often bombarded with visual information and must prioritize specific visual features based on their current needs. The neuronal circuits that detect and relay visual features have been well-studied. Yet, much less is known about how an animal adjusts its visual attention as its goals or environmental conditions change. During social behaviors, flies need to focus on nearby flies. Here, we study how the flow of visual information is altered when female Drosophila enter an aggressive state. From the connectome, we identified three state-dependent circuit motifs poised to selectively amplify the response of an aggressive female to fly-sized visual objects: convergence of excitatory inputs from neurons conveying select visual features and internal state; dendritic disinhibition of select visual feature detectors; and a switch that toggles between two visual feature detectors. Using cell-type-specific genetic tools, together with behavioral and neurophysiological analyses, we show that each of these circuit motifs function during female aggression. We reveal that features of this same switch operate in males during courtship pursuit, suggesting that disparate social behaviors may share circuit mechanisms. Our work provides a compelling example of using the connectome to infer circuit mechanisms that underlie dynamic processing of sensory signals.
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Affiliation(s)
| | - Tom Hindmarsh Sten
- Laboratory of Neurophysiology and Behavior, The Rockefeller University, New York, NY, USA
| | - Nathan Klapoetke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Mei Shao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Alice A Robie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Adam L Taylor
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Kristin M Branson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Adriane Otopalik
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Vanessa Ruta
- Laboratory of Neurophysiology and Behavior, The Rockefeller University, New York, NY, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
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39
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Clements J, Goina C, Hubbard PM, Kawase T, Olbris DJ, Otsuna H, Svirskas R, Rokicki K. NeuronBridge: an intuitive web application for neuronal morphology search across large data sets. BMC Bioinformatics 2024; 25:114. [PMID: 38491365 PMCID: PMC10943809 DOI: 10.1186/s12859-024-05732-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
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'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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Affiliation(s)
- Jody Clements
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Cristian Goina
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Philip M Hubbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Takashi Kawase
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Donald J Olbris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Robert Svirskas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Konrad Rokicki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA.
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40
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Cheong HSJ, Boone KN, Bennett MM, Salman F, Ralston JD, Hatch K, Allen RF, Phelps AM, Cook AP, Phelps JS, Erginkaya M, Lee WCA, Card GM, Daly KC, Dacks AM. Organization of an ascending circuit that conveys flight motor state in Drosophila. Curr Biol 2024; 34:1059-1075.e5. [PMID: 38402616 PMCID: PMC10939832 DOI: 10.1016/j.cub.2024.01.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/08/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024]
Abstract
Natural behaviors are a coordinated symphony of motor acts that drive reafferent (self-induced) sensory activation. Individual sensors cannot disambiguate exafferent (externally induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to carry out adaptive behaviors through corollary discharge circuits (CDCs), which provide predictive motor signals from motor pathways to sensory processing and other motor pathways. Yet, how CDCs comprehensively integrate into the nervous system remains unexplored. Here, we use connectomics, neuroanatomical, physiological, and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs) in Drosophila, which function as a predictive CDC in other insects. Both AHN pairs receive input primarily from a partially overlapping population of descending neurons, especially from DNg02, which controls wing motor output. Using Ca2+ imaging and behavioral recordings, we show that AHN activation is correlated to flight behavior and precedes wing motion. Optogenetic activation of DNg02 is sufficient to activate AHNs, indicating that AHNs are activated by descending commands in advance of behavior and not as a consequence of sensory input. Downstream, each AHN pair targets predominantly non-overlapping networks, including those that process visual, auditory, and mechanosensory information, as well as networks controlling wing, haltere, and leg sensorimotor control. These results support the conclusion that the AHNs provide a predictive motor signal about wing motor state to mostly non-overlapping sensory and motor networks. Future work will determine how AHN signaling is driven by other descending neurons and interpreted by AHN downstream targets to maintain adaptive sensorimotor performance.
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Affiliation(s)
- Han S J Cheong
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Kaitlyn N Boone
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Marryn M Bennett
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Farzaan Salman
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Jacob D Ralston
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Kaleb Hatch
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Raven F Allen
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Alec M Phelps
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Andrew P Cook
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Mert Erginkaya
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon 1400-038, Portugal
| | - Wei-Chung A Lee
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Gwyneth M Card
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Kevin C Daly
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26505, USA
| | - Andrew M Dacks
- Department of Biology, West Virginia University, Morgantown, WV 26505, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26505, USA.
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41
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Cardona A. Wiring up for controlled flight. eLife 2024; 13:e95989. [PMID: 38436656 PMCID: PMC10911749 DOI: 10.7554/elife.95989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
A map showing how neurons that process motion are wired together in the visual system of fruit flies provides new insights into how animals navigate and remain stable when flying.
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Affiliation(s)
- Albert Cardona
- MRC Laboratory of Molecular Biology, University of CambridgeCambridgeUnited Kingdom
- Department of Physiology, Development and Neuroscience, University of CambridgeCambridgeUnited Kingdom
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42
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Castaneda AN, Huda A, Whitaker IBM, Reilly JE, Shelby GS, Bai H, Ni L. Functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango in Drosophila (FLIPSOT). PLoS Genet 2024; 20:e1011190. [PMID: 38483970 PMCID: PMC10965055 DOI: 10.1371/journal.pgen.1011190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 03/26/2024] [Accepted: 02/20/2024] [Indexed: 03/27/2024] Open
Abstract
A population of neurons interconnected by synapses constitutes a neural circuit, which performs specific functions upon activation. It is essential to identify both anatomical and functional entities of neural circuits to comprehend the components and processes necessary for healthy brain function and the changes that characterize brain disorders. To date, few methods are available to study these two aspects of a neural circuit simultaneously. In this study, we developed FLIPSOT, or functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango. FLIPSOT uses (1) trans-Tango to access postsynaptic neurons genetically, (2) optogenetic approaches to activate (FLIPSOTa) or inhibit (FLIPSOTi) postsynaptic neurons in a random and sparse manner, and (3) fluorescence markers tagged with optogenetic genes to visualize these neurons. Therefore, FLIPSOT allows using a presynaptic driver to identify the behavioral function of individual postsynaptic neurons. It is readily applied to identify functions of individual postsynaptic neurons and has the potential to be adapted for use in mammalian circuits.
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Affiliation(s)
- Allison N. Castaneda
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Ainul Huda
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Iona B. M. Whitaker
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Julianne E. Reilly
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Grace S. Shelby
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Hua Bai
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Lina Ni
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
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43
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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44
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Tao L, Ayembem D, Barranca VJ, Bhandawat V. Neurons underlying aggressive actions that are shared by both males and females in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582148. [PMID: 38464020 PMCID: PMC10925114 DOI: 10.1101/2024.02.26.582148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless , a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.
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45
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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46
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Pospisil DA, Aragon MJ, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Jefferis GSXE, Murthy M, Pillow JW. From connectome to effectome: learning the causal interaction map of the fly brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.31.564922. [PMID: 37961285 PMCID: PMC10635032 DOI: 10.1101/2023.10.31.564922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.
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Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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47
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Tegethoff L, Briggman KL. Quantitative evaluation of embedding resins for volume electron microscopy. Front Neurosci 2024; 18:1286991. [PMID: 38406585 PMCID: PMC10884229 DOI: 10.3389/fnins.2024.1286991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Optimal epoxy resin embedding is crucial for obtaining consistent serial sections from large tissue samples, especially for block faces spanning >1 mm2. We report a method to quantify non-uniformity in resin curing using block hardness measurements from block faces. We identify conditions that lead to non-uniform curing as well as a procedure to monitor the hardness of blocks for a wide range of common epoxy resins used for volume electron microscopy. We also assess cutting repeatability and uniformity by quantifying the transverse and sectional cutting forces during ultrathin sectioning using a sample-mounted force sensor. Our findings indicate that screening and optimizing resin formulations is required to achieve the best repeatability in terms of section thickness. Finally, we explore the encapsulation of irregularly shaped tissue samples in a gelatin matrix prior to epoxy resin embedding to yield more uniform sections.
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Affiliation(s)
| | - Kevin L. Briggman
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
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48
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. Curr Biol 2024; 34:531-540.e5. [PMID: 38228148 PMCID: PMC10872223 DOI: 10.1016/j.cub.2023.12.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.
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Affiliation(s)
- Ivo G Ros
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Jaison J Omoto
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Michael H Dickinson
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA.
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49
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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50
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Simpson JH. Descending control of motor sequences in Drosophila. Curr Opin Neurobiol 2024; 84:102822. [PMID: 38096757 PMCID: PMC11215313 DOI: 10.1016/j.conb.2023.102822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 02/18/2024]
Abstract
The descending neurons connecting the fly's brain to its ventral nerve cord respond to sensory stimuli and evoke motor programs of varying complexity. Anatomical characterization of the descending neurons and their synaptic connections suggests how these circuits organize movements, while optogenetic manipulation of their activity reveals what behaviors they can induce. Monitoring their responses to sensory stimuli or during behavior performance indicates what information they may encode. Recent advances in all three approaches make the descending neurons an excellent place to better understand the sensorimotor integration and transformation required for nervous systems to govern the motor sequences that constitute animal behavior.
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Affiliation(s)
- Julie H Simpson
- Dept. Molecular Cellular and Developmental Biology and Neuroscience Research Institute, University of California Santa Barbara, USA.
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