1
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Syed DS, 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. Nature 2024; 631:369-377. [PMID: 38926579 DOI: 10.1038/s41586-024-07600-z] [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: 06/02/2023] [Accepted: 05/23/2024] [Indexed: 06/28/2024]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 to analyse 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. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct 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, Seattle, WA, USA
| | - Anthony W Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Su-Yee J Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Zurich, Switzerland
| | - Ran Lu
- Zetta AI, LLC, Sherrill, NY, USA
| | | | - Kisuk Lee
- Zetta AI, LLC, Sherrill, NY, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Dodam Ih
- Zetta AI, LLC, Sherrill, NY, USA
| | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, 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, Boston, MA, USA.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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2
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Azevedo A, Lesser E, Phelps JS, Mark B, Elabbady L, Kuroda S, Sustar A, Moussa A, Khandelwal A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Cook A, 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, Dickinson M, Pacureanu A, Seung HS, Macrina T, Lee WCA, Tuthill JC. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 2024; 631:360-368. [PMID: 38926570 DOI: 10.1038/s41586-024-07389-x] [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: 06/02/2023] [Accepted: 04/04/2024] [Indexed: 06/28/2024]
Abstract
A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
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Affiliation(s)
- Anthony Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Avinash Khandelwal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Su-Yee J Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions, Zurich, Switzerland
| | - Ran Lu
- Zetta AI, Sherrill, NJ, USA
| | | | - Kisuk Lee
- Zetta AI, Sherrill, NJ, USA
- 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
| | | | | | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, 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, Boston, MA, USA.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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3
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Stürner T, Brooks P, Capdevila LS, Morris BJ, Javier A, Fang S, Gkantia M, Cachero S, Beckett IR, Champion AS, Moitra I, Richards A, Klemm F, Kugel L, Namiki S, Cheong HS, Kovalyak J, Tenshaw E, Parekh R, Schlegel P, Phelps JS, Mark B, Dorkenwald S, Bates AS, Matsliah A, Yu SC, McKellar CE, Sterling A, Seung S, Murthy M, Tuthill J, Lee WCA, Card GM, Costa M, Jefferis GS, Eichler K. Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.596633. [PMID: 38895426 PMCID: PMC11185702 DOI: 10.1101/2024.06.04.596633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and information bottleneck connecting the brain and the ventral nerve cord (VNC, spinal cord analogue) and comprises diverse populations of descending (DN), ascending (AN) and sensory ascending neurons, which are crucial for sensorimotor signalling and control. Integrating three separate EM datasets, we now provide a complete connectomic description of the ascending and descending neurons of the female nervous system of Drosophila and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions have been matched across hemispheres, datasets and sexes. Crucially, we have also matched 51% of DN cell types to light level data defining specific driver lines as well as classifying all ascending populations. We use these results to reveal the general architecture, tracts, neuropil innervation and connectivity of neck connective neurons. We observe connected chains of descending and ascending neurons spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analysis of circuits implicated in sex-related behaviours, including female ovipositor extrusion (DNp13), male courtship (DNa12/aSP22) and song production (AN hemilineage 08B). Our work represents the first EM-level circuit analyses spanning the entire central nervous system of an adult animal.
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Affiliation(s)
- Tomke Stürner
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Paul Brooks
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | - Billy J. Morris
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexandre Javier
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Siqi Fang
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marina Gkantia
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Cachero
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | | | - Andrew S. Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Ilina Moitra
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alana Richards
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Finja Klemm
- Genetics Department, Leipzig University, Leipzig, Germany
| | - Leonie Kugel
- Genetics Department, Leipzig University, Leipzig, Germany
| | - Shigehiro Namiki
- Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Han S.J. Cheong
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Zuckerman Institute, Columbia University, New York, United States
| | - Julie Kovalyak
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Emily Tenshaw
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
- Brain Mind Institute & Institute of Bioengineering, EPFL, 1015 Lausanne, Switzerland
| | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, USA
| | - Alexander S. Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, USA
| | - Mala Murthy
- Computer Science Department, Princeton University, USA
| | - John Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Wei-Chung A. Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
- FM Kirby Neurobiology Center, Boston Children’s Hospital, Boston, MA, USA
| | - Gwyneth M. Card
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Zuckerman Institute, Columbia University, New York, United States
| | - Marta Costa
- 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
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Genetics Department, Leipzig University, Leipzig, Germany
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4
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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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5
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Dallmann CJ, Luo Y, Agrawal S, Chou GM, Cook A, Brunton BW, Tuthill JC. Presynaptic inhibition selectively suppresses leg proprioception in behaving Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.20.563322. [PMID: 37961558 PMCID: PMC10634730 DOI: 10.1101/2023.10.20.563322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Controlling arms and legs requires feedback from proprioceptive sensory neurons that detect joint position and movement. Proprioceptive feedback must be tuned for different behavioral contexts, but the underlying circuit mechanisms remain poorly understood. Using calcium imaging in behaving Drosophila, we find that the axons of position-encoding leg proprioceptors are active across behaviors, whereas the axons of movement-encoding leg proprioceptors are suppressed during walking and grooming. Using connectomics, we identify a specific class of interneurons that provide GABAergic presynaptic inhibition to the axons of movement-encoding proprioceptors. The predominant synaptic inputs to these interneurons are descending neurons, suggesting they are driven by predictions of leg movement originating in the brain. Calcium imaging from both the interneurons and their descending inputs confirmed that their activity is correlated with self-generated but not passive leg movements. Overall, our findings elucidate a neural circuit for suppressing specific proprioceptive feedback signals during self-generated movements.
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Affiliation(s)
- Chris J. Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Present address: Department of Neurobiology and Genetics, Julius-Maximilians-University of Würzburg, Würzburg, Germany
| | - Yichen Luo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Present address: School of Neuroscience, Virginia Tech, Blacksburg, VA, USA
| | - Grant M. Chou
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- 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
- Lead contact
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6
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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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7
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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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8
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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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9
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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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10
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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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11
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Rubin GM, Aso Y. New genetic tools for mushroom body output neurons in Drosophila. eLife 2024; 12:RP90523. [PMID: 38270577 PMCID: PMC10945696 DOI: 10.7554/elife.90523] [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] [Indexed: 01/26/2024] Open
Abstract
How memories of past events influence behavior is a key question in neuroscience. The major associative learning center in Drosophila, the mushroom body (MB), communicates to the rest of the brain through mushroom body output neurons (MBONs). While 21 MBON cell types have their dendrites confined to small compartments of the MB lobes, analysis of EM connectomes revealed the presence of an additional 14 MBON cell types that are atypical in having dendritic input both within the MB lobes and in adjacent brain regions. Genetic reagents for manipulating atypical MBONs and experimental data on their functions have been lacking. In this report we describe new cell-type-specific GAL4 drivers for many MBONs, including the majority of atypical MBONs that extend the collection of MBON driver lines we have previously generated (Aso et al., 2014a; Aso et al., 2016; Aso et al., 2019). Using these genetic reagents, we conducted optogenetic activation screening to examine their ability to drive behaviors and learning. These reagents provide important new tools for the study of complex behaviors in Drosophila.
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Affiliation(s)
- Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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12
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Viragh E, Asztalos L, Fenckova M, Szlanka T, Gyorgypal Z, Kovacs K, IntHout J, Cizek P, Konda M, Szucs E, Zvara A, Biro J, Csapo E, Lukacsovich T, Hegedus Z, Puskas L, Schenck A, Asztalos Z. Pre-Pulse Inhibition of an escape response in adult fruit fly, Drosophila melanogaster. RESEARCH SQUARE 2024:rs.3.rs-3853873. [PMID: 38343805 PMCID: PMC10854311 DOI: 10.21203/rs.3.rs-3853873/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Pre-Pulse Inhibition (PPI) is a neural process where suppression of a startle response is elicited by preceding the startling stimulus (Pulse) with a weak, non-startling one (Pre-Pulse). Defective PPI is widely employed as a behavioural endophenotype in humans and mammalian disorder-relevant models for neuropsychiatric disorders. We have developed a user-friendly, semi-automated, high-throughput-compatible Drosophila light-off jump response PPI paradigm, with which we demonstrate that PPI, with similar parameters measured in mammals, exists in adults of this model organism. We report that Drosophila PPI is affected by reduced expression of Dysbindin and both reduced and increased expression of Nmdar1 (N-methyl-D-aspartate receptor 1), perturbations associated with schizophrenia. Studying the biology of PPI in an organism that offers a plethora of genetic tools and a complex and well characterized connectome will greatly facilitate our efforts to gain deeper insight into the aetiology of human mental disorders, while reducing the need for mammalian models.
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Affiliation(s)
- Erika Viragh
- Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
- Aktogen Hungary Ltd., Szeged, Hungary
| | - Lenke Asztalos
- Aktogen Hungary Ltd., Szeged, Hungary
- Aktogen Ltd., Department of Genetics, University of Cambridge, Cambridge, United Kingdom; Current address: Aktogen Ltd. Ramsey, Huntingdon, United Kingdom
| | - Michaela Fenckova
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Molecular Biology and Genetics, Faculty of Science, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czechia
| | - Tamas Szlanka
- Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
- Aktogen Hungary Ltd., Szeged, Hungary
| | - Zoltan Gyorgypal
- Institute of Biophysics & Core Facilities, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Karoly Kovacs
- Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary
| | - Joanna IntHout
- Department for Health Evidence (HEV), Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pavel Cizek
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mihaly Konda
- Aktogen Hungary Ltd., Szeged, Hungary
- Voalaz Ltd., Szeged, Hungary
| | | | - Agnes Zvara
- Laboratory of Functional Genomics, HUN-REN Biological Research Centre Szeged, Hungary
| | | | | | | | - Zoltan Hegedus
- Institute of Biophysics & Core Facilities, HUN-REN Biological Research Centre, Szeged, Hungary
| | - Laszlo Puskas
- Laboratory of Functional Genomics, HUN-REN Biological Research Centre Szeged, Hungary
| | - Annette Schenck
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Zoltan Asztalos
- Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary
- Aktogen Hungary Ltd., Szeged, Hungary
- Aktogen Ltd., Department of Genetics, University of Cambridge, Cambridge, United Kingdom; Current address: Aktogen Ltd. Ramsey, Huntingdon, United Kingdom
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13
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Ishida IG, Sethi S, Mohren TL, Abbott L, Maimon G. Neuronal calcium spikes enable vector inversion in the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.24.568537. [PMID: 38077032 PMCID: PMC10705278 DOI: 10.1101/2023.11.24.568537] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
A typical neuron signals to downstream cells when it is depolarized and firing sodium spikes. Some neurons, however, also fire calcium spikes when hyperpolarized. The function of such bidirectional signaling remains unclear in most circuits. Here we show how a neuron class that participates in vector computation in the fly central complex employs hyperpolarization-elicited calcium spikes to invert two-dimensional mathematical vectors. When cells switch from firing sodium to calcium spikes, this leads to a ~180° realignment between the vector encoded in the neuronal population and the fly's internal heading signal, thus inverting the vector. We show that the calcium spikes rely on the T-type calcium channel Ca-α1T, and argue, via analytical and experimental approaches, that these spikes enable vector computations in portions of angular space that would otherwise be inaccessible. These results reveal a seamless interaction between molecular, cellular and circuit properties for implementing vector math in the brain.
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Affiliation(s)
- Itzel G. Ishida
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York NY, USA
| | - Sachin Sethi
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York NY, USA
| | - Thomas L. Mohren
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York NY, USA
| | - L.F. Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York NY, USA
| | - Gaby Maimon
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York NY, USA
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14
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Zhao A, Nern A, Koskela S, Dreher M, Erginkaya M, Laughland CW, Ludwigh H, Thomson A, Hoeller J, Parekh R, Romani S, Bock DD, Chiappe E, Reiser MB. A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562634. [PMID: 37904921 PMCID: PMC10614863 DOI: 10.1101/2023.10.16.562634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.
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Affiliation(s)
- Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sanna Koskela
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Mert Erginkaya
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Connor W Laughland
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Henrique Ludwigh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Alex Thomson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Judith Hoeller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, USA
| | - Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
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15
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Lyu C, Li Z, Luo L. Toward building a library of cell type-specific drivers across developmental stages. Proc Natl Acad Sci U S A 2023; 120:e2312196120. [PMID: 37590431 PMCID: PMC10466085 DOI: 10.1073/pnas.2312196120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023] Open
Affiliation(s)
- Cheng Lyu
- HHMI, Stanford University, Stanford, CA94305
- Department of Biology, Stanford University, Stanford, CA94305
| | - Zhuoran Li
- HHMI, Stanford University, Stanford, CA94305
- Department of Biology, Stanford University, Stanford, CA94305
| | - Liqun Luo
- HHMI, Stanford University, Stanford, CA94305
- Department of Biology, Stanford University, Stanford, CA94305
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16
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Chen YCD, Chen YC, Rajesh R, Shoji N, Jacy M, Lacin H, Erclik T, Desplan C. Using single-cell RNA sequencing to generate predictive cell-type-specific split-GAL4 reagents throughout development. Proc Natl Acad Sci U S A 2023; 120:e2307451120. [PMID: 37523539 PMCID: PMC10410749 DOI: 10.1073/pnas.2307451120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
Cell-type-specific tools facilitate the identification and functional characterization of the distinct cell types that form the complexity of neuronal circuits. A large collection of existing genetic tools in Drosophila relies on enhancer activity to label different subsets of cells and has been extremely useful in analyzing functional circuits in adults. However, these enhancer-based GAL4 lines often do not reflect the expression of nearby gene(s) as they only represent a small portion of the full gene regulatory elements. While genetic intersectional techniques such as the split-GAL4 system further improve cell-type-specificity, it requires significant time and resources to screen through combinations of enhancer expression patterns. Here, we use existing developmental single-cell RNA sequencing (scRNAseq) datasets to select gene pairs for split-GAL4 and provide a highly efficient and predictive pipeline (scMarco) to generate cell-type-specific split-GAL4 lines at any time during development, based on the native gene regulatory elements. These gene-specific split-GAL4 lines can be generated from a large collection of coding intronic MiMIC/CRIMIC lines or by CRISPR knock-in. We use the developing Drosophila visual system as a model to demonstrate the high predictive power of scRNAseq-guided gene-specific split-GAL4 lines in targeting known cell types, annotating clusters in scRNAseq datasets as well as in identifying novel cell types. Lastly, the gene-specific split-GAL4 lines are broadly applicable to any other Drosophila tissue. Our work opens new avenues for generating cell-type-specific tools for the targeted manipulation of distinct cell types throughout development and represents a valuable resource for the Drosophila community.
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Affiliation(s)
| | - Yen-Chung Chen
- Department of Biology, New York University, New York,NY10003
| | - Raghuvanshi Rajesh
- Department of Biology, New York University, New York,NY10003
- Center for Genomics and Systems Biology, New York University, Abu Dhabi51133, United Arab Emirates
| | - Nathalie Shoji
- Department of Biology, New York University, New York,NY10003
| | - Maisha Jacy
- Department of Biology, New York University, New York,NY10003
| | - Haluk Lacin
- Division of Biological and Biomedical Systems, University of Missouri - Kansas City, Kansas City, MO64110
| | - Ted Erclik
- Department of Biology and Cell, University of Toronto - Mississauga, Mississauga, ONL5L 1C6, Canada
- Department of Systems Biology, University of Toronto - Mississauga, Mississauga, ONL5L 1C6, Canada
| | - Claude Desplan
- Department of Biology, New York University, New York,NY10003
- Center for Genomics and Systems Biology, New York University, Abu Dhabi51133, United Arab Emirates
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17
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Richhariya S, Shin D, Le JQ, Rosbash M. Dissecting neuron-specific functions of circadian genes using modified cell-specific CRISPR approaches. Proc Natl Acad Sci U S A 2023; 120:e2303779120. [PMID: 37428902 PMCID: PMC10629539 DOI: 10.1073/pnas.2303779120] [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] [Received: 03/06/2023] [Accepted: 06/07/2023] [Indexed: 07/12/2023] Open
Abstract
Circadian behavioral rhythms in Drosophila melanogaster are regulated by about 75 pairs of brain neurons. They all express the core clock genes but have distinct functions and gene expression profiles. To understand the importance of these distinct molecular programs, neuron-specific gene manipulations are essential. Although RNAi based methods are standard to manipulate gene expression in a cell-specific manner, they are often ineffective, especially in assays involving smaller numbers of neurons or weaker Gal4 drivers. We and others recently exploited a neuron-specific CRISPR-based method to mutagenize genes within circadian neurons. Here, we further explore this approach to mutagenize three well-studied clock genes: the transcription factor gene vrille, the photoreceptor gene Cryptochrome (cry), and the neuropeptide gene Pdf (pigment dispersing factor). The CRISPR-based strategy not only reproduced their known phenotypes but also assigned cry function for different light-mediated phenotypes to discrete, different subsets of clock neurons. We further tested two recently published methods for temporal regulation in adult neurons, inducible Cas9 and the auxin-inducible gene expression system. The results were not identical, but both approaches successfully showed that the adult-specific knockout of the neuropeptide Pdf reproduces the canonical loss-of-function mutant phenotypes. In summary, a CRISPR-based strategy is a highly effective, reliable, and general method to temporally manipulate gene function in specific adult neurons.
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18
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, 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, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, 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
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The 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
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, 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
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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19
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Manubens-Gil L, Zhou Z, Chen H, Ramanathan A, Liu X, Liu Y, Bria A, Gillette T, Ruan Z, Yang J, Radojević M, Zhao T, Cheng L, Qu L, Liu S, Bouchard KE, Gu L, Cai W, Ji S, Roysam B, Wang CW, Yu H, Sironi A, Iascone DM, Zhou J, Bas E, Conde-Sousa E, Aguiar P, Li X, Li Y, Nanda S, Wang Y, Muresan L, Fua P, Ye B, He HY, Staiger JF, Peter M, Cox DN, Simonneau M, Oberlaender M, Jefferis G, Ito K, Gonzalez-Bellido P, Kim J, Rubel E, Cline HT, Zeng H, Nern A, Chiang AS, Yao J, Roskams J, Livesey R, Stevens J, Liu T, Dang C, Guo Y, Zhong N, Tourassi G, Hill S, Hawrylycz M, Koch C, Meijering E, Ascoli GA, Peng H. BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. Nat Methods 2023; 20:824-835. [PMID: 37069271 DOI: 10.1038/s41592-023-01848-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 03/14/2023] [Indexed: 04/19/2023]
Abstract
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
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Affiliation(s)
- Linus Manubens-Gil
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zhi Zhou
- Microsoft Corporation, Redmond, WA, USA
| | | | - Arvind Ramanathan
- Computing, Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL, USA
| | | | - Yufeng Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | - Todd Gillette
- Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Zongcai Ruan
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jian Yang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | | | - Ting Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Li Cheng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Lei Qu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Anhui University, Hefei, China
| | | | - Kristofer E Bouchard
- Scientific Data Division and Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, CA, USA
- Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, CA, USA
| | - Lin Gu
- RIKEN AIP, Tokyo, Japan
- Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, Tokyo, Japan
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, New South Wales, Australia
| | - Shuiwang Ji
- Texas A&M University, College Station, TX, USA
| | - Badrinath Roysam
- Cullen College of Engineering, University of Houston, Houston, TX, USA
| | - Ching-Wei Wang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Hongchuan Yu
- National Centre for Computer Animation, Bournemouth University, Poole, UK
| | | | - Daniel Maxim Iascone
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Jie Zhou
- Department of Computer Science, Northern Illinois University, DeKalb, IL, USA
| | | | - Eduardo Conde-Sousa
- i3S, Instituto de Investigação E Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
- INEB, Instituto de Engenharia Biomédica, Universidade Do Porto, Porto, Portugal
| | - Paulo Aguiar
- i3S, Instituto de Investigação E Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yujie Li
- Allen Institute for Brain Science, Seattle, WA, USA
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Yuan Wang
- Program in Neuroscience, Department of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA
| | - Leila Muresan
- Cambridge Advanced Imaging Centre, University of Cambridge, Cambridge, UK
| | - Pascal Fua
- Computer Vision Laboratory, EPFL, Lausanne, Switzerland
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Hai-Yan He
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Jochen F Staiger
- Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August- University Göttingen, Goettingen, Germany
| | - Manuel Peter
- Department of Stem Cell and Regenerative Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Daniel N Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Michel Simonneau
- 42 ENS Paris-Saclay, CNRS, CentraleSupélec, LuMIn, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Marcel Oberlaender
- Max Planck Group: In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
| | - Gregory Jefferis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Kei Ito
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Institute for Quantitative Biosciences, University of Tokyo, Tokyo, Japan
- Institute of Zoology, Biocenter Cologne, University of Cologne, Cologne, Germany
| | | | - Jinhyun Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Edwin Rubel
- Virginia Merrill Bloedel Hearing Research Center, University of Washington, Seattle, WA, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ann-Shyn Chiang
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
| | | | - Jane Roskams
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Zoology, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rick Livesey
- Zayed Centre for Rare Disease Research, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Janine Stevens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Chinh Dang
- Virginia Merrill Bloedel Hearing Research Center, University of Washington, Seattle, WA, USA
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
| | - Ning Zhong
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan
| | | | - Sean Hill
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China.
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Laturney M, Sterne GR, Scott K. Mating activates neuroendocrine pathways signaling hunger in Drosophila females. eLife 2023; 12:e85117. [PMID: 37184218 PMCID: PMC10229122 DOI: 10.7554/elife.85117] [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: 11/23/2022] [Accepted: 05/13/2023] [Indexed: 05/16/2023] Open
Abstract
Mated females reallocate resources to offspring production, causing changes to nutritional requirements and challenges to energy homeostasis. Although observed across species, the neural and endocrine mechanisms that regulate the nutritional needs of mated females are not well understood. Here, we find that mated Drosophila melanogaster females increase sugar intake, which is regulated by the activity of sexually dimorphic insulin receptor (Lgr3) neurons. In virgins, Lgr3+ cells have reduced activity as they receive inhibitory input from active, female-specific pCd-2 cells, restricting sugar intake. During copulation, males deposit sex peptide into the female reproductive tract, which silences a three-tier mating status circuit and initiates the female postmating response. We show that pCd-2 neurons also become silenced after mating due to the direct synaptic input from the mating status circuit. Thus, in mated females pCd-2 inhibition is attenuated, activating downstream Lgr3+ neurons and promoting sugar intake. Together, this circuit transforms the mated signal into a long-term hunger signal. Our results demonstrate that the mating circuit alters nutrient sensing centers to increase feeding in mated females, providing a mechanism to increase intake in anticipation of the energetic costs associated with reproduction.
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Affiliation(s)
| | | | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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