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] [Grants] [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] [Grants] [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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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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4
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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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5
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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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6
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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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7
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Sorensen SA, Gouwens NW, Wang Y, Mallory M, Budzillo A, Dalley R, Lee B, Gliko O, Kuo HC, Kuang X, Mann R, Ahmadinia L, Alfiler L, Baftizadeh F, Baker K, Bannick S, Bertagnolli D, Bickley K, Bohn P, Brown D, Bomben J, Brouner K, Chen C, Chen K, Chvilicek M, Collman F, Daigle T, Dawes T, de Frates R, Dee N, DePartee M, Egdorf T, El-Hifnawi L, Enstrom R, Esposito L, Farrell C, Gala R, Glomb A, Gamlin C, Gary A, Goldy J, Gu H, Hadley K, Hawrylycz M, Henry A, Hill D, Hirokawa KE, Huang Z, Johnson K, Juneau Z, Kebede S, Kim L, Lee C, Lesnar P, Li A, Glomb A, Li Y, Liang E, Link K, Maxwell M, McGraw M, McMillen DA, Mukora A, Ng L, Ochoa T, Oldre A, Park D, Pom CA, Popovich Z, Potekhina L, Rajanbabu R, Ransford S, Reding M, Ruiz A, Sandman D, Siverts L, Smith KA, Stoecklin M, Sulc J, Tieu M, Ting J, Trinh J, Vargas S, Vumbaco D, Walker M, Wang M, Wanner A, Waters J, Williams G, Wilson J, Xiong W, Lein E, Berg J, Kalmbach B, Yao S, Gong H, Luo Q, Ng L, Sümbül U, Jarsky T, Yao Z, Tasic B, Zeng H. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.25.568393. [PMID: 38168270 PMCID: PMC10760188 DOI: 10.1101/2023.11.25.568393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
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Affiliation(s)
| | | | - Yun Wang
- Allen Institute for Brain Science
| | | | | | | | | | | | | | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | | | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Kai Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science
| | | | | | | | | | | | | | | | | | | | | | | | - Hong Gu
- Allen Institute for Brain Science
| | | | | | | | | | | | - Zili Huang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | - Lisa Kim
- Allen Institute for Brain Science
| | | | | | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | | | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | - Zoran Popovich
- University of Washington, Dept. of Computer Science and Engineering
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Ed Lein
- Allen Institute for Brain Science
| | - Jim Berg
- Allen Institute for Brain Science
| | | | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qingming Luo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Lydia Ng
- Allen Institute for Brain Science
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