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Gradwell MA, Ozeri-Engelhard N, Eisdorfer JT, Laflamme OD, Gonzalez M, Upadhyay A, Medlock L, Shrier T, Patel KR, Aoki A, Gandhi M, Abbas-Zadeh G, Oputa O, Thackray JK, Ricci M, George A, Yusuf N, Keating J, Imtiaz Z, Alomary SA, Bohic M, Haas M, Hernandez Y, Prescott SA, Akay T, Abraira VE. Multimodal sensory control of motor performance by glycinergic interneurons of the mouse spinal cord deep dorsal horn. Neuron 2024; 112:1302-1327.e13. [PMID: 38452762 DOI: 10.1016/j.neuron.2024.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/31/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024]
Abstract
Sensory feedback is integral for contextually appropriate motor output, yet the neural circuits responsible remain elusive. Here, we pinpoint the medial deep dorsal horn of the mouse spinal cord as a convergence point for proprioceptive and cutaneous input. Within this region, we identify a population of tonically active glycinergic inhibitory neurons expressing parvalbumin. Using anatomy and electrophysiology, we demonstrate that deep dorsal horn parvalbumin-expressing interneuron (dPV) activity is shaped by convergent proprioceptive, cutaneous, and descending input. Selectively targeting spinal dPVs, we reveal their widespread ipsilateral inhibition onto pre-motor and motor networks and demonstrate their role in gating sensory-evoked muscle activity using electromyography (EMG) recordings. dPV ablation altered limb kinematics and step-cycle timing during treadmill locomotion and reduced the transitions between sub-movements during spontaneous behavior. These findings reveal a circuit basis by which sensory convergence onto dorsal horn inhibitory neurons modulates motor output to facilitate smooth movement and context-appropriate transitions.
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Affiliation(s)
- Mark A Gradwell
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nofar Ozeri-Engelhard
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Jaclyn T Eisdorfer
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olivier D Laflamme
- Dalhousie PhD program, Dalhousie University, Halifax, NS, Canada; Department of Medical Neuroscience, Atlantic Mobility Action Project, Brain Repair Center, Dalhousie University, Halifax, NS, Canada
| | - Melissa Gonzalez
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Aman Upadhyay
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Laura Medlock
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Tara Shrier
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Komal R Patel
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Adin Aoki
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Melissa Gandhi
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Gloria Abbas-Zadeh
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Olisemaka Oputa
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Joshua K Thackray
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Human Genetics Institute of New Jersey, Rutgers University, The State University of New Jersey, Piscataway, NJ, USA; Tourette International Collaborative Genetics Study (TIC Genetics)
| | - Matthew Ricci
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Arlene George
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nusrath Yusuf
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; Neuroscience PhD program, Rutgers Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Jessica Keating
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Zarghona Imtiaz
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Simona A Alomary
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Manon Bohic
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Michael Haas
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Yurdiana Hernandez
- W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Steven A Prescott
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Turgay Akay
- Department of Medical Neuroscience, Atlantic Mobility Action Project, Brain Repair Center, Dalhousie University, Halifax, NS, Canada
| | - Victoria E Abraira
- Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA; W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA.
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Russ DE, Cross RBP, Li L, Koch SC, Matson KJE, Yadav A, Alkaslasi MR, Lee DI, Le Pichon CE, Menon V, Levine AJ. A harmonized atlas of mouse spinal cord cell types and their spatial organization. Nat Commun 2021; 12:5722. [PMID: 34588430 PMCID: PMC8481483 DOI: 10.1038/s41467-021-25125-1] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022] Open
Abstract
Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.
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Affiliation(s)
- Daniel E Russ
- Division of Cancer Epidemiology and Genetics, Data Science Research Group, National Cancer Institute, NIH, Rockville, MD, USA
| | - Ryan B Patterson Cross
- Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Li Li
- Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Stephanie C Koch
- Department of Neuroscience, Physiology and Pharmacology, Division of Biosciences, University College London, London, UK
| | - Kaya J E Matson
- Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Archana Yadav
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY, USA
| | - Mor R Alkaslasi
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA.,Department of Neuroscience, Brown University, Providence, RI, USA
| | - Dylan I Lee
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY, USA
| | - Claire E Le Pichon
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Vilas Menon
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY, USA
| | - Ariel J Levine
- Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.
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Bandet MV, Dong B, Winship IR. Distinct patterns of activity in individual cortical neurons and local networks in primary somatosensory cortex of mice evoked by square-wave mechanical limb stimulation. PLoS One 2021; 16:e0236684. [PMID: 33914738 PMCID: PMC8084136 DOI: 10.1371/journal.pone.0236684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 04/15/2021] [Indexed: 11/19/2022] Open
Abstract
Artificial forms of mechanical limb stimulation are used within multiple fields of study to determine the level of cortical excitability and to map the trajectory of neuronal recovery from cortical damage or disease. Square-wave mechanical or electrical stimuli are often used in these studies, but a characterization of sensory-evoked response properties to square-waves with distinct fundamental frequencies but overlapping harmonics has not been performed. To distinguish between somatic stimuli, the primary somatosensory cortex must be able to represent distinct stimuli with unique patterns of activity, even if they have overlapping features. Thus, mechanical square-wave stimulation was used in conjunction with regional and cellular imaging to examine regional and cellular response properties evoked by different frequencies of stimulation. Flavoprotein autofluorescence imaging was used to map the somatosensory cortex of anaesthetized C57BL/6 mice, and in vivo two-photon Ca2+ imaging was used to define patterns of neuronal activation during mechanical square-wave stimulation of the contralateral forelimb or hindlimb at various frequencies (3, 10, 100, 200, and 300 Hz). The data revealed that neurons within the limb associated somatosensory cortex responding to various frequencies of square-wave stimuli exhibit stimulus-specific patterns of activity. Subsets of neurons were found to have sensory-evoked activity that is either primarily responsive to single stimulus frequencies or broadly responsive to multiple frequencies of limb stimulation. High frequency stimuli were shown to elicit more population activity, with a greater percentage of the population responding and greater percentage of cells with high amplitude responses. Stimulus-evoked cell-cell correlations within these neuronal networks varied as a function of frequency of stimulation, such that each stimulus elicited a distinct pattern that was more consistent across multiple trials of the same stimulus compared to trials at different frequencies of stimulation. The variation in cortical response to different square-wave stimuli can thus be represented by the population pattern of supra-threshold Ca2+ transients, the magnitude and temporal properties of the evoked activity, and the structure of the stimulus-evoked correlation between neurons.
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Affiliation(s)
- Mischa V. Bandet
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
- Neurochemical Research Unit, University of Alberta, Edmonton, Alberta, Canada
| | - Bin Dong
- Neurochemical Research Unit, University of Alberta, Edmonton, Alberta, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Ian R. Winship
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
- Neurochemical Research Unit, University of Alberta, Edmonton, Alberta, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
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