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Huilgol D, Levine JM, Galbavy W, Wang BS, Josh Huang Z. Orderly specification and precise laminar deployment of cortical glutamatergic projection neuron types through intermediate progenitors. bioRxiv 2024:2024.03.01.582863. [PMID: 38645016 PMCID: PMC11027211 DOI: 10.1101/2024.03.01.582863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The cerebral cortex comprises diverse types of glutamatergic projection neurons (PNs) generated from radial glial progenitors (RGs) through either direct neurogenesis or indirect neurogenesis (iNG) via intermediate progenitors (IPs). A foundational concept in corticogenesis is the "inside-out" model whereby successive generations of PNs sequentially migrate to deep then progressively more superficial layers, but its biological significance remains unclear; and the role of iNG in this process is unknown. Using genetic strategies linking PN birth-dating to projection mapping in mice, we found that the laminar deployment of IP-derived PNs substantially deviate from an inside-out rule: PNs destined to non-consecutive layers are generated at the same time, and different PN types of the same layer are generated at non-contiguous times. The overarching scheme of iNG is the sequential specification and precise laminar deployment of projection-defined PN types, which may contribute to the orderly assembly of cortical output channels and processing streams. HIGHLIGHTS - Each IP is fate-restricted to generate a pair of near-identical PNs - Corticogenesis involves the orderly generation of fate-restricted IP temporal cohorts - IP temporal cohorts sequentially as well as concurrently specify multiple PN types - The deployment of PN types to specific layers does not follow an inside-out order.
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Huilgol D, Levine JM, Galbavy W, Wang BS, He M, Suryanarayana SM, Huang ZJ. Direct and indirect neurogenesis generate a mosaic of distinct glutamatergic projection neuron types in cerebral cortex. Neuron 2023; 111:2557-2569.e4. [PMID: 37348506 PMCID: PMC10527425 DOI: 10.1016/j.neuron.2023.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 02/27/2023] [Accepted: 05/23/2023] [Indexed: 06/24/2023]
Abstract
Variations in size and complexity of the cerebral cortex result from differences in neuron number and composition, rooted in evolutionary changes in direct and indirect neurogenesis (dNG and iNG) that are mediated by radial glia and intermediate progenitors (IPs), respectively. How dNG and iNG differentially contribute to neuronal number, diversity, and connectivity are unknown. Establishing a genetic fate-mapping method to differentially visualize dNG and iNG in mice, we found that while both dNG and iNG contribute to all cortical structures, iNG contributes the largest relative proportions to the hippocampus and neocortex. Within the neocortex, whereas dNG generates all major glutamatergic projection neuron (PN) classes, iNG differentially amplifies and diversifies PNs within each class; the two pathways generate distinct PN types and assemble fine mosaics of lineage-based cortical subnetworks. Our results establish a ground-level lineage framework for understanding cortical development and evolution by linking foundational progenitor types and neurogenic pathways to PN types.
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Affiliation(s)
- Dhananjay Huilgol
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Jesse M Levine
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, Stony Brook, NY 11794, USA
| | - William Galbavy
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Program in Neuroscience, Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY 11794, USA
| | - Bor-Shuen Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Miao He
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurobiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | | | - Z Josh Huang
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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3
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - 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, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Xiangning 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, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jing Yuan
- 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, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - 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, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Qingming Luo
- 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, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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4
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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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5
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Matho KS, Huilgol D, Galbavy W, He M, Kim G, An X, Lu J, Wu P, Di Bella DJ, Shetty AS, Palaniswamy R, Hatfield J, Raudales R, Narasimhan A, Gamache E, Levine JM, Tucciarone J, Szelenyi E, Harris JA, Mitra PP, Osten P, Arlotta P, Huang ZJ. Genetic dissection of the glutamatergic neuron system in cerebral cortex. Nature 2021; 598:182-187. [PMID: 34616069 PMCID: PMC8494647 DOI: 10.1038/s41586-021-03955-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/25/2021] [Indexed: 11/09/2022]
Abstract
Diverse types of glutamatergic pyramidal neurons mediate the myriad processing streams and output channels of the cerebral cortex1,2, yet all derive from neural progenitors of the embryonic dorsal telencephalon3,4. Here we establish genetic strategies and tools for dissecting and fate-mapping subpopulations of pyramidal neurons on the basis of their developmental and molecular programs. We leverage key transcription factors and effector genes to systematically target temporal patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen temporally inducible mouse Cre and Flp knock-in driver lines to enable the combinatorial targeting of major progenitor types and projection classes. Combinatorial strategies confer viral access to subsets of pyramidal neurons defined by developmental origin, marker expression, anatomical location and projection targets. These strategies establish an experimental framework for understanding the hierarchical organization and developmental trajectory of subpopulations of pyramidal neurons that assemble cortical processing networks and output channels.
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Affiliation(s)
- Katherine S Matho
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Dhananjay Huilgol
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - William Galbavy
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience, Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
| | - Miao He
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Gukhan Kim
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Jiangteng Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Shanghai Jiaotong University Medical School, Shanghai, China
| | - Priscilla Wu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Daniela J Di Bella
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Ashwin S Shetty
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Joshua Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Ricardo Raudales
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience, Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
| | - Arun Narasimhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Eric Gamache
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Jesse M Levine
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
| | - Jason Tucciarone
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
- Department of Psychiatry, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Eric Szelenyi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Julie A Harris
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA.
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA.
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6
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Azim S, Nicholson J, Rebecchi MJ, Galbavy W, Feng T, Rizwan S, Reinsel RA, Kaczocha M, Benveniste H. Interleukin-6 and leptin levels are associated with preoperative pain severity in patients with osteoarthritis but not with acute pain after total knee arthroplasty. Knee 2018; 25:25-33. [PMID: 29325836 DOI: 10.1016/j.knee.2017.12.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 11/27/2017] [Accepted: 12/02/2017] [Indexed: 02/02/2023]
Abstract
BACKGROUND Identifying drivers of pain that can serve as novel drug targets is important for improving perioperative analgesia. Total knee arthroplasty (TKA) is associated with significant postoperative pain. Cytokines contribute to the pathophysiology of osteoarthritis (OA) and associated pain. However, the influence of perioperative cytokine levels after TKA surgery upon postoperative pain remains unexplored. METHODS We designed a prospective observational study to profile three proinflammatory cytokines, interleukin-6 (IL-6), tumor necrosis factor α (TNFα), and leptin in serum, synovial, and cerebrospinal fluid of TKA patients perioperatively to determine associations between cytokine levels and pain. We characterized time-trajectories in cytokines pre- and post-surgery and explored their relationships to pain across gender. RESULTS Preoperative pain, measured by functional pain disability scores (PDQ), was predictive of postoperative pain. There were no gender differences in severity of preoperative pain or acute postoperative pain. Serum IL-6, serum leptin, and synovial fluid leptin were positively correlated with body mass index and preoperative pain severity. Stratification of patients by gender revealed strong correlations between serum IL-6, leptin, and PDQ only in females, suggesting that females may be more sensitive to the nociceptive actions of these cytokines. Although serum IL-6 increased dramatically (and TNFα increased modestly) four hours after surgery and remained elevated at 72h; they were not associated with the severity of acute postoperative pain. CONCLUSIONS Our data suggest that while preoperative chronic pain is predictive of the severity of acute postoperative pain in TKA patients, the pre- and post-operative inflammatory status does not predict postoperative pain.
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Affiliation(s)
- Syed Azim
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States.
| | - James Nicholson
- Department of Orthopaedics, Stony Brook University, Stony Brook, NY, United States
| | - Mario J Rebecchi
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States
| | - William Galbavy
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States
| | - Tian Feng
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Sabeen Rizwan
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States
| | - Ruth A Reinsel
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States
| | - Martin Kaczocha
- Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States.
| | - Helene Benveniste
- Department of Anesthesiology, Yale University, New Haven, United States.
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Galbavy W, Lu Y, Kaczocha M, Puopolo M, Liu L, Rebecchi MJ. Transcriptomic evidence of a para-inflammatory state in the middle aged lumbar spinal cord. Immun Ageing 2017; 14:9. [PMID: 28413428 PMCID: PMC5390443 DOI: 10.1186/s12979-017-0091-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 04/05/2017] [Indexed: 11/10/2022]
Abstract
BACKGROUND We have previously reported elevated expression of multiple pro-inflammatory markers in the lumbar spinal cord (LSC) of middle-aged male rats compared to young adults suggesting a para-inflammatory state develops in the LSC by middle age, a time that in humans is associated with the greatest pain prevalence and persistence. The goal of the current study was to examine the transcriptome-wide gene expression differences between young and middle aged LSC. METHODS Young (3 month) and middle-aged (17 month) naïve Fisher 344 rats (n = 5 per group) were euthanized, perfused with heparinized saline, and the LSC were removed. RESULTS ~70% of 31,000 coding sequences were detected. After normalization, ~ 1100 showed statistically significant differential expression. Of these genes, 353 middle-aged annotated genes differed by > 1.5 fold compared to the young group. Nearly 10% of these genes belonged to the microglial sensome. Analysis of this subset revealed that the principal age-related differential pathways populated are complement, pattern recognition receptors, OX40, and various T cell regulatory pathways consistent with microglial priming and T cell invasion and modulation. Many of these pathways substantially overlap those previously identified in studies of LSC of young animals with chronic inflammatory or neuropathic pain. CONCLUSIONS Up-modulation of complement pathway, microglial priming and activation, and T cell/antigen-presenting cell communication in healthy middle-aged LSC was found. Taken together with our previous work, the results support our conclusion that an incipient or para-inflammatory state develops in the LSC in healthy middle-aged adults.
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Affiliation(s)
- William Galbavy
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
| | - Yong Lu
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
| | - Martin Kaczocha
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
| | - Michelino Puopolo
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
| | - Lixin Liu
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
| | - Mario J Rebecchi
- Department of Anesthesiology, School of Medicine, Health Sciences Center L4, Stony Brook University, Stony Brook, New York, 11794-8480 USA
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Galbavy W, Kaczocha M, Puopolo M, Liu L, Rebecchi MJ. Neuroimmune and Neuropathic Responses of Spinal Cord and Dorsal Root Ganglia in Middle Age. PLoS One 2015; 10:e0134394. [PMID: 26241743 PMCID: PMC4524632 DOI: 10.1371/journal.pone.0134394] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 07/08/2015] [Indexed: 02/07/2023] Open
Abstract
Prior studies of aging and neuropathic injury have focused on senescent animals compared to young adults, while changes in middle age, particularly in the dorsal root ganglia (DRG), have remained largely unexplored. 14 neuroimmune mRNA markers, previously associated with peripheral nerve injury, were measured in multiplex assays of lumbar spinal cord (LSC), and DRG from young and middle-aged (3, 17 month) naïve rats, or from rats subjected to chronic constriction injury (CCI) of the sciatic nerve (after 7 days), or from aged-matched sham controls. Results showed that CD2, CD3e, CD68, CD45, TNF-α, IL6, CCL2, ATF3 and TGFβ1 mRNA levels were substantially elevated in LSC from naïve middle-aged animals compared to young adults. Similarly, LSC samples from older sham animals showed increased levels of T-cell and microglial/macrophage markers. CCI induced further increases in CCL2, and IL6, and elevated ATF3 mRNA levels in LSC of young and middle-aged adults. Immunofluorescence images of dorsal horn microglia from middle-aged naïve or sham rats were typically hypertrophic with mostly thickened, de-ramified processes, similar to microglia following CCI. Unlike the spinal cord, marker expression profiles in naïve DRG were unchanged across age (except increased ATF3); whereas, levels of GFAP protein, localized to satellite glia, were highly elevated in middle age, but independent of nerve injury. Most neuroimmune markers were elevated in DRG following CCI in young adults, yet middle-aged animals showed little response to injury. No age-related changes in nociception (heat, cold, mechanical) were observed in naïve adults, or at days 3 or 7 post-CCI. The patterns of marker expression and microglial morphologies in healthy middle age are consistent with development of a para-inflammatory state involving microglial activation and T-cell marker elevation in the dorsal horn, and neuronal stress and satellite cell activation in the DRG. These changes, however, did not affect the establishment of neuropathic pain.
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Affiliation(s)
- William Galbavy
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Martin Kaczocha
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Michelino Puopolo
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Lixin Liu
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Mario J Rebecchi
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
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9
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Nicholson J, Azim S, Rebecchi MJ, Galbavy W, Feng T, Reinsel R, Rizwan S, Fowler CJ, Benveniste H, Kaczocha M. Leptin levels are negatively correlated with 2-arachidonoylglycerol in the cerebrospinal fluid of patients with osteoarthritis. PLoS One 2015; 10:e0123132. [PMID: 25835291 PMCID: PMC4383333 DOI: 10.1371/journal.pone.0123132] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 02/16/2015] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND There is compelling evidence in humans that peripheral endocannabinoid signaling is disrupted in obesity. However, little is known about the corresponding central signaling. Here, we have investigated the relationship between gender, leptin, body mass index (BMI) and levels of the endocannabinoids anandamide (AEA) and 2-arachidonoylglycerol (2-AG) in the serum and cerebrospinal fluid (CSF) of primarily overweight to obese patients with osteoarthritis. METHODOLOGY/PRINCIPAL FINDINGS Patients (20 females, 15 males, age range 44-78 years, BMI range 24-42) undergoing total knee arthroplasty for end-stage osteoarthritis were recruited for the study. Endocannabinoids were quantified by liquid chromatography - mass spectrometry. AEA and 2-AG levels in the serum and CSF did not correlate with either age or BMI. However, 2-AG levels in the CSF, but not serum, correlated negatively with CSF leptin levels (Spearman's ρ -0.48, P=0.0076, n=30). No such correlations were observed for AEA and leptin. CONCLUSIONS/SIGNIFICANCE In the patient sample investigated, there is a negative association between 2-AG and leptin levels in the CSF. This is consistent with pre-clinical studies in animals, demonstrating that leptin controls the levels of hypothalamic endocannabinoids that regulate feeding behavior.
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Affiliation(s)
- James Nicholson
- Department of Orthopedic Surgery, Stony Brook University, Stony Brook, New York, United States of America
| | - Syed Azim
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Mario J. Rebecchi
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - William Galbavy
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Tian Feng
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
| | - Ruth Reinsel
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Sabeen Rizwan
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | | | - Helene Benveniste
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail: (MK); (HB)
| | - Martin Kaczocha
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail: (MK); (HB)
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Kaczocha M, Rebecchi MJ, Ralph BP, Teng YHG, Berger WT, Galbavy W, Elmes MW, Glaser ST, Wang L, Rizzo RC, Deutsch DG, Ojima I. Inhibition of fatty acid binding proteins elevates brain anandamide levels and produces analgesia. PLoS One 2014; 9:e94200. [PMID: 24705380 PMCID: PMC3976407 DOI: 10.1371/journal.pone.0094200] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Accepted: 03/12/2014] [Indexed: 11/23/2022] Open
Abstract
The endocannabinoid anandamide (AEA) is an antinociceptive lipid that is inactivated through cellular uptake and subsequent catabolism by fatty acid amide hydrolase (FAAH). Fatty acid binding proteins (FABPs) are intracellular carriers that deliver AEA and related N-acylethanolamines (NAEs) to FAAH for hydrolysis. The mammalian brain expresses three FABP subtypes: FABP3, FABP5, and FABP7. Recent work from our group has revealed that pharmacological inhibition of FABPs reduces inflammatory pain in mice. The goal of the current work was to explore the effects of FABP inhibition upon nociception in diverse models of pain. We developed inhibitors with differential affinities for FABPs to elucidate the subtype(s) that contributes to the antinociceptive effects of FABP inhibitors. Inhibition of FABPs reduced nociception associated with inflammatory, visceral, and neuropathic pain. The antinociceptive effects of FABP inhibitors mirrored their affinities for FABP5, while binding to FABP3 and FABP7 was not a predictor of in vivo efficacy. The antinociceptive effects of FABP inhibitors were mediated by cannabinoid receptor 1 (CB1) and peroxisome proliferator-activated receptor alpha (PPARα) and FABP inhibition elevated brain levels of AEA, providing the first direct evidence that FABPs regulate brain endocannabinoid tone. These results highlight FABPs as novel targets for the development of analgesic and anti-inflammatory therapeutics.
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Affiliation(s)
- Martin Kaczocha
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail:
| | - Mario J. Rebecchi
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Brian P. Ralph
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, United States of America
| | - Yu-Han Gary Teng
- Department of Chemistry and the Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, United States of America
| | - William T. Berger
- Department of Chemistry and the Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, United States of America
| | - William Galbavy
- Department of Anesthesiology, Stony Brook University, Stony Brook, New York, United States of America
| | - Matthew W. Elmes
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, United States of America
| | - Sherrye T. Glaser
- Department of Biological Sciences, Kingsborough Community College, Brooklyn, New York, United States of America
| | - Liqun Wang
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, United States of America
| | - Robert C. Rizzo
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
| | - Dale G. Deutsch
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, New York, United States of America
| | - Iwao Ojima
- Department of Chemistry and the Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, New York, United States of America
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Galbavy W, Safaie E, Rebecchi MJ, Puopolo M. Inhibition of tetrodotoxin-resistant sodium current in dorsal root ganglia neurons mediated by D1/D5 dopamine receptors. Mol Pain 2013; 9:60. [PMID: 24283218 PMCID: PMC4220807 DOI: 10.1186/1744-8069-9-60] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Accepted: 11/22/2013] [Indexed: 12/25/2022] Open
Abstract
Background Dopaminergic fibers originating from area A11 of the hypothalamus project to different levels of the spinal cord and represent the major source of dopamine. In addition, tyrosine hydroxylase, the rate-limiting enzyme for the synthesis of catecholamines, is expressed in 8-10% of dorsal root ganglia (DRG) neurons, suggesting that dopamine may be released in the dorsal root ganglia. Dopamine has been shown to modulate calcium current in DRG neurons, but the effects of dopamine on sodium current and on the firing properties of small DRG neurons are poorly understood. Results The effects of dopamine and dopamine receptor agonists were tested on the tetrodotoxin-resistant (TTX-R) sodium current recorded from acutely dissociated small (diameter ≤ 25 μm) DRG neurons. Dopamine (20 μM) and SKF 81297 (10 μM) caused inhibition of TTX-R sodium current in small DRG neurons by 23% and 37%, respectively. In contrast, quinpirole (20 μM) had no effects on the TTX-R sodium current. Inhibition by SKF 81297 of the TTX-R sodium current was not affected when the protein kinase A (PKA) activity was blocked with the PKA inhibitory peptide (6–22), but was greatly reduced when the protein kinase C (PKC) activity was blocked with the PKC inhibitory peptide (19–36), suggesting that activation of D1/D5 dopamine receptors is linked to PKC activity. Expression of D1and D5 dopamine receptors in small DRG neurons, but not D2 dopamine receptors, was confirmed by Western blotting and immunofluorescence analysis. In current clamp experiments, the number of action potentials elicited in small DRG neurons by current injection was reduced by ~ 30% by SKF 81297. Conclusions We conclude that activation of D1/D5 dopamine receptors inhibits TTX-R sodium current in unmyelinated nociceptive neurons and dampens their intrinsic excitability by reducing the number of action potentials in response to stimulus. Increasing or decreasing levels of dopamine in the dorsal root ganglia may serve to adjust the sensitivity of nociceptors to noxious stimuli.
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Affiliation(s)
| | | | | | - Michelino Puopolo
- Department of Anesthesiology, Stony Brook Medicine, Stony Brook, NY 11794, USA.
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