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Mohan H, An X, Xu XH, Kondo H, Zhao S, Matho KS, Wang BS, Musall S, Mitra P, Huang ZJ. Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks. Nat Neurosci 2023; 26:481-494. [PMID: 36690901 PMCID: PMC10571488 DOI: 10.1038/s41593-022-01244-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 12/02/2022] [Indexed: 01/24/2023]
Abstract
The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.
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Affiliation(s)
- Hemanth Mohan
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - X Hermione Xu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Shengli Zhao
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | | | - Bor-Shuen Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Simon Musall
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Institute of Biological information Processing, Forschungszentrum Julich, Julich, Germany
| | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Z Josh Huang
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
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2
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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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3
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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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4
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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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5
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Haery L, Deverman BE, Matho KS, Cetin A, Woodard K, Cepko C, Guerin KI, Rego MA, Ersing I, Bachle SM, Kamens J, Fan M. Adeno-Associated Virus Technologies and Methods for Targeted Neuronal Manipulation. Front Neuroanat 2019; 13:93. [PMID: 31849618 PMCID: PMC6902037 DOI: 10.3389/fnana.2019.00093] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/30/2019] [Indexed: 12/14/2022] Open
Abstract
Cell-type-specific expression of molecular tools and sensors is critical to construct circuit diagrams and to investigate the activity and function of neurons within the nervous system. Strategies for targeted manipulation include combinations of classical genetic tools such as Cre/loxP and Flp/FRT, use of cis-regulatory elements, targeted knock-in transgenic mice, and gene delivery by AAV and other viral vectors. The combination of these complex technologies with the goal of precise neuronal targeting is a challenge in the lab. This report will discuss the theoretical and practical aspects of combining current technologies and establish best practices for achieving targeted manipulation of specific cell types. Novel applications and tools, as well as areas for development, will be envisioned and discussed.
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Affiliation(s)
| | - Benjamin E. Deverman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, United States
| | - Kenton Woodard
- Penn Vector Core, Gene Therapy Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Connie Cepko
- Department of Genetics, Harvard Medical School, Howard Hughes Medical Institute, Boston, MA, United States
- Department of Ophthalmology, Harvard Medical School, Howard Hughes Medical Institute, Boston, MA, United States
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Loulier K, Barry R, Mahou P, Le Franc Y, Supatto W, Matho KS, Ieng S, Fouquet S, Dupin E, Benosman R, Chédotal A, Beaurepaire E, Morin X, Livet J. Multiplex cell and lineage tracking with combinatorial labels. Neuron 2014; 81:505-20. [PMID: 24507188 DOI: 10.1016/j.neuron.2013.12.016] [Citation(s) in RCA: 115] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/03/2013] [Indexed: 10/25/2022]
Abstract
We present a method to label and trace the lineage of multiple neural progenitors simultaneously in vertebrate animals via multiaddressable genome-integrative color (MAGIC) markers. We achieve permanent expression of combinatorial labels from new Brainbow transgenes introduced in embryonic neural progenitors with electroporation of transposon vectors. In the mouse forebrain and chicken spinal cord, this approach allows us to track neural progenitor's descent during pre- and postnatal neurogenesis or perinatal gliogenesis in long-term experiments. Color labels delineate cytoarchitecture, resolve spatially intermixed clones, and specify the lineage of astroglial subtypes and adult neural stem cells. Combining colors and subcellular locations provides an expanded marker palette to individualize clones. We show that this approach is also applicable to modulate specific signaling pathways in a mosaic manner while color-coding the status of individual cells regarding induced molecular perturbations. This method opens new avenues for clonal and functional analysis in varied experimental models and contexts.
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Affiliation(s)
- Karine Loulier
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Raphaëlle Barry
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Pierre Mahou
- Laboratoire d'Optique et Biosciences, Ecole Polytechnique, Palaiseau 91128, France; CNRS, UMR 7645, Palaiseau 91128, France; INSERM, U696, Palaiseau 91128, France
| | - Yann Le Franc
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Willy Supatto
- Laboratoire d'Optique et Biosciences, Ecole Polytechnique, Palaiseau 91128, France; CNRS, UMR 7645, Palaiseau 91128, France; INSERM, U696, Palaiseau 91128, France
| | - Katherine S Matho
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Siohoi Ieng
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Stéphane Fouquet
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Elisabeth Dupin
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Ryad Benosman
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Alain Chédotal
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France
| | - Emmanuel Beaurepaire
- Laboratoire d'Optique et Biosciences, Ecole Polytechnique, Palaiseau 91128, France; CNRS, UMR 7645, Palaiseau 91128, France; INSERM, U696, Palaiseau 91128, France
| | - Xavier Morin
- Ecole Normale Supérieure, Institut de Biologie de l'ENS, IBENS, Paris 75005, France; INSERM, U1024, Paris 75005, France; CNRS, UMR 8197, Paris 75005, France.
| | - Jean Livet
- INSERM, U968, Paris 75012, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 968, Institut de la Vision, Paris 75012, France; CNRS, UMR 7210, Paris 75012, France.
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Namsolleck P, Boato F, Schwengel K, Paulis L, Matho KS, Geurts N, Thöne-Reineke C, Lucht K, Seidel K, Hallberg A, Dahlöf B, Unger T, Hendrix S, Steckelings UM. AT2-receptor stimulation enhances axonal plasticity after spinal cord injury by upregulating BDNF expression. Neurobiol Dis 2012; 51:177-91. [PMID: 23174180 DOI: 10.1016/j.nbd.2012.11.008] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Revised: 10/22/2012] [Accepted: 11/09/2012] [Indexed: 12/12/2022] Open
Abstract
It is widely accepted that the angiotensin AT2-receptor (AT2R) has neuroprotective features. In the present study we tested pharmacological AT2R-stimulation as a therapeutic approach in a model of spinal cord compression injury (SCI) in mice using the novel non-peptide AT2R-agonist, Compound 21 (C21). Complementary experiments in primary neurons and organotypic cultures served to identify underlying mechanisms. Functional recovery and plasticity of corticospinal tract (CST) fibers following SCI were monitored after application of C21 (0.3mg/kg/dayi.p.) or vehicle for 4 weeks. Organotypic co-culture of GFP-positive entorhinal cortices with hippocampal target tissue served to evaluate the impact of C21 on reinnervation. Neuronal differentiation, apoptosis and expression of neurotrophins were investigated in primary murine astrocytes and neuronal cells. C21 significantly improved functional recovery after SCI compared to controls, and this significantly correlated with the increased number of CST fibers caudal to the lesion site. In vitro, C21 significantly promoted reinnervation in organotypic brain slice co-cultures (+50%) and neurite outgrowth of primary neurons (+25%). C21-induced neurite outgrowth was absent in neurons derived from AT2R-KO mice. In primary neurons, treatment with C21 further induced RNA expression of anti-apoptotic Bcl-2 (+75.7%), brain-derived neurotrophic factor (BDNF) (+53.7%), the neurotrophin receptors TrkA (+57.4%) and TrkB (+67.9%) and a marker for neurite growth, GAP43 (+103%), but not TrkC. Our data suggest that selective AT2R-stimulation improves functional recovery in experimental spinal cord injury through promotion of axonal plasticity and through neuroprotective and anti-apoptotic mechanisms. Thus, AT2R-stimulation may be considered for the development of a novel therapeutic approach for the treatment of spinal cord injury.
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Affiliation(s)
- Pawel Namsolleck
- Center for Cardiovascular Research, Institute of Pharmacology, Charité-Universitätsmedizin Berlin, Hessische Str. 3-4, 10115 Berlin, Germany
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Mahou P, Zimmerley M, Loulier K, Matho KS, Labroille G, Morin X, Supatto W, Livet J, Débarre D, Beaurepaire E. Multicolor two-photon tissue imaging by wavelength mixing. Nat Methods 2012; 9:815-8. [PMID: 22772730 DOI: 10.1038/nmeth.2098] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2012] [Accepted: 06/10/2012] [Indexed: 12/20/2022]
Abstract
We achieve simultaneous two-photon excitation of three chromophores with distinct absorption spectra using synchronized pulses from a femtosecond laser and an optical parametric oscillator. The two beams generate separate multiphoton processes, and their spatiotemporal overlap provides an additional two-photon excitation route, with submicrometer overlay of the color channels. We report volume and live multicolor imaging of 'Brainbow'-labeled tissues as well as simultaneous three-color fluorescence and third-harmonic imaging of fly embryos.
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Affiliation(s)
- Pierre Mahou
- Laboratory for Optics and Biosciences, Ecole Polytechnique, Centre National de la Recherche Scientifique UMR 7645 and INSERM U696, Palaiseau, France
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Piaton G, Aigrot MS, Williams A, Moyon S, Tepavcevic V, Moutkine I, Gras J, Matho KS, Schmitt A, Soellner H, Huber AB, Ravassard P, Lubetzki C. Class 3 semaphorins influence oligodendrocyte precursor recruitment and remyelination in adult central nervous system. ACTA ACUST UNITED AC 2011; 134:1156-67. [PMID: 21421691 DOI: 10.1093/brain/awr022] [Citation(s) in RCA: 125] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Oligodendrocyte precursor cells, which persist in the adult central nervous system, are the main source of central nervous system remyelinating cells. In multiple sclerosis, some demyelinated plaques exhibit an oligodendroglial depopulation, raising the hypothesis of impaired oligodendrocyte precursor cell recruitment. Developmental studies identified semaphorins 3A and 3F as repulsive and attractive guidance cues for oligodendrocyte precursor cells, respectively. We previously reported their increased expression in experimental demyelination and in multiple sclerosis. Here, we show that adult oligodendrocyte precursor cells, like their embryonic counterparts, express class 3 semaphorin receptors, neuropilins and plexins and that neuropilin expression increases after demyelination. Using gain and loss of function experiments in an adult murine demyelination model, we demonstrate that semaphorin 3A impairs oligodendrocyte precursor cell recruitment to the demyelinated area. In contrast, semaphorin 3F overexpression accelerates not only oligodendrocyte precursor cell recruitment, but also remyelination rate. These data open new avenues to understand remyelination failure and promote repair in multiple sclerosis.
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Affiliation(s)
- Gabrièle Piaton
- Université Pierre et Marie Curie, Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), UMRS 975; Inserm U 975; CNRS, UMR 7225; Paris 75013, France
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