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Hergenreder T, Yang T, Ye B. The role of Down syndrome cell adhesion molecule in Down syndrome. MEDICAL REVIEW (2021) 2024; 4:31-41. [PMID: 38515781 PMCID: PMC10954295 DOI: 10.1515/mr-2023-0056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/18/2024] [Indexed: 03/23/2024]
Abstract
Down syndrome (DS) is caused by the presence of an extra copy of the entire or a portion of human chromosome 21 (HSA21). This genomic alteration leads to elevated expression of numerous HSA21 genes, resulting in a variety of health issues in individuals with DS. Among the genes located in the DS "critical region" of HSA21, Down syndrome cell adhesion molecule (DSCAM) plays an important role in neuronal development. There is a growing body of evidence underscoring DSCAM's involvement in various DS-related disorders. This review aims to provide a concise overview of the established functions of DSCAM, with a particular focus on its implications in DS. We delve into the roles that DSCAM plays in DS-associated diseases. In the concluding section of this review, we explore prospective avenues for future research to further unravel DSCAM's role in DS and opportunities for therapeutic treatments.
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Affiliation(s)
- Ty Hergenreder
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Tao Yang
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
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2
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Chen A, Sun Y, Lei Y, Li C, Liao S, Meng J, Bai Y, Liu Z, Liang Z, Zhu Z, Yuan N, Yang H, Wu Z, Lin F, Wang K, Li M, Zhang S, Yang M, Fei T, Zhuang Z, Huang Y, Zhang Y, Xu Y, Cui L, Zhang R, Han L, Sun X, Chen B, Li W, Huangfu B, Ma K, Ma J, Li Z, Lin Y, Wang H, Zhong Y, Zhang H, Yu Q, Wang Y, Liu X, Peng J, Liu C, Chen W, Pan W, An Y, Xia S, Lu Y, Wang M, Song X, Liu S, Wang Z, Gong C, Huang X, Yuan Y, Zhao Y, Chai Q, Tan X, Liu J, Zheng M, Li S, Huang Y, Hong Y, Huang Z, Li M, Jin M, Li Y, Zhang H, Sun S, Gao L, Bai Y, Cheng M, Hu G, Liu S, Wang B, Xiang B, Li S, Li H, Chen M, Wang S, Li M, Liu W, Liu X, Zhao Q, Lisby M, Wang J, Fang J, Lin Y, Xie Q, Liu Z, He J, Xu H, Huang W, Mulder J, Yang H, Sun Y, Uhlen M, Poo M, Wang J, Yao J, Wei W, Li Y, Shen Z, Liu L, Liu Z, Xu X, Li C. Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 2023; 186:3726-3743.e24. [PMID: 37442136 DOI: 10.1016/j.cell.2023.06.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/24/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
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Affiliation(s)
- Ao Chen
- BGI-Shenzhen, Shenzhen 518103, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Yidi Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Ying Lei
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Chao Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Sha Liao
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Juan Meng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yiqin Bai
- Lingang Laboratory, Shanghai 200031, China
| | - Zhen Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Nini Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hao Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Feng Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Kexin Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mei Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Shuzhen Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Tianyi Fei
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhenkun Zhuang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yiming Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong Zhang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yuanfang Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luman Cui
- BGI-Shenzhen, Shenzhen 518103, China
| | - Ruiyi Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Han
- BGI-Shenzhen, Shenzhen 518103, China
| | - Xing Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | | | - Baoqian Huangfu
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | | | - Jianyun Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhao Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yikun Lin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - He Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanqing Zhong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huifang Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaqian Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jian Peng
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Wei Chen
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Yingjie An
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shihui Xia
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanbing Lu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingli Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinxiang Song
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuai Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Chun Gong
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xin Huang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yue Yuan
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yun Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinwen Chai
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Tan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianfeng Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingyuan Zheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shengkang Li
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, China
| | | | - Yan Hong
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Min Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Mengmeng Jin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hui Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Suhong Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Gao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yinqi Bai
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Guohai Hu
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Shiping Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Bo Wang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Bin Xiang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuting Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengni Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shiwen Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Minglong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Xin Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qian Zhao
- BGI-Shenzhen, Shenzhen 518103, China
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark
| | - Jing Wang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jiao Fang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yun Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qing Xie
- BGI-Shenzhen, Shenzhen 518103, China
| | - Zhen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jie He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huatai Xu
- Lingang Laboratory, Shanghai 200031, China
| | - Wei Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jan Mulder
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | | | - Yangang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | - Muming Poo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | | | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China; CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yuxiang Li
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Wuhan, BGI, Wuhan 430074, China.
| | - Zhiming Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China.
| | - Zhiyong Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China.
| | - Chengyu Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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Jung K, Chang M, Steinecke A, Burke B, Choi Y, Oisi Y, Fitzpatrick D, Taniguchi H, Kwon HB. An adaptive behavioral control motif mediated by cortical axo-axonic inhibition. Nat Neurosci 2023; 26:1379-1393. [PMID: 37474640 PMCID: PMC10400431 DOI: 10.1038/s41593-023-01380-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/13/2023] [Indexed: 07/22/2023]
Abstract
Genetically defined subgroups of inhibitory interneurons are thought to play distinct roles in learning, but heterogeneity within these subgroups has limited our understanding of the scope and nature of their specific contributions. Here we reveal that the chandelier cell (ChC), an interneuron type that specializes in inhibiting the axon-initial segment (AIS) of pyramidal neurons, establishes cortical microcircuits for organizing neural coding through selective axo-axonic synaptic plasticity. We found that organized motor control is mediated by enhanced population coding of direction-tuned premotor neurons, with tuning refined through suppression of irrelevant neuronal activity. ChCs contribute to learning-dependent refinements by providing selective inhibitory control over individual pyramidal neurons rather than global suppression. Quantitative analysis of structural plasticity across axo-axonic synapses revealed that ChCs redistributed inhibitory weights to individual pyramidal neurons during learning. These results demonstrate an adaptive logic of the inhibitory circuit motif responsible for organizing distributed neural representations. Thus, ChCs permit efficient cortical computation in a targeted cell-specific manner.
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Affiliation(s)
- Kanghoon Jung
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Minhyeok Chang
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - André Steinecke
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - Benjamin Burke
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Youngjin Choi
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yasuhiro Oisi
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | | | - Hiroki Taniguchi
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
- Department of Pathology, Chronic Brain Injury program, Ohio State University, Columbus, OH, USA
| | - Hyung-Bae Kwon
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA.
- Max Planck Institute of Neurobiology, Martinsried, Germany.
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4
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Hong T, McBride E, Dufour BD, Falcone C, Doan M, Noctor SG, Martínez-Cerdeño V. Synaptic boutons are smaller in chandelier cell cartridges in autism. PLoS One 2023; 18:e0281477. [PMID: 37097993 PMCID: PMC10128992 DOI: 10.1371/journal.pone.0281477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 01/25/2023] [Indexed: 04/26/2023] Open
Abstract
Chandelier (Ch) cells are cortical interneurons with axon terminal structures known as cartridges that synapse on the axon initial segment of excitatory pyramidal neurons. Previous studies indicate that the number of Ch cells is decreased in autism, and that GABA receptors are decreased in the Ch cell synaptic target in the prefrontal cortex. To further identify Ch cell alterations, we examined whether the length of cartridges, and the number, density, and size of Ch cell synaptic boutons, differed in the prefrontal cortex of cases with autism versus control cases. We collected samples of postmortem human prefrontal cortex (Brodmann Area (BA) 9, 46, and 47) from 20 cases with autism and 20 age- and sex-matched control cases. Ch cells were labeled using an antibody against parvalbumin, a marker that labeles soma, cartridges, and synaptic boutons. We found no significant difference in the average length of cartridges, or in the total number or density of boutons in control subjects vs. subjects with autism. However, we found a significant decrease in the size of Ch cell boutons in those with autism. The reduced size of Ch cell boutons may result in reduced inhibitory signal transmission and impact the balance of excitation to inhibition in the prefrontal cortex in autism.
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Affiliation(s)
- Tiffany Hong
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Erin McBride
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Brett D. Dufour
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Carmen Falcone
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Mai Doan
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Stephen G. Noctor
- Department of Psychiatry and Behavioral Science, UC Davis School of Medicine, Sacramento, CA, United States of America
| | - Verónica Martínez-Cerdeño
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine and Shriners Hospitals for Children of Northern California, UC Davis School of Medicine, Sacramento, CA, United States of America
- MIND Institute, UC Davis Medical Center, Sacramento, CA, United States of America
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5
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Liu H, Caballero-Florán RN, Hergenreder T, Yang T, Hull JM, Pan G, Li R, Veling MW, Isom LL, Kwan KY, Huang ZJ, Fuerst PG, Jenkins PM, Ye B. DSCAM gene triplication causes excessive GABAergic synapses in the neocortex in Down syndrome mouse models. PLoS Biol 2023; 21:e3002078. [PMID: 37079499 PMCID: PMC10118173 DOI: 10.1371/journal.pbio.3002078] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/14/2023] [Indexed: 04/21/2023] Open
Abstract
Down syndrome (DS) is caused by the trisomy of human chromosome 21 (HSA21). A major challenge in DS research is to identify the HSA21 genes that cause specific symptoms. Down syndrome cell adhesion molecule (DSCAM) is encoded by a HSA21 gene. Previous studies have shown that the protein level of the Drosophila homolog of DSCAM determines the size of presynaptic terminals. However, whether the triplication of DSCAM contributes to presynaptic development in DS remains unknown. Here, we show that DSCAM levels regulate GABAergic synapses formed on neocortical pyramidal neurons (PyNs). In the Ts65Dn mouse model for DS, where DSCAM is overexpressed due to DSCAM triplication, GABAergic innervation of PyNs by basket and chandelier interneurons is increased. Genetic normalization of DSCAM expression rescues the excessive GABAergic innervations and the increased inhibition of PyNs. Conversely, loss of DSCAM impairs GABAergic synapse development and function. These findings demonstrate excessive GABAergic innervation and synaptic transmission in the neocortex of DS mouse models and identify DSCAM overexpression as the cause. They also implicate dysregulated DSCAM levels as a potential pathogenic driver in related neurological disorders.
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Affiliation(s)
- Hao Liu
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - René N. Caballero-Florán
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Ty Hergenreder
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Tao Yang
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jacob M. Hull
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Geng Pan
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Ruonan Li
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Macy W. Veling
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lori L. Isom
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Kenneth Y. Kwan
- Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Z. Josh Huang
- Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, United States of America
- Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, North Carolina, United States of America
| | - Peter G. Fuerst
- University of Idaho, Department of Biological Sciences, Moscow, Idaho, United States of America
| | - Paul M. Jenkins
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Bing Ye
- Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
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6
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Jung K, Chang M, Steinecke A, Berke B, Choi Y, Oisi Y, Fitzpatrick D, Taniguchi H, Kwon HB. An adaptive behavioral control motif mediated by cortical axo-axonic inhibition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.531767. [PMID: 36945592 PMCID: PMC10029003 DOI: 10.1101/2023.03.10.531767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Neural circuits are reorganized with specificity during learning. Genetically-defined subgroups of inhibitory interneurons are thought to play distinct roles in learning, but heterogeneity within these subgroups has limited our understanding of the scope and nature of their specific contributions to learning. Here we reveal that the chandelier cell (ChC), an interneuron type that specializes in inhibiting the axon-initial segment (AIS) of pyramidal neurons, establishes cortical microcircuits for organizing neural coding through selective axo-axonic synaptic plasticity. We find that organized motor control is mediated by enhanced population coding of direction-tuned premotor neurons, whose tuning is refined through suppression of irrelevant neuronal activity. ChCs are required for learning-dependent refinements via providing selective inhibitory control over pyramidal neurons rather than global suppression. Quantitative analysis on structural plasticity of axo-axonic synapses revealed that ChCs redistributed inhibitory weights to individual pyramidal neurons during learning. These results demonstrate an adaptive logic of the inhibitory circuit motif responsible for organizing distributed neural representations. Thus, ChCs permit efficient cortical computation in a target cell specific manner, which highlights the significance of interneuron diversity.
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Affiliation(s)
- Kanghoon Jung
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
- Current address: Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Minhyeok Chang
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- equally contributed
| | - André Steinecke
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
- equally contributed
| | - Benjamin Berke
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Youngjin Choi
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Yasuhiro Oisi
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
| | - David Fitzpatrick
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
| | - Hiroki Taniguchi
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
- Department of Pathology, Chronic Brain Injury program, Ohio State University, Columbus, Ohio 43210, USA
| | - Hyung-Bae Kwon
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- Max Planck Florida Institute for Neuroscience, Jupiter, Florida 33458, USA
- Max Planck Institute of Neurobiology, Martinsried 82152, Germany
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7
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Negwer M, Bosch B, Bormann M, Hesen R, Lütje L, Aarts L, Rossing C, Nadif Kasri N, Schubert D. FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis. Gigascience 2022; 12:giad035. [PMID: 37222748 PMCID: PMC10205001 DOI: 10.1093/gigascience/giad035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/15/2023] [Accepted: 04/26/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory's use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2's CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline. FINDINGS For more precise alignment, we add a landmark-based atlas registration to ClearMap's functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap's threshold-based approach: Ilastik's Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells. CONCLUSIONS As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
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Affiliation(s)
- Moritz Negwer
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Bram Bosch
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Maren Bormann
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Rick Hesen
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Lukas Lütje
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Lynn Aarts
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Carleen Rossing
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Nael Nadif Kasri
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboudumc, Donders Institute for Brain, Cognition and Behaviour, 6500 HB Nijmegen, The Netherlands
| | - Dirk Schubert
- Department of Cognitive Neuroscience, Radboudumc, Donders Institute for Brain, Cognition and Behaviour, 6500 HB Nijmegen, The Netherlands
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8
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Jung K, Choi Y, Kwon HB. Cortical control of chandelier cells in neural codes. Front Cell Neurosci 2022; 16:992409. [PMID: 36299494 PMCID: PMC9588934 DOI: 10.3389/fncel.2022.992409] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/05/2022] [Indexed: 11/28/2022] Open
Abstract
Various cortical functions arise from the dynamic interplay of excitation and inhibition. GABAergic interneurons that mediate synaptic inhibition display significant diversity in cell morphology, electrophysiology, plasticity rule, and connectivity. These heterogeneous features are thought to underlie their functional diversity. Emerging attention on specific properties of the various interneuron types has emphasized the crucial role of cell-type specific inhibition in cortical neural processing. However, knowledge is still limited on how each interneuron type forms distinct neural circuits and regulates network activity in health and disease. To dissect interneuron heterogeneity at single cell-type precision, we focus on the chandelier cell (ChC), one of the most distinctive GABAergic interneuron types that exclusively innervate the axon initial segments (AIS) of excitatory pyramidal neurons. Here we review the current understanding of the structural and functional properties of ChCs and their implications in behavioral functions, network activity, and psychiatric disorders. These findings provide insights into the distinctive roles of various single-type interneurons in cortical neural coding and the pathophysiology of cortical dysfunction.
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9
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Ostos S, Aparicio G, Fernaud-Espinosa I, DeFelipe J, Muñoz A. Quantitative analysis of the GABAergic innervation of the soma and axon initial segment of pyramidal cells in the human and mouse neocortex. Cereb Cortex 2022; 33:3882-3909. [PMID: 36058205 DOI: 10.1093/cercor/bhac314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/16/2022] [Accepted: 07/17/2022] [Indexed: 11/13/2022] Open
Abstract
Perisomatic GABAergic innervation in the cerebral cortex is carried out mostly by basket and chandelier cells, which differentially participate in the control of pyramidal cell action potential output and synchronization. These cells establish multiple synapses with the cell body (and proximal dendrites) and the axon initial segment (AIS) of pyramidal neurons, respectively. Using multiple immunofluorescence, confocal microscopy and 3D quantification techniques, we have estimated the number and density of GABAergic boutons on the cell body and AIS of pyramidal neurons located through cortical layers of the human and mouse neocortex. The results revealed, in both species, that there is clear variability across layers regarding the density and number of perisomatic GABAergic boutons. We found a positive linear correlation between the surface area of the soma, or the AIS, and the number of GABAergic terminals in apposition to these 2 neuronal domains. Furthermore, the density of perisomatic GABAergic boutons was higher in the human cortex than in the mouse. These results suggest a selectivity for the GABAergic innervation of the cell body and AIS that might be related to the different functional attributes of the microcircuits in which neurons from different layers are involved in both human and mouse.
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Affiliation(s)
- Sandra Ostos
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Guillermo Aparicio
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Isabel Fernaud-Espinosa
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Javier DeFelipe
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain.,CIBERNED, Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, Avenida Monforte de Lemos, 3-5, 28029 Madrid, Spain
| | - Alberto Muñoz
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain.,Departamento de Biología Celular, Universidad Complutense, José Antonio Novais 12, 28040 Madrid, Spain
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10
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Schneider-Mizell CM, Bodor AL, Collman F, Brittain D, Bleckert A, Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Zhuang J, Nandi A, Hu B, Buchanan J, Takeno MM, Torres R, Mahalingam G, Bumbarger DJ, Li Y, Chartrand T, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Popovych S, Wong W, Castro M, Jordan CS, Froudarakis E, Becker L, Suckow S, Reimer J, Tolias AS, Anastassiou CA, Seung HS, Reid RC, da Costa NM. Structure and function of axo-axonic inhibition. eLife 2021; 10:e73783. [PMID: 34851292 PMCID: PMC8758143 DOI: 10.7554/elife.73783] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
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Affiliation(s)
| | - Agnes L Bodor
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Adam Bleckert
- Allen Institute for Brain SciencesSeattleUnited States
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jun Zhuang
- Allen Institute for Brain SciencesSeattleUnited States
| | - Anirban Nandi
- Allen Institute for Brain SciencesSeattleUnited States
| | - Brian Hu
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain SciencesSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Yang Li
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Lynne Becker
- Allen Institute for Brain SciencesSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain SciencesSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | - Costas A Anastassiou
- Allen Institute for Brain SciencesSeattleUnited States
- Department of Neurology, University of British ColumbiaVancouverCanada
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - R Clay Reid
- Allen Institute for Brain SciencesSeattleUnited States
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11
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Rougier NP, Detorakis GI. Randomized Self-Organizing Map. Neural Comput 2021; 33:2241-2273. [PMID: 34310672 DOI: 10.1162/neco_a_01406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/05/2021] [Indexed: 11/04/2022]
Abstract
We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensional tasks, as well as on the MNIST handwritten digits data set and validated using spectral analysis and topological data analysis tools. We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.
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Affiliation(s)
- Nicolas P Rougier
- Inria Bordeaux Sud-Ouest, Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS UMR 5293, and LaBRI, Université de Bordeaux, Institut Polytechnique de Bordeaux, CNRS UMR 5800
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12
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Turegano-Lopez M, Santuy A, DeFelipe J, Merchan-Perez A. Size, Shape, and Distribution of Multivesicular Bodies in the Juvenile Rat Somatosensory Cortex: A 3D Electron Microscopy Study. Cereb Cortex 2021; 30:1887-1901. [PMID: 31665237 PMCID: PMC7132939 DOI: 10.1093/cercor/bhz211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/30/2019] [Accepted: 08/16/2019] [Indexed: 12/27/2022] Open
Abstract
Multivesicular bodies (MVBs) are membrane-bound organelles that belong to the endosomal pathway. They participate in the transport, sorting, storage, recycling, degradation, and release of multiple substances. They interchange cargo with other organelles and participate in their renovation and degradation. We have used focused ion beam milling and scanning electron microscopy (FIB-SEM) to obtain stacks of serial sections from the neuropil of the somatosensory cortex of the juvenile rat. Using dedicated software, we have 3D-reconstructed 1618 MVBs. The mean density of MVBs was 0.21 per cubic micron. They were unequally distributed between dendrites (39.14%), axons (18.16%), and nonsynaptic cell processes (42.70%). About one out of five MVBs (18.16%) were docked on mitochondria, representing the process by which the endosomal pathway participates in mitochondrial maintenance. Other features of MVBs, such as the presence of tubular protrusions (6.66%) or clathrin coats (19.74%) can also be interpreted in functional terms, since both are typical of early endosomes. The sizes of MVBs follow a lognormal distribution, with differences across cortical layers and cellular compartments. The mean volume of dendritic MVBs is more than twice as large as the volume of axonic MVBs. In layer I, they are smaller, on average, than in the other layers.
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Affiliation(s)
- M Turegano-Lopez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - A Santuy
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - J DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda Doctor Arce, 37, 28002 Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) ISCIII, Madrid, Spain
| | - A Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) ISCIII, Madrid, Spain.,Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
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13
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Domínguez-Álvaro M, Montero-Crespo M, Blazquez-Llorca L, Plaza-Alonso S, Cano-Astorga N, DeFelipe J, Alonso-Nanclares L. 3D Analysis of the Synaptic Organization in the Entorhinal Cortex in Alzheimer's Disease. eNeuro 2021; 8:ENEURO.0504-20.2021. [PMID: 34039651 PMCID: PMC8225407 DOI: 10.1523/eneuro.0504-20.2021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/26/2021] [Accepted: 04/16/2021] [Indexed: 01/01/2023] Open
Abstract
The entorhinal cortex (EC) is especially vulnerable in the early stages of Alzheimer's disease (AD). In particular, cognitive deficits have been linked to alterations in the upper layers of EC. In the present report, we examined Layers II and III from eight human brain autopsies (four subjects with no recorded neurologic alterations and four AD cases). We used stereological methods to assess cortical atrophy of the EC and possible changes in the volume occupied by different cortical elements (neuronal and glial cell bodies; blood vessels; and neuropil). We performed 3D ultrastructural analyses of synapses using focused ion beam/scanning electron microscopy (FIB/SEM) to examine possible alterations related to AD. At the light microscope level, we found a significantly lower volume fraction occupied by neuronal bodies in Layer III and a higher volume fraction occupied by glial cell bodies in Layer II in AD cases. At the ultrastructural level, we observed that (1) there was a significantly lower synaptic density in both layers in AD cases; (2) synapses were larger and more complex in Layer II in AD cases; and (3) there was a greater proportion of small and simple synapses in Layer III in AD cases than in control individuals. These structural differences may play a role in the anatomic basis for the impairment of cognitive functions in AD.
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Affiliation(s)
- M Domínguez-Álvaro
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
| | - M Montero-Crespo
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
| | - L Blazquez-Llorca
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
- Sección Departamental de Anatomía y Embriología (Veterinaria), Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - S Plaza-Alonso
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
| | - N Cano-Astorga
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
| | - J DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
| | - L Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III 28031, Madrid, Spain
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14
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Montero-Crespo M, Domínguez-Álvaro M, Alonso-Nanclares L, DeFelipe J, Blazquez-Llorca L. Three-dimensional analysis of synaptic organization in the hippocampal CA1 field in Alzheimer's disease. Brain 2021; 144:553-573. [PMID: 33324984 PMCID: PMC8240746 DOI: 10.1093/brain/awaa406] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 09/07/2020] [Accepted: 09/20/2020] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia, characterized by a persistent and progressive impairment of cognitive functions. Alzheimer's disease is typically associated with extracellular deposits of amyloid-β peptide and accumulation of abnormally phosphorylated tau protein inside neurons (amyloid-β and neurofibrillary pathologies). It has been proposed that these pathologies cause neuronal degeneration and synaptic alterations, which are thought to constitute the major neurobiological basis of cognitive dysfunction in Alzheimer's disease. The hippocampal formation is especially vulnerable in the early stages of Alzheimer's disease. However, the vast majority of electron microscopy studies have been performed in animal models. In the present study, we performed an extensive 3D study of the neuropil to investigate the synaptic organization in the stratum pyramidale and radiatum in the CA1 field of Alzheimer's disease cases with different stages of the disease, using focused ion beam/scanning electron microscopy (FIB/SEM). In cases with early stages of Alzheimer's disease, the synapse morphology looks normal and we observed no significant differences between control and Alzheimer's disease cases regarding the synaptic density, the ratio of excitatory and inhibitory synapses, or the spatial distribution of synapses. However, differences in the distribution of postsynaptic targets and synaptic shapes were found. Furthermore, a lower proportion of larger excitatory synapses in both strata were found in Alzheimer's disease cases. Individuals in late stages of the disease suffered the most severe synaptic alterations, including a decrease in synaptic density and morphological alterations of the remaining synapses. Since Alzheimer's disease cases show cortical atrophy, our data indicate a reduction in the total number (but not the density) of synapses at early stages of the disease, with this reduction being much more accentuated in subjects with late stages of Alzheimer's disease. The observed synaptic alterations may represent a structural basis for the progressive learning and memory dysfunctions seen in Alzheimer's disease cases.
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Affiliation(s)
- Marta Montero-Crespo
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Av. Doctor Arce, 37, 28002 Madrid, Spain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Marta Domínguez-Álvaro
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Lidia Alonso-Nanclares
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Av. Doctor Arce, 37, 28002 Madrid, Spain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, c/Valderrebollo, 5, 28031 Madrid, Spain
| | - Javier DeFelipe
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Av. Doctor Arce, 37, 28002 Madrid, Spain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, c/Valderrebollo, 5, 28031 Madrid, Spain
| | - Lidia Blazquez-Llorca
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, c/Valderrebollo, 5, 28031 Madrid, Spain
- Departamento de Psicobiología, Facultad de Psicología, Universidad Nacional de Educación a Distancia (UNED), c/Juan del Rosal, 10, 28040 Madrid, Spain
- Sección Departamental de Anatomía y Embriología (Veterinaria), Facultad de Veterinaria, Universidad Complutense de Madrid, Av. Puerta de Hierro, s/n, 28040 Madrid, Spain
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15
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Yang JM, Shen CJ, Chen XJ, Kong Y, Liu YS, Li XW, Chen Z, Gao TM, Li XM. erbb4 Deficits in Chandelier Cells of the Medial Prefrontal Cortex Confer Cognitive Dysfunctions: Implications for Schizophrenia. Cereb Cortex 2020; 29:4334-4346. [PMID: 30590426 DOI: 10.1093/cercor/bhy316] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/18/2018] [Accepted: 11/21/2018] [Indexed: 12/20/2022] Open
Abstract
erbb4 is a known susceptibility gene for schizophrenia. Chandelier cells (ChCs, also known as axo-axonic cells) are a distinct GABAergic interneuron subtype that exclusively target the axonal initial segment, which is the site of pyramidal neuron action potential initiation. ChCs are a source of ErbB4 expression and alterations in ChC-pyramidal neuron connectivity occur in the medial prefrontal cortex (mPFC) of schizophrenic patients and animal models of schizophrenia. However, the contribution of ErbB4 in mPFC ChCs to the pathogenesis of schizophrenia remains unknown. By conditional deletion or knockdown of ErbB4 from mPFC ChCs, we demonstrated that ErbB4 deficits led to impaired ChC-pyramidal neuron connections and cognitive dysfunctions. Furthermore, the cognitive dysfunctions were normalized by L-838417, an agonist of GABAAα2 receptors enriched in the axonal initial segment. Given that cognitive dysfunctions are a core symptom of schizophrenia, our results may provide a new perspective for understanding the etiology of schizophrenia and suggest that GABAAα2 receptors may be potential pharmacological targets for its treatment.
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Affiliation(s)
- Jian-Ming Yang
- Center for Neuroscience and Department of Neurology of Second Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University School of Medicine, Hangzhou, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Chen-Jie Shen
- Center for Neuroscience and Department of Neurology of Second Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao-Juan Chen
- Center for Neuroscience and Department of Neurology of Second Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Kong
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yi-Si Liu
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiao-Wen Li
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zhong Chen
- Department of Pharmacology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tian-Ming Gao
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiao-Ming Li
- Center for Neuroscience and Department of Neurology of Second Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University School of Medicine, Hangzhou, China
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16
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Domínguez-Álvaro M, Montero-Crespo M, Blazquez-Llorca L, DeFelipe J, Alonso-Nanclares L. 3D Ultrastructural Study of Synapses in the Human Entorhinal Cortex. Cereb Cortex 2020; 31:410-425. [PMID: 32887978 PMCID: PMC7727377 DOI: 10.1093/cercor/bhaa233] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/24/2020] [Accepted: 07/28/2020] [Indexed: 01/01/2023] Open
Abstract
The entorhinal cortex (EC) is a brain region that has been shown to be essential for memory functions and spatial navigation. However, detailed three-dimensional (3D) synaptic morphology analysis and identification of postsynaptic targets at the ultrastructural level have not been performed before in the human EC. In the present study, we used Focused Ion Beam/Scanning Electron Microscopy to perform a 3D analysis of the synapses in the neuropil of medial EC in layers II and III from human brain autopsies. Specifically, we studied synaptic structural parameters of 3561 synapses, which were fully reconstructed in 3D. We analyzed the synaptic density, 3D spatial distribution, and type (excitatory and inhibitory), as well as the shape and size of each synaptic junction. Moreover, the postsynaptic targets of synapses could be clearly determined. The present work constitutes a detailed description of the synaptic organization of the human EC, which is a necessary step to better understand the functional organization of this region in both health and disease.
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Affiliation(s)
- M Domínguez-Álvaro
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid 28223, Spain
| | - M Montero-Crespo
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid 28223, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37 Madrid, 28002, Spain
| | - L Blazquez-Llorca
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid 28223, Spain.,Depto. Psicobiología, Facultad de Psicología, Universidad Nacional de Educación a Distancia (UNED), c/Juan del Rosal, 10, Madrid 28040, Spain
| | - J DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid 28223, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37 Madrid, 28002, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), c/Valderrebollo, 5, Madrid 28031, Spain
| | - L Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid. Pozuelo de Alarcón, Madrid 28223, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Avda. Doctor Arce, 37 Madrid, 28002, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), c/Valderrebollo, 5, Madrid 28031, Spain
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17
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Staiger JF, Petersen CCH. Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception. Physiol Rev 2020; 101:353-415. [PMID: 32816652 DOI: 10.1152/physrev.00019.2019] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.
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Affiliation(s)
- Jochen F Staiger
- University Medical Center Göttingen, Institute for Neuroanatomy, Göttingen, Germany; and Laboratory of Sensory Processing, Faculty of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Carl C H Petersen
- University Medical Center Göttingen, Institute for Neuroanatomy, Göttingen, Germany; and Laboratory of Sensory Processing, Faculty of Life Sciences, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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18
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Gallo NB, Paul A, Van Aelst L. Shedding Light on Chandelier Cell Development, Connectivity, and Contribution to Neural Disorders. Trends Neurosci 2020; 43:565-580. [PMID: 32564887 PMCID: PMC7392791 DOI: 10.1016/j.tins.2020.05.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/06/2020] [Accepted: 05/07/2020] [Indexed: 02/04/2023]
Abstract
Chandelier cells (ChCs) are a unique type of GABAergic interneuron that selectively innervate the axon initial segment (AIS) of excitatory pyramidal neurons; the subcellular domain where action potentials are initiated. The proper genesis and maturation of ChCs is critical for regulating neural ensemble firing in the neocortex throughout development and adulthood. Recently, genetic and molecular studies have shed new light on the complex innerworkings of ChCs in health and disease. This review presents an overview of recent studies on the developmental origins, migratory properties, and morphology of ChCs. In addition, attention is given to newly identified molecules regulating ChC morphogenesis and connectivity as well as recent work linking ChC dysfunction to neural disorders, including schizophrenia, epilepsy, and autism spectrum disorder (ASD).
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Affiliation(s)
- Nicholas B Gallo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA; Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Anirban Paul
- Department of Neural and Behavioral Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Linda Van Aelst
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA.
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19
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Montero-Crespo M, Dominguez-Alvaro M, Rondon-Carrillo P, Alonso-Nanclares L, DeFelipe J, Blazquez-Llorca L. Three-dimensional synaptic organization of the human hippocampal CA1 field. eLife 2020; 9:e57013. [PMID: 32690133 PMCID: PMC7375818 DOI: 10.7554/elife.57013] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022] Open
Abstract
The hippocampal CA1 field integrates a wide variety of subcortical and cortical inputs, but its synaptic organization in humans is still unknown due to the difficulties involved studying the human brain via electron microscope techniques. However, we have shown that the 3D reconstruction method using Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) can be applied to study in detail the synaptic organization of the human brain obtained from autopsies, yielding excellent results. Using this technology, 24,752 synapses were fully reconstructed in CA1, revealing that most of them were excitatory, targeting dendritic spines and displaying a macular shape, regardless of the layer examined. However, remarkable differences were observed between layers. These data constitute the first extensive description of the synaptic organization of the neuropil of the human CA1 region.
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Affiliation(s)
- Marta Montero-Crespo
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC)MadridSpain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
| | - Marta Dominguez-Alvaro
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
| | - Patricia Rondon-Carrillo
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
| | - Lidia Alonso-Nanclares
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC)MadridSpain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIIIMadridSpain
| | - Javier DeFelipe
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC)MadridSpain
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIIIMadridSpain
| | - Lidia Blazquez-Llorca
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadridSpain
- Departamento de Psicobiología, Facultad de Psicología, Universidad Nacional de Educación a Distancia (UNED)MadridSpain
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20
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Kikuchi T, Gonzalez-Soriano J, Kastanauskaite A, Benavides-Piccione R, Merchan-Perez A, DeFelipe J, Blazquez-Llorca L. Volume Electron Microscopy Study of the Relationship Between Synapses and Astrocytes in the Developing Rat Somatosensory Cortex. Cereb Cortex 2020; 30:3800-3819. [PMID: 31989178 PMCID: PMC7233003 DOI: 10.1093/cercor/bhz343] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/20/2019] [Indexed: 12/11/2022] Open
Abstract
In recent years, numerous studies have shown that astrocytes play an important role in neuronal processing of information. One of the most interesting findings is the existence of bidirectional interactions between neurons and astrocytes at synapses, which has given rise to the concept of “tripartite synapses” from a functional point of view. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to examine in 3D the relationship of synapses with astrocytes that were previously labeled by intracellular injections in the rat somatosensory cortex. We observed that a large number of synapses (32%) had no contact with astrocytic processes. The remaining synapses (68%) were in contact with astrocytic processes, either at the level of the synaptic cleft (44%) or with the pre- and/or post-synaptic elements (24%). Regarding synaptic morphology, larger synapses with more complex shapes were most frequently found within the population that had the synaptic cleft in contact with astrocytic processes. Furthermore, we observed that although synapses were randomly distributed in space, synapses that were free of astrocytic processes tended to form clusters. Overall, at least in the developing rat neocortex, the concept of tripartite synapse only seems to be applicable to a subset of synapses.
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Affiliation(s)
- Toko Kikuchi
- Center for Biosciences and Informatics, School of Fundamental Science and Technology, Graduate School of Science and Technology, Keio University, 223-8522 Kanagawa, Japan.,Department of Fundamental Neuroscience, University of Lausanne, 1015 Lausanne, Switzerland
| | - Juncal Gonzalez-Soriano
- Departamento de Anatomía, Facultad de Veterinaria, Universidad Complutense, 28040 Madrid, Spain
| | - Asta Kastanauskaite
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.,Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal, CSIC, 28002 Madrid, Spain
| | - Angel Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.,Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.,Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal, CSIC, 28002 Madrid, Spain
| | - Lidia Blazquez-Llorca
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.,Departamento de Psicobiología, Facultad de Psicología, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain
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21
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D'Souza RD, Bista P, Meier AM, Ji W, Burkhalter A. Spatial Clustering of Inhibition in Mouse Primary Visual Cortex. Neuron 2019; 104:588-600.e5. [PMID: 31623918 DOI: 10.1016/j.neuron.2019.09.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 08/08/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022]
Abstract
Whether mouse visual cortex contains orderly feature maps is debated. The overlapping pattern of geniculocortical inputs with M2 muscarinic acetylcholine receptor-rich patches in layer 1 (L1) suggests a non-random architecture. Here, we found that L1 inputs from the lateral posterior thalamus (LP) avoid patches and target interpatches. Channelrhodopsin-2-assisted mapping of excitatory postsynaptic currents (EPSCs) in L2/3 shows that the relative excitation of parvalbumin-expressing interneurons (PVs) and pyramidal neurons (PNs) by dLGN, LP, and cortical feedback is distinct and depends on whether the neurons reside in clusters aligned with patches or interpatches. Paired recordings from PVs and PNs show that unitary inhibitory postsynaptic currents (uIPSCs) are larger in interpatches than in patches. The spatial clustering of inhibition is matched by dense clustering of PV terminals in interpatches. The results show that the excitation/inhibition balance across V1 is organized into patch and interpatch subnetworks, which receive distinct long-range inputs and are specialized for the processing of distinct spatiotemporal features.
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Affiliation(s)
- Rinaldo D D'Souza
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Pawan Bista
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Andrew M Meier
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Weiqing Ji
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Andreas Burkhalter
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA.
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22
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Wang X, Tucciarone J, Jiang S, Yin F, Wang BS, Wang D, Jia Y, Jia X, Li Y, Yang T, Xu Z, Akram MA, Wang Y, Zeng S, Ascoli GA, Mitra P, Gong H, Luo Q, Huang ZJ. Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells. Cell Rep 2019; 26:3145-3159.e5. [PMID: 30865900 PMCID: PMC7863572 DOI: 10.1016/j.celrep.2019.02.040] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 11/19/2018] [Accepted: 02/08/2019] [Indexed: 10/27/2022] Open
Abstract
Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of neuron types. Combining genetic labeling with high-resolution, large-volume light microscopy, we established a single neuron anatomy platform that resolves, registers, and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segments, consist of multiple subtypes distinguished by highly laminar-specific soma position and dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. This platform will facilitate genetically targeted, high-resolution, and scalable single neuron anatomy in the mouse brain.
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Affiliation(s)
- Xiaojun Wang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jason Tucciarone
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, Stony Brook, NY 11790, USA
| | - Siqi Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Fangfang Yin
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Bor-Shuen Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Dingkang Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43221, USA
| | - Yao Jia
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xueyan Jia
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yuxin Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Tao Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhengchao Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Masood A Akram
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Yusu Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH 43221, USA
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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23
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Cortical interneuron function in autism spectrum condition. Pediatr Res 2019; 85:146-154. [PMID: 30367159 DOI: 10.1038/s41390-018-0214-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 10/11/2018] [Accepted: 10/15/2018] [Indexed: 12/28/2022]
Abstract
Cortical interneurons (INs) are a diverse group of neurons that project locally and shape the function of neural networks throughout the brain. Multiple lines of evidence suggest that a proper balance of glutamate and GABA signaling is essential for both the proper function and development of the brain. Dysregulation of this system may lead to neurodevelopmental disorders, including autism spectrum condition (ASC). We evaluate the development and function of INs in rodent and human models and examine how neurodevelopmental dysfunction may produce core symptoms of ASC. Finding common physiological mechanisms that underlie neurodevelopmental disorders may lead to novel pharmacological targets and candidates that could improve the cognitive and emotional symptoms associated with ASC.
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24
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Rougier NP. A Density-Driven Method for the Placement of Biological Cells Over Two-Dimensional Manifolds. Front Neuroinform 2018; 12:12. [PMID: 29615887 PMCID: PMC5869184 DOI: 10.3389/fninf.2018.00012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 02/27/2018] [Indexed: 11/13/2022] Open
Abstract
We introduce a graphical method originating from the computer graphics domain that is used for the arbitrary placement of cells over a two-dimensional manifold. Using a bitmap image whose luminance provides cell density, this method guarantees a discrete distribution of the positions of the cells respecting the local density. This method scales to any number of cells, allows one to specify arbitrary enclosing shapes and provides a scalable and versatile alternative to the more classical assumption of a uniform spatial distribution. The method is illustrated on a discrete homogeneous neural field, on the distribution of cones and rods in the retina and on the neural density of a flattened piece of cortex.
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Affiliation(s)
- Nicolas P Rougier
- UMR5293 Institut des Maladies Neurodégénératives (IMN), Bordeaux, France.,UMR5800 Laboratoire Bordelais de Recherche en Informatique (LaBRI), Talence, France.,Inria Bordeaux - Sud-Ouest Research Centre, Talence, France
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25
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Domínguez-Álvaro M, Montero-Crespo M, Blazquez-Llorca L, Insausti R, DeFelipe J, Alonso-Nanclares L. Three-dimensional analysis of synapses in the transentorhinal cortex of Alzheimer's disease patients. Acta Neuropathol Commun 2018; 6:20. [PMID: 29499755 PMCID: PMC5834884 DOI: 10.1186/s40478-018-0520-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 02/17/2018] [Indexed: 12/11/2022] Open
Abstract
Synaptic dysfunction or loss in early stages of Alzheimer’s disease (AD) is thought to be a major structural correlate of cognitive dysfunction. Early loss of episodic memory, which occurs at the early stage of AD, is closely associated with the progressive degeneration of medial temporal lobe (MTL) structures of which the transentorhinal cortex (TEC) is the first affected area. However, no ultrastructural studies have been performed in this region in human brain samples from AD patients. In the present study, we have performed a detailed three-dimensional (3D) ultrastructural analysis using focused ion beam/scanning electron microscopy (FIB/SEM) to investigate possible synaptic alterations in the TEC of patients with AD. Surprisingly, the analysis of the density, morphological features and spatial distribution of synapses in the neuropil showed no significant differences between AD and control samples. However, light microscopy studies showed that cortical thickness of the TEC was severely reduced in AD samples, but there were no changes in the volume occupied by neuronal and glial cell bodies, blood vessels, and neuropil. Thus, the present results indicate that there is a dramatic loss of absolute number of synapses, while the morphology of synaptic junctions and synaptic spatial distribution are maintained. How these changes affect cognitive impairment in AD remains to be elucidated.
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26
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Abstract
Cortical networks are composed of glutamatergic excitatory projection neurons and local GABAergic inhibitory interneurons that gate signal flow and sculpt network dynamics. Although they represent a minority of the total neocortical neuronal population, GABAergic interneurons are highly heterogeneous, forming functional classes based on their morphological, electrophysiological, and molecular features, as well as connectivity and in vivo patterns of activity. Here we review our current understanding of neocortical interneuron diversity and the properties that distinguish cell types. We then discuss how the involvement of multiple cell types, each with a specific set of cellular properties, plays a crucial role in diversifying and increasing the computational power of a relatively small number of simple circuit motifs forming cortical networks. We illustrate how recent advances in the field have shed light onto the mechanisms by which GABAergic inhibition contributes to network operations.
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27
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Feldmeyer D, Qi G, Emmenegger V, Staiger JF. Inhibitory interneurons and their circuit motifs in the many layers of the barrel cortex. Neuroscience 2017; 368:132-151. [PMID: 28528964 DOI: 10.1016/j.neuroscience.2017.05.027] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Revised: 05/11/2017] [Accepted: 05/12/2017] [Indexed: 10/19/2022]
Abstract
Recent years have seen substantial progress in studying the structural and functional properties of GABAergic interneurons and their roles in the neuronal networks of barrel cortex. Although GABAergic interneurons represent only about 12% of the total number of neocortical neurons, they are extremely diverse with respect to their structural and functional properties. It has become clear that barrel cortex interneurons not only serve the maintenance of an appropriate excitation/inhibition balance but also are directly involved in sensory processing. In this review we present different interneuron types and their axonal projection pattern framework in the context of the laminar and columnar organization of the barrel cortex. The main focus is here on the most prominent interneuron types, i.e. basket cells, chandelier cells, Martinotti cells, bipolar/bitufted cells and neurogliaform cells, but interneurons with more unusual axonal domains will also be mentioned. We describe their developmental origin, their classification with respect to molecular, morphological and intrinsic membrane and synaptic properties. Most importantly, we will highlight the most prominent circuit motifs these interneurons are involved in and in which way they serve feed-forward inhibition, feedback inhibition and disinhibition. Finally, this will be put into context to their functional roles in sensory signal perception and processing in the whisker system and beyond.
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Affiliation(s)
- Dirk Feldmeyer
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, D-52074 Aachen, Germany; Jülich Aachen Research Alliance, Translational Brain Medicine (JARA Brain), D-52074 Aachen, Germany.
| | - Guanxiao Qi
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany
| | - Vishalini Emmenegger
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, D-52074 Aachen, Germany
| | - Jochen F Staiger
- Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August-University, Göttingen D-37075, Germany.
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28
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Anton-Sanchez L, Bielza C, Benavides-Piccione R, DeFelipe J, Larrañaga P. Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons. Neuroinformatics 2016; 14:453-64. [PMID: 27345531 PMCID: PMC5010609 DOI: 10.1007/s12021-016-9309-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. We aimed to study the existence of an optimal neuronal design for different types of cortical GABAergic neurons. To do this, we hypothesized that both the axonal and dendritic trees of individual neurons optimize brain connectivity in terms of wiring length. We took the branching points of real three-dimensional neuronal reconstructions of the axonal and dendritic trees of different types of cortical interneurons and searched for the minimal wiring arborization structure that respects the branching points. We compared the minimal wiring arborization with real axonal and dendritic trees. We tested this optimization problem using a new approach based on graph theory and evolutionary computation techniques. We concluded that neuronal wiring is near-optimal in most of the tested neurons, although the wiring length of dendritic trees is generally nearer to the optimum. Therefore, wiring economy is related to the way in which neuronal arborizations grow irrespective of the marked differences in the morphology of the examined interneurons.
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Affiliation(s)
- Laura Anton-Sanchez
- Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
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29
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Naka A, Adesnik H. Inhibitory Circuits in Cortical Layer 5. Front Neural Circuits 2016; 10:35. [PMID: 27199675 PMCID: PMC4859073 DOI: 10.3389/fncir.2016.00035] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 04/18/2016] [Indexed: 01/19/2023] Open
Abstract
Inhibitory neurons play a fundamental role in cortical computation and behavior. Recent technological advances, such as two photon imaging, targeted in vivo recording, and molecular profiling, have improved our understanding of the function and diversity of cortical interneurons, but for technical reasons most work has been directed towards inhibitory neurons in the superficial cortical layers. Here we review current knowledge specifically on layer 5 (L5) inhibitory microcircuits, which play a critical role in controlling cortical output. We focus on recent work from the well-studied rodent barrel cortex, but also draw on evidence from studies in primary visual cortex and other cortical areas. The diversity of both deep inhibitory neurons and their pyramidal cell targets make this a challenging but essential area of study in cortical computation and sensory processing.
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Affiliation(s)
- Alexander Naka
- The Helen Wills Neuroscience Institute, University of California Berkeley Berkeley, CA, USA
| | - Hillel Adesnik
- The Helen Wills Neuroscience Institute, University of California BerkeleyBerkeley, CA, USA; Department of Molecular and Cell Biology, University of California BerkeleyBerkeley, CA, USA
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30
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Wang Y, Zhang P, Wyskiel DR. Chandelier Cells in Functional and Dysfunctional Neural Circuits. Front Neural Circuits 2016; 10:33. [PMID: 27199673 PMCID: PMC4854894 DOI: 10.3389/fncir.2016.00033] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 04/08/2016] [Indexed: 01/08/2023] Open
Abstract
Chandelier cells (ChCs; also called axo-axonic cells) are a specialized GABAergic interneuron subtype that selectively innervates pyramidal neurons at the axon initial segment (AIS), the site of action potential generation. ChC connectivity allows for powerful yet precise modulation of large populations of pyramidal cells, suggesting ChCs have a critical role in brain functions. Dysfunctions in ChC connectivity are associated with brain disorders such as epilepsy and schizophrenia; however, whether this is causative, contributory or compensatory is not known. A likely stumbling block toward mechanistic discoveries and uncovering potential therapeutic targets is the apparent lack of rudimentary understanding of ChCs. For example, whether cortical ChCs are inhibitory or excitatory remains unresolved, and thus whether altered ChC activity results in altered inhibition or excitation is not clear. Recent studies have shed some light onto this excitation-inhibition controversy. In addition, new findings have identified preferential cell-type connectivities established by cortical ChCs, greatly expanding our understanding of the role of ChCs in the cortical microcircuit. Here we aim to bring more attention to ChC connectivity to better understand its role in neural circuits, address whether ChCs are inhibitory or excitatory in light of recent findings and discuss ChC dysfunctions in brain disorders.
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Affiliation(s)
- Yiqing Wang
- Department of Pharmacology, University of VirginiaCharlottesville, VA, USA; Department of Chemistry, University of VirginiaCharlottesville, VA, USA
| | - Peng Zhang
- Department of Pharmacology, University of Virginia Charlottesville, VA, USA
| | - Daniel R Wyskiel
- Department of Pharmacology, University of Virginia Charlottesville, VA, USA
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