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Hosseini Fin NS, Yip A, Scott JT, Teo L, Homman-Ludiye J, Bourne JA. Developmental dynamics of marmoset prefrontal cortical SST and PV interneuron networks highlight primate-specific features. Development 2025; 152:dev204254. [PMID: 40292611 DOI: 10.1242/dev.204254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 04/22/2025] [Indexed: 04/30/2025]
Abstract
The primate prefrontal cortex (PFC) undergoes protracted postnatal development, crucial for the emergence of cognitive control and executive function. Central to this maturation are inhibitory interneurons (INs), particularly parvalbumin-expressing (PV+) and somatostatin-expressing (SST+) subtypes, which regulate cortical circuit timing and plasticity. While rodent models have provided foundational insights into IN development, the trajectory of postmigratory maturation in primates remains largely uncharted. In this study, we characterized the expression of PV, SST, the chloride transporter KCC2, and the ion channels Kv3.1b and Nav1.1 across six PFC regions (areas 8aD, 8aV, 9, 46, 11 and 47L) in the postnatal marmoset. We report a prolonged maturation of PV+ INs into adolescence, accompanied by progressive upregulation of ion channels that support high-frequency firing. In contrast, SST+ INs show a postnatal decline in density, diverging from rodent developmental patterns. These findings reveal distinct, cell type-specific maturation dynamics in the primate PFC and offer a developmental framework for understanding how inhibitory circuit refinement may underlie vulnerability to neurodevelopmental disorders.
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Affiliation(s)
- Nafiseh S Hosseini Fin
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton, VIC 3800, Australia
| | - Adrian Yip
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton, VIC 3800, Australia
| | - Jack T Scott
- Section on Cellular and Cognitive Neurodevelopment, Systems Neurodevelopment Laboratory, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Leon Teo
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton, VIC 3800, Australia
| | - Jihane Homman-Ludiye
- Monash MicroImaging, 15 Innovation Walk, Monash University, Clayton, VIC 3800, Australia
| | - James A Bourne
- Section on Cellular and Cognitive Neurodevelopment, Systems Neurodevelopment Laboratory, National Institute of Mental Health, Bethesda, MD 20892, USA
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2
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Takeda H, Okamoto M, Takahashi H, Buyantogtokh B, Kishi N, Okano H, Kamiguchi H, Tsugawa H. Dual fragmentation via collision-induced and oxygen attachment dissociations using water and its radicals for C=C position-resolved lipidomics. Commun Chem 2025; 8:148. [PMID: 40360765 PMCID: PMC12075507 DOI: 10.1038/s42004-025-01525-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2024] [Accepted: 04/15/2025] [Indexed: 05/15/2025] Open
Abstract
Oxygen attachment dissociation (OAD) is a tandem mass spectrometry (MS/MS) technique for annotating the positions of double bonds (C=C) in complex lipids. Although OAD has been used for untargeted lipidomics, its availability has been limited to the positive ion mode, requiring the independent use of a collision-induced dissociation (CID) method. In this study, we demonstrated the OAD MS/MS technique in the negative-ion mode for profiling phosphatidylserines, phosphatidylglycerols, phosphatidylinositols, and sulfatides, where the fragmentation mechanism remained consistent with that in the positive ion mode. Furthermore, we proposed optimal conditions for the simultaneous acquisition of CID- and OAD-specific fragment ions, termed OAciD, where oxygen atoms and hydroxy radicals facilitate C=C position-specific fragmentation, while residual water vapor induces cleavage of low-energy covalent bonds as observed in CID. Finally, theoretical fragment ions were implemented in MS-DIAL 5 to accelerate C=C position-resolved untargeted lipidomics. The OAciD methodology was used to illuminate brain region-specific marmoset lipidomes with C=C positional information, including the estimation of C=C positional isomer ratios. We also characterized the profiles of polyunsaturated fatty acid-containing lipids, finding that lipids containing omega-3 fatty acids were enriched in the cerebellum, whereas those containing omega-6 fatty acids were more abundant in the hippocampus and frontal lobe.
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Affiliation(s)
- Hiroaki Takeda
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan.
- RIKEN Center for Brain Science, Wako, Saitama, Japan.
| | - Mami Okamoto
- Shimadzu Corporation, 1 Nishinokyo-Kuwabaracho Nakagyo-ku, Kyoto, Japan
| | | | - Bujinlkham Buyantogtokh
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
| | - Noriyuki Kishi
- RIKEN Center for Brain Science, Wako, Saitama, Japan
- Keio Regenerative Medicine Research Center, Kawasaki, Kanagawa, Japan
| | - Hideyuki Okano
- RIKEN Center for Brain Science, Wako, Saitama, Japan
- Keio Regenerative Medicine Research Center, Kawasaki, Kanagawa, Japan
| | | | - Hiroshi Tsugawa
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan.
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan.
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
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3
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Chitsaz D, Rowley CD, Uccelli NA, Lefebvre S, Krahn AI, Reintsch WE, Durcan TM, Tardif CL, Kennedy TE. Multiplex Immunofluorescent Batch Labeling of Marmoset Brain Sections. Brain Behav 2025; 15:e70308. [PMID: 40181645 PMCID: PMC11968781 DOI: 10.1002/brb3.70308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 11/29/2024] [Accepted: 01/12/2025] [Indexed: 04/05/2025] Open
Abstract
PURPOSE The common marmoset is a small nonhuman primate that has emerged as a valuable animal model in neuroscience research. Accurate analysis of brain tissue is crucial to understand marmoset neurophysiology and to model neurodegenerative diseases. Many studies to date have complemented magnetic resonance imaging (MRI) with histochemical staining rather than immunofluorescent labeling, which can generate more informative and higher resolution images. There is a need for high-throughput immunolabeling and imaging methodologies to generate resources for the burgeoning marmoset field, particularly brain histology atlases to display the organization of different cell types and other structures. METHODS AND FINDINGS Here, we have characterized a set of marmoset-compatible fluorescent dyes and antibodies that label myelin, axons, dendrites, and the iron-storage protein ferritin, and developed a batch-style multiplex immunohistochemistry protocol to uniformly process large numbers of tissue slides for multiple cell-type specific markers. CONCLUSION We provide a practical guide for researchers interested in harnessing the potential of marmoset models to advance understanding of brain structure, function, and pathophysiology.
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Affiliation(s)
- Daryan Chitsaz
- Integrated Program in NeuroscienceMcGill UniversityMontréalQuebecCanada
- Department of Neurology & Neurosurgery, Montreal Neurological Institute‐HospitalMcGill UniversityMontréalQuebecCanada
| | | | - Nonthué A. Uccelli
- Department of Neurology & Neurosurgery, Montreal Neurological Institute‐HospitalMcGill UniversityMontréalQuebecCanada
| | - Sarah Lefebvre
- Cognitive Neuroscience Unit, Montreal Neurological Institute‐HospitalMcGill UniversityMontrealQuebecCanada
| | - Andrea I. Krahn
- The Neuro's Early Drug Discovery Unit (EDDU)McGill UniversityMontrealQuebecCanada
| | - Wolfgang E. Reintsch
- The Neuro's Early Drug Discovery Unit (EDDU)McGill UniversityMontrealQuebecCanada
| | - Thomas M. Durcan
- Integrated Program in NeuroscienceMcGill UniversityMontréalQuebecCanada
- Department of Neurology & Neurosurgery, Montreal Neurological Institute‐HospitalMcGill UniversityMontréalQuebecCanada
- The Neuro's Early Drug Discovery Unit (EDDU)McGill UniversityMontrealQuebecCanada
| | - Christine L. Tardif
- Integrated Program in NeuroscienceMcGill UniversityMontréalQuebecCanada
- Department of Neurology & Neurosurgery, Montreal Neurological Institute‐HospitalMcGill UniversityMontréalQuebecCanada
- Department of Biomedical Engineering, Faculty of Medicine and Health SciencesMcGill UniversityMontréalQuebecCanada
| | - Timothy E. Kennedy
- Integrated Program in NeuroscienceMcGill UniversityMontréalQuebecCanada
- Department of Neurology & Neurosurgery, Montreal Neurological Institute‐HospitalMcGill UniversityMontréalQuebecCanada
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4
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Wu Y, Gao X, Liu Z, Wang P, Wu Z, Li Y, Zhang T, Liu T, Liu T, Li X. Decoding cortical folding patterns in marmosets using machine learning and large language model. Neuroimage 2025; 308:121031. [PMID: 39864569 DOI: 10.1016/j.neuroimage.2025.121031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 01/14/2025] [Accepted: 01/15/2025] [Indexed: 01/28/2025] Open
Abstract
Macroscale neuroimaging results have revealed significant differences in the structural and functional connectivity patterns of gyri and sulci in the primate cerebral cortex. Despite these findings, understanding these differences at the molecular level has remained challenging. This study leverages a comprehensive dataset of whole-brain in situ hybridization (ISH) data from marmosets, with updates continuing through 2024, to systematically analyze cortical folding patterns. Utilizing advanced machine learning algorithm and large language model (LLM), we identified genes with significant transcriptomic differences between concave (sulci) and convex (gyri) cortical patterns. Further, gene enrichment analysis, neural migration analysis, and axon guidance pathway analysis were employed to elucidate the molecular mechanisms underlying these structural and functional differences. Our findings provide new insights into the molecular basis of cortical folding, demonstrating the potential of LLM in enhancing our understanding of brain structural and functional connectivity.
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Affiliation(s)
- Yue Wu
- College of Science, North China University of Science and Technology, Tangshan, China
| | - Xuesong Gao
- College of Science, North China University of Science and Technology, Tangshan, China
| | - Zhengliang Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
| | - Pengcheng Wang
- Department of Electrical & Computer Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, Canada
| | - Zihao Wu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
| | - Yiwei Li
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, United States
| | - Tao Liu
- College of Science, North China University of Science and Technology, Tangshan, China.
| | - Xiao Li
- School of information science and technology, Northwest University, Xi'an, China.
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5
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Rollin IZ, Papoti D, Bishop M, Szczupak D, Corigliano MR, Hitchens TK, Zhang B, Pell SKA, Guretse SS, Dureux A, Murai T, Sukoff Rizzo SJ, Klassen LM, Zeman P, Gilbert KM, Menon RS, Lin MK, Everling S, Silva AC, Schaeffer DJ. An Open Access Resource for Marmoset Neuroscientific Apparatus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.12.623252. [PMID: 39605348 PMCID: PMC11601486 DOI: 10.1101/2024.11.12.623252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
The use of the common marmoset (Callithrix jacchus) for neuroscientific inquiry has grown precipitously over the past two decades. Despite windfalls of grant support from funding initiatives in North America, Europe, and Asia to model human brain diseases in the marmoset, marmoset-specific apparatus are of sparse availability from commercial vendors and thus are often developed and reside within individual laboratories. Through our collective research efforts, we have designed and vetted myriad designs for awake or anesthetized magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), as well as focused ultrasound (FUS), electrophysiology, optical imaging, surgery, and behavior in marmosets across the age-span. This resource makes these designs openly available, reducing the burden of de novo development across the marmoset field. The computer-aided-design (CAD) files are publicly available through the Marmoset Brain Connectome (MBC) resource (https://www.marmosetbrainconnectome.org/apparatus/) and include dozens of downloadable CAD assemblies, software and online calculators for marmoset neuroscience. In addition, we make available a variety of vetted touchscreen and task-based fMRI code and stimuli. Here, we highlight the online interface and the development and validation of a few yet unpublished resources: Software to automatically extract the head morphology of a marmoset from a CT and produce a 3D printable helmet for awake neuroimaging, and the design and validation of 8-channel and 14-channel receive arrays for imaging deep structures during anatomical and functional MRI.
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Affiliation(s)
- Isabela Zimmermann Rollin
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, USA
| | - Daniel Papoti
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Departamento de Física, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Mitchell Bishop
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Michael R. Corigliano
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - T. Kevin Hitchens
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bei Zhang
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, USA
| | - Sarah K. A. Pell
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Simeon S. Guretse
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Dureux
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Takeshi Murai
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Stacey J. Sukoff Rizzo
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - L. Martyn Klassen
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Peter Zeman
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Kyle M. Gilbert
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Ravi S. Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Meng-Kuan Lin
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Afonso C. Silva
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - David J. Schaeffer
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, USA
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6
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Schroeder ME, McCormack DM, Metzner L, Kang J, Li KX, Yu E, Levandowski KM, Zaniewski H, Zhang Q, Boyden ES, Krienen FM, Feng G. Astrocyte regional specialization is shaped by postnatal development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.11.617802. [PMID: 39416060 PMCID: PMC11482951 DOI: 10.1101/2024.10.11.617802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Astrocytes are an abundant class of glial cells with critical roles in neural circuit assembly and function. Though many studies have uncovered significant molecular distinctions between astrocytes from different brain regions, how this regionalization unfolds over development is not fully understood. We used single-nucleus RNA sequencing to characterize the molecular diversity of brain cells across six developmental stages and four brain regions in the mouse and marmoset brain. Our analysis of over 170,000 single astrocyte nuclei revealed striking regional heterogeneity among astrocytes, particularly between telencephalic and diencephalic regions, at all developmental time points surveyed in both species. At the stages sampled, most of the region patterning was private to astrocytes and not shared with neurons or other glial types. Though astrocytes were already regionally patterned in late embryonic stages, this region-specific astrocyte gene expression signature changed dramatically over postnatal development, and its composition suggests that regional astrocytes further specialize postnatally to support their local neuronal circuits. Comparing across species, we found divergence in the expression of astrocytic region- and age-differentially expressed genes and the timing of astrocyte maturation relative to birth between mouse and marmoset, as well as hundreds of species differentially expressed genes. Finally, we used expansion microscopy to show that astrocyte morphology is largely conserved across gray matter regions of prefrontal cortex, striatum, and thalamus in the mouse, despite substantial molecular divergence.
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Affiliation(s)
- Margaret E Schroeder
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | | | - Lukas Metzner
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Jinyoung Kang
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Katelyn X Li
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Eunah Yu
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Kirsten M Levandowski
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Qiangge Zhang
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Yang Tan Collective, MIT, Cambridge, MA, USA
- Center for Neurobiological Engineering and K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Yang Tan Collective, MIT, Cambridge, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
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7
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Hao S, Zhu X, Huang Z, Yang Q, Liu H, Wu Y, Zhan Y, Dong Y, Li C, Wang H, Haasdijk E, Wu Z, Li S, Yan H, Zhu L, Guo S, Wang Z, Ye A, Lin Y, Cui L, Tan X, Liu H, Wang M, Chen J, Zhong Y, Du W, Wang G, Lai T, Cao M, Yang T, Xu Y, Li L, Yu Q, Zhuang Z, Xia Y, Lei Y, An Y, Cheng M, Zhao Y, Han L, Yuan Y, Song X, Song Y, Gu L, Liu C, Lin X, Wang R, Wang Z, Wang Y, Li S, Li H, Song J, Chen M, Zhou W, Yuan N, Sun S, Wang S, Chen Y, Zheng M, Fang J, Zhang R, Zhang S, Chai Q, Liu J, Wei W, He J, Zhou H, Sun Y, Liu Z, Liu C, Yao J, Liang Z, Xu X, Poo M, Li C, De Zeeuw CI, Shen Z, Liu Z, Liu L, Liu S, Sun Y, Liu C. Cross-species single-cell spatial transcriptomic atlases of the cerebellar cortex. Science 2024; 385:eado3927. [PMID: 39325889 DOI: 10.1126/science.ado3927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/14/2024] [Indexed: 09/28/2024]
Abstract
The molecular and cellular organization of the primate cerebellum remains poorly characterized. We obtained single-cell spatial transcriptomic atlases of macaque, marmoset, and mouse cerebella and identified primate-specific cell subtypes, including Purkinje cells and molecular-layer interneurons, that show different expression of the glutamate ionotropic receptor Delta type subunit 2 (GRID2) gene. Distinct gene expression profiles were found in anterior, posterior, and vestibular regions in all species, whereas region-selective gene expression was predominantly observed in the granular layer of primates and in the Purkinje layer of mice. Gene expression gradients in the cerebellar cortex matched well with functional connectivity gradients revealed with awake functional magnetic resonance imaging, with more lobule-specific differences between primates and mice than between two primate species. These comprehensive atlases and comparative analyses provide the basis for understanding cerebellar evolution and function.
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Affiliation(s)
| | - Xiaojia Zhu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zhi Huang
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Qianqian Yang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Hean Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Wu
- BGI Research, Hangzhou 310030, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Dong
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- Lingang Laboratory, Shanghai 200031, China
| | - Chao Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - He Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Elize Haasdijk
- Department of Neuroscience, Erasmus MC, 3015 GE Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, 1105 BA Amsterdam, Netherlands
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Shenglong Li
- BGI Research, Hangzhou 310030, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lijing Zhu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Zefang Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aojun Ye
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Luman Cui
- BGI Research, Shenzhen 518083, China
| | - Xing Tan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Mingli Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- Lingang Laboratory, Shanghai 200031, China
| | - Jing Chen
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yanqing Zhong
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wensi Du
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Guangling Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tingting Lai
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Mengdi Cao
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tao Yang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yuanfang Xu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ling Li
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Qian Yu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Ying Xia
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ying Lei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yingjie An
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengnan Cheng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yun Zhao
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lei Han
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yue Yuan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xinxiang Song
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yumo Song
- BGI Research, Shenzhen 518083, China
| | - Liqin Gu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chang Liu
- BGI Research, Shenzhen 518083, China
| | | | - Ruiqi Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Yang Wang
- BGI Research, Shenzhen 518083, China
| | - Shenyu Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jingjing Song
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengni Chen
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wanqiu Zhou
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Nini Yuan
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Suhong Sun
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shiwen Wang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mingyuan Zheng
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jiao Fang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ruiyi Zhang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuzhen Zhang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qinwen Chai
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jiabing Liu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China
| | - Jie He
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haibo Zhou
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangang Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhen Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chuanyu Liu
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | | | - Zhifeng Liang
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xun Xu
- BGI Research, Hangzhou 310030, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Muming Poo
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Chengyu Li
- Lingang Laboratory, Shanghai 200031, China
| | - Chris I De Zeeuw
- Department of Neuroscience, Erasmus MC, 3015 GE Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, 1105 BA Amsterdam, Netherlands
| | - Zhiming Shen
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Zhiyong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Longqi Liu
- BGI Research, Hangzhou 310030, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Shiping Liu
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
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8
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Fin NSH, Yip A, Teo L, Homman-Ludiye J, Bourne JA. Developmental dynamics of the prefrontal cortical SST and PV interneuron networks: Insights from the monkey highlight human-specific features. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.602904. [PMID: 39026896 PMCID: PMC11257587 DOI: 10.1101/2024.07.10.602904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The primate prefrontal cortex (PFC) is a quintessential hub of cognitive functions. Amidst its intricate neural architecture, the interplay of distinct neuronal subtypes, notably parvalbumin (PV) and somatostatin (SST) interneurons (INs), emerge as a cornerstone in sculpting cortical circuitry and governing cognitive processes. While considerable strides have been made in elucidating the developmental trajectory of these neurons in rodent models, our understanding of their postmigration developmental dynamics in primates still needs to be studied. Disruptions to this developmental trajectory can compromise IN function, impairing signal gating and circuit modulation within cortical networks. This study examined the expression patterns of PV and SST, ion transporter KCC2, and ion channel subtypes Kv3.1b, and Nav1.1 - associated with morphophysiological stages of development in the postnatal marmoset monkey in different frontal cortical regions (granular areas 8aD, 8aV, 9, 46; agranular areas 11, 47L). Our results demonstrate that the maturation of PV+ INs extends into adolescence, characterized by discrete epochs associated with specific expression dynamics of ion channel subtypes. Interestingly, we observed a postnatal decrease in SST interneurons, contrasting with studies in rodents. This endeavor broadens our comprehension of primate cortical development and furnishes invaluable insights into the etiology and pathophysiology of neurodevelopmental disorders characterized by perturbations in PV and SST IN function.
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Affiliation(s)
- Nafiseh S Hosseini Fin
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton Vic., 3800, Australia
| | - Adrian Yip
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton Vic., 3800, Australia
| | - Leon Teo
- Australian Regenerative Medicine Institute, 15 Innovation Walk, Monash University, Clayton Vic., 3800, Australia
| | - Jihane Homman-Ludiye
- Monash MicroImaging, 15 Innovation Walk, Monash University, Clayton, VIC, 3800, Australia
| | - James A Bourne
- Section on Cellular and Cognitive Neurodevelopment, Systems Neurodevelopment Laboratory, National Institute of Mental Health, Bethesda, MD, 20892, USA
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9
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Ding SL. Lamination, Borders, and Thalamic Projections of the Primary Visual Cortex in Human, Non-Human Primate, and Rodent Brains. Brain Sci 2024; 14:372. [PMID: 38672021 PMCID: PMC11048015 DOI: 10.3390/brainsci14040372] [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: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
The primary visual cortex (V1) is one of the most studied regions of the brain and is characterized by its specialized and laminated layer 4 in human and non-human primates. However, studies aiming to harmonize the definition of the cortical layers and borders of V1 across rodents and primates are very limited. This article attempts to identify and harmonize the molecular markers and connectional patterns that can consistently link corresponding cortical layers of V1 and borders across mammalian species and ages. V1 in primates has at least two additional and unique layers (L3b2 and L3c) and two sublayers of layer 4 (L4a and L4b) compared to rodent V1. In all species examined, layers 4 and 3b of V1 receive strong inputs from the (dorsal) lateral geniculate nucleus, and V1 is mostly surrounded by the secondary visual cortex except for one location where V1 directly abuts area prostriata. The borders of primate V1 can also be clearly identified at mid-gestational ages using gene markers. In rodents, a novel posteromedial extension of V1 is identified, which expresses V1 marker genes and receives strong inputs from the lateral geniculate nucleus. This V1 extension was labeled as the posterior retrosplenial cortex and medial secondary visual cortex in the literature and brain atlases. Layer 6 of the rodent and primate V1 originates corticothalamic projections to the lateral geniculate, lateral dorsal, and reticular thalamic nuclei and the lateroposterior-pulvinar complex with topographic organization. Finally, the direct geniculo-extrastriate (particularly the strong geniculo-prostriata) projections are probably major contributors to blindsight after V1 lesions. Taken together, compared to rodents, primates, and humans, V1 has at least two unique middle layers, while other layers are comparable across species and display conserved molecular markers and similar connections with the visual thalamus with only subtle differences.
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Affiliation(s)
- Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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10
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Hu W, Foord C, Hsu J, Fan L, Corley MJ, Bhatia TN, Xu S, Belchikov N, He Y, Pang AP, Lanjewar SN, Jarroux J, Joglekar A, Milner TA, Ndhlovu LC, Zhang J, Butelman E, Sloan SA, Lee VM, Gan L, Tilgner HU. ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581897. [PMID: 38464236 PMCID: PMC10925193 DOI: 10.1101/2024.02.24.581897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Multimodal measurements have become widespread in genomics, however measuring open chromatin accessibility and splicing simultaneously in frozen brain tissues remains unconquered. Hence, we devised Single-Cell-ISOform-RNA sequencing coupled with the Assay-for-Transposase-Accessible-Chromatin (ScISOr-ATAC). We utilized ScISOr-ATAC to assess whether chromatin and splicing alterations in the brain convergently affect the same cell types or divergently different ones. We applied ScISOr-ATAC to three major conditions: comparing (i) the Rhesus macaque (Macaca mulatta) prefrontal cortex (PFC) and visual cortex (VIS), (ii) cross species divergence of Rhesus macaque versus human PFC, as well as (iii) dysregulation in Alzheimer's disease in human PFC. We found that among cortical-layer biased excitatory neuron subtypes, splicing is highly brain-region specific for L3-5/L6 IT_RORB neurons, moderately specific in L2-3 IT_CUX2.RORB neurons and unspecific in L2-3 IT_CUX2 neurons. In contrast, at the chromatin level, L2-3 IT_CUX2.RORB neurons show the highest brain-region specificity compared to other subtypes. Likewise, when comparing human and macaque PFC, strong evolutionary divergence on one molecular modality does not necessarily imply strong such divergence on another molecular level in the same cell type. Finally, in Alzheimer's disease, oligodendrocytes show convergently high dysregulation in both chromatin and splicing. However, chromatin and splicing dysregulation most strongly affect distinct oligodendrocyte subtypes. Overall, these results indicate that chromatin and splicing can show convergent or divergent results depending on the performed comparison, justifying the need for their concurrent measurement to investigate complex systems. Taken together, ScISOr-ATAC allows for the characterization of single-cell splicing and chromatin patterns and the comparison of sample groups in frozen brain samples.
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Affiliation(s)
- Wen Hu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Careen Foord
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Justine Hsu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Li Fan
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Tarun N Bhatia
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Natan Belchikov
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
- Physiology, Biophysics & Systems Biology Program, Weill Cornell Medicine, New York, NY, USA
| | - Yi He
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Alina Ps Pang
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Samantha N Lanjewar
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Julien Jarroux
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Anoushka Joglekar
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
| | - Teresa A Milner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Lishomwa C Ndhlovu
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Eduardo Butelman
- Neuropsychoimaging of Addiction and Related Conditions Research Program, Dept. of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven A Sloan
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Virginia My Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Li Gan
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute
| | - Hagen U Tilgner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA
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11
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Chang YT, Lee YJ, Haque M, Chang HC, Javed S, Lin YC, Cho Y, Abramovitz J, Chin G, Khamis A, Raja R, Murai KK, Huang WH. Comparative analyses of the Smith-Magenis syndrome protein RAI1 in mice and common marmoset monkeys. J Comp Neurol 2024; 532:e25589. [PMID: 38289192 DOI: 10.1002/cne.25589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 11/11/2023] [Accepted: 12/17/2023] [Indexed: 02/01/2024]
Abstract
Retinoic acid-induced 1 (RAI1) encodes a transcriptional regulator critical for brain development and function. RAI1 haploinsufficiency in humans causes a syndromic autism spectrum disorder known as Smith-Magenis syndrome (SMS). The neuroanatomical distribution of RAI1 has not been quantitatively analyzed during the development of the prefrontal cortex, a brain region critical for cognitive function and social behaviors and commonly implicated in autism spectrum disorders, including SMS. Here, we performed comparative analyses to uncover the evolutionarily convergent and divergent expression profiles of RAI1 in major cell types during prefrontal cortex maturation in common marmoset monkeys (Callithrix jacchus) and mice (Mus musculus). We found that while RAI1 in both species is enriched in neurons, the percentage of excitatory neurons that express RAI1 is higher in newborn mice than in newborn marmosets. By contrast, RAI1 shows similar neural distribution in adult marmosets and adult mice. In marmosets, RAI1 is expressed in several primate-specific cell types, including intralaminar astrocytes and MEIS2-expressing prefrontal GABAergic neurons. At the molecular level, we discovered that RAI1 forms a protein complex with transcription factor 20 (TCF20), PHD finger protein 14 (PHF14), and high mobility group 20A (HMG20A) in the marmoset brain. In vitro assays in human cells revealed that TCF20 regulates RAI1 protein abundance. This work demonstrates that RAI1 expression and protein interactions are largely conserved but with some unique expression in primate-specific cells. The results also suggest that altered RAI1 abundance could contribute to disease features in disorders caused by TCF20 dosage imbalance.
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Affiliation(s)
- Ya-Ting Chang
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Yu-Ju Lee
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Minza Haque
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Hao-Cheng Chang
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Sehrish Javed
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Yu Cheng Lin
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Yoobin Cho
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Joseph Abramovitz
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Gabriella Chin
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Asma Khamis
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Reesha Raja
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Keith K Murai
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Wei-Hsiang Huang
- Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, McGill University, Montréal, Québec, Canada
- Brain Repair and Integrative Neuroscience Program, The Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
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12
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Krienen FM, Levandowski KM, Zaniewski H, del Rosario RC, Schroeder ME, Goldman M, Wienisch M, Lutservitz A, Beja-Glasser VF, Chen C, Zhang Q, Chan KY, Li KX, Sharma J, McCormack D, Shin TW, Harrahill A, Nyase E, Mudhar G, Mauermann A, Wysoker A, Nemesh J, Kashin S, Vergara J, Chelini G, Dimidschstein J, Berretta S, Deverman BE, Boyden E, McCarroll SA, Feng G. A marmoset brain cell census reveals regional specialization of cellular identities. SCIENCE ADVANCES 2023; 9:eadk3986. [PMID: 37824615 PMCID: PMC10569717 DOI: 10.1126/sciadv.adk3986] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 09/26/2023] [Indexed: 10/14/2023]
Abstract
The mammalian brain is composed of many brain structures, each with its own ontogenetic and developmental history. We used single-nucleus RNA sequencing to sample over 2.4 million brain cells across 18 locations in the common marmoset, a New World monkey primed for genetic engineering, and examined gene expression patterns of cell types within and across brain structures. The adult transcriptomic identity of most neuronal types is shaped more by developmental origin than by neurotransmitter signaling repertoire. Quantitative mapping of GABAergic types with single-molecule FISH (smFISH) reveals that interneurons in the striatum and neocortex follow distinct spatial principles, and that lateral prefrontal and other higher-order cortical association areas are distinguished by high proportions of VIP+ neurons. We use cell type-specific enhancers to drive AAV-GFP and reconstruct the morphologies of molecularly resolved interneuron types in neocortex and striatum. Our analyses highlight how lineage, local context, and functional class contribute to the transcriptional identity and biodistribution of primate brain cell types.
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Affiliation(s)
- Fenna M. Krienen
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kirsten M. Levandowski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Heather Zaniewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ricardo C.H. del Rosario
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Margaret E. Schroeder
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Martin Wienisch
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alyssa Lutservitz
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Victoria F. Beja-Glasser
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cindy Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Qiangge Zhang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ken Y. Chan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Katelyn X. Li
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jitendra Sharma
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dana McCormack
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tay Won Shin
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Andrew Harrahill
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Eric Nyase
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gagandeep Mudhar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Abigail Mauermann
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Josselyn Vergara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gabriele Chelini
- Center for Mind/Brain Sciences, University of Trento, Piazza della Manifattura n.1, Rovereto (TN) 38068, Italy
| | - Jordane Dimidschstein
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sabina Berretta
- Basic Neuroscience Division, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin E. Deverman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ed Boyden
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Steven A. McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guoping Feng
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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13
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Skibbe H, Rachmadi MF, Nakae K, Gutierrez CE, Hata J, Tsukada H, Poon C, Schlachter M, Doya K, Majka P, Rosa MGP, Okano H, Yamamori T, Ishii S, Reisert M, Watakabe A. The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain. PLoS Biol 2023; 21:e3002158. [PMID: 37384809 DOI: 10.1371/journal.pbio.3002158] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023] Open
Abstract
The primate brain has unique anatomical characteristics, which translate into advanced cognitive, sensory, and motor abilities. Thus, it is important that we gain insight on its structure to provide a solid basis for models that will clarify function. Here, we report on the implementation and features of the Brain/MINDS Marmoset Connectivity Resource (BMCR), a new open-access platform that provides access to high-resolution anterograde neuronal tracer data in the marmoset brain, integrated to retrograde tracer and tractography data. Unlike other existing image explorers, the BMCR allows visualization of data from different individuals and modalities in a common reference space. This feature, allied to an unprecedented high resolution, enables analyses of features such as reciprocity, directionality, and spatial segregation of connections. The present release of the BMCR focuses on the prefrontal cortex (PFC), a uniquely developed region of the primate brain that is linked to advanced cognition, including the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset. Moreover, the inclusion of tractography data from diffusion MRI allows systematic analyses of this noninvasive modality against gold-standard cellular connectivity data, enabling detection of false positives and negatives, which provide a basis for future development of tractography. This paper introduces the BMCR image preprocessing pipeline and resources, which include new tools for exploring and reviewing the data.
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Affiliation(s)
- Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | | | - Ken Nakae
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Aichi, Japan
| | - Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Hiromichi Tsukada
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
- Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Aichi, Japan
| | - Charissa Poon
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Matthias Schlachter
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Marcello G P Rosa
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Tetsuo Yamamori
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Shin Ishii
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Marco Reisert
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Stereotactic and Functional Neurosurgery, Medical Center of the University of Freiburg, Freiburg Im Breisgau, Germany
- Medical Faculty of the University of Freiburg, Freiburg Im Breisgau, Germany
- Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center-University of Freiburg, Freiburg Im Breisgau, Germany
| | - Akiya Watakabe
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
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14
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Zhu X, Yan H, Zhan Y, Feng F, Wei C, Yao YG, Liu C. An anatomical and connectivity atlas of the marmoset cerebellum. Cell Rep 2023; 42:112480. [PMID: 37163375 DOI: 10.1016/j.celrep.2023.112480] [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: 10/18/2022] [Revised: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.
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Affiliation(s)
- Xiaojia Zhu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Furui Feng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuanyao Wei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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15
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Hong D, Iakoucheva LM. Therapeutic strategies for autism: targeting three levels of the central dogma of molecular biology. Transl Psychiatry 2023; 13:58. [PMID: 36792602 PMCID: PMC9931756 DOI: 10.1038/s41398-023-02356-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/17/2023] Open
Abstract
The past decade has yielded much success in the identification of risk genes for Autism Spectrum Disorder (ASD), with many studies implicating loss-of-function (LoF) mutations within these genes. Despite this, no significant clinical advances have been made so far in the development of therapeutics for ASD. Given the role of LoF mutations in ASD etiology, many of the therapeutics in development are designed to rescue the haploinsufficient effect of genes at the transcriptional, translational, and protein levels. This review will discuss the various therapeutic techniques being developed from each level of the central dogma with examples including: CRISPR activation (CRISPRa) and gene replacement at the DNA level, antisense oligonucleotides (ASOs) at the mRNA level, and small-molecule drugs at the protein level, followed by a review of current delivery methods for these therapeutics. Since central nervous system (CNS) penetrance is of utmost importance for ASD therapeutics, it is especially necessary to evaluate delivery methods that have higher efficiency in crossing the blood-brain barrier (BBB).
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Affiliation(s)
- Derek Hong
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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16
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [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: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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17
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Fukunishi Y, Higo J, Kasahara K. Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles. Biophys Rev 2022; 14:1423-1447. [PMID: 36465086 PMCID: PMC9703445 DOI: 10.1007/s12551-022-01015-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
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Affiliation(s)
- Yoshifumi Fukunishi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Junichi Higo
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-Ku, Kobe, Hyogo 650-0047 Japan ,Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
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18
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Tong C, Liu C, Zhang K, Bo B, Xia Y, Yang H, Feng Y, Liang Z. Multimodal analysis demonstrating the shaping of functional gradients in the marmoset brain. Nat Commun 2022; 13:6584. [PMID: 36329036 PMCID: PMC9633775 DOI: 10.1038/s41467-022-34371-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
The discovery of functional gradients introduce a new perspective in understanding the cortical spectrum of intrinsic dynamics, as it captures major axes of functional connectivity in low-dimensional space. However, how functional gradients arise and dynamically vary remains poorly understood. In this study, we investigated the biological basis of functional gradients using awake resting-state fMRI, retrograde tracing and gene expression datasets in marmosets. We found functional gradients in marmosets showed a sensorimotor-to-visual principal gradient followed by a unimodal-to-multimodal gradient, resembling functional gradients in human children. Although strongly constrained by structural wirings, functional gradients were dynamically modulated by arousal levels. Utilizing a reduced model, we uncovered opposing effects on gradient dynamics by structural connectivity (inverted U-shape) and neuromodulatory input (U-shape) with arousal fluctuations, and dissected the contribution of individual neuromodulatory receptors. This study provides insights into biological basis of functional gradients by revealing the interaction between structural connectivity and ascending neuromodulatory system.
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Affiliation(s)
- Chuanjun Tong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Cirong Liu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Kaiwei Zhang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ying Xia
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Hao Yang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China.
| | - Zhifeng Liang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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19
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Lin JP, Kelly HM, Song Y, Kawaguchi R, Geschwind DH, Jacobson S, Reich DS. Transcriptomic architecture of nuclei in the marmoset CNS. Nat Commun 2022; 13:5531. [PMID: 36130924 PMCID: PMC9492672 DOI: 10.1038/s41467-022-33140-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/02/2022] [Indexed: 11/11/2022] Open
Abstract
To understand the cellular composition and region-specific specialization of white matter - a disease-relevant, glia-rich tissue highly expanded in primates relative to rodents - we profiled transcriptomes of ~500,000 nuclei from 19 tissue types of the central nervous system of healthy common marmoset and mapped 87 subclusters spatially onto a 3D MRI atlas. We performed cross-species comparison, explored regulatory pathways, modeled regional intercellular communication, and surveyed cellular determinants of neurological disorders. Here, we analyze this resource and find strong spatial segregation of microglia, oligodendrocyte progenitor cells, and astrocytes. White matter glia are diverse, enriched with genes involved in stimulus-response and biomolecule modification, and predicted to interact with other resident cells more extensively than their gray matter counterparts. Conversely, gray matter glia preserve the expression of neural tube patterning genes into adulthood and share six transcription factors that restrict transcriptome complexity. A companion Callithrix jacchus Primate Cell Atlas (CjPCA) is available through https://cjpca.ninds.nih.gov .
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Affiliation(s)
- Jing-Ping Lin
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Hannah M Kelly
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Yeajin Song
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Riki Kawaguchi
- Psychiatry, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Psychiatry, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Departments of Neurology and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Steven Jacobson
- Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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20
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Chen SQ, Chen CH, Xiang XJ, Zhang SY, Ding SL. Chemoarchitecture of area prostriata in adult and developing mice: Comparison with presubiculum and parasubiculum. J Comp Neurol 2022; 530:2486-2517. [PMID: 35593198 DOI: 10.1002/cne.25346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 11/11/2022]
Abstract
Retrosplenial area 29e, which was a cortical region described mostly in earlier rodent literature, is often included in the dorsal presubiculum (PrSd) or postsubiculum (PoS) in modern literature and commonly used brain atlases. Recent anatomical and molecular studies have revealed that retrosplenial area 29e belongs to the superficial layers of area prostriata, which in primates is found to be important in fast analysis of quickly moving objects in far peripheral visual field. As in primates, the prostriata in rodents adjoins area 29 (granular retrosplenial area), area 30 (agranular retrosplenial area), medial visual cortex, PrSd/PoS, parasubiculum (PaS), and postrhinal cortex (PoR). The present study aims to reveal the chemoarchitecture of the prostriata versus PrSd/PoS or PaS by means of a systematic survey of gene expression patterns in adult and developing mouse brains. First, we find many genes that display differential expression across the prostriata, PrSd/PoS, and PaS and that show obvious laminar expression patterns. Second, we reveal subsets of genes that selectively express in the dorsal or ventral parts of the prostriata, suggesting the existence of at least two subdivisions. Third, we detect some genes that shows differential expression in the prostriata of postnatal mouse brains from adjoining regions, thus enabling identification of the developing area prostriata. Fourth, gene expression difference of the prostriata from the medial primary visual cortex and PoR is also observed. Finally, molecular and connectional features of the prostriata in rodents and nonhuman primates are discussed and compared.
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Affiliation(s)
- Sheng-Qiang Chen
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Institute of Neuroscience, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Chang-Hui Chen
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Institute of Neuroscience, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xiao-Jun Xiang
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Institute of Neuroscience, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Shun-Yu Zhang
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Institute of Neuroscience, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Song-Lin Ding
- Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.,Institute of Neuroscience, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.,Allen Institute for Brain Science, Seattle, Washington, USA
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21
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Ogata K, Kadono F, Hirai Y, Inoue KI, Takada M, Karube F, Fujiyama F. Conservation of the Direct and Indirect Pathway Dichotomy in Mouse Caudal Striatum With Uneven Distribution of Dopamine Receptor D1- and D2-Expressing Neurons. Front Neuroanat 2022; 16:809446. [PMID: 35185482 PMCID: PMC8854186 DOI: 10.3389/fnana.2022.809446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
The striatum is one of the key nuclei for adequate control of voluntary behaviors and reinforcement learning. Two striatal projection neuron types, expressing either dopamine receptor D1 (D1R) or dopamine receptor D2 (D2R) constitute two independent output routes: the direct or indirect pathways, respectively. These pathways co-work in balance to achieve coordinated behavior. Two projection neuron types are equivalently intermingled in most striatal space. However, recent studies revealed two atypical zones in the caudal striatum: the zone in which D1R-neurons are the minor population (D1R-poor zone) and that in which D2R-neurons are the minority (D2R-poor zone). It remains obscure as to whether these imbalanced zones have similar properties on axonal projections and electrophysiology compared to other striatal regions. Based on morphological experiments in mice using immunofluorescence, in situ hybridization, and neural tracing, here, we revealed that the poor zones densely projected to the globus pallidus and substantia nigra pars lateralis, with a few collaterals in substantia nigra pars reticulata and compacta. Similar to that in other striatal regions, D1R-neurons were the direct pathway neurons. We also showed that the membrane properties of projection neurons in the poor zones were largely similar to those in the conventional striatum using in vitro electrophysiological recording. In addition, the poor zones existed irrespective of the age or sex of mice. We also identified the poor zones in the common marmoset as well as other rodents. These results suggest that the poor zones in the caudal striatum follow the conventional projection patterns irrespective of the imbalanced distribution of projection neurons. The poor zones could be an innate structure and common in mammals. The unique striatal zones possessing highly restricted projections could relate to functions different from those of motor-related striatum.
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Affiliation(s)
- Kumiko Ogata
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
| | - Fuko Kadono
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yasuharu Hirai
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Ken-ichi Inoue
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Japan
| | - Masahiko Takada
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Japan
| | - Fuyuki Karube
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- *Correspondence: Fuyuki Karube,
| | - Fumino Fujiyama
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
- Fumino Fujiyama,
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22
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Scott JT, Bourne JA. Modelling behaviors relevant to brain disorders in the nonhuman primate: Are we there yet? Prog Neurobiol 2021; 208:102183. [PMID: 34728308 DOI: 10.1016/j.pneurobio.2021.102183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/30/2022]
Abstract
Recent years have seen a profound resurgence of activity with nonhuman primates (NHPs) to model human brain disorders. From marmosets to macaques, the study of NHP species offers a unique window into the function of primate-specific neural circuits that are impossible to examine in other models. Examining how these circuits manifest into the complex behaviors of primates, such as advanced cognitive and social functions, has provided enormous insights to date into the mechanisms underlying symptoms of numerous neurological and neuropsychiatric illnesses. With the recent optimization of modern techniques to manipulate and measure neural activity in vivo, such as optogenetics and calcium imaging, NHP research is more well-equipped than ever to probe the neural mechanisms underlying pathological behavior. However, methods for behavioral experimentation and analysis in NHPs have noticeably failed to keep pace with these advances. As behavior ultimately lies at the junction between preclinical findings and its translation to clinical outcomes for brain disorders, approaches to improve the integrity, reproducibility, and translatability of behavioral experiments in NHPs requires critical evaluation. In this review, we provide a unifying account of existing brain disorder models using NHPs, and provide insights into the present and emerging contributions of behavioral studies to the field.
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Affiliation(s)
- Jack T Scott
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia
| | - James A Bourne
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia.
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Takahashi M, Fukabori R, Kawasaki H, Kobayashi K, Kawakami K. The distribution of Cdh20 mRNA demarcates somatotopic subregions and subpopulations of spiny projection neurons in the rat dorsolateral striatum. J Comp Neurol 2021; 529:3655-3675. [PMID: 34240415 DOI: 10.1002/cne.25215] [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: 01/29/2021] [Revised: 06/21/2021] [Accepted: 07/02/2021] [Indexed: 11/07/2022]
Abstract
The dorsolateral striatum (DLS) of rodents is functionally subdivided into somatotopic subregions that represent each body part along both the dorsoventral and anteroposterior (A-P) axes and play crucial roles in sensorimotor functions via corticostriatal pathways. However, little is known about the spatial gene expression patterns and heterogeneity of spiny projection neurons (SPNs) within somatotopic subregions. Here, we show that the cell adhesion molecule gene Cdh20, which encodes a Type II cadherin, is expressed in discrete subregions covering the inner orofacial area and part of the forelimb area in the ventral domain of the DLS (v-DLS) in rats. Cdh20-expressing cells were localized in the v-DLS at the intermediate level of the striatum along the A-P axis and could be classified as direct-pathway SPNs or indirect-pathway SPNs. Unexpectedly, comprehensive analysis revealed that Cdh20 is expressed in SPNs in the rat DLS but not in the mouse DLS or the ferret putamen (Pu). Our observations reveal that Cdh20 expression demarcates somatotopic subregions and subpopulations of SPNs specifically in the rat DLS and suggest divergent regulation of genes differentially expressed in the v-DLS and Pu among mammals.
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Affiliation(s)
- Masanori Takahashi
- Graduate School of Medicine, Jichi Medical University, Shimotsuke, Tochigi, Japan.,Division of Cardiology and Metabolism, Center for Molecular Medicine, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Ryoji Fukabori
- Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University School of Medicine, Fukushima, Fukushima, Japan
| | - Hiroshi Kawasaki
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Kazuto Kobayashi
- Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University School of Medicine, Fukushima, Fukushima, Japan
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Onishi K, Kikuchi SS, Abe T, Tokuhara T, Shimogori T. Molecular cell identities in the mediodorsal thalamus of infant mice and marmoset. J Comp Neurol 2021; 530:963-977. [PMID: 34184265 PMCID: PMC8714865 DOI: 10.1002/cne.25203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 11/10/2022]
Abstract
The mediodorsal thalamus (MD) is a higher-order nucleus located within the central thalamus in many mammalian species. Emerging evidence from MD lesions and tracer injections suggests that the MD is reciprocally connected to the prefrontal cortex (PFC) and plays an essential role in specific cognitive processes and tasks. MD subdivisions (medial, central, and lateral) are poorly segregated at the molecular level in rodents, leading to a lack of MD subdivision-specific Cre driver mice. Moreover, this lack of molecular identifiers hinders MD subdivision- and cell-type-specific circuit formation and function analysis. Therefore, using publicly available databases, we explored molecules separately expressed in MD subdivisions. In addition to MD subdivision markers, we identified several genes expressed in a subdivision-specific combination and classified them. Furthermore, after developing medial MD (MDm) or central MD (MDc) region-specific Cre mouse lines, we identified diverse region- and layer-specific PFC projection patterns. Comparison between classified MD marker genes in mice and common marmosets, a nonhuman primate model, revealed diverging gene expression patterns. These results highlight the species-specific organization of cell types and their projections in the MD thalamus.
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Affiliation(s)
- Kohei Onishi
- Laboratory for Molecular Mechanisms of Brain Development, Center for Brain Science (CBS), RIKEN, Wako, Saitama, Japan
| | - Satomi S Kikuchi
- Laboratory for Molecular Mechanisms of Brain Development, Center for Brain Science (CBS), RIKEN, Wako, Saitama, Japan
| | - Takaya Abe
- Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Biosystems Dynamics Research (BDR), Chuou-ku, Kobe, Japan
| | - Tomoko Tokuhara
- Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Biosystems Dynamics Research (BDR), Chuou-ku, Kobe, Japan
| | - Tomomi Shimogori
- Laboratory for Molecular Mechanisms of Brain Development, Center for Brain Science (CBS), RIKEN, Wako, Saitama, Japan
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