1
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Wong MMK, Sha Z, Lütje L, Kong XZ, van Heukelum S, van de Berg WDJ, Jonkman LE, Fisher SE, Francks C. The neocortical infrastructure for language involves region-specific patterns of laminar gene expression. Proc Natl Acad Sci U S A 2024; 121:e2401687121. [PMID: 39133845 PMCID: PMC11348331 DOI: 10.1073/pnas.2401687121] [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: 01/25/2024] [Accepted: 06/27/2024] [Indexed: 08/29/2024] Open
Abstract
The language network of the human brain has core components in the inferior frontal cortex and superior/middle temporal cortex, with left-hemisphere dominance in most people. Functional specialization and interconnectivity of these neocortical regions is likely to be reflected in their molecular and cellular profiles. Excitatory connections between cortical regions arise and innervate according to layer-specific patterns. Here, we generated a gene expression dataset from human postmortem cortical tissue samples from core language network regions, using spatial transcriptomics to discriminate gene expression across cortical layers. Integration of these data with existing single-cell expression data identified 56 genes that showed differences in laminar expression profiles between the frontal and temporal language cortex together with upregulation in layer II/III and/or layer V/VI excitatory neurons. Based on data from large-scale genome-wide screening in the population, DNA variants within these 56 genes showed set-level associations with interindividual variation in structural connectivity between the left-hemisphere frontal and temporal language cortex, and with the brain-related disorders dyslexia and schizophrenia which often involve affected language. These findings identify region-specific patterns of laminar gene expression as a feature of the brain's language network.
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Affiliation(s)
- Maggie M. K. Wong
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
| | - Zhiqiang Sha
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
| | - Lukas Lütje
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
| | - Xiang-Zhen Kong
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou310058, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou311121, China
| | - Sabrina van Heukelum
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen6525 GA, The Netherlands
| | - Wilma D. J. van de Berg
- Section Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam1007 MB, The Netherlands
- Neurodegeneration, Amsterdam Neuroscience, Amsterdam1007 MB, The Netherlands
| | - Laura E. Jonkman
- Section Clinical Neuroanatomy and Biobanking, Department of Anatomy and Neurosciences, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam1007 MB, The Netherlands
- Neurodegeneration, Amsterdam Neuroscience, Amsterdam1007 MB, The Netherlands
- Brain Imaging, Amsterdam Neuroscience, Amsterdam1007 MB, The Netherlands
| | - Simon E. Fisher
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen6525 GA, The Netherlands
| | - Clyde Francks
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen6525XD, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen6525 GA, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen6525 GA, The Netherlands
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2
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Caglayan E, Konopka G. Evolutionary neurogenomics at single-cell resolution. Curr Opin Genet Dev 2024; 88:102239. [PMID: 39094380 DOI: 10.1016/j.gde.2024.102239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/20/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024]
Abstract
The human brain is composed of increasingly recognized heterogeneous cell types. Applying single-cell genomics to brain tissue can elucidate relative cell type proportions as well as differential gene expression and regulation among humans and other species. Here, we review recent studies that utilized high-throughput genomics approaches to compare brains among species at single-cell resolution. These studies identified genomic elements that are similar among species as well as evolutionary novelties on the human lineage. We focus on those human-relevant innovations and discuss the biological implications of these modifications. Finally, we discuss areas of comparative single-cell genomics that remain unexplored either due to needed technological advances or due to biological availability at the brain region or species level.
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Affiliation(s)
- Emre Caglayan
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Genevieve Konopka
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX 75390, USA.
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3
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DeCasien AR, Chiou KL, Testard C, Mercer A, Negrón-Del Valle JE, Bauman Surratt SE, González O, Stock MK, Ruiz-Lambides AV, Martínez MI, Antón SC, Walker CS, Sallet J, Wilson MA, Brent LJN, Montague MJ, Sherwood CC, Platt ML, Higham JP, Snyder-Mackler N. Evolutionary and biomedical implications of sex differences in the primate brain transcriptome. CELL GENOMICS 2024; 4:100589. [PMID: 38942023 PMCID: PMC11293591 DOI: 10.1016/j.xgen.2024.100589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 12/28/2023] [Accepted: 05/31/2024] [Indexed: 06/30/2024]
Abstract
Humans exhibit sex differences in the prevalence of many neurodevelopmental disorders and neurodegenerative diseases. Here, we generated one of the largest multi-brain-region bulk transcriptional datasets for the rhesus macaque and characterized sex-biased gene expression patterns to investigate the translatability of this species for sex-biased neurological conditions. We identify patterns similar to those in humans, which are associated with overlapping regulatory mechanisms, biological processes, and genes implicated in sex-biased human disorders, including autism. We also show that sex-biased genes exhibit greater genetic variance for expression and more tissue-specific expression patterns, which may facilitate rapid evolution of sex-biased genes. Our findings provide insights into the biological mechanisms underlying sex-biased disease and support the rhesus macaque model for the translational study of these conditions.
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Affiliation(s)
- Alex R DeCasien
- Department of Anthropology, New York University, New York, NY, USA; New York Consortium in Evolutionary Primatology, New York, NY, USA; Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA.
| | - Kenneth L Chiou
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA; School of Life Sciences, Arizona State University, Tempe, AZ, USA; Department of Psychology, University of Washington, Seattle, WA, USA; Nathan Shock Center of Excellence in the Basic Biology of Aging, University of Washington, Seattle, WA, USA.
| | - Camille Testard
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Arianne Mercer
- Department of Psychology, University of Washington, Seattle, WA, USA
| | | | | | - Olga González
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Michala K Stock
- Department of Sociology and Anthropology, Metropolitan State University of Denver, Denver, CO, USA
| | | | - Melween I Martínez
- Caribbean Primate Research Center, University of Puerto Rico, San Juan, PR, USA
| | - Susan C Antón
- Department of Anthropology, New York University, New York, NY, USA; New York Consortium in Evolutionary Primatology, New York, NY, USA
| | - Christopher S Walker
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Jérôme Sallet
- Stem Cell and Brain Research Institute, Université Lyon, Lyon, France
| | - Melissa A Wilson
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA; School of Life Sciences, Arizona State University, Tempe, AZ, USA; Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, USA
| | - Lauren J N Brent
- Centre for Research in Animal Behavior, University of Exeter, Exeter, UK
| | - Michael J Montague
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, Washington, DC, USA
| | - Michael L Platt
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA; Department of Marketing, University of Pennsylvania, Philadelphia, PA, USA
| | - James P Higham
- Department of Anthropology, New York University, New York, NY, USA; New York Consortium in Evolutionary Primatology, New York, NY, USA.
| | - Noah Snyder-Mackler
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA; School of Life Sciences, Arizona State University, Tempe, AZ, USA; Department of Psychology, University of Washington, Seattle, WA, USA; Nathan Shock Center of Excellence in the Basic Biology of Aging, University of Washington, Seattle, WA, USA; ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA.
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4
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Ruzicka WB, Mohammadi S, Fullard JF, Davila-Velderrain J, Subburaju S, Tso DR, Hourihan M, Jiang S, Lee HC, Bendl J, Voloudakis G, Haroutunian V, Hoffman GE, Roussos P, Kellis M. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024; 384:eadg5136. [PMID: 38781388 DOI: 10.1126/science.adg5136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/21/2023] [Indexed: 05/25/2024]
Abstract
The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.
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Affiliation(s)
- W Brad Ruzicka
- Laboratory for Epigenomics in Human Psychopathology, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shahin Mohammadi
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jose Davila-Velderrain
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Neurogenomics Research Center, Human Technopole, 20157 Milan, Italy
| | - Sivan Subburaju
- Laboratory for Epigenomics in Human Psychopathology, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel Reed Tso
- Laboratory for Epigenomics in Human Psychopathology, McLean Hospital, Belmont, MA 02478, USA
| | - Makayla Hourihan
- Laboratory for Epigenomics in Human Psychopathology, McLean Hospital, Belmont, MA 02478, USA
| | - Shan Jiang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hao-Chih Lee
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Neurogenomics Research Center, Human Technopole, 20157 Milan, Italy
| | - Manolis Kellis
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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5
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Xu L, Yuan Z, Zhou J, Zhao Y, Liu W, Lu S, He Z, Qiang B, Shu P, Chen Y, Peng X. Temporal transcriptomic dynamics in developing macaque neocortex. eLife 2024; 12:RP90325. [PMID: 38415809 PMCID: PMC10911584 DOI: 10.7554/elife.90325] [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] [Indexed: 02/29/2024] Open
Abstract
Despite intense research on mice, the transcriptional regulation of neocortical neurogenesis remains limited in humans and non-human primates. Cortical development in rhesus macaque is known to recapitulate multiple facets of cortical development in humans, including the complex composition of neural stem cells and the thicker supragranular layer. To characterize temporal shifts in transcriptomic programming responsible for differentiation from stem cells to neurons, we sampled parietal lobes of rhesus macaque at E40, E50, E70, E80, and E90, spanning the full period of prenatal neurogenesis. Single-cell RNA sequencing produced a transcriptomic atlas of developing parietal lobe in rhesus macaque neocortex. Identification of distinct cell types and neural stem cells emerging in different developmental stages revealed a terminally bifurcating trajectory from stem cells to neurons. Notably, deep-layer neurons appear in the early stages of neurogenesis, while upper-layer neurons appear later. While these different lineages show overlap in their differentiation program, cell fates are determined post-mitotically. Trajectories analysis from ventricular radial glia (vRGs) to outer radial glia (oRGs) revealed dynamic gene expression profiles and identified differential activation of BMP, FGF, and WNT signaling pathways between vRGs and oRGs. These results provide a comprehensive overview of the temporal patterns of gene expression leading to different fates of radial glial progenitors during neocortex layer formation.
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Affiliation(s)
- Longjiang Xu
- Institute of Medical Biology Chinese Academy of Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical CollegeKunmingChina
| | - Zan Yuan
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural UniversityWuhanChina
| | - Jiafeng Zhou
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Common Mechanism Research for Major Diseases, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
| | - Yuan Zhao
- Institute of Medical Biology Chinese Academy of Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical CollegeKunmingChina
| | - Wei Liu
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Common Mechanism Research for Major Diseases, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
| | - Shuaiyao Lu
- Institute of Medical Biology Chinese Academy of Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical CollegeKunmingChina
| | - Zhanlong He
- Institute of Medical Biology Chinese Academy of Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical CollegeKunmingChina
| | - Boqin Qiang
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Common Mechanism Research for Major Diseases, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
| | - Pengcheng Shu
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Common Mechanism Research for Major Diseases, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Yang Chen
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Common Mechanism Research for Major Diseases, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
| | - Xiaozhong Peng
- Institute of Medical Biology Chinese Academy of Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical CollegeKunmingChina
- Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Medical Primate Research Center, Neuroscience Center, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical CollegeBeijingChina
- State Key Laboratory of Respiratory Health and Multimorbidity, Chinese Academy of Medical Sciences, Peking Union Medical CollegeBeijingChina
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
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6
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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7
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Zhuang XL, Shao Y, Chen CY, Zhou L, Yao YG, Cooper DN, Zhang GJ, Wang W, Wu DD. Divergent Evolutionary Rates of Primate Brain Regions as Revealed by Genomics and Transcriptomics. Genome Biol Evol 2024; 16:evae023. [PMID: 38314830 PMCID: PMC10881106 DOI: 10.1093/gbe/evae023] [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: 04/19/2023] [Revised: 01/05/2024] [Accepted: 01/30/2024] [Indexed: 02/07/2024] Open
Abstract
Although the primate brain contains numerous functionally distinct structures that have experienced diverse genetic changes during the course of evolution and development, these changes remain to be explored in detail. Here we utilize two classic metrics from evolutionary biology, the evolutionary rate index (ERI) and the transcriptome age index (TAI), to investigate the evolutionary alterations that have occurred in each area and developmental stage of the primate brain. We observed a higher evolutionary rate for those genes expressed in the non-cortical areas during primate evolution, particularly in human, with the highest rate of evolution being exhibited at brain developmental stages between late infancy and early childhood. Further, the transcriptome age of the non-cortical areas was lower than that of the cerebral cortex, with the youngest age apparent at brain developmental stages between late infancy and early childhood. Our exploration of the evolutionary patterns manifest in each brain area and developmental stage provides important reference points for further research into primate brain evolution.
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Affiliation(s)
- Xiao-Lin Zhuang
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650204, China
| | - Yong Shao
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650204, China
| | - Chun-Yan Chen
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an 710072, China
| | - Long Zhou
- Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310000, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310000, China
| | - Yong-Gang Yao
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650204, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
| | - Guo-Jie Zhang
- Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310000, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou 310000, China
| | - Wen Wang
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an 710072, China
| | - Dong-Dong Wu
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
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8
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Yao G, Luo J, Zou T, Li J, Hu S, Yang L, Li X, Tian Y, Zhang Y, Feng K, Xu Y, Liu P. Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia. Neuroimage 2024; 285:120493. [PMID: 38086496 DOI: 10.1016/j.neuroimage.2023.120493] [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: 09/15/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.
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Affiliation(s)
- Guanqun Yao
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Jing Luo
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ting Zou
- School of Life Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China; School of Mental Health, Shanxi Medical University, Taiyuan 030001, China
| | - Shuang Hu
- Shanghai Mental Health Center, Shanghai 200030, China
| | - Langxiong Yang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xinrong Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yu Tian
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yuqi Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kun Feng
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China.
| | - Yong Xu
- Department of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, No. 3025, Shennan Middle Road, Futian Street, Futian District, Shenzhen 518031, China.
| | - Pozi Liu
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China.
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9
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Jorstad NL, Song JH, Exposito-Alonso D, Suresh H, Castro-Pacheco N, Krienen FM, Yanny AM, Close J, Gelfand E, Long B, Seeman SC, Travaglini KJ, Basu S, Beaudin M, Bertagnolli D, Crow M, Ding SL, Eggermont J, Glandon A, Goldy J, Kiick K, Kroes T, McMillen D, Pham T, Rimorin C, Siletti K, Somasundaram S, Tieu M, Torkelson A, Feng G, Hopkins WD, Höllt T, Keene CD, Linnarsson S, McCarroll SA, Lelieveldt BP, Sherwood CC, Smith K, Walsh CA, Dobin A, Gillis J, Lein ES, Hodge RD, Bakken TE. Comparative transcriptomics reveals human-specific cortical features. Science 2023; 382:eade9516. [PMID: 37824638 PMCID: PMC10659116 DOI: 10.1126/science.ade9516] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Abstract
The cognitive abilities of humans are distinctive among primates, but their molecular and cellular substrates are poorly understood. We used comparative single-nucleus transcriptomics to analyze samples of the middle temporal gyrus (MTG) from adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets to understand human-specific features of the neocortex. Human, chimpanzee, and gorilla MTG showed highly similar cell-type composition and laminar organization as well as a large shift in proportions of deep-layer intratelencephalic-projecting neurons compared with macaque and marmoset MTG. Microglia, astrocytes, and oligodendrocytes had more-divergent expression across species compared with neurons or oligodendrocyte precursor cells, and neuronal expression diverged more rapidly on the human lineage. Only a few hundred genes showed human-specific patterning, suggesting that relatively few cellular and molecular changes distinctively define adult human cortical structure.
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Affiliation(s)
| | - Janet H.T. Song
- Allen Discovery Center for Human Brain Evolution, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
| | - David Exposito-Alonso
- Allen Discovery Center for Human Brain Evolution, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Hamsini Suresh
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | | | - Fenna M. Krienen
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jennie Close
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Emily Gelfand
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Brian Long
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | | | | | - Soumyadeep Basu
- LKEB, Dept of Radiology, Leiden University Medical Center; Leiden, The Netherlands
- Computer Graphics and Visualization Group, Delft University of Technology, Delft, Netherlands
| | - Marc Beaudin
- Allen Discovery Center for Human Brain Evolution, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
| | | | - Megan Crow
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Jeroen Eggermont
- LKEB, Dept of Radiology, Leiden University Medical Center; Leiden, The Netherlands
| | | | - Jeff Goldy
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Katelyn Kiick
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Thomas Kroes
- LKEB, Dept of Radiology, Leiden University Medical Center; Leiden, The Netherlands
| | | | | | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Michael Tieu
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - William D. Hopkins
- Keeling Center for Comparative Medicine and Research, University of Texas, MD Anderson Cancer Center, Houston, TX 78602, USA
| | - Thomas Höllt
- Computer Graphics and Visualization Group, Delft University of Technology, Delft, Netherlands
| | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 981915, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Steven A. McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Boudewijn P. Lelieveldt
- LKEB, Dept of Radiology, Leiden University Medical Center; Leiden, The Netherlands
- Pattern Recognition and Bioinformatics group, Delft University of Technology, Delft, Netherlands
| | - Chet C. Sherwood
- Department of Anthropology, The George Washington University, Washington, DC 20037, USA
| | - Kimberly Smith
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
| | - Christopher A. Walsh
- Allen Discovery Center for Human Brain Evolution, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Alexander Dobin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Jesse Gillis
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Ed S. Lein
- Allen Institute for Brain Science; Seattle, WA, 98109, USA
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10
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Pollen AA, Kilik U, Lowe CB, Camp JG. Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution. Nat Rev Genet 2023; 24:687-711. [PMID: 36737647 PMCID: PMC9897628 DOI: 10.1038/s41576-022-00568-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 02/05/2023]
Abstract
Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these changes will provide insights into how we became human. Connecting human-specific genetic changes to species differences has been challenging owing to an abundance of low-effect size genetic changes, limited descriptions of phenotypic differences across development at the level of cell types and lack of experimental models. Emerging approaches for single-cell sequencing, genetic manipulation and stem cell culture now support descriptive and functional studies in defined cell types with a human or ape genetic background. In this Review, we describe how the sequencing of genomes from modern and archaic hominins, great apes and other primates is revealing human-specific genetic changes and how new molecular and cellular approaches - including cell atlases and organoids - are enabling exploration of the candidate causal factors that underlie human-specific traits.
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Affiliation(s)
- Alex A Pollen
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Umut Kilik
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Craig B Lowe
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
| | - J Gray Camp
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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11
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Royer J, Larivière S, Rodriguez-Cruces R, Cabalo DG, Tavakol S, Auer H, Ngo A, Park BY, Paquola C, Smallwood J, Jefferies E, Caciagli L, Bernasconi A, Bernasconi N, Frauscher B, Bernhardt BC. Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy. Brain 2023; 146:3923-3937. [PMID: 37082950 PMCID: PMC10473569 DOI: 10.1093/brain/awad125] [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: 11/07/2022] [Revised: 02/21/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023] Open
Abstract
Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
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Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Bo-yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Casey Paquola
- Multiscale Neuroanatomy Lab, INM-1, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | | | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, MA 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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12
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Chen A, Sun Y, Lei Y, Li C, Liao S, Meng J, Bai Y, Liu Z, Liang Z, Zhu Z, Yuan N, Yang H, Wu Z, Lin F, Wang K, Li M, Zhang S, Yang M, Fei T, Zhuang Z, Huang Y, Zhang Y, Xu Y, Cui L, Zhang R, Han L, Sun X, Chen B, Li W, Huangfu B, Ma K, Ma J, Li Z, Lin Y, Wang H, Zhong Y, Zhang H, Yu Q, Wang Y, Liu X, Peng J, Liu C, Chen W, Pan W, An Y, Xia S, Lu Y, Wang M, Song X, Liu S, Wang Z, Gong C, Huang X, Yuan Y, Zhao Y, Chai Q, Tan X, Liu J, Zheng M, Li S, Huang Y, Hong Y, Huang Z, Li M, Jin M, Li Y, Zhang H, Sun S, Gao L, Bai Y, Cheng M, Hu G, Liu S, Wang B, Xiang B, Li S, Li H, Chen M, Wang S, Li M, Liu W, Liu X, Zhao Q, Lisby M, Wang J, Fang J, Lin Y, Xie Q, Liu Z, He J, Xu H, Huang W, Mulder J, Yang H, Sun Y, Uhlen M, Poo M, Wang J, Yao J, Wei W, Li Y, Shen Z, Liu L, Liu Z, Xu X, Li C. Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 2023; 186:3726-3743.e24. [PMID: 37442136 DOI: 10.1016/j.cell.2023.06.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/24/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
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Affiliation(s)
- Ao Chen
- BGI-Shenzhen, Shenzhen 518103, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Yidi Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Ying Lei
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Chao Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Sha Liao
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Juan Meng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yiqin Bai
- Lingang Laboratory, Shanghai 200031, China
| | - Zhen Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Nini Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hao Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Feng Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Kexin Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mei Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Shuzhen Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Tianyi Fei
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhenkun Zhuang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yiming Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong Zhang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yuanfang Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luman Cui
- BGI-Shenzhen, Shenzhen 518103, China
| | - Ruiyi Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Han
- BGI-Shenzhen, Shenzhen 518103, China
| | - Xing Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | | | - Baoqian Huangfu
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | | | - Jianyun Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhao Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yikun Lin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - He Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanqing Zhong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huifang Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaqian Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jian Peng
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Wei Chen
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Yingjie An
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shihui Xia
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanbing Lu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingli Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinxiang Song
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuai Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Chun Gong
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xin Huang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yue Yuan
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yun Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinwen Chai
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Tan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianfeng Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingyuan Zheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shengkang Li
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, China
| | | | - Yan Hong
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Min Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Mengmeng Jin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hui Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Suhong Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Gao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yinqi Bai
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Guohai Hu
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Shiping Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Bo Wang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Bin Xiang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuting Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengni Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shiwen Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Minglong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Xin Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qian Zhao
- BGI-Shenzhen, Shenzhen 518103, China
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark
| | - Jing Wang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jiao Fang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yun Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qing Xie
- BGI-Shenzhen, Shenzhen 518103, China
| | - Zhen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jie He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huatai Xu
- Lingang Laboratory, Shanghai 200031, China
| | - Wei Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jan Mulder
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | | | - Yangang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | - Muming Poo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | | | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China; CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yuxiang Li
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Wuhan, BGI, Wuhan 430074, China.
| | - Zhiming Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China.
| | - Zhiyong Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China.
| | - Chengyu Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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13
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Zhuang XL, Zhang JJ, Shao Y, Ye Y, Chen CY, Zhou L, Wang ZB, Luo X, Su B, Yao YG, Cooper DN, Hu BX, Wang L, Qi XG, Lin J, Zhang GJ, Wang W, Sheng N, Wu DD. Integrative Omics Reveals Rapidly Evolving Regulatory Sequences Driving Primate Brain Evolution. Mol Biol Evol 2023; 40:msad173. [PMID: 37494289 PMCID: PMC10404817 DOI: 10.1093/molbev/msad173] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/11/2023] [Accepted: 07/14/2023] [Indexed: 07/28/2023] Open
Abstract
Although the continual expansion of the brain during primate evolution accounts for our enhanced cognitive capabilities, the drivers of brain evolution have scarcely been explored in these ancestral nodes. Here, we performed large-scale comparative genomic, transcriptomic, and epigenomic analyses to investigate the evolutionary alterations acquired by brain genes and provide comprehensive listings of innovatory genetic elements along the evolutionary path from ancestral primates to human. The regulatory sequences associated with brain-expressed genes experienced rapid change, particularly in the ancestor of the Simiiformes. Extensive comparisons of single-cell and bulk transcriptomic data between primate and nonprimate brains revealed that these regulatory sequences may drive the high expression of certain genes in primate brains. Employing in utero electroporation into mouse embryonic cortex, we show that the primate-specific brain-biased gene BMP7 was recruited, probably in the ancestor of the Simiiformes, to regulate neuronal proliferation in the primate ventricular zone. Our study provides a comprehensive listing of genes and regulatory changes along the brain evolution lineage of ancestral primates leading to human. These data should be invaluable for future functional studies that will deepen our understanding not only of the genetic basis of human brain evolution but also of inherited disease.
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Affiliation(s)
- Xiao-Lin Zhuang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Jin-Jin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yaxin Ye
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Chun-Yan Chen
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Long Zhou
- Center for Evolutionary & Organismal Biology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Center of Evolutionary & Organismal Biology, and Women’s Hospital at Zhejiang University School of Medicine, Hangzhou, Guangdong, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Guangdong, China
| | - Zheng-bo Wang
- Yunnan Key Laboratory of Primate Biomedicine Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Xin Luo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yong-Gang Yao
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, Yunnan, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | - Ben-Xia Hu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lu Wang
- College of Life Sciences, Northwest University, Xi’an, Shaanxi, China
| | - Xiao-Guang Qi
- College of Life Sciences, Northwest University, Xi’an, Shaanxi, China
| | - Jiangwei Lin
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Guo-Jie Zhang
- Center of Evolutionary & Organismal Biology, and Women’s Hospital at Zhejiang University School of Medicine, Hangzhou, Guangdong, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Guangdong, China
| | - Wen Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Nengyin Sheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
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14
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Wu J, Li Y, Huang Y, Liu L, Zhang H, Nagy C, Tan X, Cheng K, Liu Y, Pu J, Wang H, Wu Q, Perry SW, Turecki G, Wong ML, Licinio J, Zheng P, Xie P. Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors. Nat Neurosci 2023; 26:1352-1364. [PMID: 37443281 DOI: 10.1038/s41593-023-01379-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/12/2023] [Indexed: 07/15/2023]
Abstract
Major depressive disorder represents a serious public health challenge worldwide; however, the underlying cellular and molecular mechanisms are mostly unknown. Here, we profile the dorsolateral prefrontal cortex of female cynomolgus macaques with social stress-associated depressive-like behaviors using single-nucleus RNA-sequencing and spatial transcriptomics. We find gene expression changes associated with depressive-like behaviors mostly in microglia, and we report a pro-inflammatory microglia subpopulation enriched in the depressive-like condition. Single-nucleus RNA-sequencing data result in the identification of six enriched gene modules associated with depressive-like behaviors, and these modules are further resolved by spatial transcriptomics. Gene modules associated with huddle and sit alone behaviors are expressed in neurons and oligodendrocytes of the superficial cortical layer, while gene modules associated with locomotion and amicable behaviors are enriched in microglia and astrocytes in mid-to-deep cortical layers. The depressive-like behavior associated microglia subpopulation is enriched in deep cortical layers. In summary, our findings show cell-type and cortical layer-specific gene expression changes and identify one microglia subpopulation associated with depressive-like behaviors in female non-human primates.
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Affiliation(s)
- Jing Wu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Yifan Li
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Yu Huang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Lanxiang Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
- Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Hanping Zhang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Corina Nagy
- McGill Group for Suicide Studies, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Xunmin Tan
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Ke Cheng
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haiyang Wang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Jinfeng Laboratory, Chongqing, China
| | - Qingyuan Wu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, Chongqing University Three Gorges Hospital, Chongqing, China
| | - Seth W Perry
- Department of Psychiatry, College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Gustavo Turecki
- McGill Group for Suicide Studies, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ma-Li Wong
- Department of Psychiatry, College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Julio Licinio
- Department of Psychiatry, College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Peng Zheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Jinfeng Laboratory, Chongqing, China.
| | - Peng Xie
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Jinfeng Laboratory, Chongqing, China.
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15
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Wei J, Dai S, Yan Y, Li S, Yang P, Zhu R, Huang T, Li X, Duan Y, Wang Z, Ji W, Si W. Spatiotemporal proteomic atlas of multiple brain regions across early fetal to neonatal stages in cynomolgus monkey. Nat Commun 2023; 14:3917. [PMID: 37400444 PMCID: PMC10317979 DOI: 10.1038/s41467-023-39411-7] [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: 10/14/2022] [Accepted: 06/12/2023] [Indexed: 07/05/2023] Open
Abstract
Fetal stages are critical periods for brain development. However, the protein molecular signature and dynamics of the human brain remain unclear due to sampling difficulty and ethical limitations. Non-human primates present similar developmental and neuropathological features to humans. This study constructed a spatiotemporal proteomic atlas of cynomolgus macaque brain development from early fetal to neonatal stages. Here we showed that (1) the variability across stages was greater than that among brain regions, and comparisons of cerebellum vs. cerebrum and cortical vs. subcortical regions revealed region-specific dynamics across early fetal to neonatal stages; (2) fluctuations in abundance of proteins associated with neural disease suggest the risk of nervous disorder at early fetal stages; (3) cross-species analysis (human, monkey, and mouse) and comparison between proteomic and transcriptomic data reveal the proteomic specificity and genes with mRNA/protein discrepancy. This study provides insight into fetal brain development in primates.
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Affiliation(s)
- Jingkuan Wei
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Shaoxing Dai
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Yaping Yan
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Shulin Li
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
| | - Pengpeng Yang
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
| | - Ran Zhu
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
| | - Tianzhuang Huang
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Xi Li
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Yanchao Duan
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China
| | - Zhengbo Wang
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China.
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China.
| | - Weizhi Ji
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China.
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China.
- Chinese Primate Biomedical Research Alliance (CPBRA), 650500, Kunming, Yunnan, China.
| | - Wei Si
- State Key Laboratory of Primate Biomedical Research; Institute of Primate Translational Medicine, Kunming University of Science and Technology, 650500, Kunming, Yunnan, China.
- Yunnan Key Laboratory of Primate Biomedical Research, 650500, Kunming, Yunnan, China.
- Chinese Primate Biomedical Research Alliance (CPBRA), 650500, Kunming, Yunnan, China.
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16
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Di Biase MA. The Search for Unitary Mechanisms in Psychiatric Illness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:588-589. [PMID: 37286290 DOI: 10.1016/j.bpsc.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 06/09/2023]
Affiliation(s)
- Maria A Di Biase
- Departments of Psychiatry and Anatomy and Physiology, The University of Melbourne, Melbourne, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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17
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Li X, Xiao C, Qi J, Xue W, Xu X, Mu Z, Zhang J, Li CY, Ding W. STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data. Nucleic Acids Res 2023:7177883. [PMID: 37224539 DOI: 10.1093/nar/gkad419] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.
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Affiliation(s)
- Xiangshang Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chunfu Xiao
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Juntian Qi
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | | | - Xinwei Xu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- College of Life Science, Peking University, Beijing 100871, China
| | - Zelin Mu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Jie Zhang
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chuan-Yun Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Wanqiu Ding
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Bioinformatics Core, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
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18
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Sun W, Xie G, Jiang X, Khaitovich P, Han D, Liu X. Epigenetic regulation of human-specific gene expression in the prefrontal cortex. BMC Biol 2023; 21:123. [PMID: 37226244 DOI: 10.1186/s12915-023-01612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/03/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Changes in gene expression levels during brain development are thought to have played an important role in the evolution of human cognition. With the advent of high-throughput sequencing technologies, changes in brain developmental expression patterns, as well as human-specific brain gene expression, have been characterized. However, interpreting the origin of evolutionarily advanced cognition in human brains requires a deeper understanding of the regulation of gene expression, including the epigenomic context, along the primate genome. Here, we used chromatin immunoprecipitation sequencing (ChIP-seq) to measure the genome-wide profiles of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 acetylation (H3K27ac), both of which are associated with transcriptional activation in the prefrontal cortex of humans, chimpanzees, and rhesus macaques. RESULTS We found a discrete functional association, in which H3K4me3HP gain was significantly associated with myelination assembly and signaling transmission, while H3K4me3HP loss played a vital role in synaptic activity. Moreover, H3K27acHP gain was enriched in interneuron and oligodendrocyte markers, and H3K27acHP loss was enriched in CA1 pyramidal neuron markers. Using strand-specific RNA sequencing (ssRNA-seq), we first demonstrated that approximately 7 and 2% of human-specific expressed genes were epigenetically marked by H3K4me3HP and H3K27acHP, respectively, providing robust support for causal involvement of histones in gene expression. We also revealed the co-activation role of epigenetic modification and transcription factors in human-specific transcriptome evolution. Mechanistically, histone-modifying enzymes at least partially contribute to an epigenetic disturbance among primates, especially for the H3K27ac epigenomic marker. In line with this, peaks enriched in the macaque lineage were found to be driven by upregulated acetyl enzymes. CONCLUSIONS Our results comprehensively elucidated a causal species-specific gene-histone-enzyme landscape in the prefrontal cortex and highlighted the regulatory interaction that drove transcriptional activation.
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Affiliation(s)
- Weifen Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Gangcai Xie
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China
| | - Xi Jiang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China
| | - Philipp Khaitovich
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China.
- Skolkovo Institute of Science and Technology, Moscow, 121205, Russia.
| | - Dingding Han
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China.
- Department of Clinical Laboratory, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China.
| | - Xiling Liu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China.
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China.
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19
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Xue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, Liu M, Zhang Y, Liu F. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology 2023; 48:518-528. [PMID: 36253546 PMCID: PMC9852427 DOI: 10.1038/s41386-022-01474-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023]
Abstract
Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.
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Affiliation(s)
- Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Sixiang Liang
- Tianjin Anding Hospital, Tianjin, 300222, China
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100088, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yong Zhang
- Tianjin Anding Hospital, Tianjin, 300222, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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20
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Petanjek Z, Banovac I, Sedmak D, Hladnik A. Dendritic Spines: Synaptogenesis and Synaptic Pruning for the Developmental Organization of Brain Circuits. ADVANCES IN NEUROBIOLOGY 2023; 34:143-221. [PMID: 37962796 DOI: 10.1007/978-3-031-36159-3_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Synaptic overproduction and elimination is a regular developmental event in the mammalian brain. In the cerebral cortex, synaptic overproduction is almost exclusively correlated with glutamatergic synapses located on dendritic spines. Therefore, analysis of changes in spine density on different parts of the dendritic tree in identified classes of principal neurons could provide insight into developmental reorganization of specific microcircuits.The activity-dependent stabilization and selective elimination of the initially overproduced synapses is a major mechanism for generating diversity of neural connections beyond their genetic determination. The largest number of overproduced synapses was found in the monkey and human cerebral cortex. The highest (exceeding adult values by two- to threefold) and most protracted overproduction (up to third decade of life) was described for associative layer IIIC pyramidal neurons in the human dorsolateral prefrontal cortex.Therefore, the highest proportion and extraordinarily extended phase of synaptic spine overproduction is a hallmark of neural circuitry in human higher-order associative areas. This indicates that microcircuits processing the most complex human cognitive functions have the highest level of developmental plasticity. This finding is the backbone for understanding the effect of environmental impact on the development of the most complex, human-specific cognitive and emotional capacities, and on the late onset of human-specific neuropsychiatric disorders, such as autism and schizophrenia.
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Affiliation(s)
- Zdravko Petanjek
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia.
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia.
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia.
| | - Ivan Banovac
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Dora Sedmak
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ana Hladnik
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
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21
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Almeida D, Turecki G. Profiling cell-type specific gene expression in post-mortem human brain samples through laser capture microdissection. Methods 2022; 207:3-10. [PMID: 36064002 DOI: 10.1016/j.ymeth.2022.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 07/14/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
The transcriptome of a cell constitutes an essential piece of cellular identity and contributes to the multifaceted complexity and heterogeneity of cell-types within the mammalian brain. Thus, while a wealth of studies have investigated transcriptomic alterations underlying the pathophysiology of diseases of the brain, their use of bulk-tissue homogenates makes it difficult to tease apart whether observed differences are explained by disease state or cellular composition. Cell-type-specific enrichment strategies are, therefore, crucial in the context of gene expression profiling. Laser capture microdissection (LCM) is one such strategy that allows for the capture of specific cell-types, or regions of interest, under microscopic visualization. In this review, we focus on using LCM for cell-type specific gene expression profiling in post-mortem human brain samples. We begin with a discussion of various LCM systems, followed by a walk-through of each step in the LCM to gene expression profiling workflow and a description of some of the limitations associated with LCM. Throughout the review, we highlight important considerations when using LCM with post-mortem human brain samples. Whenever applicable, commercially available kits that have proven successful in the context of LCM with post-mortem human brain samples are described.
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Affiliation(s)
- Daniel Almeida
- McGill Group for Suicide Studies, Douglas Hospital Research Center, Montreal, QC, Canada, H4H 1R3
| | - Gustavo Turecki
- McGill Group for Suicide Studies, Douglas Hospital Research Center, Montreal, QC, Canada, H4H 1R3; Department of Psychiatry, McGill University, Montreal, QC, Canada, H3A 1A1.
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22
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Kaczanowska J, Ganglberger F, Chernomor O, Kargl D, Galik B, Hess A, Moodley Y, von Haeseler A, Bühler K, Haubensak W. Molecular archaeology of human cognitive traits. Cell Rep 2022; 40:111287. [PMID: 36044840 DOI: 10.1016/j.celrep.2022.111287] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 05/20/2022] [Accepted: 08/05/2022] [Indexed: 01/06/2023] Open
Abstract
The brains and minds of our human ancestors remain inaccessible for experimental exploration. Therefore, we reconstructed human cognitive evolution by projecting nonsynonymous/synonymous rate ratios (ω values) in mammalian phylogeny onto the anatomically modern human (AMH) brain. This atlas retraces human neurogenetic selection and allows imputation of ancestral evolution in task-related functional networks (FNs). Adaptive evolution (high ω values) is associated with excitatory neurons and synaptic function. It shifted from FNs for motor control in anthropoid ancestry (60-41 mya) to attention in ancient hominoids (26-19 mya) and hominids (19-7.4 mya). Selection in FNs for language emerged with an early hominin ancestor (7.4-1.7 mya) and was later accompanied by adaptive evolution in FNs for strategic thinking during recent (0.8 mya-present) speciation of AMHs. This pattern mirrors increasingly complex cognitive demands and suggests that co-selection for language alongside strategic thinking may have separated AMHs from their archaic Denisovan and Neanderthal relatives.
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Affiliation(s)
- Joanna Kaczanowska
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | | | - Olga Chernomor
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna, Medical University of Vienna, Dr. Bohr Gasse 9, 1030 Vienna, Austria
| | - Dominic Kargl
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria; Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Bence Galik
- Bioinformatics and Scientific Computing, Vienna Biocenter Core Facilities (VBCF), Dr. Bohr Gasse 3, 1030 Vienna, Austria
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nuremberg, Fahrstrasse 17, 91054 Erlangen, Germany
| | - Yoshan Moodley
- Department of Zoology, University of Venda, Private Bag X5050, Thohoyandou, Republic of South Africa
| | - Arndt von Haeseler
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna, Medical University of Vienna, Dr. Bohr Gasse 9, 1030 Vienna, Austria; Faculty of Computer Science, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
| | - Katja Bühler
- VRVis Research Center, Donau-City Strasse 11, 1220 Vienna, Austria
| | - Wulf Haubensak
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria; Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria.
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23
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Higo N. Motor Cortex Plasticity During Functional Recovery Following Brain Damage. JOURNAL OF ROBOTICS AND MECHATRONICS 2022. [DOI: 10.20965/jrm.2022.p0700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Although brain damage causes functional impairment, it is often followed by partial or total recovery of function. Recovery is believed to occur primarily because of brain plasticity. Both human and animal studies have significantly contributed to uncovering the neuronal basis of plasticity. Recent advances in brain imaging technology have enabled the investigation of plastic changes in living human brains. In addition, animal experiments have revealed detailed changes at the neural and genetic levels. In this review, plasticity in motor-related areas of the cerebral cortex, which is one of the most well-studied areas of the neocortex in terms of plasticity, is reviewed. In addition, the potential of technological interventions to enhance plasticity and promote functional recovery following brain damage is discussed. Novel neurorehabilitation technologies are expected to be established based on the emerging research on plasticity from the last several decades.
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24
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Hong H, Zhao Z, Huang X, Guo C, Zhao H, Wang GD, Zhang YP, Zhao JP, Shi J, Wu QF, Jiang YH, Wang Y, Li LM, Du Z, Zhang YQ, Xiong Y. Comparative Proteome and Cis-Regulatory Element Analysis Reveals Specific Molecular Pathways Conserved in Dog and Human Brains. Mol Cell Proteomics 2022; 21:100261. [PMID: 35738554 PMCID: PMC9304787 DOI: 10.1016/j.mcpro.2022.100261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/10/2022] [Accepted: 06/17/2022] [Indexed: 11/25/2022] Open
Abstract
Brain development and function are governed by precisely regulated protein expressions in different regions. To date, multiregional brain proteomes have been systematically analyzed only for adult human and mouse brains. To understand the underpinnings of brain development and function, we generated proteomes from six regions of the postnatal brain at three developmental stages of domestic dogs (Canis familiaris), which are special among animals in terms of their remarkable human-like social cognitive abilities. Quantitative analysis of the spatiotemporal proteomes identified region-enriched synapse types at different developmental stages and differential myelination progression in different brain regions. Through integrative analysis of inter-regional expression patterns of orthologous proteins and genome-wide cis-regulatory element frequencies, we found that proteins related with myelination and hippocampus were highly correlated between dog and human but not between mouse and human, although mouse is phylogenetically closer to human. Moreover, the global expression patterns of neurodegenerative disease and autism spectrum disorder-associated proteins in dog brain more resemble human brain than in mouse brain. The high similarity of myelination and hippocampus-related pathways in dog and human at both proteomic and genetic levels may contribute to their shared social cognitive abilities. The inter-regional expression patterns of disease-associated proteins in the brain of different species provide important information to guide mechanistic and translational study using appropriate animal models.
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Affiliation(s)
- Huilin Hong
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Zhiguang Zhao
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China
| | - Xiahe Huang
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Chao Guo
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Hui Zhao
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Guo-Dong Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | | | - Jianhui Shi
- National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Qing-Feng Wu
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China
| | - Yong-Hui Jiang
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yingchun Wang
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China
| | - Lei M Li
- National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhuo Du
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China
| | - Yong Q Zhang
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China.
| | - Ying Xiong
- State Key Laboratory for Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
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25
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Nano PR, Bhaduri A. Evaluation of advances in cortical development using model systems. Dev Neurobiol 2022; 82:408-427. [PMID: 35644985 PMCID: PMC10924780 DOI: 10.1002/dneu.22879] [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: 01/05/2022] [Revised: 04/26/2022] [Accepted: 04/30/2022] [Indexed: 11/11/2022]
Abstract
Compared with that of even the closest primates, the human cortex displays a high degree of specialization and expansion that largely emerges developmentally. Although decades of research in the mouse and other model systems has revealed core tenets of cortical development that are well preserved across mammalian species, small deviations in transcription factor expression, novel cell types in primates and/or humans, and unique cortical architecture distinguish the human cortex. Importantly, many of the genes and signaling pathways thought to drive human-specific cortical expansion also leave the brain vulnerable to disease, as the misregulation of these factors is highly correlated with neurodevelopmental and neuropsychiatric disorders. However, creating a comprehensive understanding of human-specific cognition and disease remains challenging. Here, we review key stages of cortical development and highlight known or possible differences between model systems and the developing human brain. By identifying the developmental trajectories that may facilitate uniquely human traits, we highlight open questions in need of approaches to examine these processes in a human context and reveal translatable insights into human developmental disorders.
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Affiliation(s)
- Patricia R Nano
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Aparna Bhaduri
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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26
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Zhu Y, Webster MJ, Murphy CE, Middleton FA, Massa PT, Liu C, Dai R, Weickert CS. Distinct Phenotypes of Inflammation Associated Macrophages and Microglia in the Prefrontal Cortex Schizophrenia Compared to Controls. Front Neurosci 2022; 16:858989. [PMID: 35844224 PMCID: PMC9279891 DOI: 10.3389/fnins.2022.858989] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 06/01/2022] [Indexed: 12/23/2022] Open
Abstract
Approximately 40% of people with schizophrenia are classified as having "high inflammation." This subgroup has worse neuropathology than patients with "low inflammation." Thus, one would expect the resident microglia and possibly monocyte-derived macrophages infiltrating from the periphery to be "activated" in those with schizophrenia with elevated neuroinflammation. To test whether microglia and/or macrophages are associated with increased inflammatory signaling in schizophrenia, we measured microglia- and macrophage-associated transcripts in the postmortem dorsolateral prefrontal cortex of 69 controls and 72 people with schizophrenia. Both groups were stratified by neuroinflammatory status based on cortical mRNA levels of cytokines and SERPINA3. We found microglial mRNAs levels were either unchanged (IBA1 and Hexb, p > 0.20) or decreased (CD11c, <62% p < 0.001) in high inflammation schizophrenia compared to controls. Conversely, macrophage CD163 mRNA levels were increased in patients, substantially so in the high inflammation schizophrenia subgroup compared to low inflammation subgroup (>250%, p < 0.0001). In contrast, high inflammation controls did not have elevated CD163 mRNA compared to low inflammation controls (p > 0.05). The pro-inflammatory macrophage marker (CD64 mRNA) was elevated (>160%, all p < 0.05) and more related to CD163 mRNA in the high inflammation schizophrenia subgroup compared to high inflammation controls, while anti-inflammatory macrophage and cytokine markers (CD206 and IL-10 mRNAs) were either unchanged or decreased in schizophrenia. Finally, macrophage recruitment chemokine CCL2 mRNA was increased in schizophrenia (>200%, p < 0.0001) and CCL2 mRNA levels positively correlated with CD163 mRNA (r = 0.46, p < 0.0001). Collectively, our findings support the co-existence of quiescent microglia and increased pro-inflammatory macrophages in the cortex of people with schizophrenia.
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Affiliation(s)
- Yunting Zhu
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, United States
| | - Maree J. Webster
- Stanley Medical Research Institute, Rockville, MD, United States
| | - Caitlin E. Murphy
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Sydney, NSW, Australia
| | - Frank A. Middleton
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, United States
| | - Paul T. Massa
- Department of Neurology and Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, United States
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, United States
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, United States
| | - Cyndi Shannon Weickert
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, United States
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Sydney, NSW, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
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27
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Kim EH, Howard D, Chen Y, Tripathy SJ, French L. LaminaRGeneVis: A Tool to Visualize Gene Expression Across the Laminar Architecture of the Human Neocortex. Front Neuroinform 2022; 16:753770. [PMID: 35281717 PMCID: PMC8907970 DOI: 10.3389/fninf.2022.753770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
The application of RNA sequencing has enabled the characterization of genome-wide gene expression in the human brain, including distinct layers of the neocortex. Neuroanatomically, the molecular patterns that underlie the laminar organization of the neocortex can help link structure to circuitry and function. To advance our understanding of cortical architecture, we created LaminaRGeneVis, a web application that displays across-layer cortical gene expression from multiple datasets. These datasets were collected using bulk, single-nucleus, and spatial RNA sequencing methodologies and were normalized to facilitate comparisons between datasets. The online resource performs single- and multi-gene analyses to provide figures and statistics for user-friendly assessment of laminar gene expression patterns in the adult human neocortex. The web application is available at https://ethanhkim.shinyapps.io/laminargenevis/.
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Affiliation(s)
- Ethan H. Kim
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Derek Howard
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Yuxiao Chen
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Shreejoy J. Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Leon French
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- *Correspondence: Leon French,
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28
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Molecular characterization of depression trait and state. Mol Psychiatry 2022; 27:1083-1094. [PMID: 34686766 DOI: 10.1038/s41380-021-01347-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 09/23/2021] [Accepted: 10/04/2021] [Indexed: 11/09/2022]
Abstract
Major depressive disorder (MDD) is a brain disorder often characterized by recurrent episode and remission phases. The molecular correlates of MDD have been investigated in case-control comparisons, but the biological alterations associated with illness trait (regardless of clinical phase) or current state (symptomatic and remitted phases) remain largely unknown, limiting targeted drug discovery. To characterize MDD trait- and state-dependent changes, in single or recurrent depressive episode or remission, we generated transcriptomic profiles of subgenual anterior cingulate cortex of postmortem subjects in first MDD episode (n = 20), in remission after a single episode (n = 15), in recurrent episode (n = 20), in remission after recurring episodes (n = 15) and control subject (n = 20). We analyzed the data at the gene, biological pathway, and cell-specific molecular levels, investigated putative causal events and therapeutic leads. MDD-trait was associated with genes involved in inflammation, immune activation, and reduced bioenergetics (q < 0.05) whereas MDD-states were associated with altered neuronal structure and reduced neurotransmission (q < 0.05). Cell-level deconvolution of transcriptomic data showed significant change in density of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin, or vasoactive-intestinal peptide (p < 3 × 10-3). A probabilistic Bayesian-network approach showed causal roles of immune-system-activation (q < 8.67 × 10-3), cytokine-response (q < 4.79 × 10-27) and oxidative-stress (q < 2.05 × 10-3) across MDD-phases. Gene-sets associated with these putative causal changes show inverse associations with the transcriptomic effects of dopaminergic and monoaminergic ligands. The study provides first insights into distinct cellular and molecular pathologies associated with trait- and state-MDD, on plasticity mechanisms linking the two pathologies, and on a method of drug discovery focused on putative disease-causing pathways.
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29
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Salamon I, Rasin MR. Evolution of the Neocortex Through RNA-Binding Proteins and Post-transcriptional Regulation. Front Neurosci 2022; 15:803107. [PMID: 35082597 PMCID: PMC8784817 DOI: 10.3389/fnins.2021.803107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/16/2021] [Indexed: 12/24/2022] Open
Abstract
The human neocortex is undoubtedly considered a supreme accomplishment in mammalian evolution. It features a prenatally established six-layered structure which remains plastic to the myriad of changes throughout an organism’s lifetime. A fundamental feature of neocortical evolution and development is the abundance and diversity of the progenitor cell population and their neuronal and glial progeny. These evolutionary upgrades are partially enabled due to the progenitors’ higher proliferative capacity, compartmentalization of proliferative regions, and specification of neuronal temporal identities. The driving force of these processes may be explained by temporal molecular patterning, by which progenitors have intrinsic capacity to change their competence as neocortical neurogenesis proceeds. Thus, neurogenesis can be conceptualized along two timescales of progenitors’ capacity to (1) self-renew or differentiate into basal progenitors (BPs) or neurons or (2) specify their fate into distinct neuronal and glial subtypes which participate in the formation of six-layers. Neocortical development then proceeds through sequential phases of proliferation, differentiation, neuronal migration, and maturation. Temporal molecular patterning, therefore, relies on the precise regulation of spatiotemporal gene expression. An extensive transcriptional regulatory network is accompanied by post-transcriptional regulation that is frequently mediated by the regulatory interplay between RNA-binding proteins (RBPs). RBPs exhibit important roles in every step of mRNA life cycle in any system, from splicing, polyadenylation, editing, transport, stability, localization, to translation (protein synthesis). Here, we underscore the importance of RBP functions at multiple time-restricted steps of early neurogenesis, starting from the cell fate transition of transcriptionally primed cortical progenitors. A particular emphasis will be placed on RBPs with mostly conserved but also divergent evolutionary functions in neural progenitors across different species. RBPs, when considered in the context of the fascinating process of neocortical development, deserve to be main protagonists in the story of the evolution and development of the neocortex.
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30
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Higo N. Non-human Primate Models to Explore the Adaptive Mechanisms After Stroke. Front Syst Neurosci 2021; 15:760311. [PMID: 34819842 PMCID: PMC8606408 DOI: 10.3389/fnsys.2021.760311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/20/2021] [Indexed: 01/15/2023] Open
Abstract
The brain has the ability to reconstruct neural structures and functions to compensate for the brain lesions caused by stroke, although it is highly limited in primates including humans. Animal studies in which experimental lesions were induced in the brain have contributed to the current understanding of the neural mechanisms underlying functional recovery. Here, I have highlighted recent advances in non-human primate models using primate species such as macaques and marmosets, most of which have been developed to study the mechanisms underlying the recovery of motor functions after stroke. Cortical lesion models have been used to investigate motor recovery after lesions to the cortical areas involved in movements of specific body parts. Models of a focal stroke at the posterior internal capsule have also been developed to bridge the gap between the knowledge obtained by cortical lesion models and the development of intervention strategies because the severity and outcome of motor deficits depend on the degree of lesions to the region. This review will also introduce other stroke models designed to study the plastic changes associated with development and recovery from cognitive and sensory impairments. Although further validation and careful interpretation are required, considering the differences between non-human primate brains and human brains, studies using brain-lesioned non-human primates offer promise for improving translational outcomes.
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Affiliation(s)
- Noriyuki Higo
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
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31
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Shukla N, Shaban B, Gallego Romero I. Genetic Diversity in Chimpanzee Transcriptomics Does Not Represent Wild Populations. Genome Biol Evol 2021; 13:6426081. [PMID: 34788801 PMCID: PMC8633730 DOI: 10.1093/gbe/evab247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Chimpanzees (Pan troglodytes) are a genetically diverse species, consisting of four highly distinct subspecies. As humans' closest living relative, they have been a key model organism in the study of human evolution, and comparisons of human and chimpanzee transcriptomes have been widely used to characterize differences in gene expression levels that could underlie the phenotypic differences between the two species. However, the subspecies from which these transcriptomic data sets have been derived is not recorded in metadata available in the public NCBI Sequence Read Archive (SRA). Furthermore, labeling of RNA sequencing (RNA-seq) samples is for the most part inconsistent across studies, and the true number of individuals from whom transcriptomic data are available is difficult to ascertain. Thus, we have evaluated genetic diversity at the subspecies and individual level in 486 public RNA-seq samples available in the SRA, spanning the vast majority of public chimpanzee transcriptomic data. Using multiple population genetics approaches, we find that nearly all samples (96.6%) have some degree of Western chimpanzee ancestry. At the individual donor level, we identify multiple samples that have been repeatedly analyzed across different studies and identify a total of 135 genetically distinct individuals within our data, a number that falls to 89 when we exclude likely first- and second-degree relatives. Altogether, our results show that current transcriptomic data from chimpanzees are capturing low levels of genetic diversity relative to what exists in wild chimpanzee populations. These findings provide important context to current comparative transcriptomics research involving chimpanzees.
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Affiliation(s)
- Navya Shukla
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.,Melbourne Integrative Genomics, University of Melbourne, Parkville, Victoria, Australia
| | - Bobbie Shaban
- Melbourne Integrative Genomics, University of Melbourne, Parkville, Victoria, Australia
| | - Irene Gallego Romero
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.,Melbourne Integrative Genomics, University of Melbourne, Parkville, Victoria, Australia.,Centre for Stem Cell Systems, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, Australia.,Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Estonia
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32
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Barker SJ, Raju RM, Milman NEP, Wang J, Davila-Velderrain J, Gunter-Rahman F, Parro CC, Bozzelli PL, Abdurrob F, Abdelaal K, Bennett DA, Kellis M, Tsai LH. MEF2 is a key regulator of cognitive potential and confers resilience to neurodegeneration. Sci Transl Med 2021; 13:eabd7695. [PMID: 34731014 PMCID: PMC9258338 DOI: 10.1126/scitranslmed.abd7695] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Scarlett J Barker
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ravikiran M Raju
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Noah E P Milman
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jun Wang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jose Davila-Velderrain
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Fatima Gunter-Rahman
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cameron C Parro
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - P Lorenzo Bozzelli
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Fatema Abdurrob
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Karim Abdelaal
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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33
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A Biomorphic Model of Cortical Column for Content-Based Image Retrieval. ENTROPY 2021; 23:e23111458. [PMID: 34828156 PMCID: PMC8620877 DOI: 10.3390/e23111458] [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] [Received: 09/10/2021] [Revised: 10/22/2021] [Accepted: 10/28/2021] [Indexed: 11/18/2022]
Abstract
How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of “triggering” a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.
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34
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Disturbance of phylogenetic layer-specific adaptation of human brain gene expression in Alzheimer's disease. Sci Rep 2021; 11:20200. [PMID: 34642398 PMCID: PMC8511061 DOI: 10.1038/s41598-021-99760-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/28/2021] [Indexed: 11/08/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with typical neuropathological hallmarks, such as neuritic plaques and neurofibrillary tangles, preferentially found at layers III and V. The distribution of both hallmarks provides the basis for the staging of AD, following a hierarchical pattern throughout the cerebral cortex. To unravel the background of this layer-specific vulnerability, we evaluated differential gene expression of supragranular and infragranular layers and subcortical white matter in both healthy controls and AD patients. We identified AD-associated layer-specific differences involving protein-coding and non-coding sequences, most of those present in the subcortical white matter, thus indicating a critical role for long axons and oligodendrocytes in AD pathomechanism. In addition, GO analysis identified networks containing synaptic vesicle transport, vesicle exocytosis and regulation of neurotransmitter levels. Numerous AD-associated layer-specifically expressed genes were previously reported to undergo layer-specific switches in recent hominid brain evolution between layers V and III, i.e., those layers that are most vulnerable to AD pathology. Against the background of our previous finding of accelerated evolution of AD-specific gene expression, here we suggest a critical role in AD pathomechanism for this phylogenetic layer-specific adaptation of gene expression, which is most prominently seen in the white matter compartment.
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35
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Wang XL, Li L. Cell type-specific potential pathogenic genes and functional pathways in Alzheimer's Disease. BMC Neurol 2021; 21:381. [PMID: 34600516 PMCID: PMC8487122 DOI: 10.1186/s12883-021-02407-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/28/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a pervasive age-related and highly heritable neurodegenerative disorder but has no effective therapy. The complex cellular microenvironment in the AD brain impedes our understanding of pathogenesis. Thus, a comprehensive investigation of cell type-specific responses in AD is crucial to provide precise molecular and cellular targets for therapeutic development. METHODS Here, we integrated analyzed 4,441 differentially expressed genes (DEGs) that were identified from 263,370 single-cells in cortex samples by single-nucleus RNA sequencing (snRNA-seq) between 42 AD-pathology subjects and 39 normal controls within 3 studies. DEGs were analyzed in microglia, astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and endothelial cells, respectively. In each cell type, we identified both common DEGs which were observed in all 3 studies, and overlapping DEGs which have been seen in at least 2 studies. Firstly, we showed the common DEGs expression and explained the biological functions by comparing with existing literature or multil-omics signaling pathways knowledgebase. We then determined the significant modules and hub genes, and explored the biological processes using the overlapping DEGs. Finally, we identified the common and distinct dysregulated pathways using overall DEGs and overlapping DEGs in a cell type-specific manner. RESULTS Up-regulated LINGO1 has been seen in both oligodendrocytes and excitatory neurons across 3 studies. Interestingly, genes enriched in the mitochondrial module were up-regulated across all cell types, which indicates mitochondrial dysfunction in the AD brain. The estrogen signaling pathway seems to be the most common pathway that is disrupted in AD. CONCLUSION Together, these analyses provide detailed information of cell type-specific and overall transcriptional changes and pathways underlying the human AD-pathology. These findings may provide important insights for drug development to tackle this disease.
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Affiliation(s)
- Xiao-Lan Wang
- Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Université de Strasbourg, Strasbourg, France
| | - Lianjian Li
- Department of Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430061 China
- Hubei Province Academy of Traditional Chinese Medicine, Wuhan, 430076 China
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36
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Paredes O, López JB, Covantes-Osuna C, Ocegueda-Hernández V, Romo-Vázquez R, Morales JA. A Transcriptome Community-and-Module Approach of the Human Mesoconnectome. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1031. [PMID: 34441171 PMCID: PMC8393183 DOI: 10.3390/e23081031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.
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Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| | - J. Alejandro Morales
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
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37
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Zhang W, Wang W, Zhao M, Turck CW, Zhu Y, Wang GZ. Rapid Body-Wide Transcriptomic Turnover During Rhesus Macaque Perinatal Development. Front Physiol 2021; 12:690540. [PMID: 34177627 PMCID: PMC8223001 DOI: 10.3389/fphys.2021.690540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
An hourglass cup-shape pattern of regulation at the molecular level was detected during the development of the primate brain. Specifically, a peak of temporally differentially expressed genes around the time of birth has been observed in the human brain. However, to what extend this peak of regulation exists among species has not been investigated in great detail. Here, by integrating multiple large-scale transcriptome data from rhesus macaques, we confirmed that a similar differential expression peak exists during the development of the macaque brain. We also found that a similar peak exists during the development of other organs, such as liver, testis, kidney and heart. Furthermore, we found that distinct pathways are regulated in the peak period of those organs. Our results highlight the importance of co-evolution of diverse organs during critical periods of perinatal development in primates.
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Affiliation(s)
- Wenqian Zhang
- School of Life Sciences, Guizhou Normal University, Guiyang, China
| | - Wei Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Manman Zhao
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Christoph W Turck
- Max Planck Institute of Psychiatry, Proteomics and Biomarkers, Munich, Germany
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Guang-Zhong Wang
- School of Life Sciences, Guizhou Normal University, Guiyang, China.,CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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38
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Zalocusky KA, Najm R, Taubes AL, Hao Y, Yoon SY, Koutsodendris N, Nelson MR, Rao A, Bennett DA, Bant J, Amornkul DEJ, Xu Q, An A, Cisne-Thomson O, Huang Y. Neuronal ApoE upregulates MHC-I expression to drive selective neurodegeneration in Alzheimer's disease. Nat Neurosci 2021; 24:786-798. [PMID: 33958804 PMCID: PMC9145692 DOI: 10.1038/s41593-021-00851-3] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/30/2021] [Indexed: 02/07/2023]
Abstract
Selective neurodegeneration is a critical causal factor in Alzheimer's disease (AD); however, the mechanisms that lead some neurons to perish, whereas others remain resilient, are unknown. We sought potential drivers of this selective vulnerability using single-nucleus RNA sequencing and discovered that ApoE expression level is a substantial driver of neuronal variability. Strikingly, neuronal expression of ApoE-which has a robust genetic linkage to AD-correlated strongly, on a cell-by-cell basis, with immune response pathways in neurons in the brains of wild-type mice, human ApoE knock-in mice and humans with or without AD. Elimination or over-expression of neuronal ApoE revealed a causal relationship among ApoE expression, neuronal MHC-I expression, tau pathology and neurodegeneration. Functional reduction of MHC-I ameliorated tau pathology in ApoE4-expressing primary neurons and in mouse hippocampi expressing pathological tau. These findings suggest a mechanism linking neuronal ApoE expression to MHC-I expression and, subsequently, to tau pathology and selective neurodegeneration.
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Affiliation(s)
- Kelly A Zalocusky
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA.
- Gladstone Center for Translational Advancement, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Ramsey Najm
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Alice L Taubes
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Yanxia Hao
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, San Francisco, CA, USA
| | - Seo Yeon Yoon
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | - Nicole Koutsodendris
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Maxine R Nelson
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Antara Rao
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Jason Bant
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Dah-Eun J Amornkul
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, San Francisco, CA, USA
| | - Qin Xu
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, San Francisco, CA, USA
| | - Alice An
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | - Olga Cisne-Thomson
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, San Francisco, CA, USA
| | - Yadong Huang
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA.
- Gladstone Center for Translational Advancement, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA.
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39
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Agoglia RM, Sun D, Birey F, Yoon SJ, Miura Y, Sabatini K, Pașca SP, Fraser HB. Primate cell fusion disentangles gene regulatory divergence in neurodevelopment. Nature 2021; 592:421-427. [PMID: 33731928 PMCID: PMC8719633 DOI: 10.1038/s41586-021-03343-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 02/08/2021] [Indexed: 12/31/2022]
Abstract
Among primates, humans display a unique trajectory of development that is responsible for the many traits specific to our species. However, the inaccessibility of primary human and chimpanzee tissues has limited our ability to study human evolution. Comparative in vitro approaches using primate-derived induced pluripotent stem cells have begun to reveal species differences on the cellular and molecular levels1,2. In particular, brain organoids have emerged as a promising platform to study primate neural development in vitro3-5, although cross-species comparisons of organoids are complicated by differences in developmental timing and variability of differentiation6,7. Here we develop a new platform to address these limitations by fusing human and chimpanzee induced pluripotent stem cells to generate a panel of tetraploid hybrid stem cells. We applied this approach to study species divergence in cerebral cortical development by differentiating these cells into neural organoids. We found that hybrid organoids provide a controlled system for disentangling cis- and trans-acting gene-expression divergence across cell types and developmental stages, revealing a signature of selection on astrocyte-related genes. In addition, we identified an upregulation of the human somatostatin receptor 2 gene (SSTR2), which regulates neuronal calcium signalling and is associated with neuropsychiatric disorders8,9. We reveal a human-specific response to modulation of SSTR2 function in cortical neurons, underscoring the potential of this platform for elucidating the molecular basis of human evolution.
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Affiliation(s)
- Rachel M Agoglia
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Danqiong Sun
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Fikri Birey
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Se-Jin Yoon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuki Miura
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Karen Sabatini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Sergiu P Pașca
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
| | - Hunter B Fraser
- Department of Biology, Stanford University, Stanford, CA, USA.
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40
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Afridi R, Seol S, Kang HJ, Suk K. Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting. Biochem Pharmacol 2021; 188:114532. [PMID: 33773976 DOI: 10.1016/j.bcp.2021.114532] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/18/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022]
Abstract
Understanding the pathophysiological mechanisms of neuropsychiatric disorders has been a challenging quest for neurobiologists. Recent years have witnessed enormous technological advances in the field of neuroimmunology, blurring boundaries between the central nervous system and the periphery. Consequently, the discipline has expanded to cover interactions between the nervous and immune systems in health and diseases. The complex interplay between the peripheral and central immune pathways in neuropsychiatric disorders has recently been documented in various studies, but the genetic determinants remain elusive. Recent transcriptome studies have identified dysregulated genes involved in peripheral immune cell activation, blood-brain barrier integrity, glial cell activation, and synaptic plasticity in major depressive disorder, bipolar disorder, autism spectrum disorder, and schizophrenia. Herein, the key transcriptomic techniques applied in investigating differentially expressed genes and pathways responsible for altered brain-immune interactions in neuropsychiatric disorders are discussed. The application of transcriptomics that can aid in identifying molecular targets in various neuropsychiatric disorders is highlighted.
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Affiliation(s)
- Ruqayya Afridi
- Department of Pharmacology, Brain Science & Engineering Institute, BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sihwan Seol
- Department of Life Science, Chung-Ang University, Seoul, Republic of Korea
| | - Hyo Jung Kang
- Department of Life Science, Chung-Ang University, Seoul, Republic of Korea.
| | - Kyoungho Suk
- Department of Pharmacology, Brain Science & Engineering Institute, BK21 Plus KNU Biomedical Convergence Program, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
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41
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Liu H, Xu L, Fu J, Su Q, Liu N, Xu J, Tang J, Li W, Zhao F, Ding H, Liu F, Qin W, Yu C. Prefrontal Granule Cell-Related Genes and Schizophrenia. Cereb Cortex 2021; 31:2268-2277. [PMID: 33270830 DOI: 10.1093/cercor/bhaa360] [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: 06/11/2020] [Revised: 11/01/2020] [Accepted: 11/02/2020] [Indexed: 11/13/2022] Open
Abstract
Although both the granular layer of the prefrontal cortex (PFC) and schizophrenia are unique in primates, especially humans, their linkage is unclear. Here, we tested whether schizophrenia is associated with expression profiles of the granule cell (GC)-related genes in the human PFC. We identified 14 candidate GC-related genes with gradually increased expression levels along the gradient of the agranular, dysgranular, light-granular, and granular prefrontal regions based on the densely sampled gene expression data of 6 postmortem human brains, and with more than 10-fold expression in neurons than other cell types based on the single-cell RNA-sequencing data of the human PFC. These GC-related genes were functionally associated with synaptic transmission and cell development and differentiation. The identified 14 GC-related genes were significantly enriched for schizophrenia, but not for depression and bipolar disorder. The expression levels of the 4 stable schizophrenia- and GC-related genes were spatially correlated with gray matter volume differences in the PFC between patients with schizophrenia and healthy controls. This study provides a set of candidate genes for the human prefrontal GCs and links expression profiles of the GC-related genes to the prefrontal structural impairments in schizophrenia.
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Affiliation(s)
- Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Lixue Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qian Su
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin 300060, China
| | - Nana Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Fangshi Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin 300070, People's Republic of China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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42
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Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL, Tran MN, Besich Z, Tippani M, Chew J, Yin Y, Kleinman JE, Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci 2021; 24:425-436. [PMID: 33558695 PMCID: PMC8095368 DOI: 10.1038/s41593-020-00787-0] [Citation(s) in RCA: 364] [Impact Index Per Article: 121.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/18/2020] [Indexed: 12/11/2022]
Abstract
We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).
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Affiliation(s)
- Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Brianna K Barry
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Joseph L Catallini
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zachary Besich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | | | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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43
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Exploring the secrets of brain transcriptional regulation: developing methodologies, recent significant findings, and perspectives. Brain Struct Funct 2021; 226:313-322. [PMID: 33547496 DOI: 10.1007/s00429-021-02230-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 01/22/2021] [Indexed: 10/22/2022]
Abstract
Exploring and revealing the secret of the function of the human brain has been the dream of mankind and science. Delineating brain transcriptional regulation has been extremely challenging, but recent technological advances have facilitated a deeper investigation of molecular processes in the brain. Tracing the molecular regulatory mechanisms of different gene expression profiles in the brain is divergent and has made it possible to connect spatial and temporal variations in gene expression to distributed properties of brain structure and function. Here, we review the molecular diversity of the brain among rodents, non-human primates and humans. We also discuss the molecular mechanism of non-coding DNA/RNA at the transcriptional/post-transcriptional level based on recent technical advances to highlight an improved understanding of the complex transcriptional network in the brain. Spatiotemporal and single-cell transcriptomics have attempted to gain novel insight into the development and evolution of the brain as well as the progression of human diseases. Although it is clear that the field is developing and challenges remain to be resolved, the impressive recent progress provides a solid foundation to better understand the brain and evidence-based recommendations for the diagnosis and treatment of brain diseases.
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44
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45
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Kostović I. The enigmatic fetal subplate compartment forms an early tangential cortical nexus and provides the framework for construction of cortical connectivity. Prog Neurobiol 2020; 194:101883. [PMID: 32659318 DOI: 10.1016/j.pneurobio.2020.101883] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 06/05/2020] [Accepted: 07/06/2020] [Indexed: 12/19/2022]
Abstract
The most prominent transient compartment of the primate fetal cortex is the deep, cell-sparse, synapse-containing subplate compartment (SPC). The developmental role of the SPC and its extraordinary size in humans remain enigmatic. This paper evaluates evidence on the development and connectivity of the SPC and discusses its role in the pathogenesis of neurodevelopmental disorders. A synthesis of data shows that the subplate becomes a prominent compartment by its expansion from the deep cortical plate (CP), appearing well-delineated on MR scans and forming a tangential nexus across the hemisphere, consisting of an extracellular matrix, randomly distributed postmigratory neurons, multiple branches of thalamic and long corticocortical axons. The SPC generates early spontaneous non-synaptic and synaptic activity and mediates cortical response upon thalamic stimulation. The subplate nexus provides large-scale interareal connectivity possibly underlying fMR resting-state activity, before corticocortical pathways are established. In late fetal phase, when synapses appear within the CP, transient the SPC coexists with permanent circuitry. The histogenetic role of the SPC is to provide interactive milieu and capacity for guidance, sorting, "waiting" and target selection of thalamocortical and corticocortical pathways. The new evolutionary role of the SPC and its remnant white matter neurons is linked to the increasing number of associative pathways in the human neocortex. These roles attributed to the SPC are regulated using a spatiotemporal gene expression during critical periods, when pathogenic factors may disturb vulnerable circuitry of the SPC, causing neurodevelopmental cognitive circuitry disorders.
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Affiliation(s)
- Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Salata 12, 10000 Zagreb, Croatia.
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46
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Li ML, Tang H, Shao Y, Wang MS, Xu HB, Wang S, Irwin DM, Adeola AC, Zeng T, Chen L, Li Y, Wu DD. Evolution and transition of expression trajectory during human brain development. BMC Evol Biol 2020; 20:72. [PMID: 32576137 PMCID: PMC7310562 DOI: 10.1186/s12862-020-01633-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 05/26/2020] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The remarkable abilities of the human brain are distinctive features that set us apart from other animals. However, our understanding of how the brain has changed in the human lineage remains incomplete, but is essential for understanding cognition, behavior, and brain disorders in humans. Here, we compared the expression trajectory in brain development between humans and rhesus macaques (Macaca mulatta) to explore their divergent transcriptome profiles. RESULTS Results showed that brain development could be divided into two stages, with a demarcation date in a range between 25 and 26 postconception weeks (PCW) for humans and 17-23PCWfor rhesus macaques, rather than birth time that have been widely used as a uniform demarcation time of neurodevelopment across species. Dynamic network biomarker (DNB) analysis revealed that the two demarcation dates were transition phases during brain development, after which the brain transcriptome profiles underwent critical transitions characterized by highly fluctuating DNB molecules. We also found that changes between early and later brain developmental stages (as defined by the demarcation points) were substantially greater in the human brain than in the macaque brain. To explore the molecular mechanism underlying prolonged timing during early human brain development, we carried out expression heterochrony tests. Results demonstrated that compared to macaques, more heterochronic genes exhibited neoteny during early human brain development, consistent with the delayed demarcation time in the human lineage, and proving that neoteny in human brain development could be traced to the prenatal period. We further constructed transcriptional networks to explore the profile of early human brain development and identified the hub gene RBFOX1 as playing an important role in regulating early brain development. We also found RBFOX1 evolved rapidly in its non-coding regions, indicating that this gene played an important role in human brain evolution. Our findings provide evidence that RBFOX1 is a likely key hub gene in early human brain development and evolution. CONCLUSIONS By comparing gene expression profiles between humans and macaques, we found divergent expression trajectories between the two species, which deepens our understanding of the evolution of the human brain.
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Affiliation(s)
- Ming-Li Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Hui Tang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Hai-Bo Xu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - David M Irwin
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, Ontario, M5G 2C4, Canada
| | - Adeniyi C Adeola
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Tao Zeng
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
| | - Yan Li
- State Key Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming, 650091, Yunnan, China.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
- Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
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47
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Lundin A, Ricchiuto P, Clausen M, Hicks R, Falk A, Herland A. hiPS-Derived Astroglia Model Shows Temporal Transcriptomic Profile Related to Human Neural Development and Glia Competence Acquisition of a Maturing Astrocytic Identity. ACTA ACUST UNITED AC 2020; 4:e1900226. [PMID: 32402123 DOI: 10.1002/adbi.201900226] [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: 09/18/2019] [Revised: 03/02/2020] [Accepted: 03/05/2020] [Indexed: 11/10/2022]
Abstract
Astrocyte biology has a functional and cellular diversity only observed in humans. The understanding of the regulatory network governing outer radial glia (RG), responsible for the expansion of the outer subventricular zone (oSVZ), and astrocyte cellular development remains elusive, partly since relevant human material to study these features is not readily available. A human-induced pluripotent stem cell derived astrocytic model, NES-Astro, has been recently developed, with high expression of astrocyte-associated markers and high astrocyte-relevant functionality. Here it is studied how the NES-Astro phenotype develops during specification and its correlation to known RG and astrocyte characteristics in human brain development. It is demonstrated that directed differentiation of neurogenic long-term neuroepithelial stem cells undergo a neurogenic-to-gliogenic competence preferential change, acquiring a glial fate. Temporal transcript profiles of long- and small RNA corroborate previously shown neurogenic restriction by glia-associated let-7 expression. Furthermore, NES-Astro differentiation displays proposed mechanistic features important for the evolutionary expansion of the oSVZ together with an astroglia/astrocyte transcriptome. The NES-Astro generation is a straight-forward differentiation protocol from stable and expandable neuroepithelial stem cell lines derived from iPS cells. Thus, the NES-Astro is an easy-access cell system with high biological relevance for studies of mechanistic traits of glia and astrocyte.
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Affiliation(s)
- Anders Lundin
- Translational Genomics, BioPharmaceuticals R&D, Discovery Sciences, AstraZeneca, Gothenburg, Pepparedsleden 1, Mölndal, 431 83, Sweden
| | - Piero Ricchiuto
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Darwin Building, 310 Milton Rd, Cambridge, CB4 0WG, UK
| | - Maryam Clausen
- Translational Genomics, BioPharmaceuticals R&D, Discovery Sciences, AstraZeneca, Gothenburg, Pepparedsleden 1, Mölndal, 431 83, Sweden
| | - Ryan Hicks
- Translational Genomics, BioPharmaceuticals R&D, Discovery Sciences, AstraZeneca, Gothenburg, Pepparedsleden 1, Mölndal, 431 83, Sweden
| | - Anna Falk
- Department of Neuroscience, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Anna Herland
- Department of Neuroscience, Karolinska Institutet, Stockholm, 17177, Sweden.,Division of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, 10044, Sweden.,AIMES, Center for the Advancement of Integrated Medical and Engineering Sciences, Karolinska Institutet, Stockholm, 17177, Sweden
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48
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Mostajo-Radji MA, Schmitz MT, Montoya ST, Pollen AA. Reverse engineering human brain evolution using organoid models. Brain Res 2020; 1729:146582. [PMID: 31809699 PMCID: PMC7058376 DOI: 10.1016/j.brainres.2019.146582] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 11/25/2019] [Accepted: 11/29/2019] [Indexed: 02/06/2023]
Abstract
Primate brains vary dramatically in size and organization, but the genetic and developmental basis for these differences has been difficult to study due to lack of experimental models. Pluripotent stem cells and brain organoids provide a potential opportunity for comparative and functional studies of evolutionary differences, particularly during the early stages of neurogenesis. However, many challenges remain, including isolating stem cell lines from additional great ape individuals and species to capture the breadth of ape genetic diversity, improving the reproducibility of organoid models to study evolved differences in cell composition and combining multiple brain regions to capture connectivity relationships. Here, we describe strategies for identifying evolved developmental differences between humans and non-human primates and for isolating the underlying cellular and genetic mechanisms using comparative analyses, chimeric organoid culture, and genome engineering. In particular, we focus on how organoid models could ultimately be applied beyond studies of progenitor cell evolution to decode the origin of recent changes in cellular organization, connectivity patterns, myelination, synaptic development, and physiology that have been implicated in human cognition.
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Affiliation(s)
- Mohammed A Mostajo-Radji
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
| | - Matthew T Schmitz
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
| | - Sebastian Torres Montoya
- Health Co-creation Laboratory, Medellin General Hospital, Medellin, Antioquia, Colombia; Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alex A Pollen
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA; The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA.
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49
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Wang W, Wang GZ. Twin-peak temporal regulation during human neocortical development. Cell Discov 2019; 5:61. [PMID: 31871735 PMCID: PMC6915741 DOI: 10.1038/s41421-019-0129-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 09/11/2019] [Indexed: 01/05/2023] Open
Abstract
Understanding the temporal and spatial expression patterns of the human cerebral cortex is essential for expanding knowledge of its functionality. Previous analysis focused on the differentially expressed genes (DEGs) among cortical subregions revealed an hourglass model for interareal differences. However, the overall pattern of transcriptional differences during the development of every region remains to be fully explored. Here, analysing more than 800 neocortex samples from lifespan transcriptional profiles revealed that excitatory neurons are more regulated than inhibitory neurons in the foetal brain. Developmental DEGs tend to be resting state or memory encoding-related and are also involved in autism and Alzheimer’s disease. In addition, twin peaks of DEGs occur during the development of each neocortex region, with a first peak appearing in the perinatal period and an unexpected second peak appearing around childhood. Genes in these peaks have similar functions, but the second peak is more inhibitory neuron related. All these results emphasize the significance of this unique temporal regulatory pattern for human neocortical development.
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Affiliation(s)
- Wei Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
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50
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Ciesielski KTR, Stern ME, Diamond A, Khan S, Busa EA, Goldsmith TE, van der Kouwe A, Fischl B, Rosen BR. Maturational Changes in Human Dorsal and Ventral Visual Networks. Cereb Cortex 2019; 29:5131-5149. [PMID: 30927361 DOI: 10.1093/cercor/bhz053] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/26/2018] [Indexed: 11/14/2022] Open
Abstract
Developmental neuroimaging studies report the emergence of increasingly diverse cognitive functions as closely entangled with a rise-fall modulation of cortical thickness (CTh), structural cortical and white-matter connectivity, and a time-course for the experience-dependent selective elimination of the overproduced synapses. We examine which of two visual processing networks, the dorsal (DVN; prefrontal, parietal nodes) or ventral (VVN; frontal-temporal, fusiform nodes) matures first, thus leading the neuro-cognitive developmental trajectory. Three age-dependent measures are reported: (i) the CTh at network nodes; (ii) the matrix of intra-network structural connectivity (edges); and (iii) the proficiency in network-related neuropsychological tests. Typically developing children (age ~6 years), adolescents (~11 years), and adults (~21 years) were tested using multiple-acquisition structural T1-weighted magnetic resonance imaging (MRI) and neuropsychology. MRI images reconstructed into a gray/white/pial matter boundary model were used for CTh evaluation. No significant group differences in CTh and in the matrix of edges were found for DVN (except for the left prefrontal), but a significantly thicker cortex in children for VVN with reduced prefrontal ventral-fusiform connectivity and with an abundance of connections in adolescents. The higher performance in children on tests related to DVN corroborates the age-dependent MRI structural connectivity findings. The current findings are consistent with an earlier maturational course of DVN.
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Affiliation(s)
- Kristina T R Ciesielski
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA.,Pediatric Neuroscience Laboratory, Department of Psychology, Psychology Clinical Neuroscience Center, University of New Mexico, Logan Hall, Albuquerque NM 87131, USA
| | - Moriah E Stern
- Pediatric Neuroscience Laboratory, Department of Psychology, Psychology Clinical Neuroscience Center, University of New Mexico, Logan Hall, Albuquerque NM 87131, USA
| | - Adele Diamond
- Department of Psychiatry, University of British Columbia, Vancouver BC V6T2A1, Canada
| | - Sheraz Khan
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA.,Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Evelina A Busa
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA
| | - Timothy E Goldsmith
- Pediatric Neuroscience Laboratory, Department of Psychology, Psychology Clinical Neuroscience Center, University of New Mexico, Logan Hall, Albuquerque NM 87131, USA
| | - Andre van der Kouwe
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA
| | - Bruce Fischl
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA.,Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Bruce R Rosen
- Department of Radiology, MGH/MIT/HMS A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA.,Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, MA 02139, USA
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