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Zintel TM, Ely JJ, Raghanti MA, Hopkins WD, Hof PR, Sherwood CC, Kamilar JM, Bauernfeind AL, Babbitt CC. Ecological Trait Differences Are Associated with Gene Expression in the Primary Visual Cortex of Primates. Genes (Basel) 2025; 16:117. [PMID: 40004446 PMCID: PMC11855002 DOI: 10.3390/genes16020117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Revised: 01/10/2025] [Accepted: 01/14/2025] [Indexed: 02/27/2025] Open
Abstract
Primate species differ drastically from most other mammals in how they visually perceive their environments, which is particularly important for foraging, predator avoidance, and detection of social cues. BACKGROUND/OBJECTIVES Although it is well established that primates display diversity in color vision and various ecological specializations, it is not understood how visual system characteristics and ecological adaptations may be associated with gene expression levels within the primary visual cortex (V1). METHODS We performed RNA-Seq on V1 tissue samples from 28 individuals, representing 13 species of primates, including hominoids, cercopithecoids, and platyrrhines. We explored trait-dependent differential expression (DE) by contrasting species with differing visual system phenotypes and ecological traits. RESULTS Between 4-25% of genes were determined to be differentially expressed in primates that varied in type of color vision (trichromatic or polymorphic di/trichromatic), habitat use (arboreal or terrestrial), group size (large or small), and primary diet (frugivorous, folivorous, or omnivorous). CONCLUSIONS Interestingly, our DE analyses revealed that humans and chimpanzees showed the most marked differences between any two species, even though they are only separated by 6-8 million years of independent evolution. These results show a combination of species-specific and trait-dependent differences in the evolution of gene expression in the primate visual cortex.
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Affiliation(s)
- Trisha M. Zintel
- Department of Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA;
| | | | - Mary Ann Raghanti
- Department of Anthropology, Kent State University, Kent, OH 44242, USA;
| | - William D. Hopkins
- Keeling Center for Comparative Medicine and Research, The University of Texas, MD Anderson Cancer Center, Bastrop, TX 78602, USA;
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
- New York Consortium in Evolutionary Primatology, New York, NY 10065, USA
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC 20052, USA;
| | - Jason M. Kamilar
- Department of Anthropology, University of Massachusetts Amherst, Amherst, MA 01003, USA;
- Organismic and Evolutionary Biology Graduate Program, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Amy L. Bauernfeind
- Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA;
- Department of Anthropology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Courtney C. Babbitt
- Department of Biology, University of Massachusetts Amherst, Amherst, MA 01003, USA;
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Jeon H, Kim J, Kim J, Choi YK, Ho CLA, Pifferi F, Huber D, Feng L, Kim J. eLemur: A cellular-resolution 3D atlas of the mouse lemur brain. Proc Natl Acad Sci U S A 2024; 121:e2413687121. [PMID: 39630862 DOI: 10.1073/pnas.2413687121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Accepted: 11/04/2024] [Indexed: 12/07/2024] Open
Abstract
The gray mouse lemur (Microcebus murinus), one of the smallest living primates, emerges as a promising model organism for neuroscience research. This is due to its genetic similarity to humans, its evolutionary position between rodents and humans, and its primate-like features encapsulated within a rodent-sized brain. Despite its potential, the absence of a comprehensive reference brain atlas impedes the progress of research endeavors in this species, particularly at the microscopic level. Existing references have largely been confined to the macroscopic scale, lacking detailed anatomical information. Here, we present eLemur, a unique resource, comprising a repository of high-resolution brain-wide images immunostained with multiple cell type and structural markers, elucidating the cyto- and chemoarchitecture of the mouse lemur brain. Additionally, it encompasses a segmented two-dimensional reference and 3D anatomical brain atlas delineated into cortical, subcortical, and other vital regions. Furthermore, eLemur includes a comprehensive 3D cell atlas, providing densities and spatial distributions of non-neuronal and neuronal cells across the mouse lemur brain. Accessible via a web-based viewer (https://eeum-brain.com/#/lemurdatasets), the eLemur resource streamlines data sharing and integration, fostering the exploration of different hypotheses and experimental designs using the mouse lemur as a model organism. Moreover, in conjunction with the growing 3D datasets for rodents, nonhuman primates, and humans, our eLemur 3D digital framework enhances the potential for comparative analysis and translation research, facilitating the integration of extensive rodent study data into human studies.
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Affiliation(s)
- Hyungju Jeon
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Jiwon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Division of Bio-Medical Science & Technology, Korea Institute of Science and Technology-School, University of Science and Technology, Seoul 02792, South Korea
| | - Jayoung Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Division of Bio-Medical Science & Technology, Korea Institute of Science and Technology-School, University of Science and Technology, Seoul 02792, South Korea
| | - Yoon Kyoung Choi
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Department of Computer Science and Engineering, Korea University, Seoul 02841, South Korea
| | - Chun Lum Andy Ho
- Department of Basic Neurosciences, University of Geneva, Geneva 1205, Switzerland
| | - Fabien Pifferi
- Musée National d'Histoire Naturelle, Adaptive Mechanisms and Evolution, UMR7179-CNRS, Paris 75005, France
| | - Daniel Huber
- Department of Basic Neurosciences, University of Geneva, Geneva 1205, Switzerland
| | - Linqing Feng
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Jinhyun Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Division of Bio-Medical Science & Technology, Korea Institute of Science and Technology-School, University of Science and Technology, Seoul 02792, South Korea
- Department of Computer Science and Engineering, Korea University, Seoul 02841, South Korea
- Korea Institute of Science and Technology-Sungkyunkwan University Brain Research Center, Sungkyunkwan University Institute for Convergence, Sungkyunkwan University, Suwon 16419, South Korea
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3
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Qu J, Zhu R, Wu Y, Xu G, Wang D. Abnormal structural‒functional coupling patterning in progressive supranuclear palsy is associated with diverse gradients and histological features. Commun Biol 2024; 7:1195. [PMID: 39341965 PMCID: PMC11439051 DOI: 10.1038/s42003-024-06877-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024] Open
Abstract
The anatomy of the brain supports inherent processes, fostering mental abilities and eventually facilitating adaptive behavior. Recent studies have shown that progressive supranuclear palsy (PSP) is accompanied by alterations in functional and structural networks. However, how the structure and function of PSP coordinates change is not clear, and the relationships between structural‒functional coupling (SFC) and the gradient of hierarchical structure and cellular histology remain largely unknown. Here, we use neuroimaging data from two independent cohorts and a public histological dataset to investigate the relationships among the cellular histology, hierarchical structure, and SFC of PSP patients. We find that the SFC of the entire cortex in PSP is severely disrupted, with higher coupling in the visual network (VN). Moreover, coupling differences in PSP follow a macroscopic organizational principle from unimodal to transmodal gradients. Finally, we elucidate greater laminar differentiation in VN regions sensitive to SFC changes in PSP, which is related mainly to the higher cellular density and smaller size of the internal-granular layer. In conclusion, our findings provide an interpretable framework for understanding SFC changes in PSP and provide new insights into the consistency of structural and functional changes in PSP regarding hierarchical structure and cellular histology.
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Affiliation(s)
- Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China.
- Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Jinan, China.
- Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Jinan, China.
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4
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Zhang Z, Huang Y, Chen X, Li J, Yang Y, Lv L, Wang J, Wang M, Wang Y, Wang Z. State-specific Regulation of Electrical Stimulation in the Intralaminar Thalamus of Macaque Monkeys: Network and Transcriptional Insights into Arousal. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402718. [PMID: 38938001 PMCID: PMC11434125 DOI: 10.1002/advs.202402718] [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: 03/15/2024] [Revised: 06/03/2024] [Indexed: 06/29/2024]
Abstract
Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging (EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.
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Affiliation(s)
- Zhao Zhang
- Department of Anesthesiology, Huashan Hospital, Fudan University, 12 Urumqi Middle Rd, Jing'an District, Shanghai, 200040, China
| | - Yichun Huang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, State Key Laboratory of General Artificial Intelligence, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, 5 Yiheyuan Rd, Haidian District, Beijing, 100871, China
| | - Xiaoyu Chen
- Institute of Natural Sciences and School of Mathematical Sciences, Shanghai Jiao Tong University, 800 Dongchuan RD, Minhang District, Shanghai, 200240, China
| | - Jiahui Li
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, State Key Laboratory of General Artificial Intelligence, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, 5 Yiheyuan Rd, Haidian District, Beijing, 100871, China
| | - Yi Yang
- Department of Neurosurgery, Brain Computer Interface Transition Research Center, Beijing Tiantan Hospital, Capital Medical University, 119 South Fourth Ring Rd West, Fengtai District, Beijing, 100070, China
| | - Longbao Lv
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, 32 East of Jiaochang Rd, Kunming, Yunnan, 650223, China
| | - Jianhong Wang
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, 32 East of Jiaochang Rd, Kunming, Yunnan, 650223, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, 450003, China
| | - Yingwei Wang
- Department of Anesthesiology, Huashan Hospital, Fudan University, 12 Urumqi Middle Rd, Jing'an District, Shanghai, 200040, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, State Key Laboratory of General Artificial Intelligence, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, 5 Yiheyuan Rd, Haidian District, Beijing, 100871, China
- School of Biomedical Engineering, Hainan University, 58 Renmin Avenue, Haikou, Hainan, 570228, China
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5
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Ning C, Wu X, Zhao X, Lu Z, Yao X, Zhou T, Yi L, Sun Y, Wu S, Liu Z, Huang X, Gao L, Liu J. Epigenomic landscapes during prefrontal cortex development and aging in rhesus. Natl Sci Rev 2024; 11:nwae213. [PMID: 39183748 PMCID: PMC11342245 DOI: 10.1093/nsr/nwae213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 06/07/2024] [Accepted: 06/09/2024] [Indexed: 08/27/2024] Open
Abstract
The prefrontal cortex (PFC) is essential for higher-level cognitive functions. How epigenetic dynamics participates in PFC development and aging is largely unknown. Here, we profiled epigenomic landscapes of rhesus monkey PFCs from prenatal to aging stages. The dynamics of chromatin states, including higher-order chromatin structure, chromatin interaction and histone modifications are coordinated to regulate stage-specific gene transcription, participating in distinct processes of neurodevelopment. Dramatic changes of epigenetic signals occur around the birth stage. Notably, genes involved in neuronal cell differentiation and layer specification are pre-configured by bivalent promoters. We identified a cis-regulatory module and the transcription factors (TFs) associated with basal radial glia development, which was associated with large brain size in primates. These TFs include GLI3, CREB5 and SOX9. Interestingly, the genes associated with the basal radial glia (bRG)-associated cis-element module, such as SRY and SOX9, are enriched in sex differentiation. Schizophrenia-associated single nucleotide polymorphisms are more enriched in super enhancers (SEs) than typical enhancers, suggesting that SEs play an important role in neural network wiring. A cis-regulatory element of DBN1 is identified, which is critical for neuronal cell proliferation and synaptic neuron differentiation. Notably, the loss of distal chromatin interaction and H3K27me3 signal are hallmarks of PFC aging, which are associated with abnormal expression of aging-related genes and transposon activation, respectively. Collectively, our findings shed light on epigenetic mechanisms underlying primate brain development and aging.
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Affiliation(s)
- Chao Ning
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Wu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Division of HIV/AIDS and Sex-Transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), State Key Laboratory of Drug Regulatory Science, Beijing 102629, China
| | - Xudong Zhao
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Zongyang Lu
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Xuelong Yao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- GuangzhouNvwa Life Technology Co., Ltd, Guangzhou 510535, China
| | - Tao Zhou
- Shenzhen Neher Neural Plasticity Laboratory, CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lizhi Yi
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyu Sun
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuaishuai Wu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhenbo Liu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xingxu Huang
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Lei Gao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiang Liu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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6
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Ding SL. Lamination, Borders, and Thalamic Projections of the Primary Visual Cortex in Human, Non-Human Primate, and Rodent Brains. Brain Sci 2024; 14:372. [PMID: 38672021 PMCID: PMC11048015 DOI: 10.3390/brainsci14040372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
The primary visual cortex (V1) is one of the most studied regions of the brain and is characterized by its specialized and laminated layer 4 in human and non-human primates. However, studies aiming to harmonize the definition of the cortical layers and borders of V1 across rodents and primates are very limited. This article attempts to identify and harmonize the molecular markers and connectional patterns that can consistently link corresponding cortical layers of V1 and borders across mammalian species and ages. V1 in primates has at least two additional and unique layers (L3b2 and L3c) and two sublayers of layer 4 (L4a and L4b) compared to rodent V1. In all species examined, layers 4 and 3b of V1 receive strong inputs from the (dorsal) lateral geniculate nucleus, and V1 is mostly surrounded by the secondary visual cortex except for one location where V1 directly abuts area prostriata. The borders of primate V1 can also be clearly identified at mid-gestational ages using gene markers. In rodents, a novel posteromedial extension of V1 is identified, which expresses V1 marker genes and receives strong inputs from the lateral geniculate nucleus. This V1 extension was labeled as the posterior retrosplenial cortex and medial secondary visual cortex in the literature and brain atlases. Layer 6 of the rodent and primate V1 originates corticothalamic projections to the lateral geniculate, lateral dorsal, and reticular thalamic nuclei and the lateroposterior-pulvinar complex with topographic organization. Finally, the direct geniculo-extrastriate (particularly the strong geniculo-prostriata) projections are probably major contributors to blindsight after V1 lesions. Taken together, compared to rodents, primates, and humans, V1 has at least two unique middle layers, while other layers are comparable across species and display conserved molecular markers and similar connections with the visual thalamus with only subtle differences.
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Affiliation(s)
- Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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7
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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8
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Pressl C, Mätlik K, Kus L, Darnell P, Luo JD, Paul MR, Weiss AR, Liguore W, Carroll TS, Davis DA, McBride J, Heintz N. Selective vulnerability of layer 5a corticostriatal neurons in Huntington's disease. Neuron 2024; 112:924-941.e10. [PMID: 38237588 DOI: 10.1016/j.neuron.2023.12.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/18/2023] [Accepted: 12/13/2023] [Indexed: 01/30/2024]
Abstract
The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here, we employed serial fluorescence-activated nuclear sorting (sFANS), deep molecular profiling, and single-nucleus RNA sequencing (snRNA-seq) of motor-cortex samples from thirteen predominantly early stage, clinically diagnosed HD donors and selected samples from cingulate, visual, insular, and prefrontal cortices to demonstrate loss of layer 5a pyramidal neurons in HD. Extensive mHTT CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layers 6a and 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in HD cerebral cortex.
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Affiliation(s)
- Christina Pressl
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Kert Mätlik
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Laura Kus
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Paul Darnell
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Ji-Dung Luo
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - Matthew R Paul
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - Alison R Weiss
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - William Liguore
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Thomas S Carroll
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - David A Davis
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jodi McBride
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Nathaniel Heintz
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA.
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9
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Günther DM, Batiuk MY, Petukhov V, De Oliveira R, Wunderle T, Buchholz CJ, Fries P, Khodosevich K. Heterogeneity of layer 4 in visual areas of rhesus macaque cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.11.584345. [PMID: 38559123 PMCID: PMC10979896 DOI: 10.1101/2024.03.11.584345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Recently, single-cell RNA-sequencing (scRNA-seq) has enabled unprecedented insights to the cellular landscape of the brains of many different species, among them the rhesus macaque as a key animal model. Building on previous, broader surveys of the macaque brain, we closely examined five immediately neighboring areas within the visual cortex of the rhesus macaque: V1, V2, V4, MT and TEO. To facilitate this, we first devised a novel pipeline for brain spatial archive - the BrainSPACE - which enabled robust archiving and sampling from the whole unfixed brain. SnRNA-sequencing of ~100,000 nuclei from visual areas V1 and V4 revealed conservation within the GABAergic neuron subtypes, while seven and one distinct principle neuron subtypes were detected in V1 and V4, respectively, all most likely located in layer 4. Moreover, using small molecule fluorescence in situ hybridization, we identified cell type density gradients across V1, V2, V4, MT, and TEO appearing to reflect the visual hierarchy. These findings demonstrate an association between the clear areal specializations among neighboring areas with the hierarchical levels within the visual cortex of the rhesus macaque.
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Affiliation(s)
- Dorothee M. Günther
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN Nijmegen, the Netherlands
- Molecular Biotechnology and Gene Therapy, Paul-Ehrlich-Institut, 63225 Langen, Germany
| | - Mykhailo Y. Batiuk
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Viktor Petukhov
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Romain De Oliveira
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Thomas Wunderle
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Christian J. Buchholz
- Molecular Biotechnology and Gene Therapy, Paul-Ehrlich-Institut, 63225 Langen, Germany
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EN Nijmegen, the Netherlands
| | - Konstantin Khodosevich
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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10
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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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11
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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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12
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Kenwood MM, Souaiaia T, Kovner R, Fox AS, French DA, Oler JA, Roseboom PH, Riedel MK, Mueller SAL, Kalin NH. Gene expression in the primate orbitofrontal cortex related to anxious temperament. Proc Natl Acad Sci U S A 2023; 120:e2305775120. [PMID: 38011550 PMCID: PMC10710052 DOI: 10.1073/pnas.2305775120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/13/2023] [Indexed: 11/29/2023] Open
Abstract
Anxiety disorders are among the most prevalent psychiatric disorders, causing significant suffering and disability. Relative to other psychiatric disorders, anxiety disorders tend to emerge early in life, supporting the importance of developmental mechanisms in their emergence and maintenance. Behavioral inhibition (BI) is a temperament that emerges early in life and, when stable and extreme, is linked to an increased risk for the later development of anxiety disorders and other stress-related psychopathology. Understanding the neural systems and molecular mechanisms underlying this dispositional risk could provide insight into treatment targets for anxiety disorders. Nonhuman primates (NHPs) have an anxiety-related temperament, called anxious temperament (AT), that is remarkably similar to BI in humans, facilitating the design of highly translational models for studying the early risk for stress-related psychopathology. Because of the recent evolutionary divergence between humans and NHPs, many of the anxiety-related brain regions that contribute to psychopathology are highly similar in terms of their structure and function, particularly with respect to the prefrontal cortex. The orbitofrontal cortex plays a critical role in the flexible encoding and regulation of threat responses, in part through connections with subcortical structures like the amygdala. Here, we explore individual differences in the transcriptional profile of cells within the region, using laser capture microdissection and single nuclear sequencing, providing insight into the molecules underlying individual differences in AT-related function of the pOFC, with a particular focus on previously implicated cellular systems, including neurotrophins and glucocorticoid signaling.
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Affiliation(s)
- Margaux M. Kenwood
- Neuroscience Training Program, University of Wisconsin, Madison, WI53705
- Department of Psychiatry, University of Wisconsin, Madison, WI53719
| | - Tade Souaiaia
- Department of Cell Biology, State University of New York Downstate, New York, NY11228
| | - Rothem Kovner
- Yale School of Medicine, Yale University, New Haven, CT06510
| | - Andrew S. Fox
- Department of Psychology and California National Primate Research Center, University of California, Davis, CA95616
| | | | - Jonathan A. Oler
- Department of Psychiatry, University of Wisconsin, Madison, WI53719
| | | | | | | | - Ned H. Kalin
- Neuroscience Training Program, University of Wisconsin, Madison, WI53705
- Department of Psychiatry, University of Wisconsin, Madison, WI53719
- Wisconsin National Primate Research Center, University of Wisconsin, Madison, WI53715
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13
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Pressl C, Mätlik K, Kus L, Darnell P, Luo JD, Paul MR, Weiss AR, Liguore W, Carroll TS, Davis DA, McBride J, Heintz N. Selective Vulnerability of Layer 5a Corticostriatal Neurons in Huntington's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538096. [PMID: 37162977 PMCID: PMC10168234 DOI: 10.1101/2023.04.24.538096] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here we employed serial fluorescence activated nuclear sorting (sFANS), deep molecular profiling, and single nucleus RNA sequencing (snRNAseq) to demonstrate that layer 5a pyramidal neurons are vulnerable in primary motor cortex and other cortical areas of HD donors. Extensive mHTT -CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layer 6a, layer 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify the vulnerable layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT -CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in the HD cerebral cortex.
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14
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Jorstad NL, Close J, Johansen N, Yanny AM, Barkan ER, Travaglini KJ, Bertagnolli D, Campos J, Casper T, Crichton K, Dee N, Ding SL, Gelfand E, Goldy J, Hirschstein D, Kroll M, Kunst M, Lathia K, Long B, Martin N, McMillen D, Pham T, Rimorin C, Ruiz A, Shapovalova N, Shehata S, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Ward K, Callaway EM, Hof PR, Keene CD, Levi BP, Linnarsson S, Mitra PP, Smith K, Hodge RD, Bakken TE, Lein ES. Transcriptomic cytoarchitecture reveals principles of human neocortex organization. Science 2023; 382:eadf6812. [PMID: 37824655 PMCID: PMC11687949 DOI: 10.1126/science.adf6812] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
Variation in cytoarchitecture is the basis for the histological definition of cortical areas. We used single cell transcriptomics and performed cellular characterization of the human cortex to better understand cortical areal specialization. Single-nucleus RNA-sequencing of 8 areas spanning cortical structural variation showed a highly consistent cellular makeup for 24 cell subclasses. However, proportions of excitatory neuron subclasses varied substantially, likely reflecting differences in connectivity across primary sensorimotor and association cortices. Laminar organization of astrocytes and oligodendrocytes also differed across areas. Primary visual cortex showed characteristic organization with major changes in the excitatory to inhibitory neuron ratio, expansion of layer 4 excitatory neurons, and specialized inhibitory neurons. These results lay the groundwork for a refined cellular and molecular characterization of human cortical cytoarchitecture and areal specialization.
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Affiliation(s)
| | - Jennie Close
- Allen Institute for Brain Science; Seattle, WA, 98109
| | | | | | | | | | | | - Jazmin Campos
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Tamara Casper
- Allen Institute for Brain Science; Seattle, WA, 98109
| | | | - Nick Dee
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Song-Lin Ding
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Emily Gelfand
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Jeff Goldy
- Allen Institute for Brain Science; Seattle, WA, 98109
| | | | - Matthew Kroll
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Michael Kunst
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Kanan Lathia
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Brian Long
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Naomi Martin
- Allen Institute for Brain Science; Seattle, WA, 98109
| | | | | | | | - Augustin Ruiz
- Allen Institute for Brain Science; Seattle, WA, 98109
| | | | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; Stockholm, Sweden, 171 77
| | | | - Josef Sulc
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Michael Tieu
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Amy Torkelson
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Herman Tung
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Katelyn Ward
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Edward M. Callaway
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, 92037
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington; Seattle, WA, 98195
| | - Boaz P. Levi
- Allen Institute for Brain Science; Seattle, WA, 98109
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet; Stockholm, Sweden, 171 77
| | - Partha P. Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, 11724
| | | | | | | | - Ed S. Lein
- Allen Institute for Brain Science; Seattle, WA, 98109
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15
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Benjamin AS, Kording KP. A role for cortical interneurons as adversarial discriminators. PLoS Comput Biol 2023; 19:e1011484. [PMID: 37768890 PMCID: PMC10538760 DOI: 10.1371/journal.pcbi.1011484] [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: 06/29/2022] [Accepted: 08/31/2023] [Indexed: 09/30/2023] Open
Abstract
The brain learns representations of sensory information from experience, but the algorithms by which it does so remain unknown. One popular theory formalizes representations as inferred factors in a generative model of sensory stimuli, meaning that learning must improve this generative model and inference procedure. This framework underlies many classic computational theories of sensory learning, such as Boltzmann machines, the Wake/Sleep algorithm, and a more recent proposal that the brain learns with an adversarial algorithm that compares waking and dreaming activity. However, in order for such theories to provide insights into the cellular mechanisms of sensory learning, they must be first linked to the cell types in the brain that mediate them. In this study, we examine whether a subtype of cortical interneurons might mediate sensory learning by serving as discriminators, a crucial component in an adversarial algorithm for representation learning. We describe how such interneurons would be characterized by a plasticity rule that switches from Hebbian plasticity during waking states to anti-Hebbian plasticity in dreaming states. Evaluating the computational advantages and disadvantages of this algorithm, we find that it excels at learning representations in networks with recurrent connections but scales poorly with network size. This limitation can be partially addressed if the network also oscillates between evoked activity and generative samples on faster timescales. Consequently, we propose that an adversarial algorithm with interneurons as discriminators is a plausible and testable strategy for sensory learning in biological systems.
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Affiliation(s)
- Ari S. Benjamin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Konrad P. Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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16
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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: 15] [Impact Index Per Article: 7.5] [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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17
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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, et alChen 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] [Show More Authors] [Citation(s) in RCA: 86] [Impact Index Per Article: 43.0] [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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18
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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: 2.5] [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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19
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Bo T, Li J, Hu G, Zhang G, Wang W, Lv Q, Zhao S, Ma J, Qin M, Yao X, Wang M, Wang GZ, Wang Z. Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys. Nat Commun 2023; 14:1499. [PMID: 36932104 PMCID: PMC10023667 DOI: 10.1038/s41467-023-37246-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 03/06/2023] [Indexed: 03/19/2023] Open
Abstract
Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.
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Affiliation(s)
- Tingting Bo
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ganlu Hu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
| | - Ge Zhang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, 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
| | - Qian Lv
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Shaoling Zhao
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junjie Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Meng Qin
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, Shandong, China
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China.
| | - Guang-Zhong 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.
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- School of Biomedical Engineering, Hainan University, Haikou, Hainan, China.
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20
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Makowski C, Wang H, Srinivasan A, Qi A, Qiu Y, van der Meer D, Frei O, Zou J, Visscher P, Yang J, Chen CH. Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness. Proc Natl Acad Sci U S A 2023; 120:e2214834120. [PMID: 36893272 PMCID: PMC10089183 DOI: 10.1073/pnas.2214834120] [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: 08/30/2022] [Accepted: 01/18/2023] [Indexed: 03/11/2023] Open
Abstract
Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Hao Wang
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anjali Srinivasan
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anna Qi
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Yuqi Qiu
- School of Statistics, East China Normal University, Shanghai20050, China
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Jingjing Zou
- Division of Biostatistics and Bioinformatics, University of California San Diego, La Jolla, CA92093
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang310024, China
| | - Chi-Hua Chen
- Department of Radiology, University of California San Diego, La Jolla, CA92093
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21
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Arion D, Enwright JF, Gonzalez-Burgos G, Lewis DA. Differential gene expression between callosal and ipsilateral projection neurons in the monkey dorsolateral prefrontal and posterior parietal cortices. Cereb Cortex 2023; 33:1581-1594. [PMID: 35441221 PMCID: PMC9977376 DOI: 10.1093/cercor/bhac157] [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: 02/17/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/14/2022] Open
Abstract
Reciprocal connections between primate dorsolateral prefrontal (DLPFC) and posterior parietal (PPC) cortices, furnished by subsets of layer 3 pyramidal neurons (PNs), contribute to cognitive processes including working memory (WM). A different subset of layer 3 PNs in each region projects to the homotopic region of the contralateral hemisphere. These ipsilateral (IP) and callosal (CP) projections, respectively, appear to be essential for the maintenance and transfer of information during WM. To determine if IP and CP layer 3 PNs in each region differ in their transcriptomes, fluorescent retrograde tracers were used to label IP and CP layer 3 PNs in the DLPFC and PPC from macaque monkeys. Retrogradely-labeled PNs were captured by laser microdissection and analyzed by RNAseq. Numerous differentially expressed genes (DEGs) were detected between IP and CP neurons in each region and the functional pathways containing many of these DEGs were shared across regions. However, DLPFC and PPC displayed opposite patterns of DEG enrichment between IP and CP neurons. Cross-region analyses indicated that the cortical area targeted by IP or CP layer 3 PNs was a strong correlate of their transcriptome profile. These findings suggest that the transcriptomes of layer 3 PNs reflect regional, projection type and target region specificity.
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Affiliation(s)
- Dominique Arion
- Department of Psychiatry and Neuroscience, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15213, United States
| | - John F Enwright
- Department of Psychiatry and Neuroscience, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15213, United States
| | - Guillermo Gonzalez-Burgos
- Department of Psychiatry and Neuroscience, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15213, United States
| | - David A Lewis
- Department of Psychiatry and Neuroscience, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15213, United States.,Department of Neuroscience, University of Pittsburgh, A210 Langley Hall. Pittsburgh, PA 15260, United States
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22
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Khodosevich K, Sellgren CM. Neurodevelopmental disorders-high-resolution rethinking of disease modeling. Mol Psychiatry 2023; 28:34-43. [PMID: 36434058 PMCID: PMC9812768 DOI: 10.1038/s41380-022-01876-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022]
Abstract
Neurodevelopmental disorders arise due to various risk factors that can perturb different stages of brain development, and a combinatorial impact of these risk factors programs the phenotype in adulthood. While modeling the complete phenotype of a neurodevelopmental disorder is challenging, individual developmental perturbations can be successfully modeled in vivo in animals and in vitro in human cellular models. Nevertheless, our limited knowledge of human brain development restricts modeling strategies and has raised questions of how well a model corresponds to human in vivo brain development. Recent progress in high-resolution analysis of human tissue with single-cell and spatial omics techniques has enhanced our understanding of the complex events that govern the development of the human brain in health and disease. This new knowledge can be utilized to improve modeling of neurodevelopmental disorders and pave the way to more accurately portraying the relevant developmental perturbations in disease models.
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Affiliation(s)
- Konstantin Khodosevich
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
| | - Carl M Sellgren
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm Health Care Services, Stockholm County Council, Karolinska Institutet, Stockholm, Sweden.
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
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23
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Wei JR, Hao ZZ, Xu C, Huang M, Tang L, Xu N, Liu R, Shen Y, Teichmann SA, Miao Z, Liu S. Identification of visual cortex cell types and species differences using single-cell RNA sequencing. Nat Commun 2022; 13:6902. [PMID: 36371428 PMCID: PMC9653448 DOI: 10.1038/s41467-022-34590-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
The primate neocortex exerts high cognitive ability and strong information processing capacity. Here, we establish a single-cell RNA sequencing dataset of 133,454 macaque visual cortical cells. It covers major cortical cell classes including 25 excitatory neuron types, 37 inhibitory neuron types and all glial cell types. We identified layer-specific markers including HPCAL1 and NXPH4, and also identified two cell types, an NPY-expressing excitatory neuron type that expresses the dopamine receptor D3 gene; and a primate specific activity-dependent OSTN + sensory neuron type. Comparisons of our dataset with humans and mice show that the gene expression profiles differ between species in relation to genes that are implicated in the synaptic plasticity and neuromodulation of excitatory neurons. The comparisons also revealed that glutamatergic neurons may be more diverse across species than GABAergic neurons and non-neuronal cells. These findings pave the way for understanding how the primary cortex fulfills the high-cognitive functions.
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Affiliation(s)
- Jia-Ru Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Lei Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Ruifeng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK.
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China.
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK.
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, China.
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24
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Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex. Nat Commun 2022; 13:6747. [PMID: 36347848 PMCID: PMC9643508 DOI: 10.1038/s41467-022-34413-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
Single cell approaches have increased our knowledge about the cell type composition of the non-human primate (NHP), but a detailed characterization of area-specific regulatory features remains outstanding. We generated single-cell transcriptomic and chromatin accessibility (single-cell ATAC) data of 358,237 cells from prefrontal cortex (PFC), primary motor cortex (M1) and primary visual cortex (V1) of adult female cynomolgus monkey brain, and integrated this dataset with Stereo-seq (spatial enhanced resolution omics-sequencing) of the corresponding cortical areas to assign topographic information to molecular states. We identified area-specific chromatin accessible sites and their targeted genes, including the cell type-specific transcriptional regulatory network associated with excitatory neurons heterogeneity. We reveal calcium ion transport and axon guidance genes related to specialized functions of PFC and M1, identified the similarities and differences between adult macaque and human oligodendrocyte trajectories, and mapped the genetic variants and gene perturbations of human diseases to NHP cortical cells. This resource establishes a transcriptomic and chromatin accessibility combinatory regulatory landscape at a single-cell and spatially resolved resolution in NHP cortex.
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25
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Gandal MJ, Haney JR, Wamsley B, Yap CX, Parhami S, Emani PS, Chang N, Chen GT, Hoftman GD, de Alba D, Ramaswami G, Hartl CL, Bhattacharya A, Luo C, Jin T, Wang D, Kawaguchi R, Quintero D, Ou J, Wu YE, Parikshak NN, Swarup V, Belgard TG, Gerstein M, Pasaniuc B, Geschwind DH. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 2022; 611:532-539. [PMID: 36323788 PMCID: PMC9668748 DOI: 10.1038/s41586-022-05377-7] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/21/2022] [Indexed: 11/17/2022]
Abstract
Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.
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Affiliation(s)
- Michael J Gandal
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Lifespan Brain Institute at Penn Medicine and The Children's Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jillian R Haney
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Brie Wamsley
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chloe X Yap
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - Sepideh Parhami
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Prashant S Emani
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Nathan Chang
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - George T Chen
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Gil D Hoftman
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Diego de Alba
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Gokul Ramaswami
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Christopher L Hartl
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chongyuan Luo
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ting Jin
- Waisman Center and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Daifeng Wang
- Waisman Center and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Riki Kawaguchi
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Diana Quintero
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jing Ou
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Ye Emily Wu
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Neelroop N Parikshak
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Vivek Swarup
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | | | - Mark Gerstein
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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26
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Willsey HR, Willsey AJ, Wang B, State MW. Genomics, convergent neuroscience and progress in understanding autism spectrum disorder. Nat Rev Neurosci 2022; 23:323-341. [PMID: 35440779 PMCID: PMC10693992 DOI: 10.1038/s41583-022-00576-7] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 12/31/2022]
Abstract
More than a hundred genes have been identified that, when disrupted, impart large risk for autism spectrum disorder (ASD). Current knowledge about the encoded proteins - although incomplete - points to a very wide range of developmentally dynamic and diverse biological processes. Moreover, the core symptoms of ASD involve distinctly human characteristics, presenting challenges to interpreting evolutionarily distant model systems. Indeed, despite a decade of striking progress in gene discovery, an actionable understanding of pathobiology remains elusive. Increasingly, convergent neuroscience approaches have been recognized as an important complement to traditional uses of genetics to illuminate the biology of human disorders. These methods seek to identify intersection among molecular-level, cellular-level and circuit-level functions across multiple risk genes and have highlighted developing excitatory neurons in the human mid-gestational prefrontal cortex as an important pathobiological nexus in ASD. In addition, neurogenesis, chromatin modification and synaptic function have emerged as key potential mediators of genetic vulnerability. The continued expansion of foundational 'omics' data sets, the application of higher-throughput model systems and incorporating developmental trajectories and sex differences into future analyses will refine and extend these results. Ultimately, a systems-level understanding of ASD genetic risk holds promise for clarifying pathobiology and advancing therapeutics.
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Affiliation(s)
- Helen Rankin Willsey
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - A Jeremy Willsey
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Belinda Wang
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Langley Porter Psychiatric Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Langley Porter Psychiatric Institute, University of California, San Francisco, San Francisco, CA, USA.
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27
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New pathogenic insights from large animal models of neurodegenerative diseases. Protein Cell 2022; 13:707-720. [PMID: 35334073 PMCID: PMC9233730 DOI: 10.1007/s13238-022-00912-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/23/2022] [Indexed: 12/12/2022] Open
Abstract
Animal models are essential for investigating the pathogenesis and developing the treatment of human diseases. Identification of genetic mutations responsible for neurodegenerative diseases has enabled the creation of a large number of small animal models that mimic genetic defects found in the affected individuals. Of the current animal models, rodents with genetic modifications are the most commonly used animal models and provided important insights into pathogenesis. However, most of genetically modified rodent models lack overt neurodegeneration, imposing challenges and obstacles in utilizing them to rigorously test the therapeutic effects on neurodegeneration. Recent studies that used CRISPR/Cas9-targeted large animal (pigs and monkeys) have uncovered important pathological events that resemble neurodegeneration in the patient’s brain but could not be produced in small animal models. Here we highlight the unique nature of large animals to model neurodegenerative diseases as well as the limitations and challenges in establishing large animal models of neurodegenerative diseases, with focus on Huntington disease, Amyotrophic lateral sclerosis, and Parkinson diseases. We also discuss how to use the important pathogenic insights from large animal models to make rodent models more capable of recapitulating important pathological features of neurodegenerative diseases.
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28
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Enwright III JF, Arion D, MacDonald WA, Elbakri R, Pan Y, Vyas G, Berndt A, Lewis DA. Differential gene expression in layer 3 pyramidal neurons across 3 regions of the human cortical visual spatial working memory network. Cereb Cortex 2022; 32:5216-5229. [PMID: 35106549 PMCID: PMC9667185 DOI: 10.1093/cercor/bhac009] [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: 09/27/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 02/03/2023] Open
Abstract
Visual spatial working memory (vsWM) is mediated by a distributed cortical network composed of multiple nodes, including primary visual (V1), posterior parietal (PPC), and dorsolateral prefrontal (DLPFC) cortices. Feedforward and feedback information is transferred among these nodes via projections furnished by pyramidal neurons (PNs) located primarily in cortical layer 3. Morphological and electrophysiological differences among layer 3 PNs across these nodes have been reported; however, the transcriptional signatures underlying these differences have not been examined in the human brain. Here we interrogated the transcriptomes of layer 3 PNs from 39 neurotypical human subjects across 3 critical nodes of the vsWM network. Over 8,000 differentially expressed genes were detected, with more than 6,000 transcriptional differences present between layer 3 PNs in V1 and those in PPC and DLPFC. Additionally, over 600 other genes differed in expression along the rostral-to-caudal hierarchy formed by these 3 nodes. Moreover, pathway analysis revealed enrichment of genes in V1 related to circadian rhythms and in DLPFC of genes involved in synaptic plasticity. Overall, these results show robust regional differences in the transcriptome of layer 3 PNs, which likely contribute to regional specialization in their morphological and physiological features and thus in their functional contributions to vsWM.
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Affiliation(s)
- John F Enwright III
- Department of Psychiatry, University of Pittsburgh Thomas Detre Hall 3811 O'Hara Street Pittsburgh, PA 15213 United States
| | - Dominique Arion
- Department of Psychiatry, University of Pittsburgh Thomas Detre Hall 3811 O'Hara Street Pittsburgh, PA 15213 United States
| | - William A MacDonald
- Department of Pediatrics UPMC Children's Hospital of Pittsburgh 4401 Penn Avenue Pittsburgh, PA 15224-1334 United States,Health Sciences Sequencing Core 4401 Penn Avenue Rangos Research Building 8th Floor Pittsburgh, PA 15224 United States
| | - Rania Elbakri
- Department of Pediatrics UPMC Children's Hospital of Pittsburgh 4401 Penn Avenue Pittsburgh, PA 15224-1334 United States,Health Sciences Sequencing Core 4401 Penn Avenue Rangos Research Building 8th Floor Pittsburgh, PA 15224 United States
| | - Yinghong Pan
- The Institute for Precision Medicine 204 Craft Avenue, Room A412 Pittsburgh, PA 15213 United States
| | - Gopi Vyas
- The Institute for Precision Medicine 204 Craft Avenue, Room A412 Pittsburgh, PA 15213 United States
| | - Annerose Berndt
- The Institute for Precision Medicine 204 Craft Avenue, Room A412 Pittsburgh, PA 15213 United States
| | - David A Lewis
- Address correspondence to David A. Lewis, Department of Psychiatry, University of Pittsburgh, Biomedical Science Tower W1654, 3811 O’Hara Street, Pittsburgh, PA 15213-2593, United States.
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29
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Abed-Esfahani P, Darwin BC, Howard D, Wang N, Kim E, Lerch J, French L. Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation. PLoS One 2022; 17:e0262717. [PMID: 35073334 PMCID: PMC8786163 DOI: 10.1371/journal.pone.0262717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/31/2021] [Indexed: 11/19/2022] Open
Abstract
High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on an ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, Sodium Voltage-Gated Channel Beta Subunit 4 (SCN4B), may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.
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Affiliation(s)
- Pegah Abed-Esfahani
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | | | - Derek Howard
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Nick Wang
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Canada
| | - Ethan Kim
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute for Medical Science, University of Toronto, Toronto, Canada
| | - Jason Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Leon French
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute for Medical Science, University of Toronto, Toronto, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
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30
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MIYASHITA Y. Operating principles of the cerebral cortex as a six-layered network in primates: beyond the classic canonical circuit model. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2022; 98:93-111. [PMID: 35283409 PMCID: PMC8948418 DOI: 10.2183/pjab.98.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/28/2021] [Indexed: 06/14/2023]
Abstract
The cerebral cortex performs its computations with many six-layered fundamental units, collectively spreading along the cortical sheet. What is the local network structure and the operating dynamics of such a fundamental unit? Previous investigations of primary sensory areas revealed a classic "canonical" circuit model, leading to an expectation of similar circuit organization and dynamics throughout the cortex. This review clarifies the different circuit dynamics at play in the higher association cortex of primates that implements computation for high-level cognition such as memory and attention. Instead of feedforward processing of response selectivity through Layers 4 to 2/3 that the classic canonical circuit stipulates, memory recall in primates occurs in Layer 5/6 with local backward projection to Layer 2/3, after which the retrieved information is sent back from Layer 6 to lower-level cortical areas for further retrieval of nested associations of target attributes. In this review, a novel "dynamic multimode module (D3M)" in the primate association cortex is proposed, as a new "canonical" circuit model performing this operation.
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Affiliation(s)
- Yasushi MIYASHITA
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
- Juntendo University, Graduate School of Medicine, Tokyo, Japan
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McColgan P, Helbling S, Vaculčiaková L, Pine K, Wagstyl K, Attar FM, Edwards L, Papoutsi M, Wei Y, Van den Heuvel MP, Tabrizi SJ, Rees G, Weiskopf N. Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics. Hum Brain Mapp 2021; 42:4996-5009. [PMID: 34272784 PMCID: PMC8449108 DOI: 10.1002/hbm.25595] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/26/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Ultra-high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer-specific cell measures from the von Economo histology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Saskia Helbling
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Lenka Vaculčiaková
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Kerrin Pine
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Konrad Wagstyl
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | | | - Luke Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Marina Papoutsi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Yongbin Wei
- Vrije Universiteit AmsterdamComplex Traits Genetics LabAmsterdamNetherlands
| | | | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Geraint Rees
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State PhysicsFaculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, et alBakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 2021; 598:111-119. [PMID: 34616062 PMCID: PMC8494640 DOI: 10.1038/s41586-021-03465-8] [Show More Authors] [Citation(s) in RCA: 404] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 03/17/2021] [Indexed: 12/11/2022]
Abstract
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
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Affiliation(s)
| | | | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Jeroen Eggermont
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nikolai Dembrow
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jayaram Kancherla
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Baldur van Lew
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Yang Eric Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Christine S Liu
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Anup Mahurkar
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Computer Science and Engineering Program, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Orvis
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Scott Owen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nora M Reed
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Angeline Rivkin
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - William J Romanow
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xinxin Wang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | | | - Renee Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Hector Corrada Bravo
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ronna Hertzano
- Departments of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Höllt
- Computer Graphics and Visualization Group, Delt University of Technology, Delft, The Netherlands
| | - Gregory D Horwitz
- Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Boudewijn P Lelieveldt
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics group, Delft University of Technology, Delft, The Netherlands
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | | | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - William J Spain
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Owen R White
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
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Wildenberg GA, Rosen MR, Lundell J, Paukner D, Freedman DJ, Kasthuri N. Primate neuronal connections are sparse in cortex as compared to mouse. Cell Rep 2021; 36:109709. [PMID: 34525373 DOI: 10.1016/j.celrep.2021.109709] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/30/2021] [Accepted: 08/20/2021] [Indexed: 12/29/2022] Open
Abstract
Detailing how primate and mouse neurons differ is critical for creating generalized models of how neurons process information. We reconstruct 15,748 synapses in adult Rhesus macaques and mice and ask how connectivity differs on identified cell types in layer 2/3 of primary visual cortex. Primate excitatory and inhibitory neurons receive 2-5 times fewer excitatory and inhibitory synapses than similar mouse neurons. Primate excitatory neurons have lower excitatory-to-inhibitory (E/I) ratios than mouse but similar E/I ratios in inhibitory neurons. In both species, properties of inhibitory axons such as synapse size and frequency are unchanged, and inhibitory innervation of excitatory neurons is local and specific. Using artificial recurrent neural networks (RNNs) optimized for different cognitive tasks, we find that penalizing networks for creating and maintaining synapses, as opposed to neuronal firing, reduces the number of connections per node as the number of nodes increases, similar to primate neurons compared with mice.
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Affiliation(s)
- Gregg A Wildenberg
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Matt R Rosen
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Jack Lundell
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Dawn Paukner
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - David J Freedman
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Narayanan Kasthuri
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; Argonne National Laboratory, Lemont, IL 60439, USA.
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Abstract
In the mammalian neocortex, projection neuron types are sequentially generated by the same pool of neural progenitors. How neuron type specification is related to developmental timing remains unclear. To determine whether temporal gene expression in neural progenitors correlates with neuron type specification, we performed single-cell RNA sequencing (scRNA-Seq) analysis of the developing mouse neocortex. We uncovered neuroepithelial cell enriched genes such as Hmga2 and Ccnd1 when compared to radial glial cells (RGCs). RGCs display dynamic gene expression over time; for instance, early RGCs express higher levels of Hes5, and late RGCs show higher expression of Pou3f2 Interestingly, intermediate progenitor cell marker gene Eomes coexpresses temporally with known neuronal identity genes at different developmental stages, though mostly in postmitotic cells. Our results delineate neural progenitor cell diversity in the developing mouse neocortex and support that neuronal identity genes are transcriptionally evident in Eomes-positive cells.
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Hua J, Yang Z, Jiang T, Yu S. Pairwise interactions in gene expression determine a hierarchical transcriptional profile in the human brain. Sci Bull (Beijing) 2021; 66:1437-1447. [PMID: 36654370 DOI: 10.1016/j.scib.2021.01.003] [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: 06/07/2020] [Revised: 08/25/2020] [Accepted: 10/13/2020] [Indexed: 01/20/2023]
Abstract
The orchestrated expression of thousands of genes gives rise to the complexity of the human brain. However, the structures governing these myriad gene-gene interactions remain unclear. By analyzing transcription data from more than 2000 sites in six human brains, we found that pairwise interactions between genes, without considering any higher-order interactions, are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas. These findings suggest a quadratic complexity of transcriptional patterns in the human brain, which is much simpler than expected. In addition, using a pairwise interaction model, we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters, which may account for the functional and structural richness of the brain.
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Affiliation(s)
- Jiaojiao Hua
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhengyi Yang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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36
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Kondratov O, Kondratova L, Mandel RJ, Coleman K, Savage MA, Gray-Edwards HL, Ness TJ, Rodriguez-Lebron E, Bell RD, Rabinowitz J, Gamlin PD, Zolotukhin S. A comprehensive study of a 29-capsid AAV library in a non-human primate central nervous system. Mol Ther 2021; 29:2806-2820. [PMID: 34298128 DOI: 10.1016/j.ymthe.2021.07.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/07/2021] [Accepted: 07/14/2021] [Indexed: 12/27/2022] Open
Abstract
Non-human primates (NHPs) are a preferred animal model for optimizing adeno-associated virus (AAV)-mediated CNS gene delivery protocols before clinical trials. In spite of its inherent appeal, it is challenging to compare different serotypes, delivery routes, and disease indications in a well-powered, comprehensive, multigroup NHP experiment. Here, a multiplex barcode recombinant AAV (rAAV) vector-tracing strategy has been applied to a systemic analysis of 29 distinct, wild-type (WT), AAV natural isolates and engineered capsids in the CNS of eight macaques. The report describes distribution of each capsid in 15 areas of the macaques' CNS after intraparenchymal (putamen) injection, or cerebrospinal fluid (CSF)-mediated administration routes (intracisternal, intrathecal, or intracerebroventricular). To trace the vector biodistribution (viral DNA) and targeted tissues transduction (viral mRNA) of each capsid in each of the analyzed CNS areas, quantitative next-generation sequencing analysis, assisted by the digital-droplet PCR technology, was used. The report describes the most efficient AAV capsid variants targeting specific CNS areas after each route of administration using the direct side-by-side comparison of WT AAV isolates and a new generation of rationally designed capsids. The newly developed bioinformatics and visualization algorithms, applicable to the comparative analysis of several mammalian brain models, have been developed and made available in the public domain.
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Affiliation(s)
- Oleksandr Kondratov
- Department of Pediatrics, University of Florida College of Medicine, Gainesville, FL 32610, USA.
| | - Liudmyla Kondratova
- Department of Pediatrics, University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Ronald J Mandel
- Department of Neuroscience, University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Kirsten Coleman
- Powell Gene Therapy Center University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Michael A Savage
- Department of Ophthalmology and Visual Sciences, University of Alabama, Birmingham, AL 35294, USA
| | - Heather L Gray-Edwards
- Horae Gene Therapy Center, University of Massachusetts Medical School, Worcester, MA 01605, USA; Department of Radiology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Timothy J Ness
- Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | | | - Robert D Bell
- Neuroscience Research Unit, Pfizer, 610 Main Street, Cambridge, MA 02139, USA
| | - Joseph Rabinowitz
- Neuroscience Research Unit, Pfizer, 610 Main Street, Cambridge, MA 02139, USA
| | - Paul D Gamlin
- Department of Ophthalmology and Visual Sciences, University of Alabama, Birmingham, AL 35294, USA
| | - Sergei Zolotukhin
- Department of Pediatrics, University of Florida College of Medicine, Gainesville, FL 32610, USA.
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Forsyth JK, Mennigen E, Lin A, Sun D, Vajdi A, Kushan-Wells L, Ching CRK, Villalon-Reina JE, Thompson PM, Bearden CE. Prioritizing Genetic Contributors to Cortical Alterations in 22q11.2 Deletion Syndrome Using Imaging Transcriptomics. Cereb Cortex 2021; 31:3285-3298. [PMID: 33638978 PMCID: PMC8196250 DOI: 10.1093/cercor/bhab008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 03/13/2020] [Accepted: 05/02/2020] [Indexed: 11/25/2022] Open
Abstract
22q11.2 deletion syndrome (22q11DS) results from a hemizygous deletion that typically spans 46 protein-coding genes and is associated with widespread alterations in brain morphology. The specific genetic mechanisms underlying these alterations remain unclear. In the 22q11.2 ENIGMA Working Group, we characterized cortical alterations in individuals with 22q11DS (n = 232) versus healthy individuals (n = 290) and conducted spatial convergence analyses using gene expression data from the Allen Human Brain Atlas to prioritize individual genes that may contribute to altered surface area (SA) and cortical thickness (CT) in 22q11DS. Total SA was reduced in 22q11DS (Z-score deviance = -1.04), with prominent reductions in midline posterior and lateral association regions. Mean CT was thicker in 22q11DS (Z-score deviance = +0.64), with focal thinning in a subset of regions. Regional expression of DGCR8 was robustly associated with regional severity of SA deviance in 22q11DS; AIFM3 was also associated with SA deviance. Conversely, P2RX6 was associated with CT deviance. Exploratory analysis of gene targets of microRNAs previously identified as down-regulated due to DGCR8 deficiency suggested that DGCR8 haploinsufficiency may contribute to altered corticogenesis in 22q11DS by disrupting cell cycle modulation. These findings demonstrate the utility of combining neuroanatomic and transcriptomic datasets to derive molecular insights into complex, multigene copy number variants.
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Affiliation(s)
- Jennifer K Forsyth
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
| | - Eva Mennigen
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden 01307, Germany
| | - Amy Lin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
- Interdepartmental Neuroscience Program, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Daqiang Sun
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
| | - Ariana Vajdi
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
| | - Leila Kushan-Wells
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, CA 90024, USA
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA 90095, USA
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38
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Vasistha NA, Khodosevich K. The impact of (ab)normal maternal environment on cortical development. Prog Neurobiol 2021; 202:102054. [PMID: 33905709 DOI: 10.1016/j.pneurobio.2021.102054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/01/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The cortex in the mammalian brain is the most complex brain region that integrates sensory information and coordinates motor and cognitive processes. To perform such functions, the cortex contains multiple subtypes of neurons that are generated during embryogenesis. Newly born neurons migrate to their proper location in the cortex, grow axons and dendrites, and form neuronal circuits. These developmental processes in the fetal brain are regulated to a large extent by a great variety of factors derived from the mother - starting from simple nutrients as building blocks and ending with hormones. Thus, when the normal maternal environment is disturbed due to maternal infection, stress, malnutrition, or toxic substances, it might have a profound impact on cortical development and the offspring can develop a variety of neurodevelopmental disorders. Here we first describe the major developmental processes which generate neuronal diversity in the cortex. We then review our knowledge of how most common maternal insults affect cortical development, perturb neuronal circuits, and lead to neurodevelopmental disorders. We further present a concept of selective vulnerability of cortical neuronal subtypes to maternal-derived insults, where the vulnerability of cortical neurons and their progenitors to an insult depends on the time (developmental period), place (location in the developing brain), and type (unique features of a cell type and an insult). Finally, we provide evidence for the existence of selective vulnerability during cortical development and identify the most vulnerable neuronal types, stages of differentiation, and developmental time for major maternal-derived insults.
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Affiliation(s)
- Navneet A Vasistha
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
| | - Konstantin Khodosevich
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
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39
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Zarkali A, McColgan P, Leyland LA, Lees AJ, Rees G, Weil RS. Organisational and neuromodulatory underpinnings of structural-functional connectivity decoupling in patients with Parkinson's disease. Commun Biol 2021; 4:86. [PMID: 33469150 PMCID: PMC7815846 DOI: 10.1038/s42003-020-01622-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/18/2020] [Indexed: 01/01/2023] Open
Abstract
Parkinson's dementia is characterised by changes in perception and thought, and preceded by visual dysfunction, making this a useful surrogate for dementia risk. Structural and functional connectivity changes are seen in humans with Parkinson's disease, but the organisational principles are not known. We used resting-state fMRI and diffusion-weighted imaging to examine changes in structural-functional connectivity coupling in patients with Parkinson's disease, and those at risk of dementia. We identified two organisational gradients to structural-functional connectivity decoupling: anterior-to-posterior and unimodal-to-transmodal, with stronger structural-functional connectivity coupling in anterior, unimodal areas and weakened towards posterior, transmodal regions. Next, we related spatial patterns of decoupling to expression of neurotransmitter receptors. We found that dopaminergic and serotonergic transmission relates to decoupling in Parkinson's overall, but instead, serotonergic, cholinergic and noradrenergic transmission relates to decoupling in patients with visual dysfunction. Our findings provide a framework to explain the specific disorders of consciousness in Parkinson's dementia, and the neurotransmitter systems that underlie these.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Peter McColgan
- Huntington's Disease Centre, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, 1 Wakefield Street, London, WC1N 1PJ, UK
| | - Geraint Rees
- Institute of Cognitive Neuroscience, University College London, 17-19 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Consortium, University College London, London, WC1N 3BG, UK
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40
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Dienel SJ, Ciesielski AJ, Bazmi HH, Profozich EA, Fish KN, Lewis DA. Distinct Laminar and Cellular Patterns of GABA Neuron Transcript Expression in Monkey Prefrontal and Visual Cortices. Cereb Cortex 2020; 31:2345-2363. [PMID: 33338196 DOI: 10.1093/cercor/bhaa341] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 10/21/2020] [Accepted: 10/21/2020] [Indexed: 12/25/2022] Open
Abstract
The functional output of a cortical region is shaped by its complement of GABA neuron subtypes. GABA-related transcript expression differs substantially between the primate dorsolateral prefrontal cortex (DLPFC) and primary visual (V1) cortices in gray matter homogenates, but the laminar and cellular bases for these differences are unknown. Quantification of levels of GABA-related transcripts in layers 2 and 4 of monkey DLPFC and V1 revealed three distinct expression patterns: 1) transcripts with higher levels in DLPFC and layer 2 [e.g., somatostatin (SST)]; 2) transcripts with higher levels in V1 and layer 4 [e.g., parvalbumin (PV)], and 3) transcripts with similar levels across layers and regions [e.g., glutamic acid decarboxylase (GAD67)]. At the cellular level, these patterns reflected transcript- and cell type-specific differences: the SST pattern primarily reflected differences in the relative proportions of SST mRNA-positive neurons, the PV pattern primarily reflected differences in PV mRNA expression per neuron, and the GAD67 pattern reflected opposed patterns in the relative proportions of GAD67 mRNA-positive neurons and in GAD67 mRNA expression per neuron. These findings suggest that differences in the complement of GABA neuron subtypes and in gene expression levels per neuron contribute to the specialization of inhibitory neurotransmission across cortical circuits.
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Affiliation(s)
- Samuel J Dienel
- Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Andrew J Ciesielski
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Holly H Bazmi
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Elizabeth A Profozich
- Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kenneth N Fish
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA
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41
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Chongtham MC, Wang H, Thaller C, Hsiao NH, Vachkov IH, Pavlov SP, Williamson LH, Yamashima T, Stoykova A, Yan J, Eichele G, Tonchev AB. Transcriptome Response and Spatial Pattern of Gene Expression in the Primate Subventricular Zone Neurogenic Niche After Cerebral Ischemia. Front Cell Dev Biol 2020; 8:584314. [PMID: 33344448 PMCID: PMC7744782 DOI: 10.3389/fcell.2020.584314] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/20/2020] [Indexed: 11/13/2022] Open
Abstract
The main stem cell niche for neurogenesis in the adult mammalian brain is the subventricular zone (SVZ) that extends along the cerebral lateral ventricles. We aimed at characterizing the initial molecular responses of the macaque monkey SVZ to transient, global cerebral ischemia. We microdissected tissue lining the anterior horn of the lateral ventricle (SVZa) from 7 day post-ischemic and sham-operated monkeys. Transcriptomics shows that in ischemic SVZa, 541 genes were upregulated and 488 genes were down-regulated. The transcription data encompassing the upregulated genes revealed a profile typical for quiescent stem cells and astrocytes. In the primate brain the SVZ is morphologically subdivided in distinct and separate ependymal and subependymal regions. The subependymal contains predominantly neural stem cells (NSC) and differentiated progenitors. To determine in which SVZa region ischemia had evoked transcriptional upregulation, sections through control and ischemic SVZa were analyzed by high-throughput in situ hybridization for a total of 150 upregulated genes shown in the www.monkey-niche.org image database. The majority of the differentially expressed genes mapped to the subependymal layers on the striatal or callosal aspect of the SVZa. Moreover, a substantial number of upregulated genes was expressed in the ependymal layer, implicating a contribution of the ependyma to stem cell biology. The transcriptome analysis yielded several novel gene markers for primate SVZa including the apelin receptor that is strongly expressed in the primate SVZa niche upon ischemic insult.
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Affiliation(s)
- Monika C Chongtham
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Haifang Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Christina Thaller
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Nai-Hua Hsiao
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Ivan H Vachkov
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Stoyan P Pavlov
- Department of Anatomy and Cell Biology, Faculty of Medicine, Medical University, Varna, Bulgaria.,Department of Stem Cell Biology and Advanced Computational Bioimaging, Research Institute, Medical University, Varna, Bulgaria
| | - Lorenz H Williamson
- Department of Anatomy and Cell Biology, Faculty of Medicine, Medical University, Varna, Bulgaria.,Department of Stem Cell Biology and Advanced Computational Bioimaging, Research Institute, Medical University, Varna, Bulgaria
| | - Tetsumori Yamashima
- Department of Psychiatry and Behavioral Science, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Anastassia Stoykova
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Jun Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Gregor Eichele
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Anton B Tonchev
- Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.,Department of Anatomy and Cell Biology, Faculty of Medicine, Medical University, Varna, Bulgaria.,Department of Stem Cell Biology and Advanced Computational Bioimaging, Research Institute, Medical University, Varna, Bulgaria
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42
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Carter O, van Swinderen B, Leopold DA, Collin S, Maier A. Perceptual rivalry across animal species. J Comp Neurol 2020; 528:3123-3133. [PMID: 32361986 PMCID: PMC7541519 DOI: 10.1002/cne.24939] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 04/18/2020] [Accepted: 04/20/2020] [Indexed: 01/10/2023]
Abstract
This review in memoriam of Jack Pettigrew provides an overview of past and current research into the phenomenon of multistable perception across multiple animal species. Multistable perception is characterized by two or more perceptual interpretations spontaneously alternating, or rivaling, when animals are exposed to stimuli with inherent sensory ambiguity. There is a wide array of ambiguous stimuli across sensory modalities, ranging from the configural changes observed in simple line drawings, such as the famous Necker cube, to the alternating perception of entire visual scenes that can be instigated by interocular conflict. The latter phenomenon, called binocular rivalry, in particular caught the attention of the late Jack Pettigrew, who combined his interest in the neuronal basis of perception with a unique comparative biological approach that considered ambiguous sensation as a fundamental problem of sensory systems that has shaped the brain throughout evolution. Here, we examine the research findings on visual perceptual alternation and suppression in a wide variety of species including insects, fish, reptiles, and primates. We highlight several interesting commonalities across species and behavioral indicators of perceptual alternation. In addition, we show how the comparative approach provides new avenues for understanding how the brain suppresses opposing sensory signals and generates alternations in perceptual dominance.
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Affiliation(s)
- Olivia Carter
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, AUS
| | | | | | - Shaun Collin
- School of Life Sciences, La Trobe University, Melbourne, VIC, AUS
| | - Alex Maier
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
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43
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Non-Human Primate Blood-Brain Barrier and In Vitro Brain Endothelium: From Transcriptome to the Establishment of a New Model. Pharmaceutics 2020; 12:pharmaceutics12100967. [PMID: 33066641 PMCID: PMC7602447 DOI: 10.3390/pharmaceutics12100967] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/02/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022] Open
Abstract
The non-human primate (NHP)-brain endothelium constitutes an essential alternative to human in the prediction of molecule trafficking across the blood–brain barrier (BBB). This study presents a comparison between the NHP transcriptome of freshly isolated brain microcapillaries and in vitro-selected brain endothelial cells (BECs), focusing on important BBB features, namely tight junctions, receptors mediating transcytosis (RMT), ABC and SLC transporters, given its relevance as an alternative model for the molecule trafficking prediction across the BBB and identification of new brain-specific transport mechanisms. In vitro BECs conserved most of the BBB key elements for barrier integrity and control of molecular trafficking. The function of RMT via the transferrin receptor (TFRC) was characterized in this NHP-BBB model, where both human transferrin and anti-hTFRC antibody showed increased apical-to-basolateral passage in comparison to control molecules. In parallel, eventual BBB-related regional differences were Investig.igated in seven-day in vitro-selected BECs from five brain structures: brainstem, cerebellum, cortex, hippocampus, and striatum. Our analysis retrieved few differences in the brain endothelium across brain regions, suggesting a rather homogeneous BBB function across the brain parenchyma. The presently established NHP-derived BBB model closely mimics the physiological BBB, thus representing a ready-to-use tool for assessment of the penetration of biotherapeutics into the human CNS.
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44
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Sato M, Chou SJ. Editorial: The Earliest-Born Cortical Neurons as Multi-Tasking Pioneers: Expanding Roles for Subplate Neurons in Cerebral Cortex Organization and Function. Front Neuroanat 2020; 14:43. [PMID: 32982700 PMCID: PMC7479822 DOI: 10.3389/fnana.2020.00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Makoto Sato
- Department of Anatomy and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Japan.,Division of Developmental Neuroscience, United Graduate School of Child Development, Osaka University, Suita, Japan
| | - Shen-Ju Chou
- Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan
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45
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Charvet CJ. Closing the gap from transcription to the structural connectome enhances the study of connections in the human brain. Dev Dyn 2020; 249:1047-1061. [PMID: 32562584 DOI: 10.1002/dvdy.218] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/02/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022] Open
Abstract
The brain is composed of a complex web of networks but we have yet to map the structural connections of the human brain in detail. Diffusion MR imaging is a high-throughput method that relies on the principle of diffusion to reconstruct tracts (ie, pathways) across the brain. Although diffusion MR tractography is an exciting method to explore the structural connectivity of the brain in development and across species, the tractography has at times led to questionable interpretations. There are at present few if any alternative methods to trace structural pathways in the human brain. Given these limitations and the potential of diffusion MR imaging to map the human connectome, it is imperative that we develop new approaches to validate neuroimaging techniques. I discuss our recent studies integrating neuroimaging with transcriptional and anatomical variation across humans and other species over the course of development and in adulthood. Developing a novel framework to harness the potential of diffusion MR tractography provides new and exciting opportunities to study the evolution of developmental mechanisms generating variation in connections and bridge the gap between model systems to humans.
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46
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Loss of Nuclear TDP-43 Is Associated with Decondensation of LINE Retrotransposons. Cell Rep 2020; 27:1409-1421.e6. [PMID: 31042469 PMCID: PMC6508629 DOI: 10.1016/j.celrep.2019.04.003] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 02/14/2019] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
Abstract
Loss of the nuclear RNA binding protein TAR DNA binding protein-43 (TDP-43) into cytoplasmic aggregates is the strongest correlate to neurodegeneration in amyotrophic lateral sclerosis and frontotemporal degeneration. The molecular changes associated with the loss of nuclear TDP-43 in human tissues are not entirely known. Using subcellular fractionation andfluorescent-activated cell sorting to enrich for diseased neuronal nuclei without TDP-43 from post-mortem frontotemporal degeneration-amyotro-phic lateral sclerosis (FTD-ALS) human brain, we characterized the effects of TDP-43 loss on the transcriptome and chromatin accessibility. Nuclear TDP-43 loss is associated with gene expression changes that affect RNA processing, nucleocytoplas-mic transport, histone processing, and DNA damage. Loss of nuclear TDP-43 is also associated with chromatin decondensation around long interspersed nuclear elements (LINEs) and increased LINE1 DNA content. Moreover, loss of TDP-43 leads to increased retrotransposition that can be inhibited with antiretro-viral drugs, suggesting that TDP-43 neuropathology is associated with altered chromatin structure including decondensation of LINEs. Liu et al. fractionated and sorted for diseased neuronal nuclei from post-mortem FTD-ALS human brains and showed that loss of an RNA-binding protein, TDP-43, altered the transcriptome and chromatin accessibility. Their results suggest that loss of nuclear TDP-43 is associated with decondensation of LINE retrotransposons.
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47
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Kim SM, Cho SY, Kim MW, Roh SR, Shin HS, Suh YH, Geum D, Lee MA. Genome-Wide Analysis Identifies NURR1-Controlled Network of New Synapse Formation and Cell Cycle Arrest in Human Neural Stem Cells. Mol Cells 2020; 43:551-571. [PMID: 32522891 PMCID: PMC7332357 DOI: 10.14348/molcells.2020.0071] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 05/01/2020] [Accepted: 05/09/2020] [Indexed: 02/07/2023] Open
Abstract
Nuclear receptor-related 1 (Nurr1) protein has been identified as an obligatory transcription factor in midbrain dopaminergic neurogenesis, but the global set of human NURR1 target genes remains unexplored. Here, we identified direct gene targets of NURR1 by analyzing genome-wide differential expression of NURR1 together with NURR1 consensus sites in three human neural stem cell (hNSC) lines. Microarray data were validated by quantitative PCR in hNSCs and mouse embryonic brains and through comparison to published human data, including genome-wide association study hits and the BioGPS gene expression atlas. Our analysis identified ~40 NURR1 direct target genes, many of them involved in essential protein modules such as synapse formation, neuronal cell migration during brain development, and cell cycle progression and DNA replication. Specifically, expression of genes related to synapse formation and neuronal cell migration correlated tightly with NURR1 expression, whereas cell cycle progression correlated negatively with it, precisely recapitulating midbrain dopaminergic development. Overall, this systematic examination of NURR1-controlled regulatory networks provides important insights into this protein's biological functions in dopamine-based neurogenesis.
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Affiliation(s)
- Soo Min Kim
- Department of Brain Science, Ajou University School of Medicine, Suwon 6499, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Graduate School of Ajou University, Suwon 16499, Korea
| | | | - Min Woong Kim
- Department of Brain Science, Ajou University School of Medicine, Suwon 6499, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Graduate School of Ajou University, Suwon 16499, Korea
| | - Seung Ryul Roh
- Department of Brain Science, Ajou University School of Medicine, Suwon 6499, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Graduate School of Ajou University, Suwon 16499, Korea
| | - Hee Sun Shin
- Department of Brain Science, Ajou University School of Medicine, Suwon 6499, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Graduate School of Ajou University, Suwon 16499, Korea
| | - Young Ho Suh
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Dongho Geum
- Department of Medical Science, Korea University Medical School, Seoul 02841, Korea
| | - Myung Ae Lee
- Department of Brain Science, Ajou University School of Medicine, Suwon 6499, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Graduate School of Ajou University, Suwon 16499, Korea
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48
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Miyazaki H, Yamanaka T, Oyama F, Kino Y, Kurosawa M, Yamada-Kurosawa M, Yamano R, Shimogori T, Hattori N, Nukina N. FACS-array-based cell purification yields a specific transcriptome of striatal medium spiny neurons in a murine Huntington disease model. J Biol Chem 2020; 295:9768-9785. [PMID: 32499373 DOI: 10.1074/jbc.ra120.012983] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/21/2020] [Indexed: 12/27/2022] Open
Abstract
Huntington disease (HD) is a neurodegenerative disorder caused by expanded CAG repeats in the Huntingtin gene. Results from previous studies have suggested that transcriptional dysregulation is one of the key mechanisms underlying striatal medium spiny neuron (MSN) degeneration in HD. However, some of the critical genes involved in HD etiology or pathology could be masked in a common expression profiling assay because of contamination with non-MSN cells. To gain insight into the MSN-specific gene expression changes in presymptomatic R6/2 mice, a common HD mouse model, here we used a transgenic fluorescent protein marker of MSNs for purification via FACS before profiling gene expression with gene microarrays and compared the results of this "FACS-array" with those obtained with homogenized striatal samples (STR-array). We identified hundreds of differentially expressed genes (DEGs) and enhanced detection of MSN-specific DEGs by comparing the results of the FACS-array with those of the STR-array. The gene sets obtained included genes ubiquitously expressed in both MSNs and non-MSN cells of the brain and associated with transcriptional regulation and DNA damage responses. We proposed that the comparative gene expression approach using the FACS-array may be useful for uncovering the gene cascades affected in MSNs during HD pathogenesis.
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Affiliation(s)
- Haruko Miyazaki
- Laboratory of Structural Neuropathology, Graduate School of Brain Science, Doshisha University, Kyoto, Japan.,Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Laboratory for Molecular Mechanisms of Brain Development, RIKEN Center for Brain Science, Saitama, Japan.,Department of Neuroscience for Neurodegenerative Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tomoyuki Yamanaka
- Laboratory of Structural Neuropathology, Graduate School of Brain Science, Doshisha University, Kyoto, Japan.,Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Laboratory for Molecular Mechanisms of Brain Development, RIKEN Center for Brain Science, Saitama, Japan.,Department of Neuroscience for Neurodegenerative Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Fumitaka Oyama
- Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Department of Chemistry and Life Science, Kogakuin University, Tokyo, Japan
| | - Yoshihiro Kino
- Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Department of Bioinformatics and Molecular Neuropathology, Meiji Pharmaceutical University, Tokyo, Japan
| | - Masaru Kurosawa
- Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Institute for Environmental and Gender-specific Medicine, Juntendo University Graduate School of Medicine, Chiba, Japan
| | | | - Risa Yamano
- Laboratory of Structural Neuropathology, Graduate School of Brain Science, Doshisha University, Kyoto, Japan
| | - Tomomi Shimogori
- Laboratory for Molecular Mechanisms of Brain Development, RIKEN Center for Brain Science, Saitama, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Nobuyuki Nukina
- Laboratory of Structural Neuropathology, Graduate School of Brain Science, Doshisha University, Kyoto, Japan .,Laboratory for Structural Neuropathology, RIKEN Brain Science Institute, Saitama, Japan.,Laboratory for Molecular Mechanisms of Brain Development, RIKEN Center for Brain Science, Saitama, Japan.,Department of Neuroscience for Neurodegenerative Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
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49
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Friedman R. Measurements of neuronal morphological variation across the rat neocortex. Neurosci Lett 2020; 734:135077. [PMID: 32485285 DOI: 10.1016/j.neulet.2020.135077] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/20/2020] [Indexed: 11/16/2022]
Abstract
Neuron morphology is highly variable across the mammalian brain. It is thought that these attributes of neuronal cell shape, such as soma surface area and branching frequency, are determined by biological function and information processing. In this study, a large data set of neurons across the rat neocortex were clustered by their anatomical characters for evidence of distinctiveness among neocortical regions and the somatosensory layers. This data set of neuronal morphologies was compiled from 31 different lab sources with a validation procedure so that data records are potentially comparable across research studies. With this large set of heterogeneous data and by clustering analysis, this study shows that neuronal morphological traits overlap among neocortical and somatosensory regions. In the context of past neuroanatomical studies, this result is not congruent with tissue level analysis and strongly suggests further sampling of neuronal data to lessen the effect of confounding factors, such as the influence of different methodologies from use of heterogeneous samples of neuronal data.
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Affiliation(s)
- Robert Friedman
- Department of Biological Sciences, University of South Carolina, Columbia, SC, United States.
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50
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Rodrigue AL, Alexander-Bloch AF, Knowles EEM, Mathias SR, Mollon J, Koenis MMG, Perrone-Bizzozero NI, Almasy L, Turner JA, Calhoun VD, Glahn DC. Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry. Cereb Cortex 2020; 30:4899-4913. [PMID: 32318716 DOI: 10.1093/cercor/bhaa082] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/12/2020] [Accepted: 03/17/2020] [Indexed: 11/14/2022] Open
Abstract
Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.
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Affiliation(s)
- Amanda L Rodrigue
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | | | - Emma E M Knowles
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Marinka M G Koenis
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Nora I Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.,Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Laura Almasy
- Department of Genetics, Perelman School of Medicine, and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jessica A Turner
- Psychology Department, Neurosciences Institute, Georgia State University, Atlanta, GA 30303, USA.,The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Psychology Department, Neurosciences Institute, Georgia State University, Atlanta, GA 30303, USA.,The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.,Mind Research Network, Department of Psychiatry and Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
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