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Yang L, Liu F, Hahm H, Okuda T, Li X, Zhang Y, Kalyanaraman V, Heitmeier MR, Samineni VK. Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590975. [PMID: 38712231 PMCID: PMC11071495 DOI: 10.1101/2024.04.24.590975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain wide projections. However, investigating the spatial organization, transcriptional and epigenetic landscapes of brain wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.
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Affiliation(s)
- Lite Yang
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
- Neuroscience Graduate Program, Division of Biology & Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, United States
| | - Fang Liu
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Hannah Hahm
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Takao Okuda
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Xiaoyue Li
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Yufen Zhang
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Vani Kalyanaraman
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Monique R. Heitmeier
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Vijay K. Samineni
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
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2
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Xu P, Peng J, Yuan T, Chen Z, He H, Wu Z, Li T, Li X, Wang L, Gao L, Yan J, Wei W, Li CT, Luo ZG, Chen Y. High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling. eLife 2024; 13:e85419. [PMID: 38390967 PMCID: PMC10914349 DOI: 10.7554/elife.85419] [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: 12/07/2022] [Accepted: 02/22/2024] [Indexed: 02/24/2024] Open
Abstract
Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.
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Affiliation(s)
- Peibo Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jian Peng
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Tingli Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Zhaoqin Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Hui He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Ziyan Wu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Ting Li
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Xiaodong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Luyue Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of ScienceShanghaiChina
| | - Le Gao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Jun Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
- School of Future Technology, University of Chinese Academy of SciencesBeijingChina
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of ScienceShanghaiChina
- Lingang LaboratoryShanghaiChina
| | - Chengyu T Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
- School of Future Technology, University of Chinese Academy of SciencesBeijingChina
- Lingang LaboratoryShanghaiChina
| | - Zhen-Ge Luo
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Yuejun Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
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3
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Moore JR, Nemera MT, D’Souza RD, Hamagami N, Clemens AW, Beard DC, Urman A, Mendoza VR, Gabel HW. Non-CG DNA methylation and MeCP2 stabilize repeated tuning of long genes that distinguish closely related neuron types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.577861. [PMID: 38352532 PMCID: PMC10862856 DOI: 10.1101/2024.01.30.577861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
The extraordinary diversity of neuron types in the mammalian brain is delineated at the highest resolution by subtle gene expression differences that may require specialized molecular mechanisms to be maintained. Neurons uniquely express the longest genes in the genome and utilize neuron-enriched non-CG DNA methylation (mCA) together with the Rett syndrome protein, MeCP2, to control gene expression, but the function of these unique gene structures and machinery in regulating finely resolved neuron type-specific gene programs has not been explored. Here, we employ epigenomic and spatial transcriptomic analyses to discover a major role for mCA and MeCP2 in maintaining neuron type-specific gene programs at the finest scale of cellular resolution. We uncover differential susceptibility to MeCP2 loss in neuronal populations depending on global mCA levels and dissect methylation patterns and intragenic enhancer repression that drive overlapping and distinct gene regulation between neuron types. Strikingly, we show that mCA and MeCP2 regulate genes that are repeatedly tuned to differentiate neuron types at the highest cellular resolution, including spatially resolved, vision-dependent gene programs in the visual cortex. These repeatedly tuned genes display genomic characteristics, including long length, numerous intragenic enhancers, and enrichment for mCA, that predispose them to regulation by MeCP2. Thus, long gene regulation by the MeCP2 pathway maintains differential gene expression between closely-related neurons to facilitate the exceptional cellular diversity in the complex mammalian brain.
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Affiliation(s)
- J. Russell Moore
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Mati T. Nemera
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Rinaldo D. D’Souza
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Nicole Hamagami
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Adam W. Clemens
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Diana C. Beard
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Alaina Urman
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Victoria Rodriguez Mendoza
- Opportunities in Genomic Research Program, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Harrison W. Gabel
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
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4
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Malaguti M, Lebek T, Blin G, Lowell S. Enabling neighbour labelling: using synthetic biology to explore how cells influence their neighbours. Development 2024; 151:dev201955. [PMID: 38165174 PMCID: PMC10820747 DOI: 10.1242/dev.201955] [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: 09/08/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Cell-cell interactions are central to development, but exploring how a change in any given cell relates to changes in the neighbour of that cell can be technically challenging. Here, we review recent developments in synthetic biology and image analysis that are helping overcome this problem. We highlight the opportunities presented by these advances and discuss opportunities and limitations in applying them to developmental model systems.
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Affiliation(s)
- Mattias Malaguti
- Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Tamina Lebek
- Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Guillaume Blin
- Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Sally Lowell
- Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
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5
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Yang P, Jin L, Liao J, Jin K, Shao X, Li C, Qian J, Cheng J, Yu D, Guo R, Xu X, Lu X, Fan X. Revealing spatial multimodal heterogeneity in tissues with SpaTrio. CELL GENOMICS 2023; 3:100446. [PMID: 38116121 PMCID: PMC10726534 DOI: 10.1016/j.xgen.2023.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/28/2023] [Accepted: 10/26/2023] [Indexed: 12/21/2023]
Abstract
Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.
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Affiliation(s)
- Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Lijun Jin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Kaiyu Jin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Chengyu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Junyun Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dingyi Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Rongfang Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiao Xu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Xiaoyan Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Jinhua Institute of Zhejiang University, Jinhua 321016 China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
- Jinhua Institute of Zhejiang University, Jinhua 321016 China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
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6
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Zhou J, Zhang Z, Wu M, Liu H, Pang Y, Bartlett A, Peng Z, Ding W, Rivkin A, Lagos WN, Williams E, Lee CT, Miyazaki PA, Aldridge A, Zeng Q, Salinda JLA, Claffey N, Liem M, Fitzpatrick C, Boggeman L, Yao Z, Smith KA, Tasic B, Altshul J, Kenworthy MA, Valadon C, Nery JR, Castanon RG, Patne NS, Vu M, Rashid M, Jacobs M, Ito T, Osteen J, Emerson N, Lee J, Cho S, Rink J, Huang HH, Pinto-Duartec A, Dominguez B, Smith JB, O'Connor C, Zeng H, Chen S, Lee KF, Mukamel EA, Jin X, Margarita Behrens M, Ecker JR, Callaway EM. Brain-wide correspondence of neuronal epigenomics and distant projections. Nature 2023; 624:355-365. [PMID: 38092919 PMCID: PMC10719087 DOI: 10.1038/s41586-023-06823-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
Single-cell analyses parse the brain's billions of neurons into thousands of 'cell-type' clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.
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Affiliation(s)
- Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Zhuzhu Zhang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - May Wu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Yan Pang
- Systems Neurobiology Laboratories, 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
| | - Zihao Peng
- School of Mathematics and Computer Science, Nanchang University, Nanchang, China
- Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, China
| | - Wubin Ding
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Angeline Rivkin
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Will N Lagos
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Elora Williams
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Cheng-Ta Lee
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Paula Assakura Miyazaki
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - J L Angelo Salinda
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Naomi Claffey
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michelle Liem
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Conor Fitzpatrick
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Lara Boggeman
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia A Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Cynthia Valadon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- 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
| | - Neelakshi S Patne
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Minh Vu
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mohammad Rashid
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Matthew Jacobs
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Tony Ito
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hsiang-Hsuan Huang
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - António Pinto-Duartec
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bertha Dominguez
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jared B Smith
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Shengbo Chen
- Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, China
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Kuo-Fen Lee
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Xin Jin
- Center for Motor Control and Disease, Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 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.
| | - Edward M Callaway
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA.
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7
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Liu H, Zeng Q, Zhou J, Bartlett A, Wang BA, Berube P, Tian W, Kenworthy M, Altshul J, Nery JR, Chen H, Castanon RG, Zu S, Li YE, Lucero J, Osteen JK, Pinto-Duarte A, Lee J, Rink J, Cho S, Emerson N, Nunn M, O'Connor C, Wu Z, Stoica I, Yao Z, Smith KA, Tasic B, Luo C, Dixon JR, Zeng H, Ren B, Behrens MM, Ecker JR. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature 2023; 624:366-377. [PMID: 38092913 PMCID: PMC10719113 DOI: 10.1038/s41586-023-06805-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.
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Affiliation(s)
- Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bang-An Wang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Peter Berube
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Huaming Chen
- 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
| | - Songpeng Zu
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zhanghao Wu
- Sky Computing Lab, University of California, Berkeley, Berkeley, CA, USA
| | - Ion Stoica
- Sky Computing Lab, University of California, Berkeley, Berkeley, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesse R Dixon
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 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.
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8
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Falconnier C, Caparros-Roissard A, Decraene C, Lutz PE. Functional genomic mechanisms of opioid action and opioid use disorder: a systematic review of animal models and human studies. Mol Psychiatry 2023; 28:4568-4584. [PMID: 37723284 PMCID: PMC10914629 DOI: 10.1038/s41380-023-02238-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/20/2023]
Abstract
In the past two decades, over-prescription of opioids for pain management has driven a steep increase in opioid use disorder (OUD) and death by overdose, exerting a dramatic toll on western countries. OUD is a chronic relapsing disease associated with a lifetime struggle to control drug consumption, suggesting that opioids trigger long-lasting brain adaptations, notably through functional genomic and epigenomic mechanisms. Current understanding of these processes, however, remain scarce, and have not been previously reviewed systematically. To do so, the goal of the present work was to synthesize current knowledge on genome-wide transcriptomic and epigenetic mechanisms of opioid action, in primate and rodent species. Using a prospectively registered methodology, comprehensive literature searches were completed in PubMed, Embase, and Web of Science. Of the 2709 articles identified, 73 met our inclusion criteria and were considered for qualitative analysis. Focusing on the 5 most studied nervous system structures (nucleus accumbens, frontal cortex, whole striatum, dorsal striatum, spinal cord; 44 articles), we also conducted a quantitative analysis of differentially expressed genes, in an effort to identify a putative core transcriptional signature of opioids. Only one gene, Cdkn1a, was consistently identified in eleven studies, and globally, our results unveil surprisingly low consistency across published work, even when considering most recent single-cell approaches. Analysis of sources of variability detected significant contributions from species, brain structure, duration of opioid exposure, strain, time-point of analysis, and batch effects, but not type of opioid. To go beyond those limitations, we leveraged threshold-free methods to illustrate how genome-wide comparisons may generate new findings and hypotheses. Finally, we discuss current methodological development in the field, and their implication for future research and, ultimately, better care.
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Affiliation(s)
- Camille Falconnier
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, 67000, Strasbourg, France
| | - Alba Caparros-Roissard
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, 67000, Strasbourg, France
| | - Charles Decraene
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, 67000, Strasbourg, France
- Centre National de la Recherche Scientifique, Université de Strasbourg, Laboratoire de Neurosciences Cognitives et Adaptatives UMR 7364, 67000, Strasbourg, France
| | - Pierre-Eric Lutz
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, 67000, Strasbourg, France.
- Douglas Mental Health University Institute, Montreal, QC, Canada.
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9
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Zhou JL, de Guglielmo G, Ho AJ, Kallupi M, Pokhrel N, Li HR, Chitre AS, Munro D, Mohammadi P, Carrette LLG, George O, Palmer AA, McVicker G, Telese F. Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition. Nat Neurosci 2023; 26:1868-1879. [PMID: 37798411 PMCID: PMC10620093 DOI: 10.1038/s41593-023-01452-y] [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: 09/03/2022] [Accepted: 09/06/2023] [Indexed: 10/07/2023]
Abstract
The amygdala processes positive and negative valence and contributes to addiction, but the cell-type-specific gene regulatory programs involved are unknown. We generated an atlas of single-nucleus gene expression and chromatin accessibility in the amygdala of outbred rats with high and low cocaine addiction-like behaviors following prolonged abstinence. Differentially expressed genes between the high and low groups were enriched for energy metabolism across cell types. Rats with high addiction index (AI) showed increased relapse-like behaviors and GABAergic transmission in the amygdala. Both phenotypes were reversed by pharmacological inhibition of the glyoxalase 1 enzyme, which metabolizes methylglyoxal-a GABAA receptor agonist produced by glycolysis. Differences in chromatin accessibility between high and low AI rats implicated pioneer transcription factors in the basic helix-loop-helix, FOX, SOX and activator protein 1 families. We observed opposite regulation of chromatin accessibility across many cell types. Most notably, excitatory neurons had greater accessibility in high AI rats and inhibitory neurons had greater accessibility in low AI rats.
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Affiliation(s)
- Jessica L Zhou
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Aaron J Ho
- Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Marsida Kallupi
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Narayan Pokhrel
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Hai-Ri Li
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Daniel Munro
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Olivier George
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Graham McVicker
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
- Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
| | - Francesca Telese
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
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10
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Konopka G, Bhaduri A. Functional genomics and systems biology in human neuroscience. Nature 2023; 623:274-282. [PMID: 37938705 DOI: 10.1038/s41586-023-06686-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023]
Abstract
Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.
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Affiliation(s)
- Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA, USA.
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11
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Weber AI, Parthasarathy S, Borisova E, Epifanova E, Preußner M, Rusanova A, Ambrozkiewicz MC, Bessa P, Newman A, Müller L, Schaal H, Heyd F, Tarabykin V. Srsf1 and Elavl1 act antagonistically on neuronal fate choice in the developing neocortex by controlling TrkC receptor isoform expression. Nucleic Acids Res 2023; 51:10218-10237. [PMID: 37697438 PMCID: PMC10602877 DOI: 10.1093/nar/gkad703] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 07/24/2023] [Accepted: 08/15/2023] [Indexed: 09/13/2023] Open
Abstract
The seat of higher-order cognitive abilities in mammals, the neocortex, is a complex structure, organized in several layers. The different subtypes of principal neurons are distributed in precise ratios and at specific positions in these layers and are generated by the same neural progenitor cells (NPCs), steered by a spatially and temporally specified combination of molecular cues that are incompletely understood. Recently, we discovered that an alternatively spliced isoform of the TrkC receptor lacking the kinase domain, TrkC-T1, is a determinant of the corticofugal projection neuron (CFuPN) fate. Here, we show that the finely tuned balance between TrkC-T1 and the better known, kinase domain-containing isoform, TrkC-TK+, is cell type-specific in the developing cortex and established through the antagonistic actions of two RNA-binding proteins, Srsf1 and Elavl1. Moreover, our data show that Srsf1 promotes the CFuPN fate and Elavl1 promotes the callosal projection neuron (CPN) fate in vivo via regulating the distinct ratios of TrkC-T1 to TrkC-TK+. Taken together, we connect spatio-temporal expression of Srsf1 and Elavl1 in the developing neocortex with the regulation of TrkC alternative splicing and transcript stability and neuronal fate choice, thus adding to the mechanistic and functional understanding of alternative splicing in vivo.
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Affiliation(s)
- A Ioana Weber
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, Takustr. 6, 14195, Berlin, Germany
| | - Srinivas Parthasarathy
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Ekaterina Borisova
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
- Research Institute of Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634009, Tomsk, Russia
| | - Ekaterina Epifanova
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Marco Preußner
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, Takustr. 6, 14195, Berlin, Germany
| | - Alexandra Rusanova
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
- Research Institute of Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634009, Tomsk, Russia
| | - Mateusz C Ambrozkiewicz
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Paraskevi Bessa
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Andrew G Newman
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Lisa Müller
- Heinrich Heine Universität Düsseldorf, Institute of Virology, Medical Faculty, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Heiner Schaal
- Heinrich Heine Universität Düsseldorf, Institute of Virology, Medical Faculty, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Florian Heyd
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, Takustr. 6, 14195, Berlin, Germany
| | - Victor Tarabykin
- Charité Universitätsmedizin Berlin, Institute of Cell Biology and Neurobiology, Charitéplatz 1, 10117 Berlin, Germany
- Institute of Neuroscience, Lobachevsky State University of Nizhny Novgorod, 603950, Nizhny Novgorod Oblast, Russia
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12
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Lee BR, Dalley R, Miller JA, Chartrand T, Close J, Mann R, Mukora A, Ng L, Alfiler L, Baker K, Bertagnolli D, Brouner K, Casper T, Csajbok E, Donadio N, Driessens SLW, Egdorf T, Enstrom R, Galakhova AA, Gary A, Gelfand E, Goldy J, Hadley K, Heistek TS, Hill D, Hou WH, Johansen N, Jorstad N, Kim L, Kocsis AK, Kruse L, Kunst M, León G, Long B, Mallory M, Maxwell M, McGraw M, McMillen D, Melief EJ, Molnar G, Mortrud MT, Newman D, Nyhus J, Opitz-Araya X, Ozsvár A, Pham T, Pom A, Potekhina L, Rajanbabu R, Ruiz A, Sunkin SM, Szöts I, Taskin N, Thyagarajan B, Tieu M, Trinh J, Vargas S, Vumbaco D, Waleboer F, Walling-Bell S, Weed N, Williams G, Wilson J, Yao S, Zhou T, Barzó P, Bakken T, Cobbs C, Dee N, Ellenbogen RG, Esposito L, Ferreira M, Gouwens NW, Grannan B, Gwinn RP, Hauptman JS, Hodge R, Jarsky T, Keene CD, Ko AL, Korshoej AR, Levi BP, Meier K, Ojemann JG, Patel A, Ruzevick J, Silbergeld DL, Smith K, Sørensen JC, Waters J, Zeng H, Berg J, Capogna M, Goriounova NA, Kalmbach B, de Kock CPJ, Mansvelder HD, Sorensen SA, Tamas G, Lein ES, Ting JT. Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science 2023; 382:eadf6484. [PMID: 37824669 DOI: 10.1126/science.adf6484] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
Human cortex transcriptomic studies have revealed a hierarchical organization of γ-aminobutyric acid-producing (GABAergic) neurons from subclasses to a high diversity of more granular types. Rapid GABAergic neuron viral genetic labeling plus Patch-seq (patch-clamp electrophysiology plus single-cell RNA sequencing) sampling in human brain slices was used to reliably target and analyze GABAergic neuron subclasses and individual transcriptomic types. This characterization elucidated transitions between PVALB and SST subclasses, revealed morphological heterogeneity within an abundant transcriptomic type, identified multiple spatially distinct types of the primate-specialized double bouquet cells (DBCs), and shed light on cellular differences between homologous mouse and human neocortical GABAergic neuron types. These results highlight the importance of multimodal phenotypic characterization for refinement of emerging transcriptomic cell type taxonomies and for understanding conserved and specialized cellular properties of human brain cell types.
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Affiliation(s)
- Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lauren Alfiler
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Eva Csajbok
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Stan L W Driessens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Enstrom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Emily Gelfand
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Dijon Hill
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wen-Hsien Hou
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Nik Jorstad
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Agnes Katalin Kocsis
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Kunst
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Gabriela León
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Erica J Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | | | - Dakota Newman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Attila Ozsvár
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | | | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ram Rajanbabu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ildikó Szöts
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Trinh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Sara Vargas
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Vumbaco
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Natalie Weed
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julia Wilson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Zhou
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Pál Barzó
- Department of Neurosurgery, University of Szeged, 6725 Szeged, Hungary
| | - Trygve Bakken
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Richard G Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Benjamin Grannan
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Ryder P Gwinn
- Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Jason S Hauptman
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | | | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kaare Meier
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Anesthesiology, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Anoop Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jacob Ruzevick
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jens Christian Sørensen
- Department of Neurosurgery, Aarhus University Hospital, 8200 Aarhus, Denmark
- Center for Experimental Neuroscience, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Marco Capogna
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | - Huib D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, 1081 HV, Netherlands
| | | | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
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13
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Kang B, Yang Y, Hu K, Ruan X, Liu YL, Lee P, Lee J, Wang J, Zhang X. Infernape uncovers cell type-specific and spatially resolved alternative polyadenylation in the brain. Genome Res 2023; 33:1774-1787. [PMID: 37907328 PMCID: PMC10691540 DOI: 10.1101/gr.277864.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023]
Abstract
Differential polyadenylation sites (PAs) critically regulate gene expression, but their cell type-specific usage and spatial distribution in the brain have not been systematically characterized. Here, we present Infernape, which infers and quantifies PA usage from single-cell and spatial transcriptomic data and show its application in the mouse brain. Infernape uncovers alternative intronic PAs and 3'-UTR lengthening during cortical neurogenesis. Progenitor-neuron comparisons in the excitatory and inhibitory neuron lineages show overlapping PA changes in embryonic brains, suggesting that the neural proliferation-differentiation axis plays a prominent role. In the adult mouse brain, we uncover cell type-specific PAs and visualize such events using spatial transcriptomic data. Over two dozen neurodevelopmental disorder-associated genes such as Csnk2a1 and Mecp2 show differential PAs during brain development. This study presents Infernape to identify PAs from scRNA-seq and spatial data, and highlights the role of alternative PAs in neuronal gene regulation.
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Affiliation(s)
- Bowei Kang
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Yalan Yang
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Kaining Hu
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Xiangbin Ruan
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Yi-Lin Liu
- Department of Statistics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Pinky Lee
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jasper Lee
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jingshu Wang
- Department of Statistics, The University of Chicago, Chicago, Illinois 60637, USA;
| | - Xiaochang Zhang
- Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA;
- The Neuroscience Institute, The University of Chicago, Chicago, Illinois 60637, USA
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14
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Reece AS, Hulse GK. Perturbation of 3D nuclear architecture, epigenomic dysregulation and aging, and cannabinoid synaptopathy reconfigures conceptualization of cannabinoid pathophysiology: part 1-aging and epigenomics. Front Psychiatry 2023; 14:1182535. [PMID: 37732074 PMCID: PMC10507876 DOI: 10.3389/fpsyt.2023.1182535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023] Open
Abstract
Much recent attention has been directed toward the spatial organization of the cell nucleus and the manner in which three-dimensional topologically associated domains and transcription factories are epigenetically coordinated to precisely bring enhancers into close proximity with promoters to control gene expression. Twenty lines of evidence robustly implicate cannabinoid exposure with accelerated organismal and cellular aging. Aging has recently been shown to be caused by increased DNA breaks. These breaks rearrange and maldistribute the epigenomic machinery to weaken and reverse cellular differentiation, cause genome-wide DNA demethylation, reduce gene transcription, and lead to the inhibition of developmental pathways, which contribute to the progressive loss of function and chronic immune stimulation that characterize cellular aging. Both cell lineage-defining superenhancers and the superanchors that control them are weakened. Cannabis exposure phenocopies the elements of this process and reproduces DNA and chromatin breakages, reduces the DNA, RNA protein and histone synthesis, interferes with the epigenomic machinery controlling both DNA and histone modifications, induces general DNA hypomethylation, and epigenomically disrupts both the critical boundary elements and the cohesin motors that create chromatin loops. This pattern of widespread interference with developmental programs and relative cellular dedifferentiation (which is pro-oncogenic) is reinforced by cannabinoid impairment of intermediate metabolism (which locks in the stem cell-like hyper-replicative state) and cannabinoid immune stimulation (which perpetuates and increases aging and senescence programs, DNA damage, DNA hypomethylation, genomic instability, and oncogenesis), which together account for the diverse pattern of teratologic and carcinogenic outcomes reported in recent large epidemiologic studies in Europe, the USA, and elsewhere. It also accounts for the prominent aging phenotype observed clinically in long-term cannabis use disorder and the 20 characteristics of aging that it manifests. Increasing daily cannabis use, increasing use in pregnancy, and exponential dose-response effects heighten the epidemiologic and clinical urgency of these findings. Together, these findings indicate that cannabinoid genotoxicity and epigenotoxicity are prominent features of cannabis dependence and strongly indicate coordinated multiomics investigations of cannabinoid genome-epigenome-transcriptome-metabolome, chromatin conformation, and 3D nuclear architecture. Considering the well-established exponential dose-response relationships, the diversity of cannabinoids, and the multigenerational nature of the implications, great caution is warranted in community cannabinoid penetration.
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Affiliation(s)
- Albert Stuart Reece
- Division of Psychiatry, University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Gary Kenneth Hulse
- Division of Psychiatry, University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
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15
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Ament SA, Poulopoulos A. The brain's dark transcriptome: Sequencing RNA in distal compartments of neurons and glia. Curr Opin Neurobiol 2023; 81:102725. [PMID: 37196598 PMCID: PMC10524153 DOI: 10.1016/j.conb.2023.102725] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/22/2023] [Accepted: 04/02/2023] [Indexed: 05/19/2023]
Abstract
Transcriptomic approaches are powerful strategies to map the molecular diversity of cells in the brain. Single-cell genomic atlases have now been compiled for entire mammalian brains. However, complementary techniques are only just beginning to map the subcellular transcriptomes from distal cellular compartments. We review single-cell datasets alongside subtranscriptome data from the mammalian brain to explore the development of cellular and subcellular diversity. We discuss how single-cell RNA-seq misses transcripts localized away from cell bodies, which form the 'dark transcriptome' of the brain: a collection of subtranscriptomes in dendrites, axons, growth cones, synapses, and endfeet with important roles in brain development and function. Recent advances in subcellular transcriptome sequencing are beginning to reveal these elusive pools of RNA. We outline the success stories to date in uncovering the constituent subtranscriptomes of neurons and glia, as well as present the emerging toolkit that is accelerating the pace of subtranscriptome discovery.
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Affiliation(s)
- Seth A Ament
- Department of Psychiatry, UM-MIND, and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alexandros Poulopoulos
- Department of Pharmacology and UM-MIND, University of Maryland School of Medicine, Baltimore, MD, USA.
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16
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Hawrylycz M, Martone ME, Ascoli GA, Bjaalie JG, Dong HW, Ghosh SS, Gillis J, Hertzano R, Haynor DR, Hof PR, Kim Y, Lein E, Liu Y, Miller JA, Mitra PP, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray PL, Sanchez R, Regev A, Ropelewski A, Scheuermann RH, Tan SZK, Thompson CL, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey TL, Baker C, Baker KS, Baker PM, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D’Orazi F, Degatano K, Dichter B, Ding SL, Dolbeare T, Ecker JR, Fang R, Fillion-Robin JC, Fliss TP, Gee J, Gillespie T, Gouwens N, Zhang GQ, Halchenko YO, Harris NL, Herb BR, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan EA, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews KL, Mezias C, Miller MI, Mollenkopf T, Mufti S, Mungall CJ, Orvis J, Puchades MA, Qu L, Receveur JP, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven CTJ, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang YR, Zheng WJ, Zingg B. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biol 2023; 21:e3002133. [PMID: 37390046 PMCID: PMC10313015 DOI: 10.1371/journal.pbio.3002133] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2023] Open
Abstract
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
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Affiliation(s)
- Michael Hawrylycz
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Maryann E. Martone
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
- San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, Volgenau School of Engineering, George Mason University, Fairfax, Virginia, United States of America
| | - Jan G. Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Jesse Gillis
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Ronna Hertzano
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David R. Haynor
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, United States of America
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yufeng Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Jeremy A. Miller
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Partha P. Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Eran Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States of America
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Hanchuan Peng
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Patrick L. Ray
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Raymond Sanchez
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Aviv Regev
- Genentech, South San Francisco, California, United States of America
| | - Alex Ropelewski
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | | | - Shawn Zheng Kai Tan
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Carol L. Thompson
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Timothy Tickle
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hagen Tilgner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, United States of America
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Owen White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Aevermann
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - David Allemang
- Kitware Inc., Albany, New York, United States of America
| | - Seth Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas L. Athey
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Cody Baker
- CatalystNeuro, Benicia, California, United States of America
| | - Katherine S. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Pamela M. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Anita Bandrowski
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Samik Banerjee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Prajal Bishwakarma
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ambrose Carr
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Min Chen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Roni Choudhury
- Kitware Inc., Albany, New York, United States of America
| | - Jonah Cool
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Heather Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Florence D’Orazi
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Kylee Degatano
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | | | - Timothy P. Fliss
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - James Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Tom Gillespie
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Nathan Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Guo-Qiang Zhang
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hannover, New Hampshire, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Brian R. Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Gregory Hood
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Sam Horvath
- Kitware Inc., Albany, New York, United States of America
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Dorota Jarecka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Shengdian Jiang
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Farzaneh Khajouei
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elizabeth A. Kiernan
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Huseyin Kir
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Boudewijn Lelieveldt
- Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yang Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
| | - Hanqing Liu
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Lijuan Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Anup Markuhar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - James Mathews
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Kaylee L. Mathews
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Chris Mezias
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Michael I. Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Tyler Mollenkopf
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Maja A. Puchades
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Lei Qu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Joseph P. Receveur
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
- Ludwig Institute for Cancer Research, La Jolla, California, United States of America
| | - Nathan Sjoquist
- Microsoft Corporation, Seattle, Washington, United States of America
| | - Brian Staats
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Tward
- UCLA Brain Mapping Center, University of California, Los Angeles, California, United States of America
| | | | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Fangming Xie
- Department of Chemistry and Biochemistry, University of California Los Angeles, California, United States of America
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Zhixi Yun
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Yun Renee Zhang
- J. Craig Venter Institute, La Jolla, California, United States of America
| | - W. Jim Zheng
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Brian Zingg
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
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17
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Liu H, Zeng Q, Zhou J, Bartlett A, Wang BA, Berube P, Tian W, Kenworthy M, Altshul J, Nery JR, Chen H, Castanon RG, Zu S, Li YE, Lucero J, Osteen JK, Pinto-Duarte A, Lee J, Rink J, Cho S, Emerson N, Nunn M, O'Connor C, Yao Z, Smith KA, Tasic B, Zeng H, Luo C, Dixon JR, Ren B, Behrens MM, Ecker JR. Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.536509. [PMID: 37131654 PMCID: PMC10153407 DOI: 10.1101/2023.04.16.536509] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.
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Affiliation(s)
- Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bang-An Wang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Peter Berube
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Huaming Chen
- 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
| | - Songpeng Zu
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chongyuan Luo
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
| | - Jesse R Dixon
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 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
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18
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Zhang M, Pan X, Jung W, Halpern A, Eichhorn SW, Lei Z, Cohen L, Smith KA, Tasic B, Yao Z, Zeng H, Zhuang X. A molecularly defined and spatially resolved cell atlas of the whole mouse brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531348. [PMID: 36945367 PMCID: PMC10028822 DOI: 10.1101/2023.03.06.531348] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3. However, these approaches have only been applied to a few brain regions1-11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in ~8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ~300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.
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Affiliation(s)
- Meng Zhang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- These authors contributed equally
| | - Xingjie Pan
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- These authors contributed equally
| | - Won Jung
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- These authors contributed equally
| | - Aaron Halpern
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Stephen W. Eichhorn
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Zhiyun Lei
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Limor Cohen
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | | | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
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19
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Wang H, Dey O, Lagos WN, Callaway EM. Diversity in spatial frequency, temporal frequency, and speed tuning across mouse visual cortical areas and layers. J Comp Neurol 2022; 530:3226-3247. [PMID: 36070574 PMCID: PMC9588602 DOI: 10.1002/cne.25404] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 11/06/2022]
Abstract
The mouse visual system consists of several visual cortical areas thought to be specialized for different visual features and/or tasks. Previous studies have revealed differences between primary visual cortex (V1) and other higher visual areas, namely, anterolateral (AL) and posteromedial (PM), and their tuning preferences for spatial and temporal frequency. However, these differences have primarily been characterized using methods that are biased toward superficial layers of cortex, such as two-photon calcium imaging. Fewer studies have investigated cell types in deeper layers of these areas and their tuning preferences. Because superficial versus deep-layer neurons and different types of deep-layer neurons are known to have different feedforward and feedback inputs and outputs, comparing the tuning preferences of these groups is important for understanding cortical visual information processing. In this study, we used extracellular electrophysiology and two-photon calcium imaging targeted toward two different layer 5 cell classes to characterize their tuning properties in V1, AL, and PM. We find that deep-layer neurons, similar to superficial layer neurons, are also specialized for different spatial and temporal frequencies, with the strongest differences between AL and V1, and AL and PM, but not V1 and PM. However, we note that the deep-layer neuron populations preferred a larger range of SFs and TFs compared to previous studies. We also find that extratelencephalically projecting layer 5 neurons are more direction selective than intratelencephalically projecting layer 5 neurons.
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Affiliation(s)
- Helen Wang
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Medical Scientist Training Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Oyshi Dey
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Willian N. Lagos
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Edward M. Callaway
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
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20
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Lloyd S, Larivée A, Lutz PE. Homeorhesis: envisaging the logic of life trajectories in molecular research on trauma and its effects. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2022; 44:65. [PMID: 36417009 DOI: 10.1007/s40656-022-00542-7] [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: 12/22/2021] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
What sets someone on a life trajectory? This question is at the heart of studies of 21st-century neurosciences that build on scientific models developed over the last 150 years that attempt to link psychopathology risk and human development. Historically, this research has documented persistent effects of singular, negative life experiences on people's subsequent development. More recently, studies have documented neuromolecular effects of early life adversity on life trajectories, resulting in models that frame lives as disproportionately affected by early negative experiences. This view is dominant, despite little evidence of the stability of the presumably early-developed molecular traits and their potential effects on phenotypes. We argue that in the context of gaps in knowledge and the need for scientists to reason across molecular and phenotypic scales, as well as time spans that can extend beyond an individual's life, specific interpretative frameworks shape the ways in which individual scientific findings are assessed. In the process, scientific reasoning oscillates between understandings of cellular homeostasis and organisms' homeorhesis, or life trajectory. Biologist and historian François Jacob described this framework as the "attitude" that researchers bring to bear on their "objects" of study. Through an analysis of, first, historical and contemporary scientific literature and then ethnographic research with neuroscientists, we consider how early life trauma came to be associated with specific psychological and neurobiological effects grounded in understandings of life trajectories. We conclude with a consideration of the conceptual, ontological, and ethical implications of interpreting life trajectories as the result of the persistence of long-embodied biological traits, persistent life environments, or both.
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Affiliation(s)
- Stephanie Lloyd
- Department of Anthropology, Université Laval, Québec, Québec, Canada.
| | - Alexandre Larivée
- Department of Anthropology, Université Laval, Québec, Québec, Canada
| | - Pierre-Eric Lutz
- Douglas Mental Health University Institute, McGill University, Montréal, Canada
- Centre National de la Recherche Scientifique, Fédération de Médecine Translationnelle de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, Université de Strasbourg, 67000, Strasbourg, France
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21
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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: 11] [Impact Index Per Article: 5.5] [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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22
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Temporally divergent regulatory mechanisms govern neuronal diversification and maturation in the mouse and marmoset neocortex. Nat Neurosci 2022; 25:1049-1058. [PMID: 35915179 PMCID: PMC9343253 DOI: 10.1038/s41593-022-01123-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/16/2022] [Indexed: 11/08/2022]
Abstract
Mammalian neocortical neurons span one of the most diverse cell type spectra of any tissue. Cortical neurons are born during embryonic development, and their maturation extends into postnatal life. The regulatory strategies underlying progressive neuronal development and maturation remain unclear. Here we present an integrated single-cell epigenomic and transcriptional analysis of individual mouse and marmoset cortical neuron classes, spanning both early postmitotic stages of identity acquisition and later stages of neuronal plasticity and circuit integration. We found that, in both species, the regulatory strategies controlling early and late stages of pan-neuronal development diverge. Early postmitotic neurons use more widely shared and evolutionarily conserved molecular regulatory programs. In contrast, programs active during later neuronal maturation are more brain- and neuron-specific and more evolutionarily divergent. Our work uncovers a temporal shift in regulatory choices during neuronal diversification and maturation in both mice and marmosets, which likely reflects unique evolutionary constraints on distinct events of neuronal development in the neocortex.
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23
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Zeng H. What is a cell type and how to define it? Cell 2022; 185:2739-2755. [PMID: 35868277 DOI: 10.1016/j.cell.2022.06.031] [Citation(s) in RCA: 118] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/20/2022]
Abstract
Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.
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Affiliation(s)
- Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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24
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Huang W, Yuan Z, Gu H. Exploring epigenomic mechanisms of neural tube defects using multi-omics methods and data. Ann N Y Acad Sci 2022; 1515:50-60. [PMID: 35666948 DOI: 10.1111/nyas.14802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Neural tube defects (NTDs) are a heterogeneous set of malformations attributed to disruption in normal neural tube closure during early embryogenesis. An in-depth understanding of NTD etiology and mechanisms remains elusive, however. Among the proposed mechanisms, epigenetic changes are thought to play an important role in the formation of NTDs. Epigenomics covers a wide spectrum of genomic DNA sequence modifications that can be investigated via high-throughput techniques. Recent advances in epigenomic technologies have enabled epigenetic studies of congenital malformations and facilitated the integration of big data into the understanding of NTDs. Herein, we review clinical epigenomic data that focuses on DNA methylation, histone modification, and miRNA alterations in human neural tissues, placental tissues, and leukocytes to explore potential mechanisms by which candidate genes affect human NTD pathogenesis. We discuss the links between epigenomics and gene regulatory mechanisms, and the effects of epigenetic alterations in human tissues on neural tube closure.
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Affiliation(s)
- Wanqi Huang
- Key Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital, China Medical University, Shenyang, China
| | - Zhengwei Yuan
- Key Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital, China Medical University, Shenyang, China
| | - Hui Gu
- Key Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital, China Medical University, Shenyang, China
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25
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Fu J, Qin W, Tong Q, Li Z, Shao Y, Liu Z, Liu C, Wang Z, Xu X. A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients. Cancer Med 2022; 11:4721-4735. [PMID: 35637633 PMCID: PMC9741990 DOI: 10.1002/cam4.4838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all-cause HCC and nonviral HCC. However, whether DNA methylation-driver genes could be used for predicting survival, the probability of hepatitis-positive HCC remains unclear. METHODS In this study, DNA methylation-driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis-positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT. RESULTS Through random forest screening and Cox progression analysis, 10 prognostic methylation-driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11-114B7.6, RP11-149I23.3, RP11-383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA-HCC and the China-HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794-0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary. CONCLUSIONS Taken together, our study explored a novel DNA methylation-driver gene risk score signature and an efficient nomogram for long-term survival prediction of hepatitis-positive HCC patients.
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Affiliation(s)
- Jie Fu
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Wei Qin
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Qing Tong
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Zhenghao Li
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Yaoli Shao
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Zhiqiang Liu
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Chun Liu
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Zicheng Wang
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina
| | - Xundi Xu
- Department of General SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaChina,Department of General SurgerySouth China Hospital of Shenzhen UniversityShenzhenChina
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26
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Duttke SH, Montilla-Perez P, Chang MW, Li H, Chen H, Carrette LLG, de Guglielmo G, George O, Palmer AA, Benner C, Telese F. Glucocorticoid Receptor-Regulated Enhancers Play a Central Role in the Gene Regulatory Networks Underlying Drug Addiction. Front Neurosci 2022; 16:858427. [PMID: 35651629 PMCID: PMC9149415 DOI: 10.3389/fnins.2022.858427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
Substance abuse and addiction represent a significant public health problem that impacts multiple dimensions of society, including healthcare, the economy, and the workforce. In 2021, over 100,000 drug overdose deaths were reported in the US, with an alarming increase in fatalities related to opioids and psychostimulants. Understanding the fundamental gene regulatory mechanisms underlying addiction and related behaviors could facilitate more effective treatments. To explore how repeated drug exposure alters gene regulatory networks in the brain, we combined capped small (cs)RNA-seq, which accurately captures nascent-like initiating transcripts from total RNA, with Hi-C and single nuclei (sn)ATAC-seq. We profiled initiating transcripts in two addiction-related brain regions, the prefrontal cortex (PFC) and the nucleus accumbens (NAc), from rats that were never exposed to drugs or were subjected to prolonged abstinence after oxycodone or cocaine intravenous self-administration (IVSA). Interrogating over 100,000 active transcription start regions (TSRs) revealed that most TSRs had hallmarks of bonafide enhancers and highlighted the KLF/SP1, RFX, and AP1 transcription factors families as central to establishing brain-specific gene regulatory programs. Analysis of rats with addiction-like behaviors versus controls identified addiction-associated repression of transcription at regulatory enhancers recognized by nuclear receptor subfamily 3 group C (NR3C) factors, including glucocorticoid receptors. Cell-type deconvolution analysis using snATAC-seq uncovered a potential role of glial cells in driving the gene regulatory programs associated with addiction-related phenotypes. These findings highlight the power of advanced transcriptomics methods to provide insight into how addiction perturbs gene regulatory programs in the brain.
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Affiliation(s)
- Sascha H. Duttke
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
- School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | | | - Max W. Chang
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Hairi Li
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Hao Chen
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science Center, Memphis, TN, United States
| | | | - Giordano de Guglielmo
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Olivier George
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Abraham A. Palmer
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Christopher Benner
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Francesca Telese
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
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27
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Hintiryan H, Dong HW. Brain Networks of Connectionally Unique Basolateral Amygdala Cell Types. Neurosci Insights 2022; 17:26331055221080175. [PMID: 35252870 PMCID: PMC8891918 DOI: 10.1177/26331055221080175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/27/2022] [Indexed: 11/25/2022] Open
Abstract
Different brain regions structurally interconnected through networks regulate behavior output. Therefore, understanding the functional organization of the brain in health and disease necessitates a foundational anatomic roadmap to its network organization. To provide this to the research community, our lab has systematically traced thousands of pathways in the mouse brain and has applied computational measures to determine the network architecture of major brain systems. Toward this effort, the brain-wide networks of the basolateral amygdalar complex (BLA) were recently generated. The data revealed uniquely connected cell types within the same BLA nucleus that were constituents of distinct neural networks. Here, we elaborate on how these connectionally unique BLA cell types fit within the larger cortico-basal ganglia and limbic networks that were previously described by our team. The significance and utility of high quality, detailed anatomic data is also discussed.
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Affiliation(s)
- Houri Hintiryan
- Department of Neurobiology, Brain Research & Artificial Intelligence Nexus (B.R.A.I.N), University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Hong-Wei Dong
- Department of Neurobiology, Brain Research & Artificial Intelligence Nexus (B.R.A.I.N), University of California, Los Angeles (UCLA), Los Angeles, CA, USA
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28
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Morales L, González-Alonso A, Desfilis E, Medina L. Precise Mapping of Otp Expressing Cells Across Different Pallial Regions Throughout Ontogenesis Using Otp-Specific Reporter Transgenic Mice. Front Neural Circuits 2022; 16:831074. [PMID: 35250495 PMCID: PMC8891171 DOI: 10.3389/fncir.2022.831074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/24/2022] [Indexed: 12/14/2022] Open
Abstract
Taking advantage of two Otp-specific reporter lines of transgenic mice (Otp-eGFP and Otp-Cre; Rpl22-HA), we identify and describe different Otp cell populations across various pallial regions, including the pallial amygdala, the piriform cortex, the mesocortex, the neocortex, and the hippocampal complex. Some of these populations can be followed throughout development, suggesting migration from external sources (for example, those of the pallial amygdala and at least some of the cingulate cortex). Other cells become visible during postnatal development (some of those in the neocortex and hippocampal formation) or in adulthood (those of the parahippocampal lobe), and seem to be produced locally. We discuss the possible role of Otp in these different populations during different moments of ontogenesis. We also analyze the connectivity patterns of some of these cells and discuss their functional implications. For example, our data suggest that Otp cells of the pallial amygdala might be engaged in networks with other Otp cells of the medial amygdala with the same embryonic origin, and may regulate specific aspects of social behavior. Regarding Otp cells in the parahippocampal lobe, they seem to be projection neurons and may regulate hippocampal function during spatial navigation and memory formation. The two reporter transgenic mice employed here provide very powerful tools for high precision studies on these different Otp cells of the pallium, but careful attention should be paid to the age and to differences between lines.
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Affiliation(s)
- Lorena Morales
- Departament de Medicina Experimental, Universitat de Lleida, Lleida, Spain
- Laboratory of Evolutionary Developmental Neurobiology, Lleida’s Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), Lleida, Spain
| | - Alba González-Alonso
- Departament de Medicina Experimental, Universitat de Lleida, Lleida, Spain
- Laboratory of Evolutionary Developmental Neurobiology, Lleida’s Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), Lleida, Spain
| | - Ester Desfilis
- Departament de Medicina Experimental, Universitat de Lleida, Lleida, Spain
- Laboratory of Evolutionary Developmental Neurobiology, Lleida’s Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), Lleida, Spain
- Serra Húnter Fellows, Lleida, Spain
| | - Loreta Medina
- Departament de Medicina Experimental, Universitat de Lleida, Lleida, Spain
- Laboratory of Evolutionary Developmental Neurobiology, Lleida’s Institute for Biomedical Research Dr. Pifarré Foundation (IRBLleida), Lleida, Spain
- Serra Húnter Fellows, Lleida, Spain
- *Correspondence: Loreta Medina, ,
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29
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Reece AS, Hulse GK. Congenital anomaly epidemiological correlates of Δ8THC across USA 2003-16: panel regression and causal inferential study. ENVIRONMENTAL EPIGENETICS 2022; 8:dvac012. [PMID: 35782486 PMCID: PMC9245652 DOI: 10.1093/eep/dvac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 05/16/2023]
Abstract
Δ8-Tetrahydrocannabinol (Δ8THC) is marketed in many US states as 'legal weed'. Concerns exist relating to class-wide genotoxic cannabinoid effects. We conducted an epidemiological investigation of Δ8THC-related genotoxicity expressed as 57 congenital anomaly (CA) rates (CARs) in the USA. CARs were taken from the Centers for Disease Control, Atlanta, Georgia. Drug exposure data were taken from the National Survey of Drug Use and Health, with a response rate of 74.1%. Ethnicity and income data were taken from the US Census Bureau. National cannabinoid exposure was taken from Drug Enforcement Agency publications and multiplied by state cannabis use data to derive state-based estimates of Δ8THC exposure. At bivariate continuous analysis, Δ8THC was associated with 23 CAs on raw CA rates, 33 CARs after correction for early termination for anomaly estimates and 41 on a categorical analysis comparing the highest and lowest exposure quintiles. At inverse probability weighted multivariable additive and interactive models lagged to 0, 2 and 4 years, Δ8THC was linked with 39, 8, 4 and 9 CAs. Chromosomal, cardiovascular, gastrointestinal, genitourinary, limb, central nervous system (CNS) and face systems were particularly affected. The minimum E-values ranged to infinity. Both the number of anomalies implicated and the effect sizes demonstrated were much greater for Δ8THC than for tobacco and alcohol combined. Δ8THC appears epidemiologically to be more strongly associated with many CAs than for tobacco and alcohol and is consistent with a cannabinoid class genotoxic/epigenotoxic effect. Quantitative causality criteria were fulfilled, and causal relationships either for Δ8THC or for cannabinoid/s, for which it is a surrogate marker, may be in operation.
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Affiliation(s)
- Albert Stuart Reece
- *Correspondence address. 39 Gladstone Rd., Highgate Hill, Brisbane, QLD, Australia. Tel: (617) +3844-4000; Fax: +(617) 3844-4015; E-mail:
| | - Gary Kenneth Hulse
- Division of Psychiatry, University of Western Australia, Crawley, WA 6009, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
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30
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Nano PR, Nguyen CV, Mil J, Bhaduri A. Cortical Cartography: Mapping Arealization Using Single-Cell Omics Technology. Front Neural Circuits 2021; 15:788560. [PMID: 34955761 PMCID: PMC8707733 DOI: 10.3389/fncir.2021.788560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 11/05/2021] [Indexed: 11/30/2022] Open
Abstract
The cerebral cortex derives its cognitive power from a modular network of specialized areas processing a multitude of information. The assembly and organization of these regions is vital for human behavior and perception, as evidenced by the prevalence of area-specific phenotypes that manifest in neurodevelopmental and psychiatric disorders. Generations of scientists have examined the architecture of the human cortex, but efforts to capture the gene networks which drive arealization have been hampered by the lack of tractable models of human neurodevelopment. Advancements in "omics" technologies, imaging, and computational power have enabled exciting breakthroughs into the molecular and structural characteristics of cortical areas, including transcriptomic, epigenomic, metabolomic, and proteomic profiles of mammalian models. Here we review the single-omics atlases that have shaped our current understanding of cortical areas, and their potential to fuel a new era of multi-omic single-cell endeavors to interrogate both the developing and adult human cortex.
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Affiliation(s)
| | | | | | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, United States
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31
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Treweek JB. A repurposed vaccine grants durable anterograde access to neural circuits. Nat Methods 2021; 18:1459-1461. [PMID: 34824478 DOI: 10.1038/s41592-021-01345-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jennifer B Treweek
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
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32
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Peng H, Xie P, Liu L, Kuang X, Wang Y, Qu L, Gong H, Jiang S, Li A, Ruan Z, Ding L, Yao Z, Chen C, Chen M, Daigle TL, Dalley R, Ding Z, Duan Y, Feiner A, He P, Hill C, Hirokawa KE, Hong G, Huang L, Kebede S, Kuo HC, Larsen R, Lesnar P, Li L, Li Q, Li X, Li Y, Li Y, Liu A, Lu D, Mok S, Ng L, Nguyen TN, Ouyang Q, Pan J, Shen E, Song Y, Sunkin SM, Tasic B, Veldman MB, Wakeman W, Wan W, Wang P, Wang Q, Wang T, Wang Y, Xiong F, Xiong W, Xu W, Ye M, Yin L, Yu Y, Yuan J, Yuan J, Yun Z, Zeng S, Zhang S, Zhao S, Zhao Z, Zhou Z, Huang ZJ, Esposito L, Hawrylycz MJ, Sorensen SA, Yang XW, Zheng Y, Gu Z, Xie W, Koch C, Luo Q, Harris JA, Wang Y, Zeng H. Morphological diversity of single neurons in molecularly defined cell types. Nature 2021; 598:174-181. [PMID: 34616072 PMCID: PMC8494643 DOI: 10.1038/s41586-021-03941-1] [Citation(s) in RCA: 139] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 08/24/2021] [Indexed: 12/23/2022]
Abstract
Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.
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Affiliation(s)
- Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA, USA.
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
| | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Lei Qu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Shengdian Jiang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zongcai Ruan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengya Chen
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | | | | | - Zhangcan Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yanjun Duan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Aaron Feiner
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ping He
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Chris Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Guodong Hong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Lei Huang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Longfei Li
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Qi Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Li
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - An Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | | | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Thuc Nghi Nguyen
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Qiang Ouyang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jintao Pan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yuanyuan Song
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | | | - Matthew B Veldman
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Wan Wan
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Peng Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tao Wang
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Yaping Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Feng Xiong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Wenjie Xu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Min Ye
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Lulu Yin
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yang Yu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jia Yuan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zhixi Yun
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Shichen Zhang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Sujun Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zijun Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | | | | | | | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Zhongze Gu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Wei Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | | | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
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33
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34
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Zhang M, Eichhorn SW, Zingg B, Yao Z, Cotter K, Zeng H, Dong H, Zhuang X. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 2021; 598:137-143. [PMID: 34616063 PMCID: PMC8494645 DOI: 10.1038/s41586-021-03705-x] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 06/08/2021] [Indexed: 11/09/2022]
Abstract
A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.
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Affiliation(s)
- Meng Zhang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Stephen W Eichhorn
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Brian Zingg
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kaelan Cotter
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongwei Dong
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Department of Physics, Harvard University, Cambridge, MA, USA.
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35
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Liu H, Zhou J, Tian W, Luo C, Bartlett A, Aldridge A, Lucero J, Osteen JK, Nery JR, Chen H, Rivkin A, Castanon RG, Clock B, Li YE, Hou X, Poirion OB, Preissl S, Pinto-Duarte A, O'Connor C, Boggeman L, Fitzpatrick C, Nunn M, Mukamel EA, Zhang Z, Callaway EM, Ren B, Dixon JR, Behrens MM, Ecker JR. DNA methylation atlas of the mouse brain at single-cell resolution. Nature 2021; 598:120-128. [PMID: 34616061 PMCID: PMC8494641 DOI: 10.1038/s41586-020-03182-8] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.
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Affiliation(s)
- Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Chongyuan Luo
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jacinta Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Huaming Chen
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Angeline Rivkin
- 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
| | - Ben Clock
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Yang Eric Li
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Xiaomeng Hou
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Olivier B Poirion
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Lara Boggeman
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Conor Fitzpatrick
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Zhuzhu Zhang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Edward M Callaway
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Institute of Genomic Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Jesse R Dixon
- Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 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.
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