1
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Carmona LM, Thomas ED, Smith K, Tasic B, Costa RM, Nelson A. Topographical and cell type-specific connectivity of rostral and caudal forelimb corticospinal neuron populations. Cell Rep 2024; 43:113993. [PMID: 38551963 DOI: 10.1016/j.celrep.2024.113993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/07/2024] [Accepted: 03/07/2024] [Indexed: 04/09/2024] Open
Abstract
Corticospinal neurons (CSNs) synapse directly on spinal neurons, a diverse assortment of cells with unique structural and functional properties necessary for body movements. CSNs modulating forelimb behavior fractionate into caudal forelimb area (CFA) and rostral forelimb area (RFA) motor cortical populations. Despite their prominence, the full diversity of spinal neurons targeted by CFA and RFA CSNs is uncharted. Here, we use anatomical and RNA sequencing methods to show that CSNs synapse onto a remarkably selective group of spinal cell types, favoring inhibitory populations that regulate motoneuron activity and gate sensory feedback. CFA and RFA CSNs target similar spinal neuron types, with notable exceptions that suggest that these populations differ in how they influence behavior. Finally, axon collaterals of CFA and RFA CSNs target similar brain regions yet receive highly divergent inputs. These results detail the rules of CSN connectivity throughout the brain and spinal cord for two regions critical for forelimb behavior.
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Affiliation(s)
- Lina Marcela Carmona
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Eric D Thomas
- Allen Institute for Brain Science, Allen Institute, Seattle, WA, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Allen Institute, Seattle, WA, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Allen Institute, Seattle, WA, USA
| | - Rui M Costa
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Allen Institute for Brain Science, Allen Institute, Seattle, WA, USA
| | - Anders Nelson
- Center for Neural Science, New York University, New York, NY 10003, USA.
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2
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven CTJ, Sullivan HA, Sun N, Kellis M, Tasic B, Wickersham I, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. eLife 2024; 12:RP87866. [PMID: 38319699 PMCID: PMC10942611 DOI: 10.7554/elife.87866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024] Open
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4130 retrogradely labeled cells and 2914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shenqin Yao
- Allen Institute for Brain ScienceSeattleUnited States
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | | | - Heather Anne Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | | | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Xiaoyin Chen
- Allen Institute for Brain ScienceSeattleUnited States
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3
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Winter CC, Jacobi A, Su J, Chung L, van Velthoven CTJ, Yao Z, Lee C, Zhang Z, Yu S, Gao K, Duque Salazar G, Kegeles E, Zhang Y, Tomihiro MC, Zhang Y, Yang Z, Zhu J, Tang J, Song X, Donahue RJ, Wang Q, McMillen D, Kunst M, Wang N, Smith KA, Romero GE, Frank MM, Krol A, Kawaguchi R, Geschwind DH, Feng G, Goodrich LV, Liu Y, Tasic B, Zeng H, He Z. A transcriptomic taxonomy of mouse brain-wide spinal projecting neurons. Nature 2023; 624:403-414. [PMID: 38092914 PMCID: PMC10719099 DOI: 10.1038/s41586-023-06817-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
The brain controls nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from the brain to the spinal cord. However, a comprehensive molecular characterization of brain-wide SPNs is still lacking. Here we transcriptionally profiled a total of 65,002 SPNs, identified 76 region-specific SPN types, and mapped these types into a companion atlas of the whole mouse brain1. This taxonomy reveals a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs from the cortex, red nucleus and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) heterogeneous populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands related to the activities of the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain and reticular formation for 'gain setting' of brain-spinal signals. In addition, this atlas revealed a LIM homeobox transcription factor code that parcellates the reticulospinal neurons into five molecularly distinct and spatially segregated populations. Finally, we found transcriptional signatures of a subset of SPNs with large soma size and correlated these with fast-firing electrophysiological properties. Together, this study establishes a comprehensive taxonomy of brain-wide SPNs and provides insight into the functional organization of SPNs in mediating brain control of bodily functions.
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Affiliation(s)
- Carla C Winter
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- PhD Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Harvard-MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Anne Jacobi
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- F. Hoffman-La Roche, pRED, Basel, Switzerland.
| | - Junfeng Su
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Leeyup Chung
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zicong Zhang
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Shuguang Yu
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Kun Gao
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Geraldine Duque Salazar
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Evgenii Kegeles
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- PhD Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Yu Zhang
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Makenzie C Tomihiro
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Yiming Zhang
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Zhiyun Yang
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Junjie Zhu
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Jing Tang
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Xuan Song
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Ryan J Donahue
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Qing Wang
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | | | | | - Ning Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Gabriel E Romero
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Michelle M Frank
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Alexandra Krol
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Riki Kawaguchi
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lisa V Goodrich
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Yuanyuan Liu
- Somatosensation and Pain Unit, National Institute of Dental and Craniofacial Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Zhigang He
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
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4
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Zu S, Li YE, Wang K, Armand EJ, Mamde S, Amaral ML, Wang Y, Chu A, Xie Y, Miller M, Xu J, Wang Z, Zhang K, Jia B, Hou X, Lin L, Yang Q, Lee S, Li B, Kuan S, Liu H, Zhou J, Pinto-Duarte A, Lucero J, Osteen J, Nunn M, Smith KA, Tasic B, Yao Z, Zeng H, Wang Z, Shang J, Behrens MM, Ecker JR, Wang A, Preissl S, Ren B. Single-cell analysis of chromatin accessibility in the adult mouse brain. Nature 2023; 624:378-389. [PMID: 38092917 PMCID: PMC10719105 DOI: 10.1038/s41586-023-06824-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.
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Affiliation(s)
- 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
- Department of Neurosurgery and Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Kangli Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Sainath Mamde
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Maria Luisa Amaral
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Yuelai Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Andre Chu
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Yang Xie
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Jie Xu
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Zhaoning Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Kai Zhang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Bojing Jia
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Xiaomeng Hou
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Lin Lin
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Qian Yang
- Center for Epigenomics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Seoyeon Lee
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Samantha Kuan
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Jacinta Lucero
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michael Nunn
- Howard Hughes Medical Institute, 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
| | - Zihan Wang
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jingbo Shang
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | | | - Joseph R Ecker
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Allen Wang
- Center for Epigenomics, 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
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - 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.
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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Yao Z, van Velthoven CTJ, Kunst M, Zhang M, McMillen D, Lee C, Jung W, Goldy J, Abdelhak A, Aitken M, Baker K, Baker P, Barkan E, Bertagnolli D, Bhandiwad A, Bielstein C, Bishwakarma P, Campos J, Carey D, Casper T, Chakka AB, Chakrabarty R, Chavan S, Chen M, Clark M, Close J, Crichton K, Daniel S, DiValentin P, Dolbeare T, Ellingwood L, Fiabane E, Fliss T, Gee J, Gerstenberger J, Glandon A, Gloe J, Gould J, Gray J, Guilford N, Guzman J, Hirschstein D, Ho W, Hooper M, Huang M, Hupp M, Jin K, Kroll M, Lathia K, Leon A, Li S, Long B, Madigan Z, Malloy J, Malone J, Maltzer Z, Martin N, McCue R, McGinty R, Mei N, Melchor J, Meyerdierks E, Mollenkopf T, Moonsman S, Nguyen TN, Otto S, Pham T, Rimorin C, Ruiz A, Sanchez R, Sawyer L, Shapovalova N, Shepard N, Slaughterbeck C, Sulc J, Tieu M, Torkelson A, Tung H, Valera Cuevas N, Vance S, Wadhwani K, Ward K, Levi B, Farrell C, Young R, Staats B, Wang MQM, Thompson CL, Mufti S, Pagan CM, Kruse L, Dee N, Sunkin SM, Esposito L, Hawrylycz MJ, Waters J, Ng L, Smith K, Tasic B, Zhuang X, Zeng H. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 2023; 624:317-332. [PMID: 38092916 PMCID: PMC10719114 DOI: 10.1038/s41586-023-06812-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | | | - Meng Zhang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | | | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Won Jung
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Pamela Baker
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Daniel Carey
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Min Chen
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Scott Daniel
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - James Gee
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - James Gray
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Mike Huang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kelly Jin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Su Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zach Madigan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Naomi Martin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rachel McCue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ryan McGinty
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas Mei
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jose Melchor
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Sven Otto
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Lane Sawyer
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Noah Shepard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shane Vance
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Rob Young
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Staats
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang M, Pan X, Jung W, Halpern AR, Eichhorn SW, Lei Z, Cohen L, Smith KA, Tasic B, Yao Z, Zeng H, Zhuang X. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 2023; 624:343-354. [PMID: 38092912 PMCID: PMC10719103 DOI: 10.1038/s41586-023-06808-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3, including several brain regions (for example, refs. 1-11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict 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 foundation for functional investigations of neural circuits and their dysfunction in health and disease.
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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
| | - Xingjie Pan
- 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
| | - Won Jung
- 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
| | - Aaron R Halpern
- 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
| | - Zhiyun Lei
- 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
| | - Limor Cohen
- 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
| | | | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, 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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9
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Sorensen SA, Gouwens NW, Wang Y, Mallory M, Budzillo A, Dalley R, Lee B, Gliko O, Kuo HC, Kuang X, Mann R, Ahmadinia L, Alfiler L, Baftizadeh F, Baker K, Bannick S, Bertagnolli D, Bickley K, Bohn P, Brown D, Bomben J, Brouner K, Chen C, Chen K, Chvilicek M, Collman F, Daigle T, Dawes T, de Frates R, Dee N, DePartee M, Egdorf T, El-Hifnawi L, Enstrom R, Esposito L, Farrell C, Gala R, Glomb A, Gamlin C, Gary A, Goldy J, Gu H, Hadley K, Hawrylycz M, Henry A, Hill D, Hirokawa KE, Huang Z, Johnson K, Juneau Z, Kebede S, Kim L, Lee C, Lesnar P, Li A, Glomb A, Li Y, Liang E, Link K, Maxwell M, McGraw M, McMillen DA, Mukora A, Ng L, Ochoa T, Oldre A, Park D, Pom CA, Popovich Z, Potekhina L, Rajanbabu R, Ransford S, Reding M, Ruiz A, Sandman D, Siverts L, Smith KA, Stoecklin M, Sulc J, Tieu M, Ting J, Trinh J, Vargas S, Vumbaco D, Walker M, Wang M, Wanner A, Waters J, Williams G, Wilson J, Xiong W, Lein E, Berg J, Kalmbach B, Yao S, Gong H, Luo Q, Ng L, Sümbül U, Jarsky T, Yao Z, Tasic B, Zeng H. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. bioRxiv 2023:2023.11.25.568393. [PMID: 38168270 PMCID: PMC10760188 DOI: 10.1101/2023.11.25.568393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
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Affiliation(s)
| | | | - Yun Wang
- Allen Institute for Brain Science
| | | | | | | | | | | | | | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | | | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Kai Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science
| | | | | | | | | | | | | | | | | | | | | | | | - Hong Gu
- Allen Institute for Brain Science
| | | | | | | | | | | | - Zili Huang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | - Lisa Kim
- Allen Institute for Brain Science
| | | | | | - 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
| | | | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | - Zoran Popovich
- University of Washington, Dept. of Computer Science and Engineering
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Ed Lein
- Allen Institute for Brain Science
| | - Jim Berg
- Allen Institute for Brain Science
| | | | | | - 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
| | - Qingming Luo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Lydia Ng
- Allen Institute for Brain Science
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10
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Liwang JK, Kronman FA, Minteer JA, Wu YT, Vanselow DJ, Ben-Simon Y, Taormina M, Parmaksiz D, Way SW, Zeng H, Tasic B, Ng L, Kim Y. epDevAtlas: Mapping GABAergic cells and microglia in postnatal mouse brains. bioRxiv 2023:2023.11.24.568585. [PMID: 38045330 PMCID: PMC10690281 DOI: 10.1101/2023.11.24.568585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
During development, brain regions follow encoded growth trajectories. Compared to classical brain growth charts, high-definition growth charts could quantify regional volumetric growth and constituent cell types, improving our understanding of typical and pathological brain development. Here, we create high-resolution 3D atlases of the early postnatal mouse brain, using Allen CCFv3 anatomical labels, at postnatal days (P) 4, 6, 8, 10, 12, and 14, and determine the volumetric growth of different brain regions. We utilize 11 different cell type-specific transgenic animals to validate and refine anatomical labels. Moreover, we reveal region-specific density changes in γ-aminobutyric acid-producing (GABAergic), cortical layer-specific cell types, and microglia as key players in shaping early postnatal brain development. We find contrasting changes in GABAergic neuronal densities between cortical and striatal areas, stabilizing at P12. Moreover, somatostatin-expressing cortical interneurons undergo regionally distinct density reductions, while vasoactive intestinal peptide-expressing interneurons show no significant changes. Remarkably, microglia transition from high density in white matter tracks to gray matter at P10, and show selective density increases in sensory processing areas that correlate with the emergence of individual sensory modalities. Lastly, we create an open-access web-visualization ( https://kimlab.io/brain-map/epDevAtlas ) for cell-type growth charts and developmental atlases for all postnatal time points.
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11
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Carmona LM, Thomas ET, Smith K, Tasic B, Costa RM, Nelson A. Topographical and cell type-specific connectivity of rostral and caudal forelimb corticospinal neuron populations. bioRxiv 2023:2023.11.17.567623. [PMID: 38014164 PMCID: PMC10680840 DOI: 10.1101/2023.11.17.567623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Corticospinal neurons (CSNs) synapse directly on spinal neurons, a diverse group of neurons with unique structural and functional properties necessary for body movements. CSNs modulating forelimb behavior fractionate into caudal forelimb area (CFA) and rostral forelimb area (RFA) motor cortical populations. Despite their prominence, no studies have mapped the diversity of spinal cell types targeted by CSNs, let alone compare CFA and RFA populations. Here we use anatomical and RNA-sequencing methods to show that CSNs synapse onto a remarkably selective group of spinal cell types, favoring inhibitory populations that regulate motoneuron activity and gate sensory feedback. CFA and RFA CSNs target similar spinal cell types, with notable exceptions that suggest these populations differ in how they influence behavior. Finally, axon collaterals of CFA and RFA CSNs target similar brain regions yet receive surprisingly divergent inputs. These results detail the rules of CSN connectivity throughout the brain and spinal cord for two regions critical for forelimb behavior.
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12
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven C, Sullivan H, Sun N, Kellis M, Tasic B, Wickersham IR, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. bioRxiv 2023:2023.03.16.532873. [PMID: 36993334 PMCID: PMC10055146 DOI: 10.1101/2023.03.16.532873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain Science, Seattle, WA
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Lingang Laboratory, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Metcela Inc., Kawasaki, Kanagawa, Japan
| | | | - Heather Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Ian R. Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
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13
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Chartrand T, Dalley R, Close J, Goriounova NA, Lee BR, Mann R, Miller JA, Molnar G, Mukora A, Alfiler L, Baker K, Bakken TE, Berg J, Bertagnolli D, Braun T, Brouner K, Casper T, Csajbok EA, Dee N, Egdorf T, Enstrom R, Galakhova AA, Gary A, Gelfand E, Goldy J, Hadley K, Heistek TS, Hill D, Jorstad N, Kim L, Kocsis AK, Kruse L, Kunst M, Leon G, Long B, Mallory M, McGraw M, McMillen D, Melief EJ, Mihut N, Ng L, Nyhus J, Oláh G, Ozsvár A, Omstead V, Peterfi Z, Pom A, Potekhina L, Rajanbabu R, Rozsa M, Ruiz A, Sandle J, Sunkin SM, Szots I, Tieu M, Toth M, Trinh J, Vargas S, Vumbaco D, Williams G, Wilson J, Yao Z, Barzo P, Cobbs C, Ellenbogen RG, Esposito L, Ferreira M, Gouwens NW, Grannan B, Gwinn RP, Hauptman JS, Jarsky T, Keene CD, Ko AL, Koch C, Ojemann JG, Patel A, Ruzevick J, Silbergeld DL, Smith K, Sorensen SA, Tasic B, Ting JT, Waters J, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Kalmbach B, Lein ES. Morphoelectric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex. Science 2023; 382:eadf0805. [PMID: 37824667 DOI: 10.1126/science.adf0805] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 09/09/2023] [Indexed: 10/14/2023]
Abstract
Neocortical layer 1 (L1) is a site of convergence between pyramidal-neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neurons with extensive L1 branching, but whether L1 interneurons are similarly diverse is underexplored. Using Patch-seq recordings from human neurosurgical tissue, we identified four transcriptomic subclasses with mouse L1 homologs, along with distinct subtypes and types unmatched in mouse L1. Subclass and subtype comparisons showed stronger transcriptomic differences in human L1 and were correlated with strong morphoelectric variability along dimensions distinct from mouse L1 variability. Accompanied by greater layer thickness and other cytoarchitecture changes, these findings suggest that L1 has diverged in evolution, reflecting the demands of regulating the expanded human neocortical circuit.
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Affiliation(s)
| | | | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Natalia A Goriounova
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Gabor Molnar
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Eva Adrienn Csajbok
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Anna A Galakhova
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Tim S Heistek
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nik Jorstad
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Agnes Katalin Kocsis
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Erica J Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Norbert Mihut
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Gáspár Oláh
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Attila Ozsvár
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Zoltan Peterfi
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Marton Rozsa
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Joanna Sandle
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Ildiko Szots
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Martin Toth
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | | | - Sara Vargas
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Julia Wilson
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Pal Barzo
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | | | | | | | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Benjamin Grannan
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Jason S Hauptman
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Anoop Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Jacob Ruzevick
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | | | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Christiaan P J de Kock
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Huib D Mansvelder
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Gabor Tamas
- Research Group for Cortical Microcircuits of the Hungarian Academy of Science, University of Szeged, Szeged, Hungary
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
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14
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Mich JK, Sunil S, Johansen N, Martinez RA, Leytze M, Gore BB, Mahoney JT, Ben-Simon Y, Bishaw Y, Brouner K, Campos J, Canfield R, Casper T, Dee N, Egdorf T, Gary A, Gibson S, Goldy J, Groce EL, Hirschstein D, Loftus L, Lusk N, Malone J, Martin NX, Monet D, Omstead V, Opitz-Araya X, Oster A, Pom CA, Potekhina L, Reding M, Rimorin C, Ruiz A, Sedeño-Cortés AE, Shapovalova NV, Taormina M, Taskin N, Tieu M, Valera Cuevas NJ, Weed N, Way S, Yao Z, McMillen DA, Kunst M, McGraw M, Thyagarajan B, Waters J, Bakken TE, Yao S, Smith KA, Svoboda K, Podgorski K, Kojima Y, Horwitz GD, Zeng H, Daigle TL, Lein ES, Tasic B, Ting JT, Levi BP. Enhancer-AAVs allow genetic access to oligodendrocytes and diverse populations of astrocytes across species. bioRxiv 2023:2023.09.20.558718. [PMID: 37790503 PMCID: PMC10542530 DOI: 10.1101/2023.09.20.558718] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Proper brain function requires the assembly and function of diverse populations of neurons and glia. Single cell gene expression studies have mostly focused on characterization of neuronal cell diversity; however, recent studies have revealed substantial diversity of glial cells, particularly astrocytes. To better understand glial cell types and their roles in neurobiology, we built a new suite of adeno-associated viral (AAV)-based genetic tools to enable genetic access to astrocytes and oligodendrocytes. These oligodendrocyte and astrocyte enhancer-AAVs are highly specific (usually > 95% cell type specificity) with variable expression levels, and our astrocyte enhancer-AAVs show multiple distinct expression patterns reflecting the spatial distribution of astrocyte cell types. To provide the best glial-specific functional tools, several enhancer-AAVs were: optimized for higher expression levels, shown to be functional and specific in rat and macaque, shown to maintain specific activity in epilepsy where traditional promoters changed activity, and used to drive functional transgenes in astrocytes including Cre recombinase and acetylcholine-responsive sensor iAChSnFR. The astrocyte-specific iAChSnFR revealed a clear reward-dependent acetylcholine response in astrocytes of the nucleus accumbens during reinforcement learning. Together, this collection of glial enhancer-AAVs will enable characterization of astrocyte and oligodendrocyte populations and their roles across species, disease states, and behavioral epochs.
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15
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Bounds HA, Sadahiro M, Hendricks WD, Gajowa M, Gopakumar K, Quintana D, Tasic B, Daigle TL, Zeng H, Oldenburg IA, Adesnik H. All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse. Cell Rep 2023; 42:112909. [PMID: 37542722 PMCID: PMC10755854 DOI: 10.1016/j.celrep.2023.112909] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/23/2023] [Accepted: 07/14/2023] [Indexed: 08/07/2023] Open
Abstract
Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.
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Affiliation(s)
- Hayley A Bounds
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Masato Sadahiro
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - William D Hendricks
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Marta Gajowa
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Karthika Gopakumar
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Quintana
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ian Antón Oldenburg
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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16
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Jin K, Yao Z, van Velthoven CTJ, Kaplan ES, Glattfelder K, Barlow ST, Boyer G, Carey D, Casper T, Chakka AB, Chakrabarty R, Clark M, Departee M, Desierto M, Gary A, Gloe J, Goldy J, Guilford N, Guzman J, Hirschstein D, Lee C, Liang E, Pham T, Reding M, Ronellenfitch K, Ruiz A, Sevigny J, Shapovalova N, Shulga L, Sulc J, Torkelson A, Tung H, Levi B, Sunkin SM, Dee N, Esposito L, Smith K, Tasic B, Zeng H. Cell-type specific molecular signatures of aging revealed in a brain-wide transcriptomic cell-type atlas. bioRxiv 2023:2023.07.26.550355. [PMID: 38168182 PMCID: PMC10760145 DOI: 10.1101/2023.07.26.550355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Biological aging can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function. Aging is a complex and dynamic process which influences distinct cell types in a myriad of ways. The cellular architecture of the mammalian brain is heterogeneous and diverse, making it challenging to identify precise areas and cell types of the brain that are more susceptible to aging than others. Here, we present a high-resolution single-cell RNA sequencing dataset containing ~1.2 million high-quality single-cell transcriptomic profiles of brain cells from young adult and aged mice across both sexes, including areas spanning the forebrain, midbrain, and hindbrain. We find age-associated gene expression signatures across nearly all 130+ neuronal and non-neuronal cell subclasses we identified. We detect the greatest gene expression changes in non-neuronal cell types, suggesting that different cell types in the brain vary in their susceptibility to aging. We identify specific, age-enriched clusters within specific glial, vascular, and immune cell types from both cortical and subcortical regions of the brain, and specific gene expression changes associated with cell senescence, inflammation, decrease in new myelination, and decreased vasculature integrity. We also identify genes with expression changes across multiple cell subclasses, pointing to certain mechanisms of aging that may occur across wide regions or broad cell types of the brain. Finally, we discover the greatest gene expression changes in cell types localized to the third ventricle of the hypothalamus, including tanycytes, ependymal cells, and Tbx3+ neurons found in the arcuate nucleus that are part of the neuronal circuits regulating food intake and energy homeostasis. These findings suggest that the area surrounding the third ventricle in the hypothalamus may be a hub for aging in the mouse brain. Overall, we reveal a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal aging that will serve as a foundation for the investigation of functional changes in the aging process and the interaction of aging and diseases.
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Affiliation(s)
- Kelly Jin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Daniel Carey
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Max Departee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Josh Sevigny
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
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17
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Xie F, Armand EJ, Yao Z, Liu H, Bartlett A, Behrens MM, Li YE, Lucero JD, Luo C, Nery JR, Pinto-Duarte A, Poirion OB, Preissl S, Rivkin AC, Tasic B, Zeng H, Ren B, Ecker JR, Mukamel EA. Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes. Cell Genom 2023; 3:100342. [PMID: 37492103 PMCID: PMC10363915 DOI: 10.1016/j.xgen.2023.100342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 03/09/2023] [Accepted: 05/17/2023] [Indexed: 07/27/2023]
Abstract
Single-cell sequencing could help to solve the fundamental challenge of linking millions of cell-type-specific enhancers with their target genes. However, this task is confounded by patterns of gene co-expression in much the same way that genetic correlation due to linkage disequilibrium confounds fine-mapping in genome-wide association studies (GWAS). We developed a non-parametric permutation-based procedure to establish stringent statistical criteria to control the risk of false-positive associations in enhancer-gene association studies (EGAS). We applied our procedure to large-scale transcriptome and epigenome data from multiple tissues and species, including the mouse and human brain, to predict enhancer-gene associations genome wide. We tested the functional validity of our predictions by comparing them with chromatin conformation data and causal enhancer perturbation experiments. Our study shows how controlling for gene co-expression enables robust enhancer-gene linkage using single-cell sequencing data.
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Affiliation(s)
- Fangming Xie
- Department of Physics, University of California San Diego, La Jolla, CA 92037, USA
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Ethan J. Armand
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92037, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - M. Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Yang Eric Li
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Jacinta D. Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Chongyuan Luo
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Joseph R. Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Olivier B. Poirion
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92037, USA
- The Jackson Laboratory, Farmington, CT, USA
| | - Sebastian Preissl
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92037, USA
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Angeline C. Rivkin
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Eran A. Mukamel
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92037, USA
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18
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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19
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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20
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Yao Z, van Velthoven CTJ, Kunst M, Zhang M, McMillen D, Lee C, Jung W, Goldy J, Abdelhak A, Baker P, Barkan E, Bertagnolli D, Campos J, Carey D, Casper T, Chakka AB, Chakrabarty R, Chavan S, Chen M, Clark M, Close J, Crichton K, Daniel S, Dolbeare T, Ellingwood L, Gee J, Glandon A, Gloe J, Gould J, Gray J, Guilford N, Guzman J, Hirschstein D, Ho W, Jin K, Kroll M, Lathia K, Leon A, Long B, Maltzer Z, Martin N, McCue R, Meyerdierks E, Nguyen TN, Pham T, Rimorin C, Ruiz A, Shapovalova N, Slaughterbeck C, Sulc J, Tieu M, Torkelson A, Tung H, Cuevas NV, Wadhwani K, Ward K, Levi B, Farrell C, Thompson CL, Mufti S, Pagan CM, Kruse L, Dee N, Sunkin SM, Esposito L, Hawrylycz MJ, Waters J, Ng L, Smith KA, Tasic B, Zhuang X, Zeng H. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. bioRxiv 2023:2023.03.06.531121. [PMID: 37034735 PMCID: PMC10081189 DOI: 10.1101/2023.03.06.531121] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled, and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Meng Zhang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | | | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Won Jung
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Pamela Baker
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Daniel Carey
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Min Chen
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Scott Daniel
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - James Gee
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - James Gray
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kelly Jin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Naomi Martin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rachel McCue
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Department of Physics, Harvard University, Cambridge, MA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
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21
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Yao S, Wang Q, Hirokawa KE, Ouellette B, Ahmed R, Bomben J, Brouner K, Casal L, Caldejon S, Cho A, Dotson NI, Daigle TL, Egdorf T, Enstrom R, Gary A, Gelfand E, Gorham M, Griffin F, Gu H, Hancock N, Howard R, Kuan L, Lambert S, Lee EK, Luviano J, Mace K, Maxwell M, Mortrud MT, Naeemi M, Nayan C, Ngo NK, Nguyen T, North K, Ransford S, Ruiz A, Seid S, Swapp J, Taormina MJ, Wakeman W, Zhou T, Nicovich PR, Williford A, Potekhina L, McGraw M, Ng L, Groblewski PA, Tasic B, Mihalas S, Harris JA, Cetin A, Zeng H. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex. Nat Neurosci 2023; 26:350-364. [PMID: 36550293 PMCID: PMC10039800 DOI: 10.1038/s41593-022-01219-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
Identification of structural connections between neurons is a prerequisite to understanding brain function. Here we developed a pipeline to systematically map brain-wide monosynaptic input connections to genetically defined neuronal populations using an optimized rabies tracing system. We used mouse visual cortex as the exemplar system and revealed quantitative target-specific, layer-specific and cell-class-specific differences in its presynaptic connectomes. The retrograde connectivity indicates the presence of ventral and dorsal visual streams and further reveals topographically organized and continuously varying subnetworks mediated by different higher visual areas. The visual cortex hierarchy can be derived from intracortical feedforward and feedback pathways mediated by upper-layer and lower-layer input neurons. We also identify a new role for layer 6 neurons in mediating reciprocal interhemispheric connections. This study expands our knowledge of the visual system connectomes and demonstrates that the pipeline can be scaled up to dissect connectivity of different cell populations across the mouse brain.
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Affiliation(s)
- Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | | | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andy Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Kat North
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Thomas Zhou
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
- CNC Program, Stanford University, Palo Alto, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
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22
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McGovern DJ, Ly A, Ecton KL, Huynh DT, Prévost ED, Gonzalez SC, McNulty CJ, Rau AR, Hentges ST, Daigle TL, Tasic B, Baratta MV, Root DH. Ventral tegmental area glutamate neurons mediate nonassociative consequences of stress. Mol Psychiatry 2022:10.1038/s41380-022-01858-3. [PMID: 36437312 PMCID: PMC10375863 DOI: 10.1038/s41380-022-01858-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/29/2022]
Abstract
Exposure to trauma is a risk factor for the development of a number of mood disorders, and may enhance vulnerability to future adverse life events. Recent data demonstrate that ventral tegmental area (VTA) neurons expressing the vesicular glutamate transporter 2 (VGluT2) signal and causally contribute to behaviors that involve aversive or threatening stimuli. However, it is unknown whether VTA VGluT2 neurons regulate transsituational outcomes of stress and whether these neurons are sensitive to stressor controllability. This work adapted an operant mouse paradigm to examine the impact of stressor controllability on VTA VGluT2 neuron function as well as the role of VTA VGluT2 neurons in mediating transsituational stressor outcomes. Uncontrollable (inescapable) stress, but not physically identical controllable (escapable) stress, produced social avoidance and exaggerated fear in male mice. Uncontrollable stress in females led to exploratory avoidance of a novel brightly lit environment. Both controllable and uncontrollable stressors increased VTA VGluT2 neuronal activity, and chemogenetic silencing of VTA VGluT2 neurons prevented the behavioral sequelae of uncontrollable stress in male and female mice. Further, we show that stress activates multiple genetically-distinct subtypes of VTA VGluT2 neurons, especially those that are VGluT2+VGaT+, as well as lateral habenula neurons receiving synaptic input from VTA VGluT2 neurons. Our results provide causal evidence that mice can be used for identifying stressor controllability circuitry and that VTA VGluT2 neurons contribute to transsituational stressor outcomes, such as social avoidance, exaggerated fear, or anxiety-like behavior that are observed within trauma-related disorders.
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Affiliation(s)
- Dillon J McGovern
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Annie Ly
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Koy L Ecton
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - David T Huynh
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Emily D Prévost
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Shamira C Gonzalez
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Connor J McNulty
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US
| | - Andrew R Rau
- Department of Biomedical Sciences, Colorado State University, 1617 Campus Delivery, Fort Collins, 80523, CO, US
- Center for Structural and Functional Neuroscience, Division of Biological Sciences, University of Montana, Missoula, 59812, MT, US
| | - Shane T Hentges
- Department of Biomedical Sciences, Colorado State University, 1617 Campus Delivery, Fort Collins, 80523, CO, US
- Department of Integrative Physiology and Neuroscience, Washington State University, Pullman, 99164, WA, US
| | - Tanya L Daigle
- Allen Institute for Brain Science, 615 Westlake. Avenue North, Seattle, 98109, WA, US
| | - Bosiljka Tasic
- Allen Institute for Brain Science, 615 Westlake. Avenue North, Seattle, 98109, WA, US
| | - Michael V Baratta
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US.
| | - David H Root
- Department of Psychology and Neuroscience, University of Colorado Boulder, 2860 Wilderness Pl, Boulder, 80301, CO, US.
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23
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Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA. Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep 2022; 41:111659. [PMID: 36351398 PMCID: PMC9797078 DOI: 10.1016/j.celrep.2022.111659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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24
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Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA. Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep 2022; 40:111176. [PMID: 35947954 PMCID: PMC9793758 DOI: 10.1016/j.celrep.2022.111176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 01/28/2022] [Accepted: 07/18/2022] [Indexed: 12/30/2022] Open
Abstract
Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.
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Affiliation(s)
- Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA,These authors contributed equally
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland,These authors contributed equally
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Soo Yeun Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yina Wei
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Zhejiang Lab, Hangzhou City, Zhejiang Province 311121, China
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Costas A. Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Lead contact,Correspondence:
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25
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Campagnola L, Seeman SC, Chartrand T, Kim L, Hoggarth A, Gamlin C, Ito S, Trinh J, Davoudian P, Radaelli C, Kim MH, Hage T, Braun T, Alfiler L, Andrade J, Bohn P, Dalley R, Henry A, Kebede S, Mukora A, Sandman D, Williams G, Larsen R, Teeter C, Daigle TL, Berry K, Dotson N, Enstrom R, Gorham M, Hupp M, Lee SD, Ngo K, Nicovich PR, Potekhina L, Ransford S, Gary A, Goldy J, McMillen D, Pham T, Tieu M, Siverts L, Walker M, Farrell C, Schroedter M, Slaughterbeck C, Cobb C, Ellenbogen R, Gwinn RP, Keene CD, Ko AL, Ojemann JG, Silbergeld DL, Carey D, Casper T, Crichton K, Clark M, Dee N, Ellingwood L, Gloe J, Kroll M, Sulc J, Tung H, Wadhwani K, Brouner K, Egdorf T, Maxwell M, McGraw M, Pom CA, Ruiz A, Bomben J, Feng D, Hejazinia N, Shi S, Szafer A, Wakeman W, Phillips J, Bernard A, Esposito L, D’Orazi FD, Sunkin S, Smith K, Tasic B, Arkhipov A, Sorensen S, Lein E, Koch C, Murphy G, Zeng H, Jarsky T. Local connectivity and synaptic dynamics in mouse and human neocortex. Science 2022; 375:eabj5861. [PMID: 35271334 PMCID: PMC9970277 DOI: 10.1126/science.abj5861] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.
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Affiliation(s)
| | | | | | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Shinya Ito
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Travis Hage
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Alex Henry
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nadia Dotson
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Charles Cobb
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Richard Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Ryder P Gwinn
- Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, WA, USA
| | - C. Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA,Regional Epilepsy Center at Harborview Medical Center, Seattle, WA, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA,Regional Epilepsy Center at Harborview Medical Center, Seattle, WA, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Daniel Carey
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shu Shi
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Susan Sunkin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Gabe Murphy
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA,Corresponding author:
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26
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Zhuang J, Wang Y, Ouellette ND, Turschak E, Larsen RS, Takasaki KT, Daigle TL, Tasic B, Waters J, Zeng H, Reid RC. Contributed Session II: Motion/direction-sensitive thalamic neurons project extensively to the middle layers of primary visual cortex. J Vis 2022. [DOI: 10.1167/jov.22.3.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
| | - Yun Wang
- Allen Institute for Brain Science
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27
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Condylis C, Ghanbari A, Manjrekar N, Bistrong K, Yao S, Yao Z, Nguyen TN, Zeng H, Tasic B, Chen JL. Dense functional and molecular readout of a circuit hub in sensory cortex. Science 2022; 375:eabl5981. [PMID: 34990233 DOI: 10.1126/science.abl5981] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Although single-cell transcriptomics of the neocortex has uncovered more than 300 putative cell types, whether this molecular classification predicts distinct functional roles is unclear. We combined two-photon calcium imaging with spatial transcriptomics to functionally and molecularly investigate cortical circuits. We characterized behavior-related responses across major neuronal subclasses in layers 2 or 3 of the primary somatosensory cortex as mice performed a tactile working memory task. We identified an excitatory intratelencephalic cell type, Baz1a, that exhibits high tactile feature selectivity. Baz1a neurons homeostatically maintain stimulus responsiveness during altered experience and show persistent enrichment of subsets of immediately early genes. Functional and anatomical connectivity reveals that Baz1a neurons residing in upper portions of layers 2 or 3 preferentially innervate somatostatin-expressing inhibitory neurons. This motif defines a circuit hub that orchestrates local sensory processing in superficial layers of the neocortex.
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Affiliation(s)
- Cameron Condylis
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Center for Neurophotonics, Boston University, Boston, MA 02215, USA
| | - Abed Ghanbari
- Department of Biology, Boston University, Boston, MA 02215, USA
| | | | - Karina Bistrong
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Shenqin Yao
- 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
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Center for Neurophotonics, Boston University, Boston, MA 02215, USA.,Department of Biology, Boston University, Boston, MA 02215, USA.,Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
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28
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Berg J, Sorensen SA, Ting JT, Miller JA, Chartrand T, Buchin A, Bakken TE, Budzillo A, Dee N, Ding SL, Gouwens NW, Hodge RD, Kalmbach B, Lee C, Lee BR, Alfiler L, Baker K, Barkan E, Beller A, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Chong P, Crichton K, Dalley R, de Frates R, Desta T, Lee SD, D'Orazi F, Dotson N, Egdorf T, Enstrom R, Farrell C, Feng D, Fong O, Furdan S, Galakhova AA, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Goriounova NA, Gratiy S, Graybuck L, Gu H, Hadley K, Hansen N, Heistek TS, Henry AM, Heyer DB, Hill D, Hill C, Hupp M, Jarsky T, Kebede S, Keene L, Kim L, Kim MH, Kroll M, Latimer C, Levi BP, Link KE, Mallory M, Mann R, Marshall D, Maxwell M, McGraw M, McMillen D, Melief E, Mertens EJ, Mezei L, Mihut N, Mok S, Molnar G, Mukora A, Ng L, Ngo K, Nicovich PR, Nyhus J, Olah G, Oldre A, Omstead V, Ozsvar A, Park D, Peng H, Pham T, Pom CA, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Ruiz A, Sandman D, Sulc J, Sunkin SM, Szafer A, Szemenyei V, Thomsen ER, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Waleboer F, Ward K, Wilbers R, Williams G, Yao Z, Yoon JG, Anastassiou C, Arkhipov A, Barzo P, Bernard A, Cobbs C, de Witt Hamer PC, Ellenbogen RG, Esposito L, Ferreira M, Gwinn RP, Hawrylycz MJ, Hof PR, Idema S, Jones AR, Keene CD, Ko AL, Murphy GJ, Ng L, Ojemann JG, Patel AP, Phillips JW, Silbergeld DL, Smith K, Tasic B, Yuste R, Segev I, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Koch C, Lein ES. Author Correction: Human neocortical expansion involves glutamatergic neuron diversification. Nature 2022; 601:E12. [PMID: 34992294 PMCID: PMC8770134 DOI: 10.1038/s41586-021-04322-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Allison Beller
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kris Bickley
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tsega Desta
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Szabina Furdan
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chris Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lisa Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Caitlin Latimer
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Desiree Marshall
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Erica Melief
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Eline J Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Leona Mezei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Norbert Mihut
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Gaspar Olah
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Attila Ozsvar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Viktor Szemenyei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Pal Barzo
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander Idema
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Anoop P Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Rafael Yuste
- NeuroTechnology Center, Columbia University, New York, NY, USA
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences and Department of Neurobiology, The Hebrew University Jerusalem, Jerusalem, Israel
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA. .,Department of Neurological Surgery, University of Washington, Seattle, WA, USA.
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29
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Zhuang J, Wang Y, Ouellette ND, Turschak EE, Larsen RS, Takasaki KT, Daigle TL, Tasic B, Waters J, Zeng H, Reid RC. Laminar distribution and arbor density of two functional classes of thalamic inputs to primary visual cortex. Cell Rep 2021; 37:109826. [PMID: 34644562 PMCID: PMC8572142 DOI: 10.1016/j.celrep.2021.109826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/18/2021] [Accepted: 09/21/2021] [Indexed: 11/02/2022] Open
Abstract
Motion/direction-sensitive and location-sensitive neurons are the two major functional types in mouse visual thalamus that project to the primary visual cortex (V1). It is under debate whether motion/direction-sensitive inputs preferentially target the superficial layers in V1, as opposed to the location-sensitive inputs, which preferentially target the middle layers. Here, by using calcium imaging to measure the activity of motion/direction-sensitive and location-sensitive axons in V1, we find evidence against these cell-type-specific laminar biases at the population level. Furthermore, using an approach to reconstruct axon arbors with identified in vivo response types, we show that, at the single-axon level, the motion/direction-sensitive axons project more densely to the middle layers than the location-sensitive axons. Overall, our results demonstrate that motion/direction-sensitive thalamic neurons project extensively to the middle layers of V1 at both the population and single-cell levels, providing further insight into the organization of thalamocortical projection in the mouse visual system.
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Affiliation(s)
- Jun Zhuang
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Rylan S Larsen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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30
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Berg J, Sorensen SA, Ting JT, Miller JA, Chartrand T, Buchin A, Bakken TE, Budzillo A, Dee N, Ding SL, Gouwens NW, Hodge RD, Kalmbach B, Lee C, Lee BR, Alfiler L, Baker K, Barkan E, Beller A, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Chong P, Crichton K, Dalley R, de Frates R, Desta T, Lee SD, D'Orazi F, Dotson N, Egdorf T, Enstrom R, Farrell C, Feng D, Fong O, Furdan S, Galakhova AA, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Goriounova NA, Gratiy S, Graybuck L, Gu H, Hadley K, Hansen N, Heistek TS, Henry AM, Heyer DB, Hill D, Hill C, Hupp M, Jarsky T, Kebede S, Keene L, Kim L, Kim MH, Kroll M, Latimer C, Levi BP, Link KE, Mallory M, Mann R, Marshall D, Maxwell M, McGraw M, McMillen D, Melief E, Mertens EJ, Mezei L, Mihut N, Mok S, Molnar G, Mukora A, Ng L, Ngo K, Nicovich PR, Nyhus J, Olah G, Oldre A, Omstead V, Ozsvar A, Park D, Peng H, Pham T, Pom CA, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Ruiz A, Sandman D, Sulc J, Sunkin SM, Szafer A, Szemenyei V, Thomsen ER, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Waleboer F, Ward K, Wilbers R, Williams G, Yao Z, Yoon JG, Anastassiou C, Arkhipov A, Barzo P, Bernard A, Cobbs C, de Witt Hamer PC, Ellenbogen RG, Esposito L, Ferreira M, Gwinn RP, Hawrylycz MJ, Hof PR, Idema S, Jones AR, Keene CD, Ko AL, Murphy GJ, Ng L, Ojemann JG, Patel AP, Phillips JW, Silbergeld DL, Smith K, Tasic B, Yuste R, Segev I, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Koch C, Lein ES. Human neocortical expansion involves glutamatergic neuron diversification. Nature 2021; 598:151-158. [PMID: 34616067 PMCID: PMC8494638 DOI: 10.1038/s41586-021-03813-8] [Citation(s) in RCA: 111] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/07/2021] [Indexed: 11/09/2022]
Abstract
The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.
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Affiliation(s)
- Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Allison Beller
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kris Bickley
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tsega Desta
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Szabina Furdan
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Anna A Galakhova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chris Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lisa Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Caitlin Latimer
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Desiree Marshall
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Erica Melief
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Eline J Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Leona Mezei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Norbert Mihut
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Gabor Molnar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Gaspar Olah
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Attila Ozsvar
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Viktor Szemenyei
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Femke Waleboer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA, USA
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Pal Barzo
- Department of Neurosurgery, University of Szeged, Szeged, Hungary
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sander Idema
- Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Anoop P Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | | | | | - Rafael Yuste
- NeuroTechnology Center, Columbia University, New York, NY, USA
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences and Department of Neurobiology, The Hebrew University Jerusalem, Jerusalem, Israel
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit, Amsterdam, The Netherlands
| | - Gabor Tamas
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, University of Szeged, Szeged, Hungary
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA. .,Department of Neurological Surgery, University of Washington, Seattle, WA, USA.
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Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 2021; 598:111-119. [PMID: 34616062 PMCID: PMC8494640 DOI: 10.1038/s41586-021-03465-8] [Citation(s) in RCA: 258] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 03/17/2021] [Indexed: 12/11/2022]
Abstract
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
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Affiliation(s)
| | | | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Jeroen Eggermont
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nikolai Dembrow
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jayaram Kancherla
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Baldur van Lew
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Yang Eric Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Christine S Liu
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Anup Mahurkar
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Computer Science and Engineering Program, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Orvis
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Scott Owen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nora M Reed
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Angeline Rivkin
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - William J Romanow
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xinxin Wang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | | | - Renee Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Hector Corrada Bravo
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ronna Hertzano
- Departments of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Höllt
- Computer Graphics and Visualization Group, Delt University of Technology, Delft, The Netherlands
| | - Gregory D Horwitz
- Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Boudewijn P Lelieveldt
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics group, Delft University of Technology, Delft, The Netherlands
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | | | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - William J Spain
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Owen R White
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
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Yao Z, Liu H, Xie F, Fischer S, Adkins RS, Aldridge AI, Ament SA, Bartlett A, Behrens MM, Van den Berge K, Bertagnolli D, de Bézieux HR, Biancalani T, Booeshaghi AS, Bravo HC, Casper T, Colantuoni C, Crabtree J, Creasy H, Crichton K, Crow M, Dee N, Dougherty EL, Doyle WI, Dudoit S, Fang R, Felix V, Fong O, Giglio M, Goldy J, Hawrylycz M, Herb BR, Hertzano R, Hou X, Hu Q, Kancherla J, Kroll M, Lathia K, Li YE, Lucero JD, Luo C, Mahurkar A, McMillen D, Nadaf NM, Nery JR, Nguyen TN, Niu SY, Ntranos V, Orvis J, Osteen JK, Pham T, Pinto-Duarte A, Poirion O, Preissl S, Purdom E, Rimorin C, Risso D, Rivkin AC, Smith K, Street K, Sulc J, Svensson V, Tieu M, Torkelson A, Tung H, Vaishnav ED, Vanderburg CR, van Velthoven C, Wang X, White OR, Huang ZJ, Kharchenko PV, Pachter L, Ngai J, Regev A, Tasic B, Welch JD, Gillis J, Macosko EZ, Ren B, Ecker JR, Zeng H, Mukamel EA. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 2021; 598:103-110. [PMID: 34616066 PMCID: PMC8494649 DOI: 10.1038/s41586-021-03500-8] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 03/26/2021] [Indexed: 12/30/2022]
Abstract
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ricky S Adkins
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anna Bartlett
- Genomic Analysis 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
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
| | | | - Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Héctor Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | | | - Carlo Colantuoni
- Johns Hopkins School of Medicine, Department of Neurology, Baltimore, MD, USA
- Johns Hopkins School of Medicine, Department of Neuroscience, Baltimore, MD, USA
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Jonathan Crabtree
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Wayne I Doyle
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Sandrine Dudoit
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA
| | - Victor Felix
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ronna Hertzano
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jayaram Kancherla
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yang Eric Li
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Jacinta D Lucero
- Computational Neurobiology 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
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Naeem M Nadaf
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Vasilis Ntranos
- University of California, San Francisco, San Francisco, CA, USA
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Elizabeth Purdom
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
| | | | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Angeline C Rivkin
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Kelly Street
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Xinxin Wang
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Owen R White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Lior Pachter
- California Institute of Technology, Pasadena, CA, USA
| | - John Ngai
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA, USA
| | | | - Joshua D Welch
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, 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
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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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: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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Booeshaghi AS, Yao Z, van Velthoven C, Smith K, Tasic B, Zeng H, Pachter L. Isoform cell-type specificity in the mouse primary motor cortex. Nature 2021; 598:195-199. [PMID: 34616073 PMCID: PMC8494650 DOI: 10.1038/s41586-021-03969-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/27/2021] [Indexed: 12/17/2022]
Abstract
Full-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types-including isoform shifts between cell types that are masked in gene-level analysis-as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.
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Affiliation(s)
- A Sina Booeshaghi
- Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
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36
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Kalmbach BE, Hodge RD, Jorstad NL, Owen S, de Frates R, Yanny AM, Dalley R, Mallory M, Graybuck LT, Radaelli C, Keene CD, Gwinn RP, Silbergeld DL, Cobbs C, Ojemann JG, Ko AL, Patel AP, Ellenbogen RG, Bakken TE, Daigle TL, Dee N, Lee BR, McGraw M, Nicovich PR, Smith K, Sorensen SA, Tasic B, Zeng H, Koch C, Lein ES, Ting JT. Signature morpho-electric, transcriptomic, and dendritic properties of human layer 5 neocortical pyramidal neurons. Neuron 2021; 109:2914-2927.e5. [PMID: 34534454 PMCID: PMC8570452 DOI: 10.1016/j.neuron.2021.08.030] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 01/20/2021] [Accepted: 08/23/2021] [Indexed: 11/18/2022]
Abstract
In the neocortex, subcerebral axonal projections originate largely from layer 5 (L5) extratelencephalic-projecting (ET) neurons. The unique morpho-electric properties of these neurons have been mainly described in rodents, where retrograde tracers or transgenic lines can label them. Similar labeling strategies are infeasible in the human neocortex, rendering the translational relevance of findings in rodents unclear. We leveraged the recent discovery of a transcriptomically defined L5 ET neuron type to study the properties of human L5 ET neurons in neocortical brain slices derived from neurosurgeries. Patch-seq recordings, where transcriptome, physiology, and morphology were assayed from the same cell, revealed many conserved morpho-electric properties of human and rodent L5 ET neurons. Divergent properties were often subtler than differences between L5 cell types within these two species. These data suggest a conserved function of L5 ET neurons in the neocortical hierarchy but also highlight phenotypic divergence possibly related to functional specialization of human neocortex.
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Affiliation(s)
- Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA.
| | | | | | - Scott Owen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Matt Mallory
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Ryder P Gwinn
- Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery and Alvord Brain Tumor Center, University of Washington, Seattle, WA 98195, USA
| | - Charles Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA; Regional Epilepsy Center, Harborview Medical Center, Seattle, WA 98104, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA; Regional Epilepsy Center, Harborview Medical Center, Seattle, WA 98104, USA
| | - Anoop P Patel
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Richard G Ellenbogen
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA
| | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Neurological Surgery, University of Washington School of Medicine, 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; The Washington National Primate Research Center, University of Washington, Seattle, WA 98195, USA.
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Bakken TE, van Velthoven CTJ, Menon V, Hodge RD, Yao Z, Nguyen TN, Graybuck LT, Horwitz GD, Bertagnolli D, Goldy J, Yanny AM, Garren E, Parry S, Casper T, Shehata SI, Barkan ER, Szafer A, Levi BP, Dee N, Smith KA, Sunkin SM, Bernard A, Phillips J, Hawrylycz MJ, Koch C, Murphy GJ, Lein E, Zeng H, Tasic B. Single-cell and single-nucleus RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates, and humans. eLife 2021; 10:e64875. [PMID: 34473054 PMCID: PMC8412930 DOI: 10.7554/elife.64875] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/18/2021] [Indexed: 12/11/2022] Open
Abstract
Abundant evidence supports the presence of at least three distinct types of thalamocortical (TC) neurons in the primate dorsal lateral geniculate nucleus (dLGN) of the thalamus, the brain region that conveys visual information from the retina to the primary visual cortex (V1). Different types of TC neurons in mice, humans, and macaques have distinct morphologies, distinct connectivity patterns, and convey different aspects of visual information to the cortex. To investigate the molecular underpinnings of these cell types, and how these relate to differences in dLGN between human, macaque, and mice, we profiled gene expression in single nuclei and cells using RNA-sequencing. These efforts identified four distinct types of TC neurons in the primate dLGN: magnocellular (M) neurons, parvocellular (P) neurons, and two types of koniocellular (K) neurons. Despite extensively documented morphological and physiological differences between M and P neurons, we identified few genes with significant differential expression between transcriptomic cell types corresponding to these two neuronal populations. Likewise, the dominant feature of TC neurons of the adult mouse dLGN is high transcriptomic similarity, with an axis of heterogeneity that aligns with core vs. shell portions of mouse dLGN. Together, these data show that transcriptomic differences between principal cell types in the mature mammalian dLGN are subtle relative to the observed differences in morphology and cortical projection targets. Finally, alignment of transcriptome profiles across species highlights expanded diversity of GABAergic neurons in primate versus mouse dLGN and homologous types of TC neurons in primates that are distinct from TC neurons in mouse.
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Affiliation(s)
| | | | - Vilas Menon
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Neurology, Columbia University Medical CenterNew YorkUnited States
| | | | - Zizhen Yao
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Gregory D Horwitz
- Washington National Primate Research Center and Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | | | - Jeff Goldy
- Allen Institute for Brain ScienceSeattleUnited States
| | | | - Emma Garren
- Allen Institute for Brain ScienceSeattleUnited States
| | - Sheana Parry
- Allen Institute for Brain ScienceSeattleUnited States
| | - Tamara Casper
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Aaron Szafer
- Allen Institute for Brain ScienceSeattleUnited States
| | - Boaz P Levi
- Allen Institute for Brain ScienceSeattleUnited States
| | - Nick Dee
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Amy Bernard
- Allen Institute for Brain ScienceSeattleUnited States
| | - John Phillips
- Allen Institute for Brain ScienceSeattleUnited States
| | | | - Christof Koch
- Allen Institute for Brain ScienceSeattleUnited States
| | - Gabe J Murphy
- Allen Institute for Brain ScienceSeattleUnited States
| | - Ed Lein
- Allen Institute for Brain ScienceSeattleUnited States
| | - Hongkui Zeng
- Allen Institute for Brain ScienceSeattleUnited States
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38
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Lee BR, Budzillo A, Hadley K, Miller JA, Jarsky T, Baker K, Hill D, Kim L, Mann R, Ng L, Oldre A, Rajanbabu R, Trinh J, Vargas S, Braun T, Dalley RA, Gouwens NW, Kalmbach BE, Kim TK, Smith KA, Soler-Llavina G, Sorensen S, Tasic B, Ting JT, Lein E, Zeng H, Murphy GJ, Berg J. Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization. eLife 2021; 10:e65482. [PMID: 34387544 PMCID: PMC8428855 DOI: 10.7554/elife.65482] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.
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Affiliation(s)
- Brian R Lee
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | - Tim Jarsky
- Allen Institute for Brain ScienceSeattleUnited States
| | | | - DiJon Hill
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lisa Kim
- Allen Institute for Brain ScienceSeattleUnited States
| | - Rusty Mann
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lindsay Ng
- Allen Institute for Brain ScienceSeattleUnited States
| | - Aaron Oldre
- Allen Institute for Brain ScienceSeattleUnited States
| | - Ram Rajanbabu
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jessica Trinh
- Allen Institute for Brain ScienceSeattleUnited States
| | - Sara Vargas
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | - Brian E Kalmbach
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Tae Kyung Kim
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | | | - Jonathan T Ting
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Ed Lein
- Allen Institute for Brain ScienceSeattleUnited States
| | - Hongkui Zeng
- Allen Institute for Brain ScienceSeattleUnited States
| | - Gabe J Murphy
- Allen Institute for Brain ScienceSeattleUnited States
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Jim Berg
- Allen Institute for Brain ScienceSeattleUnited States
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39
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Yao Z, van Velthoven CTJ, Nguyen TN, Goldy J, Sedeno-Cortes AE, Baftizadeh F, Bertagnolli D, Casper T, Chiang M, Crichton K, Ding SL, Fong O, Garren E, Glandon A, Gouwens NW, Gray J, Graybuck LT, Hawrylycz MJ, Hirschstein D, Kroll M, Lathia K, Lee C, Levi B, McMillen D, Mok S, Pham T, Ren Q, Rimorin C, Shapovalova N, Sulc J, Sunkin SM, Tieu M, Torkelson A, Tung H, Ward K, Dee N, Smith KA, Tasic B, Zeng H. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 2021; 184:3222-3241.e26. [PMID: 34004146 PMCID: PMC8195859 DOI: 10.1016/j.cell.2021.04.021] [Citation(s) in RCA: 346] [Impact Index Per Article: 115.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 02/09/2021] [Accepted: 04/14/2021] [Indexed: 12/23/2022]
Abstract
The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning. We profiled ∼1.3 million cells covering the entire adult mouse isocortex and HPF and derived a transcriptomic cell-type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types. Contrary to the traditional view of HPF as having a simpler cellular organization, we discover a complete set of glutamatergic types in HPF homologous to all major subclasses found in the six-layered isocortex, suggesting that HPF and the isocortex share a common circuit organization. We also identify large-scale continuous and graded variations of cell types along isocortical depth, across the isocortical sheet, and in multiple dimensions in hippocampus and subiculum. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation and begins to shed light on its underlying relationship with the development, evolution, connectivity, and function of these two brain structures.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Megan Chiang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - James Gray
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Stephanie Mok
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Qingzhong Ren
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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40
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Park J, Choi W, Tiesmeyer S, Long B, Borm LE, Garren E, Nguyen TN, Tasic B, Codeluppi S, Graf T, Schlesner M, Stegle O, Eils R, Ishaque N. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat Commun 2021; 12:3545. [PMID: 34112806 PMCID: PMC8192952 DOI: 10.1038/s41467-021-23807-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 05/14/2021] [Indexed: 12/24/2022] Open
Abstract
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
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Affiliation(s)
- Jeongbin Park
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wonyl Choi
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Sebastian Tiesmeyer
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lars E Borm
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Simone Codeluppi
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Graf
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Matthias Schlesner
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Roland Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital, Heidelberg, Germany.
| | - Naveed Ishaque
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
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41
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van der Veen B, Kapanaiah SKT, Kilonzo K, Steele-Perkins P, Jendryka MM, Schulz S, Tasic B, Yao Z, Zeng H, Akam T, Nicholson JR, Liss B, Nissen W, Pekcec A, Kätzel D. Control of impulsivity by G i-protein signalling in layer-5 pyramidal neurons of the anterior cingulate cortex. Commun Biol 2021; 4:662. [PMID: 34079054 PMCID: PMC8172539 DOI: 10.1038/s42003-021-02188-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 05/06/2021] [Indexed: 12/19/2022] Open
Abstract
Pathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders.
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Affiliation(s)
| | | | - Kasyoka Kilonzo
- Institute of Applied Physiology, Ulm University, Ulm, Germany
| | | | | | - Stefanie Schulz
- Institute of Applied Physiology, Ulm University, Ulm, Germany
| | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Janet R Nicholson
- Boehringer Ingelheim Pharma GmbH & Co. KG, Div. Research Germany, Biberach an der Riss, Germany
| | - Birgit Liss
- Institute of Applied Physiology, Ulm University, Ulm, Germany
- Linacre College and New College, University of Oxford, Oxford, UK
| | - Wiebke Nissen
- Boehringer Ingelheim Pharma GmbH & Co. KG, Div. Research Germany, Biberach an der Riss, Germany
| | - Anton Pekcec
- Boehringer Ingelheim Pharma GmbH & Co. KG, Div. Research Germany, Biberach an der Riss, Germany
| | - Dennis Kätzel
- Institute of Applied Physiology, Ulm University, Ulm, Germany.
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42
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Ding SL, Yao Z, Hirokawa KE, Nguyen TN, Graybuck LT, Fong O, Bohn P, Ngo K, Smith KA, Koch C, Phillips JW, Lein ES, Harris JA, Tasic B, Zeng H. Distinct Transcriptomic Cell Types and Neural Circuits of the Subiculum and Prosubiculum along the Dorsal-Ventral Axis. Cell Rep 2021; 31:107648. [PMID: 32433957 DOI: 10.1016/j.celrep.2020.107648] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 02/23/2020] [Accepted: 04/22/2020] [Indexed: 01/02/2023] Open
Abstract
Subicular regions play important roles in spatial processing and many cognitive functions, and these are mainly attributed to the subiculum (Sub) rather than the prosubiculum (PS). Using single-cell RNA sequencing, we identify 27 transcriptomic cell types residing in sub-domains of the Sub and PS. Based on in situ expression of reliable transcriptomic markers, the precise boundaries of the Sub and PS are consistently defined along the dorsoventral axis. Using these borders to evaluate Cre-line specificity and tracer injections, we find bona fide Sub projections topographically to structures important for spatial processing and navigation. In contrast, the PS sends its outputs to widespread brain regions crucial for motivation, emotion, reward, stress, anxiety, and fear. The Sub and PS, respectively, dominate dorsal and ventral subicular regions and receive different afferents. These results reveal two molecularly and anatomically distinct circuits centered in the Sub and PS, respectively, providing a consistent explanation for historical data and a clearer foundation for future studies.
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Affiliation(s)
- Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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43
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Mich JK, Graybuck LT, Hess EE, Mahoney JT, Kojima Y, Ding Y, Somasundaram S, Miller JA, Kalmbach BE, Radaelli C, Gore BB, Weed N, Omstead V, Bishaw Y, Shapovalova NV, Martinez RA, Fong O, Yao S, Mortrud M, Chong P, Loftus L, Bertagnolli D, Goldy J, Casper T, Dee N, Opitz-Araya X, Cetin A, Smith KA, Gwinn RP, Cobbs C, Ko AL, Ojemann JG, Keene CD, Silbergeld DL, Sunkin SM, Gradinaru V, Horwitz GD, Zeng H, Tasic B, Lein ES, Ting JT, Levi BP. Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex. Cell Rep 2021; 34:108754. [PMID: 33789096 PMCID: PMC8163032 DOI: 10.1016/j.celrep.2021.108754] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/07/2020] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
Viral genetic tools that target specific brain cell types could transform basic neuroscience and targeted gene therapy. Here, we use comparative open chromatin analysis to identify thousands of human-neocortical-subclass-specific putative enhancers from across the genome to control gene expression in adeno-associated virus (AAV) vectors. The cellular specificity of reporter expression from enhancer-AAVs is established by molecular profiling after systemic AAV delivery in mouse. Over 30% of enhancer-AAVs produce specific expression in the targeted subclass, including both excitatory and inhibitory subclasses. We present a collection of Parvalbumin (PVALB) enhancer-AAVs that show highly enriched expression not only in cortical PVALB cells but also in some subcortical PVALB populations. Five vectors maintain PVALB-enriched expression in primate neocortex. These results demonstrate how genome-wide open chromatin data mining and cross-species AAV validation can be used to create the next generation of non-species-restricted viral genetic tools.
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Affiliation(s)
- John K Mich
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | - Erik E Hess
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Yoshiko Kojima
- Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - Yi Ding
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Bryan B Gore
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Natalie Weed
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Luke Loftus
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ali Cetin
- Department of Biology and Applied Physics, Stanford University, Stanford, CA, USA
| | | | - Ryder P Gwinn
- Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Charles Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA; Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA; Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery and Alvord Brain Tumor Center, University of Washington, Seattle, WA, USA
| | | | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gregory D Horwitz
- Washington National Primate Research Center, University of Washington, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA; Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA; Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA; Washington National Primate Research Center, University of Washington, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA, USA.
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44
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Dudok B, Klein PM, Hwaun E, Lee BR, Yao Z, Fong O, Bowler JC, Terada S, Sparks FT, Szabo GG, Farrell JS, Berg J, Daigle TL, Tasic B, Dimidschstein J, Fishell G, Losonczy A, Zeng H, Soltesz I. Alternating sources of perisomatic inhibition during behavior. Neuron 2021; 109:997-1012.e9. [PMID: 33529646 PMCID: PMC7979482 DOI: 10.1016/j.neuron.2021.01.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/02/2020] [Accepted: 01/04/2021] [Indexed: 12/30/2022]
Abstract
Interneurons expressing cholecystokinin (CCK) and parvalbumin (PV) constitute two key GABAergic controllers of hippocampal pyramidal cell output. Although the temporally precise and millisecond-scale inhibitory regulation of neuronal ensembles delivered by PV interneurons is well established, the in vivo recruitment patterns of CCK-expressing basket cell (BC) populations has remained unknown. We show in the CA1 of the mouse hippocampus that the activity of CCK BCs inversely scales with both PV and pyramidal cell activity at the behaviorally relevant timescales of seconds. Intervention experiments indicated that the inverse coupling of CCK and PV GABAergic systems arises through a mechanism involving powerful inhibitory control of CCK BCs by PV cells. The tightly coupled complementarity of two key microcircuit regulatory modules demonstrates a novel form of brain-state-specific segregation of inhibition during spontaneous behavior.
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Affiliation(s)
- Barna Dudok
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Peter M Klein
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Ernie Hwaun
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - John C Bowler
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Satoshi Terada
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Gergely G Szabo
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Jordan S Farrell
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jordane Dimidschstein
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gord Fishell
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Blavatnik Institute, Department of Neurobiology, Boston, MA 02115, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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45
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Gala R, Budzillo A, Baftizadeh F, Miller J, Gouwens N, Arkhipov A, Murphy G, Tasic B, Zeng H, Hawrylycz M, Sümbül U. Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nat Comput Sci 2021; 1:120-127. [PMID: 35356158 DOI: 10.24433/co.4098627.v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.
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46
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Gala R, Budzillo A, Baftizadeh F, Miller J, Gouwens N, Arkhipov A, Murphy G, Tasic B, Zeng H, Hawrylycz M, Sümbül U. Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nat Comput Sci 2021; 1:120-127. [PMID: 35356158 PMCID: PMC8963134 DOI: 10.1038/s43588-021-00030-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.
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47
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Miller JA, Gouwens NW, Tasic B, Collman F, van Velthoven CTJ, Bakken TE, Hawrylycz MJ, Zeng H, Lein ES, Bernard A. Common cell type nomenclature for the mammalian brain. eLife 2020; 9:e59928. [PMID: 33372656 PMCID: PMC7790494 DOI: 10.7554/elife.59928] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/28/2020] [Indexed: 12/22/2022] Open
Abstract
The advancement of single-cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the common cell type nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step toward community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.
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48
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Whitesell JD, Liska A, Coletta L, Hirokawa KE, Bohn P, Williford A, Groblewski PA, Graddis N, Kuan L, Knox JE, Ho A, Wakeman W, Nicovich PR, Nguyen TN, van Velthoven CTJ, Garren E, Fong O, Naeemi M, Henry AM, Dee N, Smith KA, Levi B, Feng D, Ng L, Tasic B, Zeng H, Mihalas S, Gozzi A, Harris JA. Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network. Neuron 2020; 109:545-559.e8. [PMID: 33290731 PMCID: PMC8150331 DOI: 10.1016/j.neuron.2020.11.011] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/08/2020] [Accepted: 11/13/2020] [Indexed: 12/28/2022]
Abstract
The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN. Mouse resting-state default mode network anatomy described at high resolution in 3D Systematic axon tracing shows cortical DMN regions are preferentially interconnected Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons
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Affiliation(s)
| | - Adam Liska
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy; DeepMind, London EC4A 3TW, UK
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy; Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto, Italy
| | | | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ali Williford
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Joseph E Knox
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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49
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Gouwens NW, Sorensen SA, Baftizadeh F, Budzillo A, Lee BR, Jarsky T, Alfiler L, Baker K, Barkan E, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Crichton K, Daigle TL, Dalley R, de Frates RA, Dee N, Desta T, Lee SD, Dotson N, Egdorf T, Ellingwood L, Enstrom R, Esposito L, Farrell C, Feng D, Fong O, Gala R, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Graybuck L, Gu H, Hadley K, Hawrylycz MJ, Henry AM, Hill D, Hupp M, Kebede S, Kim TK, Kim L, Kroll M, Lee C, Link KE, Mallory M, Mann R, Maxwell M, McGraw M, McMillen D, Mukora A, Ng L, Ng L, Ngo K, Nicovich PR, Oldre A, Park D, Peng H, Penn O, Pham T, Pom A, Popović Z, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Robertson M, Ronellenfitch K, Ruiz A, Sandman D, Smith K, Sulc J, Sunkin SM, Szafer A, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Ward K, Williams G, Zhou Z, Ting JT, Arkhipov A, Sümbül U, Lein ES, Koch C, Yao Z, Tasic B, Berg J, Murphy GJ, Zeng H. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells. Cell 2020; 183:935-953.e19. [PMID: 33186530 PMCID: PMC7781065 DOI: 10.1016/j.cell.2020.09.057] [Citation(s) in RCA: 213] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 07/06/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022]
Abstract
Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.
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Affiliation(s)
| | | | | | - Agata Budzillo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lauren Alfiler
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Kris Bickley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jasmine Bomben
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Braun
- Byte Physics, Schwarzastraße 9, Berlin 12055, Germany
| | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tsega Desta
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Rachel Enstrom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Colin Farrell
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Melissa Gorham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lucas Graybuck
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tae Kyung Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Medea McGraw
- 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
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Osnat Penn
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zoran Popović
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | | | | | - Shea Ransford
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Sandman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Trinh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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50
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Kim DW, Yao Z, Graybuck LT, Kim TK, Nguyen TN, Smith KA, Fong O, Yi L, Koulena N, Pierson N, Shah S, Lo L, Pool AH, Oka Y, Pachter L, Cai L, Tasic B, Zeng H, Anderson DJ. Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior. Cell 2020; 179:713-728.e17. [PMID: 31626771 DOI: 10.1016/j.cell.2019.09.020] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 07/28/2019] [Accepted: 09/20/2019] [Indexed: 01/08/2023]
Abstract
The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.
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Affiliation(s)
- Dong-Wook Kim
- Program in Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA; Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Tae Kyung Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynn Yi
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Noushin Koulena
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Nico Pierson
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Sheel Shah
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Liching Lo
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - Allan-Hermann Pool
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Yuki Oka
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Long Cai
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David J Anderson
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA, USA; Howard Hughes Medical Institute; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA.
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