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Szelenyi ER, Fisenne D, Knox JE, Harris JA, Gornet JA, Palaniswamy R, Kim Y, Venkataraju KU, Osten P. Distributed X chromosome inactivation in brain circuitry is associated with X-linked disease penetrance of behavior. Cell Rep 2024; 43:114068. [PMID: 38614085 PMCID: PMC11107803 DOI: 10.1016/j.celrep.2024.114068] [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] [Received: 08/07/2023] [Revised: 02/05/2024] [Accepted: 03/21/2024] [Indexed: 04/15/2024] Open
Abstract
The precise anatomical degree of brain X chromosome inactivation (XCI) that is sufficient to alter X-linked disorders in females is unclear. Here, we quantify whole-brain XCI at single-cell resolution to discover a prevalent activation ratio of maternal to paternal X at 60:40 across all divisions of the adult brain. This modest, non-random XCI influences X-linked disease penetrance: maternal transmission of the fragile X mental retardation 1 (Fmr1)-knockout (KO) allele confers 55% of total brain cells with mutant X-active, which is sufficient for behavioral penetrance, while 40% produced from paternal transmission is tolerated. Local XCI mosaicism within affected maternal Fmr1-KO mice further specifies sensorimotor versus social anxiety phenotypes depending on which distinct brain circuitry is most affected, with only a 50%-55% mutant X-active threshold determining penetrance. Thus, our results define a model of X-linked disease penetrance in females whereby distributed XCI among single cells populating brain circuitries can regulate the behavioral penetrance of an X-linked mutation.
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Affiliation(s)
- Eric R Szelenyi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Program in Neuroscience, Stony Brook University, Neurobiology and Behavior, Stony Brook, NY 11794, USA.
| | - Danielle Fisenne
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Hofstra University, Hempstead, NY 11549, USA; Certerra, Inc., Farmingdale, NY 11735, USA
| | - Joseph E Knox
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - James A Gornet
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Columbia University, New York, NY 10027, USA
| | | | - Yongsoo Kim
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; College of Medicine, Penn State University, Hershey, PA 17033, USA
| | | | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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2
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Koelle S, Mastrovito D, Whitesell JD, Hirokawa KE, Zeng H, Meila M, Harris JA, Mihalas S. Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments. Netw Neurosci 2023; 7:1497-1512. [PMID: 38144695 PMCID: PMC10745083 DOI: 10.1162/netn_a_00337] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 09/10/2023] [Indexed: 12/26/2023] Open
Abstract
The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model.
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Affiliation(s)
- Samson Koelle
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Statistics, University of Washington, Seattle, WA, USA
| | | | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Marina Meila
- Department of Statistics, University of Washington, Seattle, WA, USA
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3
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Ma CY, Beck NA, Hockaday MZ, Niedziela CJ, Ritchie CA, Harris JA, Roudnitsky E, Guntaka PKR, Yeh SY, Middleton J, Norrlinger JY, Alvarez GA, Danquah SA, Yang S, Deoglas DK, Afshar S. The global distribution of oral and maxillofacial surgeons: a mixed-methods study. Int J Oral Maxillofac Surg 2023:S0901-5027(23)00198-4. [PMID: 37840001 DOI: 10.1016/j.ijom.2023.09.002] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 08/18/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023]
Abstract
Despite its role in treating the most dominant non-communicable diseases worldwide, the global workforce of oral and maxillofacial (OM) surgeons is not well-characterized. To address the current deficit in understanding of the global OM surgeon workforce and to elevate oral and maxillofacial surgery (OMS) in the global health discourse, we join other surgical specialties in evaluating global surgical capacity with a descriptive analysis of the distribution of OM surgeons worldwide. A mixed-methods study was implemented using a combination of literature review, in-country contacts, internet searches, and survey data. The survey was distributed globally from January to June 2022. Data regarding OM surgeon workforce estimates were obtained for 104 of 195 United Nations-recognized countries (53.3%). Among countries with available estimates, the median global workforce density was 0.518 OM surgeons per 100,000 population. Twenty-eight countries (26.9%) were reported to have two or fewer OM surgeons. The median OM surgeon workforce density for low-income countries was 0.015 surgeons per 100,000 population, compared to 1.087 surgeons per 100,000 population in high-income countries. low and middle-income countries countries have the least workforce density as well as the least data coverage. More work is needed to better understand the capacity of the global OM surgeon workforce and access to OMS care.
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Affiliation(s)
- C Y Ma
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - N A Beck
- Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Z Hockaday
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - C J Niedziela
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, Massachusetts, USA
| | - C A Ritchie
- Department of Oral and Maxillofacial Surgery, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - J A Harris
- Department of Oral and Maxillofacial Surgery, Jackson Memorial Hospital/University of Miami, Miami, Florida, USA
| | - E Roudnitsky
- Department of Oral and Maxillofacial Surgery, Rutgers University School of Dental Medicine, Newark, New Jersey, USA
| | - P K R Guntaka
- Division of Oral and Maxillofacial Surgery, Mount Sinai Health System, New York, USA
| | - S Y Yeh
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - J Middleton
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - J Y Norrlinger
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - G A Alvarez
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - S A Danquah
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - S Yang
- Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - D K Deoglas
- Oral and Maxillofacial Surgery Department, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - S Afshar
- Harvard School of Dental Medicine, Boston, Massachusetts, USA; Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, Massachusetts, USA; Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, Boston, Massachusetts, USA; Department of Plastic and Oral Surgery, Harvard School of Dental Medicine, Boston, Massachusetts, USA.
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4
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Ramirez DM, Whitesell JD, Bhagwat N, Thomas TL, Ajay AD, Nawaby A, Delatour B, Bay S, LaFaye P, Knox JE, Harris JA, Meeks JP, Diamond MI. Endogenous pathology in tauopathy mice progresses via brain networks. bioRxiv 2023:2023.05.23.541792. [PMID: 37293074 PMCID: PMC10245958 DOI: 10.1101/2023.05.23.541792] [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: 06/10/2023]
Abstract
Neurodegenerative tauopathies are hypothesized to propagate via brain networks. This is uncertain because we have lacked precise network resolution of pathology. We therefore developed whole-brain staining methods with anti-p-tau nanobodies and imaged in 3D PS19 tauopathy mice, which have pan-neuronal expression of full-length human tau containing the P301S mutation. We analyzed patterns of p-tau deposition across established brain networks at multiple ages, testing the relationship between structural connectivity and patterns of progressive pathology. We identified core regions with early tau deposition, and used network propagation modeling to determine the link between tau pathology and connectivity strength. We discovered a bias towards retrograde network-based propagation of tau. This novel approach establishes a fundamental role for brain networks in tau propagation, with implications for human disease.
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Affiliation(s)
- Denise M.O. Ramirez
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Jennifer D. Whitesell
- Allen Institute for Brain Science; Seattle, WA, USA
- Cajal Neuroscience; Seattle, WA, USA
| | - Nikhil Bhagwat
- Allen Institute for Brain Science; Seattle, WA, USA
- McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), McGill University; Montreal, Quebec, Canada
| | - Talitha L. Thomas
- Center for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Apoorva D. Ajay
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Ariana Nawaby
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Benoît Delatour
- Paris Brain Institute (ICM), CNRS UMR 7225, INSERM U1127, Sorbonne Université, Hôpital de la Pitié-Salpêtrière; Paris, France
| | - Sylvie Bay
- Unité de Chimie des Biomolécules, Institut Pasteur, Université Paris Cité, CNRS UMR 3523; Paris, France
| | - Pierre LaFaye
- Antibody Engineering Platform, Institut Pasteur, Université Paris Cité, CNRS UMR 3528; Paris, France
| | | | | | - Julian P. Meeks
- Department of Neuroscience, University of Rochester Medical School; Rochester, NY, USA
| | - Marc I. Diamond
- Center for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
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5
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Wang Q, Wang Y, Kuo HC, Xie P, Kuang X, Hirokawa KE, Naeemi M, Yao S, Mallory M, Ouellette B, Lesnar P, Li Y, Ye M, Chen C, Xiong W, Ahmadinia L, El-Hifnawi L, Cetin A, Sorensen SA, Harris JA, Zeng H, Koch C. Regional and cell-type-specific afferent and efferent projections of the mouse claustrum. Cell Rep 2023; 42:112118. [PMID: 36774552 PMCID: PMC10415534 DOI: 10.1016/j.celrep.2023.112118] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [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: 02/24/2022] [Revised: 12/17/2022] [Accepted: 01/30/2023] [Indexed: 02/13/2023] Open
Abstract
The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. Its regional anatomy and cell-type-specific connections in the mouse remain not fully determined. Using multimodal reference datasets, we confirmed the delineation of the mouse CLA as a single group of neurons embedded in the agranular insular cortex. We quantitatively investigated brain-wide inputs and outputs of CLA using bulk anterograde and retrograde viral tracing data and single neuron tracing data. We found that the prefrontal module has more cell types projecting to the CLA than other cortical modules, with layer 5 IT neurons predominating. We found nine morphological types of CLA principal neurons that topographically innervate functionally linked cortical targets, preferentially the midline cortical areas, secondary motor area, and entorhinal area. Together, this study provides a detailed wiring diagram of the cell-type-specific connections of the mouse CLA, laying a foundation for studying its functions at the cellular level.
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hsien-Chi Kuo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Peng Xie
- Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu, China
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | | | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Matt Mallory
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ben Ouellette
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Min Ye
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | | | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Julie A Harris
- 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.
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6
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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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7
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Harris JA, Liu R, Martins de Oliveira V, Vázquez-Montelongo EA, Henderson JA, Shen J. GPU-Accelerated All-Atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber. J Chem Theory Comput 2022; 18:7510-7527. [PMID: 36377980 PMCID: PMC10130738 DOI: 10.1021/acs.jctc.2c00586] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH conditions; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here, we report the first GPU-accelerated implementation, parametrization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pKa's of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson's correlation coefficient for the pKa shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pKa's and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pKa upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pKa downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.
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Affiliation(s)
- Julie A Harris
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland21201, United States
| | - Ruibin Liu
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland21201, United States
| | - Vinicius Martins de Oliveira
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland21201, United States.,Lilly Biotechnology Center, San Diego, California92121, United States
| | | | - Jack A Henderson
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland21201, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland21201, United States
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8
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Henderson JA, Liu R, Harris JA, Huang Y, de Oliveira VM, Shen J. A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]. Living J Comput Mol Sci 2022; 4:1563. [PMID: 36776714 PMCID: PMC9910290 DOI: 10.33011/livecoms.4.1.1563] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.
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Affiliation(s)
| | - Ruibin Liu
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Yandong Huang
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD
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9
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Xia D, Lianoglou S, Sandmann T, Calvert M, Suh JH, Thomsen E, Dugas J, Pizzo ME, DeVos SL, Earr TK, Lin CC, Davis S, Ha C, Leung AWS, Nguyen H, Chau R, Yulyaningsih E, Lopez I, Solanoy H, Masoud ST, Liang CC, Lin K, Astarita G, Khoury N, Zuchero JY, Thorne RG, Shen K, Miller S, Palop JJ, Garceau D, Sasner M, Whitesell JD, Harris JA, Hummel S, Gnörich J, Wind K, Kunze L, Zatcepin A, Brendel M, Willem M, Haass C, Barnett D, Zimmer TS, Orr AG, Scearce-Levie K, Lewcock JW, Di Paolo G, Sanchez PE. Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia. Mol Neurodegener 2022; 17:41. [PMID: 35690868 PMCID: PMC9188195 DOI: 10.1186/s13024-022-00547-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [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: 12/22/2021] [Accepted: 05/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. METHODS We engineered a novel App knock-in mouse model (AppSAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous AppSAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays. RESULTS Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The AppSAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged. DISCUSSION Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.
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Affiliation(s)
- Dan Xia
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Steve Lianoglou
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Thomas Sandmann
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Meredith Calvert
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Jung H. Suh
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Elliot Thomsen
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Jason Dugas
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Michelle E. Pizzo
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Sarah L. DeVos
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Timothy K. Earr
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Chia-Ching Lin
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Sonnet Davis
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Connie Ha
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Amy Wing-Sze Leung
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Hoang Nguyen
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Roni Chau
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Ernie Yulyaningsih
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Isabel Lopez
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Hilda Solanoy
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Shababa T. Masoud
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Chun-chi Liang
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Karin Lin
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Giuseppe Astarita
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Nathalie Khoury
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Joy Yu Zuchero
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Robert G. Thorne
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
- Department of Pharmaceutics, University of Minnesota, 9-177 Weaver-Densford Hall, 308 Harvard St. SE, Minneapolis, MN 55455 USA
| | - Kevin Shen
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158 USA
- Department of Neurology, University of California, San Francisco, CA 94158 USA
| | - Stephanie Miller
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158 USA
- Department of Neurology, University of California, San Francisco, CA 94158 USA
| | - Jorge J. Palop
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158 USA
- Department of Neurology, University of California, San Francisco, CA 94158 USA
| | | | | | | | | | - Selina Hummel
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Johannes Gnörich
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Karin Wind
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lea Kunze
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Artem Zatcepin
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Matthias Brendel
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Michael Willem
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Christian Haass
- German Center for Neurodegenerative Diseases (DZNE) Munich, 81377 Munich, Germany
- Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig- Maximilians-Universität, München, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
| | - Daniel Barnett
- Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, New York, NY USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA
- Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY USA
| | - Till S. Zimmer
- Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, New York, NY USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA
| | - Anna G. Orr
- Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, New York, NY USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA
- Neuroscience Graduate Program, Weill Cornell Medicine, New York, NY USA
| | - Kimberly Scearce-Levie
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Joseph W. Lewcock
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Gilbert Di Paolo
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
| | - Pascal E. Sanchez
- Denali Therapeutics, Inc., 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
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10
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Harris JA, Hashim E, Larson K, Caprio RM, Gordon AM, Resnick CM. Early weight gain in infants with Robin sequence after mandibular distraction. Int J Oral Maxillofac Surg 2022; 51:1305-1310. [PMID: 35177311 DOI: 10.1016/j.ijom.2022.01.018] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/16/2021] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
This retrospective cohort study was performed to assess weight gain in infants with Robin sequence (RS) treated by mandibular distraction osteogenesis (MDO). The primary outcome variable was average daily weight gain for the following time periods: (1) birth to MDO (T1), (2) MDO to distractor removal (T2), (3) distractor removal to 6 months later (T3), and (4) 6 months to 12 months following distractor removal (T4). Published growth curves were used for comparison. Differences were assessed using the Wilcoxon matched-pairs signed rank test. Twenty-two infants were included in the study. During T1, the infants had 9.47 ± 12.61 g/day less weight gain than expected (P = 0.001). However, for T2, T3, and T4, the infants demonstrated 3.48 ± 6.17 g/day (P = 0.028), 2.19 ± 4.47 g/day (P = 0.030), and 1.83 ± 3.25 g/day (P = 0.028) more weight gain than expected. Feeding tube use resulted in improved weight gain during T1 (P < 0.001), but was associated with poorer weight gain in T3 (P = 0.003) and T4 (P = 0.001). In conclusion, infants with RS treated by MDO demonstrated poorer weight gain relative to their peers between birth and the MDO operation. However, from the MDO procedure to 12 months post-distractor removal, infants who had MDO showed faster weight gain than their age-matched peers.
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Affiliation(s)
- J A Harris
- Boston Children's Hospital, Boston, Massachusetts, USA; Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - E Hashim
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Massachusetts, USA
| | - K Larson
- Division of Otolaryngology and Communication Enhancement, Boston Children's Hospital, Boston, Massachusetts, USA
| | - R M Caprio
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, Massachusetts, USA
| | - A M Gordon
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, Massachusetts, USA
| | - C M Resnick
- Harvard School of Dental Medicine, Boston, Massachusetts, USA; Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, Massachusetts, USA.
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11
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [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: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - 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, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- 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 at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - 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, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - 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
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - 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
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - 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, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - 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, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, 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 at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, 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
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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12
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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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13
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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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14
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Matho KS, Huilgol D, Galbavy W, He M, Kim G, An X, Lu J, Wu P, Di Bella DJ, Shetty AS, Palaniswamy R, Hatfield J, Raudales R, Narasimhan A, Gamache E, Levine JM, Tucciarone J, Szelenyi E, Harris JA, Mitra PP, Osten P, Arlotta P, Huang ZJ. Genetic dissection of the glutamatergic neuron system in cerebral cortex. Nature 2021; 598:182-187. [PMID: 34616069 PMCID: PMC8494647 DOI: 10.1038/s41586-021-03955-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [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: 04/23/2020] [Accepted: 08/25/2021] [Indexed: 11/09/2022]
Abstract
Diverse types of glutamatergic pyramidal neurons mediate the myriad processing streams and output channels of the cerebral cortex1,2, yet all derive from neural progenitors of the embryonic dorsal telencephalon3,4. Here we establish genetic strategies and tools for dissecting and fate-mapping subpopulations of pyramidal neurons on the basis of their developmental and molecular programs. We leverage key transcription factors and effector genes to systematically target temporal patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen temporally inducible mouse Cre and Flp knock-in driver lines to enable the combinatorial targeting of major progenitor types and projection classes. Combinatorial strategies confer viral access to subsets of pyramidal neurons defined by developmental origin, marker expression, anatomical location and projection targets. These strategies establish an experimental framework for understanding the hierarchical organization and developmental trajectory of subpopulations of pyramidal neurons that assemble cortical processing networks and output channels.
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Affiliation(s)
- Katherine S Matho
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Dhananjay Huilgol
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - William Galbavy
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience, Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
| | - Miao He
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Gukhan Kim
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Jiangteng Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Shanghai Jiaotong University Medical School, Shanghai, China
| | - Priscilla Wu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Daniela J Di Bella
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Ashwin S Shetty
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | - Joshua Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA
| | - Ricardo Raudales
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience, Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, NY, USA
| | - Arun Narasimhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Eric Gamache
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Jesse M Levine
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
| | - Jason Tucciarone
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
- Department of Psychiatry, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Eric Szelenyi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Julie A Harris
- Program in Neuroscience and Medical Scientist Training Program, Stony Brook University, New York, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, USA.
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA.
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15
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Davis EJ, Broestl L, Abdulai-Saiku S, Worden K, Bonham LW, Miñones-Moyano E, Moreno AJ, Wang D, Chang K, Williams G, Garay BI, Lobach I, Devidze N, Kim D, Anderson-Bergman C, Yu GQ, White CC, Harris JA, Miller BL, Bennett DA, Arnold AP, De Jager PL, Palop JJ, Panning B, Yokoyama JS, Mucke L, Dubal DB. A second X chromosome contributes to resilience in a mouse model of Alzheimer's disease. Sci Transl Med 2021; 12:12/558/eaaz5677. [PMID: 32848093 DOI: 10.1126/scitranslmed.aaz5677] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/21/2020] [Indexed: 12/21/2022]
Abstract
A major sex difference in Alzheimer's disease (AD) is that men with the disease die earlier than do women. In aging and preclinical AD, men also show more cognitive deficits. Here, we show that the X chromosome affects AD-related vulnerability in mice expressing the human amyloid precursor protein (hAPP), a model of AD. XY-hAPP mice genetically modified to develop testicles or ovaries showed worse mortality and deficits than did XX-hAPP mice with either gonad, indicating a sex chromosome effect. To dissect whether the absence of a second X chromosome or the presence of a Y chromosome conferred a disadvantage on male mice, we varied sex chromosome dosage. With or without a Y chromosome, hAPP mice with one X chromosome showed worse mortality and deficits than did those with two X chromosomes. Thus, adding a second X chromosome conferred resilience to XY males and XO females. In addition, the Y chromosome, its sex-determining region Y gene (Sry), or testicular development modified mortality in hAPP mice with one X chromosome such that XY males with testicles survived longer than did XY or XO females with ovaries. Furthermore, a second X chromosome conferred resilience potentially through the candidate gene Kdm6a, which does not undergo X-linked inactivation. In humans, genetic variation in KDM6A was linked to higher brain expression and associated with less cognitive decline in aging and preclinical AD, suggesting its relevance to human brain health. Our study suggests a potential role for sex chromosomes in modulating disease vulnerability related to AD.
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Affiliation(s)
- Emily J Davis
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Lauren Broestl
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Samira Abdulai-Saiku
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kurtresha Worden
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Luke W Bonham
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Elena Miñones-Moyano
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Arturo J Moreno
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dan Wang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kevin Chang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gina Williams
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Neurosciences Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Bayardo I Garay
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Iryna Lobach
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nino Devidze
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Daniel Kim
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | | | - Gui-Qiu Yu
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Charles C White
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bruce L Miller
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Arthur P Arnold
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Phil L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
| | - Jorge J Palop
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA.,Neurosciences Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA.,Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Barbara Panning
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer S Yokoyama
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Lennart Mucke
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA.,Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA.,Neurosciences Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA.,Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Dena B Dubal
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA. .,Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA.,Neurosciences Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
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16
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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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17
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Anderson KR, Harris JA, Ng L, Prins P, Memar S, Ljungquist B, Fürth D, Williams RW, Ascoli GA, Dumitriu D. Highlights from the Era of Open Source Web-Based Tools. J Neurosci 2021; 41:927-936. [PMID: 33472826 PMCID: PMC7880282 DOI: 10.1523/jneurosci.1657-20.2020] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 11/22/2020] [Accepted: 11/29/2020] [Indexed: 12/20/2022] Open
Abstract
High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.
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Affiliation(s)
- Kristin R Anderson
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Sara Memar
- Robarts Research Institute, BrainsCAN, Schulich School of Medicine & Dentistry, Western University, London, Ontario N6A 3K7, Canada
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Dani Dumitriu
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
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18
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Siegle JH, Jia X, Durand S, Gale S, Bennett C, Graddis N, Heller G, Ramirez TK, Choi H, Luviano JA, Groblewski PA, Ahmed R, Arkhipov A, Bernard A, Billeh YN, Brown D, Buice MA, Cain N, Caldejon S, Casal L, Cho A, Chvilicek M, Cox TC, Dai K, Denman DJ, de Vries SEJ, Dietzman R, Esposito L, Farrell C, Feng D, Galbraith J, Garrett M, Gelfand EC, Hancock N, Harris JA, Howard R, Hu B, Hytnen R, Iyer R, Jessett E, Johnson K, Kato I, Kiggins J, Lambert S, Lecoq J, Ledochowitsch P, Lee JH, Leon A, Li Y, Liang E, Long F, Mace K, Melchior J, Millman D, Mollenkopf T, Nayan C, Ng L, Ngo K, Nguyen T, Nicovich PR, North K, Ocker GK, Ollerenshaw D, Oliver M, Pachitariu M, Perkins J, Reding M, Reid D, Robertson M, Ronellenfitch K, Seid S, Slaughterbeck C, Stoecklin M, Sullivan D, Sutton B, Swapp J, Thompson C, Turner K, Wakeman W, Whitesell JD, Williams D, Williford A, Young R, Zeng H, Naylor S, Phillips JW, Reid RC, Mihalas S, Olsen SR, Koch C. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 2021; 592:86-92. [PMID: 33473216 PMCID: PMC10399640 DOI: 10.1038/s41586-020-03171-x] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 12/09/2020] [Indexed: 12/14/2022]
Abstract
The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.
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Affiliation(s)
| | - Xiaoxuan Jia
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | - Sam Gale
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Hannah Choi
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | | | | | | | | | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Dillan Brown
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nicolas Cain
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Andrew Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Timothy C Cox
- University of Missouri-Kansas City School of Dentistry, Kansas City, MO, USA
| | - Kael Dai
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Daniel J Denman
- Allen Institute for Brain Science, Seattle, WA, USA.,The University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | | | | | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Brian Hu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ross Hytnen
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - India Kato
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jerome Lecoq
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Fuhui Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Kat North
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Jed Perkins
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Ben Sutton
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Rob Young
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sarah Naylor
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, WA, USA.
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19
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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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20
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Ilse A, Donohue SE, Schoenfeld MA, Hopf JM, Heinze HJ, Harris JA. Unseen food images capture the attention of hungry viewers: Evidence from event-related potentials. Appetite 2020; 155:104828. [PMID: 32814119 DOI: 10.1016/j.appet.2020.104828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 06/24/2020] [Accepted: 08/09/2020] [Indexed: 10/23/2022]
Abstract
Motivationally relevant visual targets appear to capture visuospatial attention. This capture is evident behaviorally as faster and more accurate responses, and neurally as an enhanced-amplitude of the N2pc - an index of spatial attention allocation, which is observed even when observers are unaware of the target. In the case of reinforcers such as food or substances of dependence, it is likely that the motivational state of craving accompanying deprivation potentiates this capture. The automaticity of such attentional capture by reward-associated stimuli, as well as its possible interaction with craving, is as yet not completely understood, though it is likely a major explanatory factor in motivated behaviors. For the present experiment, participants completed two EEG recording sessions: one just after eating lunch (sated/non-craving), and the other following a minimum 12-h period of fasting (hungry/craving). For both sessions, participants identified food- and clothing-related targets embedded in an object-substitution masking paradigm, which yielded trials of full target visibility, as well as trials for which targets were present but undetected. Although masking equally disrupted visual awareness of both classes of targets as measured behaviorally, a three-way hunger by visibility by target interaction was observed in the neural data, with unseen food targets eliciting an enhanced N2pc. Interestingly, this subliminal attentional capture by food-related items was observed only during the "hungry" session. No such capture was evident under conditions of full visibility. These findings indicate that attentional capture by food-related images, and reflected in enhancements of the N2pc, is spurred by hunger, and that this effect can be viewed as automatic, or independent of explicit awareness of food-relevant target content.
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Affiliation(s)
- A Ilse
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany
| | - S E Donohue
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany; Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg, 39118, Germany
| | - M A Schoenfeld
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany; Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg, 39118, Germany; Kliniken Schmieder Heidelberg, Speyererhofweg 1, Heidelberg, 69117, Germany
| | - J M Hopf
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany; Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg, 39118, Germany
| | - H-J Heinze
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany
| | - J A Harris
- Otto-von-Guericke University Department of Neurology, Leipziger Straße 44, Magdeburg, 39120, Germany; Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg, 39118, Germany; Bradley University Department of Psychology, 1501 West Bradley Avenue, Peoria, IL, USA.
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21
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Coletta L, Pagani M, Whitesell JD, Harris JA, Bernhardt B, Gozzi A. Network structure of the mouse brain connectome with voxel resolution. Sci Adv 2020; 6:6/51/eabb7187. [PMID: 33355124 DOI: 10.1126/sciadv.abb7187] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 11/04/2020] [Indexed: 06/12/2023]
Abstract
Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Previous investigations of the mouse brain connectome have used discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. Here, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We report a number of previously unappreciated organizational principles in the mammalian brain, including a directional segregation of hub regions into neural sink and sources, and a strategic wiring of neuromodulatory nuclei as connector hubs and critical orchestrators of network communication. We also find that the mouse cortical connectome is hierarchically organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis, recapitulating a phylogenetically conserved feature of higher mammals. These findings advance our understanding of the foundational wiring principles of the mammalian connectome.
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Affiliation(s)
- Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto TN, Italy
| | - Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | | | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.
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22
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Winnubst J, Spruston N, Harris JA. Linking axon morphology to gene expression: a strategy for neuronal cell-type classification. Curr Opin Neurobiol 2020; 65:70-76. [PMID: 33181399 DOI: 10.1016/j.conb.2020.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/04/2020] [Accepted: 10/05/2020] [Indexed: 12/28/2022]
Abstract
To study how the brain drives cognition and behavior we need to understand its cellular composition. Advances in single-cell transcriptomics have revolutionized our ability to characterize neuronal diversity. To arrive at meaningful descriptions of cell types, however, gene expression must be linked to structural and functional properties. Axonal projection patterns are an appropriate measure, as they are diverse, change only gradually over time, and they influence and constrain circuit function. Here, we consider how efforts to map transcriptional and morphological diversity in the mouse brain could be linked to generate a modern taxonomy of the mouse brain.
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Affiliation(s)
- Johan Winnubst
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
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23
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Wang Q, Ding SL, Li Y, Royall J, Feng D, Lesnar P, Graddis N, Naeemi M, Facer B, Ho A, Dolbeare T, Blanchard B, Dee N, Wakeman W, Hirokawa KE, Szafer A, Sunkin SM, Oh SW, Bernard A, Phillips JW, Hawrylycz M, Koch C, Zeng H, Harris JA, Ng L. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 2020; 181:936-953.e20. [PMID: 32386544 PMCID: PMC8152789 DOI: 10.1016/j.cell.2020.04.007] [Citation(s) in RCA: 454] [Impact Index Per Article: 113.5] [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: 05/02/2019] [Revised: 12/12/2019] [Accepted: 04/03/2020] [Indexed: 01/25/2023]
Abstract
Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 μm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 μm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Josh Royall
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Benjamin Facer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Seung Wook Oh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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24
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Tran DMD, Harris JA, Harris IM, Livesey EJ. Motor Conflict: Revealing Involuntary Conditioned Motor Preparation Using Transcranial Magnetic Stimulation. Cereb Cortex 2019; 30:2478-2488. [DOI: 10.1093/cercor/bhz253] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Preparing actions to achieve goals, overriding habitual responses, and substituting actions that are no longer relevant are aspects of motor control often assumed to be driven by deliberate top-down processes. In the present study, we investigated whether motor control could come under involuntary control of environmental cues that have been associated with specific actions in the past. We used transcranial magnetic stimulation (TMS) to probe corticospinal excitability as an index of motor preparation, while participants performed a Go/No-Go task (i.e., an action outcome or no action outcome task) and rated what trial was expected to appear next (Go or No-Go). We found that corticospinal excitability during a warning cue for the upcoming trial closely matched recent experience (i.e., cue–outcome pairings), despite conflicting with what participants expected would appear. The results reveal that in an action–outcome task, neurophysiological indices of motor preparation show changes that are consistent with participants learning to associate a preparatory warning cue with a specific action, and are not consistent with the action that participants explicitly anticipate making. This dissociation with conscious expectancy ratings reveals that conditioned responding and motor preparation can operate independently of conscious expectancies about having to act.
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Affiliation(s)
- D M D Tran
- School of Psychology, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - J A Harris
- School of Psychology, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - I M Harris
- School of Psychology, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - E J Livesey
- School of Psychology, Faculty of Science, The University of Sydney, Camperdown, NSW, 2006, Australia
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25
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Gouwens NW, Sorensen SA, Berg J, Lee C, Jarsky T, Ting J, Sunkin SM, Feng D, Anastassiou CA, Barkan E, Bickley K, Blesie N, Braun T, Brouner K, Budzillo A, Caldejon S, Casper T, Castelli D, Chong P, Crichton K, Cuhaciyan C, Daigle TL, Dalley R, Dee N, Desta T, Ding SL, Dingman S, Doperalski A, Dotson N, Egdorf T, Fisher M, de Frates RA, Garren E, Garwood M, Gary A, Gaudreault N, Godfrey K, Gorham M, Gu H, Habel C, Hadley K, Harrington J, Harris JA, Henry A, Hill D, Josephsen S, Kebede S, Kim L, Kroll M, Lee B, Lemon T, Link KE, Liu X, Long B, Mann R, McGraw M, Mihalas S, Mukora A, Murphy GJ, Ng L, Ngo K, Nguyen TN, Nicovich PR, Oldre A, Park D, Parry S, Perkins J, Potekhina L, Reid D, Robertson M, Sandman D, Schroedter M, Slaughterbeck C, Soler-Llavina G, Sulc J, Szafer A, Tasic B, Taskin N, Teeter C, Thatra N, Tung H, Wakeman W, Williams G, Young R, Zhou Z, Farrell C, Peng H, Hawrylycz MJ, Lein E, Ng L, Arkhipov A, Bernard A, Phillips JW, Zeng H, Koch C. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 2019; 22:1182-1195. [PMID: 31209381 PMCID: PMC8078853 DOI: 10.1038/s41593-019-0417-0] [Citation(s) in RCA: 219] [Impact Index Per Article: 43.8] [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: 05/15/2018] [Accepted: 04/25/2019] [Indexed: 12/21/2022]
Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.
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Affiliation(s)
| | | | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kris Bickley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nicole Blesie
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Thomas Braun
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Agata Budzillo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Dan Castelli
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Peter Chong
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tsega Desta
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Samuel Dingman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Michael Fisher
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Keith Godfrey
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Melissa Gorham
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Caroline Habel
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alex Henry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sam Josephsen
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tracy Lemon
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Xiaoxiao Liu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Medea McGraw
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sheana Parry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jed Perkins
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Sandman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Corinne Teeter
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rob Young
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Colin Farrell
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, USA.
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, USA
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26
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Daigle TL, Madisen L, Hage TA, Valley MT, Knoblich U, Larsen RS, Takeno MM, Huang L, Gu H, Larsen R, Mills M, Bosma-Moody A, Siverts LA, Walker M, Graybuck LT, Yao Z, Fong O, Nguyen TN, Garren E, Lenz GH, Chavarha M, Pendergraft J, Harrington J, Hirokawa KE, Harris JA, Nicovich PR, McGraw MJ, Ollerenshaw DR, Smith KA, Baker CA, Ting JT, Sunkin SM, Lecoq J, Lin MZ, Boyden ES, Murphy GJ, da Costa NM, Waters J, Li L, Tasic B, Zeng H. A Suite of Transgenic Driver and Reporter Mouse Lines with Enhanced Brain-Cell-Type Targeting and Functionality. Cell 2019; 174:465-480.e22. [PMID: 30007418 DOI: 10.1016/j.cell.2018.06.035] [Citation(s) in RCA: 414] [Impact Index Per Article: 82.8] [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: 11/21/2017] [Revised: 04/12/2018] [Accepted: 06/13/2018] [Indexed: 01/05/2023]
Abstract
Modern genetic approaches are powerful in providing access to diverse cell types in the brain and facilitating the study of their function. Here, we report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.
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Affiliation(s)
- Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Linda Madisen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Travis A Hage
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ulf Knoblich
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rylan S Larsen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Marc M Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lawrence Huang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachael Larsen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maya Mills
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Miranda Walker
- 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
| | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Garreck H Lenz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Mariya Chavarha
- Departments of Neurobiology and Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Medea J McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | | | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jérôme Lecoq
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Z Lin
- Departments of Neurobiology and Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Edward S Boyden
- MIT Media Lab and McGovern Institute, Departments of Biological Engineering and Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lu Li
- 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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27
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Whitesell JD, Buckley AR, Knox JE, Kuan L, Graddis N, Pelos A, Mukora A, Wakeman W, Bohn P, Ho A, Hirokawa KE, Harris JA. Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer's disease. J Comp Neurol 2018; 527:2122-2145. [PMID: 30311654 DOI: 10.1002/cne.24555] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 09/13/2018] [Indexed: 01/08/2023]
Abstract
A variety of Alzheimer's disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid β (Aβ) and forming plaques. However, the degree to which these models mimic spatiotemporal patterns of Aβ deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of Aβ plaques across age in three APP-overexpression mouse lines (APP/PS1, Tg2576, and hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial two-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; for example, the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional Aβ deposition patterns described in AD.
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Affiliation(s)
| | | | - Joseph E Knox
- Allen Institute for Brain Science, Seattle, Washington
| | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, Washington
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, Washington
| | - Andrew Pelos
- Allen Institute for Brain Science, Seattle, Washington.,Department of Neuroscience, Pomona College, Claremont, California
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, Washington
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, Washington
| | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, Washington
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, Washington
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28
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Knox JE, Harris KD, Graddis N, Whitesell JD, Zeng H, Harris JA, Shea-Brown E, Mihalas S. High-resolution data-driven model of the mouse connectome. Netw Neurosci 2018; 3:217-236. [PMID: 30793081 PMCID: PMC6372022 DOI: 10.1162/netn_a_00066] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/31/2018] [Indexed: 11/04/2022] Open
Abstract
Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.
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Affiliation(s)
- Joseph E. Knox
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
| | - Kameron Decker Harris
- Applied Mathematics, University of Washington, Seattle, Washington, USA
- Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Eric Shea-Brown
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
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29
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Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan S, Penn O, Bakken T, Menon V, Miller J, Fong O, Hirokawa KE, Lathia K, Rimorin C, Tieu M, Larsen R, Casper T, Barkan E, Kroll M, Parry S, Shapovalova NV, Hirschstein D, Pendergraft J, Sullivan HA, Kim TK, Szafer A, Dee N, Groblewski P, Wickersham I, Cetin A, Harris JA, Levi BP, Sunkin SM, Madisen L, Daigle TL, Looger L, Bernard A, Phillips J, Lein E, Hawrylycz M, Svoboda K, Jones AR, Koch C, Zeng H. Shared and distinct transcriptomic cell types across neocortical areas. Nature 2018; 563:72-78. [PMID: 30382198 DOI: 10.1038/s41586-018-0654-5] [Citation(s) in RCA: 917] [Impact Index Per Article: 152.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 09/24/2018] [Indexed: 12/11/2022]
Abstract
The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.
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Affiliation(s)
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael N Economo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sarada Viswanathan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Osnat Penn
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Vilas Menon
- Allen Institute for Brain Science, Seattle, WA, USA.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Sheana Parry
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ian Wickersham
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Loren Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
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30
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Todman LC, Fraser FC, Corstanje R, Harris JA, Pawlett M, Ritz K, Whitmore AP. Evidence for functional state transitions in intensively-managed soil ecosystems. Sci Rep 2018; 8:11522. [PMID: 30068982 PMCID: PMC6070522 DOI: 10.1038/s41598-018-29925-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 07/17/2018] [Indexed: 11/16/2022] Open
Abstract
Soils are fundamental to terrestrial ecosystem functioning and food security, thus their resilience to disturbances is critical. Furthermore, they provide effective models of complex natural systems to explore resilience concepts over experimentally-tractable short timescales. We studied soils derived from experimental plots with different land-use histories of long-term grass, arable and fallow to determine whether regimes of extreme drying and re-wetting would tip the systems into alternative stable states, contingent on their historical management. Prior to disturbance, grass and arable soils produced similar respiration responses when processing an introduced complex carbon substrate. A distinct respiration response from fallow soil here indicated a different prior functional state. Initial dry:wet disturbances reduced the respiration in all soils, suggesting that the microbial community was perturbed such that its function was impaired. After 12 drying and rewetting cycles, despite the extreme disturbance regime, soil from the grass plots, and those that had recently been grass, adapted and returned to their prior functional state. Arable soils were less resilient and shifted towards a functional state more similar to that of the fallow soil. Hence repeated stresses can apparently induce persistent shifts in functional states in soils, which are influenced by management history.
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Affiliation(s)
- L C Todman
- Rothamsted Research, Harpenden, AL5 2JQ, UK.
| | - F C Fraser
- Cranfield University, Cranfield, Bedford, MK43 0AL, UK
| | - R Corstanje
- Cranfield University, Cranfield, Bedford, MK43 0AL, UK
| | - J A Harris
- Cranfield University, Cranfield, Bedford, MK43 0AL, UK
| | - M Pawlett
- Cranfield University, Cranfield, Bedford, MK43 0AL, UK
| | - K Ritz
- Cranfield University, Cranfield, Bedford, MK43 0AL, UK
- The University of Nottingham, Sutton Bonington Campus, Leicestershire, LE12 5RD, UK
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31
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Whitesell JD, Buckley AR, Graddis N, Kuan L, Knox JE, Naeemi M, Bohn P, Mukora A, Hirokawa KA, Harris JA. P4‐225: WHOLE BRAIN IMAGING REVEALS DISTINCT SPATIAL PATTERNS OF AMYLOID BETA DEPOSITION AND ATROPHY IN MOUSE MODELS OF ALZHEIMER'S DISEASE. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.07.046] [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: 10/28/2022]
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32
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Chatterjee S, Sullivan HA, MacLennan BJ, Xu R, Hou Y, Lavin TK, Lea NE, Michalski JE, Babcock KR, Dietrich S, Matthews GA, Beyeler A, Calhoon GG, Glober G, Whitesell JD, Yao S, Cetin A, Harris JA, Zeng H, Tye KM, Reid RC, Wickersham IR. Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons. Nat Neurosci 2018; 21:638-646. [PMID: 29507411 PMCID: PMC6503322 DOI: 10.1038/s41593-018-0091-7] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Accepted: 01/14/2018] [Indexed: 12/25/2022]
Abstract
Recombinant rabies viral vectors have proven useful for applications including retrograde targeting of projection neurons and monosynaptic tracing, but their cytotoxicity has limited their use to short-term experiments. Here we introduce a new class of double-deletion-mutant rabies viral vectors that left transduced cells alive and healthy indefinitely. Deletion of the viral polymerase gene abolished cytotoxicity and reduced transgene expression to trace levels but left vectors still able to retrogradely infect projection neurons and express recombinases, allowing downstream expression of other transgene products such as fluorophores and calcium indicators. The morphology of retrogradely targeted cells appeared unperturbed at 1 year postinjection. Whole-cell patch-clamp recordings showed no physiological abnormalities at 8 weeks. Longitudinal two-photon structural and functional imaging in vivo, tracking thousands of individual neurons for up to 4 months, showed that transduced neurons did not die but retained stable visual response properties even at the longest time points imaged.
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Affiliation(s)
| | - Heather A Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Ran Xu
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - YuanYuan Hou
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas K Lavin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas E Lea
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jacob E Michalski
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kelsey R Babcock
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephan Dietrich
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gillian A Matthews
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna Beyeler
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gwendolyn G Calhoon
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gordon Glober
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kay M Tye
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
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33
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Wang Q, Ng L, Harris JA, Feng D, Li Y, Royall JJ, Oh SW, Bernard A, Sunkin SM, Koch C, Zeng H. Organization of the connections between claustrum and cortex in the mouse. J Comp Neurol 2017. [DOI: 10.1002/cne.24183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Lydia Ng
- Allen Institute for Brain Science; Seattle Washington 98109
| | | | - David Feng
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Yang Li
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Josh J. Royall
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Seung Wook Oh
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Amy Bernard
- Allen Institute for Brain Science; Seattle Washington 98109
| | | | - Christof Koch
- Allen Institute for Brain Science; Seattle Washington 98109
| | - Hongkui Zeng
- Allen Institute for Brain Science; Seattle Washington 98109
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34
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Harris JA, Stein SW, Myrdal PB. Erratum to Evaluation of the TSI aerosol impactor 3306/3321 system using a redesigned impactor stage with solution and suspension metered-dose inhalers. AAPS PharmSciTech 2017. [DOI: 10.1208/pt070381c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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35
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Martersteck EM, Hirokawa KE, Evarts M, Bernard A, Duan X, Li Y, Ng L, Oh SW, Ouellette B, Royall JJ, Stoecklin M, Wang Q, Zeng H, Sanes JR, Harris JA. Diverse Central Projection Patterns of Retinal Ganglion Cells. Cell Rep 2017; 18:2058-2072. [PMID: 28228269 PMCID: PMC5357325 DOI: 10.1016/j.celrep.2017.01.075] [Citation(s) in RCA: 149] [Impact Index Per Article: 21.3] [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: 09/26/2016] [Revised: 12/09/2016] [Accepted: 01/27/2017] [Indexed: 11/27/2022] Open
Abstract
Understanding how >30 types of retinal ganglion cells (RGCs) in the mouse retina each contribute to visual processing in the brain will require more tools that label and manipulate specific RGCs. We screened and analyzed retinal expression of Cre recombinase using 88 transgenic driver lines. In many lines, Cre was expressed in multiple RGC types and retinal cell classes, but several exhibited more selective expression. We comprehensively mapped central projections from RGCs labeled in 26 Cre lines using viral tracers, high-throughput imaging, and a data processing pipeline. We identified over 50 retinorecipient regions and present a quantitative retina-to-brain connectivity map, enabling comparisons of target-specificity across lines. Projections to two major central targets were notably correlated: RGCs projecting to the outer shell or core regions of the lateral geniculate projected to superficial or deep layers within the superior colliculus, respectively. Retinal images and projection data are available online at http://connectivity.brain-map.org.
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Affiliation(s)
- Emily M Martersteck
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | | | - Mariah Evarts
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Xin Duan
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Seung W Oh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Joshua R Sanes
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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36
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Wang Q, Ng L, Harris JA, Feng D, Li Y, Royall JJ, Oh SW, Bernard A, Sunkin SM, Koch C, Zeng H. Organization of the connections between claustrum and cortex in the mouse. J Comp Neurol 2016; 525:1317-1346. [PMID: 27223051 PMCID: PMC5324679 DOI: 10.1002/cne.24047] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Revised: 05/10/2016] [Accepted: 05/23/2016] [Indexed: 12/19/2022]
Abstract
The connections between the claustrum and the cortex in mouse are systematically investigated with adeno-associated virus (AAV), an anterograde viral tracer. We first define the boundary and the three-dimensional structure of the claustrum based on a variety of molecular and anatomical data. From AAV injections into 42 neocortical and allocortical areas, we conclude that most cortical areas send bilateral projections to the claustrum, the majority being denser on the ipsilateral side. This includes prelimbic, infralimbic, medial, ventrolateral and lateral orbital, ventral retrosplenial, dorsal and posterior agranular insular, visceral, temporal association, dorsal and ventral auditory, ectorhinal, perirhinal, lateral entorhinal, and anteromedial, posteromedial, lateroposterior, laterointermediate, and postrhinal visual areas. In contrast, the cingulate and the secondary motor areas send denser projections to the contralateral claustrum than to the ipsilateral one. The gustatory, primary auditory, primary visual, rostrolateral visual, and medial entorhinal cortices send projections only to the ipsilateral claustrum. Primary motor, primary somatosensory and subicular areas barely send projections to either ipsi- or contralateral claustrum. Corticoclaustral projections are organized in a rough topographic manner, with variable projection strengths. We find that the claustrum, in turn, sends widespread projections preferentially to ipsilateral cortical areas with different projection strengths and laminar distribution patterns and to certain contralateral cortical areas. Our quantitative results show that the claustrum has strong reciprocal and bilateral connections with prefrontal and cingulate areas as well as strong reciprocal connections with the ipsilateral temporal and retrohippocampal areas, suggesting that it may play a crucial role in a variety of cognitive processes. J. Comp. Neurol. 525:1317-1346, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain ScienceSeattleWashington98109
| | - Lydia Ng
- Allen Institute for Brain ScienceSeattleWashington98109
| | | | - David Feng
- Allen Institute for Brain ScienceSeattleWashington98109
| | - Yang Li
- Allen Institute for Brain ScienceSeattleWashington98109
| | | | - Seung Wook Oh
- Allen Institute for Brain ScienceSeattleWashington98109
| | - Amy Bernard
- Allen Institute for Brain ScienceSeattleWashington98109
| | | | - Christof Koch
- Allen Institute for Brain ScienceSeattleWashington98109
| | - Hongkui Zeng
- Allen Institute for Brain ScienceSeattleWashington98109
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Wang Q, Henry AM, Harris JA, Oh SW, Joines KM, Nyhus J, Hirokawa KE, Dee N, Mortrud M, Parry S, Ouellette B, Caldejon S, Bernard A, Jones AR, Zeng H, Hohmann JG. Systematic comparison of adeno-associated virus and biotinylated dextran amine reveals equivalent sensitivity between tracers and novel projection targets in the mouse brain. J Comp Neurol 2015; 522:1989-2012. [PMID: 24639291 DOI: 10.1002/cne.23567] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [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: 12/20/2013] [Revised: 02/11/2014] [Accepted: 02/11/2014] [Indexed: 01/19/2023]
Abstract
As an anterograde neuronal tracer, recombinant adeno-associated virus (AAV) has distinct advantages over the widely used biotinylated dextran amine (BDA). However, the sensitivity and selectivity of AAV remain uncharacterized for many brain regions and species. To validate this tracing method further, AAV (serotype 1) was systematically compared with BDA as an anterograde tracer by injecting both tracers into three cortical and 15 subcortical regions in C57BL/6J mice. Identical parameters were used for our sequential iontophoretic injections, producing injections of AAV that were more robust in size and in density of neurons infected compared with those of BDA. However, these differences did not preclude further comparison between the tracers, because the pairs of injections were suitably colocalized and contained some percentage of double-labeled neurons. A qualitative analysis of projection patterns showed that the two tracers behave very similarly when injection sites are well matched. Additionally, a quantitative analysis of relative projection intensity for cases targeting primary motor cortex (MOp), primary somatosensory cortex (SSp), and caudoputamen (CP) showed strong agreement in the ranked order of projection intensities between the two tracers. A detailed analysis of the projections of two brain regions (SSp and MOp) revealed many targets that have not previously been described in the mouse or rat. Minor retrograde labeling of neurons was observed in all cases examined, for both AAV and BDA. Our results show that AAV has actions equivalent to those of BDA as an anterograde tracer and is suitable for analysis of neural circuitry throughout the mouse brain.
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Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, 98103
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Striedter GF, Belgard TG, Chen CC, Davis FP, Finlay BL, Güntürkün O, Hale ME, Harris JA, Hecht EE, Hof PR, Hofmann HA, Holland LZ, Iwaniuk AN, Jarvis ED, Karten HJ, Katz PS, Kristan WB, Macagno ER, Mitra PP, Moroz LL, Preuss TM, Ragsdale CW, Sherwood CC, Stevens CF, Stüttgen MC, Tsumoto T, Wilczynski W. NSF workshop report: discovering general principles of nervous system organization by comparing brain maps across species. J Comp Neurol 2014; 522:1445-53. [PMID: 24596113 DOI: 10.1002/cne.23568] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [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: 02/14/2014] [Accepted: 02/18/2014] [Indexed: 01/23/2023]
Abstract
Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system 'maps' comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of 'reference species' to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution.
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Affiliation(s)
- Georg F Striedter
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, California
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Quina LA, Tempest L, Ng L, Harris JA, Ferguson S, Jhou TC, Turner EE. Efferent pathways of the mouse lateral habenula. J Comp Neurol 2014; 523:32-60. [PMID: 25099741 DOI: 10.1002/cne.23662] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [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: 03/14/2014] [Revised: 08/14/2014] [Accepted: 08/05/2014] [Indexed: 12/13/2022]
Abstract
The lateral habenula (LHb) is part of the habenula complex of the dorsal thalamus. Recent studies of the LHb have focused on its projections to the ventral tegmental area (VTA) and rostromedial tegmental nucleus (RMTg), which contain γ-aminobutyric acid (GABA)ergic neurons that mediate reward prediction error via inhibition of dopaminergic activity. However, older studies in the rat have also identified LHb outputs to the lateral and posterior hypothalamus, median raphe, dorsal raphe, and dorsal tegmentum. Although these studies have shown that the medial and lateral divisions of the LHb have somewhat distinct projections, the topographic specificity of LHb efferents is not completely understood, and the relative extent of these projections to brainstem targets is unknown. Here we have used anterograde tracing with adeno-associated virus-mediated expression of green fluorescent protein, combined with serial two-photon tomography, to map the efferents of the LHb on a standard coordinate system for the entire mouse brain, and reconstruct the efferent pathways of the LHb in three dimensions. Using automated quantitation of fiber density, we show that in addition to the RMTg, the median raphe, caudal dorsal raphe, and pontine central gray are major recipients of LHb efferents. By using retrograde tract tracing with cholera toxin subunit B, we show that LHb neurons projecting to the hypothalamus, VTA, median raphe, caudal dorsal raphe, and pontine central gray reside in characteristic, but sometimes overlapping regions of the LHb. Together these results provide the anatomical basis for systematic studies of LHb function in neural circuits and behavior in mice. J. Comp. Neurol. 523:32-60, 2015. © 2014 Wiley Periodicals, Inc.
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Affiliation(s)
- Lely A Quina
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, 98101
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Harris JA, Hirokawa KE, Sorensen SA, Gu H, Mills M, Ng LL, Bohn P, Mortrud M, Ouellette B, Kidney J, Smith KA, Dang C, Sunkin S, Bernard A, Oh SW, Madisen L, Zeng H. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front Neural Circuits 2014; 8:76. [PMID: 25071457 PMCID: PMC4091307 DOI: 10.3389/fncir.2014.00076] [Citation(s) in RCA: 272] [Impact Index Per Article: 27.2] [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: 04/18/2014] [Accepted: 06/18/2014] [Indexed: 01/26/2023] Open
Abstract
Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization (ISH), imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute's Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain.
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Affiliation(s)
| | | | | | - Hong Gu
- Allen Institute for Brain Science Seattle, WA, USA
| | - Maya Mills
- Allen Institute for Brain Science Seattle, WA, USA
| | - Lydia L Ng
- Allen Institute for Brain Science Seattle, WA, USA
| | - Phillip Bohn
- Allen Institute for Brain Science Seattle, WA, USA
| | | | | | | | | | - Chinh Dang
- Allen Institute for Brain Science Seattle, WA, USA
| | - Susan Sunkin
- Allen Institute for Brain Science Seattle, WA, USA
| | - Amy Bernard
- Allen Institute for Brain Science Seattle, WA, USA
| | | | | | - Hongkui Zeng
- Allen Institute for Brain Science Seattle, WA, USA
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Wang Q, Henry AM, Harris JA, Oh SW, Joines KM, Nyhus J, Hirokawa KE, Dee N, Mortrud M, Parry S, Ouellette B, Caldejon S, Bernard A, Jones AR, Zeng H, Hohmann JG. Systematic comparison of adeno-associated virus and biotinylated dextran amine reveals equivalent sensitivity between tracers and novel projection targets in the mouse brain. J Comp Neurol 2014. [DOI: 10.1002/cne.23587] [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: 11/10/2022]
Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science; Seattle Washington 98103
| | - Alex M. Henry
- Allen Institute for Brain Science; Seattle Washington 98103
| | | | - Seung Wook Oh
- Allen Institute for Brain Science; Seattle Washington 98103
| | | | - Julie Nyhus
- Allen Institute for Brain Science; Seattle Washington 98103
| | | | - Nick Dee
- Allen Institute for Brain Science; Seattle Washington 98103
| | - Marty Mortrud
- Allen Institute for Brain Science; Seattle Washington 98103
| | - Sheana Parry
- Allen Institute for Brain Science; Seattle Washington 98103
| | | | | | - Amy Bernard
- Allen Institute for Brain Science; Seattle Washington 98103
| | - Allan R. Jones
- Allen Institute for Brain Science; Seattle Washington 98103
| | - Hongkui Zeng
- Allen Institute for Brain Science; Seattle Washington 98103
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Striedter GF, Belgard TG, Chen CC, Davis FP, Finlay BL, Güntürkün O, Hale ME, Harris JA, Hecht EE, Hof PR, Hofmann HA, Holland LZ, Iwaniuk AN, Jarvis ED, Karten HJ, Katz PS, Kristan WB, Macagno ER, Mitra PP, Moroz LL, Preuss TM, Ragsdale CW, Sherwood CC, Stevens CF, Stüttgen MC, Tsumoto T, Wilczynski W. NSF workshop report: discovering general principles of nervous system organization by comparing brain maps across species. Brain Behav Evol 2014; 83:1-8. [PMID: 24603302 DOI: 10.1159/000360152] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system 'maps' comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of 'reference species' to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution.
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Affiliation(s)
- Georg F Striedter
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, Calif., USA
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Beccaria G, Beccaria L, Dawson R, Gorman D, Harris JA, Hossain D. Nursing student's perceptions and understanding of intimate partner violence. Nurse Educ Today 2013; 33:907-911. [PMID: 23021564 DOI: 10.1016/j.nedt.2012.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Revised: 07/16/2012] [Accepted: 08/14/2012] [Indexed: 05/28/2023]
Abstract
Intimate partner violence (IPV) is a significant health issue in the Australian population and nurses have a role in assessment, intervention and support of families. World Health Organization Statistics indicate that as many as 61% of women, under the age of 50 have been physically abused by their partners. As nurses are in a unique position to identify, assist and support women living with IPV a greater understanding of student nurse's knowledge and attitudes may assist undergraduate programs to ensure better preparation of nurses for this role. A nurse's readiness to manage IPV may be influenced by their knowledge, attitudes, beliefs and behaviors, largely related to their self-efficacy in identifying these women (i.e. via screening procedures) and providing effective interventions. Students from all levels of the undergraduate program of an Australian regional university were invited to participate in focus groups and a subsequent survey that explored their perceptions, attitudes and knowledge of IPV. The results showed students had limited and stereotypical beliefs regarding what constitutes IPV and who perpetrates it. They indicated that they were under prepared to deal with IPV situations in clinical practice but did identify communication as a core skill required. Nursing students may not understand the significance of the issues of IPV nor fully understand the social, economic and health impacts at an individual and societal level. This may result in further under detection of the problem. The results of this study indicate a number of important implications for undergraduate nursing education curricula.
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Affiliation(s)
- Gavin Beccaria
- Department of Psychology, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
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Harris JA, Bartelt D, Campion M, Gayler BW, Jones B, Hayes A, Haynos J, Herbick S, Kling T, Lingaraj A, Singer M, Starmer H, Smith C, Webster K. The use of low-osmolar water-soluble contrast in videofluoroscopic swallowing exams. Dysphagia 2013; 28:520-7. [PMID: 23529533 DOI: 10.1007/s00455-013-9462-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2012] [Accepted: 02/19/2013] [Indexed: 12/13/2023]
Abstract
The selection of the contrast agent used during fluoroscopic exams is an important clinical decision. The purpose of this article is to document the usage of a nonionic, water-soluble contrast (iohexol) and barium contrast in adult patients undergoing fluoroscopic exams of the pharynx and/or esophagus and provide clinical indications for the use of each. For 1 year, data were collected on the use of iohexol and barium during fluoroscopic exams. The contrast agent used was selected by the speech language pathologist (SLP) or the radiologist based on the exam's indications. A total of 1,978 fluoroscopic exams were completed in the 12-month period of documentation. Of these exams, 60.6 % were completed for medical reasons and 39.4 % for surgical reasons. Fifty-five percent of the exams were performed jointly by a SLP and a radiologist and 45 % were performed by a radiologist alone. Aspiration was present in 22 % of the exams, vestibular penetration occurred in 38 %, extraluminal leakage of contrast was observed in 4.6 %, and both aspiration and leakage were seen in 1 % of the exams. In cases with aspiration, iohexol was used alone in 8 %, iohexol and barium were both used in 45 %, and barium was used alone in 47 %. In cases with extraluminal leakage, iohexol was used alone in 58 %, iohexol and barium were both used in 31 %, and barium was used alone in 11 %. No adverse effects were seen with the use of iohexol. When barium was used in cases of aspiration and extraluminal leakage, the amount of aspirated barium was small and the extraluminal barium in the instances of leakage was small. Iohexol is a useful screening contrast agent and can safely provide information, and its use reduces the risk of aspiration and the chance of leakage of large amounts of barium.
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Affiliation(s)
- Julie A Harris
- Physical and Rehabilitation Medicine, Johns Hopkins Hospital, 600 N. Wolfe Street, Baltimore, MD, 21287, USA,
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Harris JA, Wook Oh S, Zeng H. Adeno‐Associated Viral Vectors for Anterograde Axonal Tracing with Fluorescent Proteins in Nontransgenic and Cre Driver Mice. ACTA ACUST UNITED AC 2012; Chapter 1:Unit 1.20.1-18. [DOI: 10.1002/0471142301.ns0120s59] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
| | - Seung Wook Oh
- Allen Institute for Brain Science Seattle Washington
| | - Hongkui Zeng
- Allen Institute for Brain Science Seattle Washington
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Johnson AM, Vernon PA, McCarthy JM, Molson M, Harris JA, Jang KL. Nature vs nurture: Are leaders born or made? A behavior genetic investigation of leadership style. ACTA ACUST UNITED AC 2012. [DOI: 10.1375/twin.1.4.216] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractWith the recent resurgence in popularity of trait theories of leadership, it is timely to consider the genetic determination of the multiple factors comprising the leadership construct. Individual differences in personality traits have been found to be moderately to highly heritable, and so it follows that if there are reliable personality trait differences between leaders and non-leaders, then there may be a heritable component to these individual differences. Despite this connection between leadership and personality traits, however, there are no studies of the genetic basis of leadership using modern behavior genetic methodology. The present study proposes to address the lack of research in this area by examining the heritability of leadership style, as measured by self-report psychometric inventories. The Multifactor Leadership Questionnaire (MLQ), the Leadership Ability Evaluation, and the Adjective Checklist were completed by 247 adult twin pairs (183 monozygotic and 64 same-sex dizygotic). Results indicated that most of the leadership dimensions examined in this study are heritable, as are two higher level factors (resembling transactional and transformational leadership)derived from anobliquely rotated principal components factors analysis of the MLQ. Univariate analyses suggested that 48% of the variance in transactional leadership may be explained by additive heritability, and 59% of the variance in transformational leadership may be explained by non-additive (dominance) heritability. Multi-variate analyses indicatedthat most ofthe variables studiedshared substantial genetic covariance, suggesting a large overlap in the underlying genes responsible for the leadership dimensions.
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Allen VM, Ridley AM, Harris JA, Newell DG, Powell L. Influence of production system on the rate of onset of Campylobacter colonization in chicken flocks reared extensively in the United Kingdom. Br Poult Sci 2011; 52:30-9. [PMID: 21337195 DOI: 10.1080/00071668.2010.537306] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
1. Because thermophilic Campylobacter spp. are common in chicken flocks reared extensively, cross-sectional and longitudinal studies were carried out on organic and free-range farms to determine the onset of colonisation (lag phase) and likely sources of flock infection. 2. For 14 organic and 14 free range flocks, there was a difference in lag phases, with the former being colonized at a mean of 14·1 d in comparison with 31·6 d for the latter. Whereas most free-range flocks became colonized when released on to pasture, those reared organically were usually colonized at the housed brooding stage. 3. Further study of organic flocks on three farms over 7 successive crop cycles confirmed that colonisation was strongly influenced by the prevailing husbandry conditions and was not a consequence of the length of the rearing period. 4. Molecular epidemiological investigations on a farm showing the shortest lag phase, using PFGE typing with two different restriction enzymes (SmaI and KpnI) and flaA SVR sequence typing, revealed that potential sources of colonisation for organic chickens were already present on the farm at the time of chick placement. Such sources included the ante area of the brooding house, surrounding pasture and other livestock being kept on the farm. 5. Overall, the study demonstrated that, under UK conditions, the prevalence of colonisation was greater in extensive flocks (95-100%) than it was for conventional broilers (55%), similar to the situation in other countries, but all three management systems showed comparable levels of caecal carriage in positive birds (log(10)/g 6·2-6·7).
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Affiliation(s)
- V M Allen
- Department of Clinical Veterinary Science, University of Bristol, Langford, North Somerset, UK.
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48
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Harris JA, Devidze N, Verret L, Ho K, Halabisky B, Thwin MT, Kim D, Hamto P, Lo I, Yu GQ, Palop JJ, Masliah E, Mucke L. Transsynaptic progression of amyloid-β-induced neuronal dysfunction within the entorhinal-hippocampal network. Neuron 2010; 68:428-41. [PMID: 21040845 PMCID: PMC3050043 DOI: 10.1016/j.neuron.2010.10.020] [Citation(s) in RCA: 242] [Impact Index Per Article: 17.3] [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] [Accepted: 09/03/2010] [Indexed: 12/14/2022]
Abstract
The entorhinal cortex (EC) is one of the earliest affected, most vulnerable brain regions in Alzheimer's disease (AD), which is associated with amyloid-β (Aβ) accumulation in many brain areas. Selective overexpression of mutant amyloid precursor protein (APP) predominantly in layer II/III neurons of the EC caused cognitive and behavioral abnormalities characteristic of mouse models with widespread neuronal APP overexpression, including hyperactivity, disinhibition, and spatial learning and memory deficits. APP/Aβ overexpression in the EC elicited abnormalities in synaptic functions and activity-related molecules in the dentate gyrus and CA1 and epileptiform activity in parietal cortex. Soluble Aβ was observed in the dentate gyrus, and Aβ deposits in the hippocampus were localized to perforant pathway terminal fields. Thus, APP/Aβ expression in EC neurons causes transsynaptic deficits that could initiate the cortical-hippocampal network dysfunction in mouse models and human patients with AD.
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Affiliation(s)
- Julie A. Harris
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Nino Devidze
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Laure Verret
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Kaitlyn Ho
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Brian Halabisky
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Myo T. Thwin
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Daniel Kim
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Patricia Hamto
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Iris Lo
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Gui-Qiu Yu
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
| | - Jorge J. Palop
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Eliezer Masliah
- Departments of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA
- Department of Pathology, University of California, San Diego, San Diego, CA 92093, USA
| | - Lennart Mucke
- Gladstone Institute of Neurological Disease, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94158, USA
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Harris JA, Gortner RA, Lawrence JV. ON THE DIFFERENTIATION OF THE LEAF TISSUE FLUIDS OF LIGNEOUS AND HERBACEOUS PLANTS WITH RESPECT TO OSMOTIC CONCENTRATION AND ELECTRICAL CONDUCTIVITY. ACTA ACUST UNITED AC 2010; 3:343-5. [PMID: 19871870 PMCID: PMC2140441 DOI: 10.1085/jgp.3.3.343] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- J A Harris
- Department of Experimental Evolution and the Department of Botanical Research, the Carnegie Institution of Washington, Washington
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Harris JA, Devidze N, Halabisky B, Lo I, Thwin MT, Yu GQ, Bredesen DE, Masliah E, Mucke L. Many neuronal and behavioral impairments in transgenic mouse models of Alzheimer's disease are independent of caspase cleavage of the amyloid precursor protein. J Neurosci 2010; 30:372-81. [PMID: 20053918 PMCID: PMC3064502 DOI: 10.1523/jneurosci.5341-09.2010] [Citation(s) in RCA: 108] [Impact Index Per Article: 7.7] [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: 10/28/2009] [Accepted: 11/02/2009] [Indexed: 11/21/2022] Open
Abstract
Previous studies suggested that cleavage of the amyloid precursor protein (APP) at aspartate residue 664 by caspases may play a key role in the pathogenesis of Alzheimer's disease. Mutation of this site (D664A) prevents caspase cleavage and the generation of the C-terminal APP fragments C31 and Jcasp, which have been proposed to mediate amyloid-beta (Abeta) neurotoxicity. Here we compared human APP transgenic mice with (B254) and without (J20) the D664A mutation in a battery of tests. Before Abeta deposition, hAPP-B254 and hAPP-J20 mice had comparable hippocampal levels of Abeta(1-42). At 2-3 or 5-7 months of age, hAPP-B254 and hAPP-J20 mice had similar abnormalities relative to nontransgenic mice in spatial and nonspatial learning and memory, elevated plus maze performance, electrophysiological measures of synaptic transmission and plasticity, and levels of synaptic activity-related proteins. Thus, caspase cleavage of APP at position D664 and generation of C31 do not play a critical role in the development of these abnormalities.
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Affiliation(s)
- Julie A. Harris
- Gladstone Institute of Neurological Disease and
- Department of Neurology, University of California, San Francisco, San Francisco, California 94158
| | | | - Brian Halabisky
- Gladstone Institute of Neurological Disease and
- Department of Neurology, University of California, San Francisco, San Francisco, California 94158
| | - Iris Lo
- Gladstone Institute of Neurological Disease and
| | | | - Gui-Qiu Yu
- Gladstone Institute of Neurological Disease and
| | - Dale E. Bredesen
- Department of Neurology, University of California, San Francisco, San Francisco, California 94158
- Buck Institute for Age Research, Novato, California 94945, and
| | - Eliezer Masliah
- Departments of Neurosciences and Pathology, University of California, San Diego, San Diego, California 92093
| | - Lennart Mucke
- Gladstone Institute of Neurological Disease and
- Department of Neurology, University of California, San Francisco, San Francisco, California 94158
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