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Samavat M, Bartol TM, Harris KM, Sejnowski TJ. Synaptic Information Storage Capacity Measured With Information Theory. Neural Comput 2024; 36:781-802. [PMID: 38658027 DOI: 10.1162/neco_a_01659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/02/2024] [Indexed: 04/26/2024]
Abstract
Variation in the strength of synapses can be quantified by measuring the anatomical properties of synapses. Quantifying precision of synaptic plasticity is fundamental to understanding information storage and retrieval in neural circuits. Synapses from the same axon onto the same dendrite have a common history of coactivation, making them ideal candidates for determining the precision of synaptic plasticity based on the similarity of their physical dimensions. Here, the precision and amount of information stored in synapse dimensions were quantified with Shannon information theory, expanding prior analysis that used signal detection theory (Bartol et al., 2015). The two methods were compared using dendritic spine head volumes in the middle of the stratum radiatum of hippocampal area CA1 as well-defined measures of synaptic strength. Information theory delineated the number of distinguishable synaptic strengths based on nonoverlapping bins of dendritic spine head volumes. Shannon entropy was applied to measure synaptic information storage capacity (SISC) and resulted in a lower bound of 4.1 bits and upper bound of 4.59 bits of information based on 24 distinguishable sizes. We further compared the distribution of distinguishable sizes and a uniform distribution using Kullback-Leibler divergence and discovered that there was a nearly uniform distribution of spine head volumes across the sizes, suggesting optimal use of the distinguishable values. Thus, SISC provides a new analytical measure that can be generalized to probe synaptic strengths and capacity for plasticity in different brain regions of different species and among animals raised in different conditions or during learning. How brain diseases and disorders affect the precision of synaptic plasticity can also be probed.
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Affiliation(s)
- Mohammad Samavat
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, U.S.A.
| | - Thomas M Bartol
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, U.S.A.
| | - Kristen M Harris
- Center for Learning and Memory and Department of Neuroscience, University of Texas at Austin, Austin, TX 78712, U.S.A.
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA 92037, U.S.A
- Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, U.S.A.
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2
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Samavat M, Bartol TM, Bromer C, Hubbard DD, Hanka DC, Kuwajima M, Mendenhall JM, Parker PH, Bowden JB, Abraham WC, Sejnowski TJ, Harris KM. Long-Term Potentiation Produces a Sustained Expansion of Synaptic Information Storage Capacity in Adult Rat Hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.12.574766. [PMID: 38260636 PMCID: PMC10802612 DOI: 10.1101/2024.01.12.574766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Long-term potentiation (LTP) has become a standard model for investigating synaptic mechanisms of learning and memory. Increasingly, it is of interest to understand how LTP affects the synaptic information storage capacity of the targeted population of synapses. Here, structural synaptic plasticity during LTP was explored using three-dimensional reconstruction from serial section electron microscopy. Storage capacity was assessed by applying a new analytical approach, Shannon information theory, to delineate the number of functionally distinguishable synaptic strengths. LTP was induced by delta-burst stimulation of perforant pathway inputs to the middle molecular layer of hippocampal dentate granule cells in adult rats. Spine head volumes were measured as predictors of synaptic strength and compared between LTP and control hemispheres at 30 min and 2 hr after the induction of LTP. Synapses from the same axon onto the same dendrite were used to determine the precision of synaptic plasticity based on the similarity of their physical dimensions. Shannon entropy was measured by exploiting the frequency of spine heads in functionally distinguishable sizes to assess the degree to which LTP altered the number of bits of information storage. Outcomes from these analyses reveal that LTP expanded storage capacity; the distribution of spine head volumes was increased from 2 bits in controls to 3 bits at 30 min and 2.7 bits at 2 hr after the induction of LTP. Furthermore, the distribution of spine head volumes was more uniform across the increased number of functionally distinguishable sizes following LTP, thus achieving more efficient use of coding space across the population of synapses.
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Affiliation(s)
- Mohammad Samavat
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, UC San Diego
- Computational Neurobiology Laboratory, The Salk Institute for Biological Sciences, La Jolla, CA 92037
| | - Thomas M Bartol
- Computational Neurobiology Laboratory, The Salk Institute for Biological Sciences, La Jolla, CA 92037
| | - Cailey Bromer
- Computational Neurobiology Laboratory, The Salk Institute for Biological Sciences, La Jolla, CA 92037
| | - Dusten D Hubbard
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
| | - Dakota C Hanka
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
| | - Masaaki Kuwajima
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
| | - John M Mendenhall
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
| | - Patrick H Parker
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
| | - Jared B Bowden
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712
| | - Wickliffe C Abraham
- Department of Psychology and Brain Health Research Centre, University of Otago, Dunedin, 9016, New Zealand
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Sciences, La Jolla, CA 92037
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093
| | - Kristen M Harris
- Center for Learning and Memory, The University of Texas at Austin, Austin, TX 78712
- Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712
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3
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Venkatraman K, Lee CT, Garcia GC, Mahapatra A, Milshteyn D, Perkins G, Kim K, Pasolli HA, Phan S, Lippincott‐Schwartz J, Ellisman MH, Rangamani P, Budin I. Cristae formation is a mechanical buckling event controlled by the inner mitochondrial membrane lipidome. EMBO J 2023; 42:e114054. [PMID: 37933600 PMCID: PMC10711667 DOI: 10.15252/embj.2023114054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023] Open
Abstract
Cristae are high-curvature structures in the inner mitochondrial membrane (IMM) that are crucial for ATP production. While cristae-shaping proteins have been defined, analogous lipid-based mechanisms have yet to be elucidated. Here, we combine experimental lipidome dissection with multi-scale modeling to investigate how lipid interactions dictate IMM morphology and ATP generation. When modulating phospholipid (PL) saturation in engineered yeast strains, we observed a surprisingly abrupt breakpoint in IMM topology driven by a continuous loss of ATP synthase organization at cristae ridges. We found that cardiolipin (CL) specifically buffers the inner mitochondrial membrane against curvature loss, an effect that is independent of ATP synthase dimerization. To explain this interaction, we developed a continuum model for cristae tubule formation that integrates both lipid and protein-mediated curvatures. This model highlighted a snapthrough instability, which drives IMM collapse upon small changes in membrane properties. We also showed that cardiolipin is essential in low-oxygen conditions that promote PL saturation. These results demonstrate that the mechanical function of cardiolipin is dependent on the surrounding lipid and protein components of the IMM.
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Affiliation(s)
- Kailash Venkatraman
- Department of Chemistry and BiochemistryUniversity of California San DiegoLa JollaCAUSA
| | - Christopher T Lee
- Department of Mechanical and Aerospace EngineeringUniversity of California San DiegoLa JollaCAUSA
| | - Guadalupe C Garcia
- Computational Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaCAUSA
| | - Arijit Mahapatra
- Department of Mechanical and Aerospace EngineeringUniversity of California San DiegoLa JollaCAUSA
- Present address:
Applied Physical SciencesUniversity of North Carolina Chapel HillChapel HillNCUSA
| | - Daniel Milshteyn
- Department of Chemistry and BiochemistryUniversity of California San DiegoLa JollaCAUSA
| | - Guy Perkins
- National Center for Microscopy and Imaging Research, Center for Research in Biological SystemsUniversity of California San DiegoLa JollaCAUSA
| | - Keun‐Young Kim
- National Center for Microscopy and Imaging Research, Center for Research in Biological SystemsUniversity of California San DiegoLa JollaCAUSA
| | - H Amalia Pasolli
- Howard Hughes Medical InstituteAshburnVAUSA
- Present address:
Electron Microscopy Resource CenterThe Rockefeller UniversityNew YorkNYUSA
| | - Sebastien Phan
- National Center for Microscopy and Imaging Research, Center for Research in Biological SystemsUniversity of California San DiegoLa JollaCAUSA
| | | | - Mark H Ellisman
- National Center for Microscopy and Imaging Research, Center for Research in Biological SystemsUniversity of California San DiegoLa JollaCAUSA
| | - Padmini Rangamani
- Department of Mechanical and Aerospace EngineeringUniversity of California San DiegoLa JollaCAUSA
| | - Itay Budin
- Department of Chemistry and BiochemistryUniversity of California San DiegoLa JollaCAUSA
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4
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Venkatraman K, Lee CT, Garcia GC, Mahapatra A, Milshteyn D, Perkins G, Kim KY, Pasolli HA, Phan S, Lippincott-Schwartz J, Ellisman MH, Rangamani P, Budin I. Cristae formation is a mechanical buckling event controlled by the inner membrane lipidome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532310. [PMID: 36993370 PMCID: PMC10054968 DOI: 10.1101/2023.03.13.532310] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Cristae are high curvature structures in the inner mitochondrial membrane (IMM) that are crucial for ATP production. While cristae-shaping proteins have been defined, analogous mechanisms for lipids have yet to be elucidated. Here we combine experimental lipidome dissection with multi-scale modeling to investigate how lipid interactions dictate IMM morphology and ATP generation. When modulating phospholipid (PL) saturation in engineered yeast strains, we observed a surprisingly abrupt breakpoint in IMM topology driven by a continuous loss of ATP synthase organization at cristae ridges. We found that cardiolipin (CL) specifically buffers the IMM against curvature loss, an effect that is independent of ATP synthase dimerization. To explain this interaction, we developed a continuum model for cristae tubule formation that integrates both lipid and protein-mediated curvatures. The model highlighted a snapthrough instability, which drives IMM collapse upon small changes in membrane properties. We also showed that CL is essential in low oxygen conditions that promote PL saturation. These results demonstrate that the mechanical function of CL is dependent on the surrounding lipid and protein components of the IMM.
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Affiliation(s)
- Kailash Venkatraman
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093
| | - Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA 92093
| | - Guadalupe C Garcia
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla CA 92097
| | - Arijit Mahapatra
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA 92093
| | - Daniel Milshteyn
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093
| | - Guy Perkins
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093
| | - Keun-Young Kim
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093
| | - H Amalia Pasolli
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn VA 20147
| | - Sebastien Phan
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093
| | | | - Mark H Ellisman
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, University of California San Diego, La Jolla, CA 92093
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA 92093
| | - Itay Budin
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093
- Lead contact
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5
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Keto L, Manninen T. CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations. Neuroinformatics 2023; 21:483-500. [PMID: 37133688 PMCID: PMC10406679 DOI: 10.1007/s12021-023-09627-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 05/04/2023]
Abstract
Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.
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Affiliation(s)
- Laura Keto
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
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6
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Abdellah M, Cantero JJG, Guerrero NR, Foni A, Coggan JS, Calì C, Agus M, Zisis E, Keller D, Hadwiger M, Magistretti PJ, Markram H, Schürmann F. Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. Brief Bioinform 2022; 24:6847753. [PMID: 36434788 PMCID: PMC9851302 DOI: 10.1093/bib/bbac491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/27/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022] Open
Abstract
Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.
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Affiliation(s)
- Marwan Abdellah
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
| | | | - Nadir Román Guerrero
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Alessandro Foni
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,Neuroscience Institute Cavalieri Ottolenghi (NICO) Orbassano, Italy,Department of Neuroscience, University of Torino Torino, Italy
| | - Marco Agus
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar
| | - Eleftherios Zisis
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Felix Schürmann
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
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7
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Efficient metadata mining of web-accessible neural morphologies. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:94-102. [PMID: 34022302 PMCID: PMC8602463 DOI: 10.1016/j.pbiomolbio.2021.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 01/03/2023]
Abstract
Advancements in neuroscience research have led to steadily accelerating data production and sharing. The online community repository of neural reconstructions NeuroMorpho.Org grew from fewer than 1000 digitally traced neurons in 2006 to more than 140,000 cells today, including glia that now constitute 10.1% of the content. Every reconstruction consists of a detailed 3D representation of branch geometry and connectivity in a standardized format, from which a collection of morphometric features is extracted and stored. Moreover, each entry in the database is accompanied by rich metadata annotation describing the animal subject, anatomy, and experimental details. The rapid expansion of this resource in the past decade was accompanied by a parallel rise in the complexity of the available information, creating both opportunities and challenges for knowledge mining. Here, we introduce a new summary reporting functionality, allowing NeuroMorpho.Org users to efficiently download digests of metadata and morphometrics from multiple groups of similar cells for further analysis. We demonstrate the capabilities of the tool for both glia and neurons and present an illustrative statistical analysis of the resulting data.
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8
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Chen W, Hepburn I, Martyushev A, De Schutter E. Modeling Neurons in 3D at the Nanoscale. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:3-24. [DOI: 10.1007/978-3-030-89439-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Mendelsohn R, Garcia GC, Bartol TM, Lee CT, Khandelwal P, Liu E, Spencer DJ, Husar A, Bushong EA, Phan S, Perkins G, Ellisman MH, Skupin A, Sejnowski TJ, Rangamani P. Morphological principles of neuronal mitochondria. J Comp Neurol 2021; 530:886-902. [PMID: 34608995 DOI: 10.1002/cne.25254] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/02/2021] [Accepted: 09/09/2021] [Indexed: 01/01/2023]
Abstract
In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like adenosine triphosphate and heat often represent mitochondria as idealized geometries, and therefore, can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial transmission electron microscopy (TEM) tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.64 nanometer isotropic voxels. The resulting in-silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs present across this population of mitochondria, which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.
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Affiliation(s)
- Rachel Mendelsohn
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Guadalupe C Garcia
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Thomas M Bartol
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Christopher T Lee
- Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA
| | - Priya Khandelwal
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Emily Liu
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Donald J Spencer
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Adam Husar
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Eric A Bushong
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neuroscience, School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Sebastien Phan
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neuroscience, School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Guy Perkins
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neuroscience, School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Mark H Ellisman
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neuroscience, School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Alexander Skupin
- National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neuroscience, School of Medicine, University of California San Diego, La Jolla, California, USA.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, USA
| | - Terrence J Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA.,Division of Biological Sciences, University of California San Diego, La Jolla, California, USA
| | - Padmini Rangamani
- Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA
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10
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Ozsvár A, Komlósi G, Oláh G, Baka J, Molnár G, Tamás G. Predominantly linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons. eLife 2021; 10:65634. [PMID: 34308838 PMCID: PMC8360660 DOI: 10.7554/elife.65634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 07/14/2021] [Indexed: 01/13/2023] Open
Abstract
Summation of ionotropic receptor-mediated responses is critical in neuronal computation by shaping input-output characteristics of neurons. However, arithmetics of summation for metabotropic signals are not known. We characterized the combined ionotropic and metabotropic output of neocortical neurogliaform cells (NGFCs) using electrophysiological and anatomical methods in the rat cerebral cortex. These experiments revealed that GABA receptors are activated outside release sites and confirmed coactivation of putative NGFCs in superficial cortical layers in vivo. Triple recordings from presynaptic NGFCs converging to a postsynaptic neuron revealed sublinear summation of ionotropic GABAA responses and linear summation of metabotropic GABAB responses. Based on a model combining properties of volume transmission and distributions of all NGFC axon terminals, we predict that in 83% of cases one or two NGFCs can provide input to a point in the neuropil. We suggest that interactions of metabotropic GABAergic responses remain linear even if most superficial layer interneurons specialized to recruit GABAB receptors are simultaneously active.
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Affiliation(s)
- Attila Ozsvár
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Gergely Komlósi
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Gáspár Oláh
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Judith Baka
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Gábor Molnár
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Gábor Tamás
- MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
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11
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Lee CT, Laughlin JG, Angliviel de La Beaumelle N, Amaro RE, McCammon JA, Ramamoorthi R, Holst M, Rangamani P. 3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries. PLoS Comput Biol 2020; 16:e1007756. [PMID: 32251448 PMCID: PMC7162555 DOI: 10.1371/journal.pcbi.1007756] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 04/16/2020] [Accepted: 03/01/2020] [Indexed: 12/17/2022] Open
Abstract
Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.
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Affiliation(s)
- Christopher T. Lee
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California, United States of America
| | - Justin G. Laughlin
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California, United States of America
| | - Nils Angliviel de La Beaumelle
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California, United States of America
| | - Rommie E. Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, United States of America
| | - J. Andrew McCammon
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, United States of America
| | - Ravi Ramamoorthi
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, United States of America
| | - Michael Holst
- Department of Mathematics, University of California, San Diego, La Jolla, California, United States of America
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California, United States of America
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12
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Long-term potentiation expands information content of hippocampal dentate gyrus synapses. Proc Natl Acad Sci U S A 2018; 115:E2410-E2418. [PMID: 29463730 DOI: 10.1073/pnas.1716189115] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
An approach combining signal detection theory and precise 3D reconstructions from serial section electron microscopy (3DEM) was used to investigate synaptic plasticity and information storage capacity at medial perforant path synapses in adult hippocampal dentate gyrus in vivo. Induction of long-term potentiation (LTP) markedly increased the frequencies of both small and large spines measured 30 minutes later. This bidirectional expansion resulted in heterosynaptic counterbalancing of total synaptic area per unit length of granule cell dendrite. Control hemispheres exhibited 6.5 distinct spine sizes for 2.7 bits of storage capacity while LTP resulted in 12.9 distinct spine sizes (3.7 bits). In contrast, control hippocampal CA1 synapses exhibited 4.7 bits with much greater synaptic precision than either control or potentiated dentate gyrus synapses. Thus, synaptic plasticity altered total capacity, yet hippocampal subregions differed dramatically in their synaptic information storage capacity, reflecting their diverse functions and activation histories.
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13
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Puwal S, Roth BJ, Basser PJ. Heterogeneous anisotropic magnetic susceptibility of the myelin-water layers causes local magnetic field perturbations in axons. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3628. [PMID: 27731911 PMCID: PMC6130896 DOI: 10.1002/nbm.3628] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Revised: 06/23/2016] [Accepted: 08/17/2016] [Indexed: 05/23/2023]
Abstract
One goal of MRI is to determine the myelin water fraction in neural tissue. One approach is to measure the reduction in T2 * arising from microscopic perturbations in the magnetic field caused by heterogeneities in the magnetic susceptibility of myelin. In this paper, analytic expressions for the induced magnetic field distribution are derived within and around an axon, assuming that the myelin susceptibility is anisotropic. Previous models considered the susceptibility to be piecewise continuous, whereas this model considers a sinusoidally varying susceptibility. Many conclusions are common in both models. When the magnetic field is applied perpendicular to the axon, the magnetic field in the intraaxonal space is uniformly perturbed, the magnetic field in the myelin sheath oscillates between the lipid and water layers, and the magnetic field in the extracellular space just outside the myelin sheath is heterogeneous. These field heterogeneities cause the spins to dephase, shortening T2 *. When the magnetic field is applied along the axon, the field is homogeneous within water-filled regions, including between lipid layers. Therefore the spins do not dephase and the magnetic susceptibility has no effect on T2 *. Generally, the response of an axon is given as the superposition of these two contributions. The sinusoidal model uses a different set of approximations compared with the piecewise model, so their common predictions indicate that the models are not too sensitive to the details of the myelin-water distribution. Other predictions, such as the sensitivity to water diffusion between myelin and water layers, may highlight differences between the two approaches. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Steffan Puwal
- Department of Physics, Oakland University, Rochester, MI, USA
| | - Bradley J Roth
- Department of Physics, Oakland University, Rochester, MI, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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14
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Bartol TM, Bromer C, Kinney J, Chirillo MA, Bourne JN, Harris KM, Sejnowski TJ. Nanoconnectomic upper bound on the variability of synaptic plasticity. eLife 2015; 4:e10778. [PMID: 26618907 PMCID: PMC4737657 DOI: 10.7554/elife.10778] [Citation(s) in RCA: 141] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 11/29/2015] [Indexed: 12/15/2022] Open
Abstract
Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes. DOI:http://dx.doi.org/10.7554/eLife.10778.001 What is the memory capacity of a human brain? The storage capacity in a computer memory is measured in bits, each of which can have a value of 0 or 1. In the brain, information is stored in the form of synaptic strength, a measure of how strongly activity in one neuron influences another neuron to which it is connected. The number of different strengths can be measured in bits. The total storage capacity of the brain therefore depends on both the number of synapses and the number of distinguishable synaptic strengths. Structurally, neurons consist of a cell body that influences other neurons through a cable-like axon. The cell body bears numerous short branches called dendrites, which are covered in tiny protrusions, or “spines”. Most excitatory synapses are formed between the axon of one neuron and a dendritic spine on another. When two neurons on either side of a synapse are active simultaneously, that synapse becomes stronger, a form of memory. The dendritic spine also becomes larger to accommodate the extra molecular machinery needed to support a stronger synapse. Some axons form two or more synapses with the same dendrite, but on different dendritic spines. These synapses should be the same strength because they will have experienced the same history of neural activity. Bartol et al. used a technique called serial section electron microscopy to create a 3D reconstruction of part of the brain that allowed the sizes of the dendritic spines these synapses form on to be compared. This revealed that the synaptic areas and volumes of the spine heads were nearly identical. This remarkable similarity can be used to estimate the number of bits of information that a single synapse can store, since the size of dendritic spines and their synapses can be used as proxies for synaptic strength. Measurements in a small cube of brain tissue revealed 26 different dendritic spine sizes, each associated with a distinct synaptic strength. This number translates into a storage capacity of roughly 4.7 bits of information per synapse. This estimate is markedly higher than previous suggestions. It implies that the total memory capacity of the brain – with its many trillions of synapses – may have been underestimated by an order of magnitude. Additional measurements in the same and other brain regions are needed to confirm this possibility. DOI:http://dx.doi.org/10.7554/eLife.10778.002
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Affiliation(s)
- Thomas M Bartol
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States
| | - Cailey Bromer
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States
| | - Justin Kinney
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States
| | - Michael A Chirillo
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, United States
| | - Jennifer N Bourne
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, United States
| | - Kristen M Harris
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, United States
| | - Terrence J Sejnowski
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, United States.,Division of Biological Sciences, University of California, San Diego, San Diego, United States
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15
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Bartol TM, Keller DX, Kinney JP, Bajaj CL, Harris KM, Sejnowski TJ, Kennedy MB. Computational reconstitution of spine calcium transients from individual proteins. Front Synaptic Neurosci 2015; 7:17. [PMID: 26500546 PMCID: PMC4595661 DOI: 10.3389/fnsyn.2015.00017] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 09/11/2015] [Indexed: 11/24/2022] Open
Abstract
We have built a stochastic model in the program MCell that simulates Ca(2+) transients in spines from the principal molecular components believed to control Ca(2+) entry and exit. Proteins, with their kinetic models, are located within two segments of dendrites containing 88 intact spines, centered in a fully reconstructed 6 × 6 × 5 μm(3) cube of hippocampal neuropil. Protein components include AMPA- and NMDA-type glutamate receptors, L- and R-type voltage-dependent Ca(2+) channels, Na(+)/Ca(2+) exchangers, plasma membrane Ca(2+) ATPases, smooth endoplasmic reticulum Ca(2+) ATPases, immobile Ca(2+) buffers, and calbindin. Kinetic models for each protein were taken from published studies of the isolated proteins in vitro. For simulation of electrical stimuli, the time course of voltage changes in the dendritic spine was generated with the desired stimulus in the program NEURON. Voltage-dependent parameters were then continuously re-adjusted during simulations in MCell to reproduce the effects of the stimulus. Nine parameters of the model were optimized within realistic experimental limits by a process that compared results of simulations to published data. We find that simulations in the optimized model reproduce the timing and amplitude of Ca(2+) transients measured experimentally in intact neurons. Thus, we demonstrate that the characteristics of individual isolated proteins determined in vitro can accurately reproduce the dynamics of experimentally measured Ca(2+) transients in spines. The model will provide a test bed for exploring the roles of additional proteins that regulate Ca(2+) influx into spines and for studying the behavior of protein targets in the spine that are regulated by Ca(2+) influx.
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Affiliation(s)
- Thomas M. Bartol
- Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological StudiesLa Jolla, CA, USA
- Center for Theoretical Biological Physics, University of CaliforniaSan Diego, La Jolla, CA, USA
| | - Daniel X. Keller
- Center for Theoretical Biological Physics, University of CaliforniaSan Diego, La Jolla, CA, USA
- Neurosciences Department, University of CaliforniaSan Diego, La Jolla, CA, USA
| | - Justin P. Kinney
- Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological StudiesLa Jolla, CA, USA
| | - Chandrajit L. Bajaj
- Department of Computer Science, Center for Computational Visualization, University of TexasAustin, TX, USA
| | - Kristen M. Harris
- Department of Neuroscience, Center for Learning and Memory, University of TexasAustin, TX, USA
| | - Terrence J. Sejnowski
- Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological StudiesLa Jolla, CA, USA
- Center for Theoretical Biological Physics, University of CaliforniaSan Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of CaliforniaSan Diego, La Jolla, CA, USA
| | - Mary B. Kennedy
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadena, CA, USA
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16
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Harris KM, Spacek J, Bell ME, Parker PH, Lindsey LF, Baden AD, Vogelstein JT, Burns R. A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development. Sci Data 2015; 2:150046. [PMID: 26347348 PMCID: PMC4555877 DOI: 10.1038/sdata.2015.46] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 08/12/2015] [Indexed: 12/29/2022] Open
Abstract
Resurgent interest in synaptic circuitry and plasticity has emphasized the importance of 3D reconstruction from serial section electron microscopy (3DEM). Three volumes of hippocampal CA1 neuropil from adult rat were imaged at X-Y resolution of ~2 nm on serial sections of ~50-60 nm thickness. These are the first densely reconstructed hippocampal volumes. All axons, dendrites, glia, and synapses were reconstructed in a cube (~10 μm(3)) surrounding a large dendritic spine, a cylinder (~43 μm(3)) surrounding an oblique dendritic segment (3.4 μm long), and a parallelepiped (~178 μm(3)) surrounding an apical dendritic segment (4.9 μm long). The data provide standards for identifying ultrastructural objects in 3DEM, realistic reconstructions for modeling biophysical properties of synaptic transmission, and a test bed for enhancing reconstruction tools. Representative synapses are quantified from varying section planes, and microtubules, polyribosomes, smooth endoplasmic reticulum, and endosomes are identified and reconstructed in a subset of dendrites. The original images, traces, and Reconstruct software and files are freely available and visualized at the Open Connectome Project (Data Citation 1).
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Affiliation(s)
- Kristen M Harris
- Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, 1 University Station C7000 , Austin, Texas 78712, USA
| | - Josef Spacek
- Department of Pathology, Charles University at Prague, Faculty of Medicine , 500 35 Hradec Kralove, Czech Republic
| | - Maria Elizabeth Bell
- Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, 1 University Station C7000 , Austin, Texas 78712, USA
| | - Patrick H Parker
- Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, 1 University Station C7000 , Austin, Texas 78712, USA
| | - Laurence F Lindsey
- Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, 1 University Station C7000 , Austin, Texas 78712, USA
| | - Alexander D Baden
- Department of Computer Science, Institute for Data Intensive Science and Engineering, Johns Hopkins University, 160 Malone Hall, 3400 N. Charles St. , Baltimore, Maryland 21218, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Clark Hall Room 317C, 3400 N. Charles St. , Baltimore, Maryland 21218, USA
| | - Randal Burns
- Department of Computer Science, Institute for Data Intensive Science and Engineering, Johns Hopkins University, 160 Malone Hall, 3400 N. Charles St. , Baltimore, Maryland 21218, USA
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