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Zhao J, Yu X, Shentu X, Li D. The application and development of electron microscopy for three-dimensional reconstruction in life science: a review. Cell Tissue Res 2024; 396:1-18. [PMID: 38416172 DOI: 10.1007/s00441-024-03878-7] [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: 10/17/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
Imaging technologies have played a pivotal role in advancing biological research by enabling visualization of biological structures and processes. While traditional electron microscopy (EM) produces two-dimensional images, emerging techniques now allow high-resolution three-dimensional (3D) characterization of specimens in situ, meeting growing needs in molecular and cellular biology. Combining transmission electron microscopy (TEM) with serial sectioning inaugurated 3D imaging, attracting biologists seeking to explore cell ultrastructure and driving advancement of 3D EM reconstruction. By comprehensively and precisely rendering internal structure and distribution, 3D TEM reconstruction provides unparalleled ultrastructural insights into cells and molecules, holding tremendous value for elucidating structure-function relationships and broadly propelling structural biology. Here, we first introduce the principle of 3D reconstruction of cells and tissues by classical approaches in TEM and then discuss modern technologies utilizing TEM and on new SEM-based as well as cryo-electron microscope (cryo-EM) techniques. 3D reconstruction techniques from serial sections, electron tomography (ET), and the recent single-particle analysis (SPA) are examined; the focused ion beam scanning electron microscopy (FIB-SEM), the serial block-face scanning electron microscopy (SBF-SEM), and automatic tape-collecting lathe ultramicrotome (ATUM-SEM) for 3D reconstruction of large volumes are discussed. Finally, we review the challenges and development prospects of these technologies in life science. It aims to provide an informative reference for biological researchers.
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Affiliation(s)
- Jingjing Zhao
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xiaoping Yu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xuping Shentu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Danting Li
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China.
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2
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Ma F, Kaufmann R, Sedzicki J, Cseresnyés Z, Dehio C, Hoeppener S, Figge MT, Heintzmann R. Guided-deconvolution for correlative light and electron microscopy. PLoS One 2023; 18:e0282803. [PMID: 36893111 PMCID: PMC9997956 DOI: 10.1371/journal.pone.0282803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/22/2023] [Indexed: 03/10/2023] Open
Abstract
Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. The EM images only contain contrast information. Therefore, some of the detailed structures cannot be specified from these images alone, especially when different cell organelle are contacted. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. This applies to living cells structures before fixation as well as previously fixed sample. It attempts to automatically assign fluorescence-labeled structures to structural details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes. We tested our approach on simulations, correlative data of multi-color beads and previously published data of biological samples.
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Affiliation(s)
- Fengjiao Ma
- Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Jena, Thuringia, Germany
- Leibniz Institute of Photonic Technology, Jena, Thuringia, Germany
- Jena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany
| | - Rainer Kaufmann
- Centre for Structural Systems Biology, Hamburg, Germany
- Department of Physics, University of Hamburg, Hamburg, Germany
| | | | - Zoltán Cseresnyés
- Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Thuringia, Germany
| | | | - Stephanie Hoeppener
- Jena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany
- Laboratory of Organic Chemistry and Macromolecular Chemistry, University of Jena, Jena, Thuringia, Germany
| | - Marc Thilo Figge
- Jena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany
- Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Thuringia, Germany
- Institute of Microbiology, Faculty of Biological Sciences, University of Jena, Jena, Thuringia, Germany
| | - Rainer Heintzmann
- Institute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Jena, Thuringia, Germany
- Leibniz Institute of Photonic Technology, Jena, Thuringia, Germany
- Jena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany
- * E-mail:
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3
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Santella A, Kolotuev I, Kizilyaprak C, Bao Z. Cross-modality synthesis of EM time series and live fluorescence imaging. eLife 2022; 11:77918. [PMID: 35666127 PMCID: PMC9213002 DOI: 10.7554/elife.77918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/05/2022] [Indexed: 11/13/2022] Open
Abstract
Analyses across imaging modalities allow the integration of complementary spatiotemporal information about brain development, structure, and function. However, systematic atlasing across modalities is limited by challenges to effective image alignment. We combine highly spatially resolved electron microscopy (EM) and highly temporally resolved time-lapse fluorescence microscopy (FM) to examine the emergence of a complex nervous system in Caenorhabditis elegans embryogenesis. We generate an EM time series at four classic developmental stages and create a landmark-based co-optimization algorithm for cross-modality image alignment, which handles developmental heterochrony among datasets to achieve accurate single-cell level alignment. Synthesis based on the EM series and time-lapse FM series carrying different cell-specific markers reveals critical dynamic behaviors across scales of identifiable individual cells in the emergence of the primary neuropil, the nerve ring, as well as a major sensory organ, the amphid. Our study paves the way for systematic cross-modality data synthesis in C. elegans and demonstrates a powerful approach that may be applied broadly.
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Affiliation(s)
- Anthony Santella
- Molecular Cytology Core, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Irina Kolotuev
- Electron Microscopy Facility, University of Lausanne, Lausanne, Switzerland
| | | | - Zhirong Bao
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
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4
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Takaya E, Takeichi Y, Ozaki M, Kurihara S. Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels. J Neurosci Methods 2021; 351:109066. [PMID: 33417965 DOI: 10.1016/j.jneumeth.2021.109066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/29/2020] [Accepted: 01/02/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Segmentation of electron microscopic continuous section images by deep learning has attracted attention as a technique to reduce the cost of annotation for researchers attempting to make observations using 3D reconstruction methods. However, when the observed samples are rare, or scanning circumstances are unstable, pursuing generalization performance for newly obtained samples is not appropriate. NEW METHODS We assume a transductive setting that predicts all labels in a dataset from only partially obtained labels while avoiding the pursuit of generalization performance for unknown data. Then, we propose sequential semi-supervised segmentation (4S), which semi-automatically extracts neural regions from electron microscopy image stacks. This method focuses on the fact that adjacent images have a strong correlation in serial images. Our 4S repeats training, inference, and pseudo-labeling using a minimal number of teacher labels and performs segmentation on all slices. RESULT Our experiments using two types of serial section images showed effectiveness in terms of both quality and quantity. In addition, we experimentally clarified the effect of the number and position of teacher labels on performance. COMPARISON WITH EXISTING METHODS Compared with supervised learning when a small number of labeled data was obtained, the performance of the proposed method was shown to be superior. CONCLUSION Our 4S leverages a limited number of labeled data and a large amount of unlabeled data to extract neural regions from serial image stacks in a transductive setting. We plan to develop this method as a core module of a general-purpose annotation tool in our future work.
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Affiliation(s)
- Eichi Takaya
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Kanagawa, Japan.
| | - Yusuke Takeichi
- Department of Biology, Graduate School of Science, Kobe University, Kobe, Japan
| | - Mamiko Ozaki
- Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, Kobe, Japan; Division of Strategic Research of the Humanosphere, Research Institute of Sustainable Humanosphere, Kyoto University, Kyoto, Japan; KYOUSEI Science Center for Life and Nature, Nara Women's University, Nara, Japan; RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Satoshi Kurihara
- Department of Industrial and Systems Engineering, Faculty of Science and Technology, Keio University, Kanagawa, Japan
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5
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Reimann MW, Gevaert M, Shi Y, Lu H, Markram H, Muller E. A null model of the mouse whole-neocortex micro-connectome. Nat Commun 2019; 10:3903. [PMID: 31467291 PMCID: PMC6715727 DOI: 10.1038/s41467-019-11630-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 07/25/2019] [Indexed: 11/27/2022] Open
Abstract
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.
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Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Michael Gevaert
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Ying Shi
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Huanxiang Lu
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Eilif Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
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6
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Abdollahzadeh A, Belevich I, Jokitalo E, Tohka J, Sierra A. Automated 3D Axonal Morphometry of White Matter. Sci Rep 2019; 9:6084. [PMID: 30988411 PMCID: PMC6465365 DOI: 10.1038/s41598-019-42648-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 04/02/2019] [Indexed: 12/14/2022] Open
Abstract
Axonal structure underlies white matter functionality and plays a major role in brain connectivity. The current literature on the axonal structure is based on the analysis of two-dimensional (2D) cross-sections, which, as we demonstrate, is precarious. To be able to quantify three-dimensional (3D) axonal morphology, we developed a novel pipeline, called ACSON (AutomatiC 3D Segmentation and morphometry Of axoNs), for automated 3D segmentation and morphometric analysis of the white matter ultrastructure. The automated pipeline eliminates the need for time-consuming manual segmentation of 3D datasets. ACSON segments myelin, myelinated and unmyelinated axons, mitochondria, cells and vacuoles, and analyzes the morphology of myelinated axons. We applied the pipeline to serial block-face scanning electron microscopy images of the corpus callosum of sham-operated (n = 2) and brain injured (n = 3) rats 5 months after the injury. The 3D morphometry showed that cross-sections of myelinated axons were elliptic rather than circular, and their diameter varied substantially along their longitudinal axis. It also showed a significant reduction in the myelinated axon diameter of the ipsilateral corpus callosum of rats 5 months after brain injury, indicating ongoing axonal alterations even at this chronic time-point.
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Affiliation(s)
- Ali Abdollahzadeh
- Biomedical Imaging Unit, A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ilya Belevich
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Eija Jokitalo
- Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Jussi Tohka
- Biomedical Imaging Unit, A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Alejandra Sierra
- Biomedical Imaging Unit, A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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7
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Horne JA, Langille C, McLin S, Wiederman M, Lu Z, Xu CS, Plaza SM, Scheffer LK, Hess HF, Meinertzhagen IA. A resource for the Drosophila antennal lobe provided by the connectome of glomerulus VA1v. eLife 2018; 7:37550. [PMID: 30382940 PMCID: PMC6234030 DOI: 10.7554/elife.37550] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 10/31/2018] [Indexed: 02/06/2023] Open
Abstract
Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in Drosophila melanogaster. Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The fruitless-positive, sexually dimorphic VA1v included >11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of >150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having >50 connections; these form 88% of the glomerulus' pre/postsynaptic sites.
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Affiliation(s)
- Jane Anne Horne
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada
| | - Carlie Langille
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada
| | - Sari McLin
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada
| | - Meagan Wiederman
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada
| | - Zhiyuan Lu
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada.,Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
| | - Louis K Scheffer
- Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
| | - Ian A Meinertzhagen
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, Canada.,Janelia Research Campus, Howard Hughes Medical Institute, Virginia, United States
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8
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Wanner AA, Vishwanathan A. Methods for Mapping Neuronal Activity to Synaptic Connectivity: Lessons From Larval Zebrafish. Front Neural Circuits 2018; 12:89. [PMID: 30410437 PMCID: PMC6209671 DOI: 10.3389/fncir.2018.00089] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 09/28/2018] [Indexed: 12/29/2022] Open
Abstract
For a mechanistic understanding of neuronal circuits in the brain, a detailed description of information flow is necessary. Thereby it is crucial to link neuron function to the underlying circuit structure. Multiphoton calcium imaging is the standard technique to record the activity of hundreds of neurons simultaneously. Similarly, recent advances in high-throughput electron microscopy techniques allow for the reconstruction of synaptic resolution wiring diagrams. These two methods can be combined to study both function and structure in the same specimen. Due to its small size and optical transparency, the larval zebrafish brain is one of the very few vertebrate systems where both, activity and connectivity of all neurons from entire, anatomically defined brain regions, can be analyzed. Here, we describe different methods and the tools required for combining multiphoton microscopy with dense circuit reconstruction from electron microscopy stacks of entire brain regions in the larval zebrafish.
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Affiliation(s)
- Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Ashwin Vishwanathan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
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9
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Berger DR, Seung HS, Lichtman JW. VAST (Volume Annotation and Segmentation Tool): Efficient Manual and Semi-Automatic Labeling of Large 3D Image Stacks. Front Neural Circuits 2018; 12:88. [PMID: 30386216 PMCID: PMC6198149 DOI: 10.3389/fncir.2018.00088] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 09/24/2018] [Indexed: 11/13/2022] Open
Abstract
Recent developments in serial-section electron microscopy allow the efficient generation of very large image data sets but analyzing such data poses challenges for software tools. Here we introduce Volume Annotation and Segmentation Tool (VAST), a freely available utility program for generating and editing annotations and segmentations of large volumetric image (voxel) data sets. It provides a simple yet powerful user interface for real-time exploration and analysis of large data sets even in the Petabyte range.
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Affiliation(s)
- Daniel R Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
| | - H Sebastian Seung
- Computer Science Department, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
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10
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Abstract
New reconstruction techniques are generating connectomes of unprecedented size. These must be analyzed to generate human comprehensible results. The analyses being used fall into three general categories. The first is interactive tools used during reconstruction, to help guide the effort, look for possible errors, identify potential cell classes, and answer other preliminary questions. The second type of analysis is support for formal documents such as papers and theses. Scientific norms here require that the data be archived and accessible, and the analysis reproducible. In contrast to some other “omic” fields such as genomics, where a few specific analyses dominate usage, connectomics is rapidly evolving and the analyses used are often specific to the connectome being analyzed. These analyses are typically performed in a variety of conventional programming language, such as Matlab, R, Python, or C++, and read the connectomic data either from a file or through database queries, neither of which are standardized. In the short term we see no alternative to the use of specific analyses, so the best that can be done is to publish the analysis code, and the interface by which it reads connectomic data. A similar situation exists for archiving connectome data. Each group independently makes their data available, but there is no standardized format and long-term accessibility is neither enforced nor funded. In the long term, as connectomics becomes more common, a natural evolution would be a central facility for storing and querying connectomic data, playing a role similar to the National Center for Biotechnology Information for genomes. The final form of analysis is the import of connectome data into downstream tools such as neural simulation or machine learning. In this process, there are two main problems that need to be addressed. First, the reconstructed circuits contain huge amounts of detail, which must be intelligently reduced to a form the downstream tools can use. Second, much of the data needed for these downstream operations must be obtained by other methods (such as genetic or optical) and must be merged with the extracted connectome.
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11
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Pichat J, Iglesias JE, Yousry T, Ourselin S, Modat M. A Survey of Methods for 3D Histology Reconstruction. Med Image Anal 2018; 46:73-105. [DOI: 10.1016/j.media.2018.02.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 02/02/2018] [Accepted: 02/14/2018] [Indexed: 02/08/2023]
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12
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Chen X, Zhang X, Zhong Q, Sun Q, Peng J, Gong H, Yuan J. Simultaneous acquisition of neuronal morphology and cytoarchitecture in the same Golgi-stained brain. BIOMEDICAL OPTICS EXPRESS 2018; 9:230-244. [PMID: 29359099 PMCID: PMC5772577 DOI: 10.1364/boe.9.000230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 12/06/2017] [Accepted: 12/12/2017] [Indexed: 05/14/2023]
Abstract
Acquiring an accurate orientation reference is a prerequisite for precisely analysing the morphological features of Golgi-stained neurons in the whole brain. However, the same reflective imaging contrast of Golgi staining for morphology and Nissl staining for cytoarchitecture leads to the failure of distinguishing soma morphology and simultaneously co-locate cytoarchitecture. Here, we developed the dual-mode micro-optical sectioning tomography (dMOST) method to simultaneously image the reflective and fluorescent signals in three dimensions. We evaluated the feasibility of real-time fluorescent counterstaining on Golgi-stained brain tissue. With our system, we acquired whole-brain data sets of physiological and pathological Golgi-stained mouse model brains with fluorescence-labelled anatomical annotation at single-neuron resolution. We also obtained the neuronal morphology of macaque monkey brain tissue using this method. The results show that real-time acquisition of the co-located cytoarchitecture reference in the same brain greatly facilitates the precise morphological analysis of Golgi-stained neurons.
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Affiliation(s)
- Xiao Chen
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaoyu Zhang
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qiuyuan Zhong
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qingtao Sun
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jie Peng
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hui Gong
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jing Yuan
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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13
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Abstract
The brain is a network of neurons, one that generates behaviour, and knowing the former is crucial to understanding the latter. Identifying the exact network of synaptic connections, or connectome, of the fly's central nervous system is now a major objective in Drosophila neurobiology, one that has been initiated in several laboratories, especially the Janelia Research Campus of the Howard Hughes Medical Institute. Progress is most advanced in the optic neuropiles of the visual system. The effort to derive a connectome from these and other neuropile regions is proceeding by various methods of electron microscopy, especially focused-ion beam milling scanning electron microscopy, and relies upon - but is to be carefully distinguished from - published light microscopic methods that reveal the projections of genetically labelled cell types. The latter reveal those neurons that come into close proximity and are therefore candidate synaptic partners. Synaptic partnerships are not in fact reliably revealed by such candidate pairs, anatomical connections often revealing unexpected pathways. Synaptic partnerships identified from ultrastructural features provide a strong heuristic basis to interpret not only functional interactions between identified neurons, but also a powerful means to predict such interactions, and suggest functional pathways not readily predicted from existing experimental evidence. The analysis of circuit function may proceed cell by cell, by examining the behavioural outcome of either interrupting or restoring function to any one element in an anatomically defined circuit, but can be foiled by degeneracy in pathway elements. Circuit information can also be used to identify and analyse circuit motifs, and their role in higher-order network properties. These attempts in Drosophila anticipate parallel attempts in other systems, notably the inner plexiform layer of the vertebrate retina, and augment the one complete connectome already available to us, that available for 30 years in the nematode Caenorhabditis elegans.
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Affiliation(s)
- Ian A Meinertzhagen
- a Department of Psychology and Neuroscience, Life Sciences Centre , Dalhousie University , Halifax , Canada ;,b Janelia Research Campus of Howard Hughes Medical Institute , Ashburn , VA , USA
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14
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Wrosch JK, Einem VV, Breininger K, Dahlmanns M, Maier A, Kornhuber J, Groemer TW. Rewiring of neuronal networks during synaptic silencing. Sci Rep 2017; 7:11724. [PMID: 28916806 PMCID: PMC5601899 DOI: 10.1038/s41598-017-11729-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 08/29/2017] [Indexed: 12/14/2022] Open
Abstract
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.
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Affiliation(s)
- Jana Katharina Wrosch
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany.
| | - Vicky von Einem
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany.,Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Katharina Breininger
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Marc Dahlmanns
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
| | - Teja Wolfgang Groemer
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany
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15
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Gal E, London M, Globerson A, Ramaswamy S, Reimann MW, Muller E, Markram H, Segev I. Rich cell-type-specific network topology in neocortical microcircuitry. Nat Neurosci 2017; 20:1004-1013. [DOI: 10.1038/nn.4576] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 05/03/2017] [Indexed: 12/14/2022]
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16
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Rapid identification of neuronal structures in electronic microscope image using novel combined multi-scale image features. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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Lee D, Huang TH, De La Cruz A, Callejas A, Lois C. Methods to investigate the structure and connectivity of the nervous system. Fly (Austin) 2017; 11:224-238. [PMID: 28277925 PMCID: PMC5552278 DOI: 10.1080/19336934.2017.1295189] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Understanding the computations that take place in neural circuits requires identifying how neurons in those circuits are connected to one another. In addition, recent research indicates that aberrant neuronal wiring may be the cause of several neurodevelopmental disorders, further emphasizing the importance of identifying the wiring diagrams of brain circuits. To address this issue, several new approaches have been recently developed. In this review, we describe several methods that are currently available to investigate the structure and connectivity of the brain, and discuss their strengths and limitations.
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Affiliation(s)
- Donghyung Lee
- a Division of Biology and Biological Engineering , California Institute of Technology , Pasadena , CA , USA
| | - Ting-Hao Huang
- a Division of Biology and Biological Engineering , California Institute of Technology , Pasadena , CA , USA
| | - Aubrie De La Cruz
- a Division of Biology and Biological Engineering , California Institute of Technology , Pasadena , CA , USA
| | - Antuca Callejas
- a Division of Biology and Biological Engineering , California Institute of Technology , Pasadena , CA , USA.,b Department of Cell Biology, School of Science , University of Extremadura , Badajoz , Spain
| | - Carlos Lois
- a Division of Biology and Biological Engineering , California Institute of Technology , Pasadena , CA , USA
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18
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Mai KKK, Kang BH. Semiautomatic Segmentation of Plant Golgi Stacks in Electron Tomograms Using 3dmod. Methods Mol Biol 2017; 1662:97-104. [PMID: 28861820 DOI: 10.1007/978-1-4939-7262-3_8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Electron tomography is a powerful tool for visualizing subcellular organelles. With the advances in cryo-fixation techniques, it is now possible to reconstruct complex structures in cells preserved close to their native states in three-dimension (3D) using electron tomography. In order to better visualize these objects, 3D models are made from outlines of organelles in individual tomographic slices, which can be used to display morphological features and quantify structural parameters. While outlines of simple organelles can be drawn by hand fairly quickly, it is possible to accelerate 3D modeling of more complex organelles by means of semiautomatic segmentation. In this chapter, we use the example of reconstructing Golgi cisternae of a plant cell into 3D models using the semiautomatic protocol.
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Affiliation(s)
- Keith Ka Ki Mai
- State Key Laboratory of Agrobiotechnology, Centre for Cell and Developmental Biology, School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Byung-Ho Kang
- State Key Laboratory of Agrobiotechnology, Centre for Cell and Developmental Biology, School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China.
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19
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Bentley B, Branicky R, Barnes CL, Chew YL, Yemini E, Bullmore ET, Vértes PE, Schafer WR. The Multilayer Connectome of Caenorhabditis elegans. PLoS Comput Biol 2016; 12:e1005283. [PMID: 27984591 PMCID: PMC5215746 DOI: 10.1371/journal.pcbi.1005283] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 01/04/2017] [Accepted: 12/05/2016] [Indexed: 11/17/2022] Open
Abstract
Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.
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Affiliation(s)
- Barry Bentley
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Robyn Branicky
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Christopher L. Barnes
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
- HHMI Janelia Research Campus, Ashburn, VA, United States of America
| | - Yee Lian Chew
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Eviatar Yemini
- Department of Biological Sciences, Columbia University, New York, NY, United States of America
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge United Kingdom
- ImmunoPsychiatry, Alternative Discovery & Development, GlaxoSmithKline R&D, Cambridge United Kingdom
| | - Petra E. Vértes
- Department of Psychiatry, University of Cambridge, Cambridge United Kingdom
| | - William R. Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
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20
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Wanner AA, Genoud C, Friedrich RW. 3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons. Sci Data 2016; 3:160100. [PMID: 27824337 PMCID: PMC5100684 DOI: 10.1038/sdata.2016.100] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 09/22/2016] [Indexed: 01/27/2023] Open
Abstract
Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm3. Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS.
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Affiliation(s)
- Adrian A. Wanner
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
- University of Basel, 4003 Basel, Switzerland
| | - Christel Genoud
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Rainer W. Friedrich
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
- University of Basel, 4003 Basel, Switzerland
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21
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Wan Y, Long F, Qu L, Xiao H, Hawrylycz M, Myers EW, Peng H. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies. Neuroinformatics 2016; 13:487-99. [PMID: 26036213 DOI: 10.1007/s12021-015-9272-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.
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Affiliation(s)
- Yinan Wan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Fuhui Long
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Allen Institute for Brain Science, Seattle, WA, USA
| | - Lei Qu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Hang Xiao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eugene W Myers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Hanchuan Peng
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA. .,Allen Institute for Brain Science, Seattle, WA, USA.
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22
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DeFelipe J, Douglas RJ, Hill SL, Lein ES, Martin KAC, Rockland KS, Segev I, Shepherd GM, Tamás G. Comments and General Discussion on "The Anatomical Problem Posed by Brain Complexity and Size: A Potential Solution". Front Neuroanat 2016; 10:60. [PMID: 27375436 PMCID: PMC4901047 DOI: 10.3389/fnana.2016.00060] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 05/18/2016] [Indexed: 02/06/2023] Open
Affiliation(s)
- Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de MadridMadrid, Spain; Instituto Cajal, Consejo Superior de Investigaciones CientíficasMadrid, Spain; Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED)Madrid, Spain
| | - Rodney J Douglas
- Institute of Neuroinformatics, Swiss Federal Institute of Technology in Zurich (ETH) and University of Zurich (UZH) Zurich, Switzerland
| | - Sean L Hill
- Blue Brain Project, Campus Biotech Geneva, Switzerland
| | - Ed S Lein
- Human Cell Types Department, Allen Institute for Brain Science Seattle, WA, USA
| | - Kevan A C Martin
- Institute of Neuroinformatics, Swiss Federal Institute of Technology in Zurich (ETH) and University of Zurich (UZH) Zurich, Switzerland
| | - Kathleen S Rockland
- Department of Anatomy and Neurobiology, Boston University School of MedicineBoston, MA, USA; Cold Spring Harbor Laboratory, Cold Spring HarborNY, USA
| | - Idan Segev
- Departments of Neurobiology, The Hebrew University of JerusalemJerusalem, Israel; The Interdisciplinary Center for Neural Computation, The Hebrew University of JerusalemJerusalem, Israel; Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of JerusalemJerusalem, Israel
| | - Gordon M Shepherd
- Department of Neurobiology, Yale School of Medicine New Haven, CT, USA
| | - 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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23
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Seyedhosseini M, Tasdizen T. Semantic Image Segmentation with Contextual Hierarchical Models. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2016; 38:951-64. [PMID: 26336116 PMCID: PMC4844369 DOI: 10.1109/tpami.2015.2473846] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).
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24
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Papp EA, Leergaard TB, Csucs G, Bjaalie JG. Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections. Front Neuroinform 2016; 10:11. [PMID: 27148038 PMCID: PMC4835481 DOI: 10.3389/fninf.2016.00011] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 02/26/2016] [Indexed: 01/11/2023] Open
Abstract
Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.
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Affiliation(s)
- Eszter A Papp
- Institute of Basic Medical Sciences, University of Oslo Oslo, Norway
| | | | - Gergely Csucs
- Institute of Basic Medical Sciences, University of Oslo Oslo, Norway
| | - Jan G Bjaalie
- Institute of Basic Medical Sciences, University of Oslo Oslo, Norway
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25
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Schneider-Mizell CM, Gerhard S, Longair M, Kazimiers T, Li F, Zwart MF, Champion A, Midgley FM, Fetter RD, Saalfeld S, Cardona A. Quantitative neuroanatomy for connectomics in Drosophila. eLife 2016; 5. [PMID: 26990779 PMCID: PMC4811773 DOI: 10.7554/elife.12059] [Citation(s) in RCA: 166] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2015] [Accepted: 01/31/2016] [Indexed: 12/18/2022] Open
Abstract
Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity. DOI:http://dx.doi.org/10.7554/eLife.12059.001 The nervous system contains cells called neurons, which connect to each other to form circuits that send and process information. Each neuron receives and transmits signals to other neurons via very small junctions called synapses. Neurons are shaped a bit like trees, and most input synapses are located in the tiniest branches. Understanding the architecture of a neuron’s branches is important to understand the role that a particular neuron plays in processing information. Therefore, neuroscientists strive to reconstruct the architecture of these branches and how they connect to one another using imaging techniques. One imaging technique known as serial electron microscopy generates highly detailed images of neural circuits. However, reconstructing neural circuits from such images is notoriously time consuming and error prone. These errors could result in the reconstructed circuit being very different than the real-life circuit. For example, an error that leads to missing out a large branch could result in researchers failing to notice many important connections in the circuit. On the other hand, some errors may not matter much because the neurons share other synapses that are included in the reconstruction. To understand what effect errors have on the reconstructed circuits, neuroscientists need to have a more detailed understanding of the relationship between the shape of a neuron, its synaptic connections to other neurons, and where errors commonly occur. Here, Schneider-Mizell, Gerhard et al. study this relationship in detail and then devise a faster reconstruction method that uses the shape and other properties of neurons without sacrificing accuracy. The method includes a way to include data from the shape of neurons in the circuit wiring diagrams, revealing circuit patterns that would otherwise go unnoticed. The experiments use serial electron microscopy images of neurons from fruit flies and show that, from the tiniest larva to the adult fly, neurons form synapses with each other in a similar way. Most errors in the reconstruction only affect the tips of the smallest branches, which generally only host a single synapse. Such omissions do not have a big effect on the reconstructed circuit because strongly connected neurons make multiple synapses onto each other. Schneider-Mizell, Gerhard et al.'s approach will help researchers to reconstruct neural circuits and analyze them more effectively than was possible before. The algorithms and tools developed in this study are available in an open source software package so that they can be used by other researchers in the future. DOI:http://dx.doi.org/10.7554/eLife.12059.002
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Affiliation(s)
| | - Stephan Gerhard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.,Institute of Neuroinformatics, University of Zurich, Zürich, Switzerland.,Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
| | - Mark Longair
- Institute of Neuroinformatics, University of Zurich, Zürich, Switzerland.,Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Maarten F Zwart
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Andrew Champion
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Frank M Midgley
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Richard D Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Albert Cardona
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
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26
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Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy. J Neurosci Methods 2016; 264:16-24. [PMID: 26928258 DOI: 10.1016/j.jneumeth.2016.02.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 01/18/2016] [Accepted: 02/22/2016] [Indexed: 11/24/2022]
Abstract
BACKGROUND Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. NEW METHOD The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. RESULTS For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. COMPARISON WITH EXISTING METHODS Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. CONCLUSION Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.
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27
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Abstract
A quiet revolution is under way in technologies used for nanoscale cellular imaging. Focused ion beams, previously restricted to the materials sciences and semiconductor fields, are rapidly becoming powerful tools for ultrastructural imaging of biological samples. Cell and tissue architecture, as preserved in plastic-embedded resin or in plunge-frozen form, can be investigated in three dimensions by scanning electron microscopy imaging of freshly created surfaces that result from the progressive removal of material using a focused ion beam. The focused ion beam can also be used as a sculpting tool to create specific specimen shapes such as lamellae or needles that can be analyzed further by transmission electron microscopy or by methods that probe chemical composition. Here we provide an in-depth primer to the application of focused ion beams in biology, including a guide to the practical aspects of using the technology, as well as selected examples of its contribution to the generation of new insights into subcellular architecture and mechanisms underlying host-pathogen interactions.
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28
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Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-random Features of Cortical Synaptic Wiring. PLoS Comput Biol 2016; 12:e1004759. [PMID: 26866369 PMCID: PMC4750861 DOI: 10.1371/journal.pcbi.1004759] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 01/18/2016] [Indexed: 11/19/2022] Open
Abstract
Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, for example, above-chance bidirectionality and an overrepresentation of certain triangular motifs. Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns, and particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a power law distribution of synaptic lifetimes, and a tendency for stronger synapses to be more stable over time. Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex. While previous work has modeled some of the individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring. The problem of how the brain wires itself up has important implications for the understanding of both brain development and cognition. The microscopic structure of the circuits of the adult neocortex, often considered the seat of our highest cognitive abilities, is still poorly understood. Recent experiments have provided a first set of findings on the structural features of these circuits, but it is unknown how these features come about and how they are maintained. Here we present a neural network model that shows how these features might come about. It gives rise to numerous connectivity features, which have been observed in experiments, but never before simultaneously produced by a single model. Our model explains the development of these structural features as the result of a process of self-organization. The results imply that only a few simple mechanisms and constraints are required to produce, at least to the first approximation, various characteristic features of a typical fragment of brain microcircuitry. In the absence of any of these mechanisms, simultaneous production of all desired features fails, suggesting a minimal set of necessary mechanisms for their production.
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29
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Givon LE, Lazar AA. Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain. PLoS One 2016; 11:e0146581. [PMID: 26751378 PMCID: PMC4709234 DOI: 10.1371/journal.pone.0146581] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 12/18/2015] [Indexed: 11/23/2022] Open
Abstract
We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules' local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly's entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel's model integration by combining independently developed models of the retina and lamina neuropils in the fly's visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel's ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel's communication performance both over the number of interface ports exposed by an emulation's constituent modules and the total number of modules comprised by an emulation.
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Affiliation(s)
- Lev E. Givon
- Department of Electrical Engineering, Columbia University, New York, NY 10027, United States of America
| | - Aurel A. Lazar
- Department of Electrical Engineering, Columbia University, New York, NY 10027, United States of America
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Calì C, Baghabra J, Boges DJ, Holst GR, Kreshuk A, Hamprecht FA, Srinivasan M, Lehväslaiho H, Magistretti PJ. Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. J Comp Neurol 2016; 524:23-38. [PMID: 26179415 PMCID: PMC5042088 DOI: 10.1002/cne.23852] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 07/01/2015] [Accepted: 07/02/2015] [Indexed: 02/01/2023]
Abstract
Advances in the application of electron microscopy (EM) to serial imaging are opening doors to new ways of analyzing cellular structure. New and improved algorithms and workflows for manual and semiautomated segmentation allow us to observe the spatial arrangement of the smallest cellular features with unprecedented detail in full three-dimensions. From larger samples, higher complexity models can be generated; however, they pose new challenges to data management and analysis. Here we review some currently available solutions and present our approach in detail. We use the fully immersive virtual reality (VR) environment CAVE (cave automatic virtual environment), a room in which we are able to project a cellular reconstruction and visualize in 3D, to step into a world created with Blender, a free, fully customizable 3D modeling software with NeuroMorph plug-ins for visualization and analysis of EM preparations of brain tissue. Our workflow allows for full and fast reconstructions of volumes of brain neuropil using ilastik, a software tool for semiautomated segmentation of EM stacks. With this visualization environment, we can walk into the model containing neuronal and astrocytic processes to study the spatial distribution of glycogen granules, a major energy source that is selectively stored in astrocytes. The use of CAVE was key to the observation of a nonrandom distribution of glycogen, and led us to develop tools to quantitatively analyze glycogen clustering and proximity to other subcellular features.
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Affiliation(s)
- Corrado Calì
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Jumana Baghabra
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Daniya J. Boges
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Glendon R. Holst
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Anna Kreshuk
- Heidelberg Collaboratory for Image Processing (HCI)University of Heidelberg69115 HeidelbergGermany
| | - Fred A. Hamprecht
- Heidelberg Collaboratory for Image Processing (HCI)University of Heidelberg69115 HeidelbergGermany
| | - Madhusudhanan Srinivasan
- KAUST Visualization Lab (KVL)King Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Heikki Lehväslaiho
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
| | - Pierre J. Magistretti
- Biological and Environmental Science and Engineering divisionKing Abdullah University of Science and Technology23955‐6900 ThuwalSaudi Arabia
- Brain Mind InstituteÉcole Polytechnique Fédérale de Lausanne (EPFL)1005 LausanneSwitzerland
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31
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Okano H, Miyawaki A, Kasai K. Brain/MINDS: brain-mapping project in Japan. Philos Trans R Soc Lond B Biol Sci 2015; 370:rstb.2014.0310. [PMID: 25823872 PMCID: PMC4387516 DOI: 10.1098/rstb.2014.0310] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas.
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Affiliation(s)
- Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Brain Science Institute RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Atsushi Miyawaki
- Laboratory for Cell Function Dynamics, Brain Science Institute RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, Schmidhuber J, Laptev D, Dwivedi S, Buhmann JM, Liu T, Seyedhosseini M, Tasdizen T, Kamentsky L, Burget R, Uher V, Tan X, Sun C, Pham TD, Bas E, Uzunbas MG, Cardona A, Schindelin J, Seung HS. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat 2015; 9:142. [PMID: 26594156 PMCID: PMC4633678 DOI: 10.3389/fnana.2015.00142] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/19/2015] [Indexed: 11/13/2022] Open
Abstract
To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.
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Affiliation(s)
- Ignacio Arganda-Carreras
- UMR1318 French National Institute for Agricultural Research-AgroParisTech, French National Institute for Agricultural Research Centre de Versailles-Grignon, Institut Jean-Pierre Bourgin Versailles, France
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Daniel R Berger
- Center for Brain Science, Harvard University Cambridge, MA, USA
| | - Dan Cireşan
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Alessandro Giusti
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Luca M Gambardella
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Jürgen Schmidhuber
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Dmitry Laptev
- Department of Computer Science, ETH Zurich Zurich, Switzerland
| | - Sarvesh Dwivedi
- Department of Computer Science, ETH Zurich Zurich, Switzerland
| | | | - Ting Liu
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Mojtaba Seyedhosseini
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Tolga Tasdizen
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Lee Kamentsky
- Imaging Platform, Broad Institute Cambridge, MA, USA
| | - Radim Burget
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic
| | - Vaclav Uher
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic
| | - Xiao Tan
- School of Engineering and Information Technology, University of New South Wales Canberra, ACT, Australia
| | - Changming Sun
- Digital Productivity Flagship, Commonwealth Scientific and Industrial Research Organisation North Ryde, NSW, Australia
| | - Tuan D Pham
- Department of Biomedical Engineering, The Institute of Technology, Linkoping University Linkoping, Sweden
| | - Erhan Bas
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Mustafa G Uzunbas
- Computer Science Department, Rutgers University New Brunswick, NJ, USA
| | - Albert Cardona
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Johannes Schindelin
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison Madison, WI, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA
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Synaptic circuits and their variations within different columns in the visual system of Drosophila. Proc Natl Acad Sci U S A 2015; 112:13711-6. [PMID: 26483464 DOI: 10.1073/pnas.1509820112] [Citation(s) in RCA: 147] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form ≥ 20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.
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Reimann MW, King JG, Muller EB, Ramaswamy S, Markram H. An algorithm to predict the connectome of neural microcircuits. Front Comput Neurosci 2015; 9:120. [PMID: 26500529 PMCID: PMC4597796 DOI: 10.3389/fncom.2015.00120] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 05/22/2015] [Indexed: 11/18/2022] Open
Abstract
Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue—the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity.
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Affiliation(s)
- Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus Geneva, Switzerland
| | - James G King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus Geneva, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus Geneva, Switzerland
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35
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Sigal YM, Speer CM, Babcock HP, Zhuang X. Mapping Synaptic Input Fields of Neurons with Super-Resolution Imaging. Cell 2015; 163:493-505. [PMID: 26435106 PMCID: PMC4733473 DOI: 10.1016/j.cell.2015.08.033] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2015] [Revised: 07/22/2015] [Accepted: 08/12/2015] [Indexed: 01/28/2023]
Abstract
As a basic functional unit in neural circuits, each neuron integrates input signals from hundreds to thousands of synapses. Knowledge of the synaptic input fields of individual neurons, including the identity, strength, and location of each synapse, is essential for understanding how neurons compute. Here, we developed a volumetric super-resolution reconstruction platform for large-volume imaging and automated segmentation of neurons and synapses with molecular identity information. We used this platform to map inhibitory synaptic input fields of On-Off direction-selective ganglion cells (On-Off DSGCs), which are important for computing visual motion direction in the mouse retina. The reconstructions of On-Off DSGCs showed a GABAergic, receptor subtype-specific input field for generating direction selective responses without significant glycinergic inputs for mediating monosynaptic crossover inhibition. These results demonstrate unique capabilities of this super-resolution platform for interrogating neural circuitry.
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Affiliation(s)
- Yaron M Sigal
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Colenso M Speer
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Hazen P Babcock
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA.
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36
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Wanner AA, Kirschmann MA, Genoud C. Challenges of microtome-based serial block-face scanning electron microscopy in neuroscience. J Microsc 2015; 259:137-142. [PMID: 25907464 PMCID: PMC4745002 DOI: 10.1111/jmi.12244] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Accepted: 02/11/2015] [Indexed: 01/14/2023]
Abstract
Serial block-face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation. Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape-collecting ultramicrotome, focused ion-beam scanning electron microscopy and SBEM (microtome serial block-face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools. In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines.
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Affiliation(s)
- A A Wanner
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058, Basel, Switzerland
| | - M A Kirschmann
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058, Basel, Switzerland
| | - C Genoud
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058, Basel, Switzerland
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37
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Uzunbas MG, Chen C, Metaxas D. An efficient conditional random field approach for automatic and interactive neuron segmentation. Med Image Anal 2015. [PMID: 26210001 DOI: 10.1016/j.media.2015.06.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
We present a new graphical-model-based method for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. For automated reconstruction, our learning based model selects a collection of nodes from a hierarchical merging tree as the proposed segmentation. More specifically, this is achieved by training a conditional random field (CRF) whose underlying graph is the watershed merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our results are comparable to the results of state-of-the-art methods. Furthermore, both the inference and the training are very efficient as the graph is tree-structured. The problem of neuron segmentation requires extremely high segmentation quality. Therefore, proofreading, namely, interactively correcting mistakes of the automatic method, is a necessary module in the pipeline. Based on our efficient tree-structured inference algorithm, we develop an interactive segmentation framework which only selects locations where the model is uncertain for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Only giving a limited number of choices makes the user interaction very efficient. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.
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Affiliation(s)
- Mustafa Gokhan Uzunbas
- Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA.
| | - Chao Chen
- Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA.
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA.
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Parag T, Chakraborty A, Plaza S, Scheffer L. A context-aware delayed agglomeration framework for electron microscopy segmentation. PLoS One 2015; 10:e0125825. [PMID: 26018659 PMCID: PMC4446358 DOI: 10.1371/journal.pone.0125825] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 03/26/2015] [Indexed: 11/18/2022] Open
Abstract
Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a “delayed” scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets.
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Affiliation(s)
- Toufiq Parag
- Janelia Research Campus, HHMI, Ashburn, VA, USA
- * E-mail:
| | - Anirban Chakraborty
- Department of Diagnostic Radiology, National University of Singapore, Singapore
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Szalkai B, Kerepesi C, Varga B, Grolmusz V. The Budapest Reference Connectome Server v2.0. Neurosci Lett 2015; 595:60-2. [PMID: 25862487 DOI: 10.1016/j.neulet.2015.03.071] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 03/30/2015] [Indexed: 10/23/2022]
Abstract
The connectomes of different human brains are pairwise distinct: we cannot talk about an abstract "graph of the brain". Two typical connectomes, however, have quite a few common graph edges that may describe the same connections between the same cortical areas. The Budapest Reference Connectome Server v2.0 generates the common edges of the connectomes of 96 distinct cortexes, each with 1015 vertices, computed from 96 MRI data sets of the Human Connectome Project. The user may set numerous parameters for the identification and filtering of common edges, and the graphs are downloadable in both csv and GraphML formats; both formats carry the anatomical annotations of the vertices, generated by the FreeSurfer program. The resulting consensus graph is also automatically visualized in a 3D rotating brain model on the website. The consensus graphs, generated with various parameter settings, can be used as reference connectomes based on different, independent MRI images, therefore they may serve as reduced-error, low-noise, robust graph representations of the human brain. The webserver is available at http://connectome.pitgroup.org.
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Affiliation(s)
- Balázs Szalkai
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Csaba Kerepesi
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; Uratim Ltd., H-1118 Budapest, Hungary.
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40
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Jones C, Liu T, Cohan NW, Ellisman M, Tasdizen T. Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images. J Neurosci Methods 2015; 246:13-21. [PMID: 25769273 DOI: 10.1016/j.jneumeth.2015.03.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Revised: 02/27/2015] [Accepted: 03/03/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. NEW METHOD We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. RESULTS We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. COMPARISON WITH EXISTING METHODS Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method. CONCLUSION Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.
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Affiliation(s)
- Cory Jones
- Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States
| | - Ting Liu
- Scientific Computing and Imaging Institute, University of Utah, United States; School of Computing, University of Utah, United States
| | - Nathaniel Wood Cohan
- National Center for Microscopy and Imaging Research, University of California, San Diego, United States
| | - Mark Ellisman
- National Center for Microscopy and Imaging Research, University of California, San Diego, United States
| | - Tolga Tasdizen
- Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States; School of Computing, University of Utah, United States.
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Kaynig V, Vazquez-Reina A, Knowles-Barley S, Roberts M, Jones TR, Kasthuri N, Miller E, Lichtman J, Pfister H. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med Image Anal 2015; 22:77-88. [PMID: 25791436 DOI: 10.1016/j.media.2015.02.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Revised: 11/02/2014] [Accepted: 02/06/2015] [Indexed: 01/14/2023]
Abstract
Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a 27,000 μm(3) volume of brain tissue over a cube of 30 μm in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles.
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Affiliation(s)
- Verena Kaynig
- School of Engineering and Applied Sciences, Harvard University, United States
| | - Amelio Vazquez-Reina
- School of Engineering and Applied Sciences, Harvard University, United States; Department of Computer Science at Tufts University, United States
| | | | - Mike Roberts
- School of Engineering and Applied Sciences, Harvard University, United States
| | - Thouis R Jones
- School of Engineering and Applied Sciences, Harvard University, United States; Department of Molecular and Cellular Biology, Harvard University, United States
| | - Narayanan Kasthuri
- Department of Molecular and Cellular Biology, Harvard University, United States
| | - Eric Miller
- Department of Computer Science at Tufts University, United States
| | - Jeff Lichtman
- Department of Molecular and Cellular Biology, Harvard University, United States
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, United States
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42
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Miranda K, Girard-Dias W, Attias M, de Souza W, Ramos I. Three dimensional reconstruction by electron microscopy in the life sciences: An introduction for cell and tissue biologists. Mol Reprod Dev 2015; 82:530-47. [PMID: 25652003 DOI: 10.1002/mrd.22455] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 12/10/2014] [Indexed: 12/26/2022]
Abstract
Early applications of transmission electron microscopy (TEM) in the life sciences have contributed tremendously to our current understanding at the subcellular level. Initially limited to two-dimensional representations of three-dimensional (3D) objects, this approach has revolutionized the fields of cellular and structural biology-being instrumental for determining the fine morpho-functional characterization of most cellular structures. Electron microscopy has progressively evolved towards the development of tools that allow for the 3D characterization of different structures. This was done with the aid of a wide variety of techniques, which have become increasingly diverse and highly sophisticated. We start this review by examining the principles of 3D reconstruction of cells and tissues using classical approaches in TEM, and follow with a discussion of the modern approaches utilizing TEM as well as on new scanning electron microscopy-based techniques. 3D reconstruction techniques from serial sections and (cryo) electron-tomography are examined, and the recent applications of focused ion beam-scanning microscopes and serial-block-face techniques for the 3D reconstruction of large volumes are discussed. Alternative low-cost techniques and more accessible approaches using basic transmission or field emission scanning electron microscopes are also examined.
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Affiliation(s)
- Kildare Miranda
- Laboratório de Ultraestrutura Celular Hertha Meyer, Instituto de Biofísica, Carlos Chagas Filho and Instituto Nacional de Ciência e Tecnologia em Biologia Estrutural e Bioimagens-Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.,Diretoria de Metrologia Aplicada a Ciências da Vida, Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO), Xer, é, m, Rio de Janeiro, Brazil
| | - Wendell Girard-Dias
- Laboratório de Ultraestrutura Celular Hertha Meyer, Instituto de Biofísica, Carlos Chagas Filho and Instituto Nacional de Ciência e Tecnologia em Biologia Estrutural e Bioimagens-Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcia Attias
- Laboratório de Ultraestrutura Celular Hertha Meyer, Instituto de Biofísica, Carlos Chagas Filho and Instituto Nacional de Ciência e Tecnologia em Biologia Estrutural e Bioimagens-Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Wanderley de Souza
- Laboratório de Ultraestrutura Celular Hertha Meyer, Instituto de Biofísica, Carlos Chagas Filho and Instituto Nacional de Ciência e Tecnologia em Biologia Estrutural e Bioimagens-Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.,Diretoria de Metrologia Aplicada a Ciências da Vida, Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO), Xer, é, m, Rio de Janeiro, Brazil
| | - Isabela Ramos
- Laboratório de Bioquímica de Insetos, Instituto de Bioquímica Médica, Leopoldo de Meis -Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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43
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Esposito U, Giugliano M, Vasilaki E. Adaptation of short-term plasticity parameters via error-driven learning may explain the correlation between activity-dependent synaptic properties, connectivity motifs and target specificity. Front Comput Neurosci 2015; 8:175. [PMID: 25688203 PMCID: PMC4310301 DOI: 10.3389/fncom.2014.00175] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 12/31/2014] [Indexed: 01/09/2023] Open
Abstract
The anatomical connectivity among neurons has been experimentally found to be largely non-random across brain areas. This means that certain connectivity motifs occur at a higher frequency than would be expected by chance. Of particular interest, short-term synaptic plasticity properties were found to colocalize with specific motifs: an over-expression of bidirectional motifs has been found in neuronal pairs where short-term facilitation dominates synaptic transmission among the neurons, whereas an over-expression of unidirectional motifs has been observed in neuronal pairs where short-term depression dominates. In previous work we found that, given a network with fixed short-term properties, the interaction between short- and long-term plasticity of synaptic transmission is sufficient for the emergence of specific motifs. Here, we introduce an error-driven learning mechanism for short-term plasticity that may explain how such observed correspondences develop from randomly initialized dynamic synapses. By allowing synapses to change their properties, neurons are able to adapt their own activity depending on an error signal. This results in more rich dynamics and also, provided that the learning mechanism is target-specific, leads to specialized groups of synapses projecting onto functionally different targets, qualitatively replicating the experimental results of Wang and collaborators.
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Affiliation(s)
- Umberto Esposito
- Department Computer Science, University of Sheffield Sheffield, UK
| | - Michele Giugliano
- Department Computer Science, University of Sheffield Sheffield, UK ; Theoretical Neurobiology and Neuroengineering Laboratory, Department Biomedical Sciences, University of Antwerp Antwerp, Belgium ; Laboratory of Neural Microcircuitry, Brain Mind Institute, Swiss Federal Institute of Technology of Lausanne École Polytechnique Fédérale de Lausanne, Switzerland
| | - Eleni Vasilaki
- Department Computer Science, University of Sheffield Sheffield, UK ; Theoretical Neurobiology and Neuroengineering Laboratory, Department Biomedical Sciences, University of Antwerp Antwerp, Belgium ; INSIGNEO Institute for in Silico Medicine, University of Sheffield Sheffield, UK
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Edwards J, Daniel E, Kinney J, Bartol T, Sejnowski T, Johnston D, Harris K, Bajaj C. VolRoverN: enhancing surface and volumetric reconstruction for realistic dynamical simulation of cellular and subcellular function. Neuroinformatics 2014; 12:277-89. [PMID: 24100964 DOI: 10.1007/s12021-013-9205-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Establishing meaningful relationships between cellular structure and function requires accurate morphological reconstructions. In particular, there is an unmet need for high quality surface reconstructions to model subcellular and synaptic interactions among neurons and glia at nanometer resolution. We address this need with VolRoverN, a software package that produces accurate, efficient, and automated 3D surface reconstructions from stacked 2D contour tracings. While many techniques and tools have been developed in the past for 3D visualization of cellular structure, the reconstructions from VolRoverN meet specific quality criteria that are important for dynamical simulations. These criteria include manifoldness, water-tightness, lack of self- and object-object-intersections, and geometric accuracy. These enhanced surface reconstructions are readily extensible to any cell type and are used here on spiny dendrites with complex morphology and axons from mature rat hippocampal area CA1. Both spatially realistic surface reconstructions and reduced skeletonizations are produced and formatted by VolRoverN for easy input into analysis software packages for neurophysiological simulations at multiple spatial and temporal scales ranging from ion electro-diffusion to electrical cable models.
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Affiliation(s)
- John Edwards
- Department of Computer Science, ICES, The University of Texas, Austin, TX, USA
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46
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Nakae K, Ikegaya Y, Ishikawa T, Oba S, Urakubo H, Koyama M, Ishii S. A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging. PLoS Comput Biol 2014; 10:e1003949. [PMID: 25393874 PMCID: PMC4230777 DOI: 10.1371/journal.pcbi.1003949] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 09/29/2014] [Indexed: 11/18/2022] Open
Abstract
Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.
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Affiliation(s)
- Ken Nakae
- Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Yuji Ikegaya
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Center for Information and Neural Networks, Suita City, Osaka, Japan
- * E-mail: (YI); (SI)
| | - Tomoe Ishikawa
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Shigeyuki Oba
- Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Hidetoshi Urakubo
- Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Masanori Koyama
- Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Shin Ishii
- Integrated Systems Biology Laboratory, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto, Japan
- * E-mail: (YI); (SI)
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Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations. PLoS Comput Biol 2014; 10:e1003921. [PMID: 25340814 PMCID: PMC4207466 DOI: 10.1371/journal.pcbi.1003921] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 09/17/2014] [Indexed: 12/15/2022] Open
Abstract
Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models. Computational models of neurons and neural networks provide a valuable avenue to test our understanding of brain regions and to make predictions to guide future experimentation. Each neuron has a unique dendritic tree, features of which can vary depending on the location of the neuron within the particular brain region. In this study, we generated a complete population of dendritic trees for the most numerous type of neuron in the hippocampus, the dentate gyrus granule cell, using a realistic three-dimensional structural context to drive the generation process. Morphological properties can now be studied at the level of complete neuronal populations, and this work provides a foundation to build upon in the construction of large-scale, data-driven neuroanatomical and network models.
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Brain-mapping projects using the common marmoset. Neurosci Res 2014; 93:3-7. [PMID: 25264372 DOI: 10.1016/j.neures.2014.08.014] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Revised: 08/21/2014] [Accepted: 08/22/2014] [Indexed: 11/23/2022]
Abstract
Globally, there is an increasing interest in brain-mapping projects, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative project in the USA, the Human Brain Project (HBP) in Europe, and the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project in Japan. These projects aim to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain. Brain/MINDS is focused on structural and functional mapping of the common marmoset (Callithrix jacchus) brain. This non-human primate has numerous advantages for brain mapping, including a well-developed frontal cortex and a compact brain size, as well as the availability of transgenic technologies. In the present review article, we discuss strategies for structural and functional mapping of the marmoset brain and the relation of the common marmoset to other animals models.
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49
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DeBello WM, McBride TJ, Nichols GS, Pannoni KE, Sanculi D, Totten DJ. Input clustering and the microscale structure of local circuits. Front Neural Circuits 2014; 8:112. [PMID: 25309336 PMCID: PMC4162353 DOI: 10.3389/fncir.2014.00112] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 08/28/2014] [Indexed: 11/13/2022] Open
Abstract
The recent development of powerful tools for high-throughput mapping of synaptic networks promises major advances in understanding brain function. One open question is how circuits integrate and store information. Competing models based on random vs. structured connectivity make distinct predictions regarding the dendritic addressing of synaptic inputs. In this article we review recent experimental tests of one of these models, the input clustering hypothesis. Across circuits, brain regions and species, there is growing evidence of a link between synaptic co-activation and dendritic location, although this finding is not universal. The functional implications of input clustering and future challenges are discussed.
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Affiliation(s)
- William M DeBello
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA
| | - Thomas J McBride
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA ; PLOS Medicine San Francisco, CA, USA
| | - Grant S Nichols
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA
| | - Katy E Pannoni
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA
| | - Daniel Sanculi
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA
| | - Douglas J Totten
- Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California-Davis Davis, CA, USA
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50
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Abstract
One of the most fascinating challenges in neuroscience is the reconstruction of the connectivity map of the brain. Recent years have seen a rapid expansion in the field of connectomics, whose aim is to trace this map and understand its relationship with neural computation. Many different approaches, ranging from electron and optical microscopy to magnetic resonance imaging, have been proposed to address the connectomics challenge on various spatial scales and in different species. Here, we review the main technological advances in the microscopy techniques applied to connectomics, highlighting the potential and limitations of the different methods. Finally, we briefly discuss the role of connectomics in the Human Brain Project, the Future and Emerging Technologies (FET) Flagship recently approved by the European Commission.
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