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Khosrozadeh A, Seeger R, Witz G, Radecke J, Sørensen JB, Zuber B. CryoVesNet: A dedicated framework for synaptic vesicle segmentation in cryo-electron tomograms. J Cell Biol 2025; 224:e202402169. [PMID: 39446113 DOI: 10.1083/jcb.202402169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 07/26/2024] [Accepted: 09/20/2024] [Indexed: 10/25/2024] Open
Abstract
Cryo-electron tomography (cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is highly complex, requiring image segmentation for visualization and quantification of subcellular structures. Due to noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Cryo-ET study of the spatial organization of SVs and their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Accurate SV segmentation is a prerequisite to obtaining a faithful connectivity representation. Hundreds of SVs are present in a synapse, and their manual segmentation is a bottleneck. We addressed this by designing a workflow consisting of a convolutional network followed by post-processing steps. Alongside, we provide an interactive tool for accurately segmenting spherical vesicles. Our pipeline can in principle segment spherical vesicles in any cell type as well as extracellular and in vitro spherical vesicles.
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Affiliation(s)
- Amin Khosrozadeh
- Institute of Anatomy, University of Bern , Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern , Bern, Switzerland
| | - Raphaela Seeger
- Institute of Anatomy, University of Bern , Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern , Bern, Switzerland
| | - Guillaume Witz
- Data Science Lab, University of Bern , Bern, Switzerland
| | - Julika Radecke
- Institute of Anatomy, University of Bern , Bern, Switzerland
- Department of Neuroscience, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Diamond Light Source Ltd. , Didcot, UK
| | - Jakob B Sørensen
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | - Benoît Zuber
- Institute of Anatomy, University of Bern , Bern, Switzerland
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2
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Markert SM. Studying zebrafish nervous system structure and function in health and disease with electron microscopy. Dev Growth Differ 2023; 65:502-516. [PMID: 37740826 DOI: 10.1111/dgd.12890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 09/25/2023]
Abstract
Zebrafish (Danio rerio) is a well-established model for studying the nervous system. Findings in zebrafish often inform studies on human diseases of the nervous system and provide crucial insight into disease mechanisms. The functions of the nervous system often rely on communication between neurons. Signal transduction is achieved via release of signaling molecules in the form of neuropeptides or neurotransmitters at synapses. Snapshots of membrane dynamics of these processes are imaged by electron microscopy. Electron microscopy can reveal ultrastructure and thus synaptic processes. This is crucial both for mapping synaptic connections and for investigating synaptic functions. In addition, via volumetric electron microscopy, the overall architecture of the nervous system becomes accessible, where structure can inform function. Electron microscopy is thus of particular value for studying the nervous system. However, today a plethora of electron microscopy techniques and protocols exist. Which technique is most suitable highly depends on the research question and scope as well as on the type of tissue that is examined. This review gives an overview of the electron microcopy techniques used on the zebrafish nervous system. It aims to give researchers a guide on which techniques are suitable for their specific questions and capabilities as well as an overview of the capabilities of electron microscopy in neurobiological research in the zebrafish model.
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Raimondi A, Ilacqua N, Pellegrini L. Liver inter-organelle membrane contact sites revealed by serial section electron tomography. Methods Cell Biol 2023; 177:101-123. [PMID: 37451764 DOI: 10.1016/bs.mcb.2022.12.021] [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] [Indexed: 02/12/2023]
Abstract
Inter-organelle membrane contact sites (MCSs) are defined as areas of close proximity between the membranes of two organelles (10-80nm). They have been implicated in many physiological processes such as Ca++, lipids or small molecules transfer, organelles biogenesis or dynamic and have an important role in many cellular processes such as apoptosis, autophagy, and signaling. Since the distance and the extent of these contacts are in the nanometer range, high resolution techniques are ideal for imaging these structures. It is for this reason that transmission electron microscopy (TEM) has been considered the gold standard for MCSs visualization and the first technique that described them. However, often TEM analysis is limited to 2D lacking information on the 3D association between the organelles involved in MCSs. To fully describe the complex architecture of MSCs and to unveil their role in cellular physiology a 3D analysis is required. This chapter provides a method for the analysis of MCSs using serial section electron tomography (ssET), a technique able to reconstruct in 3D at nanometer resolution cellular and subcellular structures. By applying this procedure, it was possible to elucidate the role of the contacts between Endoplasmic Reticulum (ER) and other organelles in liver lipid metabolism.
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Affiliation(s)
- Andrea Raimondi
- Experimental Imaging Centre, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Nicolò Ilacqua
- Mitochondria Biology Laboratory, Brain Research Center, Quebec, QC, Canada; Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Luca Pellegrini
- Mitochondria Biology Laboratory, Brain Research Center, Quebec, QC, Canada; Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec, QC, Canada; Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
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4
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Dandekar T, Kunz M. Design Principles of a Cell. Bioinformatics 2023. [DOI: 10.1007/978-3-662-65036-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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Franco-Barranco D, Pastor-Tronch J, González-Marfil A, Muñoz-Barrutia A, Arganda-Carreras I. Deep learning based domain adaptation for mitochondria segmentation on EM volumes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106949. [PMID: 35753105 DOI: 10.1016/j.cmpb.2022.106949] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/05/2022] [Accepted: 06/09/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species. METHODS We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation. RESULTS We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets. CONCLUSIONS The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.
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Affiliation(s)
- Daniel Franco-Barranco
- Dept. of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain; Donostia International Physics Center (DIPC), Spain.
| | - Julio Pastor-Tronch
- Dept. of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain
| | - Aitor González-Marfil
- Dept. of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain
| | - Arrate Muñoz-Barrutia
- Universidad Carlos III de Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Spain
| | - Ignacio Arganda-Carreras
- Dept. of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Spain; Donostia International Physics Center (DIPC), Spain; Ikerbasque, Basque Foundation for Science, Spain
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Imbrosci B, Schmitz D, Orlando M. Automated Detection and Localization of Synaptic Vesicles in Electron Microscopy Images. eNeuro 2022; 9:ENEURO.0400-20.2021. [PMID: 34983830 PMCID: PMC8805189 DOI: 10.1523/eneuro.0400-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/04/2021] [Accepted: 11/23/2021] [Indexed: 11/21/2022] Open
Abstract
Information transfer and integration in the brain occurs at chemical synapses and is mediated by the fusion of synaptic vesicles filled with neurotransmitter. Synaptic vesicle dynamic spatial organization regulates synaptic transmission as well as synaptic plasticity. Because of their small size, synaptic vesicles require electron microscopy (EM) for their imaging, and their analysis is conducted manually. The manual annotation and segmentation of the hundreds to thousands of synaptic vesicles, is highly time consuming and limits the throughput of data collection. To overcome this limitation, we built an algorithm, mainly relying on convolutional neural networks (CNNs), capable of automatically detecting and localizing synaptic vesicles in electron micrographs. The algorithm was trained on murine synapses but we show that it works well on synapses from different species, ranging from zebrafish to human, and from different preparations. As output, we provide the vesicle count and coordinates, the nearest neighbor distance (nnd) and the estimate of the vesicles area. We also provide a graphical user interface (GUI) to guide users through image analysis, result visualization, and manual proof-reading. The application of our algorithm is especially recommended for images produced by transmission EM. Since this type of imaging is used routinely to investigate presynaptic terminals, our solution will likely be of interest for numerous research groups.
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Affiliation(s)
- Barbara Imbrosci
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin 10117, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Dietmar Schmitz
- German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin 10117, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
- NeuroCure Cluster of Excellence, Berlin 10117, Germany
- Bernstein Center for Computational Neuroscience (BCCN) Berlin, Berlin 10115, Germany
- Einstein Center for Neurosciences (ECN) Berlin, Berlin 10117, Germany
- Max-Delbrück-Centrum (MDC) for Molecular Medicine, Berlin 13125, Germany
| | - Marta Orlando
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
- NeuroCure Cluster of Excellence, Berlin 10117, Germany
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Markert SM, Skoruppa M, Yu B, Mulcahy B, Zhen M, Gao S, Sendtner M, Stigloher C. Overexpression of an ALS-associated FUS mutation in C. elegans disrupts NMJ morphology and leads to defective neuromuscular transmission. Biol Open 2020; 9:bio055129. [PMID: 33148607 PMCID: PMC7746668 DOI: 10.1242/bio.055129] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/27/2020] [Indexed: 12/26/2022] Open
Abstract
The amyotrophic lateral sclerosis (ALS) neurodegenerative disorder has been associated with multiple genetic lesions, including mutations in the gene for fused in sarcoma (FUS), a nuclear-localized RNA/DNA-binding protein. Neuronal expression of the pathological form of FUS proteins in Caenorhabditis elegans results in mislocalization and aggregation of FUS in the cytoplasm, and leads to impairment of motility. However, the mechanisms by which the mutant FUS disrupts neuronal health and function remain unclear. Here we investigated the impact of ALS-associated FUS on motor neuron health using correlative light and electron microscopy, electron tomography, and electrophysiology. We show that ectopic expression of wild-type or ALS-associated human FUS impairs synaptic vesicle docking at neuromuscular junctions. ALS-associated FUS led to the emergence of a population of large, electron-dense, and filament-filled endosomes. Electrophysiological recording revealed reduced transmission from motor neurons to muscles. Together, these results suggest a pathological effect of ALS-causing FUS at synaptic structure and function organization.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Sebastian M Markert
- University of Würzburg, Biocenter, Imaging Core Facility, Am Hubland, Würzburg 97074, Germany
| | - Michael Skoruppa
- University Hospital Würzburg, Institute of Clinical Neurobiology, Versbacherstraße 5, 97080 Würzburg, Germany
| | - Bin Yu
- Huazhong University of Science and Technology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Wuhan 430074, China
| | - Ben Mulcahy
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Mei Zhen
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
- University of Toronto, Department of Molecular Genetics, Physiology and Institute of Medical Science, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada
| | - Shangbang Gao
- Huazhong University of Science and Technology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Wuhan 430074, China
| | - Michael Sendtner
- University Hospital Würzburg, Institute of Clinical Neurobiology, Versbacherstraße 5, 97080 Würzburg, Germany
| | - Christian Stigloher
- University of Würzburg, Biocenter, Imaging Core Facility, Am Hubland, Würzburg 97074, Germany
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8
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Gómez-de-Mariscal E, Maška M, Kotrbová A, Pospíchalová V, Matula P, Muñoz-Barrutia A. Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images. Sci Rep 2019; 9:13211. [PMID: 31519998 PMCID: PMC6744556 DOI: 10.1038/s41598-019-49431-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 08/05/2019] [Indexed: 02/07/2023] Open
Abstract
Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.
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Affiliation(s)
- Estibaliz Gómez-de-Mariscal
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain
| | - Martin Maška
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - Anna Kotrbová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 611 37, Czech Republic
| | - Vendula Pospíchalová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, 611 37, Czech Republic
| | - Pavel Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - Arrate Muñoz-Barrutia
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain.
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9
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Kaltdorf KV, Theiss M, Markert SM, Zhen M, Dandekar T, Stigloher C, Kollmannsberger P. Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning. PLoS One 2018; 13:e0205348. [PMID: 30296290 PMCID: PMC6175533 DOI: 10.1371/journal.pone.0205348] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 09/24/2018] [Indexed: 11/18/2022] Open
Abstract
Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.
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Affiliation(s)
- Kristin Verena Kaltdorf
- Imaging Core Facility, Biocenter, University of Würzburg, Würzburg, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | - Maria Theiss
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
| | | | - Mei Zhen
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- * E-mail: (PK); (CS); (TD)
| | - Christian Stigloher
- Imaging Core Facility, Biocenter, University of Würzburg, Würzburg, Germany
- * E-mail: (PK); (CS); (TD)
| | - Philip Kollmannsberger
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany
- * E-mail: (PK); (CS); (TD)
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Baldwin PR, Tan YZ, Eng ET, Rice WJ, Noble AJ, Negro CJ, Cianfrocco MA, Potter CS, Carragher B. Big data in cryoEM: automated collection, processing and accessibility of EM data. Curr Opin Microbiol 2017; 43:1-8. [PMID: 29100109 DOI: 10.1016/j.mib.2017.10.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 09/27/2017] [Accepted: 10/09/2017] [Indexed: 11/24/2022]
Abstract
The scope and complexity of cryogenic electron microscopy (cryoEM) data has greatly increased, and will continue to do so, due to recent and ongoing technical breakthroughs that have led to much improved resolutions for macromolecular structures solved using this method. This big data explosion includes single particle data as well as tomographic tilt series, both generally acquired as direct detector movies of ∼10-100 frames per image or per tilt-series. We provide a brief survey of the developments leading to the current status, and describe existing cryoEM pipelines, with an emphasis on the scope of data acquisition, methods for automation, and use of cloud storage and computing.
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Affiliation(s)
- Philip R Baldwin
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Yong Zi Tan
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Edward T Eng
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - William J Rice
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Alex J Noble
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Carl J Negro
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Michael A Cianfrocco
- Life Sciences Institute and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Clinton S Potter
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Bridget Carragher
- The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
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