1
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Kolotuev I. Work smart, not hard: How array tomography can help increase the ultrastructure data output. J Microsc 2024; 295:42-60. [PMID: 37626455 DOI: 10.1111/jmi.13217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
Transmission electron microscopy has been essential for understanding cell biology for over six decades. Volume electron microscopy tools, such as serial block face and focused ion beam scanning electron microscopy acquisition, brought a new era to ultrastructure analysis. 'Array Tomography' (AT) refers to sequential image acquisition of resin-embedded sample sections on a large support (coverslip, glass slide, silicon wafers) for immunolabelling with multiple fluorescent labels, occasionally combined with ultrastructure observation. Subsequently, the term was applied to generating and imaging a series of sections to acquire a 3D representation of a structure using scanning electron microscopy (SEM). Although this is a valuable application, the potential of AT is to facilitate many tasks that are difficult or even impossible to obtain by Transmission Electron Microscopy (TEM). Due to the straightforward nature and versatility of AT sample preparation and image acquisition, the technique can be applied practically to any biological sample for selected sections or volume electron microscopy analysis. Furthermore, in addition to the benefits described here, AT is compatible with morphological analysis, multiplex immunolabelling, immune-gold labelling, and correlative light and electron microscopy workflow applicable for single cells, tissue and small organisms. This versatility makes AT attractive not only for basic research but as a diagnostic tool with a simplified routine.
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Affiliation(s)
- Irina Kolotuev
- Electron Microscopy Facility, University of Lausanne, Lausanne, Switzerland
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2
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Torres R, Takasaki K, Gliko O, Laughland C, Yu WQ, Turschak E, Hellevik A, Balaram P, Perlman E, Sümbül U, Reid RC. A scalable and modular computational pipeline for axonal connectomics: automated tracing and assembly of axons across serial sections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.11.598365. [PMID: 38915568 PMCID: PMC11195148 DOI: 10.1101/2024.06.11.598365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Progress in histological methods and in microscope technology has enabled dense staining and imaging of axons over large brain volumes, but tracing axons over such volumes requires new computational tools for 3D reconstruction of data acquired from serial sections. We have developed a computational pipeline for automated tracing and volume assembly of densely stained axons imaged over serial sections, which leverages machine learning-based segmentation to enable stitching and alignment with the axon traces themselves. We validated this segmentation-driven approach to volume assembly and alignment of individual axons over centimeter-scale serial sections and show the application of the output traces for analysis of local orientation and for proofreading over aligned volumes. The pipeline is scalable, and combined with recent advances in experimental approaches, should enable new studies of mesoscale connectivity and function over the whole human brain.
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3
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Nkosi NC, Basson AK, Ntombela ZG, Dlamini NG, Pullabhotla RVSR. Green Synthesis, Characterization and Application of Silver Nanoparticles Using Bioflocculant: A Review. Bioengineering (Basel) 2024; 11:492. [PMID: 38790359 PMCID: PMC11117625 DOI: 10.3390/bioengineering11050492] [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: 04/26/2024] [Revised: 05/08/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
Nanotechnology has emerged as an effective means of removing contaminants from water. Traditional techniques for producing nanoparticles, such as physical methods (condensation and evaporation) and chemical methods (oxidation and reduction), have demonstrated high efficiency. However, these methods come with certain drawbacks, including the significant energy requirement and the use of costly and hazardous chemicals that may cause nanoparticles to adhere to surfaces. To address these limitations, researchers are actively developing alternative procedures that are cost-effective, environmentally safe, and user-friendly. One promising approach involves biological synthesis, which utilizes plants or microorganisms as reducing and capping agents. This review discusses various methods of nanoparticle synthesis, with a focus on biological synthesis using naturally occurring bioflocculants from microorganisms. Bioflocculants offer several advantages, including harmlessness, biodegradability, and minimal secondary pollution. Furthermore, the review covers the characterization of synthesized nanoparticles, their antimicrobial activity, and cytotoxicity. Additionally, it explores the utilization of these NPs in water purification and dye removal processes.
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Affiliation(s)
- Nkanyiso C. Nkosi
- Biochemistry and Microbiology Department, Faculty of Science, Agriculture, and Engineering, P/Bag X1001, University of Zululand, KwaDlangezwa 3886, South Africa; (A.K.B.); (Z.G.N.); (N.G.D.)
| | - Albertus K. Basson
- Biochemistry and Microbiology Department, Faculty of Science, Agriculture, and Engineering, P/Bag X1001, University of Zululand, KwaDlangezwa 3886, South Africa; (A.K.B.); (Z.G.N.); (N.G.D.)
| | - Zuzingcebo G. Ntombela
- Biochemistry and Microbiology Department, Faculty of Science, Agriculture, and Engineering, P/Bag X1001, University of Zululand, KwaDlangezwa 3886, South Africa; (A.K.B.); (Z.G.N.); (N.G.D.)
| | - Nkosinathi G. Dlamini
- Biochemistry and Microbiology Department, Faculty of Science, Agriculture, and Engineering, P/Bag X1001, University of Zululand, KwaDlangezwa 3886, South Africa; (A.K.B.); (Z.G.N.); (N.G.D.)
| | - Rajasekhar V. S. R. Pullabhotla
- Chemistry Department, Faculty of Science, Agriculture, and Engineering, P/Bag X1001, University of Zululand, KwaDlangezwa 3886, South Africa
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4
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Müller A, Schmidt D, Albrecht JP, Rieckert L, Otto M, Galicia Garcia LE, Fabig G, Solimena M, Weigert M. Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nat Protoc 2024; 19:1436-1466. [PMID: 38424188 DOI: 10.1038/s41596-024-00957-5] [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: 03/07/2023] [Accepted: 11/24/2023] [Indexed: 03/02/2024]
Abstract
Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.
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Affiliation(s)
- Andreas Müller
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany.
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany.
- German Center for Diabetes Research, Neuherberg, Germany.
| | - Deborah Schmidt
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
| | - Jan Philipp Albrecht
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany
| | - Lucas Rieckert
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Maximilian Otto
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Leticia Elizabeth Galicia Garcia
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Gunar Fabig
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Dresden, Dresden, Germany
| | - Michele Solimena
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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5
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Celii B, Papadopoulos S, Ding Z, Fahey PG, Wang E, Papadopoulos C, Kunin AB, Patel S, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yu SC, Yin W, Xenes D, Kitchell LM, Rivlin PK, Rose VA, Bishop CA, Wester B, Froudarakis E, Walker EY, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Pitkow X, Tolias AS, Reimer J. NEURD: automated proofreading and feature extraction for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532674. [PMID: 36993282 PMCID: PMC10055177 DOI: 10.1101/2023.03.14.532674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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6
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Wang Y, Wang Y, Zhang X, Du J, Zhang T, Xu B. Brain topology improved spiking neural network for efficient reinforcement learning of continuous control. Front Neurosci 2024; 18:1325062. [PMID: 38694900 PMCID: PMC11062182 DOI: 10.3389/fnins.2024.1325062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/27/2024] [Indexed: 05/04/2024] Open
Abstract
The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.
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Affiliation(s)
- Yongjian Wang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yansong Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xinhe Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Tielin Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
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7
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Son R, Yamazawa K, Oguchi A, Suga M, Tamura M, Yanagita M, Murakawa Y, Kume S. Morphomics via next-generation electron microscopy. J Mol Cell Biol 2024; 15:mjad081. [PMID: 38148118 PMCID: PMC11167312 DOI: 10.1093/jmcb/mjad081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/02/2022] [Accepted: 12/23/2023] [Indexed: 12/28/2023] Open
Abstract
The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.
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Affiliation(s)
- Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kenji Yamazawa
- Advanced Manufacturing Support Team, RIKEN Center for Advanced Photonics, Wako 351-0198, Japan
| | - Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mitsuo Suga
- Multimodal Microstructure Analysis Unit, RIKEN–JEOL Collaboration Center, Kobe 650-0047, Japan
| | - Masaru Tamura
- Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Research Center, Tsukuba 305-0074, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
- IFOM—The FIRC Institute of Molecular Oncology, Milan 20139, Italy
| | - Satoshi Kume
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan
- Center for Health Science Innovation, Osaka City University, Osaka 530-0011, Japan
- Osaka Electro-Communication University, Neyagawa 572-8530, Japan
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8
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Cano-Astorga N, Plaza-Alonso S, Turegano-Lopez M, Rodrigo-Rodríguez J, Merchan-Perez A, DeFelipe J. Unambiguous identification of asymmetric and symmetric synapses using volume electron microscopy. Front Neuroanat 2024; 18:1348032. [PMID: 38645671 PMCID: PMC11026665 DOI: 10.3389/fnana.2024.1348032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/08/2024] [Indexed: 04/23/2024] Open
Abstract
The brain contains thousands of millions of synapses, exhibiting diverse structural, molecular, and functional characteristics. However, synapses can be classified into two primary morphological types: Gray's type I and type II, corresponding to Colonnier's asymmetric (AS) and symmetric (SS) synapses, respectively. AS and SS have a thick and thin postsynaptic density, respectively. In the cerebral cortex, since most AS are excitatory (glutamatergic), and SS are inhibitory (GABAergic), determining the distribution, size, density, and proportion of the two major cortical types of synapses is critical, not only to better understand synaptic organization in terms of connectivity, but also from a functional perspective. However, several technical challenges complicate the study of synapses. Potassium ferrocyanide has been utilized in recent volume electron microscope studies to enhance electron density in cellular membranes. However, identifying synaptic junctions, especially SS, becomes more challenging as the postsynaptic densities become thinner with increasing concentrations of potassium ferrocyanide. Here we describe a protocol employing Focused Ion Beam Milling and Scanning Electron Microscopy for studying brain tissue. The focus is on the unequivocal identification of AS and SS types. To validate SS observed using this protocol as GABAergic, experiments with immunocytochemistry for the vesicular GABA transporter were conducted on fixed mouse brain tissue sections. This material was processed with different concentrations of potassium ferrocyanide, aiming to determine its optimal concentration. We demonstrate that using a low concentration of potassium ferrocyanide (0.1%) improves membrane visualization while allowing unequivocal identification of synapses as AS or SS.
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Affiliation(s)
- Nicolás Cano-Astorga
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- PhD Program in Neuroscience, Autonoma de Madrid University-Cajal Institute, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergio Plaza-Alonso
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Turegano-Lopez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - José Rodrigo-Rodríguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Angel Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
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9
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Schneider-Mizell CM, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Elabbady L, Gamlin C, Kapner D, Kinn S, Mahalingam G, Seshamani S, Suckow S, Takeno M, Torres R, Yin W, Dorkenwald S, Bae JA, Castro MA, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Reimer J, Tolias AS, Seung HS, Reid RC, Collman F, Maçarico da Costa N. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.23.525290. [PMID: 36747710 PMCID: PMC9900837 DOI: 10.1101/2023.01.23.525290] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA
| | | | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA
| | | | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Kisuk Lee
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology
| | - Kai Li
- Computer Science Department, Princeton University
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
- Department of Electrical and Computer Engineering, Rice University
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA
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10
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Popovych S, Macrina T, Kemnitz N, Castro M, Nehoran B, Jia Z, Bae JA, Mitchell E, Mu S, Trautman ET, Saalfeld S, Li K, Seung HS. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat Commun 2024; 15:289. [PMID: 38177169 PMCID: PMC10767115 DOI: 10.1038/s41467-023-44354-0] [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: 04/08/2022] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
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Affiliation(s)
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Kai Li
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Computer Science Department, Princeton University, Princeton, NJ, USA.
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11
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Fiedler KR, Olszta MJ, Yano KH, Doty C, Hopkins D, Akers S, Spurgeon SR. Evaluating Stage Motion for Automated Electron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1931-1939. [PMID: 37832144 DOI: 10.1093/micmic/ozad108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 08/15/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
Precise control is an essential and elusive quality of emerging self-driving transmission electron microscopes (TEMs). It is widely understood these instruments must be capable of performing rapid, high-volume, and arbitrary movements for practical self-driving operation. However, stage movements are difficult to automate at scale, owing to mechanical instability, hysteresis, and thermal drift. Such difficulties pose major barriers to artificial intelligence-directed microscope designs that require repeatable, precise movements. To guide design of emerging instruments, it is necessary to understand the behavior of existing mechanisms to identify rate limiting steps for full autonomy. Here, we describe a general framework to evaluate stage motion in any TEM. We define metrics to evaluate stage degrees of freedom, propose solutions to improve performance, and comment on fundamental limits to automated experimentation using present hardware.
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Affiliation(s)
- Kevin R Fiedler
- College of Arts and Sciences, Washington State University-Tri-Cities, Richland, WA 99354, USA
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Matthew J Olszta
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Kayla H Yano
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Christina Doty
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Derek Hopkins
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Sarah Akers
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Steven R Spurgeon
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Department of Physics, University of Washington, Seattle, WA 98195, USA
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12
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Venkadesh S, Santarelli A, Boesen T, Dong HW, Ascoli GA. Combinatorial quantification of distinct neural projections from retrograde tracing. Nat Commun 2023; 14:7271. [PMID: 37949860 PMCID: PMC10638408 DOI: 10.1038/s41467-023-43124-2] [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: 01/28/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. A source region projecting to n targets could have 2n-1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k target regions (k < n), one can experimentally count the cells expressing different color combinations in the source region. The neuronal counts for different color combinations from n-choose-k experiments provide constraints for a model that is robustly solvable using evolutionary algorithms. Here, we demonstrate this method's reliability for 4 targets using simulated triple injection experiments. Furthermore, we illustrate the experimental application of this framework by quantifying the projections of male mouse primary motor cortex to the primary and secondary somatosensory and motor cortices.
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Affiliation(s)
- Siva Venkadesh
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, 22030, USA
| | - Anthony Santarelli
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90089, USA
| | - Giorgio A Ascoli
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, 22030, USA.
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, 22030, USA.
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13
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Kievits AJ, Duinkerken BHP, Fermie J, Lane R, Giepmans BNG, Hoogenboom JP. Optical STEM detection for scanning electron microscopy. Ultramicroscopy 2023; 256:113877. [PMID: 37931528 DOI: 10.1016/j.ultramic.2023.113877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/08/2023]
Abstract
Recent advances in electron microscopy techniques have led to a significant scale up in volumetric imaging of biological tissue. The throughput of electron microscopes, however, remains a limiting factor for the volume that can be imaged in high resolution within reasonable time. Faster detection methods will improve throughput. Here, we have characterized and benchmarked a novel detection technique for scanning electron microscopy: optical scanning transmission electron microscopy (OSTEM). A qualitative and quantitative comparison was performed between OSTEM, secondary and backscattered electron detection and annular dark field detection in scanning transmission electron microscopy. Our analysis shows that OSTEM produces images similar to backscattered electron detection in terms of contrast, resolution and signal-to-noise ratio. OSTEM can complement large scale imaging with (scanning) transmission electron microscopy and has the potential to speed up imaging in single-beam scanning electron microscope.
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Affiliation(s)
- Arent J Kievits
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
| | - B H Peter Duinkerken
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Ryan Lane
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jacob P Hoogenboom
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
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14
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Karlupia N, Schalek RL, Wu Y, Meirovitch Y, Wei D, Charney AW, Kopell BH, Lichtman JW. Immersion Fixation and Staining of Multicubic Millimeter Volumes for Electron Microscopy-Based Connectomics of Human Brain Biopsies. Biol Psychiatry 2023; 94:352-360. [PMID: 36740206 PMCID: PMC10397365 DOI: 10.1016/j.biopsych.2023.01.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023]
Abstract
Connectomics allows mapping of cells and their circuits at the nanometer scale in volumes of approximately 1 mm3. Given that the human cerebral cortex can be 3 mm in thickness, larger volumes are required. Larger-volume circuit reconstructions of human brain are limited by 1) the availability of fresh biopsies; 2) the need for excellent preservation of ultrastructure, including extracellular space; and 3) the requirement of uniform staining throughout the sample, among other technical challenges. Cerebral cortical samples from neurosurgical patients are available owing to lead placement for deep brain stimulation. Described here is an immersion fixation, heavy metal staining, and tissue processing method that consistently provides excellent ultrastructure throughout human and rodent surgical brain samples of volumes 2 × 2 × 2 mm3 and up to 37 mm3 with one dimension ≤2 mm. This method should allow synapse-level circuit analysis in samples from patients with psychiatric and neurologic disorders.
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Affiliation(s)
- Neha Karlupia
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts.
| | - Richard L Schalek
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Donglai Wei
- Department of Computer Science, Boston College, Boston, Massachusetts
| | | | - Brian H Kopell
- Center for Neuromodulation, Department of Neurosurgery, The Icahn School of Medicine, Mount Sinai, New York, New York
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts.
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15
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Chen B, Kayiranga A, Ge M, Ciais P, Zhang H, Black A, Xiao X, Yuan W, Zeng Z, Piao S. Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century. GLOBAL CHANGE BIOLOGY 2023; 29:3421-3432. [PMID: 36949006 DOI: 10.1111/gcb.16688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/11/2023] [Indexed: 05/16/2023]
Abstract
The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.
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Affiliation(s)
- Baozhang Chen
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, China
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, 210023, Nanjing, China
| | - Alphonse Kayiranga
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
- University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Mengyu Ge
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, 210044, Nanjing, China
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Huifang Zhang
- State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Andy Black
- Faculty of Land and Food Systems, University of British Columbia, British Columbia, Vancouver, Canada
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Oklahoma, Norman, USA
| | - Wenping Yuan
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai Key Laboratory of Dynamics Urban Climate and Ecology, Sun Yat-sen University, 510245, Guangdong, Zhuhai, China
| | - Zhenzhong Zeng
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, 100871, Beijing, China
- Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 100085, Beijing, China
- Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, 100085, Beijing, China
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16
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Song K, Feng Z, Helmstaedter M. High-contrast en bloc staining of mouse whole-brain and human brain samples for EM-based connectomics. Nat Methods 2023:10.1038/s41592-023-01866-3. [PMID: 37156843 DOI: 10.1038/s41592-023-01866-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/27/2023] [Indexed: 05/10/2023]
Abstract
Connectomes of human cortical gray matter require high-contrast homogeneously stained samples sized at least 2 mm on a side, and a mouse whole-brain connectome requires samples sized at least 5-10 mm on a side. Here we report en bloc staining and embedding protocols for these and other applications, removing a key obstacle for connectomic analyses at the mammalian whole-brain level.
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Affiliation(s)
- Kun Song
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Zhihui Feng
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
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17
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Ding Z, Fahey PG, Papadopoulos S, Wang EY, Celii B, Papadopoulos C, Kunin AB, Chang A, Fu J, Ding Z, Patel S, Ponder K, Muhammad T, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Froudarakis E, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Walker EY, Pitkow X, Reimer J, Tolias AS. Functional connectomics reveals general wiring rule in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.531369. [PMID: 36993398 PMCID: PMC10054929 DOI: 10.1101/2023.03.13.531369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.
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Affiliation(s)
- Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Eric Y Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Brendan Celii
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Christos Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Alexander B Kunin
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Mathematics, Creighton University, Omaha, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Manuel A Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Erick Cobos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dan Kapner
- Allen Institute for Brain Science, Seattle, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sam Kinn
- Allen Institute for Brain Science, Seattle, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, USA
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Emmanouil Froudarakis
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Fabian Sinz
- Institute for Bioinformatics and Medical Informatics, University Tübingen, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, USA
| | - Edgar Y Walker
- Department of Physiology and Biophysics, University of Washington, Seattle, USA
- Computational Neuroscience Center, University of Washington, Seattle, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
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18
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Gamlin CR, Schneider-Mizell CM, Mallory M, Elabbady L, Gouwens N, Williams G, Mukora A, Dalley R, Bodor A, Brittain D, Buchanan J, Bumbarger D, Kapner D, Kinn S, Mahalingam G, Seshamani S, Takeno M, Torres R, Yin W, Nicovich PR, Bae JA, Castro MA, Dorkenwald S, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Yu S, Berg J, Jarsky T, Lee B, Seung HS, Zeng H, Reid RC, Collman F, da Costa NM, Sorensen SA. Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533857. [PMID: 36993629 PMCID: PMC10055412 DOI: 10.1101/2023.03.22.533857] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types 1 . Neural cell types have previously been defined by morphology 2, 3 , electrophysiology 4, 5 , transcriptomic expression 6-8 , connectivity 9-13 , or even a combination of such modalities 14-16 . More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20 . Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21 . It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25 , were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.
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19
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Wilson A, Babadi M. SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning. PATTERNS 2023; 4:100693. [PMID: 37123442 PMCID: PMC10140600 DOI: 10.1016/j.patter.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 01/25/2023] [Indexed: 03/09/2023]
Abstract
3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.
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20
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Steinmann SN, Wang Q, Seh ZW. How machine learning can accelerate electrocatalysis discovery and optimization. MATERIALS HORIZONS 2023; 10:393-406. [PMID: 36541226 DOI: 10.1039/d2mh01279k] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.
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Affiliation(s)
| | - Qing Wang
- Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Chimie UMR 5182, Lyon, France.
| | - Zhi Wei Seh
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, 138634, Singapore.
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21
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Conrad R, Narayan K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset. Cell Syst 2023; 14:58-71.e5. [PMID: 36657391 PMCID: PMC9883049 DOI: 10.1016/j.cels.2022.12.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/10/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 106 image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan Conrad
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda 20892, Maryland, USA.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick 21702, Maryland, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda 20892, Maryland, USA.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick 21702, Maryland, USA
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22
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Venkadesh S, Santarelli A, Boesen T, Dong H, Ascoli GA. Combinatorial quantification of distinct neural projections from retrograde tracing. RESEARCH SQUARE 2023:rs.3.rs-2454289. [PMID: 36711802 PMCID: PMC9882684 DOI: 10.21203/rs.3.rs-2454289/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify the distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. For a source region projecting to n targets, there are 2n - 1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k regions (k < n), one can experimentally count the cells expressing different combinations of colors in the source region1,2. Such an experiment can be performed for n choose k combinations. The counts of cells with different color combinations from all experiments provide constraints for a system of equations that include 2n - 1 unknown variables, each corresponding to the count of neurons for a projection pattern. Evolutionary algorithms prove to be effective at solving the resultant system of equations, thus allowing the determination of the counts of neurons with each of the possible projection patterns. Numerical analysis of simulated 4 choose 3 retrograde injection experiments using surrogate data demonstrates reliable and precise count estimates for all projection neuron types. We illustrate the experimental application of this framework by quantifying the projections of mouse primary motor cortex to four prominent targets: the primary and secondary somatosensory and motor cortices.
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Affiliation(s)
- Siva Venkadesh
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, Virginia 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, Virginia 22030, USA
| | - Anthony Santarelli
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Hongwei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90089, USA
| | - Giorgio A. Ascoli
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, Virginia 22030, USA
- Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, Virginia 22030, USA
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23
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Sheridan A, Nguyen TM, Deb D, Lee WCA, Saalfeld S, Turaga SC, Manor U, Funke J. Local shape descriptors for neuron segmentation. Nat Methods 2023; 20:295-303. [PMID: 36585455 PMCID: PMC9911350 DOI: 10.1038/s41592-022-01711-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/01/2022] [Indexed: 12/31/2022]
Abstract
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
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Affiliation(s)
- Arlo Sheridan
- grid.443970.dHHMI Janelia, Ashburn, VA USA ,grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
| | - Tri M. Nguyen
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | | | - Wei-Chung Allen Lee
- grid.38142.3c000000041936754XF.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
| | | | | | - Uri Manor
- grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
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24
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Abstract
Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.
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25
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Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Wong W, Castro M, Jordan CS, Wilson AM, Froudarakis E, Buchanan J, Takeno MM, Torres R, Mahalingam G, Collman F, Schneider-Mizell CM, Bumbarger DJ, Li Y, Becker L, Suckow S, Reimer J, Tolias AS, Macarico da Costa N, Reid RC, Seung HS. Binary and analog variation of synapses between cortical pyramidal neurons. eLife 2022; 11:e76120. [PMID: 36382887 PMCID: PMC9704804 DOI: 10.7554/elife.76120] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Agnes L Bodor
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain ScienceSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | | | - Yang Li
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lynne Becker
- Allen Institute for Brain ScienceSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | | | - R Clay Reid
- Allen Institute for Brain ScienceSeattleUnited States
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
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26
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Wang Y, LeDue JM, Murphy TH. Multiscale imaging informs translational mouse modeling of neurological disease. Neuron 2022; 110:3688-3710. [PMID: 36198319 DOI: 10.1016/j.neuron.2022.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/26/2022] [Accepted: 09/06/2022] [Indexed: 11/05/2022]
Abstract
Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.
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Affiliation(s)
- Yundi Wang
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
| | - Jeffrey M LeDue
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
| | - Timothy H Murphy
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada.
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27
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Conze C, Trushina NI, Holtmannspötter M, Rierola M, Attanasio S, Bakota L, Piehler J, Brandt R. Super-resolution imaging and quantitative analysis of microtubule arrays in model neurons show that epothilone D increases the density but decreases the length and straightness of microtubules in axon-like processes. Brain Res Bull 2022; 190:234-243. [PMID: 36244582 PMCID: PMC9634454 DOI: 10.1016/j.brainresbull.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/03/2022] [Accepted: 10/06/2022] [Indexed: 11/29/2022]
Abstract
Microtubules are essential for the development of neurons and the regulation of their structural plasticity. Microtubules also provide the structural basis for the long-distance transport of cargo. Various factors influence the organization and dynamics of neuronal microtubules, and disturbance of microtubule regulation is thought to play a central role in neurodegenerative diseases. However, imaging and quantitative assessment of the microtubule organization in the densely packed neuronal processes is challenging. The development of super-resolution techniques combined with the use of nanobodies offers new possibilities to visualize microtubules in neurites in high resolution. In combination with recently developed computational analysis tools, this allows automated quantification of neuronal microtubule organization with high precision. Here we have implemented three-dimensional DNA-PAINT (Point Accumulation in Nanoscale Topography), a single-molecule localization microscopy (SMLM) technique, which allows us to acquire 3D arrays of the microtubule lattice in axons of model neurons (neuronally differentiated PC12 cells) and dendrites of primary neurons. For the quantitative analysis of the microtubule organization, we used the open-source software package SMLM image filament extractor (SIFNE). We found that treatment with nanomolar concentrations of the microtubule-targeting drug epothilone D (EpoD) increased microtubule density in axon-like processes of model neurons and shifted the microtubule length distribution to shorter ones, with a mean microtubule length of 2.39 µm (without EpoD) and 1.98 µm (with EpoD). We also observed a significant decrease in microtubule straightness after EpoD treatment. The changes in microtubule density were consistent with live-cell imaging measurements of ensemble microtubule dynamics using a previously established Fluorescence Decay After Photoactivation (FDAP) assay. For comparison, we determined the organization of the microtubule array in dendrites of primary hippocampal neurons. We observed that dendritic microtubules have a very similar length distribution and straightness compared to microtubules in axon-like processes of a neuronal cell line. Our data show that super-resolution imaging of microtubules followed by algorithm-based image analysis represents a powerful tool to quantitatively assess changes in microtubule organization in neuronal processes, useful to determine the effect of microtubule-modulating conditions. We also provide evidence that the approach is robust and can be applied to neuronal cell lines or primary neurons, both after incorporation of labeled tubulin and by anti-tubulin antibody staining.
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Affiliation(s)
- Christian Conze
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | | | | | - Marina Rierola
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Simone Attanasio
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Lidia Bakota
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Jacob Piehler
- Center for Cellular Nanoanalytics, Osnabrück University, Osnabrück, Germany; Division of Biophysics, Osnabrück University, Osnabrück, Germany
| | - Roland Brandt
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany; Center for Cellular Nanoanalytics, Osnabrück University, Osnabrück, Germany; Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany.
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28
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Hong B, Liu J, Zhai H, Liu J, Shen L, Chen X, Xie Q, Han H. Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes. BMC Bioinformatics 2022; 23:453. [PMID: 36316652 PMCID: PMC9623997 DOI: 10.1186/s12859-022-04991-6] [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: 05/25/2022] [Accepted: 10/17/2022] [Indexed: 11/14/2022] Open
Abstract
Background Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. Results In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. Conclusions We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.
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Affiliation(s)
- Bei Hong
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Liu
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Zhai
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jiazheng Liu
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lijun Shen
- grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Chen
- grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qiwei Xie
- grid.28703.3e0000 0000 9040 3743Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology, Beijing, China
| | - Hua Han
- grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China ,grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China ,grid.507732.4CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
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29
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Chen X, Kuner T, Blanpied TA. Editorial: Quantifying and controlling the nano-architecture of neuronal synapses. Front Synaptic Neurosci 2022; 14:1024073. [PMID: 36160915 PMCID: PMC9491271 DOI: 10.3389/fnsyn.2022.1024073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Xiaobing Chen
- Laboratory of Neurobiology, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Xiaobing Chen
| | - Thomas Kuner
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
- Thomas Kuner
| | - Thomas A. Blanpied
- School of Medicine, University of Maryland, Baltimore, MD, United States
- Thomas A. Blanpied
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30
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Xu M, Kumar A, LeBeau JM. Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:1-9. [PMID: 36062363 DOI: 10.1017/s1431927622012193] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.
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Affiliation(s)
- Michael Xu
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Abinash Kumar
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James M LeBeau
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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31
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Matejek B, Franzmeyer T, Wei D, Wang X, Zhao J, Palagyi K, Lichtman JW, Pfister H. Scalable Biologically-Aware Skeleton Generation for Connectomic Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2360-2370. [PMID: 35377840 DOI: 10.1109/tmi.2022.3164343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
As connectomic datasets exceed hundreds of terabytes in size, accurate and efficient skeleton generation of the label volumes has evolved into a critical component of the computation pipeline used for analysis, evaluation, visualization, and error correction. We propose a novel topological thinning strategy that uses biological-constraints to produce accurate centerlines from segmented neuronal volumes while still maintaining biologically relevant properties. Current methods are either agnostic to the underlying biology, have non-linear running times as a function of the number of input voxels, or both. First, we eliminate from the input segmentation biologically-infeasible bubbles, pockets of voxels incorrectly labeled within a neuron, to improve segmentation accuracy, allow for more accurate centerlines, and increase processing speed. Next, a Convolutional Neural Network (CNN) detects cell bodies from the input segmentation, allowing us to anchor our skeletons to the somata. Lastly, a synapse-aware topological thinning approach produces expressive skeletons for each neuron with a nearly one-to-one correspondence between endpoints and synapses. We simultaneously estimate geometric properties of neurite width and geodesic distance between synapse and cell body, improving accuracy by 47.5% and 62.8% over baseline methods. We separate the skeletonization process into a series of computation steps, leveraging data-parallel strategies to increase throughput significantly. We demonstrate our results on over 1250 neurons and neuron fragments from three different species, processing over one million voxels per second per CPU with linear scalability.
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Mahalingam G, Torres R, Kapner D, Trautman ET, Fliss T, Seshamani S, Perlman E, Young R, Kinn S, Buchanan J, Takeno MM, Yin W, Bumbarger DJ, Gwinn RP, Nyhus J, Lein E, Smith SJ, Reid RC, Khairy KA, Saalfeld S, Collman F, Macarico da Costa N. A scalable and modular automated pipeline for stitching of large electron microscopy datasets. eLife 2022; 11:76534. [PMID: 35880860 PMCID: PMC9427110 DOI: 10.7554/elife.76534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 07/10/2022] [Indexed: 11/13/2022] Open
Abstract
Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 108 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.
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Affiliation(s)
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, United States
| | - Daniel Kapner
- Allen Institute for Brain Science, Seattle, United States
| | - Eric T Trautman
- Scientific Computing, Janelia Research Campus, Ashburn, United States
| | - Tim Fliss
- Allen Institute for Brain Science, Seattle, United States
| | | | | | - Rob Young
- Allen Institute for Brain Science, Seattle, United States
| | - Samuel Kinn
- Allen Institute for Brain Science, Seattle, United States
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, United States
| | - Marc M Takeno
- Allen Institute for Brain Science, Seattle, United States
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, United States
| | | | - Ryder P Gwinn
- Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, United States
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, United States
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, United States
| | - Steven J Smith
- Allen Institute for Brain Science, Seattle, United States
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, United States
| | - Khaled A Khairy
- St. Jude Children's Research Hospital, Memphis, United States
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Kievits AJ, Lane R, Carroll EC, Hoogenboom JP. How innovations in methodology offer new prospects for volume electron microscopy. J Microsc 2022; 287:114-137. [PMID: 35810393 PMCID: PMC9546337 DOI: 10.1111/jmi.13134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 11/29/2022]
Abstract
Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.
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Affiliation(s)
- Arent J. Kievits
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
| | - Ryan Lane
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
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Peddie CJ, Genoud C, Kreshuk A, Meechan K, Micheva KD, Narayan K, Pape C, Parton RG, Schieber NL, Schwab Y, Titze B, Verkade P, Aubrey A, Collinson LM. Volume electron microscopy. NATURE REVIEWS. METHODS PRIMERS 2022; 2:51. [PMID: 37409324 PMCID: PMC7614724 DOI: 10.1038/s43586-022-00131-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 07/07/2023]
Abstract
Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
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Affiliation(s)
- Christopher J. Peddie
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | - Christel Genoud
- Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Kimberly Meechan
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Constantin Pape
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert G. Parton
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicole L. Schieber
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Yannick Schwab
- Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Aubrey Aubrey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy M. Collinson
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
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nGauge: Integrated and Extensible Neuron Morphology Analysis in Python. Neuroinformatics 2022; 20:755-764. [PMID: 35247136 PMCID: PMC9720862 DOI: 10.1007/s12021-022-09573-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge's data compatibility.
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Olszta M, Hopkins D, Fiedler KR, Oostrom M, Akers S, Spurgeon SR. An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:1-11. [PMID: 35686442 DOI: 10.1017/s1431927622012065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.
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Affiliation(s)
- Matthew Olszta
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Derek Hopkins
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Kevin R Fiedler
- College of Arts and Sciences, Washington State University - Tri-Cities, Richland, WA 99354, USA
| | - Marjolein Oostrom
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Sarah Akers
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Steven R Spurgeon
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Department of Physics, University of Washington, Seattle, WA 98195, USA
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Kent RS, Briggs EM, Colon BL, Alvarez C, Silva Pereira S, De Niz M. Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research. Front Cell Infect Microbiol 2022; 12:900878. [PMID: 35734575 PMCID: PMC9207352 DOI: 10.3389/fcimb.2022.900878] [Citation(s) in RCA: 2] [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: 03/21/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.
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Affiliation(s)
- Robyn S. Kent
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, United States
| | - Emma M. Briggs
- Institute for Immunology and Infection Research, School of Biological Sciences, University Edinburgh, Edinburgh, United Kingdom
- Wellcome Centre for Integrative Parasitology, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Beatrice L. Colon
- Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Catalina Alvarez
- de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Sara Silva Pereira
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Mariana De Niz
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- Institut Pasteur, Paris, France
- *Correspondence: Mariana De Niz,
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38
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Harris KM, Hubbard DD, Kuwajima M, Abraham WC, Bourne JN, Bowden JB, Haessly A, Mendenhall JM, Parker PH, Shi B, Spacek J. Dendritic Spine Density Scales with Microtubule Number in Rat Hippocampal Dendrites. Neuroscience 2022; 489:84-97. [PMID: 35218884 PMCID: PMC9038701 DOI: 10.1016/j.neuroscience.2022.02.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 12/14/2022]
Abstract
Microtubules deliver essential resources to and from synapses. Three-dimensional reconstructions in rat hippocampus reveal a sampling bias regarding spine density that needs to be controlled for dendrite caliber and resource delivery based on microtubule number. The strength of this relationship varies across dendritic arbors, as illustrated for area CA1 and dentate gyrus. In both regions, proximal dendrites had more microtubules than distal dendrites. For CA1 pyramidal cells, spine density was greater on thicker than thinner dendrites in stratum radiatum, or on the more uniformly thin terminal dendrites in stratum lacunosum moleculare. In contrast, spine density was constant across the cone shaped arbor of tapering dendrites from dentate granule cells. These differences suggest that thicker dendrites supply microtubules to subsequent dendritic branches and local dendritic spines, whereas microtubules in thinner dendrites need only provide resources to local spines. Most microtubules ran parallel to dendrite length and associated with long, presumably stable mitochondria, which occasionally branched into lateral dendritic branches. Short, presumably mobile, mitochondria were tethered to microtubules that bent and appeared to direct them into a thin lateral branch. Prior work showed that dendritic segments with the same number of microtubules had elevated resources in subregions of their dendritic shafts where spine synapses had enlarged, and spine clusters had formed. Thus, additional microtubules were not required for redistribution of resources locally to growing spines or synapses. These results provide new understanding about the potential for microtubules to regulate resource delivery to and from dendritic branches and locally among dendritic spines.
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Affiliation(s)
- Kristen M Harris
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States.
| | - Dusten D Hubbard
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Masaaki Kuwajima
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Wickliffe C Abraham
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Jennifer N Bourne
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Jared B Bowden
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Andrea Haessly
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - John M Mendenhall
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Patrick H Parker
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Bitao Shi
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Josef Spacek
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
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Chang S, Shen L, Li L, Chen X, Han H. Denoising of scanning electron microscope images for biological ultrastructure enhancement. J Bioinform Comput Biol 2022; 20:2250007. [PMID: 35469552 DOI: 10.1142/s021972002250007x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. Source code is available on GitHub (https://github.com/VictorCSheng/VSID-Net).
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Affiliation(s)
- Sheng Chang
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Lijun Shen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100190, P. R. China.,The Center for Excellence in Brain, Science and Intelligence Technology, CAS, Shanghai 200031, P. R. China.,National Laboratory of Pattern Recognition, CASIA, Beijing 100190, P. R. China
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40
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Turegano-Lopez M, Santuy A, Kastanauskaite A, Rodriguez JR, DeFelipe J, Merchan-Perez A. Single-Neuron Labeling in Fixed Tissue and Targeted Volume Electron Microscopy. Front Neuroanat 2022; 16:852057. [PMID: 35528948 PMCID: PMC9070053 DOI: 10.3389/fnana.2022.852057] [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: 01/10/2022] [Accepted: 03/15/2022] [Indexed: 11/16/2022] Open
Abstract
The structural complexity of nervous tissue makes it very difficult to unravel the connectivity between neural elements at different scales. Numerous methods are available to trace long-range projections at the light microscopic level, and to identify the actual synaptic connections at the electron microscopic level. However, correlating mesoscopic and nanoscopic scales in the same cell, cell population or brain region is a problematic, laborious and technically demanding task. Here we present an effective method for the 3D reconstruction of labeled subcellular structures at the ultrastructural level, after single-neuron labeling in fixed tissue. The brain is fixed by intracardial perfusion of aldehydes and thick vibratome sections (250 μm) are obtained. Single cells in these vibratome sections are intracellularly injected with horseradish peroxidase (HRP), so that the cell body and its processes can be identified. The thick sections are later flat-embedded in epoxy resin and re-sectioned into a series of thinner (7 μm) sections. The sections containing the regions of interest of the labeled cells are then imaged with automated focused ion beam milling and scanning electron microscopy (FIB-SEM), acquiring long series of high-resolution images that can be reconstructed, visualized, and analyzed in 3D. With this methodology, we can accurately select any cellular segment at the light microscopic level (e.g., proximal, intermediate or distal dendrites, collateral branches, axonal segments, etc.) and analyze its synaptic connections at the electron microscopic level, along with other ultrastructural features. Thus, this method not only facilitates the mapping of the synaptic connectivity of single-labeled neurons, but also the analysis of the surrounding neuropil. Since the labeled processes can be located at different layers or subregions, this method can also be used to obtain data on the differences in local synaptic organization that may exist at different portions of the labeled neurons.
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Affiliation(s)
- Marta Turegano-Lopez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
- Ph.D. Program in Neuroscience, Universidad Autónoma de Madrid – Instituto Cajal, Madrid, Spain
| | - Andrea Santuy
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Asta Kastanauskaite
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Jose-Rodrigo Rodriguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain
| | - Angel Merchan-Perez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, Spain
- Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
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Bonney SK, Coelho-Santos V, Huang SF, Takeno M, Kornfeld J, Keller A, Shih AY. Public Volume Electron Microscopy Data: An Essential Resource to Study the Brain Microvasculature. Front Cell Dev Biol 2022; 10:849469. [PMID: 35450291 PMCID: PMC9016339 DOI: 10.3389/fcell.2022.849469] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/21/2022] [Indexed: 01/09/2023] Open
Abstract
Electron microscopy is the primary approach to study ultrastructural features of the cerebrovasculature. However, 2D snapshots of a vascular bed capture only a small fraction of its complexity. Recent efforts to synaptically map neuronal circuitry using volume electron microscopy have also sampled the brain microvasculature in 3D. Here, we perform a meta-analysis of 7 data sets spanning different species and brain regions, including two data sets from the MICrONS consortium that have made efforts to segment vasculature in addition to all parenchymal cell types in mouse visual cortex. Exploration of these data have revealed rich information for detailed investigation of the cerebrovasculature. Neurovascular unit cell types (including, but not limited to, endothelial cells, mural cells, perivascular fibroblasts, microglia, and astrocytes) could be discerned across broad microvascular zones. Image contrast was sufficient to identify subcellular details, including endothelial junctions, caveolae, peg-and-socket interactions, mitochondria, Golgi cisternae, microvilli and other cellular protrusions of potential significance to vascular signaling. Additionally, non-cellular structures including the basement membrane and perivascular spaces were visible and could be traced between arterio-venous zones along the vascular wall. These explorations revealed structural features that may be important for vascular functions, such as blood-brain barrier integrity, blood flow control, brain clearance, and bioenergetics. They also identified limitations where accuracy and consistency of segmentation could be further honed by future efforts. The purpose of this article is to introduce these valuable community resources within the framework of cerebrovascular research. We do so by providing an assessment of their vascular contents, identifying features of significance for further study, and discussing next step ideas for refining vascular segmentation and analysis.
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Affiliation(s)
- Stephanie K. Bonney
- Center for Developmental Biology and Regenerative Medicine, Seattle Children’s Research Institute, Seattle, WA, United States
| | - Vanessa Coelho-Santos
- Center for Developmental Biology and Regenerative Medicine, Seattle Children’s Research Institute, Seattle, WA, United States
| | - Sheng-Fu Huang
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Neuroscience Center Zürich, University of Zürich and ETH Zürich, Zürich, Switzerland
| | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA, United States
| | | | - Annika Keller
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
- Neuroscience Center Zürich, University of Zürich and ETH Zürich, Zürich, Switzerland
- *Correspondence: Annika Keller, ; Andy Y. Shih,
| | - Andy Y. Shih
- Center for Developmental Biology and Regenerative Medicine, Seattle Children’s Research Institute, Seattle, WA, United States
- Department of Pediatrics, University of Washington, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- *Correspondence: Annika Keller, ; Andy Y. Shih,
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Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains. Neuroinformatics 2022; 20:525-536. [PMID: 35182359 DOI: 10.1007/s12021-022-09569-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2022] [Indexed: 01/04/2023]
Abstract
Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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Wu J, Turner N, Bae JA, Vishwanathan A, Seung HS. RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations. Front Neuroinform 2022; 16:828169. [PMID: 35311003 PMCID: PMC8924549 DOI: 10.3389/fninf.2022.828169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/30/2022] Open
Abstract
Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.
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Affiliation(s)
- Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- *Correspondence: Jingpeng Wu,
| | - Nicholas Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Computer Science, Princeton University, Princeton, NJ, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, United States
| | - Ashwin Vishwanathan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
- Department of Computer Science, Princeton University, Princeton, NJ, United States
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45
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Zhang Z, Wu Y, Lin N, Yin S, Meng Z. Monitoring Clinical-Pathological Grading of Hepatocellular Carcinoma Using MicroRNA-Guided Semiconducting Polymer Dots. ACS APPLIED MATERIALS & INTERFACES 2022; 14:7717-7730. [PMID: 35112844 DOI: 10.1021/acsami.1c24191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNAs (miRNAs) are a class of small, noncoding RNAs involved in nearly all genetic central dogma processes and human biological behavior, which also play a significant role in the pathological activity of tumors, such as gene transcription, protein translation, and exosome secretion. Therefore, through the navigation of certain specific miRNAs, we can trace the specific physiological processes or image some specific tissues. Designing and accurately positioning microRNA (miRNA)-sensitive fluorescent nanoprobes with benign specificity and recognition in cells or tissues are a challenging research field. To solve the difficulties, we introduce four semiconducting polymer dots (Pdots) as nanoprobes linked by specific miRNA antisense sequences for monitoring the pathological grading by the variation in miRNA expression. Based on the base pairing principle, these miRNA-sensitive Pdots could bind to specific miRNAs within the cancerous cells. As impacted by the background of different pathology gradings, the proportions of the four hepatocellular carcinoma (HCC)-specific miRNAs within the cancerous cell are different, and the pathological grading of the patient tissues can be determined by comparing the palette combinations. The short single-stranded RNA-functionalized Pdots, which have excellent microRNA sensitivity, are observed in an experimental cell model and a series of tissue specimens from HCC patients for the first time. Using the Förster (or fluorescence) resonance energy transfer (FRET) model of Pdots and Cy3dt tag to simulate in vivo miRNA detection, the superior sensitivity and specificity of these nanoprobes are verified. The interference of subjective factors in traditional single/bis-dye emission intensity detection is abandoned, and multiple label staining is used to enhance sensitivity further and reduce the false-positive rate. The feasibility exhibited by this novel staining method is verified in normal hepatocellular HCC cell lines and 16 frozen ultrathin tissue sections, which are employed to quantify pathological grading-related color presentation systems for clinical doctors and pathologists' use. The intelligently designed miRNA-guided Pdots will emerge as an ideal platform with promising biological imaging.
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Affiliation(s)
- Ze Zhang
- Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University, Jilin University, No. 126 Xiantai Street, Changchun, Jilin 130000, P. R. China
| | - Yuyang Wu
- State Key Laboratory of Integrated Optoelectronic, College of Electronic Science and Engineering, Jilin University, No. 2699 Qianjin Street, Changchun 130012, P. R. China
| | - Nan Lin
- Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, No. 2699 Qianjin Street, Changchun 130012, P. R. China
| | - Shengyan Yin
- State Key Laboratory of Integrated Optoelectronic, College of Electronic Science and Engineering, Jilin University, No. 2699 Qianjin Street, Changchun 130012, P. R. China
| | - Zihui Meng
- Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University, Jilin University, No. 126 Xiantai Street, Changchun, Jilin 130000, P. R. China
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46
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Friedrichsen K, Ramakrishna P, Hsiang JC, Valkova K, Kerschensteiner D, Morgan JL. Reconstructing neural circuits using multiresolution correlated light and electron microscopy. Front Neural Circuits 2022; 16:753496. [PMID: 36338333 PMCID: PMC9635852 DOI: 10.3389/fncir.2022.753496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Correlated light and electron microscopy (CLEM) can be used to combine functional and molecular characterizations of neurons with detailed anatomical maps of their synaptic organization. Here we describe a multiresolution approach to CLEM (mrCLEM) that efficiently targets electron microscopy (EM) imaging to optically characterized cells while maintaining optimal tissue preparation for high-throughput EM reconstruction. This approach hinges on the ease with which arrays of sections collected on a solid substrate can be repeatedly imaged at different scales using scanning electron microscopy. We match this multiresolution EM imaging with multiresolution confocal mapping of the aldehyde-fixed tissue. Features visible in lower resolution EM correspond well to features visible in densely labeled optical maps of fixed tissue. Iterative feature matching, starting with gross anatomical correspondences and ending with subcellular structure, can then be used to target high-resolution EM image acquisition and annotation to cells of interest. To demonstrate this technique and range of images used to link live optical imaging to EM reconstructions, we provide a walkthrough of a mouse retinal light to EM experiment as well as some examples from mouse brain slices.
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Affiliation(s)
- Karl Friedrichsen
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Pratyush Ramakrishna
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Jen-Chun Hsiang
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Katia Valkova
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Daniel Kerschensteiner
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Josh L Morgan
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
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47
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Wildenberg G, Sorokina A, Koranda J, Monical A, Heer C, Sheffield M, Zhuang X, McGehee D, Kasthuri B. Partial connectomes of labeled dopaminergic circuits reveal non-synaptic communication and axonal remodeling after exposure to cocaine. eLife 2021; 10:71981. [PMID: 34965204 PMCID: PMC8716107 DOI: 10.7554/elife.71981] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/29/2021] [Indexed: 12/15/2022] Open
Abstract
Dopaminergic (DA) neurons exert profound influences on behavior including addiction. However, how DA axons communicate with target neurons and how those communications change with drug exposure remains poorly understood. We leverage cell type-specific labeling with large volume serial electron microscopy to detail DA connections in the nucleus accumbens (NAc) of the mouse (Mus musculus) before and after exposure to cocaine. We find that individual DA axons contain different varicosity types based on their vesicle contents. Spatially ordering along individual axons further suggests that varicosity types are non-randomly organized. DA axon varicosities rarely make specific synapses (<2%, 6/410), but instead are more likely to form spinule-like structures (15%, 61/410) with neighboring neurons. Days after a brief exposure to cocaine, DA axons were extensively branched relative to controls, formed blind-ended 'bulbs' filled with mitochondria, and were surrounded by elaborated glia. Finally, mitochondrial lengths increased by ~2.2 times relative to control only in DA axons and NAc spiny dendrites after cocaine exposure. We conclude that DA axonal transmission is unlikely to be mediated via classical synapses in the NAc and that the major locus of anatomical plasticity of DA circuits after exposure to cocaine are large-scale axonal re-arrangements with correlated changes in mitochondria.
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Affiliation(s)
- Gregg Wildenberg
- Department of Neurobiology, University of Chicago, Chicago, United States.,Argonne National Laboratory, Lemont, United States
| | - Anastasia Sorokina
- Department of Neurobiology, University of Chicago, Chicago, United States.,Argonne National Laboratory, Lemont, United States
| | - Jessica Koranda
- Department of Neurobiology, University of Chicago, Chicago, United States
| | - Alexis Monical
- Department of Anesthesia & Critical Care, University of Chicago, Chicago, United States
| | - Chad Heer
- Department of Neurobiology, University of Chicago, Chicago, United States
| | - Mark Sheffield
- Department of Neurobiology, University of Chicago, Chicago, United States
| | - Xiaoxi Zhuang
- Department of Neurobiology, University of Chicago, Chicago, United States
| | - Daniel McGehee
- Department of Anesthesia & Critical Care, University of Chicago, Chicago, United States
| | - Bobby Kasthuri
- Department of Neurobiology, University of Chicago, Chicago, United States.,Argonne National Laboratory, Lemont, United States
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48
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Schneider-Mizell CM, Bodor AL, Collman F, Brittain D, Bleckert A, Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Zhuang J, Nandi A, Hu B, Buchanan J, Takeno MM, Torres R, Mahalingam G, Bumbarger DJ, Li Y, Chartrand T, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Popovych S, Wong W, Castro M, Jordan CS, Froudarakis E, Becker L, Suckow S, Reimer J, Tolias AS, Anastassiou CA, Seung HS, Reid RC, da Costa NM. Structure and function of axo-axonic inhibition. eLife 2021; 10:e73783. [PMID: 34851292 PMCID: PMC8758143 DOI: 10.7554/elife.73783] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
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Affiliation(s)
| | - Agnes L Bodor
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Adam Bleckert
- Allen Institute for Brain SciencesSeattleUnited States
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jun Zhuang
- Allen Institute for Brain SciencesSeattleUnited States
| | - Anirban Nandi
- Allen Institute for Brain SciencesSeattleUnited States
| | - Brian Hu
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain SciencesSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Yang Li
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Lynne Becker
- Allen Institute for Brain SciencesSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain SciencesSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | - Costas A Anastassiou
- Allen Institute for Brain SciencesSeattleUnited States
- Department of Neurology, University of British ColumbiaVancouverCanada
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - R Clay Reid
- Allen Institute for Brain SciencesSeattleUnited States
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49
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Lewczuk B, Szyryńska N. Field-Emission Scanning Electron Microscope as a Tool for Large-Area and Large-Volume Ultrastructural Studies. Animals (Basel) 2021; 11:ani11123390. [PMID: 34944167 PMCID: PMC8698110 DOI: 10.3390/ani11123390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Ultrastructural studies of cells and tissues are usually performed using transmission electron microscopy (TEM), which enables imaging at the highest possible resolution. The weak point of TEM is the limited ability to analyze the ultrastructure of large areas and volumes of biological samples. This limitation can be overcome by using modern field-emission scanning electron microscopy (FE-SEM) with high-sensitivity detection, which enables the creation of TEM-like images from the flat surfaces of resin-embedded biological specimens. Several FE-SEM-based techniques for two- and three-dimensional ultrastructural studies of cells, tissues, organs, and organisms have been developed in the 21st century. These techniques have created a new era in structural biology and have changed the role of the scanning electron microscope (SEM) in biological and medical laboratories. Since the premiere of the first commercially available SEM in 1965, these instruments were used almost exclusively to obtain topographical information over a large range of magnifications. Currently, FE-SEM offers many attractive possibilities in the studies of cell and tissue ultrastructure, and they are presented in this review. Abstract The development of field-emission scanning electron microscopes for high-resolution imaging at very low acceleration voltages and equipped with highly sensitive detectors of backscattered electrons (BSE) has enabled transmission electron microscopy (TEM)-like imaging of the cut surfaces of tissue blocks, which are impermeable to the electron beam, or tissue sections mounted on the solid substrates. This has resulted in the development of methods that simplify and accelerate ultrastructural studies of large areas and volumes of biological samples. This article provides an overview of these methods, including their advantages and disadvantages. The imaging of large sample areas can be performed using two methods based on the detection of transmitted electrons or BSE. Effective imaging using BSE requires special fixation and en bloc contrasting of samples. BSE imaging has resulted in the development of volume imaging techniques, including array tomography (AT) and serial block-face imaging (SBF-SEM). In AT, serial ultrathin sections are collected manually on a solid substrate such as a glass and silicon wafer or automatically on a tape using a special ultramicrotome. The imaging of serial sections is used to obtain three-dimensional (3D) information. SBF-SEM is based on removing the top layer of a resin-embedded sample using an ultramicrotome inside the SEM specimen chamber and then imaging the exposed surface with a BSE detector. The steps of cutting and imaging the resin block are repeated hundreds or thousands of times to obtain a z-stack for 3D analyses.
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50
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Ivanchenko MV, Indzhykulian AA, Corey DP. Electron Microscopy Techniques for Investigating Structure and Composition of Hair-Cell Stereociliary Bundles. Front Cell Dev Biol 2021; 9:744248. [PMID: 34746139 PMCID: PMC8569945 DOI: 10.3389/fcell.2021.744248] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/28/2021] [Indexed: 11/18/2022] Open
Abstract
Hair cells—the sensory cells of the vertebrate inner ear—bear at their apical surfaces a bundle of actin-filled protrusions called stereocilia, which mediate the cells’ mechanosensitivity. Hereditary deafness is often associated with morphological disorganization of stereocilia bundles, with the absence or mislocalization within stereocilia of specific proteins. Thus, stereocilia bundles are closely examined to understand most animal models of hereditary hearing loss. Because stereocilia have a diameter less than a wavelength of light, light microscopy is not adequate to reveal subtle changes in morphology or protein localization. Instead, electron microscopy (EM) has proven essential for understanding stereocilia bundle development, maintenance, normal function, and dysfunction in disease. Here we review a set of EM imaging techniques commonly used to study stereocilia, including optimal sample preparation and best imaging practices. These include conventional and immunogold transmission electron microscopy (TEM) and scanning electron microscopy (SEM), as well as focused-ion-beam scanning electron microscopy (FIB-SEM), which enables 3-D serial reconstruction of resin-embedded biological structures at a resolution of a few nanometers. Parameters for optimal sample preparation, fixation, immunogold labeling, metal coating and imaging are discussed. Special attention is given to protein localization in stereocilia using immunogold labeling. Finally, we describe the advantages and limitations of these EM techniques and their suitability for different types of studies.
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Affiliation(s)
- Maryna V Ivanchenko
- Department of Neurobiology, Harvard Medical School, Boston, MA, United States
| | - Artur A Indzhykulian
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States
| | - David P Corey
- Department of Neurobiology, Harvard Medical School, Boston, MA, United States
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