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Zheng Z, Own CS, Wanner AA, Koene RA, Hammerschmith EW, Silversmith WM, Kemnitz N, Lu R, Tank DW, Seung HS. Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy. Nat Commun 2024; 15:6860. [PMID: 39127683 PMCID: PMC11316758 DOI: 10.1038/s41467-024-50846-4] [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: 12/15/2022] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.
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Affiliation(s)
- Zhihao Zheng
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Paul Scherrer Institute, Villigen, Switzerland
| | | | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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2
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Galaz-Montoya JG. The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography. Front Mol Biosci 2024; 11:1390858. [PMID: 38868297 PMCID: PMC11167099 DOI: 10.3389/fmolb.2024.1390858] [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: 02/24/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024] Open
Abstract
Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.
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Affiliation(s)
- Jesús G. Galaz-Montoya
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, United States
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3
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Fahy K, Kapishnikov S, Donnellan M, McEnroe T, O'Reilly F, Fyans W, Sheridan P. Laboratory based correlative cryo-soft X-ray tomography and cryo-fluorescence microscopy. Methods Cell Biol 2024; 187:293-320. [PMID: 38705628 DOI: 10.1016/bs.mcb.2024.02.033] [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] [Indexed: 05/07/2024]
Abstract
Cryo-soft X-ray tomography is the unique technology that can image whole intact cells in 3D under normal and pathological conditions without labelling or fixation, at high throughput and spatial resolution. The sample preparation is relatively straightforward; requiring just fast freezing of the specimen before transfer to the microscope for imaging. It is also possible to image chemically fixed samples where necessary. The technique can be correlated with cryo fluorescence microscopy to localize fluorescent proteins to organelles within the whole cell volume. Cryo-correlated light and soft X-ray tomography is particularly useful for the study of gross morphological changes brought about by disease or drugs. For example, viral fluorescent tags can be co-localized to sites of viral replication in the soft X-ray volume. In general this approach is extremely useful in the study of complex 3D organelle structure, nanoparticle uptake or in the detection of rare events in the context of whole cell structure. The main challenge of soft X-ray tomography is that the soft X-ray illumination required for imaging has heretofore only been available at a small number of synchrotron labs worldwide. Recently, a compact device with a footprint small enough to fit in a standard laboratory setting has been deployed ("the SXT-100") and is routinely imaging cryo prepared samples addressing a variety of disease and drug research applications. The SXT-100 facilitates greater access to this powerful technique and greatly increases the scope and throughput of potential research projects. Furthermore, the availability of cryo-soft X-ray tomography in the laboratory will accelerate the development of novel correlative and multimodal workflows by integration with light and electron microscope based approaches. It also allows for co-location of this powerful imaging modality at BSL3 labs or other facilities where safety or intellectual property considerations are paramount. Here we describe the compact SXT-100 microscope along with its novel integrated cryo-fluorescence imaging capability.
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Affiliation(s)
- Kenneth Fahy
- SiriusXT Ltd., Stillorgan Industrial Park, Dublin, Ireland.
| | | | | | - Tony McEnroe
- SiriusXT Ltd., Stillorgan Industrial Park, Dublin, Ireland
| | - Fergal O'Reilly
- SiriusXT Ltd., Stillorgan Industrial Park, Dublin, Ireland; University College Dublin, School of Physics, Dublin, Ireland; University College Dublin, School of Biology and Environmental Sciences, Dublin, Ireland
| | - William Fyans
- SiriusXT Ltd., Stillorgan Industrial Park, Dublin, Ireland
| | - Paul Sheridan
- SiriusXT Ltd., Stillorgan Industrial Park, Dublin, Ireland
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4
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Schubert PJ, Saxena R, Kornfeld J. DeepFocus: fast focus and astigmatism correction for electron microscopy. Nat Commun 2024; 15:948. [PMID: 38296974 PMCID: PMC10830472 DOI: 10.1038/s41467-024-45042-3] [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: 04/29/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
High-throughput 2D and 3D scanning electron microscopy, which relies on automation and dependable control algorithms, requires high image quality with minimal human intervention. Classical focus and astigmatism correction algorithms attempt to explicitly model image formation and subsequently aberration correction. Such models often require parameter adjustments by experts when deployed to new microscopes, challenging samples, or imaging conditions to prevent unstable convergence, making them hard to use in practice or unreliable. Here, we introduce DeepFocus, a purely data-driven method for aberration correction in scanning electron microscopy. DeepFocus works under very low signal-to-noise ratio conditions, reduces processing times by more than an order of magnitude compared to the state-of-the-art method, rapidly converges within a large aberration range, and is easily recalibrated to different microscopes or challenging samples.
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Affiliation(s)
- P J Schubert
- Max Planck Institute for Biological Intelligence, Martinsried, 82152, Germany
| | - R Saxena
- Max Planck Institute for Biological Intelligence, Martinsried, 82152, Germany
| | - J Kornfeld
- Max Planck Institute for Biological Intelligence, Martinsried, 82152, Germany.
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5
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Holland ND, Holland LZ. Serial block-face scanning electron microscopy of the tail tip of post-metamorphic amphioxus finds novel myomeres with odd shapes and unusually prominent sclerocoels. J Morphol 2024; 285:e21667. [PMID: 38100741 DOI: 10.1002/jmor.21667] [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: 11/09/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
Serial block-face scanning electron microscopy of the tail tip of post-metamorphic amphioxus (Branchiostoma floridae) revealed some terminal myomeres never been seen before with other techniques. The morphology of these myomeres differed markedly from the chevron shapes of their more anterior counterparts. Histologically, these odd-shaped myomeres ranged from empty vesicles bordered by undifferentiated cells to ventral sacs composed of well-developed myotome, dermatome, and sclerotome. Strikingly, several of these ventral sacs gave rise to a nipple-like dorsal projection composed either entirely of sclerotome or a mixture of sclerotome and myotome. Considered as a whole, from posterior to anterior, these odd-shaped posterior myomeres suggested that their more substantial ventral part may represent the ventral limb of a chevron, while the delicate projection represents a nascent dorsal limb. This scenario contrasts with formation of chevron-shaped myomeres along most of the antero-posterior axis. Although typical chevron formation in amphioxus is surprisingly poorly studied, it seems to be attained by a dorso-ventral extension of the myomere accompanied by the assumption of a V-shape; this is similar to what happens (at least superficially) in developing fishes. Another unusual feature of the odd-shaped posterior myomeres of amphioxus is their especially distended sclerocoels. One possible function for these might be to protect the posterior end of the central nervous system from trauma when the animals burrow into the substratum.
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Affiliation(s)
- Nicholas D Holland
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California, USA
| | - Linda Z Holland
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California, USA
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6
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Boelts J, Harth P, Gao R, Udvary D, Yáñez F, Baum D, Hege HC, Oberlaender M, Macke JH. Simulation-based inference for efficient identification of generative models in computational connectomics. PLoS Comput Biol 2023; 19:e1011406. [PMID: 37738260 PMCID: PMC10550169 DOI: 10.1371/journal.pcbi.1011406] [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: 03/06/2023] [Revised: 10/04/2023] [Accepted: 08/01/2023] [Indexed: 09/24/2023] Open
Abstract
Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.
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Affiliation(s)
- Jan Boelts
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Philipp Harth
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Richard Gao
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Daniel Udvary
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Felipe Yáñez
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Daniel Baum
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Hans-Christian Hege
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Marcel Oberlaender
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Free University Amsterdam, Amsterdam, Netherlands
| | - Jakob H. Macke
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
- Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
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7
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Dorkenwald S, Schneider-Mizell CM, Brittain D, Halageri A, Jordan C, Kemnitz N, Castro MA, Silversmith W, Maitin-Shephard J, Troidl J, Pfister H, Gillet V, Xenes D, Bae JA, Bodor AL, Buchanan J, Bumbarger DJ, Elabbady L, Jia Z, Kapner D, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Reid RC, da Costa NM, Seung HS, Collman F. CAVE: Connectome Annotation Versioning Engine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550598. [PMID: 37546753 PMCID: PMC10402030 DOI: 10.1101/2023.07.26.550598] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.
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Affiliation(s)
- 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
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manual A. Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Valentin Gillet
- Lund University, Department of Biology, Lund Vision Group, Lund, Sweden
| | - Daniel Xenes
- Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, 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
| | | | | | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
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8
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Wen C, Matsumoto M, Sawada M, Sawamoto K, Kimura KD. Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks. Sci Rep 2023; 13:7109. [PMID: 37217545 DOI: 10.1038/s41598-023-34232-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/26/2023] [Indexed: 05/24/2023] Open
Abstract
Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks.
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Affiliation(s)
- Chentao Wen
- Graduate School of Science, Nagoya City University, Nagoya, Japan.
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Mami Matsumoto
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Masato Sawada
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Kazunobu Sawamoto
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
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Buswinka CJ, Nitta H, Osgood RT, Indzhykulian AA. SKOOTS: Skeleton oriented object segmentation for mitochondria. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.05.539611. [PMID: 37214838 PMCID: PMC10197543 DOI: 10.1101/2023.05.05.539611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The segmentation of individual instances of mitochondria from imaging datasets is informative, yet time-consuming to do by hand, sparking interest in developing automated algorithms using deep neural networks. Existing solutions for various segmentation tasks are largely optimized for one of two types of biomedical imaging: high resolution three-dimensional (whole neuron segmentation in volumetric electron microscopy datasets) or two-dimensional low resolution (whole cell segmentation of light microscopy images). The former requires consistently predictable boundaries to segment large structures, while the latter is boundary invariant but struggles with segmentation of large 3D objects without downscaling. Mitochondria in whole cell 3D EM datasets often occupy the challenging middle ground: large with ambiguous borders, limiting accuracy with existing tools. To rectify this, we have developed skeleton oriented object segmentation (SKOOTS); a new segmentation approach which efficiently handles large, densely packed mitochondria. We show that SKOOTS can accurately, and efficiently, segment 3D mitochondria in previously difficult situations. Furthermore, we will release a new, manually annotated, 3D mitochondria segmentation dataset. Finally, we show this approach can be extended to segment objects in 3D light microscopy datasets. These results bridge the gap between existing segmentation approaches and increases the accessibility for three-dimensional biomedical image analysis.
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Affiliation(s)
- Christopher J Buswinka
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, USA
| | - Hidetomi Nitta
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, USA
| | - Richard T Osgood
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Artur A Indzhykulian
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
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10
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Ji P, Wang Y, Peron T, Li C, Nagler J, Du J. Structure and function in artificial, zebrafish and human neural networks. Phys Life Rev 2023; 45:74-111. [PMID: 37182376 DOI: 10.1016/j.plrev.2023.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Abstract
Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.
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Affiliation(s)
- Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yufan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China
| | - Thomas Peron
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos 13566-590, São Paulo, Brazil.
| | - Chunhe Li
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
| | - Jan Nagler
- Deep Dynamics, Frankfurt School of Finance & Management, Frankfurt, Germany; Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management, Frankfurt, Germany
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.
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11
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Cordero Cervantes D, Khare H, Wilson AM, Mendoza ND, Coulon-Mahdi O, Lichtman JW, Zurzolo C. 3D reconstruction of the cerebellar germinal layer reveals tunneling connections between developing granule cells. SCIENCE ADVANCES 2023; 9:eadf3471. [PMID: 37018410 PMCID: PMC10075961 DOI: 10.1126/sciadv.adf3471] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/02/2023] [Indexed: 06/19/2023]
Abstract
The difficulty of retrieving high-resolution, in vivo evidence of the proliferative and migratory processes occurring in neural germinal zones has limited our understanding of neurodevelopmental mechanisms. Here, we used a connectomic approach using a high-resolution, serial-sectioning scanning electron microscopy volume to investigate the laminar cytoarchitecture of the transient external granular layer (EGL) of the developing cerebellum, where granule cells coordinate a series of mitotic and migratory events. By integrating image segmentation, three-dimensional reconstruction, and deep-learning approaches, we found and characterized anatomically complex intercellular connections bridging pairs of cerebellar granule cells throughout the EGL. Connected cells were either mitotic, migratory, or transitioning between these two cell stages, displaying a chronological continuum of proliferative and migratory events never previously observed in vivo at this resolution. This unprecedented ultrastructural characterization poses intriguing hypotheses about intercellular connectivity between developing progenitors and its possible role in the development of the central nervous system.
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Affiliation(s)
- Diégo Cordero Cervantes
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
- Université Paris-Saclay, 91405 Orsay, France
| | - Harshavardhan Khare
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
| | - Alyssa Michelle Wilson
- Department of Neurology, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nathaly Dongo Mendoza
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
- Research Center in Bioengineering, Universidad de Ingeniería y Tecnología-UTEC, Lima 15049, Peru
| | - Orfane Coulon-Mahdi
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
| | - Jeff William Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Chiara Zurzolo
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
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12
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Holland ND, Holland LZ. Cephalochordate Hemocytes: First Demonstration for Asymmetron lucayanum (Bahamas Lancelet) Plus Augmented Description for Branchiostoma floridae (Florida Amphioxus). THE BIOLOGICAL BULLETIN 2023; 244:71-81. [PMID: 37725696 DOI: 10.1086/726774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
AbstractWithin phylum Chordata, the subphylum Cephalochordata (amphioxus and lancelets) has figured large in considerations of the evolutionary origin of the vertebrates. To date, these discussions have been predominantly based on knowledge of a single cephalochordate genus (Branchiostoma), almost to the exclusion of the other two genera (Asymmetron and Epigonichthys). This uneven pattern is illustrated by cephalochordate hematology, until now known entirely from work done on Branchiostoma. The main part of the present study is to describe hemocytes in the dorsal aorta of a species of Asymmetron by serial block-face scanning electron microscopy. This technique, which demonstrates three-dimensional fine structure, showed that the hemocytes have a relatively uniform morphology characterized by an oval shape and scanty cytoplasm. Ancillary information is also included for Branchiostoma hemocytes, known from previous studies to have relatively abundant cytoplasm; our serial block-face scanning electron microscopy provides more comprehensive views of the highly variable shapes of these cells, which typically extend one or several pseudopodium-like protrusions. The marked difference in hemocyte morphology found between Asymmetron and Branchiostoma was unexpected and directs attention to investigating comparable cells in the genus Epigonichthys. A broader knowledge of the hemocytes in all three cephalochordate genera would provide more balanced insights into the evolution of vertebrate hematopoiesis.
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13
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 89] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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14
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Kislinger G, Niemann C, Rodriguez L, Jiang H, Fard MK, Snaidero N, Schumacher AM, Kerschensteiner M, Misgeld T, Schifferer M. Neurons on tape: Automated Tape Collecting Ultramicrotomy-mediated volume EM for targeting neuropathology. Methods Cell Biol 2023; 177:125-170. [PMID: 37451765 DOI: 10.1016/bs.mcb.2023.01.012] [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] [Indexed: 03/19/2023]
Abstract
In this chapter, we review Automated Tape Collecting Ultramicrotomy (ATUM), which, among other array tomography methods, substantially simplified large-scale volume electron microscopy (vEM) projects. vEM reveals biological structures at nanometer resolution in three dimensions and resolves ambiguities of two-dimensional representations. However, as the structures of interest-like disease hallmarks emerging from neuropathology-are often rare but the field of view is small, this can easily turn a vEM project into a needle in a haystack problem. One solution for this is correlated light and electron microscopy (CLEM), providing tissue context, dynamic and molecular features before switching to targeted vEM to hone in on the object's ultrastructure. This requires precise coordinate transfer between the two imaging modalities (e.g., by micro computed tomography), especially for block face vEM which relies on physical destruction of sections. With array tomography methods, serial ultrathin sections are collected into a tissue library, thus allowing storage of precious samples like human biopsies and enabling repetitive imaging at different resolution levels for an SEM-based search strategy. For this, ATUM has been developed to reliably collect serial ultrathin sections via a conveyor belt onto a plastic tape that is later mounted onto silicon wafers for serial scanning EM (SEM). The ATUM-SEM procedure is highly modular and can be divided into sample preparation, serial ultramicrotomy onto tape, mounting, serial image acquisition-after which the acquired image stacks can be used for analysis. Here, we describe the steps of this workflow and how ATUM-SEM enables targeting and high resolution imaging of specific structures. ATUM-SEM is widely applicable. To illustrate this, we exemplify the approach by reconstructions of focal pathology in an Alzheimer mouse model and CLEM of a specific cortical synapse.
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Affiliation(s)
- Georg Kislinger
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Cornelia Niemann
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Lucia Rodriguez
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Hanyi Jiang
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Maryam K Fard
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nicolas Snaidero
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; Hertie institute for Clinical Brain Research, Tuebingen University Hospital, Tuebingen, Germany
| | - Adrian-Minh Schumacher
- Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Martin Kerschensteiner
- Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Thomas Misgeld
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
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15
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Loconte V, Chen J, Vanslembrouck B, Ekman AA, McDermott G, Le Gros MA, Larabell CA. Soft X-ray tomograms provide a structural basis for whole-cell modeling. FASEB J 2023; 37:e22681. [PMID: 36519968 PMCID: PMC10107707 DOI: 10.1096/fj.202200253r] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/13/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
Developing in silico models that accurately reflect a whole, functional cell is an ongoing challenge in biology. Current efforts bring together mathematical models, probabilistic models, visual representations, and data to create a multi-scale description of cellular processes. A realistic whole-cell model requires imaging data since it provides spatial constraints and other critical cellular characteristics that are still impossible to obtain by calculation alone. This review introduces Soft X-ray Tomography (SXT) as a powerful imaging technique to visualize and quantify the mesoscopic (~25 nm spatial scale) organelle landscape in whole cells. SXT generates three-dimensional reconstructions of cellular ultrastructure and provides a measured structural framework for whole-cell modeling. Combining SXT with data from disparate technologies at varying spatial resolutions provides further biochemical details and constraints for modeling cellular mechanisms. We conclude, based on the results discussed here, that SXT provides a foundational dataset for a broad spectrum of whole-cell modeling experiments.
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Affiliation(s)
- Valentina Loconte
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Jian‐Hua Chen
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Bieke Vanslembrouck
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Axel A. Ekman
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Gerry McDermott
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Mark A. Le Gros
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
| | - Carolyn A. Larabell
- Department of AnatomyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Molecular Biophysics and Integrated Bioimaging DivisionLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
- National Center for X‐ray TomographyAdvanced Light SourceBerkeleyCaliforniaUSA
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16
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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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17
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Svara F, Förster D, Kubo F, Januszewski M, Dal Maschio M, Schubert PJ, Kornfeld J, Wanner AA, Laurell E, Denk W, Baier H. Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain. Nat Methods 2022; 19:1357-1366. [PMID: 36280717 PMCID: PMC9636024 DOI: 10.1038/s41592-022-01621-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 08/22/2022] [Indexed: 12/29/2022]
Abstract
Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm3. We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system.
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Affiliation(s)
- Fabian Svara
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
- ariadne.ai ag, Buchrain, Switzerland
| | - Dominique Förster
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Fumi Kubo
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Center for Frontier Research, National Institute of Genetics, Mishima, Japan
| | | | - Marco Dal Maschio
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Jörgen Kornfeld
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Paul Scherrer Institute (PSI), Villigen, Switzerland
| | - Eva Laurell
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Winfried Denk
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Herwig Baier
- Max Planck Institute for Biological Intelligence, Martinsried, Germany.
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18
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Schubert PJ, Dorkenwald S, Januszewski M, Klimesch J, Svara F, Mancu A, Ahmad H, Fee MS, Jain V, Kornfeld J. SyConn2: dense synaptic connectivity inference for volume electron microscopy. Nat Methods 2022; 19:1367-1370. [PMID: 36280715 PMCID: PMC9636020 DOI: 10.1038/s41592-022-01624-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 08/24/2022] [Indexed: 12/28/2022]
Abstract
The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.
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Affiliation(s)
| | - Sven Dorkenwald
- Max Planck Institute of Neurobiology, Martinsried, Germany
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Computer Science Department, Princeton, NJ, USA
| | | | | | - Fabian Svara
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
- ariadne.ai ag, Buchrain, Switzerland
| | - Andrei Mancu
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Hashir Ahmad
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Michale S Fee
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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19
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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] [Key Words] [MESH Headings] [Grants] [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
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Ryan Lane
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | | | - Jacob P. Hoogenboom
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
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20
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Holland ND, Holland LZ, Somorjai IML. Three-dimensional fine structure of fibroblasts and other mesodermally derived tissues in the dermis of adults of the Bahamas lancelet (Chordata, Cephalohordata), as seen by serial block-face scanning electron microscopy. J Morphol 2022; 283:1289-1298. [PMID: 35971624 DOI: 10.1002/jmor.21502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 11/07/2022]
Abstract
Tissues of adult cephalochordates include sparsely distributed fibroblasts. Previous work on these cells has left unsettled such questions as their developmental origin, range of functions, and even their overall shape. Here, we describe fibroblasts of a cephalochordate, the Bahamas lancelet, Asymmetron lucayanum, by serial block-face scanning electron microscopy to demonstrate their three-dimensional (3D) distribution and fine structure in a 0.56-mm length of the tail. The technique reveals in detail their position, abundance, and morphology. In the region studied, we found only 20 fibroblasts, well separated from one another. Each was strikingly stellate with long cytoplasmic processes rather similar to those of a vertebrate telocyte, a possibly fortuitous resemblance that is considered in the discussion section. In the cephalochordate dermis, the fibroblasts were never linked with one another, although they occasionally formed close associations of unknown significance with other cell types. The fibroblasts, in spite of their name, showed no signs of directly synthesizing fibrillar collagen. Instead, they appeared to be involved in the production of nonfibrous components of the extracellular matrix-both by the release of coarsely granular dense material and by secretion of more finely granular material by the local breakdown of their cytoplasmic processes. For context, the 3D structures of two other mesoderm-derived tissues (the midline mesoderm and the posteriormost somite) are also described for the region studied.
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Affiliation(s)
- Nicholas D Holland
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California, USA
| | - Linda Z Holland
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California, USA
| | - Ildiko M L Somorjai
- School of Biology, University of Saint Andrews, St. Andrews, Fife, Scotland, UK
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21
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Computational methods for ultrastructural analysis of synaptic complexes. Curr Opin Neurobiol 2022; 76:102611. [PMID: 35952541 DOI: 10.1016/j.conb.2022.102611] [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: 02/28/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/21/2022]
Abstract
Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.
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22
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Phoulady A, May N, Choi H, Suleiman Y, Shahbazmohamadi S, Tavousi P. Rapid high-resolution volumetric imaging via laser ablation delayering and confocal imaging. Sci Rep 2022; 12:12277. [PMID: 35853990 PMCID: PMC9296531 DOI: 10.1038/s41598-022-16519-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/11/2022] [Indexed: 12/03/2022] Open
Abstract
Acquiring detailed 3D images of samples is needed for conducting thorough investigations in a wide range of applications. Doing so using nondestructive methods such as X-ray computed tomography (X-ray CT) has resolution limitations. Destructive methods, which work based on consecutive delayering and imaging of the sample, face a tradeoff between throughput and resolution. Using focused ion beam (FIB) for delayering, although high precision, is low throughput. On the other hand, mechanical methods that can offer fast delayering, are low precision and may put the sample integrity at risk. Herein, we propose to use femtosecond laser ablation as a delayering method in combination with optical and confocal microscopy as the imaging technique for performing rapid 3D imaging. The use of confocal microscopy provides several advantages. First, it eliminates the 3D image distortion resulting from non-flat layers, caused by the difference in laser ablation rate of different materials. It further allows layer height variations to be maintained within a small range. Finally, it enables material characterization based on the processing of material ablation rate at different locations. The proposed method is applied on a printed circuit board (PCB), and the results are validated and compared with the X-ray CT image of the PCB part.
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23
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Ali MA, Bollmann JH. Motion vision: Course control in the developing visual system. Curr Biol 2022; 32:R520-R523. [PMID: 35671725 DOI: 10.1016/j.cub.2022.04.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
As we move around, the image pattern on our retina is constantly changing. Nervous systems have evolved to detect such global 'optic flow' patterns. A new study reveals how optic flow is encoded in the larval zebrafish brain and could be used for the estimation of self-motion.
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Affiliation(s)
- Mir Ahsan Ali
- Institute of Biology I, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Johann H Bollmann
- Institute of Biology I, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany; Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany.
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24
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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] [Key Words] [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
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25
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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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26
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Shi R, Wang W, Li Z, He L, Sheng K, Ma L, Du K, Jiang T, Huang T. U-RISC: An Annotated Ultra-High-Resolution Electron Microscopy Dataset Challenging the Existing Deep Learning Algorithms. Front Comput Neurosci 2022; 16:842760. [PMID: 35480847 PMCID: PMC9038176 DOI: 10.3389/fncom.2022.842760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/23/2022] [Indexed: 11/27/2022] Open
Abstract
Connectomics is a developing field aiming at reconstructing the connection of the neural system at the nanometer scale. Computer vision technology, especially deep learning methods used in image processing, has promoted connectomic data analysis to a new era. However, the performance of the state-of-the-art (SOTA) methods still falls behind the demand of scientific research. Inspired by the success of ImageNet, we present an annotated ultra-high resolution image segmentation dataset for cell membrane (U-RISC), which is the largest cell membrane-annotated electron microscopy (EM) dataset with a resolution of 2.18 nm/pixel. Multiple iterative annotations ensured the quality of the dataset. Through an open competition, we reveal that the performance of current deep learning methods still has a considerable gap from the human level, different from ISBI 2012, on which the performance of deep learning is closer to the human level. To explore the causes of this discrepancy, we analyze the neural networks with a visualization method, which is an attribution analysis. We find that the U-RISC requires a larger area around a pixel to predict whether the pixel belongs to the cell membrane or not. Finally, we integrate the currently available methods to provide a new benchmark (0.67, 10% higher than the leader of the competition, 0.61) for cell membrane segmentation on the U-RISC and propose some suggestions in developing deep learning algorithms. The U-RISC dataset and the deep learning codes used in this study are publicly available.
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Affiliation(s)
- Ruohua Shi
- Beijing Academy of Artificial Intelligence, Beijing, China
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
| | - Wenyao Wang
- Beijing Academy of Artificial Intelligence, Beijing, China
| | - Zhixuan Li
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
| | - Liuyuan He
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
| | - Kaiwen Sheng
- Beijing Academy of Artificial Intelligence, Beijing, China
| | - Lei Ma
- Beijing Academy of Artificial Intelligence, Beijing, China
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing, China
- *Correspondence: Kai Du
| | - Tingting Jiang
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
- Tingting Jiang
| | - Tiejun Huang
- Beijing Academy of Artificial Intelligence, Beijing, China
- National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China
- Institute for Artificial Intelligence, Peking University, Beijing, China
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27
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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] [Key Words] [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
| | - 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
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28
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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: 77] [Impact Index Per Article: 38.5] [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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29
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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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30
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Lane R, Wolters AHG, Giepmans BNG, Hoogenboom JP. Integrated Array Tomography for 3D Correlative Light and Electron Microscopy. Front Mol Biosci 2022; 8:822232. [PMID: 35127826 PMCID: PMC8809480 DOI: 10.3389/fmolb.2021.822232] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/15/2021] [Indexed: 12/22/2022] Open
Abstract
Volume electron microscopy (EM) of biological systems has grown exponentially in recent years due to innovative large-scale imaging approaches. As a standalone imaging method, however, large-scale EM typically has two major limitations: slow rates of acquisition and the difficulty to provide targeted biological information. We developed a 3D image acquisition and reconstruction pipeline that overcomes both of these limitations by using a widefield fluorescence microscope integrated inside of a scanning electron microscope. The workflow consists of acquiring large field of view fluorescence microscopy (FM) images, which guide to regions of interest for successive EM (integrated correlative light and electron microscopy). High precision EM-FM overlay is achieved using cathodoluminescent markers. We conduct a proof-of-concept of our integrated workflow on immunolabelled serial sections of tissues. Acquisitions are limited to regions containing biological targets, expediting total acquisition times and reducing the burden of excess data by tens or hundreds of GBs.
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Affiliation(s)
- Ryan Lane
- Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Anouk H. G. Wolters
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Ben N. G. Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, Netherlands
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31
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Furuta T, Yamauchi K, Okamoto S, Takahashi M, Kakuta S, Ishida Y, Takenaka A, Yoshida A, Uchiyama Y, Koike M, Isa K, Isa T, Hioki H. Multi-scale light microscopy/electron microscopy neuronal imaging from brain to synapse with a tissue clearing method, ScaleSF. iScience 2022; 25:103601. [PMID: 35106459 PMCID: PMC8786651 DOI: 10.1016/j.isci.2021.103601] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/19/2021] [Accepted: 12/06/2021] [Indexed: 12/02/2022] Open
Abstract
The mammalian brain is organized over sizes that span several orders of magnitude, from synapses to the entire brain. Thus, a technique to visualize neural circuits across multiple spatial scales (multi-scale neuronal imaging) is vital for deciphering brain-wide connectivity. Here, we developed this technique by coupling successive light microscopy/electron microscopy (LM/EM) imaging with a glutaraldehyde-resistant tissue clearing method, ScaleSF. Our multi-scale neuronal imaging incorporates (1) brain-wide macroscopic observation, (2) mesoscopic circuit mapping, (3) microscopic subcellular imaging, and (4) EM imaging of nanoscopic structures, allowing seamless integration of structural information from the brain to synapses. We applied this technique to three neural circuits of two different species, mouse striatofugal, mouse callosal, and marmoset corticostriatal projection systems, and succeeded in simultaneous interrogation of their circuit structure and synaptic connectivity in a targeted way. Our multi-scale neuronal imaging will significantly advance the understanding of brain-wide connectivity by expanding the scales of objects. Successive light microscopy/electron microscopy in optically cleared tissues Multi-scale neuronal imaging from the entire brain to individual synapses Simultaneous interrogation of neural circuit structure and synaptic connectivity Zooming-in to scarce synaptic contacts with the successive imaging
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Affiliation(s)
- Takahiro Furuta
- Department of Oral Anatomy and Neurobiology, Graduate School of Dentistry, Osaka University, Suita, Osaka 565-0871, Japan
| | - Kenta Yamauchi
- Department of Neuroanatomy, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Advanced Research Institute for Health Sciences, Juntendo University, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Shinichiro Okamoto
- Department of Neuroanatomy, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Advanced Research Institute for Health Sciences, Juntendo University, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Megumu Takahashi
- Department of Neuroanatomy, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Neuroscience, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Soichiro Kakuta
- Laboratory of Morphology and Image Analysis, Biomedical Research Core Facilities, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Yoko Ishida
- Department of Neuroanatomy, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Advanced Research Institute for Health Sciences, Juntendo University, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Aya Takenaka
- Department of Oral Anatomy and Neurobiology, Graduate School of Dentistry, Osaka University, Suita, Osaka 565-0871, Japan
| | - Atsushi Yoshida
- Department of Oral Anatomy and Neurobiology, Graduate School of Dentistry, Osaka University, Suita, Osaka 565-0871, Japan
| | - Yasuo Uchiyama
- Department of Cellular and Molecular Neuropathology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Masato Koike
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Advanced Research Institute for Health Sciences, Juntendo University, Bunkyo-Ku, Tokyo 113-8421, Japan
| | - Kaoru Isa
- Department of Neuroscience, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Tadashi Isa
- Department of Neuroscience, Graduate School of Medicine, Kyoto University, Kyoto, Kyoto 606-8501, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Hiroyuki Hioki
- Department of Neuroanatomy, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Cell Biology and Neuroscience, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Department of Multi-Scale Brain Structure Imaging, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo 113-8421, Japan
- Corresponding author
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Lee K, Lu R, Luther K, Seung HS. Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3801-3811. [PMID: 34270419 PMCID: PMC8692755 DOI: 10.1109/tmi.2021.3097826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A "metric graph" on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network. Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex "self-contact" motifs. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.
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33
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Liu J, Wang S, Lu Y, Wang H, Wang F, Qiu M, Xie Q, Han H, Hua Y. Aligned Organization of Synapses and Mitochondria in Auditory Hair Cells. Neurosci Bull 2021; 38:235-248. [PMID: 34837647 PMCID: PMC8975952 DOI: 10.1007/s12264-021-00801-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/25/2021] [Indexed: 10/19/2022] Open
Abstract
Recent studies have revealed great functional and structural heterogeneity in the ribbon-type synapses at the basolateral pole of the isopotential inner hair cell (IHC). This feature is believed to be critical for audition over a wide dynamic range, but whether the spatial gradient of ribbon morphology is fine-tuned in each IHC and how the mitochondrial network is organized to meet local energy demands of synaptic transmission remain unclear. By means of three-dimensional electron microscopy and artificial intelligence-based algorithms, we demonstrated the cell-wide structural quantification of ribbons and mitochondria in mature mid-cochlear IHCs of mice. We found that adjacent IHCs in staggered pairs differ substantially in cell body shape and ribbon morphology gradient as well as mitochondrial organization. Moreover, our analysis argues for a location-specific arrangement of correlated ribbon and mitochondrial function at the basolateral IHC pole.
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Affiliation(s)
- Jing Liu
- grid.9227.e0000000119573309National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408 China ,grid.507732.4CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031 China
| | - Shengxiong Wang
- grid.24516.340000000123704535Putuo People’s Hospital, Tongji University, Shanghai, 200060 China ,grid.16821.3c0000 0004 0368 8293Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China
| | - Yan Lu
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China ,grid.412523.3Department of Otolaryngology–Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai, 200125 China ,grid.16821.3c0000 0004 0368 8293Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China ,grid.412987.10000 0004 0630 1330Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, 200125 China
| | - Haoyu Wang
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China ,grid.412523.3Department of Otolaryngology–Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai, 200125 China ,grid.16821.3c0000 0004 0368 8293Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China ,grid.412987.10000 0004 0630 1330Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, 200125 China
| | - Fangfang Wang
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125 China
| | - Miaoxin Qiu
- grid.24516.340000000123704535Putuo People’s Hospital, Tongji University, Shanghai, 200060 China
| | - Qiwei Xie
- grid.28703.3e0000 0000 9040 3743Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology, Beijing, 100124 China
| | - Hua Han
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
| | - Yunfeng Hua
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China. .,Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, 200125, China. .,Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China. .,Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, 200125, China.
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34
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Endo M, Maruoka H, Okabe S. Advanced Technologies for Local Neural Circuits in the Cerebral Cortex. Front Neuroanat 2021; 15:757499. [PMID: 34803616 PMCID: PMC8595196 DOI: 10.3389/fnana.2021.757499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/13/2021] [Indexed: 11/13/2022] Open
Abstract
The neural network in the brain can be viewed as an integrated system assembled from a large number of local neural circuits specialized for particular brain functions. Activities of neurons in local neural circuits are thought to be organized both spatially and temporally under the rules optimized for their roles in information processing. It is well perceived that different areas of the mammalian neocortex have specific cognitive functions and distinct computational properties. However, the organizational principles of the local neural circuits in different cortical regions have not yet been clarified. Therefore, new research principles and related neuro-technologies that enable efficient and precise recording of large-scale neuronal activities and synaptic connections are necessary. Innovative technologies for structural analysis, including tissue clearing and expansion microscopy, have enabled super resolution imaging of the neural circuits containing thousands of neurons at a single synapse resolution. The imaging resolution and volume achieved by new technologies are beyond the limits of conventional light or electron microscopic methods. Progress in genome editing and related technologies has made it possible to label and manipulate specific cell types and discriminate activities of multiple cell types. These technologies will provide a breakthrough for multiscale analysis of the structure and function of local neural circuits. This review summarizes the basic concepts and practical applications of the emerging technologies and new insight into local neural circuits obtained by these technologies.
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Affiliation(s)
| | | | - Shigeo Okabe
- Department of Cellular Neurobiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
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35
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Moreno Manrique JF, Voit PR, Windsor KE, Karla AR, Rodriguez SR, Beaudoin GMJ. SynapseJ: An Automated, Synapse Identification Macro for ImageJ. Front Neural Circuits 2021; 15:731333. [PMID: 34675779 PMCID: PMC8524137 DOI: 10.3389/fncir.2021.731333] [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: 06/26/2021] [Accepted: 08/25/2021] [Indexed: 12/03/2022] Open
Abstract
While electron microscopy represents the gold standard for detection of synapses, a number of limitations prevent its broad applicability. A key method for detecting synapses is immunostaining for markers of pre- and post-synaptic proteins, which can infer a synapse based upon the apposition of the two markers. While immunostaining and imaging techniques have improved to allow for identification of synapses in tissue, analysis and identification of these appositions are not facile, and there has been a lack of tools to accurately identify these appositions. Here, we delineate a macro that uses open-source and freely available ImageJ or FIJI for analysis of multichannel, z-stack confocal images. With use of a high magnification with a high NA objective, we outline two methods to identify puncta in either sparsely or densely labeled images. Puncta from each channel are used to eliminate non-apposed puncta and are subsequently linked with their cognate from the other channel. These methods are applied to analysis of a pre-synaptic marker, bassoon, with two different post-synaptic markers, gephyrin and N-methyl-d-aspartate (NMDA) receptor subunit 1 (NR1). Using gephyrin as an inhibitory, post-synaptic scaffolding protein, we identify inhibitory synapses in basolateral amygdala, central amygdala, arcuate and the ventromedial hypothalamus. Systematic variation of the settings identify the parameters most critical for this analysis. Identification of specifically overlapping puncta allows for correlation of morphometry data between each channel. Finally, we extend the analysis to only examine puncta overlapping with a cytoplasmic marker of specific cell types, a distinct advantage beyond electron microscopy. Bassoon puncta are restricted to virally transduced, pedunculopontine tegmental nucleus (PPN) axons expressing yellow fluorescent protein. NR1 puncta are restricted to tyrosine hydroxylase labeled dopaminergic neurons of the substantia nigra pars compacta (SNc). The macro identifies bassoon-NR1 overlap throughout the image, or those only restricted to the PPN-SNc connections. Thus, we have extended the available analysis tools that can be used to study synapses in situ. Our analysis code is freely available and open-source allowing for further innovation.
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Affiliation(s)
| | - Parker R Voit
- Department of Biology, Trinity University, San Antonio, TX, United States
| | - Kathryn E Windsor
- Department of Biology, Trinity University, San Antonio, TX, United States
| | - Aamuktha R Karla
- Department of Biology, Trinity University, San Antonio, TX, United States
| | - Sierra R Rodriguez
- Department of Biology, Trinity University, San Antonio, TX, United States
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36
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Schifferer M, Snaidero N, Djannatian M, Kerschensteiner M, Misgeld T. Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology. Front Neuroanat 2021; 15:732506. [PMID: 34720890 PMCID: PMC8548362 DOI: 10.3389/fnana.2021.732506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/24/2021] [Indexed: 11/13/2022] Open
Abstract
Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a "needle-in-the-haystack" problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are "one-shot" imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, "multi-shot" approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.
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Affiliation(s)
- Martina Schifferer
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Nicolas Snaidero
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
- Hertie Institute for Clinical Brain Research, Tübingen, Germany
| | - Minou Djannatian
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
| | - Martin Kerschensteiner
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute of Clinical Neuroimmunology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
- Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-University Munich, Munich, Germany
| | - Thomas Misgeld
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
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37
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Schwarz MK, Kubitscheck U. Expansion light sheet fluorescence microscopy of extended biological samples: Applications and perspectives. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 168:33-36. [PMID: 34626664 DOI: 10.1016/j.pbiomolbio.2021.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/25/2021] [Accepted: 09/30/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Martin K Schwarz
- Institute Experimental Epileptology and Cognition Research (EECR), University of Bonn Medical School, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
| | - Ulrich Kubitscheck
- Institute of Physical and Theoretical Chemistry, University of Bonn, Wegelerstr. 12, 53115, Bonn, Germany
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38
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Yook JS, Kim J, Kim J. Convergence Circuit Mapping: Genetic Approaches From Structure to Function. Front Syst Neurosci 2021; 15:688673. [PMID: 34234652 PMCID: PMC8255632 DOI: 10.3389/fnsys.2021.688673] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022] Open
Abstract
Understanding the complex neural circuits that underpin brain function and behavior has been a long-standing goal of neuroscience. Yet this is no small feat considering the interconnectedness of neurons and other cell types, both within and across brain regions. In this review, we describe recent advances in mouse molecular genetic engineering that can be used to integrate information on brain activity and structure at regional, cellular, and subcellular levels. The convergence of structural inputs can be mapped throughout the brain in a cell type-specific manner by antero- and retrograde viral systems expressing various fluorescent proteins and genetic switches. Furthermore, neural activity can be manipulated using opto- and chemo-genetic tools to interrogate the functional significance of this input convergence. Monitoring neuronal activity is obtained with precise spatiotemporal resolution using genetically encoded sensors for calcium changes and specific neurotransmitters. Combining these genetically engineered mapping tools is a compelling approach for unraveling the structural and functional brain architecture of complex behaviors and malfunctioned states of neurological disorders.
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Affiliation(s)
- Jang Soo Yook
- Center for Functional Connectomics, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Jihyun Kim
- Center for Functional Connectomics, Korea Institute of Science and Technology (KIST), Seoul, South Korea.,Department of Integrated Biomedical and Life Sciences, Graduate School, Korea University, Seoul, South Korea
| | - Jinhyun Kim
- Center for Functional Connectomics, Korea Institute of Science and Technology (KIST), Seoul, South Korea.,Department of Integrated Biomedical and Life Sciences, Graduate School, Korea University, Seoul, South Korea
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39
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Du M, Di Z(W, Gürsoy D, Xian RP, Kozorovitskiy Y, Jacobsen C. Upscaling X-ray nanoimaging to macroscopic specimens. J Appl Crystallogr 2021; 54:386-401. [PMID: 33953650 PMCID: PMC8056767 DOI: 10.1107/s1600576721000194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.
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Affiliation(s)
- Ming Du
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Zichao (Wendy) Di
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - R. Patrick Xian
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Yevgenia Kozorovitskiy
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
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40
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Raman DV, O'Leary T. Frozen algorithms: how the brain's wiring facilitates learning. Curr Opin Neurobiol 2021; 67:207-214. [PMID: 33508698 PMCID: PMC8202511 DOI: 10.1016/j.conb.2020.12.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/21/2020] [Accepted: 12/30/2020] [Indexed: 12/03/2022]
Abstract
Synapses and neural connectivity are plastic and shaped by experience. But to what extent does connectivity itself influence the ability of a neural circuit to learn? Insights from optimization theory and AI shed light on how learning can be implemented in neural circuits. Though abstract in their nature, learning algorithms provide a principled set of hypotheses on the necessary ingredients for learning in neural circuits. These include the kinds of signals and circuit motifs that enable learning from experience, as well as an appreciation of the constraints that make learning challenging in a biological setting. Remarkably, some simple connectivity patterns can boost the efficiency of relatively crude learning rules, showing how the brain can use anatomy to compensate for the biological constraints of known synaptic plasticity mechanisms. Modern connectomics provides rich data for exploring this principle, and may reveal how brain connectivity is constrained by the requirement to learn efficiently.
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Affiliation(s)
- Dhruva V Raman
- Department of Engineering, University of Cambridge, United Kingdom
| | - Timothy O'Leary
- Department of Engineering, University of Cambridge, United Kingdom.
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41
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Lane R, Vos Y, Wolters AHG, Kessel LV, Chen SE, Liv N, Klumperman J, Giepmans BNG, Hoogenboom JP. Optimization of negative stage bias potential for faster imaging in large-scale electron microscopy. JOURNAL OF STRUCTURAL BIOLOGY-X 2021; 5:100046. [PMID: 33763642 PMCID: PMC7973379 DOI: 10.1016/j.yjsbx.2021.100046] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/17/2020] [Accepted: 01/27/2021] [Indexed: 11/24/2022]
Abstract
The use of a negative bias potential was empirically optimized for tissue imaging with SEM. Optimized bias potential leads to a factor 20 increase in imaging speeds as well as an order of magnitude improvement to SNR. SNR increase results from a combination of BSE acceleration and detector response. Similar increases to SNR can be obtained when a magnetic immersion field is combined with a negative bias potential. Stage bias can be applied within an integrated fluorescence and electron microscope allowing for fast correlative imaging of tissue sections.
Large-scale electron microscopy (EM) allows analysis of both tissues and macromolecules in a semi-automated manner, but acquisition rate forms a bottleneck. We reasoned that a negative bias potential may be used to enhance signal collection, allowing shorter dwell times and thus increasing imaging speed. Negative bias potential has previously been used to tune penetration depth in block-face imaging. However, optimization of negative bias potential for application in thin section imaging will be needed prior to routine use and application in large-scale EM. Here, we present negative bias potential optimized through a combination of simulations and empirical measurements. We find that the use of a negative bias potential generally results in improvement of image quality and signal-to-noise ratio (SNR). The extent of these improvements depends on the presence and strength of a magnetic immersion field. Maintaining other imaging conditions and aiming for the same image quality and SNR, the use of a negative stage bias can allow for a 20-fold decrease in dwell time, thus reducing the time for a week long acquisition to less than 8 h. We further show that negative bias potential can be applied in an integrated correlative light electron microscopy (CLEM) application, allowing fast acquisition of a high precision overlaid LM-EM dataset. Application of negative stage bias potential will thus help to solve the current bottleneck of image acquisition of large fields of view at high resolution in large-scale microscopy.
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Affiliation(s)
- Ryan Lane
- Imaging Physics, Delft University of Technology, The Netherlands
| | - Yoram Vos
- Imaging Physics, Delft University of Technology, The Netherlands
| | - Anouk H G Wolters
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, The Netherlands
| | - Luc van Kessel
- Imaging Physics, Delft University of Technology, The Netherlands
| | - S Elisa Chen
- Cell Biology, Center for Molecular Medicine, University Medical Center Utrecht, The Netherlands
| | - Nalan Liv
- Cell Biology, Center for Molecular Medicine, University Medical Center Utrecht, The Netherlands
| | - Judith Klumperman
- Cell Biology, Center for Molecular Medicine, University Medical Center Utrecht, The Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, The Netherlands
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42
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Friedrich RW, Wanner AA. Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish. Annu Rev Neurosci 2021; 44:275-293. [PMID: 33730512 DOI: 10.1146/annurev-neuro-110220-013050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations.
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Affiliation(s)
- Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; .,Faculty of Natural Sciences, University of Basel, 4003 Basel, Switzerland
| | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA;
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43
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Anderson KR, Harris JA, Ng L, Prins P, Memar S, Ljungquist B, Fürth D, Williams RW, Ascoli GA, Dumitriu D. Highlights from the Era of Open Source Web-Based Tools. J Neurosci 2021; 41:927-936. [PMID: 33472826 PMCID: PMC7880282 DOI: 10.1523/jneurosci.1657-20.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 11/22/2020] [Accepted: 11/29/2020] [Indexed: 12/20/2022] Open
Abstract
High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.
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Affiliation(s)
- Kristin R Anderson
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Sara Memar
- Robarts Research Institute, BrainsCAN, Schulich School of Medicine & Dentistry, Western University, London, Ontario N6A 3K7, Canada
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Dani Dumitriu
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
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44
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Minehart JA, Speer CM. A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits. Front Synaptic Neurosci 2021; 12:615059. [PMID: 33469427 PMCID: PMC7813761 DOI: 10.3389/fnsyn.2020.615059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/07/2020] [Indexed: 12/23/2022] Open
Abstract
A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.
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Affiliation(s)
| | - Colenso M. Speer
- Department of Biology, University of Maryland, College Park, MD, United States
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Hua Y, Ding X, Wang H, Wang F, Lu Y, Neef J, Gao Y, Moser T, Wu H. Electron Microscopic Reconstruction of Neural Circuitry in the Cochlea. Cell Rep 2021; 34:108551. [PMID: 33406431 DOI: 10.1016/j.celrep.2020.108551] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/25/2020] [Accepted: 12/03/2020] [Indexed: 02/02/2023] Open
Abstract
Recent studies reveal great diversity in the structure, function, and efferent innervation of afferent synaptic connections between the cochlear inner hair cells (IHCs) and spiral ganglion neurons (SGNs), which likely enables audition to process a wide range of sound pressures. By performing an extensive electron microscopic (EM) reconstruction of the neural circuitry in the mature mouse organ of Corti, we demonstrate that afferent SGN dendrites differ in abundance and composition of efferent innervation in a manner dependent on their afferent synaptic connectivity with IHCs. SGNs that sample glutamate release from several presynaptic ribbons receive more efferent innervation from lateral olivocochlear projections than those driven by a single ribbon. Next to the prevailing unbranched SGN dendrites, we found branched SGN dendrites that can contact several ribbons of 1-2 IHCs. Unexpectedly, medial olivocochlear neurons provide efferent innervation of SGN dendrites, preferring those forming single-ribbon, pillar-side synapses. We propose a fine-tuning of afferent and efferent SGN innervation.
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Affiliation(s)
- Yunfeng Hua
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, China; Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China; Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt/Main, Germany.
| | - Xu Ding
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, China; Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Haoyu Wang
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fangfang Wang
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Lu
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jakob Neef
- Institute for Auditory Neuroscience, University Medical Center Göttingen, Göttingen, Germany; Auditory Neuroscience Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Yunge Gao
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, China; Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Tobias Moser
- Institute for Auditory Neuroscience, University Medical Center Göttingen, Göttingen, Germany; Auditory Neuroscience Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany; Multiscale Bioimaging Cluster of Excellence, University of Göttingen, Göttingen, Germany.
| | - Hao Wu
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, China; Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China; Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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46
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Conte D, Borisyuk R, Hull M, Roberts A. A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry. J Neurosci Methods 2020; 351:109062. [PMID: 33383055 DOI: 10.1016/j.jneumeth.2020.109062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network. NEW METHOD We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files. RESULTS Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types. COMPARISON WITH EXISTING METHODS Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording. CONCLUSIONS This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.
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Affiliation(s)
- Deborah Conte
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
| | - Roman Borisyuk
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, United Kingdom; Institute of Mathematical Problems of Biology, the Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, 142290, Russia; School of Computing, Engineering and Mathematics, University of Plymouth, PL4 8AA, United Kingdom.
| | - Mike Hull
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom; Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
| | - Alan Roberts
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
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Abstract
The formation of the human brain, which contains nearly 100 billion neurons making an average of 1000 connections each, represents an astonishing feat of self-organization. Despite impressive progress, our understanding of how neurons form the nervous system and enable function is very fragmentary, especially for the human brain. New technologies that produce large volumes of high-resolution measurements-big data-are now being brought to bear on this problem. Single-cell molecular profiling methods allow the exploration of neural diversity with increasing spatial and temporal resolution. Advances in human genetics are shedding light on the genetic architecture of neurodevelopmental disorders, and new approaches are revealing plausible neurobiological mechanisms underlying these conditions. Here, we review the opportunities and challenges of integrating large-scale genomics and genetics for the study of brain development.
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Affiliation(s)
| | - Oscar Marín
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE1 1UL, UK. .,MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK
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48
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Cachero S, Gkantia M, Bates AS, Frechter S, Blackie L, McCarthy A, Sutcliffe B, Strano A, Aso Y, Jefferis GSXE. BAcTrace, a tool for retrograde tracing of neuronal circuits in Drosophila. Nat Methods 2020; 17:1254-1261. [PMID: 33139893 PMCID: PMC7610425 DOI: 10.1038/s41592-020-00989-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 10/05/2020] [Indexed: 11/26/2022]
Abstract
Animal behavior is encoded in neuronal circuits in the brain. To elucidate the function of these circuits, it is necessary to identify, record from and manipulate networks of connected neurons. Here we present BAcTrace (Botulinum Activated Tracer), a genetically encoded, retro-grade, transsynaptic labelling system. BAcTrace is based on C. botulinum neurotoxin A, Botox, which we have engineered to travel retrogradely between neurons to activate an otherwise silent transcription factor. We validate BAcTrace at three neuronal connections in the Drosophila olfactory system. We show that BAcTrace-mediated labeling allows electrophysiological recordings of connected neurons. Finally, in a challenging circuit with highly divergent connections, BAcTrace correctly identifies 12 out of 16 connections, which were previously observed by electron microscopy.
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Affiliation(s)
- Sebastian Cachero
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.
| | - Marina Gkantia
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alexander S Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Shahar Frechter
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Laura Blackie
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Amy McCarthy
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
| | - Ben Sutcliffe
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alessio Strano
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,Department of Cancer and Developmental Biology & Zayed Centre for Research into Rare Disease in Children, University College London, London, UK
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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49
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Yin W, Brittain D, Borseth J, Scott ME, Williams D, Perkins J, Own CS, Murfitt M, Torres RM, Kapner D, Mahalingam G, Bleckert A, Castelli D, Reid D, Lee WCA, Graham BJ, Takeno M, Bumbarger DJ, Farrell C, Reid RC, da Costa NM. A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy. Nat Commun 2020; 11:4949. [PMID: 33009388 PMCID: PMC7532165 DOI: 10.1038/s41467-020-18659-3] [Citation(s) in RCA: 53] [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: 11/27/2019] [Accepted: 08/28/2020] [Indexed: 11/18/2022] Open
Abstract
Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months. Over 26,500 ultrathin tissue sections from the same block were imaged, yielding a dataset of more than 2 petabytes. The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpixel per sec with six microscopes running in parallel. This work demonstrates the feasibility of acquiring EM datasets at the scale of cortical microcircuits in multiple brain regions and species.
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50
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Miroschnikow A, Schlegel P, Pankratz MJ. Making Feeding Decisions in the Drosophila Nervous System. Curr Biol 2020; 30:R831-R840. [DOI: 10.1016/j.cub.2020.06.036] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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