1
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Wu JY, Cho SJ, Descant K, Li PH, Shapson-Coe A, Januszewski M, Berger DR, Meyer C, Casingal C, Huda A, Liu J, Ghashghaei T, Brenman M, Jiang M, Scarborough J, Pope A, Jain V, Stein JL, Guo J, Yasuda R, Lichtman JW, Anton ES. Mapping of neuronal and glial primary cilia contactome and connectome in the human cerebral cortex. Neuron 2024; 112:41-55.e3. [PMID: 37898123 PMCID: PMC10841524 DOI: 10.1016/j.neuron.2023.09.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 07/25/2023] [Accepted: 09/22/2023] [Indexed: 10/30/2023]
Abstract
Primary cilia act as antenna receivers of environmental signals and enable effective neuronal or glial responses. Disruption of their function is associated with circuit disorders. To understand the signals these cilia receive, we comprehensively mapped cilia's contacts within the human cortical connectome using serial-section EM reconstruction of a 1 mm3 cortical volume, spanning the entire cortical thickness. We mapped the "contactome" of cilia emerging from neurons and astrocytes in every cortical layer. Depending on the layer and cell type, cilia make distinct patterns of contact. Primary cilia display cell-type- and layer-specific variations in size, shape, and microtubule axoneme core, which may affect their signaling competencies. Neuronal cilia are intrinsic components of a subset of cortical synapses and thus a part of the connectome. This diversity in the structure, contactome, and connectome of primary cilia endows each neuron or glial cell with a unique barcode of access to the surrounding neural circuitry.
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Affiliation(s)
- Jun Yao Wu
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Su-Ji Cho
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Katherine Descant
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Peter H Li
- Google Research, Mountain View, CA 94043, USA
| | - Alexander Shapson-Coe
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Cailyn Meyer
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Cristine Casingal
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Ariba Huda
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Jiaqi Liu
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Tina Ghashghaei
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Mikayla Brenman
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Michelle Jiang
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Joseph Scarborough
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Art Pope
- Google Research, Mountain View, CA 94043, USA
| | - Viren Jain
- Google Research, Mountain View, CA 94043, USA
| | - Jason L Stein
- UNC Neuroscience Center and the Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Jiami Guo
- Department of Cell Biology and Anatomy, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ryohei Yasuda
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA.
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
| | - E S Anton
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA.
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2
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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3
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Lu X, Wu Y, Schalek RL, Meirovitch Y, Berger DR, Lichtman JW. A Scalable Staining Strategy for Whole-Brain Connectomics. bioRxiv 2023:2023.09.26.558265. [PMID: 37808722 PMCID: PMC10557665 DOI: 10.1101/2023.09.26.558265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.
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Affiliation(s)
- Xiaotang Lu
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Richard L. Schalek
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Daniel R. Berger
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
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4
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Ofer N, Berger DR, Kasthuri N, Lichtman JW, Yuste R. Cover Image. Dev Neurobiol 2021. [DOI: 10.1002/dneu.22841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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5
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Ofer N, Berger DR, Kasthuri N, Lichtman JW, Yuste R. Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies. Dev Neurobiol 2021; 81:746-757. [PMID: 33977655 DOI: 10.1002/dneu.22829] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 01/20/2023]
Abstract
Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in most mammalian neurons. Spines, with a bulbous head connected to the dendrite by a thin neck, have a variety of morphologies that likely impact their functional properties. Nevertheless, the question of whether spines belong to distinct morphological subtypes is still open. Addressing this quantitatively requires clear identification and measurements of spine necks. Recent advances in electron microscopy enable large-scale systematic reconstructions of spines with nanometer precision in 3D. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spine structures can be rigorously separated into heads and necks, enabling morphological measurements of spine necks. We then used a database of spine morphological parameters to explore the potential existence of different spine classes. Without exception, our analysis revealed unimodal distributions of individual morphological parameters of spine heads and necks, without evidence for subtypes of spines. The postsynaptic density size was strongly correlated with the spine head volume. The spine neck diameter, but not the neck length, was also correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data reveal a lack of morphological subtypes of spines and indicate that the spine neck length and head volume must be independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.
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Affiliation(s)
- Netanel Ofer
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Daniel R Berger
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA
| | | | - Jeff W Lichtman
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Rafael Yuste
- Neurotechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA.,Donostia International Physics Center, DIPC, San Sebastian, Spain
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6
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Abstract
Objectives Platelet-rich plasma (PRP) is being used increasingly often in the clinical setting to treat tendon-related pathologies. Yet the optimal PRP preparations to promote tendon healing in different patient populations are poorly defined. Here, we sought to determine whether increasing the concentration of platelet-derived proteins within a derivative of PRP, platelet lysate (PL), enhances tenocyte proliferation and migration in vitro, and whether the mitogenic properties of PL change with donor age. Methods Concentrated PLs from both young (< 50 years) and aged (> 50 years) donors were prepared by exposing pooled PRP to a series of freeze-thaw cycles followed by dilution in plasma, and the levels of several platelet-derived proteins were measured using multiplex immunoassay technology. Human tenocytes were cultured with PLs to simulate a clinically relevant PRP treatment range, and cell growth and migration were assessed using DNA quantitation and gap closure assays, respectively. Results Platelet-derived protein levels increased alongside higher PL concentrations, and PLs from both age groups improved tenocyte proliferation relative to control conditions. However, PLs from aged donors yielded a dose-response relationship in tenocyte behaviour, with higher PL concentrations resulting in increased tenocyte proliferation and migration. Conversely, no significant differences in tenocyte behaviour were detected when increasing the concentration of PLs from younger donors. Conclusion Higher PL concentrations, when prepared from the PRP of aged but not young donors, were more effective than lower PL concentrations at promoting tenocyte proliferation and migration in vitro. Cite this article: D. R. Berger, C. J. Centeno, N. J. Steinmetz. Platelet lysates from aged donors promote human tenocyte proliferation and migration in a concentration-dependent manner. Bone Joint Res 2019;8:32–40. DOI: 10.1302/2046-3758.81.BJR-2018-0164.R1.
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Affiliation(s)
- D R Berger
- Interventional Orthopedics Foundation, Broomfield, Colorado, USA
| | - C J Centeno
- Centeno-Schultz Clinic, Broomfield, Colorado, USA; Board Chairman, Interventional Orthopedics Foundation, Broomfield, Colorado, USA; Chief Medical Officer, Regenexx, LLC, Broomfield, Colorado, USA
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7
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Berger DR, Seung HS, Lichtman JW. VAST (Volume Annotation and Segmentation Tool): Efficient Manual and Semi-Automatic Labeling of Large 3D Image Stacks. Front Neural Circuits 2018; 12:88. [PMID: 30386216 PMCID: PMC6198149 DOI: 10.3389/fncir.2018.00088] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 09/24/2018] [Indexed: 11/13/2022] Open
Abstract
Recent developments in serial-section electron microscopy allow the efficient generation of very large image data sets but analyzing such data poses challenges for software tools. Here we introduce Volume Annotation and Segmentation Tool (VAST), a freely available utility program for generating and editing annotations and segmentations of large volumetric image (voxel) data sets. It provides a simple yet powerful user interface for real-time exploration and analysis of large data sets even in the Petabyte range.
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Affiliation(s)
- Daniel R Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
| | - H Sebastian Seung
- Computer Science Department, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
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8
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Quadrato G, Nguyen T, Macosko EZ, Sherwood JL, Min Yang S, Berger DR, Maria N, Scholvin J, Goldman M, Kinney JP, Boyden ES, Lichtman JW, Williams ZM, McCarroll SA, Arlotta P. Cell diversity and network dynamics in photosensitive human brain organoids. Nature 2017; 545:48-53. [PMID: 28445462 DOI: 10.1038/nature22047] [Citation(s) in RCA: 744] [Impact Index Per Article: 106.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 03/07/2017] [Indexed: 12/18/2022]
Abstract
In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.
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Affiliation(s)
- Giorgia Quadrato
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Tuan Nguyen
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Evan Z Macosko
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John L Sherwood
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Sung Min Yang
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Daniel R Berger
- Department of Cellular and Molecular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Natalie Maria
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Jorg Scholvin
- Departments of Biological Engineering and Brain and Cognitive Sciences, MIT Media Lab and McGovern Institute, MIT, Cambridge, Massachusetts 02139, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | | | - Edward S Boyden
- Departments of Biological Engineering and Brain and Cognitive Sciences, MIT Media Lab and McGovern Institute, MIT, Cambridge, Massachusetts 02139, USA
| | - Jeff W Lichtman
- Department of Cellular and Molecular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
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9
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Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, Schmidhuber J, Laptev D, Dwivedi S, Buhmann JM, Liu T, Seyedhosseini M, Tasdizen T, Kamentsky L, Burget R, Uher V, Tan X, Sun C, Pham TD, Bas E, Uzunbas MG, Cardona A, Schindelin J, Seung HS. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat 2015; 9:142. [PMID: 26594156 PMCID: PMC4633678 DOI: 10.3389/fnana.2015.00142] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/19/2015] [Indexed: 11/13/2022] Open
Abstract
To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.
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Affiliation(s)
- Ignacio Arganda-Carreras
- UMR1318 French National Institute for Agricultural Research-AgroParisTech, French National Institute for Agricultural Research Centre de Versailles-Grignon, Institut Jean-Pierre Bourgin Versailles, France
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Daniel R Berger
- Center for Brain Science, Harvard University Cambridge, MA, USA
| | - Dan Cireşan
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Alessandro Giusti
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Luca M Gambardella
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Jürgen Schmidhuber
- Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland
| | - Dmitry Laptev
- Department of Computer Science, ETH Zurich Zurich, Switzerland
| | - Sarvesh Dwivedi
- Department of Computer Science, ETH Zurich Zurich, Switzerland
| | | | - Ting Liu
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Mojtaba Seyedhosseini
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Tolga Tasdizen
- Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA
| | - Lee Kamentsky
- Imaging Platform, Broad Institute Cambridge, MA, USA
| | - Radim Burget
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic
| | - Vaclav Uher
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic
| | - Xiao Tan
- School of Engineering and Information Technology, University of New South Wales Canberra, ACT, Australia
| | - Changming Sun
- Digital Productivity Flagship, Commonwealth Scientific and Industrial Research Organisation North Ryde, NSW, Australia
| | - Tuan D Pham
- Department of Biomedical Engineering, The Institute of Technology, Linkoping University Linkoping, Sweden
| | - Erhan Bas
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Mustafa G Uzunbas
- Computer Science Department, Rutgers University New Brunswick, NJ, USA
| | - Albert Cardona
- Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA
| | - Johannes Schindelin
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison Madison, WI, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA
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10
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Hayworth KJ, Morgan JL, Schalek R, Berger DR, Hildebrand DGC, Lichtman JW. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Front Neural Circuits 2014; 8:68. [PMID: 25018701 PMCID: PMC4073626 DOI: 10.3389/fncir.2014.00068] [Citation(s) in RCA: 170] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 06/05/2014] [Indexed: 11/13/2022] Open
Abstract
The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly—the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments.
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Affiliation(s)
| | - Josh L Morgan
- Department of Molecular and Cell Biology, Harvard University Cambridge, MA, USA
| | - Richard Schalek
- Department of Molecular and Cell Biology, Harvard University Cambridge, MA, USA
| | - Daniel R Berger
- Department of Molecular and Cell Biology, Harvard University Cambridge, MA, USA
| | | | - Jeff W Lichtman
- Department of Molecular and Cell Biology, Harvard University Cambridge, MA, USA
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11
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Tomassy GS, Berger DR, Chen HH, Kasthuri N, Hayworth KJ, Vercelli A, Seung HS, Lichtman JW, Arlotta P. Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex. Science 2014; 344:319-24. [PMID: 24744380 PMCID: PMC4122120 DOI: 10.1126/science.1249766] [Citation(s) in RCA: 346] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Myelin is a defining feature of the vertebrate nervous system. Variability in the thickness of the myelin envelope is a structural feature affecting the conduction of neuronal signals. Conversely, the distribution of myelinated tracts along the length of axons has been assumed to be uniform. Here, we traced high-throughput electron microscopy reconstructions of single axons of pyramidal neurons in the mouse neocortex and built high-resolution maps of myelination. We find that individual neurons have distinct longitudinal distribution of myelin. Neurons in the superficial layers displayed the most diversified profiles, including a new pattern where myelinated segments are interspersed with long, unmyelinated tracts. Our data indicate that the profile of longitudinal distribution of myelin is an integral feature of neuronal identity and may have evolved as a strategy to modulate long-distance communication in the neocortex.
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Affiliation(s)
- Giulio Srubek Tomassy
- Department of Stem Cell and Regenerative Biology, Harvard University, 7 Divinity Avenue, Cambridge MA 02138
| | - Daniel R. Berger
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139
| | - Hsu-Hsin Chen
- Department of Stem Cell and Regenerative Biology, Harvard University, 7 Divinity Avenue, Cambridge MA 02138
| | - Narayanan Kasthuri
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138
| | - Kenneth J. Hayworth
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138
| | - Alessandro Vercelli
- Neuroscience Institute Cavalieri Ottolenghi, Neuroscience Institute of Turin, Corso M. d’Azeglio 52, Turin, Italy10126
| | - H. Sebastian Seung
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, 7 Divinity Avenue, Cambridge MA 02138
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12
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Burns R, Roncal WG, Kleissas D, Lillaney K, Manavalan P, Perlman E, Berger DR, Bock DD, Chung K, Grosenick L, Kasthuri N, Weiler NC, Deisseroth K, Kazhdan M, Lichtman J, Reid RC, Smith SJ, Szalay AS, Vogelstein JT, Vogelstein RJ. The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. Sci Stat Database Manag 2013:10.1145/2484838.2484870. [PMID: 24401992 PMCID: PMC3881956 DOI: 10.1145/2484838.2484870] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.
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Affiliation(s)
- Randal Burns
- Department of Computer Science and the Institute for Data Intensive Engineering and Science, Johns Hopkins University
| | | | - Dean Kleissas
- Department of Statistical Science and Mathematics and the Institute for Brain Science, Duke University
| | - Kunal Lillaney
- Janelia Farm Research Campus, Howard Hughes Medical Institute
| | - Priya Manavalan
- Department of Molecular and Cellular Biology, Harvard University
| | - Eric Perlman
- Department of Computational Neuroscience, Massachusetts Institute of Technology
| | | | - Davi D Bock
- Department of Physics and Astronomy and the Institute for Data Intensive Engineering and Science, Johns Hopkins University
| | | | - Logan Grosenick
- Department of Molecular and Cellular Physiology, Stanford University
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Tapia JC, Wylie JD, Kasthuri N, Hayworth KJ, Schalek R, Berger DR, Guatimosim C, Seung HS, Lichtman JW. Pervasive synaptic branch removal in the mammalian neuromuscular system at birth. Neuron 2012; 74:816-29. [PMID: 22681687 DOI: 10.1016/j.neuron.2012.04.017] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2012] [Indexed: 11/16/2022]
Abstract
VIDEO ABSTRACT Using light and serial electron microscopy, we show profound refinements in motor axonal branching and synaptic connectivity before and after birth. Embryonic axons become maximally connected just before birth when they innervate ∼10-fold more muscle fibers than in maturity. In some developing muscles, axons innervate almost every muscle fiber. At birth, each neuromuscular junction is coinnervated by approximately ten highly intermingled axons (versus one in adults). Extensive die off of terminal branches occurs during the first several postnatal days, leading to much sparser arbors that still span the same territory. Despite the extensive pruning, total axoplasm per neuron increases as axons elongate, thicken, and add more synaptic release sites on their remaining targets. Motor axons therefore initially establish weak connections with nearly all available postsynaptic targets but, beginning at birth, massively redistribute synaptic resources, concentrating many more synaptic sites on many fewer muscle fibers. Analogous changes in connectivity may occur in the CNS.
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Affiliation(s)
- Juan C Tapia
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
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MacNeilage PR, Banks MS, Berger DR, Bülthoff HH. A Bayesian model of the disambiguation of gravitoinertial force by visual cues. Exp Brain Res 2006; 179:263-90. [PMID: 17136526 DOI: 10.1007/s00221-006-0792-0] [Citation(s) in RCA: 155] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2005] [Accepted: 10/31/2006] [Indexed: 11/28/2022]
Abstract
The otoliths are stimulated in the same fashion by gravitational and inertial forces, so otolith signals are ambiguous indicators of self-orientation. The ambiguity can be resolved with added visual information indicating orientation and acceleration with respect to the earth. Here we present a Bayesian model of the statistically optimal combination of noisy vestibular and visual signals. Likelihoods associated with sensory measurements are represented in an orientation/acceleration space. The likelihood function associated with the otolith signal illustrates the ambiguity; there is no unique solution for self-orientation or acceleration. Likelihood functions associated with other sensory signals can resolve this ambiguity. In addition, we propose two priors, each acting on a dimension in the orientation/acceleration space: the idiotropic prior and the no-acceleration prior. We conducted experiments using a motion platform and attached visual display to examine the influence of visual signals on the interpretation of the otolith signal. Subjects made pitch and acceleration judgments as the vestibular and visual signals were manipulated independently. Predictions of the model were confirmed: (1) visual signals affected the interpretation of the otolith signal, (2) less variable signals had more influence on perceived orientation and acceleration than more variable ones, and (3) combined estimates were more precise than single-cue estimates. We also show that the model can explain some well-known phenomena including the perception of upright in zero gravity, the Aubert effect, and the somatogravic illusion.
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Affiliation(s)
- Paul R MacNeilage
- Vision Science Program, University of California, Berkeley, CA 94720, USA.
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Summerbell RK, Berger DR. Rearrangements of α-Halogenated Ethers. II.1 The Preparation and Some Reactions of 2,3-Diphenyl-p-dioxene. J Am Chem Soc 2002. [DOI: 10.1021/ja01512a032] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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De Wall SL, Wang K, Berger DR, Watanabe S, Hernandez JC, Gokel GW. Azacrown Ethers as Amphiphile Headgroups: Formation of Stable Aggregates from Two- and Three-Armed Lariat Ethers. J Org Chem 1997. [DOI: 10.1021/jo970929a] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Stephen L. De Wall
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
| | - Keshi Wang
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
| | - Daniel R. Berger
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
| | - Shigeru Watanabe
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
| | - Jeanette C. Hernandez
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
| | - George W. Gokel
- Bioorganic Chemistry Program and Department of Molecular Biology & Pharmacology, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8103, St. Louis, Missouri 63110
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