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Zehtabian A, Fuchs J, Eickholt BJ, Ewers H. Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons. Neuroscience 2024:S0306-4522(24)00218-5. [PMID: 38838980 DOI: 10.1016/j.neuroscience.2024.05.024] [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: 12/19/2023] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.
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Affiliation(s)
- Amin Zehtabian
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195 Berlin, Germany.
| | - Joachim Fuchs
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Molecular Biology and Biochemistry, Virchowweg 6, 10117 Berlin, Germany
| | - Britta J Eickholt
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Molecular Biology and Biochemistry, Virchowweg 6, 10117 Berlin, Germany
| | - Helge Ewers
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195 Berlin, Germany
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2
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Chen S, Loper J, Zhou P, Paninski L. Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. PLoS Comput Biol 2022; 18:e1009991. [PMID: 35395020 PMCID: PMC9020678 DOI: 10.1371/journal.pcbi.1009991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 04/20/2022] [Accepted: 03/07/2022] [Indexed: 11/19/2022] Open
Abstract
Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.
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Affiliation(s)
- Shuonan Chen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Jackson Loper
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
| | - Pengcheng Zhou
- Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
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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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Yu X, Creamer MS, Randi F, Sharma AK, Linderman SW, Leifer AM. Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training. eLife 2021; 10:e66410. [PMID: 34259623 PMCID: PMC8367385 DOI: 10.7554/elife.66410] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 07/13/2021] [Indexed: 11/25/2022] Open
Abstract
We present an automated method to track and identify neurons in C. elegans, called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL (Yemini et al., 2021). Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group (Chaudhary et al., 2021). Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.
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Affiliation(s)
- Xinwei Yu
- Department of Physics, Princeton UniversityPrincetonUnited States
| | - Matthew S Creamer
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Francesco Randi
- Department of Physics, Princeton UniversityPrincetonUnited States
| | - Anuj K Sharma
- Department of Physics, Princeton UniversityPrincetonUnited States
| | - Scott W Linderman
- Department of Statistics, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences Institute, Stanford UniversityStanfordUnited States
| | - Andrew M Leifer
- Department of Physics, Princeton UniversityPrincetonUnited States
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
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Rabinowitch I. What would a synthetic connectome look like? Phys Life Rev 2019; 33:1-15. [PMID: 31296448 DOI: 10.1016/j.plrev.2019.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/25/2019] [Indexed: 02/07/2023]
Abstract
A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.
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Affiliation(s)
- Ithai Rabinowitch
- Department of Medical Neurobiology, IMRIC - Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, 9112002, Israel.
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6
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Kebschull JM. DNA sequencing in high-throughput neuroanatomy. J Chem Neuroanat 2019; 100:101653. [PMID: 31173871 DOI: 10.1016/j.jchemneu.2019.101653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 03/30/2019] [Accepted: 05/24/2019] [Indexed: 01/15/2023]
Abstract
Mapping brain connectivity at single cell resolution is critical for understanding brain structure. For decades, such mapping has been principally approached with microscopy techniques, aiming to visualize neurons and their connections. However, these techniques are often very labor intensive and do not scale well to the complexity of mammalian brains. We recently leveraged the speed and parallelization of DNA sequencing to map the projections of thousands of single neurons in single experiments, and to map cortical mesoscale connectivity in single mice. Here, I review the state of sequencing-based neuroanatomy, and discuss future directions in synaptic connectivity mapping and comparative connectomics.
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Rolnick D, Dyer EL. Generative models and abstractions for large-scale neuroanatomy datasets. Curr Opin Neurobiol 2019; 55:112-120. [PMID: 30878806 PMCID: PMC8449855 DOI: 10.1016/j.conb.2019.02.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 01/09/2019] [Accepted: 02/07/2019] [Indexed: 01/09/2023]
Abstract
Neural datasets are increasing rapidly in both resolution and volume. In neuroanatomy, this trend has been accelerated by innovations in imaging technology. As full datasets are impractical and unnecessary for many applications, it is important to identify abstractions that distill useful features of neural structure, organization, and anatomy. In this review article, we discuss several such abstractions and highlight recent algorithmic advances in working with these models. In particular, we discuss the use of generative models in neuroanatomy; such models may be considered 'meta-abstractions' that capture distributions over other abstractions.
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Affiliation(s)
- David Rolnick
- School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Eva L Dyer
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Gao R, Asano SM, Upadhyayula S, Pisarev I, Milkie DE, Liu TL, Singh V, Graves A, Huynh GH, Zhao Y, Bogovic J, Colonell J, Ott CM, Zugates C, Tappan S, Rodriguez A, Mosaliganti KR, Sheu SH, Pasolli HA, Pang S, Xu CS, Megason SG, Hess H, Lippincott-Schwartz J, Hantman A, Rubin GM, Kirchhausen T, Saalfeld S, Aso Y, Boyden ES, Betzig E. Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution. Science 2019; 363:eaau8302. [PMID: 30655415 PMCID: PMC6481610 DOI: 10.1126/science.aau8302] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 11/30/2018] [Indexed: 12/20/2022]
Abstract
Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire Drosophila brain. These included synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.
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Affiliation(s)
- Ruixuan Gao
- MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Shoh M Asano
- MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
| | - Srigokul Upadhyayula
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Department of Cell Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, 200 Longwood Avenue, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Igor Pisarev
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Daniel E Milkie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tsung-Li Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ved Singh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Austin Graves
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Grace H Huynh
- MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Yongxin Zhao
- MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Jennifer Colonell
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Carolyn M Ott
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Christopher Zugates
- arivis AG, 1875 Connecticut Avenue NW, 10th floor, Washington, DC 20009, USA
| | - Susan Tappan
- MBF Bioscience, 185 Allen Brook Lane, Suite 101, Williston, VT 05495, USA
| | - Alfredo Rodriguez
- MBF Bioscience, 185 Allen Brook Lane, Suite 101, Williston, VT 05495, USA
| | - Kishore R Mosaliganti
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Shu-Hsien Sheu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - H Amalia Pasolli
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Song Pang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Sean G Megason
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Harald Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | | - Adam Hantman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tom Kirchhausen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Department of Cell Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, 200 Longwood Avenue, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Edward S Boyden
- MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
- MIT Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- Koch Institute, MIT, Cambridge, MA 02139, USA
| | - Eric Betzig
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
- Department of Physics, University of California, Berkeley, CA 94720, USA
- Howard Hughes Medical Institute, Berkeley, CA 94720, USA
- Helen Wills Neuroscience Institute, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Wassie AT, Zhao Y, Boyden ES. Expansion microscopy: principles and uses in biological research. Nat Methods 2018; 16:33-41. [PMID: 30573813 DOI: 10.1038/s41592-018-0219-4] [Citation(s) in RCA: 243] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 10/10/2018] [Indexed: 01/08/2023]
Abstract
Many biological investigations require 3D imaging of cells or tissues with nanoscale spatial resolution. We recently discovered that preserved biological specimens can be physically expanded in an isotropic fashion through a chemical process. Expansion microscopy (ExM) allows nanoscale imaging of biological specimens with conventional microscopes, decrowds biomolecules in support of signal amplification and multiplexed readout chemistries, and makes specimens transparent. We review the principles of how ExM works, advances in the technology made by our group and others, and its applications throughout biology and medicine.
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Affiliation(s)
- Asmamaw T Wassie
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yongxin Zhao
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Edward S Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. .,McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Kornfeld J, Denk W. Progress and remaining challenges in high-throughput volume electron microscopy. Curr Opin Neurobiol 2018; 50:261-267. [PMID: 29758457 DOI: 10.1016/j.conb.2018.04.030] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 04/27/2018] [Accepted: 04/30/2018] [Indexed: 01/04/2023]
Abstract
Recent advances in the effectiveness of the automatic extraction of neural circuits from volume electron microscopy data have made us more optimistic that the goal of reconstructing the nervous system of an entire adult mammal (or bird) brain can be achieved in the next decade. The progress on the data analysis side-based mostly on variants of convolutional neural networks-has been particularly impressive, but improvements in the quality and spatial extent of published VEM datasets are substantial. Methodologically, the combination of hot-knife sample partitioning and ion milling stands out as a conceptual advance while the multi-beam scanning electron microscope promises to remove the data-acquisition bottleneck.
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Affiliation(s)
- Jörgen Kornfeld
- Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Planegg, Germany.
| | - Winfried Denk
- Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Planegg, Germany
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