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Dense neuronal reconstruction through X-ray holographic nano-tomography
Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light (LM) and electron microscopy (EM). Here, we show that X-ray holographic nano-tomography (XNH) can image millimeter-sca...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354006/ https://www.ncbi.nlm.nih.gov/pubmed/32929244 http://dx.doi.org/10.1038/s41593-020-0704-9 |
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author | Kuan, Aaron T. Phelps, Jasper S. Thomas, Logan A. Nguyen, Tri M. Han, Julie Chen, Chiao-Lin Azevedo, Anthony W. Tuthill, John C. Funke, Jan Cloetens, Peter Pacureanu, Alexandra Allen Lee, Wei-Chung |
author_facet | Kuan, Aaron T. Phelps, Jasper S. Thomas, Logan A. Nguyen, Tri M. Han, Julie Chen, Chiao-Lin Azevedo, Anthony W. Tuthill, John C. Funke, Jan Cloetens, Peter Pacureanu, Alexandra Allen Lee, Wei-Chung |
author_sort | Kuan, Aaron T. |
collection | PubMed |
description | Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light (LM) and electron microscopy (EM). Here, we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100 nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells, and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery. |
format | Online Article Text |
id | pubmed-8354006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-83540062021-08-10 Dense neuronal reconstruction through X-ray holographic nano-tomography Kuan, Aaron T. Phelps, Jasper S. Thomas, Logan A. Nguyen, Tri M. Han, Julie Chen, Chiao-Lin Azevedo, Anthony W. Tuthill, John C. Funke, Jan Cloetens, Peter Pacureanu, Alexandra Allen Lee, Wei-Chung Nat Neurosci Article Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light (LM) and electron microscopy (EM). Here, we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100 nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells, and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery. 2020-09-14 2020-12 /pmc/articles/PMC8354006/ /pubmed/32929244 http://dx.doi.org/10.1038/s41593-020-0704-9 Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Kuan, Aaron T. Phelps, Jasper S. Thomas, Logan A. Nguyen, Tri M. Han, Julie Chen, Chiao-Lin Azevedo, Anthony W. Tuthill, John C. Funke, Jan Cloetens, Peter Pacureanu, Alexandra Allen Lee, Wei-Chung Dense neuronal reconstruction through X-ray holographic nano-tomography |
title | Dense neuronal reconstruction through X-ray holographic nano-tomography |
title_full | Dense neuronal reconstruction through X-ray holographic nano-tomography |
title_fullStr | Dense neuronal reconstruction through X-ray holographic nano-tomography |
title_full_unstemmed | Dense neuronal reconstruction through X-ray holographic nano-tomography |
title_short | Dense neuronal reconstruction through X-ray holographic nano-tomography |
title_sort | dense neuronal reconstruction through x-ray holographic nano-tomography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354006/ https://www.ncbi.nlm.nih.gov/pubmed/32929244 http://dx.doi.org/10.1038/s41593-020-0704-9 |
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