Isotropic reconstruction for electron tomography with deep learning.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
29 10 2022
Historique:
received: 14 09 2021
accepted: 05 10 2022
pubmed: 31 10 2022
medline: 2 11 2022
entrez: 30 10 2022
Statut: epublish

Résumé

Cryogenic electron tomography (cryoET) allows visualization of cellular structures in situ. However, anisotropic resolution arising from the intrinsic "missing-wedge" problem has presented major challenges in visualization and interpretation of tomograms. Here, we have developed IsoNet, a deep learning-based software package that iteratively reconstructs the missing-wedge information and increases signal-to-noise ratio, using the knowledge learned from raw tomograms. Without the need for sub-tomogram averaging, IsoNet generates tomograms with significantly reduced resolution anisotropy. Applications of IsoNet to three representative types of cryoET data demonstrate greatly improved structural interpretability: resolving lattice defects in immature HIV particles, establishing architecture of the paraflagellar rod in Eukaryotic flagella, and identifying heptagon-containing clathrin cages inside a neuronal synapse of cultured cells. Therefore, by overcoming two fundamental limitations of cryoET, IsoNet enables functional interpretation of cellular tomograms without sub-tomogram averaging. Its application to high-resolution cellular tomograms should also help identify differently oriented complexes of the same kind for sub-tomogram averaging.

Identifiants

pubmed: 36309499
doi: 10.1038/s41467-022-33957-8
pii: 10.1038/s41467-022-33957-8
pmc: PMC9617606
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

6482

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM071940
Pays : United States
Organisme : NIH HHS
ID : S10 OD018111
Pays : United States
Organisme : NCRR NIH HHS
ID : S10 RR023057
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Yun-Tao Liu (YT)

Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China.
California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Microbiology, Immunology and Molecular Genetics, UCLA, Los Angeles, CA, 90095, USA.

Heng Zhang (H)

Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Department of Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China.

Hui Wang (H)

California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Microbiology, Immunology and Molecular Genetics, UCLA, Los Angeles, CA, 90095, USA.
Department of Bioengineering, UCLA, Los Angeles, CA, 90095, USA.

Chang-Lu Tao (CL)

Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Interdisciplinary Center for Brain Information, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

Guo-Qiang Bi (GQ)

Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China. gqbi@ustc.edu.cn.
Interdisciplinary Center for Brain Information, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. gqbi@ustc.edu.cn.
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China. gqbi@ustc.edu.cn.

Z Hong Zhou (ZH)

California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA. Hong.Zhou@UCLA.edu.
Department of Microbiology, Immunology and Molecular Genetics, UCLA, Los Angeles, CA, 90095, USA. Hong.Zhou@UCLA.edu.
Department of Bioengineering, UCLA, Los Angeles, CA, 90095, USA. Hong.Zhou@UCLA.edu.

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Classifications MeSH