Böhning, J, Bharat, TAM and Collins, SM orcid.org/0000-0002-5151-6360 (2022) Compressed sensing for electron cryotomography and high-resolution subtomogram averaging of biological specimens. Structure, 30 (3). pp. 408-417. ISSN 0969-2126
Abstract
Cryoelectron tomography (cryo-ET) and subtomogram averaging (STA) allow direct visualization and structural studies of biological macromolecules in their native cellular environment, in situ. Often, low signal-to-noise ratios in tomograms, low particle abundance within the cell, and low throughput in typical cryo-ET workflows severely limit the obtainable structural information. To help mitigate these limitations, here we apply a compressed sensing approach using 3D second-order total variation (CS-TV2) to tomographic reconstruction. We show that CS-TV2 increases the signal-to-noise ratio in tomograms, enhancing direct visualization of macromolecules, while preserving high-resolution information up to the secondary structure level. We show that, particularly with small datasets, CS-TV2 allows improvement of the resolution of STA maps. We further demonstrate that the CS-TV2 algorithm is applicable to cellular specimens, leading to increased visibility of molecular detail within tomograms. This work highlights the potential of compressed sensing-based reconstruction algorithms for cryo-ET and in situ structural biology.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jan 2022 12:14 |
Last Modified: | 25 Feb 2025 14:41 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.str.2021.12.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182725 |