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Isotropic reconstruction for electron tomography with deep learning

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  • Yun-Tao Liu

    (University of Science and Technology of China
    University of California, Los Angeles (UCLA)
    Immunology and Molecular Genetics, UCLA)

  • Heng Zhang

    (University of Science and Technology of China
    University of Science and Technology of China)

  • Hui Wang

    (University of California, Los Angeles (UCLA)
    Immunology and Molecular Genetics, UCLA
    UCLA)

  • Chang-Lu Tao

    (University of Science and Technology of China
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Guo-Qiang Bi

    (University of Science and Technology of China
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Z. Hong Zhou

    (University of California, Los Angeles (UCLA)
    Immunology and Molecular Genetics, UCLA
    UCLA)

Abstract

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.

Suggested Citation

  • Yun-Tao Liu & Heng Zhang & Hui Wang & Chang-Lu Tao & Guo-Qiang Bi & Z. Hong Zhou, 2022. "Isotropic reconstruction for electron tomography with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33957-8
    DOI: 10.1038/s41467-022-33957-8
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    Cited by:

    1. Haonan Zhang & Yan Li & Yanan Liu & Dongyu Li & Lin Wang & Kai Song & Keyan Bao & Ping Zhu, 2023. "A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Nikita Balyschew & Artsemi Yushkevich & Vasilii Mikirtumov & Ricardo M. Sanchez & Thiemo Sprink & Mikhail Kudryashev, 2023. "Streamlined structure determination by cryo-electron tomography and subtomogram averaging using TomoBEAR," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Xueming Meng & Cong Xu & Jiawei Li & Benhua Qiu & Jiajun Luo & Qin Hong & Yujie Tong & Chuyu Fang & Yanyan Feng & Rui Ma & Xiangyi Shi & Cheng Lin & Chen Pan & Xueliang Zhu & Xiumin Yan & Yao Cong, 2024. "Multi-scale structures of the mammalian radial spoke and divergence of axonemal complexes in ependymal cilia," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Michelle M. Shimogawa & Angeline S. Wijono & Hui Wang & Jiayan Zhang & Jihui Sha & Natasha Szombathy & Sabeeca Vadakkan & Paula Pelayo & Keya Jonnalagadda & James Wohlschlegel & Z. Hong Zhou & Kent L., 2023. "FAP106 is an interaction hub for assembling microtubule inner proteins at the cilium inner junction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Conny Leistner & Martin Wilkinson & Ailidh Burgess & Megan Lovatt & Stanley Goodbody & Yong Xu & Susan Deuchars & Sheena E. Radford & Neil A. Ranson & René A. W. Frank, 2023. "The in-tissue molecular architecture of β-amyloid pathology in the mammalian brain," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Rebeccah A. Warmack & Ailiena O. Maggiolo & Andres Orta & Belinda B. Wenke & James B. Howard & Douglas C. Rees, 2023. "Structural consequences of turnover-induced homocitrate loss in nitrogenase," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Un Seng Chio & Eugene Palovcak & Anton A. A. Smith & Henriette Autzen & Elise N. Muñoz & Zanlin Yu & Feng Wang & David A. Agard & Jean-Paul Armache & Geeta J. Narlikar & Yifan Cheng, 2024. "Functionalized graphene-oxide grids enable high-resolution cryo-EM structures of the SNF2h-nucleosome complex without crosslinking," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Zhen Hou & Frank Nightingale & Yanan Zhu & Craig MacGregor-Chatwin & Peijun Zhang, 2023. "Structure of native chromatin fibres revealed by Cryo-ET in situ," Nature Communications, Nature, vol. 14(1), pages 1-7, December.

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