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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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    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.
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    3. 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.
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    6. Bryan S. Sibert & Joseph Y. Kim & Jie E. Yang & Zunlong Ke & Christopher C. Stobart & Martin L. Moore & Elizabeth R. Wright, 2024. "Assembly of respiratory syncytial virus matrix protein lattice and its coordination with fusion glycoprotein trimers," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
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    8. Iskander Khusainov & Natalie Romanov & Camille Goemans & Beata Turoňová & Christian E. Zimmerli & Sonja Welsch & Julian D. Langer & Athanasios Typas & Martin Beck, 2024. "Bactericidal effect of tetracycline in E. coli strain ED1a may be associated with ribosome dysfunction," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Liming Zheng & Jie Xu & Weihua Wang & Xiaoyin Gao & Chao Zhao & Weijun Guo & Luzhao Sun & Hang Cheng & Fanhao Meng & Buhang Chen & Weiyu Sun & Xia Jia & Xiong Zhou & Kai Wu & Zhongfan Liu & Feng Ding , 2024. "Self-assembled superstructure alleviates air-water interface effect in cryo-EM," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    10. Tim Schulte & Antonio Chaves-Sanjuan & Valentina Speranzini & Kevin Sicking & Melissa Milazzo & Giulia Mazzini & Paola Rognoni & Serena Caminito & Paolo Milani & Chiara Marabelli & Alessandro Corbelli, 2024. "Helical superstructures between amyloid and collagen in cardiac fibrils from a patient with AL amyloidosis," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    11. Kaitlyn M. Abe & Gan Li & Qixiang He & Timothy Grant & Ci Ji Lim, 2024. "Small LEA proteins mitigate air-water interface damage to fragile cryo-EM samples during plunge freezing," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    12. Yitang Zhang & Maofei Chen & Xudong Chen & Minghui Zhang & Jian Yin & Zi Yang & Xin Gao & Sensen Zhang & Maojun Yang, 2024. "Molecular architecture of the mammalian 2-oxoglutarate dehydrogenase complex," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. 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.
    14. 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.
    15. 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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