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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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    References listed on IDEAS

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