Isotropic reconstruction for electron tomography with deep learning
Yun-Tao Liu,
Heng Zhang,
Hui Wang,
Chang-Lu Tao,
Guo-Qiang Bi () and
Z. Hong Zhou ()
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Yun-Tao Liu: University of Science and Technology of China
Heng Zhang: University of Science and Technology of China
Hui Wang: University of California, Los Angeles (UCLA)
Chang-Lu Tao: University of Science and Technology of China
Guo-Qiang Bi: University of Science and Technology of China
Z. Hong Zhou: University of California, Los Angeles (UCLA)
Nature Communications, 2022, vol. 13, issue 1, 1-17
Abstract:
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.
Date: 2022
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DOI: 10.1038/s41467-022-33957-8
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