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bioRxiv · 10.1101/2021.07.17.452128

Isotropic Reconstruction of Electron Tomograms with Deep Learning

Liu, Y.-T.·Zhang, H.·Wang, H.·Tao, C.-L.·Bi, G.-Q.·Zhou, Z. H.
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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 near-atomic resolution sub-tomogram averaging.

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Liu, Y.-T., Zhang, H., Wang, H., Tao, C.-L., Bi, G.-Q., Zhou, Z. H.. 2021-07-19. Isotropic Reconstruction of Electron Tomograms with Deep Learning. https://doi.org/10.1101/2021.07.17.452128

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