Researchers made breakthrough in reconstruction for cryogenic electron tomography

In a study published in Nature Communication recently, a team led by Prof. BI Guoqiang from the University of Science and Technology of China (USTC) and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), together with collaborators from the United States, developed a software package named IsoNet for the isotropic reconstruction for cryogenic electron tomography (cryoET). Their work effectively solved the intrinsic "missing-wedge" problem and low signal-to-noise ratio problems in cryoET. Anisotropic resolution caused by the intrinsic "missing-wedge" problem has long been a challenge when using CryoET for the visualization of cellular structures. To solve this, the team developed IsoNet, a software package based on iterative self-supervised deep learning artificial neural network. Using the rotated cryoET tomographic 3D reconstruction data as the training set, their algorithm is able to perform missing-edge correction on the cryoET data. Simultaneously, a denoising process is added to the IsoNet, allowing the artificial neural network to recover missing information and denoise tomographic 3D data simultaneously.