Nature Biotechnology
06 October 2022
Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes
Chang Qiao1,2,11, Di Li3,11, Yong Liu3,11, Siwei Zhang3,4,11, Kan Liu1, Chong Liu3,4, Yuting Guo3, Tao Jiang3,4, Chuyu Fang5, Nan Li6, Yunmin Zeng1, Kangmin He6,7, Xueliang Zhu5, Jennifer Lippincott-Schwartz8✉, Qionghai Dai12,9,10✉ and Dong Li3,4✉
1 Department of Automation, Tsinghua University, Beijing, China.
2 Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
3 National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
4 College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
5 State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
6 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
7 College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China.
8 Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
9 Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, Beijing, China.
10 Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China.
11 These authors contributed equally: Chang Qiao, Di Li, Yong Liu, Siwei Zhang.
10.1038/s41587-022-01471-3
Artistic rendering of a system for noise reduction in super-resolution images. Qiao et al. incorporate prior knowledge of illumination patterns into a deep learning algorithm and apply this method to sustained live cell imaging.
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