Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging

Rong Chen, Xiao Tang, Yuxuan Zhao, Zeyu Shen, Meng Zhang, Yusheng Shen, Tiantian Li, Casper Ho Yin Chung, Lijuan Zhang, Ji Wang, Binbin Cui, Peng Fei, Yusong Guo (), Shengwang Du () and Shuhuai Yao ()
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Rong Chen: The Hong Kong University of Science and Technology
Xiao Tang: The Hong Kong University of Science and Technology
Yuxuan Zhao: Huazhong University of Science and Technology
Zeyu Shen: The Hong Kong University of Science and Technology
Meng Zhang: Huazhong University of Science and Technology
Yusheng Shen: The Hong Kong University of Science and Technology
Tiantian Li: The Hong Kong University of Science and Technology
Casper Ho Yin Chung: The Hong Kong University of Science and Technology
Lijuan Zhang: Guizhou University
Ji Wang: The Hong Kong University of Science and Technology
Binbin Cui: The Hong Kong University of Science and Technology
Peng Fei: Huazhong University of Science and Technology
Yusong Guo: The Hong Kong University of Science and Technology
Shengwang Du: The Hong Kong University of Science and Technology
Shuhuai Yao: The Hong Kong University of Science and Technology

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Here we develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method which utilizes a subpixel edge map and a multicomponent optimization strategy to guide the neural network to reconstruct a super-resolution image from a single frame of a diffraction-limited image. Under a tolerable signal density and an affordable signal-to-noise ratio, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics such as interplays between mitochondria and endoplasmic reticulum, the vesicle transport along microtubules, and the endosome fusion and fission. Moreover, its adaptability to different microscopes and spectra makes it a useful tool for various imaging systems.

Date: 2023
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DOI: 10.1038/s41467-023-38452-2

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