Abstract
Observing subcellular structural dynamics in living cells has become the goal of super-resolution (SR) fluorescence microscopy. Among typical SRM techniques, structured illumination microscopy (SIM) stands out for its fast imaging speed and low photobleaching. However, 2D-SIM requires nine raw images to obtain a SR image, leading to undesirable artifacts in the fast dynamics of live-cell imaging. In this paper, we propose a single-frame structured illumination microscopy (SF-SIM) method based on deep learning that achieves SR imaging using only a single image modulated by a hexagonal lattice pattern. The SF-SIM method used the prior knowledge to complete the structure enhancement of SR images in the spatial domain and the expansion of the Fourier spectrum through deep learning, achieving the same resolution as conventional 2D-SIM. Temporal resolution is improved nine times, and photobleaching is reduced by 2.4 times compared to conventional 2D-SIM. Based on this, we observed the fast dynamics of multiple subcellular structures and the dynamic interaction of two organelles. The SF-SIM methods provide a powerful tool for live-cell imaging.
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