Self-supervised denoising for structured illumination microscopy enables long-term super-resolution live-cell imaging

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.05.535684v1?rss=1 Authors: Chen, X., Qiao, C., Jiang, T., Liu, J., Meng, Q., Zeng, Y., Chen, H., Zhang, Y., Li, X., Zhang, G., Li, Y., Qiao, H., Wu, J., Tan, S., Li, D., Dai, Q. Abstract: Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 10-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to ground truth (GT). Moreover, the proposed method is compatible with multiple SIM modalities such as total internal reflective fluorescence SIM (TIRF-SIM), three-dimensional SIM (3D-SIM), and lattice light-sheet SIM (LLS-SIM). With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various bioprocesses, revealing the clustered distribution of clathrin coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC