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bioRxiv · 10.1101/2021.10.08.463746

Deep-Learning Super-Resolution Microscopy Reveals Nanometer-Scale Intracellular Dynamics at the Millisecond Temporal Resolution

Chen, R.·Tang, X.·Shen, Z.·Shen, Y.·Li, T.·Wang, J.·Cui, B.·Guo, Y.·Du, S.·Yao, S.
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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 in thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Based on deep learning networks, we develop a single-frame super-resolution microscopy (SFSRM) approach that reconstructs a super-resolution image from a single frame of a diffraction-limited image to support live-cell super-resolution imaging at a [~]20 nm spatial resolution and a temporal resolution of up to 10 ms over thousands of time points. We demonstrate that our SFSRM method enables the visualization of the dynamics of vesicle transport at a millisecond temporal resolution in the dense and vibrant microtubule network in live cells. Moreover, the well-trained network model can be used with different live-cell imaging systems, such as confocal and light-sheet microscopes, making super-resolution microscopy accessible to nonexperts.

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Chen, R., Tang, X., Shen, Z., Shen, Y., Li, T., Wang, J., Cui, B., Guo, Y., Du, S., Yao, S.. 2021-10-09. Deep-Learning Super-Resolution Microscopy Reveals Nanometer-Scale Intracellular Dynamics at the Millisecond Temporal Resolution. https://doi.org/10.1101/2021.10.08.463746

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