Deep-Learning Accelerated Super-Resolution Radial Fluctuations (Srrf) Enables Real-Time Live Cell Imaging

Abstract

The Super-resolution radial fluctuations (SRRF) algorithm analyzes radial and temporal fluorescence intensity fluctuations in an image sequence, which typically includes tens or hundreds of raw images to generate one super-resolution image. At present, most of the SRRF applications rely on large amounts of raw images or tedious post-processing and computation, thus are not feasible for real-time live cell super-resolution imaging. Here, we developed a novel deep learning accelerated SRRF method, which significantly reduced the requirement of raw images for super-resolution reconstruction. Our results showed that by using only 5 low signal-to-noise ratio (SNR) images, we were able to achieve super-resolution SRRF reconstruction to a comparable resolution as the traditional method. We demonstrated that by integration of GPU computing and the sliding window reconstruction method, the dynamic contraction of microtubules and the interactions between microtubules and clathrin-coated pits (CCPs) can be visualized in real-time with super-resolution. In summary, we established the deep learning accelerated SRRF method, which permits real-time, long-term and multi-color live cell super-resolution imaging, and we anticipate it will have vast biomedical applications.

Keywords: Super-resolution imaging, deep learning, SRRF reconstruction, TIRFM, U-Net