DNA-PAINT Imaging Accelerated by Machine Learning - Zhu, Min; Zhang, Luhao; Jin, Luhong; Chen, Jincheng; Zhang, Yongdeng; Xu, Yingke (Frontiers in Chemistry, 2022)
DNA point accumulation in nanoscale topography (DNA-PAINT) is an easy-to-implement approach for localization-based super-resolution imaging. Conventional DNA-PAINT imaging typically requires tens of thousands frames of raw data to reconstruct one super-resolution image, which prevents its potential application for live imaging. Here, we introduce a new DNA-PAINT labeling method that allows for imaging of microtubules with both DNA-PAINT and widefield illumination. We develop a U-Net based neural network, namely U-PAINT to accelerate DNA-PAINT imaging from a widefield fluorescent image and a sparse single molecule localization image. Comparing with conventional method, U-PAINT only needs one tenth of original raw data, which permits fast imaging and reconstruction of super-resolution microtubules and can be adopted to analyze other SMLM datasets. We anticipate that this machine learning method enables faster and even live-cell DNA-PAINT imaging in the future.