Deep learning-enabled fast DNA-PAINT imaging in cells

Min Zhu; Luhao Zhang; Luhong Jin; Yunyue Chen; Haixu Yang; Baohua Ji; Yingke Xu

doi:10.52601/bpr.2023.230014

Deep learning-enabled fast DNA-PAINT imaging in cells

Biophys Rep. 2023 Aug 31;9(4):177-187. doi: 10.52601/bpr.2023.230014.

Authors

Min Zhu¹, Luhao Zhang^{1

2}, Luhong Jin¹, Yunyue Chen¹, Haixu Yang¹, Baohua Ji³, Yingke Xu^{1

2

4}

Affiliations

¹ Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine for Clinical Evaluation and Translational Research, Zhejiang University, Hangzhou 310027, China.
² Binjiang Institute of Zhejiang University, Hangzhou 310053, China.
³ Department of Engineering Mechanics, Biomechanics and Biomaterials Laboratory, Zhejiang University, Hangzhou 310027, China.
⁴ Department of Endocrinology, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou 310051, China.

Abstract

DNA-based point accumulation in nanoscale topography (DNA-PAINT) is a well-established technique for single-molecule localization microscopy (SMLM), enabling resolution of up to a few nanometers. Traditionally, DNA-PAINT involves the utilization of tens of thousands of single-molecule fluorescent images to generate a single super-resolution image. This process can be time-consuming, which makes it unfeasible for many researchers. Here, we propose a simplified DNA-PAINT labeling method and a deep learning-enabled fast DNA-PAINT imaging strategy for subcellular structures, such as microtubules. By employing our method, super-resolution reconstruction can be achieved with only one-tenth of the raw data previously needed, along with the option of acquiring the widefield image. As a result, DNA-PAINT imaging is significantly accelerated, making it more accessible to a wider range of biological researchers.

Keywords: DNA-PAINT; Deep learning; SMLM; Super-resolution imaging; U-Net.