PDF(Pubmed)
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.
摘要：
纳米尺度形貌中基于DNA的点积累（DNA-PAINT）是一种用于单分子定位显微镜（SMLM）的成熟技术，使分辨率达到几纳米。传统上,DNA-PAINT涉及利用成千上万的单分子荧光图像来生成单个超分辨率图像。这个过程可能很耗时，这对许多研究人员来说是不可行的。这里,我们提出了一种简化的DNA-PAINT标记方法和一种基于深度学习的亚细胞结构快速DNA-PAINT成像策略，如微管。通过使用我们的方法，超分辨率重建可以用以前需要的原始数据的十分之一来实现，以及获取广域图像的选项。因此,DNA-PAINT成像明显加速,使其更容易被更广泛的生物研究人员所接受。
