Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

超分辨显微成像技术是细胞生物学中研究细胞器结构、相互作用和蛋白质功能的强大工具,其具有突破光学衍射极限的分辨能力,从纳米尺度上为细胞生物学提供了新的分析手段,对生命科学相关领域具有重大意义。然而,受衍射极限的影响,超分辨显微镜的轴向分辨率相比于横向分辨率要更难以提高,这导致实现细胞结构亚百纳米分辨率的三维成像更为困难。从受激辐射损耗显微术和单分子定位显微术这两种主流技术出发,对目前存在的多种三维成像技术进行了原理介绍和特点分析,最后对其未来发展方向进行了展望。