DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Journal of Innovative Optical Health Sciences, Ahead of Print. 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 dist ribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus imp...
Source: Journal of Innovative Optical Health Sciences - Category: Biomedical Science Authors: Source Type: research