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

Qihang Song Jr.; Cheng Wu Jr.; Jianming Huang Jr.; Zhiwei Zhou Jr.; Zhen-Li Huang Sr.; Zhengxia Wang Sr.

doi:10.1117/12.2684912

29 November 2023 DecodeSTORM: a user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Qihang Song Jr., Cheng Wu Jr., Jianming Huang Jr., Zhiwei Zhou Jr., Zhen-Li Huang Sr., Zhengxia Wang Sr.

Author Affiliations +

Proceedings Volume 12766, Advanced Optical Imaging Technologies VI; 127660O (2023) https://doi.org/10.1117/12.2684912
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Data in single-molecule localization microscopy (SMLM) contain a large amount of biological information, and accurate quantitative analysis of these data is crucial for studying cellular functions at the biomolecular level. Current SMLM analysis tools often rely on a single method and do not fully consider the potential effects of imaging artifacts on the accuracy of analysis. Here we developed an easy-to-use ImageJ plugin called DecodeSTORM, which integrates multiple quantitative analysis methods (including segmentation, clustering, spatial statistics and co-localization), and also provides various artifact correction methods (including drift correction and localization filtering). Users are free to combine these methods as needed to improve the accuracy of quantitative analysis. DecodeSTORM aims to provide an easy data analysis tool for biological users who are looking for a more accurate data analysis in SMLM.

Citation Download Citation

Qihang Song Jr., Cheng Wu Jr., Jianming Huang Jr., Zhiwei Zhou Jr., Zhen-Li Huang Sr., and Zhengxia Wang Sr. "DecodeSTORM: a user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy", Proc. SPIE 12766, Advanced Optical Imaging Technologies VI, 127660O (29 November 2023); https://doi.org/10.1117/12.2684912

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