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.
With the development of fluorescence super-resolution microscopic imaging technology, super-resolution optical microscopic imaging technology, which breaks through optical diffraction, has rapidly developed into one of the powerful research technologies in the field of life sciences due to its nanometer-level spatial resolution and low damage to samples. Since 2016, deep learning-based methods have been proposed for reconstructing super-resolution microscopy images. In this paper, we make two improvements based on the Deep-STORM network, first replacing the original full convolution with the dilated convolution. Then, the skip connection was added on top of the dilated convolution to reconstruct the microtubule dataset of the EPFL·SMLM challenge site. The experimental results show that NMSEs are reduced by 36.84% and 37.99% respectively on the two improved methods compared to CEL0. Compared to FALCON, NMSEs are reduced by 24.69% and 25.84% respectively. Compared to Deep-STORM, the NMSEs are reduced by 2.2% and 3.35% respectively. Using this method, super-resolution microscopic images at the nanometer-level can be reconstructed quickly and accurately, thus meeting the requirement to observe subcellular microtubule structures beyond the diffraction limit.
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