As an emerging therapeutic strategy, proteolysis-targeting chimeras (PROTACs) have been proven to be superior to traditional drugs in many aspects. However, due to their unique mechanism of action, existing methods for evaluating the degradation still have many limitations, which seriously restricts the development of PROTACs. In this methodological study, using direct stochastic optical reconstruction microscopy (dSTORM)-based single-cell protein quantitative analysis, we systematically investigated the dynamic degradation characteristics of FLT3 protein during PROTACs treatment. We found that the distribution of FLT3 varies between FLT3-ITD mutation and FLT3-WT cells. PROTACs had an obvious time-course effect on protein degradation and present two distinct phases; this provided a basis for deciding when to evaluate protein degradation. High concentrations of PROTACs were more effective than long-time administration because a higher D max was achieved. Two-color dSTORM-based colocalization analysis efficiently detected the proportion of ternary complexes, making it very useful in screening PROTACs. Taken together, our findings show that the dSTORM method is an ideal tool for evaluating PROTACs and will accelerate the development of new PROTACs.

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.

Single molecule localization microscopy (SMLM) enables the analysis and quantification of protein complexes at the nanoscale. Using clustering analysis methods, quantitative information about protein complexes (for example, the size, density, number, and the distribution of nearest neighbors) can be extracted from coordinate-based SMLM data. However, since a final super-resolution image in SMLM is usually reconstructed from point clouds that contain millions of localizations, current popular clustering methods are not fast enough to enable daily quantification on such a big dataset. Here, we provide a fast and accurate clustering analysis method called FACAM, which is modified from the Alpha Shape method (a point dataset analysis method used in many fields). By taking advantage of parallel computation, FACAM is able to process millions of localizations in less than an hour, which is at least 10 times faster than the popular DBSCAN method. Furthermore, FACAM adaptively determines the segmentation threshold, and thus overcomes the problem of user-defined parameters. Using simulation and experimental datasets, we verified the advantages of FACAM over other reported clustering methods (including Ripley’s H, DBSCAN, and ClusterViSu).

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