dSTORMQuant: A Python Package for Post-Processing and Quantitative Analysis of SMLM datasets

Abstract

SummarySingle-molecule localization microscopy techniques, such as (direct) stochastic optical reconstruction microscopy ((d)STORM) and photo-activated localization microscopy (PALM) enable the visualization of subcellular molecular organization beyond the diffraction limit of conventional light microscopy. Not only is data acquisition rather slow, but the downstream analysis of localization datasets often remains computationally challenging and time-consuming. Consequently, the complexity and duration of data processing often limit experiments to the acquisition and analysis of only small numbers of cells or regions of interest, thereby restricting the statistical power and biological reliability of SMLM studies. To address this limitation, we developed an open-source Python-based package for automated, high-throughput post-processing and quantitative analysis of SMLM localization data, enabling efficient and straightforward handling of extensive datasets with minimal manual intervention. Availability and implementationdSTORMQuant (source code and documentation) are freely available on GitHub at https://github.com/BCMM-Bielefeld-University/dSTORMQuant under GPL v3 license.