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Single-molecule localization microscopy (SMLM) relies on the temporally separated emission of individual molecules. While the acquired image is still diffraction limited, due to their sparsity, each molecule can be localized at nanometer precision and their coordinates accumulated to form a super-resolved image. But the power of SMLM goes far beyond generating images. Computational analysis tools allow extracting the spatial organization, colocalization, clustering, and dynamics of individual molecules, which has transformed the study of nanoscale biology over the past decade. This wiki gives an introduction to SMLM and mostly practical information to get you started with your own experiments or data analysis.
Here I describe the common SMLM data analysis workflow starting from localization over filtering and image reconstruction.
This page describes how to perform a single-molecule calibration experiment and provides some scripts to extract quantitative information. I provide and demonstrate analysis of a test dataset of Alexa647-labelled antibodies. Blinking characteristics can be used in the following to group (merge) blinking events and estimate molecule quantities.
Here you'll see how to perform a basic spatial analysis using simple density-based clustering and refer to more complex analysis schemes.
The tracking page describes how to link molecule positions between frames to extract molecule diffusion properties from live cell experiments.
SMLM is not possible without the right software tools. A number of excellent open-source software platforms are available trough the SMLM community. While I provide some of my personal analysis scripts in this repository, I summarize what else is available and give main applications.
If you want to know more. I further provide references throughout the other sections.