Analysis of Super-resolution Single Molecule Localization Microscopy Data: a tutorial

Abstract

The diffraction of light imposes a fundamental limit on the resolution of light microscopes. This limit can be circumvented by creating and exploiting independent behaviors of the sample at length scales below the diffraction limit. In super-resolution single molecule localization microscopy (SMLM), the independence arises from individual fluorescent labels stochastically switching between dark and fluorescent states, which in turn allows the pinpointing of fluorophores post experimentally using a sequence of acquired sparse image frames. Finally, the resulting list of fluorophore coordinates is utilized to produce high resolution images or to gain quantitative insight into the underlying biological structures. Therefore, image processing and post-processing are essential stages of SMLM. Here, we review the latest progress on SMLM data processing and post-processing.

References in corpus (4)

• Precise Particle Tracking Against a Complicated Background: Polynomial Fitting with Gaussian Weight
• Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy
• BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets
• Real-space post-processing correction of thermal drift and piezoelectric actuator nonlinearities in scanning tunneling microscope images