Fluorophore localization algorithms for super-resolution microscopy.

Super-resolution localization microscopy methods provide powerful new capabilities for probing biology at the nanometer scale via fluorescence. These methods rely on two key innovations: switchable fluorophores (which blink on and off and can be sequentially imaged) and powerful localization algorithms (which estimate the positions of the fluorophores in the images). These techniques have spurred a flurry of innovation in algorithm development over the last several years. In this Review, we survey the fundamental issues for single-fluorophore fitting routines, localization algorithms based on principles other than fitting, three-dimensional imaging, dipole imaging and techniques for estimating fluorophore positions from images of multiple activated fluorophores. We offer practical advice for users and adopters of algorithms, and we identify areas for further development.

Fast maximum likelihood algorithm for localization of fluorescent molecules.

A common task in microscopy is to fit an image of a fluorescent probe to a point spread function (PSF) in order to estimate the position of the probe. The PSF is often approximated as a Gaussian for mathematical simplicity. We show that the separable property of the Gaussian PSF enables a reduction of computational time from O(L2) to O(L), where L is the width (in pixels) of the image. When tested on realistic simulated data, our algorithm is able to localize the probes with precision close to the Cramér-Rao lower bound.