An expectation-maximization approach to quantifying protein stoichiometry with single-molecule imaging.

Abstract

Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets, such as the abundance and stoichiometry of macromolecular complexes. These algorithms often require knowledge of the complicated photophysical properties of photoswitchable fluorophores. Here, we develop a calibration-free approach to quantitative SMLM built upon the observation that most photoswitchable fluorophores emit a geometrically distributed number of blinks before photobleaching. From a statistical model of a mixture of monomers, dimers and trimers, the method employs an adapted expectation-maximization algorithm to learn the protomer fractions while simultaneously determining the single-fluorophore blinking distribution. To illustrate the utility of our approach, we benchmark it on both simulated datasets and experimental datasets assembled from SMLM images of fluorescently labeled DNA nanostructures. An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/. Supplementary data are available at Bioinformatics Advances online.