An Expectation-maximization Approach to Quantifying Protein Stoichiometry with Single-molecule Imaging

Overview

Journal Bioinform Adv

Publisher Oxford University Press

Specialty Biology

Date 2023 Jan 26

PMID 36700088

Authors

Artittaya Boonkird

Daniel F Nino

Joshua N Milstein

Affiliations

Soon will be listed here.

Abstract

Motivation: 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.

Results: 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.

Availability And Implementation: An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/.

Supplementary Information: Supplementary data are available at online.

Citing Articles

Single-molecule counting applied to the study of GPCR oligomerization.

Milstein J, Nino D, Zhou X, Gradinaru C Biophys J. 2022; 121(17):3175-3187.

PMID: 35927960 PMC: 9463696. DOI: 10.1016/j.bpj.2022.07.034.

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