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

Area of Science:

• Biophysics
• Microscopy
• Computational Biology

Background:

• Single-molecule localization microscopy (SMLM) offers nanometer-scale imaging of cellular structures.
• Quantitative analysis of SMLM data requires understanding complex fluorophore photophysics.
• Existing algorithms for macromolecular complex analysis in SMLM are limited by fluorophore property dependencies.

Purpose of the Study:

• To develop a calibration-free quantitative SMLM method.
• To determine molecular complex stoichiometry and abundance from SMLM data.
• To simultaneously characterize single-fluorophore blinking properties.

Main Methods:

• Utilized a statistical model based on the geometric distribution of fluorophore blinking events.
• Applied an adapted expectation-maximization algorithm.
• Validated the method on simulated and experimental SMLM datasets of DNA nanostructures.

Main Results:

• Developed a calibration-free approach for quantitative SMLM.
• Successfully determined protomer fractions (monomers, dimers, trimers) and single-fluorophore blinking distributions.
• Demonstrated the method's utility on diverse SMLM datasets.

Conclusions:

• The new method simplifies quantitative SMLM analysis by removing the need for calibration.
• Accurate determination of molecular stoichiometry and fluorophore properties is achievable.
• The approach enhances the quantitative capabilities of SMLM for biological research.