Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting.

Area of Science:

• Biophysics
• Cell Biology
• Microscopy

Background:

• Cell membranes exhibit complex protein and lipid distributions.
• Super-resolution fluorescence microscopy and electron microscopy are key tools for probing membrane heterogeneity.
• Quantifying molecular clustering is crucial for understanding cellular processes.

Purpose of the Study:

• To present an analytical method using correlation functions to quantify clustering in 2D super-resolution fluorescence localization and electron microscopy images.
• To quantify the contribution of over-counting labeled molecules to apparent self-clustering.
• To calculate the effective lateral resolution of images and analyze molecular distributions in cell membranes.

Main Methods:

• Utilized pair auto-correlation and cross-correlation functions to analyze image data.
• Applied the method to quantify the distribution of the IgE receptor (FcεRI) on RBL-2H3 mast cell membranes.
• Employed stochastic optical reconstruction microscopy (STORM/dSTORM) and scanning electron microscopy (SEM) for image acquisition.

Main Results:

• Apparent clustering from over-counting varies inversely with surface density and does not depend on the number of counts per molecule.
• Over-counting does not cause apparent co-clustering in double-label experiments.
• Correcting for over-counting revealed weak but significant self-clustering of FcεRI in fluorescence data, but none in SEM data.

Conclusions:

• The developed correlation function method accurately quantifies molecular clustering and corrects for over-counting artifacts in microscopy.
• Over-counting significantly contributes to apparent FcεRI clustering in fluorescence imaging, an effect mitigated by cross-correlation analysis.
• The method provides a robust approach to analyze membrane heterogeneity and molecular distributions using advanced imaging techniques.