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Updated: Apr 1, 2026

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling

Published on: March 20, 2026

Bayesian cluster identification in single-molecule localization microscopy data.

Patrick Rubin-Delanchy¹, Garth L Burn², Juliette Griffié²

¹School of Mathematics, Heilbronn Institute for Mathematical Research, University of Bristol, Bristol, UK.

|October 6, 2015

Summary

This summary is machine-generated.

We developed a Bayesian method to analyze molecular clustering from super-resolution microscopy data. This approach accurately identifies molecular clusters, even with varying localization precision, using simulated and experimental T cell data.

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Last Updated: Apr 1, 2026

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Published on: June 30, 2018

Area of Science:

Biophysics
Cell Biology
Microscopy

Background:

Super-resolution microscopy techniques like photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) generate precise molecular coordinate data.
Analyzing molecular patterns for clustering remains a challenge despite numerous localization algorithms.

Purpose of the Study:

To present a novel model-based Bayesian approach for evaluating molecular cluster assignments from super-resolution microscopy data.
To incorporate individual localization precisions into cluster analysis.

Main Methods:

Utilized Ripley's K function to generate initial cluster assignment proposals.
Developed a Bayesian framework to rigorously evaluate these cluster proposals.
Accounted for the precision of each molecular localization.

Main Results:

Validated the Bayesian approach using simulated datasets with known clustering properties.
Applied the method to experimental data examining CD3ζ subunit clustering in human T cells.
Demonstrated the method's effectiveness in both resting and activated T cells.

Conclusions:

The presented Bayesian method provides a robust framework for analyzing molecular clustering in super-resolution microscopy.
This approach enhances the understanding of molecular organization and its functional implications, particularly in immune cell signaling.