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Updated: Nov 1, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Detecting structural heterogeneity in single-molecule localization microscopy data.

Teun A P M Huijben¹, Hamidreza Heydarian¹, Alexander Auer^2,3

¹Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.

Nature Communications

|June 19, 2021

Summary

This summary is machine-generated.

This study introduces unsupervised classification for single-molecule microscopy, accurately distinguishing particle structures without prior knowledge. The method effectively identifies rare structures and conformational variations in biological samples.

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Last Updated: Nov 1, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Published on: September 5, 2019

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Area of Science:

Biophysics
Structural Biology
Microscopy

Background:

Single-molecule localization microscopy (SMLM) enhances resolution but struggles with sample heterogeneity.
Particle fusion in SMLM improves signal but overlooks structural variations.
Understanding conformational variability is crucial for biological processes.

Purpose of the Study:

To develop an unsupervised method for classifying structurally different particles in SMLM data.
To address the limitations of particle fusion methods in capturing heterogeneity.
To enable the analysis of conformational variability without prior structural information.

Main Methods:

Implemented a-priori knowledge-free unsupervised classification.
Utilized the Bhattacharya cost function as a dissimilarity metric for particle comparison.
Applied the method to analyze DNA-origami structures and nuclear pore complexes.

Main Results:

Achieved 96% classification accuracy on mixtures of up to four DNA-origami structures.
Successfully detected rare origami classes present at a 2% rate.
Captured variations in the ellipticity of nuclear pore complexes, indicating conformational changes.

Conclusions:

Unsupervised classification effectively resolves structural heterogeneity in SMLM.
The Bhattacharya cost function is a robust metric for distinguishing particle conformations.
This approach advances the analysis of complex biological structures at the single-molecule level.