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Updated: Mar 14, 2026

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

Automatic Bayesian single molecule identification for localization microscopy.

Yunqing Tang¹, Johnny Hendriks², Thomas Gensch²

¹National Center for Nanoscience and Technology of China, Beijing 100190, P.R. China.

Scientific Reports

|September 20, 2016

Summary

This summary is machine-generated.

This study introduces Auto-Bayes, an automated method for analyzing single molecule localization microscopy (SMLM) data. Auto-Bayes removes user bias, improving molecule detection accuracy in SMLM imaging.

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Last Updated: Mar 14, 2026

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

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Published on: June 30, 2018

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

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

Published on: September 5, 2019

Area of Science:

Biophysics
Microscopy
Computational Biology

Background:

Single molecule localization microscopy (SMLM) is a powerful imaging technique in life sciences.
Current SMLM data analysis relies on subjective user-defined parameters, introducing bias.
Objective and automated analysis is needed to enhance SMLM data interpretation.

Purpose of the Study:

To develop an automated method, Auto-Bayes, for unbiased SMLM data analysis.
To eliminate human subjectivity in SMLM image processing.
To improve the accuracy and reliability of SMLM data analysis.

Main Methods:

Introduction of the Auto-Bayes method for automatic SMLM data analysis.
Utilizing photoelectron count as a primary emitter selection characteristic.
Demonstration of applicability to any bimodally distributed characteristic for emitter identification.

Main Results:

The Auto-Bayes method successfully automates SMLM data analysis, removing user bias.
Generated emitter reliability maps aid in assessing the quality of SMLM structures.
The method outperformed existing software in molecule detection during the ISBI 2015 challenge (Jaccard index).

Conclusions:

Auto-Bayes offers an objective and highly accurate approach to SMLM data analysis.
The method has the potential to become a standard tool in life science imaging.
Automated analysis significantly enhances the reliability and discoverability of SMLM-based biological structures.