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Updated: Jan 18, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

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Published on: June 24, 2013

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.

Daniel Krentzel^1,2, Matouš Elphick^3,4,5,6, Marie-Charlotte Domart³

¹Imaging and Modeling Unit, Institut Pasteur, Université Paris Cité, Paris, France. daniel.krentzel@pasteur.fr.

|September 10, 2025

Summary

This summary is machine-generated.

This study introduces CLEM-Reg, an automated algorithm for aligning volume correlative light and electron microscopy (vCLEM) datasets. This method significantly speeds up vCLEM analysis and improves protein localization accuracy.

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Area of Science:

Cell Biology
Microscopy Techniques
Bioinformatics

Background:

Volume correlative light and electron microscopy (vCLEM) combines fluorescence and ultrastructural imaging.
Current vCLEM alignment methods are manual, time-consuming, and subjective.
Accurate alignment is crucial for correlating fluorescent signals with cellular structures.

Purpose of the Study:

To develop an automated algorithm for 3D alignment of vCLEM datasets.
To reduce the time and subjectivity associated with vCLEM data processing.
To enable precise localization of fluorescently labeled proteins within ultrastructural context.

Main Methods:

Development of CLEM-Reg, an algorithm using probabilistic point cloud registration.
Derivation of point clouds from segmentations of common structures in light and electron microscopy data.
Application of state-of-the-art open-source segmentation methods.

Main Results:

CLEM-Reg automates vCLEM dataset alignment in minutes, drastically reducing processing time.
Achieved submicron accuracy in correlating fluorescent signals to target structures in electron microscopy.
Successfully identified TGN46-positive transport carriers involved in protein trafficking.

Conclusions:

CLEM-Reg offers a robust and efficient solution for vCLEM data alignment.
Automated alignment enhances the accuracy and throughput of vCLEM studies.
The algorithm facilitates the study of protein trafficking and cellular organization.