Correcting Image Distortion in Expansion Microscopy Using 3D-Aligner.

Area of Science:

• Microscopy and Imaging Technologies
• Biotechnology and Bioimaging
• Computational Biology

Background:

• Expansion microscopy (ExM) offers super-resolution imaging by physically enlarging specimens, overcoming the diffraction limit for nanoscale biological visualization.
• Despite advances, ExM imaging accuracy is compromised by sample drift during acquisition, distorting 3D reconstructions and quantitative analysis.
• Existing methods struggle to accurately correct drift in diverse ExM datasets with varying expansion factors and labels.

Purpose of the Study:

• To develop and validate a computational tool for correcting sample drift in ExM datasets.
• To enhance the reliability of 3D reconstruction and quantitative analysis in super-resolution microscopy.
• To provide a user-friendly solution applicable to both ExM and conventional fluorescence microscopy.

Main Methods:

• Developed 3D-Aligner, a software utilizing background feature detection and matching across image stacks to determine drift trajectories.
• Implemented a nanometer-scale alignment algorithm to computationally correct sample movement.
• Validated the software's performance on ExM datasets with different expansion factors and fluorescent labels.

Main Results:

• 3D-Aligner accurately corrects sample drift in ExM datasets, restoring structural fidelity.
• The software demonstrates robust performance across varying expansion factors and labeling conditions.
• 3D-Aligner outperforms conventional registration tools and is applicable to non-ExM fluorescence microscopy data.

Conclusions:

• 3D-Aligner provides a precise and user-friendly solution for mitigating sample drift in ExM imaging.
• This computational approach enables reliable 3D reconstruction and quantitative assessment of nanoscale biological structures.
• The software enhances the utility of ExM and other fluorescence microscopy techniques for advanced biological research.