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

Expansion microscopy (ExM) is an innovative and cost-effective super-resolution imaging technique that enables nanoscale visualization of biological structures using conventional fluorescence microscopes. By physically enlarging biological specimens, ExM circumvents the diffraction limit and has become an indispensable tool in cell biology. Ongoing methodological advances have further enhanced its spatial resolution, labeling versatility, and compatibility with diverse sample types. However, ExM imaging is often hindered by sample drift during image acquisition, caused by subtle movements of the expanded hydrogel. This drift can distort three-dimensional reconstruction, compromising both visualization accuracy and quantitative analysis. To overcome this limitation, we developed 3D-Aligner, an advanced and user-friendly image analysis software that computationally corrects sample drift in fluorescence microscopy datasets, including but not limited to those acquired using ExM. The algorithm accurately determines drift trajectories across image stacks by detecting and matching stable background features, enabling nanometer-scale alignment to restore structural fidelity. We demonstrate that 3D-Aligner robustly corrects drift across ExM datasets with varying expansion factors and fluorescent labels. This protocol provides a comprehensive, step-by-step workflow for implementing drift correction in ExM datasets, ensuring reliable three-dimensional imaging and quantitative assessment. Key features • 3D-Aligner precisely corrects sample drift in expansion microscopy (ExM) datasets, enabling reliable 3D reconstruction and robust quantitative analysis. • Utilizes background feature detection and feature matching across z-planes to achieve nanoscale-precision drift correction. • 3D-Speckler, which is a MATLAB-based software platform, offers a customizable and user-friendly interface. • Outperforms conventional registration tools across varying expansion factors and labeling conditions and is equally applicable to non-ExM datasets.