Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy

Area of Science:

• Microscopy and image analysis
• Biophysics
• Computational biology

Background:

• Assessing spatial correlation between structures in microscopic images is crucial.
• Traditional colocalization methods have limitations, including strict overlap requirements for fluorescent markers, often unmet with super-resolution imaging.
• A new approach is needed for accurate colocalization analysis, especially with advanced imaging techniques.

Purpose of the Study:

• To develop a novel colocalization method that overcomes the limitations of existing algorithms.
• To create an easy-to-use ImageJ/Fiji plugin for spatial correlation analysis.

Main Methods:

• Developed a new ImageJ/Fiji plugin named Colocalization by cross-correlation (CCC).
• Employs cross-correlation over space to identify spatial correlations as a function of distance.
• Compatible with 3D and time-lapse images.

Main Results:

• The CCC plugin successfully identifies spatial correlations without requiring fluorescent marker overlap.
• Provides more comprehensive results compared to traditional methods.
• Generates novel images highlighting correlating labeled structures.

Conclusions:

• Colocalization by cross-correlation (CCC) is a versatile, powerful, and user-friendly tool for spatial correlation analysis in microscopy.
• The CCC plugin is readily available through Fiji update sites.
• Comprehensive documentation and source code are accessible online.