KCBC - a correlation-based method for co-localization analysis of super-resolution microscopy images using bivariate...

Area of Science:

• Biophysics
• Microscopy
• Computational Biology

Background:

• Super-resolution microscopy provides nanoscale molecular localization data.
• Accurate co-localization analysis is crucial for understanding molecular interactions.
• Existing methods may suffer from false positives and noise.

Purpose of the Study:

• To develop a novel correlation-based co-localization analysis method for super-resolution microscopy images.
• To quantify local and global molecular co-localization between different species.
• To address limitations of existing methods, such as false positives and variance.

Main Methods:

• Utilized bivariate Ripley's K functions to measure spatial correlations.
• Developed the Kernel-based Correlation of Bivariate K-functions (KCBC) method.
• Calculated local KCBC values within concentric rings and averaged for global quantification.

Main Results:

• KCBC method demonstrated unbiasedness in extensive simulations.
• The method effectively filters noise and corrects random over-counting.
• KCBC showed increased power and reduced variance compared to other methods.

Conclusions:

• KCBC is a robust and effective method for co-localization analysis in super-resolution microscopy.
• The method accurately quantifies molecular spatial relationships, enhancing biological insights.
• Demonstrated applicability and effectiveness on real super-resolution mitochondria image data.