Model-free quantification and visualization of colocalization in fluorescence images.

Area of Science:

• Cell Biology
• Biophysics
• Image Analysis

Background:

• In situ spatial association of fluorescently tagged biomolecules reveals biological relationships.
• Pearson's Correlation Coefficient (PCC) and Spearman's Rank Coefficient (SRC) measure linear and monotonic correlations, respectively, but struggle with complex biological signals.

Purpose of the Study:

• To adapt information theory methods for measuring statistical dependence between two probes' concentrations.
• To provide a more general and robust approach for quantifying biomolecular association in fluorescence microscopy.

Main Methods:

• Applied information theory concepts to measure statistical dependence between probe concentrations.
• Compared the novel approach against PCC and SRC for analyzing biological images.

Main Results:

• The information theory-based method offers more intuitive and robust results than PCC or SRC.
• The new method is less susceptible to outliers and multiple complex relationships.
• A visualization technique was developed to highlight regions of probe association.

Conclusions:

• The developed information theory approach provides a more accurate and robust quantification and visualization of probe association in fluorescence images.
• This method enhances the interpretation of spatial relationships between biomolecules in situ.