Image Processing for Bioluminescence Resonance Energy Transfer Measurement-BRET-Analyzer

Area of Science:

• Quantitative live-cell imaging
• Biophysics and biochemistry
• Cellular signaling dynamics

Background:

• Live recordings of signaling pathways and protein-protein interactions are increasingly common using ratiometric measurements like Bioluminescence Resonance Energy Transfer (BRET) Imaging.
• Accurate and reproducible analysis of this quantitative imaging data is essential for reliable interpretation.
• Existing tools may lack the systematic approach needed for complex ratiometric data.

Purpose of the Study:

• To develop and present an open-source toolset, BRET-Analyzer for Fiji, to address the need for systematic analysis of ratiometric imaging data.
• To provide a comprehensive solution for image processing and ratio quantification in live-cell imaging experiments.
• To enable precise and reproducible quantification of dynamic biological processes.

Main Methods:

• Development of an open-source toolset (BRET-Analyzer) integrated with Fiji.
• Implementation of key image processing steps: background subtraction, temporal alignment, composite thresholding for sample limits, pixel-wise ratio calculation, and pseudocolor intensity mapping.
• Quantification of ratio mean intensity and standard deviation within selected regions of interest.

Main Results:

• BRET-Analyzer systematically analyzes images from background subtraction to ratio quantification.
• The toolset accurately reconstitutes and quantifies ratiometric images in space and time, even in heterogeneous subcellular volumes.
• Demonstrated improved precision in defining specimen limits compared to standard analysis, enabling detailed tracking of protein interaction dynamics in neuronal compartments over time.

Conclusions:

• BRET-Analyzer offers a complete, versatile, and efficient solution for automated, reproducible, and meaningful image ratio analysis.
• The toolset facilitates the study of dynamic biological processes, such as scaffold protein interactions in live neurons.
• Availability of the open-source toolset and tutorial promotes wider adoption and advancement in quantitative live-cell imaging analysis.