GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy

Area of Science:

• Biophysics
• Microscopy
• Computational Biology

Background:

• Effective image analysis is crucial for fluorescence microscopy.
• Traditional phasor analysis has limitations in complex biological samples.
• Need for advanced, accessible tools in fluorescence imaging.

Purpose of the Study:

• To introduce GSLab, an open-source platform for advanced phasor analysis in fluorescence microscopy.
• To enhance traditional phasor analysis with machine learning and quantitative unmixing.
• To provide researchers with comprehensive tools for complex biological imaging challenges.

Main Methods:

• Development of an open-source software platform, GSLab.
• Integration of machine learning-based clustering for phasor data.
• Implementation of real-time monitoring and quantitative unmixing algorithms.
• Compatibility with commercial and custom fluorescence microscopy systems.

Main Results:

• GSLab enhances traditional phasor analysis with advanced computational tools.
• The platform enables machine learning-based clustering of fluorescence lifetime and spectral data.
• Quantitative unmixing of fluorescent species is achieved through GSLab's features.
• Real-time monitoring capabilities improve experimental workflow.

Conclusions:

• GSLab provides a powerful, open-source solution for fluorescence microscopy image analysis.
• The platform empowers researchers to address complex biological questions using advanced phasor analysis.
• GSLab facilitates deeper insights into biological systems through enhanced imaging techniques.