VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics

Area of Science:

• Cellular and Molecular Biology
• Biophysics
• Microscopy and Imaging

Background:

• Environment-sensitive probes are crucial for studying cellular homeostasis using spectral microscopy.
• Existing open-source spectral image processing packages have limited functionality.
• There is a need for advanced, user-friendly software for detailed spectral image analysis.

Purpose of the Study:

• To introduce VISION, a novel stand-alone Python software for comprehensive spectral image analysis.
• To provide advanced single-cell segmentation and biophysical parameter calculation capabilities.
• To facilitate the study of cellular properties and responses to external factors.

Main Methods:

• Development of a Python-based software package named VISION.
• Implementation of batch processing for multidimensional spectral images.
• Integration of advanced single-cell segmentation and user-defined spectral mathematical operations.
• Capability for 3D and temporal mapping of cellular properties.

Main Results:

• VISION offers enhanced applicability for spectral analysis beyond classical methods.
• The software enables calculation of biophysical and metabolic parameters at the single-cell level.
• Demonstrated application in studying drug effects, membrane fluidity, mitochondrial potential, protein distribution, and vesicle nanodomains.
• Successful implementation of a graphical user interface for ease of use.

Conclusions:

• VISION significantly expands the capabilities for processing and analyzing spectral microscopy data.
• The software provides a powerful tool for investigating complex cellular dynamics and biophysical properties.
• VISION's user-friendly interface and broad applicability make it valuable for diverse research in cell biology and biophysics.