An integrated image analysis platform to quantify signal transduction in single cells.

Area of Science:

• Cell Biology
• Biophysics
• Systems Biology

Background:

• Microscopy yields rich single-cell data but quantitative analysis remains challenging.
• Extracting dynamic cellular behaviors from time-lapse microscopy requires robust image analysis tools.

Purpose of the Study:

• To develop and validate YeastQuant, an integrated platform for automated image analysis and quantitative single-cell measurements from microscopy.
• To demonstrate YeastQuant's utility in studying dynamic signal transduction processes in yeast.

Main Methods:

• YeastQuant integrates a database with a graphical user interface and MATLAB for automated image segmentation and quantification.
• Implemented three distinct cell segmentation methods suitable for various experimental conditions.
• Developed image analysis protocols for measuring cellular readouts like reporter gene expression and protein translocation.

Main Results:

• YeastQuant successfully quantified fluorescent reporter expression dynamics under osmotic stress, revealing gene expression noise.
• Analyzed dynamic nuclear translocation of endogenous proteins, providing a rapid measure of pathway activity.
• Demonstrated YeastQuant's versatility in analyzing diverse microscopy-based cellular readouts.

Conclusions:

• YeastQuant offers a versatile, expandable platform for improved image analysis and quantification of microscopy data.
• Facilitates dynamic single-cell measurements crucial for building mathematical models of cellular signaling pathways.
• Enhances the extraction of quantitative biological insights from complex microscopy datasets.