HYPHAEdelity: a quantitative image analysis tool for assessing peripheral whole colony filamentation

Area of Science:

• Microbiology
• Cell Biology
• Computational Biology

Background:

• Saccharomyces cerevisiae exhibits phenotypic switching, transitioning from ellipsoidal cells to elongated chains in response to nutrient stress (nitrogen or carbon starvation).
• Assessing this filamentation response typically involves analyzing whole colony morphology in 2D images, a process that is labor-intensive and difficult to scale for large datasets.

Purpose of the Study:

• To develop and validate HYPHAEdelity, a software tool for semi-automating the quantification of yeast filamentation in 2D colony images.
• To provide a more efficient and accurate method for measuring the magnitude of peripheral whole colony yeast filamentation.

Main Methods:

• Development of the HYPHAEdelity software utilizing the OpenCV Python library for image analysis.
• Quantification of filamentous growth (f-measure) by calculating the difference between outer boundary area (hyphal projections) and inner colony boundary area.
• Validation of HYPHAEdelity against manual pixel counting and other automated software using 2D top-down images of Saccharomyces cerevisiae colonies.

Main Results:

• HYPHAEdelity accurately quantifies yeast filamentation by measuring the f-measure, representing the area of filamentous growth.
• The software's results demonstrated comparability to manual pixel enumeration methods.
• HYPHAEdelity proved to be more accurate than existing whole colony filamentation software solutions.

Conclusions:

• HYPHAEdelity offers a validated, semi-automated approach to quantify yeast filamentation, overcoming limitations of manual assessment.
• The developed software enhances the efficiency and accuracy of analyzing phenotypic switching in Saccharomyces cerevisiae.
• This tool facilitates larger-scale studies of yeast stress responses and colony morphology.