Automated Yeast Colony Quantification for Fatty Acid Sensitivity Screening

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Project Description

This project aims to develop an automated image analysis pipeline for a genome-wide yeast screen to identify mutants sensitive to fatty acids. The screen involves spotting yeast mutant arrays on media containing different types of fatty acids. At the core of this project is the generation of a robust, automated segmentation and quantification pipeline to accurately measure colony surface area as a proxy for growth. Key aspects of the image analysis pipeline include:

• Automated colony segmentation from plate images

• Accurate quantification of colony surface area

• Data processing and statistical analysis to identify significant growth defects

• Scalability to handle larger, high-density arrays

This automated approach will significantly accelerate the screening process, allowing for rapid identification of hits. Moreover, the pipeline’s adaptability makes it valuable not only for this specific project but also for future screening and Synthetic Genetic Array (SGA) projects in our lab and potentially for other scientists in the yeast genetics community. The overall significance of this project lies in its potential to uncover new insights into lipid metabolism, cellular adaptation to fatty acid exposure, and potentially lipotoxicity mechanisms. These findings will contribute to our understanding of fundamental biological processes and their implications for human health.