Quantifying Competitive Fitness in Yeast with High-Throughput Fluorescence Microscopy Imaging

Area of Science:

• Evolutionary Biology
• Microbial Ecology
• Quantitative Biology

Background:

• Competitive fitness is crucial for understanding organismal adaptation and resource competition.
• Traditional microbial competitive fitness assays are time-consuming and labor-intensive, involving manual colony counting.
• Existing methods lack the throughput and automation needed for large-scale studies.

Purpose of the Study:

• To introduce a novel, high-throughput method for quantifying microbial competitive fitness.
• To replace traditional, labor-intensive competitive fitness assays with an automated, image-based approach.
• To provide a comprehensive protocol for implementing this new methodology.

Main Methods:

• Utilizing fluorescence microscopic imaging to distinguish between competing microbial populations.
• Employing machine-learning-enabled image analysis for automated cell counting.
• Integrating sample preparation, microscopy, image analysis, and statistical calculations in R.

Main Results:

• The developed method provides accurate and reproducible quantitative measurements of competitive fitness.
• This high-throughput approach significantly reduces the labor and time required compared to traditional methods.
• The protocol includes detailed instructions and scripts for seamless implementation.

Conclusions:

• This automated fluorescence microscopy and machine learning method offers an efficient and reliable way to assess microbial competitive fitness.
• The technique is broadly applicable to various microbial systems and evolutionary studies.
• This advancement facilitates high-throughput screening and detailed analysis of competitive dynamics.