Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.

Area of Science:

• Cell Biology
• Proteomics
• Systems Biology

Background:

• Proteomics generates large-scale quantitative data, but in vivo systems approaches for proteome analysis are underdeveloped.
• Understanding protein abundance and localization is crucial for deciphering cellular functions.

Purpose of the Study:

• To develop and apply a systems approach for evaluating protein abundance and localization on a proteome scale in vivo.
• To map the yeast proteome's localization and survey its dynamics under various chemical and genetic stimuli.

Main Methods:

• Utilized the yeast GFP-fusion collection combined with automated genetics and high-throughput microscopy.
• Developed ensemble binary classifiers for single-cell localization data analysis.
• Analyzed over 20 million cells to identify dynamic proteome changes.

Main Results:

• Generated localization maps for approximately 3,000 proteins across 16 localization classes.
• Identified dynamic proteins that redistribute among multiple localizations in response to hydroxyurea, rapamycin, and in an rpd3Δ mutant.
• Collected quantitative, proteome-wide abundance and localization data.

Conclusions:

• The developed pipeline provides a robust method for large-scale proteome analysis in vivo.
• Quantitative data facilitates comparative studies, single-cell analyses, modeling, and prediction of protein behavior.
• Revealed dynamic proteome responses to specific cellular perturbations.