A quantitative spatial proteomics analysis of proteome turnover in human cells

Area of Science:

• Proteomics
• Cell Biology
• Biochemistry

Background:

• Measuring endogenous protein properties like expression and localization is challenging.
• mRNA levels do not reliably predict protein levels or other properties.

Purpose of the Study:

• To develop and apply a strategy for characterizing endogenous human protein expression, localization, synthesis, degradation, and turnover rates.
• To analyze these properties across different subcellular compartments.

Main Methods:

• Combined pulse-labeling and spatial proteomics.
• Quantitative mass spectrometry with stable isotope labeling.
• Specialized data analysis software (PepTracker) for quantification and visualization.

Main Results:

• Quantified 80,098 peptides from 8,041 HeLa proteins, determining their spatial distribution (cytoplasm, nucleus, nucleolus).
• Calculated protein abundance, synthesis, degradation, and turnover rates, revealing expression levels varying by seven orders of magnitude.
• Found average turnover rate of ~20 h, with higher abundance proteins having longer half-lives and identified PEST motifs in fast turnover proteins.

Conclusions:

• The developed method enables comprehensive characterization of endogenous protein dynamics.
• Protein turnover rates are linked to abundance and PEST motifs.
• Subcellular localization influences protein turnover rates, particularly for subunits of large complexes.