A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling

Area of Science:

• Proteomics
• Systems Biology
• Biochemistry

Background:

• Understanding protein dynamics is crucial for comprehending organismal development, health, and disease.
• Previous methods for analyzing protein turnover were limited in scale and scope.

Purpose of the Study:

• To develop and validate a robust data processing pipeline for analyzing large-scale proteome dynamics using nitrogen-15 (15N) metabolic labeling.
• To enable the accurate determination of individual protein turnover rate constants.

Main Methods:

• Utilized in vivo metabolic labeling with 15N to trace protein synthesis and turnover.
• Developed a novel data processing pipeline integrating mass spectrometry database search engines with specialized modules.
• Implemented modules for cross-extraction of 15N-ion intensities, computation of isotopic incorporation distributions, and aggregation of peptide data into protein curves.

Main Results:

• Successfully traced label incorporation in over 1700 proteins simultaneously.
• Enabled determination of protein turnover rate constants across a three-order-of-magnitude dynamic range.
• Demonstrated the necessity of parameter optimization and noise reduction for accurate data processing.

Conclusions:

• The developed data processing pipeline is essential for large-scale investigations of proteome dynamics.
• This methodology provides a deeper understanding of healthy development, well-being, and disease progression.
• The approach facilitates comprehensive analysis of protein turnover rates in complex biological systems.