Global Extraction from Parallel Reaction Monitoring to Quantify Background Peptides for Improved Normalization and...

Area of Science:

• Proteomics
• Mass Spectrometry
• Quantitative Biology

Background:

• Targeted proteomics relies on heavy isotope-labeled standards for accurate quantification.
• Input sample amount, determined by spectrophotometry, significantly impacts mass spectrometry results.
• Current workflows lack methods to detect or correct errors from sample input variations.

Purpose of the Study:

• To develop a novel workflow for identifying and quantifying background peptides in parallel reaction monitoring (PRM) experiments.
• To utilize these background peptides for post-acquisition normalization, reducing variance from input sample amount differences.
• To enhance the accuracy of targeted proteomics by addressing limitations in sample input determination.

Main Methods:

• Developed the global extraction from parallel reaction monitoring (PRM) workflow.
• Identified and quantified thousands of endogenous background peptides within PRM data.
• Applied intensity-based, post-acquisition normalization using identified background peptides.

Main Results:

• Successfully identified and quantified numerous background peptides inherent to PRM experiments.
• Demonstrated the ability to normalize for variations in input sample amount, improving quantitative accuracy.
• Applied the workflow to a xenograft study, enhancing human protein quantification in mixed-tissue samples and providing quality control metrics.

Conclusions:

• The global extraction from parallel reaction monitoring (PRM) workflow effectively addresses inaccuracies in targeted proteomics.
• Utilizing background peptides for normalization significantly improves quantitative precision and reliability.
• This approach offers a robust method for sample quality control and enhances data integrity in complex biological samples.