Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Accurate protein quantification is crucial for understanding biological processes and disease.
• Shotgun proteomics using isobaric tags for relative and absolute quantification (iTRAQ) is a powerful technique but requires robust methodologies for reliable results.
• Existing methods face challenges with variance and bias in complex human samples.

Purpose of the Study:

• To establish criteria for reliable protein quantification.
• To develop an accurate and precise method for protein quantification using iTRAQ mass spectrometry.
• To investigate the impact of experimental variables on iTRAQ data quality.

Main Methods:

• Utilized thousands of peptide measurements from complex human samples (A549 cell lysate) analyzed across three mass spectrometry platforms.
• Investigated effects of sample amount, fractionation, fragmentation energy, and instrument platform on iTRAQ reporter ion data.
• Developed a methodology incorporating duplicate samples for experimental validation and peptide weighting based on reporter ion intensity.

Main Results:

• Identified key experimental variables influencing variance and bias in iTRAQ quantification.
• Developed a protein quantification methodology that reduces relative error by weighting peptides.
• Demonstrated the methodology's effectiveness in cancer cell line experiments and clinical lung cancer tissue samples.
• Introduced a method to assess quantification confidence for proteins with few peptides.

Conclusions:

• A robust methodology for improved protein quantification in shotgun proteomics was developed.
• The experimental design and algorithms significantly decreased relative protein quantification error in complex biological samples.
• The approach provides a reliable way to assess protein quantification, especially for proteins with limited peptide evidence.