Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Quantitative proteomics using isobaric tags for relative and absolute quantitation (iTRAQ) is crucial for biological research.
• Ensuring accuracy and reproducibility in iTRAQ experiments across different organisms is essential for reliable data interpretation.
• Optimizing experimental strategies can improve proteome coverage and the precision of protein quantification.

Purpose of the Study:

• To evaluate the impact of multiple LC-MS/MS analyses on proteome coverage and quantification accuracy in iTRAQ experiments.
• To assess the reliability of protein identifications, including single-peptide identifications, using a stringent double database search strategy.
• To determine the reproducibility of iTRAQ quantification values across multiple sample injections.

Main Methods:

• Performed 10 iTRAQ experiments using three model organisms: Saccharomyces cerevisiae, Sulfolobus solfataricus, and Synechocystis sp.
• Employed a double database search strategy to minimize false positive rates to <3%.
• Conducted multiple LC-MS/MS analyses per sample and assessed protein quantification reproducibility using coefficient of variation (CV).

Main Results:

• Multiple LC-MS/MS analyses significantly increased proteome coverage: 6% in S. cerevisiae, 33% in S. solfataricus, and 50% in Synechocystis sp.
• The double database search strategy ensured high reliability for protein identifications, including those based on single peptides.
• iTRAQ quantification values demonstrated high reproducibility, with an average CV of 0.09 across all organisms.

Conclusions:

• Recommending multiple analyses of iTRAQ samples is crucial for achieving greater proteome coverage.
• This strategy enhances the precision and reproducibility of protein quantification in complex biological samples.
• The findings support the utility of iTRAQ coupled with optimized data analysis for robust proteomic studies.