Defining, Comparing, and Improving iTRAQ Quantification in Mass Spectrometry Proteomics Data

Abstract

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples. Recent developments in methods and instruments for mass spectrometry enable quantitative proteomics analysis of complex samples with good coverage (1-4). Several techniques for quantification by mass spectrometry exist, both using isotopic labeling and label free methods (5, 6). Quantification by isotopic labeling can be done on precursor ion level or by quantifying isobaric label fragments in fragment spectra. Isotope-coded affinity tag (7), isobaric tags for relative and absolute quantification (iTRAQ) 1 (8), and stable isotope labeling by amino acids in cell culture (SILAC) (9) are among the most commonly used labeling methods based on stable isotopes. iTRAQ allows for simultaneous relative quantification of up to eight samples within a single run. Quantification by mass spectrometry is however a challenge, and several factors contribute to the uncertainty in the quantitative estimate; differences in labeling efficiency, protein digestion, precursor mixing, ion suppression, peak detection, data preprocessing, and data analysis (10). The quality of quantitation methods can be measured in terms of precision ...

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

“…A total of 16 factors associated with PSM quality were evaluated, including reporter ion intensity, precursor ion intensity, m / z , MH + , hydrophobicity, 51 SpScore, XCorr, charge state, ion inject time, isolation interference, mass accuracy, missed cleavage, retention time, matched ions, total ions, as well as ion match rate. In addition to the previously described effect of reporter ion intensity 36 (Figure 2b), the precursor ion MH + , m / z , and hydrophobicity score of each PSM were also closely correlated to the PSM-level FPR (Figure 2c–e). …”

Strategies based on sum of peak intensities may better reflect protein expression than peak area integration in iTRAQ protein expression measurement by LC-MS/MS using a TripleTOF 5600+ platform

“…Since its first description by Ross et al in 2004, iTRAQ has been widely used for multiplexed analysis of proteomes. The advantages and drawbacks of iTRAQ have been widely addressed in the literature, following the many developments of technical optimizations, analysis strategies and tools to improve quantitation precision and accuracy (21)(22)(23)(24). However, there is still no consensual technique in sample preparation and analysis or data processing.…”

iTRAQ as a method for optimization: Enhancing peptide recovery after gel fractionation

“…One of the main challenges in interpreting iTRAQ data is the underestimation of the fold change, partially caused by interfering masses in the silent region of the reporters and mixed MS/MS . Here, we present a new workflow for method optimization by means of isobaric tags (such as iTRAQ) to overcome these challenges without the need of complex data analysis tools.…”

“…and mixed MS/MS [3,4]. Here, we present a new workflow for method optimization by means of isobaric tags (such as iTRAQ) to overcome these challenges without the need of complex data analysis tools.…”