iTRAQ Underestimation in Simple and Complex Mixtures: “The Good, the Bad and the Ugly”

Abstract

The increasing popularity of iTRAQ for quantitative proteomics applications makes it necessary to evaluate its relevance, accuracy, and precision for biological interpretation. Here, we have assessed (a) the accuracy and precision of iTRAQ quantification in a controlled experimental setup, using low- and high-complexity protein mixtures; and (b) the potential pitfalls that hamper the applicability and attainable dynamic range of iTRAQ: isotopic contamination, background interference, and signal-to-noise ratio. Our data suggest greater dynamic crosstalk between interfering factors affecting underestimations, and that these interferences were largely scenario-specific, dependent on sample complexity. The good is the potential for iTRAQ to provide accurate quantification spanning 2 orders of magnitude. This potential is however limited by two factors. (1) The bad: the existence of isotopic impurities that can be corrected for; provided accurate isotopic factors are at one's disposal. (2) The ugly: we demonstrate here the interference of mixed MS/MS contribution occurring during precursor selection, an issue that is currently very difficult to minimize. In light of our results, we propose a list of advice for iTRAQ data analysis that could routinely ameliorate quantitative interpretation of proteomic data sets.

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

“…Mean log ratio (VSN1ϪVSN2) have problems with accuracy by systematically underestimating ratios (23)(24)(25)(26)(27). To assess this question, we prepared a sample with proteins at known ratios.…”

“…This effect was seen on both data collected with a QSTAR and with a QTof Premier, suggesting that the effect is ubiquitous and not dependent on the MS technology used. It has been suggested that this underestimation arises from contaminating peptides with similar m/z ratios during ion selection prior to collision-induced dissociation (23,27). A quantitative model reveals that when the relative amount of contamination is the same within an experiment a linear relationship between observed and true ratios is expected and would be independent of signal strength.…”

“…Typically, ratiometric normalization has been used to address this tag bias where all peptide ratios are multiplied by a global normalization factor determined to center the ratio distribution on 1 (19,22). Even after such normalization, concerns have been raised that iTRAQ has imperfect accuracy with ratios shrunken toward 1, and this underestimation has been reported across multiple MS platforms (23)(24)(25)(26)(27). It has been suggested that this underestimation arises from co-eluting peptides with similar m/z values, which are co-selected during ion selection and co-fragmented during CID (23,27).…”

“…Even after such normalization, concerns have been raised that iTRAQ has imperfect accuracy with ratios shrunken toward 1, and this underestimation has been reported across multiple MS platforms (23)(24)(25)(26)(27). It has been suggested that this underestimation arises from co-eluting peptides with similar m/z values, which are co-selected during ion selection and co-fragmented during CID (23,27). As the majority of these will be at a 1:1 ratio across the reporter ion tags (as required for normalization in iTRAQ experiments), they will contribute a background value equally to each of the iTRAQ reporter ion signals and diminish the computed ratios.…”

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

“…Several studies have shown that iTRAQ labeling is associated with bias; fold changes are compressed toward one (11)(12)(13)(14). It has been suggested that this underestimation of fold change is caused by co-eluting peptides with similar m/z values that are isolated together, creating mixed iTRAQ intensities in complex samples (14).…”