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.

The iTRAQ (isobaric tags for relative and absolute quantification) technique is widely employed in proteomic workflows requiring relative quantification. Here, we review the iTRAQ literature; in particular, we focus on iTRAQ usage in relation to other commonly used quantitative techniques e.g. stable isotope labelling in culture (SILAC), label-free methods and selected reaction monitoring (SRM). As a result, we identify several issues arising with respect to iTRAQ. Perhaps frustratingly, iTRAQ's attractiveness has been undermined by a number of technical and analytical limitations: it may not be truly quantitative, as the changes in abundance reported will generally be underestimated. We discuss weaknesses and strengths of iTRAQ as a methodology for relative quantification in the light of this and other technical issues. We focus on technical developments targeted at iTRAQ accuracy and precision, use of 4-plex over 8-plex reagents and application of iTRAQ to post-translational modification (PTM) workflows. We also discuss iTRAQ in relation to label-free approaches, to which iTRAQ is losing ground.

Nostoc punctiforme ATCC 29133 is a photoautotrophic cyanobacterium with the capacity to fix atmospheric N 2. Its ability to mediate this process is similar to that described for Nostoc sp. PCC 7120, where vegetative cells differentiate into heterocysts. Quantitative proteomic investigations at both the filament level and the heterocyst level are presented using isobaric tagging technology (iTRAQ), with 721 proteins at the 95% confidence interval quantified across both studies. Observations from both experiments yielded findings confirmatory of both transcriptional studies, and published Nostoc sp. PCC 7120 iTRAQ data. N. punctiforme exhibits similar metabolic trends, though changes in a number of metabolic pathways are less pronounced than in Nostoc sp. PCC 7120. Results also suggest a number of proteins that may benefit from future investigations. These include ATP dependent Zn-proteases, N-reserve degraders and also redox balance proteins. Complementary proteomic data sets from both organisms present key precursor knowledge that is important for future cyanobacterial biohydrogen research.