iTRAQ underestimation in simple and complex mixtures: "the good, the bad and the ugly".
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.
Ow
Saw Yen
SY
ChELSI Institute, Chemical and Process Engineering, University of Sheffield, Mappin Street, S1 3JD Sheffield, United Kingdom.
Salim
Malinda
M
Noirel
Josselin
J
Evans
Caroline
C
Rehman
Ishtiaq
I
Wright
Phillip C
PC
D000818:Animals; D002852:Chromatography, Ion Exchange / Q000379:methods; D011506:Proteins / Q000032:analysis*; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods
D011506:Proteins; D020543:Proteome