iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics

Abstract

Labeling of primary amines on peptides with reagents containing stable isotopes is a commonly used technique in quantitative mass spectrometry. Isobaric labeling techniques such as iTRAQ™ or TMT™ allow for relative quantification of peptides based on ratios of reporter ions in the low m/z region of spectra produced by precursor ion fragmentation. In contrast, nonisobaric labeling with mTRAQ™ yields precursors with different masses that can be directly quantified in MS1 spectra. In this study, we compare iTRAQ- and mTRAQ-based quantification of peptides and phosphopeptides derived from EGF-stimulated HeLa cells. Both labels have identical chemical structures, therefore precursor ion- and fragment ion-based quantification can be directly compared. Our results indicate that iTRAQ labeling has an additive effect on precursor intensities, whereas mTRAQ labeling leads to more redundant MS2 scanning events caused by triggering on the same peptide with different mTRAQ labels. We found that iTRAQ labeling quantified nearly threefold more phosphopeptides (12,129 versus 4,448) and nearly twofold more proteins (2,699 versus 1,597) than mTRAQ labeling. Although most key proteins in the EGFR signaling network were quantified with both techniques, iTRAQ labeling allowed quantification of twice as many kinases. Accuracy of reporter ion quantification by iTRAQ is adversely affected by peptides that are cofragmented in the same precursor isolation window, dampening observed ratios toward unity. However, because of tighter overall iTRAQ ratio distributions, the percentage of statistically significantly regulated phosphopeptides and proteins detected by iTRAQ and mTRAQ was similar. We observed a linear correlation of logarithmic iTRAQ to mTRAQ ratios over two orders of magnitude, indicating a possibility to correct iTRAQ ratios by an average compression factor. Spike-in experiments using peptides of defined ratios in a background of nonregulated peptides show that iTRAQ quantification is less accurate but not as variable as mTRAQ quantification.

Fig. 1.

A, Labeling strategy for comparative evaluation of iTRAQ and mTRAQ tags. Peptides were labeled with the indicated iTRAQ and mTRAQ reagents for combined phosphoproteome and proteome analysis. B, Selection of optimal instrument methods for analysis of iTRAQ- and mTRAQ-labeled peptides. Unfractionated proteome samples (1 ug) and phosphoproteome samples (enriched from 250 μg peptides) were analyzed for iTRAQ samples with a CID/HCD-Top8 method, whereas for mTRAQ we compared CID-Top16 acquisition to HCD-Top8. Note that duty cycle times were for all instrument methods ∼3.1 s.

Fig. 2.

Experimental workflow for global phosphoproteome and proteome analysis. After EGF-stimulation cells were lysed, digested with trypsin and labeled either with iTRAQ or mTRAQ reagents. Samples were mixed and separated via SCX: 90% of total peptides were combined into 12 phosphopeptide samples and further enriched for phosphorylated peptides with IMAC beads, whereas 10% of total peptides were combined into 16 whole proteome samples. All samples were analyzed on a LTQ Oribtrap Velos instrument with 140 min runs. iTRAQ samples were acquired with CID/HCD-Top8 and mTRAQ samples with CID-Top16 methods. Peptides were identified and quantified with Spectrum Mill.

Fig. 3.

iTRAQ labeling yields 2.7-fold more phosphopeptides that were reproducibly quantified in two biological replicates. Scatterplots with marginal histograms illustrate log₂ iTRAQ and mTRAQ ratios for the 10 min (A) and 24 h (B) EGF stimulation time points. Mean (m) and standard deviation (sd) values for the frequency distribution of each experiment were derived by Gaussian modeling. Dashed green lines indicate p value thresholds (BH FDR p < 0.05) for up- and down-regulation. Robust linear regression is shown in blue and the corresponding 99% confidence interval in red.

Fig. 4.

iTRAQ labeling results in 1.7-fold more quantified proteins. Scatterplots with marginal histograms of log₂ ratios show all proteins that were quantified with three or more distinct peptides in two biological replicates. Mean (m) and standard deviation (sd) values for the frequency distribution of each experiment were derived by Gaussian modeling. Dashed green lines indicate p value thresholds (BH FDR p < 0.05) for up- and down-regulation. Robust linear regression is shown in blue and the corresponding 99% confidence interval in red.

Fig. 5.

Uniform iTRAQ ratio compression results in a similar percentage of regulated phosphopeptides and proteins as in mTRAQ data sets. Overlapping phosphopeptides and proteins are shown for iTRAQ and mTRAQ in scatterplots with marginal histograms. Mean (m) and standard deviation (sd) values for the frequency distribution of each experiment were derived by Gaussian modeling. Dashed green lines indicate upper and lower cutoff thresholds at BH FDR p < 0.05. Note the linearity of iTRAQ compared with mTRAQ ratios. X indicates the slope and r the Pearson correlation coefficient of the linear regression analysis.

Fig. 6.

iTRAQ quantification is more precise but less accurate than mTRAQ. A, GluC peptide spike-in experiments to test accuracy and variability of quantification. HeLa proteins were digested with endoproteinase GluC, labeled with three different iTRAQ or mTRAQ labels, mixed in defined ratios and spiked into fractions 6 and 8 of the whole proteome analysis. GluC-derived peptides were less than 20% of all identified peptides for iTRAQ as well as mTRAQ samples. Median ratios and coefficients of variation (CV) are shown for all spike-in GluC-derived peptides in a 1:1:1 background of tryptic peptides derived from biological samples. iTRAQ ratios show a strong compression, whereas mTRAQ peptides have much higher CV values. B, Simplified precursor isolation purity model of iTRAQ ratio compression. Effect of PIP at constant values is shown on iTRAQ ratio compression with real log₂ values on the abscissa and modeled log₂ values on the ordinate. Equations (1) and (2) simulate the contribution of 1:1 background reporter ions to the reporter ions of the peptide of interest based on constant PIP values. The extent of the linear trend in the sigmoidal function is dependent on the PIP value, which at PIP = 75% spans two orders of magnitude in linear space. C, Linear relationship of logarithmic GluC peptide ratios measured with and without tryptic background peptides. Scatterplot depicts log₂ iTRAQ ratios of GluC peptides with and without background interference. In red, median ratios of all GluC peptides are shown at a given mixing ratio as well as the corresponding linear regression line. Note that after correction via the slope of the linear regression, corrected iTRAQ ratios are quite close to the original ratios without background.