Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Abstract

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variancestabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variancestabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry. Molecular & Cellular Proteomics 9:1885-1897, 2010.Different techniques are being used and developed in the field of proteomics to allow quantitative comparison of samples between one state and another. These can be divided into gel-(1-4) or mass spectrometry-based (5-8) techniques. Comparative studies have found that each technique has strengths and weaknesses and plays a complementary role in proteomics (9, 10). There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11, 12) or isobaric tags for relative or absolute quantitation (iTRAQ) 1 (13, 14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to mos...

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).…”

“…Spike-in of proteins has the benefit of looking at a small controlled set of peptides and how they behave in the studied system. This strategy has been used in several of the previously mentioned papers that address iTRAQ quantification (11)(12)(13)(14). However, the number of data points studied may be unlikely to represent the complexity of a real biological sample, which often contains thousands of proteins (24).…”

“…Concerning precision, iTRAQ data has been reported to exhibit variance heterogeneity. The coefficient of variance (CV) of the signal depends on the intensity, with larger CV for low intensity peaks (11,12,15,16). Measurements of iTRAQ intensities for quantification are made in the MS/MS spectra of the peptides, and thereafter 1 The abbreviations used are: iTRAQ, isobaric tags for relative and absolute quantification; SILAC, stable isotope labeling by amino acids in cell culture; CV, coefficient of variance; PSM, peptide spectrum match; HCD, higher-energy collisional dissociation; CID, collision induced dissociation; FDR, false discovery rate; PQPQ, protein quantification by peptide quality control; RMSE, root mean square error; RMSE s , scaled root mean square error; RSD, relative standard deviation.…”

“…There are several different approaches for combining the iTRAQ peptide data to compute a reliable protein ratio. Methods to improve the protein quantification by addressing the variance heterogeneity have been based on excluding low intensity peptide data (17,18), weighting the peptide data according to intensity (18 -21) or stabilizing the variance (12).…”

“…Concerning precision, iTRAQ data has been reported to exhibit variance heterogeneity. The coefficient of variance (CV) of the signal depends on the intensity, with larger CV for low intensity peaks (11,12,15,16). Measurements of iTRAQ intensities for quantification are made in the MS/MS spectra of the peptides, and thereafter combined to calculate a summarized relative protein quantity.…”