Precise protein quantification based on peptide quantification using iTRAQ™

Boehm, Andreas; Pütz, Stephanie; Altenhöfer, Daniela; Sickmann, Albert; Falk, Michael

doi:10.1186/1471-2105-8-214

Cited by 102 publications

(78 citation statements)

References 26 publications

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“…The logarithm transformation has been suggested previously for iTRAQ data with the objective of addressing the heterogeneity of variance (19,21). However, Fig.…”

Section: Heterogeneity Of Variance: Variance-mean Dependencementioning

confidence: 99%

“…Filtering, outlier removal, and weighted methods are of limited use for peptides for which only a few low intensity readings were made; however, such cases typically dominate the data sets. Even with a logarithmic transformation, heterogeneity has been reported for iTRAQ data (16,19,22). Current methods struggle to address the issue and to maintain sensitivity.…”

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confidence: 99%

“…In iTRAQ, systematic biases can arise because of inconsistencies in iTRAQ labeling efficiency and protein digestion (22). 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).…”

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confidence: 99%

“…GPS Explorer, a software package developed for iTRAQ, assumes normality in the peptide ratio for a protein once an outlier filter is applied (18). The iTRAQ package ProQuant assumes that peptide ratio data for a protein follow a log-normal distribution (19). Averaging can be via mean (20), weighted average (21,22), or weighted correlation (23).…”

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confidence: 99%

“…Some of the current approaches include outlier removal (18,25), weighted means (21,22), inclusion filters (16,22), logarithmic transformation (19), and weighted correlation analysis (23). Outlier removal methods, for example using Dixon's test, assume a normal distribution for which there is little empirical basis.…”

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confidence: 99%

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Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Karp

Huber

Sadowski

et al. 2010

Molecular & Cellular Proteomics

466

459

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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...

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“…The logarithm transformation has been suggested previously for iTRAQ data with the objective of addressing the heterogeneity of variance (19,21). However, Fig.…”

Section: Heterogeneity Of Variance: Variance-mean Dependencementioning

confidence: 99%

mentioning

confidence: 99%