Dataset Information

Discovery- and target-based protein quantification using iTRAQ and pulsed Q collision induced dissociation (PQD).

ABSTRACT: Pulsed Q collision-induced dissociation (PQD) was developed in part to facilitate detection of low-mass reporter ions using labeling reagents (e.g. iTRAQ) on LTQ platforms. It has generally been recognized that the scan speed and sensitivity of an LTQ are superior than those of an Orbitrap using the higher-energy collisional dissociation (HCD). However, the use of PQD in quantitative proteomics is limited, primarily due to the meager reproducibility of reporter ion ratios. Optimizations of PQD for iTRAQ quantification using LTQ have been reported, but a universally applicable strategy for quantifying the less abundant proteins has not been fully established. Adjustments of the AGC target, ?scan, or scan speed offer only incremental improvements in reproducibility. From our experience, however, satisfactory coefficients of variation (CVs) of reporter ion ratios were difficult to achieve using the discovery-based approach. As an alternative, we implemented a target-based approach that obviates data dependency to allow repetitive data acquisitions across chromatographic peaks. Such a strategy generates enough data points for more reliable quantification. Using cAMP treatment in S49 cell lysates and this target-based approach, we were able to validate differentially expressed proteins, which were initially identified as potential candidates using the discovery-based PQD. The target-based strategy also yielded results comparable to those obtained from HCD in an Orbitrap. Our findings should aid LTQ users who desire to explore iTRAQ quantitative proteomics but have limited access to the more costly Orbitrap or other instruments.

SUBMITTER: Wu WW

PROVIDER: S-EPMC3321122 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Publications

Discovery- and target-based protein quantification using iTRAQ and pulsed Q collision induced dissociation (PQD).

Wu Wells W WW Wang Guanghui G Insel Paul A PA Hsiao Cheng-Te CT Zou Sige S Martin Bronwen B Maudsley Stuart S Shen Rong-Fong RF

Journal of proteomics 20120303 8

Pulsed Q collision-induced dissociation (PQD) was developed in part to facilitate detection of low-mass reporter ions using labeling reagents (e.g. iTRAQ) on LTQ platforms. It has generally been recognized that the scan speed and sensitivity of an LTQ are superior than those of an Orbitrap using the higher-energy collisional dissociation (HCD). However, the use of PQD in quantitative proteomics is limited, primarily due to the meager reproducibility of reporter ion ratios. Optimizations of PQD f ...[more]

PMID: 22397766

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Project description:BACKGROUND: Mass spectrometry based quantification of peptides can be performed using the iTRAQ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level. RESULTS: A method comprising sound error estimation and statistical methods is presented that allows precise abundance analysis plus error calculation at the peptide as well as at the protein level. This yields the relevant information that is required for quantitative proteomics. Comparing the performance of our method named Quant with existing approaches the error estimation is reliable and offers information for precise bioinformatic models. Quant is shown to generate results that are consistent with those produced by ProQuant, thus validating both systems. Moreover, the results are consistent with that of Mascot 2.2. The MATLAB scripts of Quant are freely available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/, each under the GNU Lesser General Public License. CONCLUSION: The software Quant demonstrates improvements in protein quantification using iTRAQ. Precise quantification data can be obtained at the protein level when using error propagation and adequate visualization. Quant integrates both and additionally provides the possibility to obtain more reliable results by calculation of wise quality measures. Peak area integration has been replaced by sum of intensities, yielding more reliable quantification results. Additionally, Quant allows the combination of quantitative information obtained by iTRAQ with peptide and protein identifications from popular tandem MS identification tools. Hence Quant is a useful tool for the proteomics community and may help improving analysis of proteomic experimental data. In addition, we have shown that a lognormal distribution fits the data of mass spectrometry based relative peptide quantification.

Project description:We present a workflow using an ETD-optimised version of Mascot Percolator and a modified version of SLoMo (turbo-SLoMo) for analysis of phosphoproteomic data. We have benchmarked this against several database searching algorithms and phosphorylation site localisation tools and show that it offers highly sensitive and confident phosphopeptide identification and site assignment with PSM-level statistics, enabling rigorous comparison of data acquisition methods. We analysed the Plasmodium falciparum schizont phosphoproteome using for the first time, a data-dependent neutral loss-triggered-ETD (DDNL) strategy and a conventional decision-tree method. At a posterior error probability threshold of 0.01, similar numbers of PSMs were identified using both methods with a 73% overlap in phosphopeptide identifications. The false discovery rate associated with spectral pairs where DDNL CID/ETD identified the same phosphopeptide was <1%. 72% of phosphorylation site assignments using turbo-SLoMo without any score filtering, were identical and 99.8% of these cases are associated with a false localisation rate of <5%. We show that DDNL acquisition is a useful approach for phosphoproteomics and results in an increased confidence in phosphopeptide identification without compromising sensitivity or duty cycle. Furthermore, the combination of Mascot Percolator and turbo-SLoMo represents a robust workflow for phosphoproteomic data analysis using CID and ETD fragmentation.Biological significanceProtein phosphorylation is a ubiquitous post-translational modification that regulates protein function. Mass spectrometry-based approaches have revolutionised its analysis on a large-scale but phosphorylation sites are often identified by single phosphopeptides and therefore require more rigorous data analysis to unsure that sites are identified with high confidence for follow-up experiments to investigate their biological significance. The coverage and confidence of phosphoproteomic experiments can be enhanced by the use of multiple complementary fragmentation methods. Here we have benchmarked a data analysis pipeline for analysis of phosphoproteomic data generated using CID and ETD fragmentation and used it to demonstrate the utility of a data-dependent neutral loss triggered ETD fragmentation strategy for high confidence phosphopeptide identification and phosphorylation site localisation.

Dataset Information

Discovery- and target-based protein quantification using iTRAQ and pulsed Q collision induced dissociation (PQD).

Publications

Discovery- and target-based protein quantification using iTRAQ and pulsed Q collision induced dissociation (PQD).

Similar Datasets

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