Dataset Information

Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell.

ABSTRACT: Improving analytical precision is a major goal in quantitative differential proteomics as high precision ensures low numbers of outliers, a source of false positives with regard to quantification. In addition, higher precision increases statistical power, i.e., the probability to detect significant differences. With chemical labeling using isobaric tags for relative and absolute quantitation (iTRAQ) or tandem mass tag (TMT) reagents, quantification is based on the extraction of reporter ions from tandem mass spectrometry (MS/MS) spectra. We compared the performance of two versions of the LTQ Orbitrap higher energy collisional dissociation (HCD) cell with and without an axial electric field with regard to reporter ion quantification. The HCD cell with the axial electric field was designed to push fragment ions into the C-trap and this version is mounted in current Orbitrap XL ETD and Orbitrap Velos instruments. Our goal was to evaluate whether the purported improvement in ion transmission had a measurable impact on the precision of MS/MS based quantification using peptide labeling with isobaric tags. We show that the axial electric field led to an increased percentage of HCD spectra in which the complete set of reporter ions was detected and, even more important, to a reduction in overall variance, i.e., improved analytical precision of the acquired data. Notably, adequate precision of HCD-based quantification was maintained even for low precursor ion intensities of a complex biological sample. These findings may help researchers in their design of quantitative proteomics studies using isobaric tags and establish HCD-based quantification on the LTQ Orbitrap as a highly precise approach in quantitative proteomics.

SUBMITTER: Pichler P

PROVIDER: S-EPMC3270567 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell.

Pichler Peter P Köcher Thomas T Holzmann Johann J Möhring Thomas T Ammerer Gustav G Mechtler Karl K

Analytical chemistry 20110128 4

Improving analytical precision is a major goal in quantitative differential proteomics as high precision ensures low numbers of outliers, a source of false positives with regard to quantification. In addition, higher precision increases statistical power, i.e., the probability to detect significant differences. With chemical labeling using isobaric tags for relative and absolute quantitation (iTRAQ) or tandem mass tag (TMT) reagents, quantification is based on the extraction of reporter ions fro ...[more]

PMID: 21275378

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Project description:Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools. Here we present a new protocol tailored toward mapping SS; it uses readily available reagents, instruments, and software. For sample preparation, a 4-h pepsin digestion at pH 1.3 followed by an overnight trypsin digestion at pH 6.5 can maximize the release of SS-containing peptides from non-reduced proteins, while minimizing SS scrambling. For LC/MS/MS analysis, SS-containing peptides can be efficiently fragmented with HCD in a Q Exactive Orbitrap mass spectrometer, preserving SS for subsequent identification. Our bioinformatics protocol describes how we tailored our freely downloadable and easy-to-use software, Spectrum Identification Machine for Cross-Linked Peptides (SIM-XL), to minimize false identification and facilitate manual validation of SS-peptide mass spectra. To substantiate this optimized method, we've comprehensively identified 14 out of 17 known SS in BSA. SIGNIFICANCE: Comprehensive and accurate identification of SS in proteins is critical for elucidating protein structures and functions. Yet, it is far from routine to accomplish this task in many analytical or core laboratories. Numerous published methods require complex sample preparation methods, specialized mass spectrometers and cumbersome or proprietary software tools, thus cannot be easily implemented in unspecialized laboratories. Here, we describe a robust and rapid SS mapping approach that utilizes readily available reagents, instruments, and software; it can be easily implemented in any analytical core laboratories, and tested for its impact on the research community.

Project description:BackgroundMass 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.ResultsA 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.ConclusionThe 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.

Dataset Information

Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell.

Publications

Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell.

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