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Concept: Quantitative proteomics

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Isobaric labeling strategies (e.g. iTRAQ or TMT) are commonly applied in tandem mass spectrometric (MS/MS) level quantitative proteomics. However, we frequently observed missing isotope reporter ion signals in a large-scale liquid chromatography/matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometric (LC/MALDI-TOF/TOF) quantitative proteomics experiment. To understand this issue, we systematically investigated the processing of MS/MS spectra into peak lists prior to peptide identification and quantification.

Concepts: Scientific method, Mass spectrometry, Research, Proteomics, Tandem mass spectrometry, Tandem mass tags, Quantitative proteomics, SILAC

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Proteomics tools can be used to identify the differentially expressed proteins related to allergic rhinitis (AR). However, the large numbers of proteins related to AR have not yet been explored using an advanced quantitative proteomics approach, known as isobaric tags for relative and absolute quantitation (iTRAQ).

Concepts: Proteins, Scientific method, Protein, Allergy, Proteomics, Related, Quantitative proteomics, ITRAQ

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The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualisation software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: i) intensity-based (MS(1) ) label free, ii) MS(1) label-based (such as SILAC or N(15) ), iii) MS(2) tag-based (iTRAQ or tandem mass tags), and iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta-data, for SRM transitions, the mechanism for inferring peptide-level or protein-level quantitative values, and support for both label-based or label-free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis. This article is protected by copyright. All rights reserved.

Concepts: Controlled vocabulary, All rights reserved, Support, Copyright, Tandem mass tags, Quantitative proteomics, SILAC, ITRAQ

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Mass-spectrometry based proteomics has evolved as a promising technology over the last decade and is undergoing a dramatic development in a number of different areas, such as; mass spectrometric instrumentation, peptide identification algorithms and bioinformatic computational data analysis. The improved methodology allows quantitative measurement of relative or absolute protein amounts, which is essential for gaining insights into their functions and dynamics in biological systems. Several different strategies involving stable isotopes label (ICAT, ICPL, IDBEST, iTRAQ, TMT, IPTL, SILAC), label-free statistical assessment approaches (MRM, SWATH) and absolute quantification methods (AQUA) are possible, each having specific strengths and weaknesses. Inductively coupled plasma mass spectrometry (ICP-MS), which is still widely recognised as elemental detector, has recently emerged as a complementary technique to the previous methods. The new application area for ICP-MS is targeting the fast growing field of proteomics related research, allowing absolute protein quantification using suitable elemental based tags. This document describes the different stable isotope labelling methods which incorporate metabolic labelling in live cells, ICP-MS based detection and post-harvest chemical label tagging for protein quantification, in addition to summarising their pros and cons.

Concepts: Protein, Mass spectrometry, Proteomics, Isotope, Isotopes, Nuclide, Quantitative proteomics, SILAC

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Relative quantification of proteins via their enzymatically digested peptide products determines disease biomarker candidate lists in discovery studies. Isobaric label-based strategies using TMT and iTRAQ allow for up to 10 samples to be multiplexed in one experiment, but their expense limits their use. The demand for cost-effective tagging reagents capable of multiplexing many samples led us to develop an 8-plex version of our isobaric labeling reagent, DiLeu.

Concepts: Protein, Mass spectrometry, Optical fiber, Proteomics, Reagent, Label, Multiplexing, Quantitative proteomics

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Isobaric tagging reagents, such as tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ), are high-throughput methods that allow the analysis of multiple samples simultaneously, which reduces instrument time and error. Accuracy and precision of isobaric tags are limited, however, in tandem mass spectrometry (MS/MS) acquisition due to co-isolation and co-fragmentation of neighboring peptide peaks in precursor scans. Here we present a MS(3) method using pulsed-Q dissociation (PQD) in ion trap and Orbitrap instrumentation as a means to improve ratio distortion and maintain high numbers of identified and quantified proteins.

Concepts: Mass spectrometry, Accuracy and precision, Tandem mass spectrometry, Fourier transform ion cyclotron resonance, Top-down proteomics, Tandem mass tags, Quantitative proteomics, ITRAQ

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For discovering the functional correlation between the identified and quantified proteins by iTRAQ analysis, here we propose a correlation analysis method with cosine correlation coefficients as a powerful tool. iTRAQ analysis is a quantitative proteomics approach that enables identification and quantification of a large number of proteins. In order to obtain proteins responsive to Zn, Mn, or Fe mineral deficiency, we conducted iTRAQ analysis using a microsomal fraction of protein extractions from Arabidopsis root tissues. We identified and quantified 730 common proteins in three biological replicates with less than 1 % false discovery rate. To determine the role of these proteins in tolerating mineral deficiencies and their relation to each other, we calculated cosine correlation coefficients and represented the outcomes on a correlation map for visual understanding of functional relations among the identified proteins. Functionally similar proteins were gathered into the same clusters. Interestingly, a cluster of proteins (FRO2, IRT1, AHA2, PDR9/ABCG37, and GLP5) highly responsive to Fe deficiency was identified, which included both known and unknown novel proteins involved in tolerating Fe deficiency. We propose that the correlation analysis with the cosine correlation coefficients is a powerful method for finding important proteins of interest to several biological processes through comprehensive data sets.

Concepts: Scientific method, Protein, Gene, Proteomics, Quantification, Trigonometric functions, Discovery, Quantitative proteomics

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Understanding and treating vernal keratoconjunctivitis (VKC) has been a challenge because the pathogenesis is unclear and antiallergic therapy often unsuccessful. The aim of the study was to analyze peptide profiles in human tears using mass spectrometry to elucidate compositional differences between healthy subjects and patients affected by VKC.

Concepts: Immune system, Mass spectrometry, Tears, Protein mass spectrometry, Keratoconjunctivitis sicca, Quantitative proteomics, Im Sang-soo, ITRAQ

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The correct understanding of tumour development relies on the comprehensive study of proteins. They are the main orchestrators of vital processes, such as signalling pathways, which drive the carcinogenic process. Proteomic technologies can be applied to cancer research to detect differential protein expression and to assess different responses to treatment. Lung cancer is the number one cause of cancer-related death in the world. Mostly diagnosed at late stages of the disease, lung cancer has one of the lowest 5-year survival rates at 15 %. The use of different proteomic techniques such as two-dimensional gel electrophoresis (2D-PAGE), isotope labelling (ICAT, SILAC, iTRAQ) and mass spectrometry may yield new knowledge on the underlying biology of lung cancer and also allow the development of new early detection tests and the identification of changes in the cancer protein network that are associated with prognosis and drug resistance.

Concepts: Protein, Cancer, Oncology, Molecular biology, Lung cancer, Gel electrophoresis, Two-dimensional gel electrophoresis, Quantitative proteomics

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Introduction: Global protein expression profiling between healthy vs diseased states helps identifying differential expression and post-translational modifications of proteins, thereby providing better insights into the molecular changes of disease diagnosis and prognosis. In addition, analytical separation and identification of proteins from complex mixtures can provide insight into targeted drug therapy and prediction of response to different therapeutics. Areas covered: In the present review the authors summarize the readily available quantitative proteomics tools for the analytical separation and identification of target proteins in myeloid leukemia, AML in particular, and its future perspectives in its diagnostics and therapeutics. Within, the authors highlight some of the proteomics approaches such as gel-based quantitation strategies (2D, 2D-DIGE); MS-based quantitative proteomics tools (metabolic labeling (SILAC), chemical labeling (ITRAQ, ICAT)); MS techniques (MALDI-MS/MS). In addition, some of the target proteins identified using proteomics approaches in myeloid leukemia are also discussed that may encourage cancer biology investigators to undertake proteomics as a vital tool in their study. Expert opinion: With suitable, selective application of diverse set of quantitative proteomics strategies integrated with bioinformatics software and precise statistical analysis in myeloid leukemia holds tremendous promise in deciphering cancer proteome, understanding tumor pathophysiology and development of personalized molecular medicine and therapy.

Concepts: Protein, Medicine, Bioinformatics, Cancer, Molecular biology, Proteomics, Proteome, Quantitative proteomics