title: Dissecting the iTRAQ Data Analysis.
abstract: In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.
authors.affiliation: Immunology Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi, 110067, India.
authors.forename: Suruchi
authors.initials: S
authors.lastname: Aggarwal
authors.affiliation: Drug Discovery Research Center (DDRC), Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 122001, Haryana, India. amit.yadav@thsti.res.in.
authors.forename: Amit Kumar
authors.initials: AK
authors.lastname: Yadav
mesh_terms: D002853:Chromatography, Liquid; D003627:Data Interpretation, Statistical; D010455:Peptides; D020543:Proteome; D040901:Proteomics / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D053719:Tandem Mass Spectrometry; D057188:Workflow
chemical_list: D010455:Peptides; D020543:Proteome
keywords: Chemical labeling; Quantitative proteomics; Relative protein quantitation; Statistics; iTRAQ