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Protein labeling by iTRAQ: A new tool for quantitative mass spectrometry in proteome research
Author(s) -
Wiese Sebastian,
Reidegeld Kai A.,
Meyer Helmut E.,
Warscheid Bettina
Publication year - 2007
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200600422
Subject(s) - isobaric labeling , quantitative proteomics , chemistry , proteome , mass spectrometry , peptide , label free quantification , chromatography , proteomics , fragmentation (computing) , tandem mass tag , reagent , derivatization , stable isotope labeling by amino acids in cell culture , tandem mass spectrometry , biochemistry , protein mass spectrometry , biology , gene , ecology
A novel, MS‐based approach for the relative quantification of proteins, relying on the derivatization of primary amino groups in intact proteins using isobaric tag for relative and absolute quantitation (iTRAQ) is presented. Due to the isobaric mass design of the iTRAQ reagents, differentially labeled proteins do not differ in mass; accordingly, their corresponding proteolytic peptides appear as single peaks in MS scans. Because quantitative information is provided by isotope‐encoded reporter ions that can only be observed in MS/MS spectra, we analyzed the fragmentation behavior of ESI and MALDI ions of peptides generated from iTRAQ‐labeled proteins using a TOF/TOF and/or a QTOF instrument. We observed efficient liberation of reporter ions for singly protonated peptides at low‐energy collision conditions. In contrast, increased collision energies were required to liberate the iTRAQ label from lysine side chains of doubly charged peptides and, thus, to observe reporter ions suitable for relative quantification of proteins with high accuracy. We then developed a quantitative strategy that comprises labeling of intact proteins by iTRAQ followed by gel electrophoresis and peptide MS/MS analyses. As proof of principle, mixtures of five different proteins in various concentration ratios were quantified, demonstrating the general applicability of the approach presented here to quantitative MS‐based proteomics.