Literature DB >> 20465265

Quantitative analysis of mTRAQ-labeled proteome using full MS scans.

Un-Beom Kang1, Jeonghun Yeom, Hoguen Kim, Cheolju Lee.   

Abstract

Proteomic techniques are mostly used these days to identify proteins in a biological sample. Quantification of the differences between two or more physiological conditions, such as disease or no disease, has become an increasingly challenging task in proteomics. Mass tags introducing stable isotopes into peptides or proteins provide means for quantification in mass spectrometry. The mass tags are recognized by mass spectrometry and at the same time provide quantitative information. In the current study, we introduce mTRAQ for the purpose of quantification by full MS scans. Although mTRAQ reagent was initially designed for multiple reaction monitoring, we verified the utility of mTRAQ for MS1-based relative quantification using standard protein mixtures and blood plasma samples. mTRAQ-labeled peptides showed better quality MS2 spectra with increased XCorr values in a SEQUEST search output than corresponding unlabeled peptides. The improved spectral quality was due mostly to the enhanced matching of b-type ions. By combining mTRAQ with ICAT and applying them to colon cancer tissues, we identified and quantified a total of 3,320 proteins. mTRAQ covered a wider range of the proteome than did ICAT, and only 1053 proteins were shared by the two independent methods. Our results suggest the usefulness of mTRAQ, alone or in combination with ICAT, as a comparative profiling method in quantitative proteomics.

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Year:  2010        PMID: 20465265     DOI: 10.1021/pr9011014

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  14 in total

1.  Proteomic Interrogation in Cancer Biomarker.

Authors:  Un-Beom Kang
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

2.  Mass Defect-Based N,N-Dimethyl Leucine Labels for Quantitative Proteomics and Amine Metabolomics of Pancreatic Cancer Cells.

Authors:  Ling Hao; Jillian Johnson; Christopher B Lietz; Amanda Buchberger; Dustin Frost; W John Kao; Lingjun Li
Journal:  Anal Chem       Date:  2017-01-04       Impact factor: 6.986

Review 3.  Minireview: progress and challenges in proteomics data management, sharing, and integration.

Authors:  Lauren B Becnel; Neil J McKenna
Journal:  Mol Endocrinol       Date:  2012-08-17

4.  Nanoparticle-Assisted Laser Desorption/Ionization Mass Spectrometry (Nano-PALDI MS) with Py-Tag for the Analysis of Small Molecules.

Authors:  Yukina Tatsuta; Yukie Tanaka; Akari Ikeda; Shigeru Matsukawa; Hajime Katano; Shu Taira
Journal:  Mass Spectrom (Tokyo)       Date:  2017-09-15

5.  Hexapeptide libraries for enhanced protein PTM identification and relative abundance profiling in whole human saliva.

Authors:  Sricharan Bandhakavi; Susan K Van Riper; Pierre N Tawfik; Matthew D Stone; Tufia Haddad; Nelson L Rhodus; John V Carlis; Timothy J Griffin
Journal:  J Proteome Res       Date:  2011-01-14       Impact factor: 4.466

6.  Comparative profiling of plasma proteome from breast cancer patients reveals thrombospondin-1 and BRWD3 as serological biomarkers.

Authors:  Eui Jin Suh; Mohammad Humayun Kabir; Un Beom Kang; Jong Won Lee; Jonghan Yu; Dong Young Noh; Cheolju Lee
Journal:  Exp Mol Med       Date:  2012-01-31       Impact factor: 8.718

7.  Isotopic N,N-dimethyl leucine tags for absolute quantification of clusterin and apolipoprotein E in Alzheimer's disease.

Authors:  Yuan Liu; Hua Zhang; Xiaofang Zhong; Zihui Li; Henrik Zetterberg; Lingjun Li
Journal:  J Proteomics       Date:  2022-02-03       Impact factor: 4.044

8.  Increasing the throughput of sensitive proteomics by plexDIA.

Authors:  Jason Derks; Andrew Leduc; Georg Wallmann; R Gray Huffman; Matthew Willetts; Saad Khan; Harrison Specht; Markus Ralser; Vadim Demichev; Nikolai Slavov
Journal:  Nat Biotechnol       Date:  2022-07-14       Impact factor: 68.164

9.  High-throughput peptide quantification using mTRAQ reagent triplex.

Authors:  Joo Young Yoon; Jeonghun Yeom; Heebum Lee; Kyutae Kim; Seungjin Na; Kunsoo Park; Eunok Paek; Cheolju Lee
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

10.  Quantitative Phosphoproteomics Analysis of ERBB3/ERBB4 Signaling.

Authors:  Sebastian K Wandinger; Idoya Lahortiga; Kris Jacobs; Martin Klammer; Nicole Jordan; Sarah Elschenbroich; Marc Parade; Edgar Jacoby; Joannes T M Linders; Dirk Brehmer; Jan Cools; Henrik Daub
Journal:  PLoS One       Date:  2016-01-08       Impact factor: 3.240

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