Literature DB >> 17668192

Quantitative mass spectrometry in proteomics: a critical review.

Marcus Bantscheff1, Markus Schirle, Gavain Sweetman, Jens Rick, Bernhard Kuster.   

Abstract

The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data.

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Year:  2007        PMID: 17668192     DOI: 10.1007/s00216-007-1486-6

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  452 in total

1.  BPDA2d--a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass spectrometry.

Authors:  Youting Sun; Jianqiu Zhang; Ulisses Braga-Neto; Edward R Dougherty
Journal:  Bioinformatics       Date:  2011-12-06       Impact factor: 6.937

2.  msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies.

Authors:  Berend Hoekman; Rainer Breitling; Frank Suits; Rainer Bischoff; Peter Horvatovich
Journal:  Mol Cell Proteomics       Date:  2012-02-07       Impact factor: 5.911

Review 3.  Proteomic analysis of the presynaptic active zone.

Authors:  W Volknandt; M Karas
Journal:  Exp Brain Res       Date:  2012-02-22       Impact factor: 1.972

4.  Accurate mass spectrometry based protein quantification via shared peptides.

Authors:  Banu Dost; Nuno Bandeira; Xiangqian Li; Zhouxin Shen; Steven P Briggs; Vineet Bafna
Journal:  J Comput Biol       Date:  2012-03-13       Impact factor: 1.479

Review 5.  A Review on Quantitative Multiplexed Proteomics.

Authors:  Nishant Pappireddi; Lance Martin; Martin Wühr
Journal:  Chembiochem       Date:  2019-04-18       Impact factor: 3.164

6.  Targeted Proteomic Analyses of Histone H4 Acetylation Changes Associated with Homologous-Recombination-Deficient High-Grade Serous Ovarian Carcinomas.

Authors:  Stefani N Thomas; Lijun Chen; Yang Liu; Naseruddin Höti; Hui Zhang
Journal:  J Proteome Res       Date:  2017-09-14       Impact factor: 4.466

7.  Quantitative liquid chromatography-mass spectrometry-multiple reaction monitoring (LC-MS-MRM) analysis of site-specific glycoforms of haptoglobin in liver disease.

Authors:  Miloslav Sanda; Petr Pompach; Zuzana Brnakova; Jing Wu; Kepher Makambi; Radoslav Goldman
Journal:  Mol Cell Proteomics       Date:  2013-02-06       Impact factor: 5.911

8.  Identification of additional proteins in differential proteomics using protein interaction networks.

Authors:  Frederik Gwinner; Adelina E Acosta-Martin; Ludovic Boytard; Maggy Chwastyniak; Olivia Beseme; Hervé Drobecq; Sophie Duban-Deweer; Francis Juthier; Brigitte Jude; Philippe Amouyel; Florence Pinet; Benno Schwikowski
Journal:  Proteomics       Date:  2013-04       Impact factor: 3.984

Review 9.  Proteomic approaches to quantify cysteine reversible modifications in aging and neurodegenerative diseases.

Authors:  Liqing Gu; Renã A S Robinson
Journal:  Proteomics Clin Appl       Date:  2016-11-11       Impact factor: 3.494

10.  Measuring the dynamics of E. coli ribosome biogenesis using pulse-labeling and quantitative mass spectrometry.

Authors:  Stephen S Chen; Edit Sperling; Josh M Silverman; Joseph H Davis; James R Williamson
Journal:  Mol Biosyst       Date:  2012-10-30
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