Literature DB >> 23957277

Quantitative proteomic analysis of microdissected breast cancer tissues: comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches.

Ning Qing Liu1, Lennard J M Dekker, Christoph Stingl, Coşkun Güzel, Tommaso De Marchi, John W M Martens, John A Foekens, Theo M Luider, Arzu Umar.   

Abstract

Quantitative proteomics plays an important role in validation of breast-cancer-related biomarkers. In this study, we systematically compared the performance of label-free quantification (LFQ) and SILAC with shotgun and directed methods for quantifying breast-cancer-related markers in microdissected tissues. We show that LFQ leads to slightly higher coefficient of variation (CV) for protein quantification (median CV = 16.3%) than SILAC quantification (median CV = 13.7%) (P < 0.0001), but LFQ method enables ∼60% more protein quantification and is also more reproducible (∼20% more proteins were quantified in all replicate samples). Furthermore, we describe a method to accurately quantify multiple proteins within one pathway, that is, "focal adhesion pathway", in trace amounts of breast cancer tissues using a SILAC-based SRM assay. Using this SILAC-based SRM assay, we precisely quantified five "focal adhesion" proteins with good quantitative precision (CV range: 2.4-5.9%) in replicate whole tissue lysate samples and replicate microdissected samples (CV range: 5.8-16.1%). Our results show that in microdissected breast cancer tissues LFQ in combination with shotgun proteomics performed the best overall and is therefore suitable for both biomarker discovery and validation in these types of specimens. The SILAC-based SRM method can be used for the development of clinically relevant protein assays in tumor biopsies.

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Year:  2013        PMID: 23957277     DOI: 10.1021/pr4005794

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


  17 in total

1.  Ultrafast Peptide Label-Free Quantification with FlashLFQ.

Authors:  Robert J Millikin; Stefan K Solntsev; Michael R Shortreed; Lloyd M Smith
Journal:  J Proteome Res       Date:  2017-11-08       Impact factor: 4.466

2.  moFF: a robust and automated approach to extract peptide ion intensities.

Authors:  Andrea Argentini; Ludger J E Goeminne; Kenneth Verheggen; Niels Hulstaert; An Staes; Lieven Clement; Lennart Martens
Journal:  Nat Methods       Date:  2016-11-29       Impact factor: 28.547

3.  Quantitative Microproteomics Based Characterization of the Central and Peripheral Nervous System of a Mouse Model of Krabbe Disease.

Authors:  Davide Pellegrini; Ambra Del Grosso; Lucia Angella; Nadia Giordano; Marialaura Dilillo; Ilaria Tonazzini; Matteo Caleo; Marco Cecchini; Liam A McDonnell
Journal:  Mol Cell Proteomics       Date:  2019-03-29       Impact factor: 5.911

Review 4.  Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer.

Authors:  Sayem Miah; Charles A S Banks; Mark K Adams; Laurence Florens; Kiven E Lukong; Michael P Washburn
Journal:  Mol Biosyst       Date:  2016-12-20

Review 5.  Chemical cross-linking with mass spectrometry: a tool for systems structural biology.

Authors:  Juan D Chavez; James E Bruce
Journal:  Curr Opin Chem Biol       Date:  2018-08-30       Impact factor: 8.822

Review 6.  Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.

Authors:  Nathan P Manes; Aleksandra Nita-Lazar
Journal:  J Proteomics       Date:  2018-02-13       Impact factor: 4.044

7.  Evaluation of iTRAQ and SWATH-MS for the Quantification of Proteins Associated with Insulin Resistance in Human Duodenal Biopsy Samples.

Authors:  Sylvie Bourassa; Frédéric Fournier; Benjamin Nehmé; Isabelle Kelly; André Tremblay; Valéry Lemelin; Benoit Lamarche; Patrick Couture; Arnaud Droit
Journal:  PLoS One       Date:  2015-05-07       Impact factor: 3.240

8.  Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer.

Authors:  Ning Qing Liu; Christoph Stingl; Maxime P Look; Marcel Smid; René B H Braakman; Tommaso De Marchi; Anieta M Sieuwerts; Paul N Span; Fred C G J Sweep; Barbro K Linderholm; Anita Mangia; Angelo Paradiso; Luc Y Dirix; Steven J Van Laere; Theo M Luider; John W M Martens; John A Foekens; Arzu Umar
Journal:  J Natl Cancer Inst       Date:  2014-01-07       Impact factor: 13.506

Review 9.  Proteomics approaches for the studies of bone metabolism.

Authors:  Ji-Hyun Lee; Je-Yoel Cho
Journal:  BMB Rep       Date:  2014-03       Impact factor: 4.778

10.  Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress.

Authors:  Zhe Cheng; Guoshou Teo; Sabrina Krueger; Tara M Rock; Hiromi W L Koh; Hyungwon Choi; Christine Vogel
Journal:  Mol Syst Biol       Date:  2016-01-20       Impact factor: 11.429

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