Literature DB >> 23079070

Pipeline to assess the greatest source of technical variance in quantitative proteomics using metabolic labelling.

Matthew R Russell1, Kathryn S Lilley.   

Abstract

The biological variance in protein expression of interest to biologists can only be accessed if the technical variance of the protein quantification method is low compared with the biological variance. Technical variance is dependent on the protocol employed within a quantitative proteomics experiment and accumulated with every additional step. The magnitude of additional variance incurred by each step of a protocol should be determined to enable design of experiments maximally sensitive to differential protein expression. Metabolic labelling techniques for MS based quantitative proteomics enable labelled and unlabelled samples to be combined at the tissue level. It has been widely assumed, although not yet empirically verified, that early combination of samples minimises technical variance in relative quantification. This study presents a pipeline to determine the variance incurred at each stage of a common quantitative proteomics protocol involving metabolic labelling. We apply this pipeline to determine whether early combination of samples in a protocol leads to significant reduction in experimental variance. We also identify which stage within the protocol is associated with maximum variance. This provides a blueprint by which the variance associated with each stage of any protocol can be dissected and utilised to influence optimal experimental design.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23079070     DOI: 10.1016/j.jprot.2012.09.020

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  10 in total

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2.  Comprehensive analysis of protein digestion using six trypsins reveals the origin of trypsin as a significant source of variability in proteomics.

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Review 3.  What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Authors:  Christoph W Turck; Michaela D Filiou
Journal:  Mol Neuropsychiatry       Date:  2015-05-12

4.  SPACEPro: A Software Tool for Analysis of Protein Sample Cleavage for Tandem Mass Spectrometry.

Authors:  Vidur Kailash; Luis Mendoza; Robert L Moritz; Michael R Hoopmann
Journal:  J Proteome Res       Date:  2021-02-02       Impact factor: 4.466

5.  Reproducible automated phosphopeptide enrichment using magnetic TiO2 and Ti-IMAC.

Authors:  Christopher J Tape; Jonathan D Worboys; John Sinclair; Robert Gourlay; Janis Vogt; Kelly M McMahon; Matthias Trost; Douglas A Lauffenburger; Douglas J Lamont; Claus Jørgensen
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6.  Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments.

Authors:  Evelyne Maes; Dirk Valkenborg; Geert Baggerman; Hanny Willems; Bart Landuyt; Liliane Schoofs; Inge Mertens
Journal:  PLoS One       Date:  2015-03-16       Impact factor: 3.240

7.  SILAC-iPAC: a quantitative method for distinguishing genuine from non-specific components of protein complexes by parallel affinity capture.

Authors:  Johanna S Rees; Kathryn S Lilley; Antony P Jackson
Journal:  J Proteomics       Date:  2014-12-20       Impact factor: 4.044

Review 8.  What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.

Authors:  Matthew B O'Rourke; Stephanie E L Town; Penelope V Dalla; Fiona Bicknell; Naomi Koh Belic; Jake P Violi; Joel R Steele; Matthew P Padula
Journal:  Proteomes       Date:  2019-08-22

9.  The effect of peptide adsorption on signal linearity and a simple approach to improve reliability of quantification.

Authors:  Stacey Warwood; Adam Byron; Martin J Humphries; David Knight
Journal:  J Proteomics       Date:  2013-05-09       Impact factor: 4.044

10.  Comparison of pre-processing methods for multiplex bead-based immunoassays.

Authors:  Tanja K Rausch; Arne Schillert; Andreas Ziegler; Angelika Lüking; Hans-Dieter Zucht; Peter Schulz-Knappe
Journal:  BMC Genomics       Date:  2016-08-11       Impact factor: 3.969

  10 in total

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