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      Issues and Applications in Label-Free Quantitative Mass Spectrometry

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          Abstract

          To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

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          Most cited references52

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          Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.

          We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we compared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively global protein expression in cells and tissues.
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            Comparison of label-free methods for quantifying human proteins by shotgun proteomics.

            Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.
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              MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.

              New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.
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                Author and article information

                Journal
                Int J Proteomics
                Int J Proteomics
                IJPRO
                International Journal of Proteomics
                Hindawi Publishing Corporation
                2090-2166
                2090-2174
                2013
                16 January 2013
                : 2013
                : 756039
                Affiliations
                1Department of Cellular & Integrative Physiology, Biotechnology Research & Training Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
                2School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
                Author notes

                Academic Editor: Bomie Han

                Article
                10.1155/2013/756039
                3562690
                23401775
                29e83791-2c24-48fc-936d-dd2b8f461fde
                Copyright © 2013 Xianyin Lai et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 October 2012
                : 17 October 2012
                : 31 October 2012
                Categories
                Review Article

                Molecular biology
                Molecular biology

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