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      Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *

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          Abstract

          Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

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          Most cited references 54

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          Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

          Quantitative proteomics has traditionally been performed by two-dimensional gel electrophoresis, but recently, mass spectrometric methods based on stable isotope quantitation have shown great promise for the simultaneous and automated identification and quantitation of complex protein mixtures. Here we describe a method, termed SILAC, for stable isotope labeling by amino acids in cell culture, for the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid, in this case deuterated leucine (Leu-d3). We find that growth of cells maintained in these media is no different from growth in normal media as evidenced by cell morphology, doubling time, and ability to differentiate. Complete incorporation of Leu-d3 occurred after five doublings in the cell lines and proteins studied. Protein populations from experimental and control samples are mixed directly after harvesting, and mass spectrometric identification is straightforward as every leucine-containing peptide incorporates either all normal leucine or all Leu-d3. We have applied this technique to the relative quantitation of changes in protein expression during the process of muscle cell differentiation. Proteins that were found to be up-regulated during this process include glyceraldehyde-3-phosphate dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple, inexpensive, and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system.
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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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              Proteomic characterization of the human centrosome by protein correlation profiling.

              The centrosome is the major microtubule-organizing centre of animal cells and through its influence on the cytoskeleton is involved in cell shape, polarity and motility. It also has a crucial function in cell division because it determines the poles of the mitotic spindle that segregates duplicated chromosomes between dividing cells. Despite the importance of this organelle to cell biology and more than 100 years of study, many aspects of its function remain enigmatic and its structure and composition are still largely unknown. We performed a mass-spectrometry-based proteomic analysis of human centrosomes in the interphase of the cell cycle by quantitatively profiling hundreds of proteins across several centrifugation fractions. True centrosomal proteins were revealed by both correlation with already known centrosomal proteins and in vivo localization. We identified and validated 23 novel components and identified 41 likely candidates as well as the vast majority of the known centrosomal proteins in a large background of nonspecific proteins. Protein correlation profiling permits the analysis of any multiprotein complex that can be enriched by fractionation but not purified to homogeneity.
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                Author and article information

                Journal
                Mol Cell Proteomics
                Mol. Cell Proteomics
                mcprot
                mcprot
                MCP
                Molecular & Cellular Proteomics : MCP
                The American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                September 2014
                17 June 2014
                17 June 2014
                : 13
                : 9
                : 2513-2526
                Affiliations
                From the ‡Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany
                Author notes
                § To whom correspondence should be addressed: E-mail: cox@ 123456biochem.mpg.de or mmann@ 123456biochem.mpg.de .
                Article
                M113.031591
                10.1074/mcp.M113.031591
                4159666
                24942700
                © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

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