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      The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics

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          The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides.

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          Most cited references 36

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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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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.

              The Paragon Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user interface that removes the user expertise requirement, presenting control settings in the language of the laboratory that are translated to optimal algorithmic settings. To validate this new algorithm, a comparison with Mascot is presented for a series of analogous searches to explore the relative impact of increasing search space probed with Mascot by relaxing the tryptic digestion conformance requirements from trypsin to semitrypsin to no enzyme and with the Paragon Algorithm using its Rapid mode and Thorough mode with and without tryptic specificity. Although they performed similarly for small search space, dramatic differences were observed in large search space. With the Paragon Algorithm, hundreds of biological and artifact modifications, all possible substitutions, and all levels of conformance to the expected digestion pattern can be searched in a single search step, yet the typical cost in search time is only 2-5 times that of conventional small search space. Despite this large increase in effective search space, there is no drastic loss of discrimination that typically accompanies the exploration of large search space.

                Author and article information

                F1000Research (London, UK )
                7 April 2014
                : 2
                [1 ]Cambridge Systems Biology Centre and Dept. of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
                [2 ]Division of Physiology and Metabolism, MRC National Institute for Medical Research, London, NW7 1AA, UK
                [1 ]The Beatson Institute for Cancer Research, Glasgow, UK
                Cambridge Systems Biology Centre (Department of Biochemistry), University of Cambridge, UK
                [1 ]Department of Cellular and Integrative Physiology, Indiana University, Indianapolis, IN, USA
                Cambridge Systems Biology Centre (Department of Biochemistry), University of Cambridge, UK
                Author notes

                Equal contributors

                Jakob Vowinckel, Floriana Capuano, Kate Campbell and Michael J. Deery designed and conducted the experiments, Kathryn S. Lilley and Markus Ralser designed the experiments and all authors contributed to writing the manuscript.

                Competing interests: The authors declare no competing interests.

                Competing interests: No competing interests were disclosed.

                Competing interests: None

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Copyright: © 2014 Vowinckel J et al.

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

                Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

                Funded by: Isaac Newton Trust
                Funded by: Wellcome Trust
                Award ID: RG 093735/Z/10/Z
                Funded by: ERC
                Award ID: 260809
                Funded by: 7th Framework Programme of the European Union
                Award ID: 262067- PRIME-XS
                This work was funded by the Isaac Newton Trust, the Wellcome Trust (RG 093735/Z/10/Z) to MR, the ERC (Starting grant 260809) to MR and the 7th Framework Programme of the European Union (262067- PRIME-XS) to KSL. M.R. is a Wellcome Trust Research Career Development and Wellcome-Beit prize fellow.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Method Article
                Protein Chemistry & Proteomics


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