Rune Matthiesen, Jakob Bunkenborg[show abstract] [hide abstract]
ABSTRACT: Mass spectrometry has been widely applied to study biomolecules and one rapidly developing field is the global analysis of proteins, proteomics. Understanding and handling mass spectrometry data is a multifaceted task that requires many decisions to be made to get the most comprehensive information from an experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools can be applied to the many analytical challenges. This introductory chapter is intended as a basic introduction to mass spectrometry (MS)-based proteomics to set the scene for newcomers and give pointers to reference material.There are many applications of mass spectrometry in proteomics and each application is associated with some analytical choices, instrumental limitations and data processing steps that depend on the aim of the study and means of conducting it. Different aspects of the proteome can be explored by choosing the right combination of sample preparation, MS instrumentation and data processing. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which are explored in greater depth in later chapters.Methods in molecular biology (Clifton, N.J.) 01/2013; 1007:1-45.
Jakob Bunkenborg, Rune Matthiesen[show abstract] [hide abstract]
ABSTRACT: Tandem mass spectrometry provides a sensitive means of analyzing the amino acid sequence of peptides and modified peptides by providing accurate mass measurements of precursor and fragment ions. Modern mass spectrometry instrumentation is capable of rapidly generating many thousands of tandem mass spectra and protein database search engines have been developed to match the experimental data to peptide candidates. In most studies there is a schism between discarding perfectly valid data and including nonsensical peptide identifications-this is currently a major bottleneck in data-analysis and it calls for an understanding of tandem mass spectrometry data. Manual evaluation of the data and perhaps experimental cross-checking of the MS data can save many months of experimental work trying to do biological follow-ups based on erroneous identifications. Especially for posttranslationally modified peptides there is a need for manual validation of the data because search algorithms seldom have been optimized for the identification of modified peptides and because there are many pitfalls for the unwary. This chapter describes some of the issues that should be considered when interpreting and validating tandem mass spectra and gives some useful tables to aid this process.Methods in molecular biology (Clifton, N.J.) 01/2013; 1007:139-71.
Article: Data extraction from proteomics raw data: an evaluation of nine tandem MS tools using a large Orbitrap data set.[show abstract] [hide abstract]
ABSTRACT: In shot-gun proteomics raw tandem MS data are processed with extraction tools to produce condensed peak lists that can be uploaded to database search engines. Many extraction tools are available but to our knowledge, a systematic comparison of such tools has not yet been carried out. Using raw data containing more than 400,000 tandem MS spectra acquired using an Orbitrap Velos we compared 9 tandem MS extraction tools, freely available as well as commercial. We compared the tools with respect to number of extracted MS/MS events, fragment ion information, number of matches, precursor mass accuracies and agreement in-between tools. Processing a primary data set with 9 different tandem MS extraction tools resulted in a low overlap of identified peptides. The tools differ by assigned charge states of precursors, precursor and fragment ion masses, and we show that peptides identified very confidently using one extraction tool might not be matched when using another tool. We also found a bias towards peptides of lower charge state when extracting fragment ion data from higher resolution raw data without deconvolution. Collecting and comparing the extracted data from the same raw data allow adjusting parameters and expectations and selecting the right tool for extraction of tandem MS data.Journal of proteomics 06/2012; 75(17):5293-303. · 5.07 Impact Factor
Peter Mortensen, Joost W Gouw, Jesper V Olsen, Shao-En Ong, Kristoffer T G Rigbolt, Jakob Bunkenborg, Jürgen Cox, Leonard J Foster, Albert J R Heck, Blagoy Blagoev, Jens S Andersen, Matthias Mann[show abstract] [hide abstract]
ABSTRACT: Mass spectrometry-based proteomics critically depends on algorithms for data interpretation. A current bottleneck in the rapid advance of proteomics technology is the closed nature and slow development cycle of vendor-supplied software solutions. We have created an open source software environment, called MSQuant, which allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS(3) score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., (15)N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., (18)O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches. MSQuant is available, including an installer and supporting scripts, at http://msquant.sourceforge.net .Journal of Proteome Research 11/2009; 9(1):393-403. · 5.11 Impact Factor
Article: Identification of thioredoxin disulfide targets using a quantitative proteomics approach based on isotope-coded affinity tags.[show abstract] [hide abstract]
ABSTRACT: Thioredoxin (Trx) is a ubiquitous protein disulfide reductase involved in a wide range of cellular redox processes. A large number of putative target proteins have been identified using proteomics approaches, but insight into target specificity at the molecular level is lacking since the reactivity of Trx toward individual disulfides has not been quantified. Here, a novel proteomics procedure is described for quantification of Trx-mediated target disulfide reduction based on thiol-specific differential labeling with the iodoacetamide-based isotope-coded affinity tag (ICAT) reagents. Briefly, protein extract of embryos from germinated barley seeds was treated +/- Trx, and thiols released from target protein disulfides were irreversibly blocked with iodoacetamide. The remaining cysteine residues in the Trx-treated and the control (-Trx) samples were then chemically reduced and labeled with the "light" (12C) and "heavy" (13C) ICAT reagent, respectively. The extent of Trx-mediated reduction was thus quantified for individual cysteine residues based on ratios of tryptic peptides labeled with the two ICAT reagents as measured by liquid chromatography coupled with mass spectrometry (LC-MS). A threshold for significant target reduction was set and disulfide targets were identified in 104 among a total of 199 identified ICAT-labeled peptides. Trx-reduced disulfides were found in several previously identified target proteins, for example, peroxiredoxin and cyclophilin, as well as from a wide range of new targets including several ribosomal proteins that point to a link between Trx h and translation. The catalytic cysteine in dehydroascorbate reductase constituted the most extensively reduced target suggesting that Trx h has an important role in the ascorbate-glutathione cycle.Journal of Proteome Research 01/2009; 7(12):5270-6. · 5.11 Impact Factor