The code, the documentation and example datasets are available open-source at www.msstats.org under the Artistic-2.0 license. The package can be downloaded from www.msstats.org or from Bioconductor www.bioconductor.org and used in an R command line workflow. The package can also be accessed as an external tool in Skyline (Broudy et al., 2014) and used via graphical user interface.

Targeted mass spectrometry by selected reaction monitoring (S/MRM) has proven to be a suitable technique for the consistent and reproducible quantification of proteins across multiple biological samples and a wide dynamic range. This performance profile is an important prerequisite for systems biology and biomedical research. However, the method is limited to the measurements of a few hundred peptides per LC-MS analysis. Recently, we introduced SWATH-MS, a combination of data independent acquisition and targeted data analysis that vastly extends the number of peptides/proteins quantified per sample, while maintaining the favorable performance profile of S/MRM. Here we applied the SWATH-MS technique to quantify changes over time in a large fraction of the proteome expressed in Saccharomyces cerevisiae in response to osmotic stress.We sampled cell cultures in biological triplicates at six time points following the application of osmotic stress and acquired single injection data independent acquisition data sets on a high-resolution 5600 tripleTOF instrument operated in SWATH mode. Proteins were quantified by the targeted extraction and integration of transition signal groups from the SWATH-MS datasets for peptides that are proteotypic for specific yeast proteins. We consistently identified and quantified more than 15,000 peptides and 2500 proteins across the 18 samples. We demonstrate high reproducibility between technical and biological replicates across all time points and protein abundances. In addition, we show that the abundance of hundreds of proteins was significantly regulated upon osmotic shock, and pathway enrichment analysis revealed that the proteins reacting to osmotic shock are mainly involved in the carbohydrate and amino acid metabolism. Overall, this study demonstrates the ability of SWATH-MS to efficiently generate reproducible, consistent, and quantitatively accurate measurements of a large fraction of a proteome across multiple samples. In systems biology and biomedical studies targeted mass spectrometry via selected reaction monitoring (SRM) 1 (also known as multiple reaction monitoring, MRM) has emerged as a powerful technique for the consistent and reproducible quantification of proteins across numerous complex samples (1-6). Optimal sets of precursor/fragment ion pairs, called transitions, uniquely represent a specific peptide. They constitute a definitive mass spectrometric assay for the detection of targeted peptides, and thus the proteins from which they derive, in the complex matrix of trypsinized biological samples (1, 7). Protein quantification is then performed by relating the intensity of the acquired transition signals to suitable reference signals. Most quantification strategies commonly used in proteomics are compatible with this method (8). Recently, the high-throughput development of S/MRM assays has been achieved via the generation of MS/MS spectral libraries from the measurements of thousands of synthetic peptides representing proteotypic peptides (9). Moreover, many experime...

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a standalone tool or in integration with the existing computational pipelines. Molecular & Cellular Proteomics 11: 10.1074/ mcp.M111.014662, 1-12, 2012.Selected reaction monitoring (SRM) 1 is a mass spectrometry technique that can accurately and reproducibly quantify proteins in complex biological mixtures (1, 2, 3). It can cover a nearly complete dynamic range of abundance of cellular proteome, with a lower boundary of detection below 50 copies per cell for single cellular organisms (3). Considerable efforts are currently invested into developing high-throughput SRM assays, even for whole proteomes (4, 5). These assays are then used to simultaneously quantify hundreds of proteins with a high degree of reproducibility across multiple samples, and as a result the assays are increasingly used in systems biology and in clinical investigations (3,6,7,8).SRM experiments quantify a priori known protein species. They require knowledge of the peptides of these proteins that are unique to the target proteins and can be observed by a mass spectrometer (9, 10), and of the mass spectrometric characteristics of these peptides such as fragment ion mass, signal intensity distribution, and optimal collision energy (2). Enzymatically digested proteins are subjected to liquid chromatography separation and are monitored in a triple quadrupole mass spectrometer, and the ion signals for an a prior...

We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a twodimensional image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional objects rotated about a single axis. We use this observation to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that -the algorithm performs very well on a range of test images composed of three-dimensional objects rotated about a single axis.

Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly used for accurate and reproducible quantification of protein analytes in complex biological mixtures. Strictly hypothesis-driven, SRM assays quantify each targeted protein by collecting measurements on its peptide fragment ions, called transitions. To achieve sensitive and accurate quantitative results, experimental design and data analysis must consistently account for the variability of the quantified transitions. This consistency is especially important in large experiments, which increasingly require profiling up to hundreds of proteins over hundreds of samples. Here we describe a robust and automated workflow for the analysis of large quantitative SRM data sets that integrates data processing, statistical protein identification and quantification, and dissemination of the results. The integrated workflow combines three software tools: mProphet for peptide identification via probabilistic scoring; SRMstats for protein significance analysis with linear mixed-effect models; and PASSEL, a public repository for storage, retrieval and query of SRM data. The input requirements for the protocol are files with SRM traces in mzXML format, and a file with a list of transitions in a text tab-separated format. The protocol is especially suited for data with heavy isotope-labeled peptide internal standards. We demonstrate the protocol on a clinical data set in which the abundances of 35 biomarker candidates were profiled in 83 blood plasma samples of subjects with ovarian cancer or benign ovarian tumors. The time frame to realize the protocol is 1-2 weeks, depending on the number of replicates used in the experiment.

Evolutionary and reproductive success of angiosperms, the most diverse group of land plants, relies on visual and olfactory cues for pollinator attraction. Previous work has focused on elucidating the developmental regulation of pathways leading to the formation of pollinator-attracting secondary metabolites such as scent compounds and flower pigments. However, to date little is known about how flowers control their entire metabolic network to achieve the highly regulated production of metabolites attracting pollinators. Integrative analysis of transcripts and metabolites in snapdragon sepals and petals over flower development performed in this study revealed a profound developmental remodeling of gene expression and metabolite profiles in petals, but not in sepals. Genes up-regulated during petal development were enriched in functions related to secondary metabolism, fatty acid catabolism, and amino acid transport, whereas down-regulated genes were enriched in processes involved in cell growth, cell wall formation, and fatty acid biosynthesis. The levels of transcripts and metabolites in pathways leading to scent formation were coordinately up-regulated during petal development, implying transcriptional induction of metabolic pathways preceding scent formation. Developmental gene expression patterns in the pathways involved in scent production were different from those of glycolysis and the pentose phosphate pathway, highlighting distinct developmental regulation of secondary metabolism and primary metabolic pathways feeding into it.

Quantitative measurement of proteins involved in insulin signaling and central metabolism in C57BL/6J and 129Sv mice subjected to a sustained high-fat diet reveals that the two strains diverge early in their response to the feeding regimen.

Eicosanoids constitute a diverse class of bioactive lipid mediators that are produced from arachidonic acid and play critical roles in cell signaling and inflammatory aspects of numerous diseases. We have previously quantified eicosanoid metabolite production in RAW264.7 macrophage cells in response to Toll-like receptor 4 signaling and analyzed the levels of transcripts coding for the enzymes involved in the eicosanoid metabolite biosynthetic pathways. We now report the quantification of changes in protein levels under similar experimental conditions in RAW264.7 macrophages by multiple reaction monitoring mass spectrometry, an accurate targeted protein quantification method. The data complete the first fully integrated genomic, proteomic, and metabolomic analysis of the eicosanoid biochemical pathway. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.014746, 1-9, 2012.Eicosanoids constitute a diverse class of bioactive lipid mediators produced from arachidonic acid that play critical roles in cell signaling and inflammatory aspects of numerous diseases (1-3). Under basal conditions, biological systems have very low levels of free fatty acids including arachidonic acid, because fatty acids are mostly found esterified in triglycerides, sterol esters, and phospholipids. The activation of specific receptors causes many downstream events, including triggering the release of free arachidonic acid from membrane phospholipids by the action of phospholipase A 2 and its subsequent conversion to oxygenated metabolites generally termed eicosanoids (4). We have previously quantified eicosanoid metabolite production in RAW264.7 macrophage cells (5, 6) in response to the Toll-like receptor 4 (TLR-4) 1 agonist Kdo 2 -lipid A (KLA), a single well defined subspecies of lipopolysaccharide and a potent inducer of macrophage inflammatory programs (7). We also carried out a fluxomic analysis of the metabolic profile as a function of time after stimulation (8) and a transcriptomic analysis as part of a broader study of the macrophage lipidome and its transcriptomic correlation (9). We now report the quantification of changes in abundances of the proteins involved in eicosanoid biosynthesis under similar experimental conditions, using multiple reaction monitoring, a quantitatively accurate targeted mass spectrometric method. The results complete the first fully integrated genomic, proteomic, and metabolomic analysis of the eicosanoid biochemical pathway using mouse RAW264.7 cells as a model system. EXPERIMENTAL PROCEDURESSample Preparation-RAW 264.7 macrophages (American Type Culture Collection, catalog number TIB-71) were seeded at a density of 1 ϫ 10 7 cells into 75-cm 2 tissue culture flasks and grown for 18 h to reach a density of ϳ2 ϫ 10 7 cells/flask in DMEM (without phenol red) supplemented with 4 mM L-glutamate, 4.5 g liter Ϫ1 D-glucose, 10% heat-inactivated FCS, and 1% penicillin/streptomycin (Invitrogen). The cells were stimulated with KLA (100 ng ml Ϫ1 final concentration) and harvested at 0.5, 1, 2, 4, 8, 12, and 24 h a...