Ching-Yun Chang

MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments

UNLABELLED MSstats is an R package for statistical relative quantification of proteins and peptides in mass spectrometry-based proteomics. Version 2.0 of MSstats supports label-free and label-based experimental workflows and data-dependent, targeted and data-independent spectral acquisition. It takes as input identified and quantified spectral peaks, and outputs a list of differentially abundant peptides or proteins, or summaries of peptide or protein relative abundance. MSstats relies on a flexible family of linear mixed models. AVAILABILITY AND IMPLEMENTATION 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.

Protein significance analysis in selected reaction monitoring (SRM) measurements

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 stand-alone tool or in integration with the existing computational pipelines.

Reproducible and Consistent Quantification of the Saccharomyces cerevisiae Proteome by SWATH-mass spectrometry

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.

Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies

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.

Developmental Changes in the Metabolic Network of Snapdragon Flowers

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.

Targeted Proteomics of the Eicosanoid Biosynthetic Pathway Completes an Integrated Genomics-Proteomics-Metabolomics Picture of Cellular Metabolism

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.

Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet.

The metabolic syndrome is a collection of risk factors including obesity, insulin resistance and hepatic steatosis, which occur together and increase the risk of diseases such as diabetes, cardiovascular disease and cancer. In spite of intense research, the complex etiology of insulin resistance and its association with the accumulation of triacylglycerides in the liver and with hepatic steatosis remains not completely understood. Here, we performed quantitative measurements of 144 proteins involved in the insulin‐signaling pathway and central metabolism in liver homogenates of two genetically well‐defined mouse strains C57BL/6J and 129Sv that were subjected to a sustained high‐fat diet. We used targeted mass spectrometry by selected reaction monitoring (SRM) to generate accurate and reproducible quantitation of the targeted proteins across 36 different samples (12 conditions and 3 biological replicates), generating one of the largest quantitative targeted proteomics data sets in mammalian tissues. Our results revealed rapid response to high‐fat diet that diverged early in the feeding regimen, and evidenced a response to high‐fat diet dominated by the activation of peroxisomal β‐oxidation in C57BL/6J and by lipogenesis in 129Sv mice.

Serum D-Lactate Concentrations in Cats with Gastrointestinal Disease

BACKGROUND Increased D-lactate concentrations cause neurological signs in humans with gastrointestinal disease. HYPOTHESIS/OBJECTIVES To determine if serum D-lactate concentrations are increased in cats with gastrointestinal disease compared to healthy controls, and if concentrations correlate with specific neurological or gastrointestinal abnormalities. ANIMALS Systematically selected serum samples submitted to the Gastrointestinal Laboratory at Texas A&M University from 100 cats with clinical signs of gastrointestinal disease and abnormal gastrointestinal function tests, and 30 healthy cats. METHODS Case-control study in which serum D- and L-lactate concentrations and retrospective data on clinical signs were compared between 30 healthy cats and 100 cats with gastrointestinal disease. Association of D-lactate concentration with tests of GI dysfunction and neurological signs was evaluated by multivariate linear and logistic regression analyses, respectively. RESULTS All 100 cats had a history of abnormal gastrointestinal signs and abnormal gastrointestinal function test results. Thirty-one cats had definitive or subjective neurological abnormalities. D-lactate concentrations of cats with gastrointestinal disease (median 0.36, range 0.04-8.33 mmol/L) were significantly higher than those in healthy controls (median 0.22, range 0.04-0.87 mmol/L; P = .022). L-lactate concentrations were not significantly different between the 2 groups of cats with gastrointestinal disease and healthy controls. D-lactate concentrations were not significantly associated with fPLI, fTLI, cobalamin, folate, or neurological abnormalities (P > .05). CONCLUSIONS AND CLINICAL IMPORTANCE D-lactate concentrations can be increased in cats with gastrointestinal disease. These findings warrant additional investigations into the role of intestinal microbiota derangements in cats with gastrointestinal disease, and the association of D-lactate and neurological abnormalities.

Targeted protein quantification using sparse reference labeling

Targeted proteomics is a method of choice for accurate and high-throughput quantification of predefined sets of proteins. Many workflows use isotope-labeled reference peptides for every target protein, which is time consuming and costly. We report a statistical approach for quantifying full protein panels with a reduced set of reference peptides. This label-sparse approach achieves accurate quantification while reducing experimental cost and time. It is implemented in the software tool SparseQuant.

Effect of heparin administration on urine protein excretion during the developmental stage of experimentally induced laminitis in horses.

OBJECTIVE To investigate the effects of heparin administration on urine protein excretion during the developmental stages of experimentally induced laminitis in horses. ANIMALS 13 horses. Procedures-Horses received unfractionated heparin (80 U/kg, SC, q 8 h; n=7) or no treatment (control group; 6) beginning 3 days prior to induction of laminitis. All horses were given 3 oligofructose loading doses (1 g/kg each) at 24-hour intervals and a laminitis induction dose (10 g of oligofructose/kg) 24 hours following the final loading dose (designated as 0 hours) via nasogastric tube. Serum glucose and insulin concentrations were measured before administration of the first loading dose (baseline) and at 0 and 24 hours; urine protein-to-creatinine (UP:C) ratio was determined at 0 hours and every 4 hours thereafter. Lameness was evaluated every 6 hours, and horses were euthanized when Obel grade 2 lameness was observed. RESULTS Mean±SD time until euthanasia did not differ significantly between the heparin-treated (28.9±6.5 hours) and control (29.0±6.9 hours) horses. The UP:C ratio was significantly increased from baseline at 20 to 28 hours after induction of laminitis (ie, 4±4 hours before lameness was evident) in control horses but did not change significantly from baseline in heparin-treated horses. Serum glucose or insulin concentration did not change significantly from baseline in either group. CONCLUSIONS AND CLINICAL RELEVANCE Urine protein excretion increased during the developmental stages of carbohydrate-induced laminitis in horses; administration of heparin prevented that increase, but did not delay onset or decrease severity of lameness.