Michael S. Bereman

Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline APPLICATION TO PROTEIN ACETYLATION AND PHOSPHORYLATION

Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models.

Development of a nanoLC LTQ Orbitrap Mass Spectrometric Method for Profiling Glycans Derived from Plasma from Healthy, Benign Tumor Control, and Epithelial Ovarian Cancer Patients

We report the development of split-less nano-flow liquid chromatography mass spectrometric analysis of glycans chemically cleaved from glycoproteins in plasma. Porous graphitized carbon operating under reverse-phase conditions and an amide-based stationary phase operating under hydrophilic interaction conditions are quantitatively compared for glycan separation. Both stationary phases demonstrated similar column efficiencies and excellent retention time reproducibility without an internal standard to correct for retention time shift. The 95% confidence intervals of the mean retention times were +/-4 s across 5 days of analysis for both stationary phases; however, the amide stationary phase was observed to be more robust. The high mass measurement accuracy of less than 2 ppm and fragmentation spectra provided highly confident identifications along with structural information. In addition, data are compared among samples derived from 10 healthy controls, 10 controls with a differential diagnosis of benign gynecologic tumors, and 10 diseased epithelial ovarian cancer patients (EOC). Two fucosylated glycans were found to be up-regulated in healthy controls and provided an accurate diagnostic value with an area under the receiver operator characteristic curve of 0.87. However, these same glycans provided a significantly less diagnostic value when used to differentiate EOC from benign tumor control samples with an area under the curve of 0.73.

The development of selected reaction monitoring methods for targeted proteomics via empirical refinement

Software advancements in the last several years have had a significant impact on proteomics from method development to data analysis. Herein, we detail a method, which uses our in‐house developed software tool termed Skyline, for empirical refinement of candidate peptides from targeted proteins. The method consists of four main steps from generation of a testable hypothesis, method development, peptide refinement, to peptide validation. The ultimate goal is to identify the best performing peptide in terms of ionization efficiency, reproducibility, specificity, and chromatographic characteristics to monitor as a proxy for protein abundance. It is important to emphasize that this method allows the user to perform this refinement procedure in the sample matrix and organism of interest with the instrumentation available. Finally, the method is demonstrated in a case study to determine the best peptide to monitor the abundance of surfactant protein B in lung aspirates.

Comparison between procedures using SDS for shotgun proteomic analyses of complex samples

Filter‐aided sample preparation (FASP) and a new sample preparation method using a modified commercial SDS removal spin column are quantitatively compared in terms of their performance for shotgun proteomic experiments in three complex proteomic samples: a Saccharomyces cerevisiae lysate (insoluble fraction), a Caenorhabditis elegans lysate (soluble fraction), and a human embryonic kidney cell line (HEK293T). The characteristics and total number of peptides and proteins identified are compared between the two procedures. The SDS spin column procedure affords a conservative fourfold improvement in throughput, is more reproducible, less expensive (i.e. requires less materials), and identifies between 30 and 107% more peptides at q≤0.01, than the FASP procedure. The peptides identified by SDS spin column are more hydrophobic than species identified by the FASP procedure as indicated by the distribution of GRAVY scores. Ultimately, these improvements correlate to as great as a 50% increase in protein identifications with two or more peptides.

Development of a Robust and High Throughput Method for Profiling N-Linked Glycans Derived from Plasma Glycoproteins by NanoLC−FTICR Mass Spectrometry

Recent investigations continue to emphasize the importance of glycosylation in various diseases including cancer. In this work, we present a step-by-step protocol describing a method for N-linked glycan profiling of plasma glycoproteins by nanoflow liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). A large experimental space was initially explored and is described herein. Three internal standards were spiked into the sample and provided normalization of plasma glycan abundance across different experimental conditions. Incubation methods and times and the effect of NP40 detergent on glycan abundance were explored. It was found that an 18-h incubation with no detergent led to the greatest ion abundance; however, data could be obtained in less than one day from raw plasma samples utilizing microwave irradiation or shorter incubation periods. The intersample precision of three different glycans was less than 5.5% (RSD) when the internal standards were added prior to the initial processing step. The high mass measurement accuracy (<3 ppm) afforded by the FTICR mass spectrometer provided confident identifications of several glycan species.

Effects of Column and Gradient Lengths on Peak Capacity and Peptide Identification in Nanoflow LC-MS/MS of Complex Proteomic Samples

AbstractReversed-phase liquid chromatography is the most commonly used separation method for shotgun proteomics. Nanoflow chromatography has emerged as the preferred chromatography method for its increased sensitivity and separation. Despite its common use, there are a wide range of parameters and conditions used across research groups. These parameters have an effect on the quality of the chromatographic separation, which is critical to maximizing the number of peptide identifications and minimizing ion suppression. Here we examined the relationship between column lengths, gradient lengths, peptide identifications, and peptide peak capacity. We found that while longer column and gradient lengths generally increase peptide identifications, the degree of improvement is dependent on both parameters and is diminished at longer column and gradients. Peak capacity, in comparison, showed a more linear increase with column and gradient lengths. We discuss the discrepancy between these two results and some of the considerations that should be taken into account when deciding on the chromatographic conditions for a proteomics experiment. FigureThe effects of column and gradient lengths on the performance of nanoflow LC-MS/MS is examined in complex proteomic samples.

Implementation of Statistical Process Control for Proteomic Experiments Via LC MS/MS

AbstractStatistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface, which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution), and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies. Fig. aᅟ

Estimating relative abundances of proteins from shotgun proteomics data

BackgroundSpectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SIN), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).ResultsWe compared the reproducibility and the linearity relative to each protein’s abundance of the four spectral counting metrics. Our analysis suggests that NSAF yields the most reproducible counts across technical and biological replicates, and both SINand NSAF achieve the best linearity.ConclusionsWith the crux spectral-counts command, Crux provides open-source modular methods to analyze mass spectrometry data for identifying and now quantifying peptides and proteins. The C++ source code, compiled binaries, spectra and sequence databases are available athttp://noble.gs.washington.edu/proj/crux-spectral-counts.

Tools for monitoring system suitability in LC MS/MS centric proteomic experiments

With advances in liquid chromatography coupled to tandem mass spectrometry technologies combined with the continued goals of biomarker discovery, clinical applications of established biomarkers, and integrating large multiomic datasets (i.e. “big data”), there remains an urgent need for robust tools to assess instrument performance (i.e. system suitability) in proteomic workflows. To this end, several freely available tools have been introduced that monitor a number of peptide identification (ID) and/or peptide ID free metrics. Peptide ID metrics include numbers of proteins, peptides, or peptide spectral matches identified from a complex mixture. Peptide ID free metrics include retention time reproducibility, full width half maximum, ion injection times, and integrated peptide intensities. The main driving force in the development of these tools is to monitor both intra‐ and interexperiment performance variability and to identify sources of variation. The purpose of this review is to summarize and evaluate these tools based on versatility, automation, vendor neutrality, metrics monitored, and visualization capabilities. In addition, the implementation of a robust system suitability workflow is discussed in terms of metrics, type of standard, and frequency of evaluation along with the obstacles to overcome prior to incorporating a more proactive approach to overall quality control in liquid chromatography coupled to tandem mass spectrometry based proteomic workflows.

Genomic and Proteomic Analysis of Schizaphis graminum Reveals Cyclophilin Proteins Are Involved in the Transmission of Cereal Yellow Dwarf Virus

Yellow dwarf viruses cause the most economically important virus diseases of cereal crops worldwide and are transmitted by aphid vectors. The identification of aphid genes and proteins mediating virus transmission is critical to develop agriculturally sustainable virus management practices and to understand viral strategies for circulative movement in all insect vectors. Two cyclophilin B proteins, S28 and S29, were identified previously in populations of Schizaphisgraminum that differed in their ability to transmit the RPV strain of Cereal yellow dwarf virus (CYDV-RPV). The presence of S29 was correlated with F2 genotypes that were efficient virus transmitters. The present study revealed the two proteins were isoforms, and a single amino acid change distinguished S28 and S29. The distribution of the two alleles was determined in 12 F2 genotypes segregating for CYDV-RPV transmission capacity and in 11 genetically independent, field-collected S . graminum biotypes. Transmission efficiency for CYDV-RPV was determined in all genotypes and biotypes. The S29 isoform was present in all genotypes or biotypes that efficiently transmit CYDV-RPV and more specifically in genotypes that efficiently transport virus across the hindgut. We confirmed a direct interaction between CYDV-RPV and both S28 and S29 using purified virus and bacterially expressed, his-tagged S28 and S29 proteins. Importantly, S29 failed to interact with a closely related virus that is transported across the aphid midgut. We tested for in vivo interactions using an aphid-virus co-immunoprecipitation strategy coupled with a bottom-up LC-MS/MS analysis using a Q Exactive mass spectrometer. This analysis enabled us to identify a third cyclophilin protein, cyclophilin A, interacting directly or in complex with purified CYDV-RPV. Taken together, these data provide evidence that both cyclophilin A and B interact with CYDV-RPV, and these interactions may be important but not sufficient to mediate virus transport from the hindgut lumen into the hemocoel.