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.

TDP-43 pathology marks a spectrum of multisystem proteinopathies including amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and sporadic inclusion body myositis. Surprisingly, it has been challenging to recapitulate this pathology, highlighting an incomplete understanding of TDP-43 regulatory mechanisms. Here we provide evidence supporting TDP-43 acetylation as a trigger for disease pathology. Using cultured cells and mouse skeletal muscle, we show that TDP-43 acetylation-mimics promote TDP-43 phosphorylation and ubiquitination, perturb mitochondria, and initiate degenerative inflammatory responses that resemble sporadic inclusion body myositis pathology. Analysis of functionally linked amyotrophic lateral sclerosis proteins revealed recruitment of p62, ubiquilin-2, and optineurin to TDP-43 aggregates. We demonstrate that TDP-43 acetylation-mimic pathology is potently suppressed by an HSF1-dependent mechanism that disaggregates TDP-43. Our study illustrates bidirectional TDP-43 processing in which TDP-43 aggregation is targeted by a coordinated chaperone response. Thus, activation or restoration of refolding mechanisms may alleviate TDP-43 aggregation in tissues that are uniquely susceptible to TDP-43 proteinopathies.

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 4 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.

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 seconds 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 amongst 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.

Statistical 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.

Novel tags are used to increase the hydrophobicity of glycans and impart a permanent charge yielding as great as a approximately 5-fold increase in electrospray response from both a standard and complex mixture.

BackgroundSpectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The 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 SIN and NSAF achieve the best linearity.ConclusionsWith the 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 at http://noble.gs.washington.edu/proj/crux-spectral-counts.

We report the first coupling of a desorption electrospray ionization (DESI) ion source to Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS) for high-resolution protein analysis. The DESI FT-ICR-MS source design is described in detail along with preliminary data obtained on peptides and proteins ranging from 1 to 5.7 kDa.