An alternative sampling algorithm for use in liquid chromatography/tandem mass spectrometry experiments

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma

The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.

Definitions of terms relating to mass spectrometry (IUPAC Recommendations 2013)

This document contains recommendations for terminology in mass spectrometry. Development of standard terms dates back to 1974 when the IUPAC Commission on Analytical Nomenclature issued recommendations on mass spectrometry terms and definitions. In 1978, the IUPAC Commission on Molecular Structure and Spectroscopy updated and extended the recommendations and made further recommendations regarding symbols, acronyms, and abbreviations. The IUPAC Physical Chemistry Division Commission on Molecular Structure and Spectroscopy’s Subcommittee on Mass Spectroscopy revised the recommended terms in 1991 and appended terms relating to vacuum technology. Some additional terms related to tandem mass spectrometry were added in 1993 and accelerator mass spectrometry in 1994. Owing to the rapid expansion of the field in the intervening years, particularly in mass spectrometry of biomolecules, a further revision of the recommendations has become necessary. This document contains a comprehensive revision of mass spectrometry terminology that represents the current consensus of the mass spectrometry community.

Effect of dynamic exclusion duration on spectral count based quantitative proteomics

To increase proteome coverage, dynamic exclusion (DE) is a widely used tool. When DE is enabled, more proteins can be identified, although the total spectral counts will decrease. To investigate the effects of DE duration on spectral-counting based quantitative proteomics, we analyzed the same sample via multidimensional protein identification technology while enabling different DE durations (15, 60, 90, 300, 600 s) or turning DE off. Normalized spectral abundance factors (NSAFs) measured for abundant proteins varied little with or without DE, while enabling DE lead to higher peptide counts, higher NSAFs, and better reproducibility of detection for proteins of relatively lower abundance. The optimal DE duration, which generated the maximum number of peptides, proteins, and peptides per protein, was observed to be 90 s in our settings. We developed a mathematical model for analyzing the effects of DE duration on peptide spectral counts. We found that the optimal DE duration depends on the average chromatographic peak width at the base of eluting peptides and mass spectrometry parameters, leading us to calculate an optimized DE duration of 97.9 s, in excellent agreement with our observations. In this study, we provide a systematic approach for the optimization of spectral counts for improved quantitative proteomics analysis.

An iterative strategy for precursor ion selection for LC-MS/MS based shotgun proteomics

Currently, the precursor ion selection strategies in LC-MS mainly choose the most prominent peptide signals for MS/MS analysis. Consequently, high-abundance proteins are identified by MS/MS of many peptides, whereas proteins of lower abundance might elude identification. We present a novel, iterative and result-driven approach for precursor ion selection that significantly increases the efficiency of an MS/MS analysis by decreasing data redundancy and analysis time. By simulating different strategies for precursor ion selection on an existing data set, we compare our method to existing result-driven strategies and evaluate its performance with regard to mass accuracy, database size, and sample complexity.