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Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization's Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

Levothyroxine as active pharmaceutical ingredient of formulations used for the treatment of hypothyroidism is distributed worldwide and taken by millions of people. An important issue in terms of compound stability is its capability to react with ambient oxygen, especially in case of long term compound storage at elevated temperature. In this study we demonstrate that ultrahigh-performance liquid chromatography coupled to UV spectrometry and high-resolution mass spectrometry (UHPLC-UV-HRMS) represent very useful approaches to investigate the influence of ambient oxygen on the degradation kinetics of levothyroxine in the solid state at enhanced degradation conditions. Moreover, the impurity pattern of oxidative degradation of levothyroxine is elucidated and classified with respect to degradation kinetics at different oxygen levels. Kinetic analysis of thyroxine bulk material at 100 °C reveals bi-phasic degradation kinetics with a distinct change in degradation phases dependent on the availability of oxygen. The results clearly show that contact of the bulk material to ambient oxygen is a key factor for fast compound degradation. Furthermore, the combination of time-resolved HRMS data and automated data processing is shown to allow insights into the kinetics and mechanism of impurity formation on individual compound basis. By comparing degradation profiles, four main classes of profiles linked to reaction pathways of thyroxine degradation were identifiable. Finally, we show the capability of automated data processing for the matching of different stressing conditions, in order to extract information about mechanistic similarities. As a result, degradation kinetics is influenced by factors like availability of oxygen, stressing time, or stressing temperature, while the degradation mechanisms appear to be conserved., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Rapid and efficient quality control according to the public authority regulations is mandatory to guarantee safety of the pharmaceuticals and to save resources in the pharmaceutical industry. In the case of so-called "grandfather products" like the synthetic thyroid hormone thyroxine, strict regulations enforce a detailed chemical analysis in order to characterize potentially toxic or pharmacologically relevant impurities. We report a straightforward workflow for the comprehensive impurity profiling of synthetic thyroid hormones and impurities employing ultrahigh-performance liquid chromatography (UHPLC) hyphenated to high-resolution mass spectrometry (HRMS). Five different batches of synthetic thyroxin were analyzed resulting in the detection of 71 impurities within 3 min total analysis time. Structural elucidation of the compounds was accomplished via a combination of accurate mass measurements, computer based calculations of molecular formulas, multistage high-resolution mass spectrometry (HRMS(n)), and nuclear magnetic resonance spectroscopy, which enabled the identification of 71 impurities, of which 47 have been unknown so far. Thirty of the latter were structurally elucidated, including products of deiodination, aliphatic chain oxidation, as well as dimeric compounds as new class of thyroid hormone derivatives. Limits of detection for the thyroid compounds were in the 6 ng/mL range for negative electrospray ionization mass spectrometric detection in full scan mode. Within day and day-to-day repeatabilities of retention times and peak areas were below 0.5% and 3.5% R.SD. The performance characteristics of the method in terms of robustness and information content clearly show that UHPLC-HRMS is adequate for the rapid and reliable detection, identification, and semiquantitative determination of trace levels of impurities in synthetic pharmaceuticals.

Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists for peptide identification algorithms but data that represents a ground truth for the evaluation of LC-MS data is limited. Hence there have been attempts to simulate such data in a controlled fashion to evaluate and compare algorithms. We present MSSimulator, a simulation software for LC-MS and LC-MS/MS experiments. Starting from a list of proteins from a FASTA file, the simulation will perform in-silico digestion, retention time prediction, ionization filtering, and raw signal simulation (including MS/MS), while providing many options to change the properties of the resulting data like elution profile shape, resolution and sampling rate. Several protocols for SILAC, iTRAQ or MS(E) are available, in addition to the usual label-free approach, making MSSimulator the most comprehensive simulator for LC-MS and LC-MS/MS data.

Mass-spectrometry-based lipidomics aims to identify as many lipid species as possible from complex biological samples. Due to the large combinatorial search space, unambiguous identification of lipid species is far from trivial. Mass ambiguities are common in direct-injection shotgun experiments, where an orthogonal separation (e.g., liquid chromatography) is missing. Using the rich information within available lipid databases, we generated a comprehensive rule set describing mass ambiguities, while taking into consideration the resolving power (and its decay) of different mass analyzers. Importantly, common adduct species and isotopic peaks are accounted for and are shown to play a major role, both for perfect mass overlaps due to identical sum formulas and resolvable mass overlaps. We identified known and hitherto unknown mass ambiguities in high- and ultrahigh resolution data, while also ranking lipid classes by their propensity to cause ambiguities. On the basis of this new set of ambiguity rules, guidelines and recommendations for experimentalists and software developers of what constitutes a solid lipid identification in both MS and MS/MS were suggested. For researchers new to the field, our results are a compact source of ambiguities which should be accounted for. These new findings also have implications for the selection of internal standards, peaks used for internal mass calibration, optimal choice of instrument resolution, and sample preparation, for example, in regard to adduct ion formation. [ABSTRACT FROM AUTHOR]