Analysis of surfactants by mass spectrometry: Coming to grips with their diversity

Mass Spectrometry Strategies for O-Glycoproteomics

Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Untargeted metabolomic analysis by ultra-high-resolution mass spectrometry for the profiling of new Italian wine varieties

The chemical composition of wine samples comprises numerous bioactive compounds responsible for unique flavor and health-promoting properties. Thus, it's important to have a complete overview of the metabolic profile of new wine products in order to obtain peculiar information in terms of their phytochemical composition, quality, and traceability. To achieve this aim, in this work, a mass spectrometry-based phytochemical screening was performed on seven new wine products from Villa D'Agri in the Basilicata region (Italy), i.e., Aglianico Bianco, Plavina, Guisana, Giosana, Malvasia ad acino piccolo, Colata Murro and Santa Sofia. Ultra-high-resolution mass spectrometry data were processed into absorption mode FT-ICR mass spectra, in order to remove artifacts and achieve a higher resolution and lower levels of noise. Accurate mass-to-charge ratio (m/z) values were converted into putative elemental formulas. Therefore, 2D van Krevelen diagrams were used as a tool to obtain molecular formula maps useful to perform a rapid and more comprehensive analysis of the wine sample metabolome. The presence of important metabolite classes, i.e., fatty acid derivatives, amino acids and peptides, carbohydrates and phenolic derivatives, was assessed. Moreover, the comparison of obtained metabolomic maps revealed some differences among profiles, suggesting their employment as metabolic fingerprints. This study shed some light on the metabolic composition of seven new Italian wine varieties, improving their value in terms of related bioactive compound content. Moreover, different metabolomic fingerprints were obtained for each of them, suggesting the use of molecular maps as innovative tool to ascertain their unique metabolic profile.

Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.