Protocol for community-created public MS/MS reference spectra within the Global Natural Products Social Molecular Networking infrastructure.

RATIONALE: A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of tandem mass spectrometry (MS/MS) reference spectra in public databases. Currently, scientists purchase databases or use public databases such as Global Natural Products Social Molecular Networking (GNPS). The MSMS-Chooser workflow is an open-source protocol for the creation of MS/MS reference spectra directly in the GNPS infrastructure. METHODS: An MSMS-Chooser Sample Template is provided and completed manually. The MSMS-Chooser Submission File and Sequence Table for data acquisition were programmatically generated. Standards from the Mass Spectrometry Metabolite Library (MSMLS) suspended in a methanol-water (1:1) solution were analyzed. Flow injection on an LC/MS/MS system was used to generate negative and positive mode data using data-dependent acquisition. The MS/MS spectra and Submission File were uploaded to MSMS-Chooser workflow in GNPS for automatic selection of MS/MS spectra. RESULTS: Data acquisition and processing required ~2 h and ~2 min, respectively, per 96-well plate using MSMS-Chooser. Analysis of the MSMLS, over 600 small molecules, using MSMS-Chooser added 889 spectra (including multiple adducts) to the public library in GNPS. Manual validation of one plate indicated accurate selection of MS/MS scans (true positive rate of 0.96 and a true negative rate of 0.99). The MSMS-Chooser output includes a table formatted for inclusion in the GNPS library as well as the ability to directly launch searches via MASST. CONCLUSIONS: MSMS-Chooser enables rapid data acquisition, data analysis (selection of MS/MS spectra), and a formatted table for inspection and upload to GNPS. Open file-format data (.mzML or.mzXML) from most mass spectrometry platforms containing MS/MS spectra can be processed using MSMS-Chooser. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.

A UHPLC-HRMS based metabolomics and chemoinformatics approach to chemically distinguish 'super foods' from a variety of plant-based foods.

The aim of this study was to investigate if the declared benefits associated with superfoods are related to a specific molecular composition. For this purpose, untargeted metabolomics and molecular networking were used to obtain an overview of all features, focusing on compounds with anti-inflammatory, antioxidant or antimicrobial properties. 565 plant-based food samples were analyzed using UHPLC-HRMS and advanced data analysis tools. The molecular networking of the whole dataset allowed identification of a greater diversity of molecules, in particular, prenol lipids, isoflavonoids and isoquinolines in superfoods, when compared with non-superfood species belonging to the same botanical family. Furthermore, in silico tools were used to expand our chemical knowledge of compounds observed in superfood samples.

Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages.

In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations.