Mass spectral libraries are collections of reference spectra, usually associated with specific analytes from which the spectra were generated, that are used for further downstream analysis of new spectra. There are many different formats used for encoding spectral libraries, but none have undergone a standardization process to ensure broad applicability to many applications. As part of the Human Proteome Organization Proteomics Standards Initiative (PSI), we have developed a standardized format for encoding spectral libraries, called mzSpecLib (https://psidev.info/mzSpecLib). It is primarily a data model that flexibly encodes metadata about the library entries using the extensible PSI-MS controlled vocabulary and can be encoded in and converted between different serialization formats. We have also developed a standardized data model and serialization for fragment ion peak annotations, called mzPAF (https://psidev.info/mzPAF). It is defined as a separate standard, since it may be used for other applications besides spectral libraries. The mzSpecLib and mzPAF standards are compatible with existing PSI standards such as ProForma 2.0 and the Universal Spectrum Identifier. The mzSpecLib and mzPAF standards have been primarily defined for peptides in proteomics applications with basic small molecule support. They could be extended in the future to other fields that need to encode spectral libraries for nonpeptidic analytes.

Introduction Liverworts are a group of non-vascular plants that possess unique metabolism not found in other plants. Many liverwort metabolites have interesting structural and biochemical characteristics, however the fluctuations of these metabolites in response to stressors is largely unknown. Objectives To investigate the metabolic stress-response of the leafy liverwort Radula complanata. Methods Five phytohormones were applied exogenously to in vitro cultured R. complanata and an untargeted metabolomic analysis was conducted. Compound classification and identification was performed with CANOPUS and SIRIUS while statistical analyses including PCA, ANOVA, and variable selection using BORUTA were conducted to identify metabolic shifts.Results It was found that R. complanata was predominantly composed of carboxylic acids and derivatives, followed by benzene and substituted derivatives, fatty acyls, organooxygen compounds, prenol lipids, and flavonoids. The PCA revealed that samples grouped based on the type of hormone applied, and the variable selection using BORUTA (Random Forest) revealed 71 identified and/or classified features that fluctuated with phytohormone application. The stress-response treatments largely reduced the production of the selected primary metabolites while the growth treatments resulted in increased production of these compounds. 4-(3-Methyl-2-butenyl)-5-phenethylbenzene-1,3-diol was identified as a biomarker for the growth treatments while GDP-hexose was identified as a biomarker for the stress-response treatments. Conclusion Exogenous phytohormone application caused clear metabolic shifts in Radula complanata that deviate from the responses of vascular plants. Further identification of the selected metabolite features can reveal metabolic biomarkers unique to liverworts and provide more insight into liverwort stress responses.

Metabolic fingerprinting is a powerful analytical technique, giving access to high-throughput identification and relative quantification of multiple metabolites. Because of short analysis times, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the preferred instrumental platform for fingerprinting, although its power in analysis of free fatty acids (FFAs) is limited. However, these metabolites are the biomarkers of human pathologies and indicators of food quality. Hence, a high-throughput method for their fingerprinting is required. Therefore, here we propose a MALDI-TOF-MS method for identification and relative quantification of FFAs in biological samples of different origins. Our approach relies on formation of monomolecular Langmuir films (LFs) at the interphase of aqueous barium acetate solution, supplemented with low amounts of 2,5-dihydroxybenzoic acid, and hexane extracts of biological samples. This resulted in detection limits of 10–13–10–14 mol and overall method linear dynamic range of at least 4 orders of magnitude with accuracy and precision within 2 and 17%, respectively. The method precision was verified with eight sample series of different taxonomies, which indicates a universal applicability of our approach. Thereby, 31 and 22 FFA signals were annotated by exact mass and identified by tandem MS, respectively. Among 20 FFAs identified in Fucus algae, 14 could be confirmed by gas chromatography-mass spectrometry.

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

IntroductionThe demand to develop efficient and reliable analytical methods for the quality control of nutraceuticals is on the rise, together with an increase in the legal requirements for safe and consistent levels of its active principles.ObjectiveTo establish a reliable model for the quality control of widely used Senna preparations used as laxatives and assess its phyto-equivalency.MethodsA comparative metabolomics approach via NMR and MS analyses was employed for the comprehensive measurement of metabolites and analyzed using chemometrics.ResultsUnder optimized conditions, 30 metabolites were simultaneously identified and quantified including anthraquinones, bianthrones, acetophenones, flavonoid conjugates, naphthalenes, phenolics, and fatty acids. Principal component analysis (PCA) was used to define relative metabolite differences among Senna preparations. Furthermore, quantitative 1H NMR (qHNMR) was employed to assess absolute metabolites levels in preparations. Results revealed that 6-hydroxy musizin or tinnevellin were correlated with active metabolites levels, suggesting the use of either of these naphthalene glycosides as markers for official Senna drugs authentication.ConclusionThis study provides the first comparative metabolomics approach utilizing NMR and UPLC–MS to reveal for secondary metabolite compositional differences in Senna preparations that could readily be applied as a reliable quality control model for its analysis.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

IntroductionThe production of marine drugs in its normal habitats is often low and depends greatly on ecological conditions. Chemical synthesis of marine drugs is not economically feasible owing to their complex structures. Biotechnology application via elicitation represents a promising tool to enhance metabolites yield that has yet to be explored in soft corals.ObjectivesStudy the elicitation impact of salicylic acid (SA) and six analogues in addition to a systemic acquired resistance inducer on secondary metabolites accumulation in the soft coral Sarcophyton ehrenbergi along with the symbiont zooxanthellae and if SA elicitation effect is extended to other coral species S. glaucum and Lobophyton pauciliforum.MethodsPost elicitation in the three corals and zooxanthella, metabolites were extracted and analyzed via UHPLC-MS coupled with chemometric tools.ResultsMultivariate data analysis of the UHPLC-MS data set revealed clear segregation of SA, amino-SA, and acetyl-SA elicited samples. An increased level ca. 6- and 8-fold of the diterpenes viz., sarcophytonolide I, sarcophine and a C28-sterol, was observed in SA and amino-SA groups, respectively. Post elicitation, the level of diepoxy-cembratriene increased 1.5-fold and 2.4-fold in 1 mM SA, and acetyl-SA (aspirin) treatment groups, respectively. S. glaucum and Lobophyton pauciliforum showed a 2-fold increase of diepoxy-cembratriene levels.ConclusionThese results suggest that SA could function as a general and somewhat selective diterpene inducing signaling molecule in soft corals. Structural consideration reveals initial structure–activity relationship (SAR) in SA derivatives that seem important for efficient diterpene and sterol elicitation.

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little “arm twisting” in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

The identification of metabolites by mass spectrometry constitutes a major bottleneck which considerably limits the throughput of metabolomics studies in biomedical or plant research. Here, we present a novel approach to analyze metabolomics data from untargeted, data-independent LC-MS/MS measurements. By integrated analysis of MS1 abundances and MS/MS spectra, the identification of regulated metabolite families is achieved. This approach offers a global view on metabolic regulation in comparative metabolomics. We implemented our approach in the web application “MetFamily”, which is freely available at http://msbi.ipb-halle.de/MetFamily/. MetFamily provides a dynamic link between the patterns based on MS1-signal intensity and the corresponding structural similarity at the MS/MS level. Structurally related metabolites are annotated as metabolite families based on a hierarchical cluster analysis of measured MS/MS spectra. Joint examination with principal component analysis of MS1 patterns, where this annotation is preserved in the loadings, facilitates the interpretation of comparative metabolomics data at the level of metabolite families. As a proof of concept, we identified two trichome-specific metabolite families from wild-type tomato Solanum habrochaites LA1777 in a fully unsupervised manner and validated our findings based on earlier publications and with NMR.

Univariate hypotheses tests such as Student’s t test or variance analysis (ANOVA) can help to answer a variety of questions in metabolomics data analysis. The statistical power of these tests depends on the setup of the experiment, the experimental design and the analytical variance of the actual observations. In this paper, we demonstrate how a well-designed pilot study prior to an experiment with the aim to find differences between e.g. several genotypes, can help to determine the variance at multiple levels ranging from biological variance, sample preparation to instrumental variances. Next, we illustrate how these variances can be used to obtain several parameters (e.g. minimum statistically significant effect, number of required replicates and error probabilities) which influence the design of the actual study. In particular, we are going to sketch how technical replicates can improve the performance of a test, when they are correctly used in the statistical analysis, e.g. with a hierarchical model. Finally, we demonstrate the process of evaluating the trade-off between different experimental designs with different replication strategies. The choice of an experimental design beyond the gut feeling can be influenced by factors such as costs, sample availability and the accuracy of of the tests. We use metabolite profiles of the model plant Arabidopsis thaliana measured on an UPLC-ESI/QqTOF-MS as real-world dataset, but the approach is equally applicable to other sample types and measurement methods like NMR based metabolomics.