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.

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.

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.

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.

Benzylisoquinoline alkaloids (BIAs) are important secondary plant metabolites and include medicinally relevant drugs, such as morphine or codeine. As the de novo synthesis of BIA backbones is (still) unfeasible, to date the opium poppy plant Papaver somniferum L. represents the main source of BIAs. The formation of BIAs is induced in poppy plants by stress conditions, such as wounding or salt treatment; however, the details about regulatory processes controlling BIA formation in opium poppy are not well studied. Environmental stresses, such as wounding or salinization, are transduced in plants by phospholipid-based signaling pathways, which involve different classes of phospholipases. Here we investigate whether pharmacological inhibition of phospholipase A2 (PLA2, inhibited by aristolochic acid (AA)) or phospholipase D (PLD; inhibited by 5-fluoro-2-indolyl des-chlorohalopemide (FIPI)) in poppy plants influences wound-induced BIA accumulation and the expression of key biosynthetic genes. We show that inhibition of PLA2 results in increased morphinan biosynthesis concomitant with reduced production of BIAs of the papaverine branch, whereas inhibition of PLD results in increased production of BIAs of the noscapine branch. The data suggest that phospholipid-dependent signaling pathways contribute to the activation of morphine biosynthesis at the expense of the production of other BIAs in poppy plants. A better understanding of the effectors and the principles of regulation of alkaloid biosynthesis might be the basis for the future genetic modification of opium poppy to optimize BIA production.

Drought is one of the most important environmental stressors resulting in increasing losses of crop plant productivity all over the world. Therefore, development of new approaches to increase the stress tolerance of crop plants is strongly desired. This requires precise and adequate modeling of drought stress. As this type of stress manifests itself as a steady decrease in the substrate water potential (ψw), agar plates infused with polyethylene glycol (PEG) are the perfect experimental tool: they are easy in preparation and provide a constantly reduced ψw, which is not possible in soil models. However, currently, this model is applicable only to seedlings and cannot be used for evaluation of stress responses in mature plants, which are obviously the most appropriate objects for drought tolerance research. To overcome this limitation, here we introduce a PEG-based agar infusion model suitable for 6–8-week-old A. thaliana plants, and characterize, to the best of our knowledge for the first time, the early drought stress responses of adult plants grown on PEG-infused agar. We describe essential alterations in the primary metabolome (sugars and related compounds, amino acids and polyamines) accompanied by qualitative and quantitative changes in protein patterns: up to 87 unique stress-related proteins were annotated under drought stress conditions, whereas further 84 proteins showed a change in abundance. The obtained proteome patterns differed slightly from those reported for seedlings and soil-based models.

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.

Drought, salinity and alkalinity are distinct forms of osmotic stress with serious impacts on rice productivity. We investigated, for a salt-sensitive rice cultivar, the response to osmotically equivalent doses of these stresses. Drought, experimentally mimicked by mannitol (single factor: osmotic stress), salinity (two factors: osmotic stress and ion toxicity), and alkalinity (three factors: osmotic stress, ion toxicity, and depletion of nutrients and protons) produced different profiles of adaptive and damage responses, both locally (in the root) as well as systemically (in the shoot). The combination of several stress factors was not necessarily additive, and we even observed cases of mitigation, when two (salinity), or three stressors (alkalinity) were compared to the single stressor (drought). The response to combinations of individual stress factors is therefore not a mere addition of the partial stress responses, but rather represents a new quality of response. We interpret this finding in a model, where the output to signaling molecules is not determined by their abundance per se, but qualitatively depends on their adequate integration into an adaptive signaling network. This output generates a systemic signal that will determine the quality of the shoot response to local concentrations of ions.

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org, a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.

Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature–feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.