Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Investigating the Metabolism of Plants Germinated in Heavy Water, D2O, and H218O-Enriched Media Using High-Resolution Mass Spectrometry

Mass spectrometry has been an essential technique for the investigation of the metabolic pathways of living organisms since its appearance at the beginning of the 20th century. Due to its capability to resolve isotopically labeled species, it can be applied together with stable isotope tracers to reveal the transformation of particular biologically relevant molecules. However, low-resolution techniques, which were used for decades, had limited capabilities for untargeted metabolomics, especially when a large number of compounds are labelled simultaneously. Such untargeted studies may provide new information about metabolism and can be performed with high-resolution mass spectrometry. Here, we demonstrate the capabilities of high-resolution mass spectrometry to obtain insights on the metabolism of a model plant, Lepidium sativum, germinated in D2O and H218O-enriched media. In particular, we demonstrated that in vivo labeling with heavy water helps to identify if a compound is being synthesized at a particular stage of germination or if it originates from seed content, and tandem mass spectrometry allows us to highlight the substructures with incorporated isotope labels. Additionally, we found in vivo labeling useful to distinguish between isomeric compounds with identical fragmentation patterns due to the differences in their formation rates that can be compared by the extent of heavy atom incorporation.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis

Non-targeted analysis (NTA) has made critical contributions in the fields of environmental chemistry and environmental health. One critical bottleneck is the lack of available analytical standards for most chemicals in the environment. Our study aims to explore a novel approach that integrates measurements of equilibrium partition ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties can be used as a fingerprint, which with the help of a machine learning algorithm can be converted into a series of functional groups (RDKit fragments), which can be used to search chemical databases. We conducted partitioning experiments using a chemical mixture containing 185 chemicals in 10 different organic solvents and water. Both a liquid chromatography quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap MS were used to assess the feasibility of the experimental method and the accuracy of the algorithm at predicting the correct functional groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error (MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated into errors in the predictions of RDKit fragments with the MAE for the QTOF method being 0.23 and for the Orbitrap method being 0.31. Our approach presents a new angle in structure elucidation for NTA and showed promise in assisting with compound identification.

Hydrogen/deuterium exchange mass spectrometry in the world of small molecules

The combined use of hydrogen/deuterium exchange (HDX) and mass spectrometry (MS), referred to as HDX-MS, is a powerful tool for exploring molecular edifices and has been used for over 60 years. Initially for structural and mechanistic investigation of low-molecular weight organic compounds, then to study protein structure and dynamics, then, the craze to study small molecules by HDX-MS accelerated and has not stopped yet. The purpose of this review is to present its different facets with particular emphasis on recent developments and applications. Reversible H/D exchanges of mobilizable protons as well as stable exchanges of non-labile hydrogen are considered whether they are taking place in solution or in the gas phase, or enzymatically in a biological media. Some fundamental principles are restated, especially for gas-phase processes, and an overview of recent applications, ranging from identification to quantification through the study of metabolic pathways, is given.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Amine additives for improved in-ESI H/D exchange

In-ESI H/D exchange is a convenient technique for analyzing small-molecular complex mixtures.

Suspect and non-target screening: the last frontier in environmental analysis

Suspect and non-target screening (SNTS) techniques are arising as new analytical strategies useful to disentangle the environmental occurrence of the thousands of exogenous chemicals present in our ecosystems. The unbiased discovery of the wide number of substances present over environmental analysis needs to find a consensus with powerful technical and computational requirements, as well as with the time-consuming unequivocal identification of discovered analytes. Within these boundaries, the potential applications of SNTS include the studies of environmental pollution in aquatic, atmospheric, solid and biological samples, the assessment of new compounds, transformation products and metabolites, contaminant prioritization, bioremediation or soil/water treatment evaluation, and retrospective data analysis, among many others. In this review, we evaluate the state of the art of SNTS techniques going over the normalized workflow from sampling and sample treatment to instrumental analysis, data processing and a brief review of the more recent applications of SNTS in environmental occurrence and exposure to xenobiotics. The main issues related to harmonization and knowledge gaps are critically evaluated and the challenges of their implementation are assessed in order to ensure a proper use of these promising techniques in the near future.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Evaluation of Nontarget Long-Term LC-HRMS Time Series Data Using Multivariate Statistical Approaches

The use of liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has steadily increased in many application fields ranging from metabolomics to environmental science. HRMS data are frequently used for nontarget screening (NTS), i.e., the search for compounds that are not previously known and where no reference substances are available. However, the large quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study, a prioritization method to handle time series in nontarget data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor the occurrence of a limited number of targeted water contaminants, NTS provides the opportunity to detect also unknown trace organic compounds (TrOCs) that are not in the focus of routine target analysis. The developed prioritization method enables reducing raw data and including identification of prioritized unknown contaminants. To that end, a five-month time series for industrial wastewaters was utilized, analyzed by liquid chromatography-time-of-flight mass spectrometry (LC-qTOF-MS), and evaluated by NTS. Following peak detection, alignment, grouping, and blank subtraction, 3303 features were obtained of wastewater treatment plant (WWTP) influent samples. Subsequently, two complementary ways for exploratory time trend detection and feature prioritization are proposed. Therefore, following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorization of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered, and prioritized. Similarities and differences in the chemical composition of wastewater samples were observed over time to reveal hidden factors accounting for the structure of the data. The detected features were reduced to 130 relevant time trends related to TrOCs for identification. Exemplarily, as proof of concept, one nontarget pollutant was identified as N-methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but also could be efficiently employed for time trend exploration in other scientific fields.

Unraveling longitudinal pollution patterns of organic micropollutants in a river by non-target screening and cluster analysis

The pollution of aquatic ecosystems with complex and largely unknown mixtures of organic micropollutants is not sufficiently addressed with current monitoring strategies based on target screening methods. In this study, we implemented an open-source workflow based on non-target screening to unravel longitudinal pollution patterns of organic micropollutants along a river course. The 47 km long Holtemme River, a tributary of the Bode River (both Saxony-Anhalt, Germany), was used as a case study. Sixteen grab samples were taken along the river and analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We applied a cluster analysis specifically designed for longitudinal data sets to identify spatial pollutant patterns and prioritize peaks for compound identification. Three main pollution patterns were identified representing pollutants entering a) from wastewater treatment plants, b) at the confluence with the Bode River and c) from diffuse and random inputs via small point sources and groundwater input. By further sub-clustering of the main patterns, source-related fingerprints were revealed. The main patterns were characterized by specific isotopologue signatures and the abundance of peaks in homologue series representing the major (pollution) sources. Furthermore, we identified 25 out of 38 representative compounds for the patterns by structure elucidation. The workflow represents an important contribution to the ongoing attempts to understand, monitor, prioritize and manage complex environmental mixtures and may be applied to other settings.

Hydrogen/Deuterium and 16O/18O-Exchange Mass Spectrometry Boosting the Reliability of Compound Identification

Accurate and reliable identification of chemical compounds is the ultimate goal of mass spectrometry analyses. Currently, identification of compounds is usually based on the measurement of the accurate mass and fragmentation spectrum, chromatographic elution time, and collisional cross section. Unfortunately, despite the growth of databases of experimentally measured MS/MS spectra (such as MzCloud and Metlin) and developing software for predicting MS/MS fragments in silico from SMILES patterns (such as MetFrag, CFM-ID, and Ms-Finder), the problem of identification is still unsolved. The major issue is that the elution time and fragmentation spectra depend considerably on the equipment used and are not the same for different LC-MS systems. It means that any additional descriptors depending only on the structure of the chemical compound will be of big help for LC-MS/MS-based omics. Our approach is based on the characterization of compounds by the number of labile hydrogen and oxygen atoms in the molecule, which can be measured using hydrogen/deuterium and ¹⁶O/¹⁸O-exchange approaches. The number of labile atoms (those from -OH, -NH, ═O, and -COOH groups) can be predicted from SMILES patterns and serves as an additional structural descriptor when performing a database search. In addition, distribution of isotope labels among MS/MS fragments can be roughly predicted by software such as MetFrag or CFM-ID. Here, we present an approach utilizing the selection of structural candidates from a database on the basis of the number of functional groups and analysis of isotope labels distribution among fragments. It was found that our approach allows reduction of the search space by a factor of 10 and considerably increases the reliability of the compound identification.