Nontargeted homologue series extraction from hyphenated high resolution mass spectrometry data

Nontarget Analysis of Legacy and Emerging PFAS in a Lithium-Ion Power Battery Recycling Park and Their Possible Toxicity Measured Using High-Throughput Phenotype Screening

Driven by the global popularity of electric vehicles and the shortage of critical raw materials for batteries, the spent lithium-ion power battery (LIPB) recycling industry has exhibited explosive growth in both quantity and scale. However, relatively little information is known about the environmental risks posed by LIPB recycling, in particular with regards to perfluoroalkyl and polyfluoroalkyl substances (PFAS). In this work, suspect screening and nontarget analysis were carried out to characterize PFAS in soil, dust, water and sediment from a LIPB recycling area. Twenty-five PFAS from nine classes were identified at confidence level 3 or above, including 13 legacy and 12 emerging PFAS, as well as two ultrashort-chain PFAS. Based on the target analysis of 16 PFAS, at least nine were detected in each environmental sample, indicating their widespread presence in a LIPB recycling area. Perfluorodecanoic acid, perfluorooctanesulfonic acid and trifluoromethanesulfonamide showed significant differences in the four phenotypic parameters (growth, movement, survival and fecundity) of Caenorhabditis elegans and were the most toxic substances in all target PFAS at an exposure concentration of 200 μM. Our project provides first-hand information on the existence and environmental risk of PFAS, facilitating the formulation of regulations and green development of the LIPB recycling industry.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography-high resolution mass spectrometry

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80-90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50-70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

APEX: an Annotation Propagation Workflow through Multiple Experimental Networks to Improve the Annotation of New Metabolite Classes in Caenorhabditis elegans

Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegans metabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow's applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.

Practical application guide for the discovery of novel PFAS in environmental samples using high resolution mass spectrometry

BACKGROUND

The intersection of the topics of high-resolution mass spectrometry (HRMS) and per- and polyfluoroalkyl substances (PFAS) bring together two disparate and complex subjects. Recently non-targeted analysis (NTA) for the discovery of novel PFAS in environmental and biological media has been shown to be valuable in multiple applications. Classical targeted analysis for PFAS using LC-MS/MS, though growing in compound coverage, is still unable to inform a holistic understanding of the PFAS burden in most samples. NTA fills at least a portion of this data gap.

OBJECTIVES

Entrance into the study of novel PFAS discovery requires identification techniques such as HRMS (e.g., QTOF and Orbitrap) instrumentation. This requires practical knowledge of best approaches depending on the purpose of the analyses. The utility of HRMS applications for PFAS discovery is unquestioned and will likely play a significant role in many future environmental and human exposure studies.

METHODS/RESULTS

PFAS have some characteristics that make them standout from most other chemicals present in samples. Through a series of tell-tale PFAS characteristics (e.g., characteristic mass defect range, homologous series and characteristic fragmentation patterns), and case studies different approaches and remaining challenges are demonstrated.

IMPACT STATEMENT

The identification of novel PFAS via non-targeted analysis using high resolution mass spectrometry is an important and difficult endeavor. This synopsis document will hopefully make current and future efforts on this topic easier to perform for novice and experienced alike. The typical time devoted to NTA PFAS investigations (weeks to months or more) may benefit from these practical steps employed.

New algorithms demonstrate untargeted detection of chemically meaningful changing units and formula assignment for HRMS data of polymeric mixtures in the open-source constellation web application

The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for tools and solutions to simplify and automate the processing of the large and complex datasets that result from these analyses. Constellation is one such of these tools, developed by our group over the last two years to perform unsupervised trend detection for repeating, polymeric units in HRMS data of complex mixtures such as natural organic matter, oil, or lignin. In this work, we develop two new unsupervised algorithms for finding chemically-meaningful changing units in HRMS data, and incorporate a molecular-formula-finding algorithm from the open-source CoreMS software package, both demonstrated here in the Constellation software environment. These algorithms are evaluated on a collection of open-source HRMS datasets containing polymeric analytes (PEG 400 and NIST standard reference material 1950, both metabolites in human plasma, as well as a swab extract containing polymers), and are able to successfully identify all known changing units in the data, including assigning the correct formulas. Through these new developments, we are excited to add to a growing body of open-source software specialized in extracting useful information from complex datasets without the high costs, technical knowledge, and processor-demand typically associated with such tools.

Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

An algorithm to classify homologous series within compound datasets

Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH2 repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues .

Homologue series detection and management in LC-MS data with homologueDiscoverer

SUMMARY

Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities.

AVAILABILITY AND IMPLEMENTATION

homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Occurrence and contamination profile of legacy and emerging per- and polyfluoroalkyl substances (PFAS) in Belgian wastewater using target, suspect and non-target screening approaches

With the growing concern regarding the health risks of per- and polyfluoroalkyl substances (PFAS), there is an increasing demand for the identification of emerging PFAS. This study provides a comprehensive investigation of legacy and emerging PFAS in 16 wastewater treatment plants (WWTPs) in Belgium using target, suspect, and non-target screening methods. Perfluorobutanoic acid (PFBA) and perfluoropentanoic acid (PFPeA) were the dominant compounds in most locations, whereas perfluorooctanoic acid (PFOA) was the most predominant PFAS in WWTP Deurne (Antwerp region). Using a suspect screening approach, 14 PFAS were annotated as confidence level (CL) of 4 or higher and 4 PFAS were annotated as CL 2a and 2b, including aqueous film forming foam (AFFF)-derived PFAS. The compound group of n:3 unsaturated fluorotelomer carboxylic acid was found using non-target screening in the wastewater from WWTP Deurne. Population exposure in a catchment area estimated using population-normalized mass loads (PNML) showed the highest value in the catchment area of WWTP Deurne, implying a potentially higher exposure to PFAS in this community.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

On-line 2D-RPLC x RPLC - HRMS to assess wastewater treatment in a pharmaceutical plant

Pharmaceutical effluents are complex media containing hundreds of compounds including active ingredients, intermediate products and unknown impurities. Bringing an industrial wastewater treatment plant (WWTP) into compliance with European directives requires a thorough analysis of the effluent. In this study, we demonstrate how online comprehensive two-dimensional liquid chromatography (on-line LC × LC) hyphenated to high resolution mass spectrometry (HRMS) can be a powerful analytical methodology to monitoring the outlet water, by analysing the content of known molecules while characterizing unknown compounds. Reversed phase liquid chromatography (RPLC) was used in both dimensions, with a penta-fluoro-phenyl silica-based column at neutral pH in the first dimension (¹D) and a C18 column at acidic pH in the second one (²D). The conditions were optimized for a total analysis time of 60 min. The variability of both retention times and peak areas was evaluated. The average standard deviation on retention times was found to be less than 0.1 s in ²D. The relative standard deviation on peak area was about 7% for run-to-run analysis. This analytical approach, applied to the pharmaceutical effluents before (inlet) and after (outlet) wastewater treatment permitted to detect 240 compounds. These included 27 priority pharmaceutical products, 8 of which were of very high priority and their concentrations could be compared to target values. The comparison of 2D-LC and 1D-LC approaches clearly highlights the power of on-line RPLC x RPLC technique, which allows both targeted quantitative analysis and non-targeted qualitative analysis of pharmaceutical effluents.

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

Chemical characterisation of Pelargonium sidoides root based on LC-QToF-MS non-target screening strategies

INTRODUCTION

Pelargonium sidoides is a member of the Geraniaceae family and it originates from the coastal regions of South Africa. In the last decades, Pelargonium sidoides root has been subjected to several surveys due to the assertion of its health benefits, such as the relief of symptoms of acute bronchitis, common cold and acute rhinosinusitis. Many studies have been conducted to reveal its naturally occurring bioactive chemicals, yet no wide-scope chemical characterisation strategies have been done using mass spectrometry.

OBJECTIVE

This research aimed to comprehensively characterise the chemical profile of Pelargonium sidoides root via high-resolution mass spectrometry.

METHODOLOGY

The Pelargonium sidoides root was extracted by a mixture of methanol: water in the proportion of 80:20. The extraction procedure included vortexing, shaking as well as the use of an ultrasound sonication bath under 40°C. After centrifugation, the supernatant was evaporated to dryness. The dry residue was reconstituted with a mixture of methanol/water (50:50, v/v), filtered and injected into an ultra-high-pressure liquid chromatography-quadruple time-of-flight mass spectrometer.

RESULTS

Overall, 33 compounds were identified in the root using suspect and non-target screening. These compounds were originated from different classes of compounds such as amino acids, phenolic acids, α-hydroxy-acids, vitamins, polyphenols, flavonoids, coumarins, coumarins glucosides, coumarin sulphates and nucleotides. Quantitative results were provided for the identified compounds, where their reference standards were available.

CONCLUSION

Some important compounds were elucidated, belonging to different classes of compounds such as antioxidants (coumarins and phenolic compounds), amino acids, nucleotides and vitamins revealing the importance of the bioactive content of this root.

Environmental Sources, Chemistry, Fate, and Transport of Per- and Polyfluoroalkyl Substances: State of the Science, Key Knowledge Gaps, and Recommendations Presented at the August 2019 SETAC Focus Topic Meeting

A Society of Environmental Toxicology and Chemistry (SETAC) Focused Topic Meeting (FTM) on the environmental management of per- and polyfluoroalkyl substances (PFAS) convened during August 2019 in Durham, North Carolina (USA). Experts from around the globe were brought together to critically evaluate new and emerging information on PFAS including chemistry, fate, transport, exposure, and toxicity. After plenary presentations, breakout groups were established and tasked to identify and adjudicate via panel discussions overarching conclusions and relevant data gaps. The present review is one in a series and summarizes outcomes of presentations and breakout discussions related to (1) primary sources and pathways in the environment, (2) sorption and transport in porous media, (3) precursor transformation, (4) practical approaches to the assessment of source zones, (5) standard and novel analytical methods with implications for environmental forensics and site management, and (6) classification and grouping from multiple perspectives. Outcomes illustrate that PFAS classification will continue to be a challenge, and additional pressing needs include increased availability of analytical standards and methods for assessment of PFAS and fate and transport, including precursor transformation. Although the state of the science is sufficient to support a degree of site-specific and flexible risk management, effective source prioritization tools, predictive fate and transport models, and improved and standardized analytical methods are needed to guide broader policies and best management practices. Environ Toxicol Chem 2021;40:3234-3260. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Background

Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS.

Results

Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-1}$$\end{document}-1, measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected).

Conclusions

Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-021-00544-w.

Data analysis strategies for the characterization of chemical contaminant mixtures. Fish as a case study

Thousands of chemicals are potentially contaminating the environment and food resources, covering a wide spectrum of molecular structures, physico-chemical properties, sources, environmental behavior and toxic profiles. Beyond the description of the individual chemicals, characterizing contaminant mixtures in related matrices has become a major challenge in ecological and human health risk assessments. Continuous analytical developments, in the fields of targeted (TA) and non-targeted analysis (NTA), have resulted in ever larger sets of data on associated chemical profiles. More than ever, the implementation of advanced data analysis strategies is essential to elucidate profiles and extract new knowledge from these large data sets. Specifically focusing on the data analysis step, this review summarizes the recent progress in integrating data analysis tools into TA and NTA workflows to address the challenging characterization of chemical mixtures in environmental and food matrices. As fish matrices are relevant in both aquatic pollution and consumer exposure perspectives, fish was chosen as the main theme to illustrate this review, although the present document is equally relevant to other food and environmental matrices. The key features of TA and NTA data sets were reviewed to illustrate the challenges associated with their analysis. Advanced filtering strategies to mine NTA data sets are presented, with a particular focus on chemical filters and discriminant analysis. Further, the applications of supervised and unsupervised multivariate analysis methods to characterize exposure to chemical mixtures, and their associated challenges, is discussed.

Using Estrogenic Activity and Nontargeted Chemical Analysis to Identify Contaminants in Sewage Sludge

Diverse organic compounds, many derived from consumer products, are found in sewage sludge worldwide. Understanding which of these poses the most significant environmental threat following land application can be investigated through a variety of predictive and cell-based toxicological techniques. Nontargeted analysis using high-resolution mass spectrometry with predictive estrogenic activity modeling was performed on sewage sludge samples from 12 wastewater treatment plants in California. Diisobutyl phthalate and dextrorphan were predicted to exhibit estrogenic activity and identified in >75% of sludge samples, signifying their universal presence and persistence. Additionally, the application of an estrogen-responsive cell bioassay revealed reductions in agonistic activity during mesophilic and thermophilic treatment but significant increases in antagonism during thermophilic treatment, which warrants further research. Ten nontarget features were identified (metoprolol, fenofibric acid, erythrohydrobupropion, oleic acid, mestranol, 4'-chlorobiphenyl-2,3-diol, medrysone, scillarenin, sudan I, and N,O-didesmethyltramadol) in treatment set samples and are considered to have influenced the in vitro estrogenic activity observed. The combination of predictive and in vitro estrogenicity with nontargeted analysis has led to confirmation of 12 estrogen-active contaminants in California sewage sludge and has highlighted the importance of evaluating both agonistic and antagonistic responses when evaluating the bioactivity of complex samples.

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.

Identification of more than 100 new compounds in the wastewater: Fate of polyethylene/polypropylene oxide copolymers and their metabolites in the aquatic environment

100 ethylene oxide (EO)/propylene oxide (PO) copolymer precursor and metabolites were detected in wastewater effluents. The homopolymers of EO and PO as well as the EO/PO copolymers are widely used as surfactants, e.g., for the production of cosmetics, pharmaceuticals and lubricants. Concomitantly, these compounds are discharged into the wastewater and the environmental fate of the PO homopolymers, also called polypropylene glycols (PPGs), and EO/PO copolymers is mostly unknown. In the present study, we identified hitherto unknown copolymer EO/PO homologous series and their metabolites in wastewater effluent. The identified compounds occur in homologous series and consist of PPGs and EO/PO copolymers, and their carbonylated, carboxylated and dicarboxylated metabolites. MBBR lab incubations of PPGs and EO/PO copolymers showed the successive degradation by cleavage of individual PO and EO groups, with high removal (>90%) in the initial 8 h for most of the copolymers. Carbonylated and carboxylated metabolites were degraded within 40 h. EO/PO copolymers with a higher number of EO and PO units showed a higher removal in MBBR and conventional activated sludge wastewater treatment plants. Polymers with lower molecular weight were initially formed by degradation of the EO/PO polymers. The mono-carboxylated metabolites were also detected in surface waters. Overall, our results provide new knowledge about degradation pathways of PO containing compounds and show the hitherto unnoticed occurrence of EO/PO copolymers and metabolites in the water cycle.

Nontargeted Discovery of Novel Contaminants in the Great Lakes Region: A Comparison of Fish Fillets and Fish Consumers

Sport fish fillets and human sera (fish consumers) were collected in the Lake Superior and Lake Michigan basin and screened for novel contaminants using the isotopic profile deconvoluted chromatogram (IPDC) algorithm. The IPDC algorithm was extended beyond traditional Cl/Br filters to detect additional potential bioaccumulative and toxic (PBT) such as perfluoroalkyl substances (PFAS). The IPDC algorithm screened for approximately 13.5 million theoretical molecular formulas. Additional algorithm modules were developed to detect data independent MS/MS fragmentation products and a retention time index calculator using a series of ¹³C-labeled perfluoroalkyl carboxylic acids (¹³C-PFCAs). Ten potential compound classes were isolated including six untargeted PFAS, six homologue groups of polyfluorinated carboxylic acids, polyfluorinated telomer alcohols (PoFTOHs), two hydroxylated polychlorobiphenyls, pesticides, herbicides, antifungals, pharmaceuticals, artificial sweeteners, and personal care products with minimal postprocessing efforts. The algorithm isolated 48 ubiquitous PoFTOHs in both fish fillet and serum of fish consumers suggesting a region wide distribution of this class of compounds. The 3, 4, and 7 fluorine substituted PoFTOH were the most abundant congeners in both biological matrices.

Characterization of water-soluble synthetic polymeric substances in wastewater using LC-HRMS/MS

Synthetic water-soluble polymeric materials are widely employed in e.g. cleaning detergents, personal care products, paints or textiles. Accordingly, these compounds reach sewage treatment plants and may enter receiving waters and the aquatic environment. Characteristically, these molecules show a polydisperse molecular weight distribution, comprising multiple repeating units, i.e. a homologous series (HS). Their analysis in environmentally relevant samples has received some attention over the last two decades, however, the majority of previous studies focused on surfactants and a molecular weight range <1000 Da. To capture a wider range on the mass versus polarity plane and extend towards less polar contaminants, a workflow was established using three different ionization strategies, namely conventional electrospray ionization, atmospheric pressure photoionization and atmospheric pressure chemical ionization. The data evaluation consisted of suspect screening of ca. 1200 suspect entries and a non-target screening of HS with pre-defined accurate mass differences using ca. 400 molecular formulas of repeating units of HS as input and repeating retention time shifts as HS indicator. To study the fate of these water-soluble polymeric substances in the wastewater treatment process, the different stages, i.e. after primary and secondary clarifier, and after ozonation followed by sand filtration, were sampled at a Swiss wastewater treatment plant. Remaining with two different ionization interfaces, ESI and APPI, in both polarities, a non-targeted screening approach led to a total number of 146 HS (each with a minimum number of 4 members), with a molecular mass of up to 1200 detected in the final effluent. Of the 146 HS, ca 15% could be associated with suspect hits and approximately 25% with transformation products of suspects. Tentative characterization or probable chemical structure could be assigned to almost half of the findings. In positive ionization mode various sugar derivatives with differing side chains, for negative mode structures with sulfonic acids, could be characterized. The number of detected HS decreased significantly over the three treatment stages. For HS detectable also in the biological and oxidative treatment stages, a change in HS distribution towards to lower mass range was often observed.

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

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.

Fluorine Mass Balance and Suspect Screening in Marine Mammals from the Northern Hemisphere

There is increasing evidence that the ∼20 routinely monitored perfluoroalkyl and polyfluoroalkyl substances (PFASs) account for only a fraction of extractable organofluorine (EOF) occurring in the environment. To assess whether PFAS exposure is being underestimated in marine mammals from the Northern Hemisphere, we performed a fluorine mass balance on liver tissues from 11 different species using a combination of targeted PFAS analysis, EOF and total fluorine determination, and suspect screening. Samples were obtained from the east coast United States (US), west and east coast of Greenland, Iceland, and Sweden from 2000 to 2017. Of the 36 target PFASs, perfluorooctane sulfonate (PFOS) dominated in all but one Icelandic and three US samples, where the 7:3 fluorotelomer carboxylic acid (7:3 FTCA) was prevalent. This is the first report of 7:3 FTCA in polar bears (∼1000 ng/g, ww) and cetaceans (<6-190 ng/g, ww). In 18 out of 25 samples, EOF was not significantly greater than fluorine concentrations derived from sum target PFASs. For the remaining 7 samples (mostly from the US east coast), 30-75% of the EOF was unidentified. Suspect screening revealed an additional 37 PFASs (not included in the targeted analysis) bringing the total to 63 detected PFASs from 12 different classes. Overall, these results highlight the importance of a multiplatform approach for accurately characterizing PFAS exposure in marine mammals.

Fluorine Mass Balance and Suspect Screening in Marine Mammals from the Northern Hemisphere

There is increasing evidence that the ∼20 routinely monitored perfluoroalkyl and polyfluoroalkyl substances (PFASs) account for only a fraction of extractable organofluorine (EOF) occurring in the environment. To assess whether PFAS exposure is being underestimated in marine mammals from the Northern Hemisphere, we performed a fluorine mass balance on liver tissues from 11 different species using a combination of targeted PFAS analysis, EOF and total fluorine determination, and suspect screening. Samples were obtained from the east coast United States (US), west and east coast of Greenland, Iceland, and Sweden from 2000 to 2017. Of the 36 target PFASs, perfluorooctane sulfonate (PFOS) dominated in all but one Icelandic and three US samples, where the 7:3 fluorotelomer carboxylic acid (7:3 FTCA) was prevalent. This is the first report of 7:3 FTCA in polar bears (∼1000 ng/g, ww) and cetaceans (<6-190 ng/g, ww). In 18 out of 25 samples, EOF was not significantly greater than fluorine concentrations derived from sum target PFASs. For the remaining 7 samples (mostly from the US east coast), 30-75% of the EOF was unidentified. Suspect screening revealed an additional 37 PFASs (not included in the targeted analysis) bringing the total to 63 detected PFASs from 12 different classes. Overall, these results highlight the importance of a multiplatform approach for accurately characterizing PFAS exposure in marine mammals.

Automated Isotopic Profile Deconvolution for High Resolution Mass Spectrometric Data (APGC-QToF) from Biological Matrices

An isotopic profile matching algorithm, the isotopic profile deconvoluted chromatogram (IPDC), was developed to screen for a wide variety of organic compounds in high-resolution mass spectrometry (HRMS) data acquired from instruments with resolution power as low as 22 000 fwhm. The algorithm initiates the screening process by generating a series of C/Br/Cl/S isotopic patterns consistent with the profiles of approximately 3 million molecular formulas for compounds with potentially persistent, bioaccumulative, and toxic (PBT) properties. To evaluate this algorithm, HRMS data were screened using these seed profiles to isolate relevant chlorinated and/or brominated compounds. Data reduction techniques included mass defect filtering and retention time prediction from estimated boiling points predicted using molecular formulas and reasonable elemental conformations. A machine learning classifier was also developed using spectrometric and chromatographic variables to minimize false positives. A scoring system was developed to rank candidate molecular formulas for an isotopic feature. The IPDC algorithm was applied to a Lake Michigan lake trout extract analyzed by atmospheric pressure gas chromatography-quadrupole time-of-flight (APGC-QToF) mass spectrometry in positive and negative modes. The IPDC algorithm detected isotopic features associated with legacy contaminants and a series of unknown halogenated features. The IPDC algorithm resolved 313 and 855 halogenated features in positive and negative modes, respectively, in Lake Michigan lake trout.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

R-MetaboList 2: A Flexible Tool for Metabolite Annotation from High-Resolution Data-Independent Acquisition Mass Spectrometry Analysis

Technological advancements have permitted the development of innovative multiplexing strategies for data independent acquisition (DIA) mass spectrometry (MS). Software solutions and extensive compound libraries facilitate the efficient analysis of MS¹ data, regardless of the analytical platform. However, the development of comparable tools for DIA data analysis has significantly lagged. This research introduces an update to the former MetaboList R package and a workflow for full-scan MS¹ and MS/MS DIA processing of metabolomic data from multiplexed liquid chromatography high-resolution mass spectrometry (LC-HRMS) experiments. When compared to the former version, new functions have been added to address isolated MS¹ and MS/MS workflows, processing of MS/MS data from stepped collision energies, performance scoring of metabolite annotations, and batch job analysis were incorporated into the update. The flexibility and efficiency of this strategy were assessed through the study of the metabolite profiles of human urine, leukemia cell culture, and medium samples analyzed by either liquid chromatography quadrupole time-of-flight (q-TOF) or quadrupole orbital (q-Orbitrap) instruments. This open-source alternative was designed to promote global metabolomic strategies based on recursive retrospective research of multiplexed DIA analysis.

Suspect Screening of Hydrocarbon Surfactants in AFFFs and AFFF-Contaminated Groundwater by High-Resolution Mass Spectrometry

Aqueous film-forming foams (AFFFs) are proprietary mixtures containing hydrocarbon surfactants and per- and polyfluoroalkyl substances (PFASs) that are used to extinguish hydrocarbon-based fuel fires. There is limited information on hydrocarbon surfactants in AFFFs and AFFF-contaminated groundwater even though hydrocarbon surfactants are more abundant (5-10% w/w) than PFASs (0.9-1.5% w/w) in AFFFs. Eight commercial AFFFs manufactured between 1988 and 2012 and 10 AFFF-contaminated groundwaters collected from near source zones of fire-fighter training areas were analyzed for suspect hydrocarbon surfactants by liquid chromatography quadrupole time-of-flight mass spectrometry. A suspect list and a homologous series detection computational tool, enviMass, were combined to screen for hydrocarbon surfactants. Nine classes of hydrocarbon surfactants were detected in AFFFs including octylphenol polyethoxylates, linear alcohol ethoxylates, ethoxylated cocoamines, alkyl ether sulfates, alkyl amido dipropionates, linear alkyl benzenesulfonates, alkyl sulfates, and polyethylene glycols. Of those, six were also found in groundwater along with diethanolamines and alkyl amido betaines, which were not found in the eight archived AFFFs. This indicates that although aerobically biodegradable, hydrocarbon surfactants likely persist in groundwater due to anaerobic aquifer conditions. To the best of our knowledge, this is the first screening for hydrocarbon surfactants in AFFFs and in AFFF-contaminated groundwater.

Targeted and Untargeted Metabolomics as an Enhanced Tool for the Detection of Pomegranate Juice Adulteration

Pomegranate juice is one of the most popular fruit juices, is well-known as a "superfood", and plays an important role in healthy diets. Due to its constantly growing demand and high value, pomegranate juice is often targeted for adulteration, especially with cheaper substitutes such as apple and red grape juice. In the present study, the potential of applying a metabolomics approach to trace pomegranate juice adulteration was investigated. A novel methodology based on high-resolution mass spectrometric analysis was developed using targeted and untargeted screening strategies to discover potential biomarkers for the reliable detection of pomegranate juice adulteration from apple and red grape juice. Robust classification and prediction models were built with the use of unsupervised and supervised techniques (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)), which were able to distinguish pomegranate juice adulteration to a level down to 1%. Characteristic m/z markers were detected, indicating pomegranate juice adulteration, and several marker compounds were identified. The results obtained from this study clearly demonstrate that Mass Spectrometry (MS)-based metabolomics have the potential to be used as a reliable screening tool for the rapid determination of food adulteration.

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

A comprehensive protocol for chemical analysis of flame combustion emissions by secondary ion mass spectrometry

RATIONALE

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is used to provide detailed information on the surface chemical composition of soot. An analytical protocol is proposed and tested on a laboratory flame, and the results are compared with our previous measurements provided by two-step laser mass spectrometry (L2MS).

METHODS

This work details: (1) the development of a dedicated apparatus to sample combustion products from atmospheric flames and deposit them on substrates suitable for TOF-SIMS analysis; (2) the choice of the deposition substrate and the material of the sampling line, and their effect on the mass spectra; (3) a method to separate the contributions of soot and condensable gas based on impact deposition; and finally (4) post-acquisition data processing.

RESULTS

Compounds produced during flame combustion are detected on the surface of different deposition substrates and attributed a molecular formula based on mass defect analysis. Silicon and titanium wafers perform similarly, while the surface roughness of glass microfiber filters results in a reduced mass resolution. The mass spectra obtained from the analysis of different locations of the deposits obtained by impaction show characteristic patterns that are attributed to soot/condensable gas.

CONCLUSIONS

A working method for the analysis of soot samples and the extraction of useful data from mass spectra is proposed. This protocol should help to avoid common experimental issues like sample contamination, while optimizing the setup performance by maximizing the achievable mass resolution.

Household Dust as a Repository of Chemical Accumulation: New Insights from a Comprehensive High-Resolution Mass Spectrometric Study

Chemical exposure in household dust poses potential risks to human health but has been studied incompletely thus far. Most analytical studies have focused on one or several compound classes, with analysis performed by either liquid or gas chromatography coupled with mass spectrometry (LC-MS or GC-MS). However, a comprehensive investigation of individual dust samples is missing. The present study comprehensively characterizes chemicals in dust by applying a combination of target, suspect, and nontarget screening approaches using both LC and GC with quadrupole time-of-flight (Q/TOF) MS. First, the extraction method was optimized to streamline detection of LC-Q/TOF and GC-Q/TOF amenable compounds and was successfully validated with over 100 target compounds. Nontarget screening with GC-Q/TOF was done by spectral deconvolution followed by a library search. Suspect screening by LC-Q/TOF was carried out with an accurate mass spectral library. Finally, LC-Q/TOF nontarget screening was carried out by extracting molecular features, acquiring tandem mass spectrometric (MS/MS) spectra, and performing compound identification by use of in silico fragmentation software tools. In total, 271 chemicals could be detected in 38 dust samples, 163 of which could be unambiguously confirmed by a reference standard. Many of them, such as the plastic leachable 7,9-di- tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione (CAS 82304-66-3) and three organofluorine compounds, are of emerging concern and their presence in dust has been underestimated. Advantages and drawbacks of the different approaches and analytical instruments are critically discussed.