Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Ascertaining sensitive exposure biomarkers of various metal(loid)s to embryo implantation

Close relationships exist between metal(loid)s exposure and embryo implantation failure (EIF) from animal and epidemiological studies. However, there are still inconsistent results and lacking of sensitive metal(loid) exposure biomarkers associated with EIF risk. We aimed to ascertain sensitive metal(loid) biomarkers to EIF and provide potential biological explanations. Candidate metal(loid) biomarkers were measured in the female hair (FH), female serum (FS), and follicular fluid (FF) with various exposure time periods. An analytical framework was established by integrating epidemiological association results, comprehensive literature searching, and knowledge-based adverse outcome pathway (AOP) networks. The sensitive biomarkers of metal(loid)s along with potential biological pathways to EIF were identified in this framework. Among the concerned 272 candidates, 45 metal(loid)s biomarkers across six time periods and three biomatrix were initially identified by single-metal(loid) analyses. Two biomarkers with counterfactual results according to literature summary results were excluded, and a total of five biomarkers were further determined from 43 remained candidates in mixture models. Finally, four sensitive metal(loid) biomarkers were eventually assessed by overlapping AOP networks information, including Se and Co in FH, and Fe and Zn in FS. AOP networks also identified key GO pathways and proteins involved in regulation of oxygen species biosynthetic, cell proliferation, and inflammatory response. Partial dependence results revealed Fe in FS and Co in FH at their low levels might be potential sensitive exposure levels for EIF. Our study provided a typical framework to screen the crucial metal(loid) biomarkers and ascertain that Se and Co in FH, and Fe and Zn in FS played an important role in embryo implantation.

[Application of multiomics mass spectrometry in the research of chemical exposome]

Environmental factors, such as environmental pollutants, behaviors, and lifestyles, are the leading causes of chronic noncommunicable diseases. Estimates indicate that approximately 50% of all deaths worldwide can be attributed to environmental factors. The exposome is defined as the totality of human environmental (i.e., all nongenetic) exposures from conception, including general external exposure (e.g., climate, education, and urban environment), specific external exposure (e.g., pollution, physical activity, and diet), and internal exposure (e.g., metabolic factors, oxidative stress, inflammation, and protein modification). As a new paradigm, this concept aims to comprehensively understand the link between human health and environmental factors. Therefore, a comprehensive measurement of the exposome, including accurate and reliable measurements of exposure to the external environment and a wide range of biological responses to the internal environment, is of great significance. The measurement of the general external exposome depends on advances in environmental sensors, personal-sensing technologies, and geographical information systems. The determination of exogenous chemicals to which individuals are exposed and endogenous chemicals that are produced or modified by external stressors relies on improvements in methodology and the development of instrumental approaches, including colorimetric, chromatographic, spectral, and mass-spectrometric methods. This article reviews the research strategies for chemical exposomes and summarizes existing exposome-measurement methods, focusing on mass spectrometry (MS)-based methods. The top-down and bottom-up approaches are commonly used in exposome studies. The bottom-up approach focuses on the identification of chemicals in the external environment (e.g., soil, water, diet, and air), whereas the top-down approach focuses on the evaluation of endogenous chemicals and biological processes in biological samples (e.g., blood, urine, and serum). Low- and high-resolution MS (LRMS and HRMS, respectively) have become the most popular methods for the direct measurement of exogenous and endogenous chemicals owing to their superior sensitivity, specificity, and dynamic range. LRMS has been widely applied in the targeted analysis of expected chemicals, whereas HRMS is a promising technique for the suspect and unknown screening of unexpected chemicals. The development of MS-based multiomics, including proteomics, metabolomics, epigenomics, and spatial omics, provides new opportunities to understand the effects of environmental exposure on human health. Metabolomics involves the sum of all low-molecular-weight metabolites in a living system. Nontargeted metabolomics can measure both endogenous and exogenous chemicals, which would directly link exposure to biological effects, internal dose, and disease pathobiology, whereas proteomics could play an important role in predicting potential adverse health outcomes and uncovering molecular mechanisms. MS imaging (MSI) is an emerging technique that provides unlabeled in-depth measurements of endogenous and exogenous molecules directly from tissue and cell sections without changing their spatial information. MSI-based spatial omics, which has been widely applied in biomarker discovery for clinical diagnosis, as well as drug and pollutant monitoring, is expected to become an effective method for exposome measurement. Integrating these response measurements from metabolomics, proteomics, spatial omics, and epigenomics will enable the generation of new hypotheses to discover the etiology of diseases caused by chemical exposure. Finally, we highlight the major challenges in achieving chemical exposome measurements.

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.

Suspect screening analysis to improve untargeted and targeted UHPLC-qToF approaches: the biodegradability of a proton pump inhibitor medicine and a natural medical device

Suspect screening and untargeted analysis using UHPLC-qToF are two advanced analytical approaches now used to achieve an extensive chemical profile of samples, which are then typically confirmed through targeted analysis. These techniques can detect a large number of chemical features simultaneously and are currently being introduced into the study of contaminants of emerging concern (CECs) and into the study of the extent of human chemical exposure (the exposome). Here is described the use of these techniques to characterize chemical mixtures derived from the OECD 301F ready biodegradability test (RBT) of a chemical and natural formulation currently used to treat reflux disease and functional dyspepsia. Untargeted analysis clearly evidenced a different behavior between formulations containing only natural products with respect to that containing synthetic and non-naturally occurring substances. Suspect screening analysis improved the untargeted analysis of the omeprazole-based medicine, leading to the tentative identification of a number of omeprazole-derived transformation products, thereby enabling their preliminary quali-quantitative evaluation. Targeted analysis was then performed to confirm the preliminary data gained from the suspect screening approach. The validation of the analytical method for the quantitative determination of omeprazole and its major metabolite, omeprazole sulphide, has provided robust data to evaluate the behavior of omeprazole during the OECD 301F test. Using advanced analytical approaches, the RBT performed on the two products under investigation confirmed that omeprazole is not readily biodegradable, while the medical device made of natural substances has proven to be readily biodegradable.

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.

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor-fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS.

Screening and prioritization of organic chemicals in a large river basin by suspect and non-target analysis

Many organic chemicals are present in aquatic environments, but how to screen and prioritize these chemicals has always been a difficult task. Here we investigated organic chemicals in the West River Basin by using a developed non-target identification workflow. A total of 957 chemicals were tentatively identified, with 96 assigned as high confidence levels by matching with reference standards, MassBank spectral library, and using CompTox Chemistry Dashboard database as the compound library for MetFrag. More pesticides and their transformation products (e.g., metolachlor ESA, acetochlor ESA, deethylatrazine, and hydroxyatrazine) were detected in the wet season due to the increasing usage. High detection of pharmaceutical and personal care products and their transformation products in the tributaries was linked to rural farming and human activities. Irbesartan that is used to treat high blood pressure was recognized in the river and positive correlations between some detected chemicals and irbesartan were observed, indicating a domestic wastewater source. Ecological risks of the identified chemicals were calculated by toxicological prioritization ranking schemes, and 24 chemicals showed high ToxPi scores in the river. The results from this study show the presence of a large number of emerging organic chemicals in our waterways, and demonstrated conceptual schemes for integrating risk assessment into a non-target screening workflow.

Investigating geographic differences in environmental chemical exposures in maternal and cord sera using non-targeted screening and silicone wristbands in California

BACKGROUND

Differential risks for adverse pregnancy outcomes may be influenced by prenatal chemical exposures, but current exposure methods may not fully capture data to identify harms and differences.

METHODS

We collected maternal and cord sera from pregnant people in Fresno and San Francisco, and screened for over 2420 chemicals using LC-QTOF/MS. We matched San Francisco participants to Fresno participants (N = 150) and compared detection frequencies. Twenty-six Fresno participants wore silicone wristbands evaluated for over 1500 chemicals using quantitative chemical analysis. We assessed whether living in tracts with higher levels of pollution according to CalEnviroScreen correlated with higher numbers of chemicals detected in sera.

RESULTS

We detected 2167 suspect chemical features across maternal and cord sera. The number of suspect chemical features was not different by city, but a higher number of suspect chemicals in cosmetics or fragrances was detected in the Fresno versus San Francisco participants' sera. We also found high levels of chemicals used in fragrances measured in the silicone wristbands. Fresno participants living in tracts with higher pesticide scores had higher numbers of suspect pesticides in their sera.

CONCLUSIONS

Multiple exposure-assessment approaches can identify exposure to many chemicals during pregnancy that have not been well-studied for health effects.

Implementing a suspect screening method to assess occupational chemical exposures among US-based hairdressers serving an ethnically diverse clientele: a pilot study

BACKGROUND

There are over 700,000 hairdressers in the United States, and it is estimated that >90% are female and 31% are Black or Hispanic/Latina. Racial and ethnic minorities in this workforce may be exposed to a unique mixture of potentially hazardous chemicals from products used and services provided. However, previous biomonitoring studies of hairdressers target a narrow list of compounds and few studies have investigated exposures among minority hairdressers.

OBJECTIVE

To assess occupational chemical exposures in a sample of US-based Black and Latina hairdressers serving an ethnically diverse clientele by analyzing urine specimens with a suspect screening method.

METHODS

Post-shift urine samples were collected from a sample of US female hairdressers (n = 23) and office workers (n = 17) and analyzed via reverse-phase liquid chromatography coupled to high-resolution mass spectrometry. Detected compounds were filtered based on peak area differences between groups and matching with a suspect screening list. When possible, compound identities were confirmed with reference standards. Possible exposure sources were evaluated for detected compounds.

RESULTS

The developed workflow allowed for the detection of 24 compounds with median peak areas ≥2x greater among hairdressers compared to office workers. Product use categories (PUCs) and harmonized functional uses were searched for these compounds, including confirmed compounds methylparaben, ethylparaben, propylparaben, and 2-naphthol. Most product use categories were associated with "personal use" and included 11 different "hair styling and care" product types (e.g., hair conditioner, hair relaxer). Functional uses for compounds without associated PUCs included fragrance, hair and skin conditioning, hair dyeing, and UV stabilizer.

SIGNIFICANCE

Our suspect screening approach detected several compounds not previously reported in biomonitoring studies of hairdressers. These results will help guide future studies to improve characterization of occupational chemical exposures in this workforce and inform exposure and risk mitigation strategies to reduce potential associated work-related health disparities.

Optimization of a method for collecting infant and toddler urine for non-target analysis using cotton pads and commercially available disposable diapers

BACKGROUND

Urine is an abundant and useful medium for measuring biomarkers related to chemical exposures in infants and children. Identification of novel biomarkers is greatly enhanced with non-targeted analysis (NTA), a powerful methodology for broad chemical analysis of environmental and biological specimens. However, collecting urine in non-toilet trained children presents many challenges, and contamination from specimen collection can impact NTA results.

OBJECTIVES

We optimized a caregiver-driven method for collecting urine from infants and children using cotton pads and commercially available disposable diapers for NTA and demonstrate its applicability to various children biomonitoring studies.

METHODS

Experiments were first performed to evaluate the effects of processing method (i.e., centrifuge vs. syringe), storage temperature, and diaper brand on recovery of urine absorbed to cotton pads. Caregivers of 11 children (<2 years) used and retained diapers (with cotton pads) to collect their child's urine for 24 h. Specimens were analyzed via a NTA method implementing an exclusion list of ions related to contamination from collection materials.

RESULTS

Centrifuging cotton pads through a small-pore membrane, compared to a manual syringe method, and storing diapers at 4 °C, compared to room temperature, resulted in larger volumes of recovered sample. This method was successfully implemented to recover urine from cotton pads collected in the field; between 5-9 diapers were collected per child in 24 h, and the total mean volume of urine recovered was 44.7 (range 26.7-71.1) mL. NTA yielded a list of compounds present in urine and/or stool that may hold promise as biomarkers of chemical exposures from a variety of sources.

IMPACT STATEMENT

Infant and children urine is a valuable matrix for studies of the early life exposome, in that numerous biological markers of exposure and outcome can be derived from a single analysis. Depending on the nature of the exposure study, it may be the case that a simple collection method that can be facilitated by caregivers of young children is desirable, especially when time-integrated samples or large volumes of urine are needed. We describe the process for development and results of an optimized method for urine collection and analysis using commercially available diapers and non-target analysis.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Assessment of exposure to pesticide mixtures in five European countries by a harmonized urinary suspect screening approach

Humans are exposed to a mixture of pesticides through diet as well as through the environment. We conducted a suspect-screening based study to describe the probability of (concomitant) exposure to a set of pesticide profiles in five European countries (Latvia, Hungary, Czech Republic, Spain and the Netherlands). We explored whether living in an agricultural area (compared to living in a peri-urban area), being a a child (compared to being an adult), and the season in which the urine sample was collected had an impact on the probability of detection of pesticides (-metabolites). In total 2088 urine samples were collected from 1050 participants (525 parent-child pairs) and analyzed through harmonized suspect screening by five different laboratories. Fourty pesticide biomarkers (either pesticide metabolites or the parent pesticides as such) relating to 29 pesticides were identified at high levels of confidence in samples across all study sites. Most frequently detected were biomarkers related to the parent pesticides acetamiprid and chlorpropham. Other biomarkers with high detection rates in at least four countries related to the parent pesticides boscalid, fludioxonil, pirimiphos-methyl, pyrimethanil, clothianidin, fluazifop and propamocarb. In 84% of the samples at least two different pesticides were detected. The median number of detected pesticides in the urine samples was 3, and the maximum was 13 pesticides detected in a single sample. The most frequently co-occurring substances were acetamiprid with chlorpropham (in 62 urine samples), and acetamiprid with tebuconazole (30 samples). Some variation in the probability of detection of pesticides (-metabolites) was observed with living in an agricultural area or season of urine sampling, though no consistent patterns were observed. We did observe differences in the probability of detection of a pesticide (metabolite) among children compared to adults, suggesting a different exposure and/or elimination patterns between adults and children. This survey demonstrates the feasibility of conducting a harmonized pan-European sample collection, combined with suspect screening to provide insight in the presence of exposure to pesticide mixtures in the European population, including agricultural areas. Future improvements could come from improved (harmonized) quantification of pesticide levels.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.

Disparities in chemical exposures among pregnant women and neonates by socioeconomic and demographic characteristics: A nontargeted approach

BACKGROUND

Exposure to environmental chemicals during pregnancy adversely affects maternal and infant health, and identifying socio-demographic differences in exposures can inform contributions to health inequities.

METHODS

We recruited 294 demographically diverse pregnant participants in San Francisco from the Mission Bay/Moffit Long (MB/ML) hospitals, which serve a primarily higher income population, and Zuckerberg San Francisco General Hospital (ZSFGH), which serves a lower income population. We collected maternal and cord sera, which we screened for 2420 unique formulas and their isomers using high-resolution mass spectrometry using LC-QTOF/MS. We assessed differences in chemical abundances across socioeconomic and demographic groups using linear regression adjusting for false discovery rate.

RESULTS

Our participants were racially diverse (31% Latinx, 16% Asian/Pacific Islander, 5% Black, 5% other or multi-race, and 43% white). A substantial portion experienced financial strain (28%) and food insecurity (20%) during pregnancy. We observed significant abundance differences in maternal (9 chemicals) and cord sera (39 chemicals) between participants who delivered at the MB/ML hospitals versus ZSFGH. Of the 39 chemical features differentially detected in cord blood, 18 were present in pesticides, one per- or poly-fluoroalkyl substance (PFAS), 21 in plasticizers, 24 in cosmetics, and 17 in pharmaceuticals; 4 chemical features had unknown sources. A chemical feature annotated as 2,4-dichlorophenol had higher abundances among Latinx compared to white participants, those delivering at ZSFGH compared to MB/ML, those with food insecurity, and those with financial strain. Post-hoc QTOF analyses indicated the chemical feature was either 2,4-dichlorophenol or 2,5-dichlorophenol, both of which have potential endocrine-disrupting effects.

CONCLUSIONS

Chemical exposures differed between delivery hospitals, likely due to underlying social conditions faced by populations served. Differential exposures to 2,4-dichlorophenol or 2,5-dichlorophenol may contribute to disparities in adverse outcomes.

Machine learning for identification of silylated derivatives from mass spectra

Motivation

Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).

Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.

Results

This study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Suspect Screening of Liquid Crystal Monomers (LCMs) in Sediment Using an Established Database Covering 1173 LCMs

Recent studies have suggested that liquid crystal monomers (LCMs) are emerging contaminants in the environment, and knowledge of this class of substances is very rare. Here, we reviewed existing LCM-related documents, i.e., publications and patents, and established a database involving 1173 LCMs. These 1173 LCMs were further calculated for their physicochemical properties, i.e., persistence (P), bioaccumulation (B), long-range transport potential (LRTP), and Arctic contamination and bioaccumulation potential (ACBAP). We found that 476 out of them were P&B chemicals (99% of them were halogenated), and 320 of them could have ACBAP properties (67% of them were halogenated). This LCM database was further applied for suspect screening of LCMs in n = 33 sediment samples by use of gas chromatography coupled to quadrupole time-of-flight mass spectrometry (GC-QTOF/MS). We tentatively identified 26 LCM formulas, which could have 43 chemical structures. Two out of these 43 suspect LCM candidates, 1-butoxy-2,3-difluoro-4-(4-propylcyclohexyl) benzene (3cH4OdFP) and 1-ethoxy-2,3-difluoro-4-(4-pentyl cyclohexyl) benzene (5cH2OdFP), were fully confirmed by a comparison of unique GC and MS characteristics with their authentic standards. Overall, our present study expanded the previous LCM database from 362 to 1173, and 1173 LCMs in this database were calculated for their physicochemical properties. Meanwhile, taking n = 33 sediment samples as an exercise, we successfully developed a suspect screening strategy tailored for LCMs, and this strategy could have promising potential to be extended to other environmental matrices.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Analysis of per- and polyfluoroalkyl substances in Houston Ship Channel and Galveston Bay following a large-scale industrial fire using ion-mobility-spectrometry-mass spectrometry

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern because of their ubiquitous presence in surface and ground water; analytical methods that can be used for rapid comprehensive exposure assessment and fingerprinting of PFAS are needed. Following the fires at the Intercontinental Terminals Company (ITC) in Deer Park, TX in 2019, large quantities of PFAS-containing firefighting foams were deployed. The release of these substances into the Houston Ship Channel/Galveston Bay (HSC/GB) prompted concerns over the extent and level of PFAS contamination. A targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based study of temporal and spatial patterns of PFAS associated with this incident revealed presence of 7 species; their levels gradually decreased over a 6-month period. Because the targeted LC-MS/MS analysis was focused on about 30 PFAS molecules, it may have missed other PFAS compounds present in firefighting foams. Therefore, we utilized untargeted LC-ion mobility spectrometry-mass spectrometry (LC-IMS-MS)-based analytical approach for a more comprehensive characterization of PFAS in these water samples. We analyzed 31 samples from 9 sites in the HSC/GB that were collected over 5 months after the incident. Our data showed that additional 19 PFAS were detected in surface water of HSC/GB, most of them decreased gradually after the incident. PFAS features detected by LC-MS/MS correlated well in abundance with LC-IMS-MS data; however, LC-IMS-MS identified a number of additional PFAS, many known to be components of firefighting foams. These findings therefore illustrate that untargeted LC-IMS-MS improved our understanding of PFAS presence in complex environmental samples.

PFAS Analysis with Ultrahigh Resolution 21T FT-ICR MS: Suspect and Nontargeted Screening with Unrivaled Mass Resolving Power and Accuracy

Per- and polyfluoroalkyl substances (PFASs) are a large family of thousands of chemicals, many of which have been identified using nontargeted time-of-flight and Orbitrap mass spectrometry methods. Comprehensive characterization of complex PFAS mixtures is critical to assess their environmental transport, transformation, exposure, and uptake. Because 21 tesla (T) Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest available mass resolving power and sub-ppm mass errors across a wide molecular weight range, we developed a nontargeted 21 T FT-ICR MS method to screen for PFASs in an aqueous film-forming foam (AFFF) using suspect screening, a targeted formula database (C, H, Cl, F, N, O, P, S; ≤865 Da), isotopologues, and Kendrick-analogous mass difference networks (KAMDNs). False-positive PFAS identifications in a natural organic matter (NOM) sample, which served as the negative control, suggested that a minimum length of 3 should be imposed when annotating CF₂-homologous series with positive mass defects. We putatively identified 163 known PFASs during suspect screening, as well as 134 novel PFASs during nontargeted screening, including a suspected polyethoxylated perfluoroalkane sulfonamide series. This study shows that 21 T FT-ICR MS analysis can provide unique insights into complex PFAS composition and expand our understanding of PFAS chemistries in impacted matrices.

Discovery and quantification of lipoamino acids in bacteria

Improving knowledge about metabolites produced by the microbiota is a key point to understand its role in human health and disease. Among them, lipoamino acid (LpAA) containing asparagine and their derivatives are bacterial metabolites which could have an impact on the host. In this study, our aim was to extend the characterization of this family. We developed a semi-targeted workflow to identify and quantify new candidates. First, the sample preparation and analytical conditions using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) were optimized. Using a theoretical homemade database, HRMS raw data were manually queried. This strategy allowed us to find 25 new LpAA conjugated to Asn, Gln, Asp, Glu, His, Leu, Ile, Lys, Phe, Trp and Val amino acids. These metabolites were then fully characterized by MS², and compared to the pure synthesized standards to validate annotation. Finally, a quantitative method was developed by LC coupled to a triple quadrupole instrument, and linearity and limit of quantification were determined. 14 new LpAA were quantified in gram positive bacteria, Lactobacilus animalis, and 12 LpAA in Escherichia coli strain Nissle 1917.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Ultra-high pressure liquid chromatography coupled to travelling wave ion mobility-time of flight mass spectrometry for the screening of pharmaceutical metabolites in wastewater samples: Application to antiretrovirals

The presence of pharmaceutical compounds in the aquatic environment is a significant environmental health concern, which is exacerbated by recent evidence of the contribution of drug metabolites to the overall pharmaceutical load. In light of a recent report of the occurrence of metabolites of antiretroviral drugs (ARVDs) in wastewater, we investigate in the present work the occurrence of further ARVD metabolites in samples obtained from a domestic wastewater treatment plant in the Western Cape, South Africa. Pharmacokinetic data indicate that ARVDs are biotransformed into several positional isomeric metabolites, only two of which have been reported wastewater samples. Given the challenges associated with the separation and identification of isomeric species in complex wastewater samples, a method based on liquid chromatography hyphenated to ion mobility spectrometry-high resolution mass spectrometry (LC-IMS-HR-MS) was implemented. Gradient LC separation was achieved on a sub-2 µm reversed phase column, while the quadrupole-time-of-flight MS was operated in data independent acquisition (DIA) mode to increase spectral coverage of detected features. A mass defect filter (MDF) template was implemented to detect ARVD metabolites with known phase I and phase II mass shifts and fractional mass differences and to filter out potential interferents. IMS proved particularly useful in filtering the MS data for co-eluting species according to arrival time to provide cleaner mass spectra. This approach allowed us to confirm the presence of two known hydroxylated efavirenz and nevirapine metabolites using authentic standards, and to tentatively identify a carboxylate metabolite of abacavir previously reported in literature. Furthermore, three hydroxylated-, two sulphated and one glucuronidated metabolite of efavirenz, two hydroxylated metabolites of nevirapine and one hydroxylated metabolite of ritonavir were tentatively or putatively identified in wastewater samples for the first time. Assignment of the metabolites is discussed in terms of high resolution fragmentation data, while collisional cross section (CCS) values measured for the detected analytes are reported to facilitate further work in this area.

The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60-69 years (China BAPE Study)

The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks. To demonstrate the exposome paradigm, we describe the rationale and design of a comprehensive biomarker and biomonitoring panel study to systematically explore the association between individual airborne exposure and adverse health outcomes. We used a combination of personal monitoring for airborne pollutants, extensive human biomonitoring, advanced omics analysis, confounding information, and statistical methods. We established an exploratory panel study of Biomarkers of Air Pollutant Exposure in Chinese people aged 60-69 years (China BAPE), which included 76 healthy residents from a representative community in Jinan City, Shandong Province. During the period between September 2018 and January 2019, we conducted prospective longitudinal monitoring with a 3-day assessment every month. This project: (1) leveraged advanced tools for personal airborne exposure monitoring (external exposures); (2) comprehensively characterized biological samples for exogenous and endogenous compounds (e.g., targeted and untargeted monitoring) and multi-omics scale measurements to explore potential biomarkers and putative toxicity pathways; and (3) systematically evaluated the relationships between personal exposure to air pollutants, and novel biomarkers of exposures and effects using exposome-wide association study approaches. These findings will contribute to our understanding of the mechanisms underlying the adverse health impacts of air pollution exposures and identify potential adverse clinical outcomes that can facilitate the development of effective prevention and targeted intervention techniques.

Noncovalently Tagged Gas Phase Complex Ions for Screening Unknown Contaminant Metabolites in Plants

Screening the metabolites of emerging organic contaminants (EOCs) from complicated biological matrices is an important but challenging task. Although stable isotope labeling (SIL) is frequently used to facilitate the identification of contaminant metabolites from redundant interfering components, the isotopically labeled reagents are expensive and difficult to synthesize, which greatly constrains the application of the SIL method. Herein, a new online noncovalent tagging method was developed for screening the metabolites of 1H-benzotriazol (BT) based on the characteristic structural moieties reserved in the metabolites. By selecting β-cyclodextrin (β-CD) as a macrocyclic tagging reagent, metabolites with the reserved moiety were expected to exhibit a characteristic shift of the mass-to-charge ratio (Δm/z = 1134.3698) after being noncovalently tagged by β-CD. Based on the characteristic mass shift, the suspected features were reduced by 1 order of magnitude, as numerous interfering species that could not be effectively tagged by β-CD were excluded. From these suspected features, two metabolites of BT that have not been reported before were successfully screened out. The significant characteristic mass shift caused by the noncovalent tagging method is easier to identify with more confidence than the previously reported SIL method. Besides, noncovalent tagging reagents can be much more accessible and less expensive than isotopically labeled reagents. Hence, this online noncovalent tagging method can be an intriguing alternative to the conventional SIL method.

Metabolome-wide association study of occupational exposure to benzene

Benzene is a recognized hematotoxin and leukemogen; however, its mechanism of action in humans remain unclear. To provide insight into the processes underlying benzene hematotoxicity, we performed high-resolution metabolomic profiling of plasma collected from a cross-sectional study of 33 healthy workers exposed to benzene (median 8-h time-weighted average exposure; 20 ppma), and 25 unexposed controls in Shanghai, China. Metabolic features associated with benzene were identified using a metabolome-wide association study (MWAS) that tested for the relationship between feature intensity and benzene exposure. MWAS identified 478 mass spectral features associated with benzene exposure at false discovery rate < 20%. Comparison to a list of 13 known benzene metabolites and metabolites predicted using a multi-component biotransformation algorithm showed five metabolites were detected, which included the known metabolites phenol and benzene diolepoxide. Metabolic pathway enrichment identified 41 pathways associated with benzene exposure, with altered pathways including carnitine shuttle, fatty acid metabolism, sulfur amino acid metabolism, glycolysis, gluconeogenesis and branched chain amino acid metabolism. These results suggest disruption to fatty acid uptake, energy metabolism and increased oxidative stress, and point towards pathways related to mitochondrial dysfunction, which has previously been linked to benzene exposure in animal models and human studies. Taken together, these results suggest benzene exposure is associated with disruption of mitochondrial pathways, and provide promising, systems biology biomarkers for risk assessment of benzene-induced hematotoxicity in humans.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Scopes of Bioanalytical Chromatography–Mass Spectrometry

The areas of application of modern bioanalytical chromatography–mass spectrometry are so extensive that any attempt to systematize them becomes subjective. It would be more correct to say that there is no such area of biology and medicine where chromatography–mass spectrometry would not find application. This article focuses on the areas of application of this technique that are either relatively new or insufficiently covered in recent reviews. State-of-the-art bioanalytical techniques have become multitargeted in terms of analytes and standardized in terms of matrices. The ability to detect trace concentrations of analytes in the presence of a huge number of biomatrix macrocomponents using chromatography–mass spectrometry is especially important for bioanalytical chemistry. In the target-oriented determination of persistent organic pollutants by chromatography–mass spectrometry, the main problem is the expansion of the list of analytes, including isomers. In the detection of exposures to unstable toxicants, the fragmented adducts of xenobiotics with biomolecules become target biomarkers along with hydrolytic metabolites. The exposome reflects the general exposure of a human being to total xenobiotics and the metabolic status reflects the physiological state of the body. Chromatography–mass spectrometry is a key technique in metabolomics. Metabolomics is currently used to solve the problems of clinical diagnostics and anti-doping control. Biological sample preparation procedures for instrumental analysis are being simplified and developed toward increasing versatility. Proteomic technologies with the use of various versions of mass spectrometry have found application in the development of new methods for diagnosing coronavirus infections.

Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products

The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.

Large-Scale Implementation and Flaw Investigation of Human Serum Suspect Screening Analysis for Industrial Chemicals

Non-targeted analysis (NTA), including both suspect screening analysis (SSA) and unknown compound analysis, has gained increasing popularity in various fields for its capability in identifying new compounds of interests. Current major challenges for NTA SSA are that (1) tremendous effort and resources are needed for large-scale identification and confirmation of suspect chemicals and (2) suspect chemicals generally show low matching rates during identification and confirmation processes. To narrow the gap between these challenges and smooth implementation of NTA SSA methodology in the biomonitoring field, we present a thorough SSA workflow for the large-scale screen, identification, and confirmation of industrial chemicals that may pose adverse health effects in pregnant women and newborns. The workflow was established in a study of 30 paired maternal and umbilical cord serum samples collected at delivery in the San Francisco Bay area. By analyzing LC-HRMS and MS/MS data, together with the assistance of a combination of resources including online MS/MS spectra libraries, online in silico fragmentation tools, and the EPA CompTox Chemicals Dashboard, we confirmed the identities of 17 chemicals, among which monoethylhexyl phthalate, 4-nitrophenol, tridecanedioic acid, and octadecanedioic acid are especially interesting due to possible toxicities and their high-volume use in industrial manufacturing. Similar to other previous studies in the SSA field, the suspect compounds show relatively low MS/MS identification (16%) and standard confirmation (8%) rates. Therefore, we also investigated origins of false positive features and unidentifiable suspected features, as well as technical obstacles encountered during the confirmation process, which would promote a better understanding of the flaw of low confirmation rate and encourage gaining more effective tools for tackling this issue in NTA SSA.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery

BACKGROUND

Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.

OBJECTIVES

We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.

DISCUSSION

The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.

CONCLUSIONS

Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.

An annotation database for chemicals of emerging concern in exposome research

BACKGROUND

Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.

OBJECTIVES

This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.

METHODS

The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.

RESULTS

All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag.

DISCUSSION

The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC-HRMS Data Processing

There is a current need to monitor human exposure to a large number of pesticides and other chemicals of emerging concern (CECs). This requires screening analysis with high confidence for these compounds and their metabolites in complex matrices, which is hampered by the fact that no reference standards are available for most metabolites. We address this challenge by a high-throughput workflow based on incubation of pesticides (or other CECs) with human liver S9, followed by solid-phase extraction, liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis, and automated data processing to generate a database (retention time, precursor m/z, and MS² spectral library) for the annotation in human samples. The metabolite prioritization consists of statistical comparisons and mass defect and m/z range filtering to obtain a subset of probable phase I metabolites, for which molecular formulas and likely metabolic transformation are retrieved. We tested the workflow on 22 pesticides, for which we could determine 91 metabolite molecular formulas which are only partly covered by the literature and/or predicted by in silico metabolization. Our workflow allows for an efficient generation of metabolite reference information, which can be used directly for annotating LC-HRMS data from human samples. A full structure elucidation of individual metabolites can be limited to those being actually present in human samples.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Optimized suspect screening approach for a comprehensive assessment of the impact of best management practices in reducing micropollutants transport in the Potomac River watershed

The vast number of chemicals potentially reaching aquatic environment pose a challenge in maintaining the quality of water resources. However, best management practices to improve water quality are typically focused on reducing nutrient transport without assessing how these practices may impact the occurrence of micropollutants. The potential for co-management of nutrients and organic micropollutants exists, but few studies have comprehensively evaluated the suite of contaminants associated with different water quality management practices (riparian zone restoration, stormwater management, etc.). Furthermore, most studies dealing with the determination of micropollutants in environmental samples include only a limited number of target analytes, leaving many contaminants undetected. To address this limitation, there has been a gradual shift in environmental monitoring from using target analysis to either suspect screening analysis (SSA) or non-targeted analysis (NTA), which relies on accurate mass measurements, mass spectral fragmentation patterns, and retention time information obtained using liquid chromatography coupled to high-resolution mass spectrometry. The work presented in this paper focuses on a wide-scope detection of micropollutants in surface water samples from the Potomac River watershed (United States). An in-house database composed of 1039 compounds based on experimental analysis of primary standards was established, and SSA workflow was optimized and applied to determine the presence of micropollutants in surface water. A total of 103 micropollutants were detected in the samples, some of which are contaminants that were not previously monitored and belong to various classes such as pharmaceuticals, personal care products, per-and polyfluoroalkyl substances and other persistent industrial chemicals. The impact of best management practices being implemented for nitrogen and phosphorus reductions were also assessed for their potential to reduce micropollutant transport. This work illustrates the advantages of suspect screening methods to determine a large number of micropollutants in environmental samples and reveals the potential to co-manage a diverse array of micropollutants based on shared transport and transformation mechanisms in watersheds.

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.

Suspect Screening, Prioritization, and Confirmation of Environmental Chemicals in Maternal-Newborn Pairs from San Francisco

Our proof-of-concept study develops a suspect screening workflow to identify and prioritize potentially ubiquitous chemical exposures in matched maternal/cord blood samples, a critical period of development for future health risks. We applied liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF/MS) to perform suspect screening for ∼3500 industrial chemicals on pilot data from 30 paired maternal and cord serum samples (n = 60). We matched 662 suspect features in positive ionization mode and 788 in negative ionization mode (557 unique formulas overall) to compounds in our database, and selected 208 of these for fragmentation analysis based on detection frequency, correlation in feature intensity between maternal and cord samples, and peak area differences by demographic characteristics. We tentatively identified 73 suspects through fragmentation spectra matching and confirmed 17 chemical features (15 unique compounds) using analytical standards. We tentatively identified 55 compounds not previously reported in the literature, the majority which have limited to no information about their sources or uses. Examples include (i) 1-(1-acetyl-2,2,6,6-tetramethylpiperidin-4-yl)-3-dodecylpyrrolidine-2,5-dione (known high production volume chemical) (ii) methyl perfluoroundecanoate and 2-perfluorooctyl ethanoic acid (two PFAS compounds); and (iii) Sumilizer GA 80 (plasticizer). Thus, our workflow demonstrates an approach to evaluating the chemical exposome to identify and prioritize chemical exposures during a critical period of development.

Ion Mobility-High-Resolution Mass Spectrometry (IM-HRMS) for the Analysis of Contaminants of Emerging Concern (CECs): Database Compilation and Application to Urine Samples

Ion mobility mass spectrometry (IM-MS)-derived collision cross section (CCS) values can serve as a valuable additional identification parameter within the analysis of compounds of emerging concern (CEC) in human matrices. This study introduces the first comprehensive database of ^DTCCS_N2 values of 148 CECs and their metabolites including bisphenols, alternative plasticizers (AP), organophosphate flame retardants (OP), perfluoroalkyl chemicals (PFAS), and others. A total of 311 ions were included in the database, whereby the ^DTCCS_N2 values for 113 compounds are reported for the first time. For 105 compounds, more than one ion is reported. Moreover, the ^DTCCS_N2 values of several isomeric CECs and their metabolites are reported to allow a distinction between isomers. Comprehensive quality assurance guidelines were implemented in the workflow of acquiring ^DTCCS_N2 values to ensure reproducible experimental conditions. The reliability and reproducibility of the complied database were investigated by analyzing pooled human urine spiked with 30 AP and OP metabolites at two concentration levels. For all investigated metabolites, the ^DTCCS_N2 values measured in urine showed a percent error of <1% in comparison to database values. ^DTCCS_N2 values of OP metabolites showed an average percent error of 0.12% (50 ng/mL in urine) and 0.15% (20 ng/mL in urine). For AP metabolites, these values were 0.10 and 0.09%, respectively. These results show that the provided database can be of great value for enhanced identification of CECs in environmental and human matrices, which can advance future suspect screening studies on CECs.

Head, Shoulders, Knees, and Toes: Placement of Wearable Passive Samplers Alters Exposure Profiles Observed

Chemical exposures are a major risk factor for many diseases. Comprehensive characterization of personal exposures is necessary to highlight chemicals of concern and factors that influence these chemical exposure dynamics. For this purpose, wearable passive samplers can be applied to assess longitudinal personal exposures to airborne contaminants. Questions remain regarding the impact of sampler placement at different locations of the body on the exposure profiles observed and how these placements affect the monitoring of seasonal dynamics in exposures. This study assessed personal air contaminant exposure using passive samplers worn in parallel across 32 participant's wrists, chest, and shoes over 24 h. Samplers were analyzed by thermal desorption gas chromatography high-resolution mass spectrometry. Personal exposure profiles were similar for about one-third of the 275 identified chemicals, irrespective of sampler placement. Signals of certain semivolatile organic compounds (SVOCs) were enhanced in shoes and, to a lesser extent, wrist samplers, as compared to those in chest samplers. Signals of volatile organic compounds were less impacted by sampler placement. Results showed that chest samplers predominantly captured more volatile exposures, as compared to those of particle-bound exposures, which may indicate predominant monitoring of chemicals via the inhalation route of exposure for chest samplers. In contrast, shoe samplers were more sensitive to particle-bound SVOCs. Seventy-one chemicals changed across participants between winter and summer in the same manner for two or more different sampler placements on the body, whereas 122 chemicals were observed to have seasonal differences in only one placement. Hence, the placement in certain cases significantly impacts exposure dynamics observed. This work shows that it is essential in epidemiological studies undertaking exposure assessment to consider the consequence of the placement of exposure monitors.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.

Network Analysis to Identify Communities Among Multiple Exposure Biomarkers Measured at Birth in Three Flemish General Population Samples

Introduction: Humans are exposed to multiple environmental chemicals via different sources resulting in complex real-life exposure patterns. Insight into these patterns is important for applications such as linkage to health effects and (mixture) risk assessment. By providing internal exposure levels of (metabolites of) chemicals, biomonitoring studies can provide snapshots of exposure patterns and factors that drive them. Presentation of biomonitoring data in networks facilitates the detection of such exposure patterns and allows for the systematic comparison of observed exposure patterns between datasets and strata within datasets. Methods: We demonstrate the use of network techniques in human biomonitoring data from cord blood samples collected in three campaigns of the Flemish Environment and Health Studies (FLEHS) (sampling years resp. 2002-2004, 2008-2009, and 2013-2014). Measured biomarkers were multiple organochlorine compounds, PFAS and metals. Comparative network analysis (CNA) was conducted to systematically compare networks between sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Results: Network techniques offered an intuitive approach to visualize complex correlation structures within human biomonitoring data. The identification of groups of highly connected biomarkers, "communities," within these networks highlighted which biomarkers should be considered collectively in the analysis and interpretation of epidemiological studies or in the design of toxicological mixture studies. Network analyses demonstrated in our example to which extent biomarker networks and its communities changed across the sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Conclusion: Network analysis is a data-driven and intuitive screening method when dealing with multiple exposure biomarkers, which can easily be upscaled to high dimensional HBM datasets, and can inform mixture risk assessment approaches.

Multi-Target Analysis and Suspect Screening of Xenobiotics in Milk by UHPLC-HRMS/MS

The development of suspect or non-target screening methods to detect xenobiotics in biological fluids is essential to properly understand the exposome and assess its adverse health effects on humans. In order to fulfil that aim, the biomonitorization of human fluids is compulsory. However, these methods are not yet extensively developed, especially for polar organic xenobiotics in biofluids such as milk, as most works are only focused on certain analytes of interest. In this work, a multi-target analysis method to determine 245 diverse xenobiotics in milk by means of Ultra High Performance Liquid Chromatography (UHPLC)-qOrbitrap was developed. Under optimal conditions, liquid milk samples were extracted with acetonitrile in the presence of anhydrous Na2SO4 and NaCl, and the extracts were cleaned-up by protein precipitation at low temperature and Captiva Non-Drip (ND)—Lipids filters. The optimized method was validated at two concentration-levels (10 ng/g and 40 ng/g) obtaining satisfactory figures of merit for more than 200 compounds. The validated multi-target method was applied to several milk samples, including commercial and breast milk, provided by 4 healthy volunteers. Moreover, the method was extended to perform suspect analysis of more than 17,000 xenobiotics. All in all, several diverse xenobiotics were detected, highlighting food additives (benzothiazole) or phytoestrogens (genistein and genistin) in commercial milk samples, and stimulants (caffeine), plasticizers (phthalates), UV filters (benzophenone), or pharmaceuticals (orlistat) in breast milk samples.