Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS.

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Rapid Screening of Consumer Products by GCxGC-HRT and Machine Learning Assisted Data Processing.

This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis.

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

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.

Chemicals of concern in personal care products used by women of color in three communities of California.

BACKGROUND: Personal care products (PCPs) may contain chemicals associated with adverse health effects. Prior studies found differences in product use by race/ethnicity and suggest some women are disproportionately exposed to chemicals of concern (CoCs). OBJECTIVE: We quantified chemicals linked to cancer, reproductive or developmental harm, or endocrine disruption in PCPs used by women of color. METHODS: We documented PCPs in stores frequented by Black, Latina, and Vietnamese women in their communities in California and CoCs on ingredient labels of 546 unique hair, skin, makeup, nail, deodorant/perfume, and intimate care products. Community partners chose 31 products for a combined targeted and suspect screen (National Institute of Standards and Technology mass spectral library search) two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) analysis to detect chemicals not on ingredient labels. RESULTS: We found that 65% of labels included CoCs, and 74% of labels had undisclosed ingredients listed as "fragrance." The most prevalent chemicals were parabens, cyclosiloxanes, and formaldehyde releasers. GCxGC-TOFMS found additional CoCs, including fragrances, solvents, preservatives, ultraviolet filters, and contaminants. SIGNIFICANCE: These findings contribute to awareness of potentially hazardous chemicals in PCPs, can help estimate disparities in chemical exposure, and complement research on health inequities due to chemical exposures from various contributors. IMPACT STATEMENT: This study is one of the first detailed assessments of chemicals of concern found in various types of PCPs used by several racial/ethnic groups. We found that over half of the 546 products selected by community partners as marketed to and/or used by them contained ingredients linked to cancer, reproductive or developmental harm, or endocrine disruption. Laboratory analysis identified additional chemicals in a subset of products, including unlabeled fragrance chemicals and contaminants. Elucidating exposures to chemicals in PCPs is important for risk assessment and health inequity research.

Postnatal persistence of nonhuman primate sex-dependent renal structural and molecular changes programmed by intrauterine growth restriction.

BACKGROUND: Poor nutrition during fetal development programs postnatal kidney function. Understanding postnatal consequences in nonhuman primates (NHP) is important for translation to our understanding the impact on human kidney function and disease risk. We hypothesized that intrauterine growth restriction (IUGR) in NHP persists postnatally, with potential molecular mechanisms revealed by Western-type diet challenge. METHODS: IUGR juvenile baboons were fed a 7-week Western diet, with kidney biopsies, blood, and urine collected before and after challenge. Transcriptomics and metabolomics were used to analyze biosamples. RESULTS: Pre-challenge IUGR kidney transcriptome and urine metabolome differed from controls. Post-challenge, sex and diet-specific responses in urine metabolite and renal signaling pathways were observed. Dysregulated mTOR signaling persisted postnatally in female pre-challenge. Post-challenge IUGR male response showed uncoordinated signaling suggesting proximal tubule injury. CONCLUSION: Fetal undernutrition impacts juvenile offspring kidneys at the molecular level suggesting early-onset blood pressure dysregulation.

An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group.

Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.

Chemical Characterization of Recycled Consumer Products Using Suspect Screening Analysis.

Recycled materials are found in many consumer products as part of a circular economy; however, the chemical content of recycled products is generally uncharacterized. A suspect screening analysis using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) was applied to 210 products (154 recycled, 56 virgin) across seven categories. Chemicals in products were tentatively identified using a standard spectral library or confirmed using chemical standards. A total of 918 probable chemical structures identified (112 of which were confirmed) in recycled materials versus 587 (110 confirmed) in virgin materials. Identified chemicals were characterized in terms of their functional use and structural class. Recycled paper products and construction materials contained greater numbers of chemicals than virgin products; 733 identified chemicals had greater occurrence in recycled compared to virgin materials. Products made from recycled materials contained greater numbers of fragrances, flame retardants, solvents, biocides, and dyes. The results were clustered to identify groups of chemicals potentially associated with unique chemical sources, and identified chemicals were prioritized for further study using high-throughput hazard and exposure information. While occurrence is not necessarily indicative of risk, these results can be used to inform the expansion of existing models or identify exposure pathways currently neglected in exposure assessments.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Attenuation of diet-induced hypothalamic inflammation following bariatric surgery in female mice.

BACKGROUND: Exposure of rodents to chronic high-fat diet (HFD) results in upregulation of inflammatory markers and proliferation of microglia within the mediobasal hypothalamus. Such hypothalamic inflammation is associated with metabolic dysfunction, central leptin resistance, and maintenance of obesity. Bariatric surgeries result in long-term stable weight loss and improved metabolic function. However, the effects of such surgical procedures on HFD-induced hypothalamic inflammation are unknown. We sought to characterize the effects of two bariatric surgical procedures, Roux-en-Y gastric bypass (RYGB) and biliary diversion (BD-IL), in female mice with particular emphasis on HFD-induced hypothalamic inflammation and microgliosis. METHODS: RYGB and BD-IL were performed on diet-induced obese (DIO) mice. Quantitative RT-PCR and fluorescent microscopy were used to evaluate hypothalamic inflammatory gene expression and microgliosis. Results were compared to lean (CD), DIO sham-surgerized mice (DIO-SHAM), and dietary weight loss (DIO-Rev) controls. RESULTS: In female mice, RYGB and BD-IL result in normalization of hypothalamic inflammatory gene expression and microgliosis within 8 weeks of surgery, despite ongoing exposure to HFD. Paralleling these results, the hypothalamic expression levels of the orexigenic neuropeptide Agrp and the anorexic response of surgical mice to exogenous leptin were comparable to lean controls (CD). In contrast, results from DIO-Rev mice were comparable to DIO-SHAM mice, despite transition back to standard rodent show and normalization of weight. CONCLUSION: Bariatric surgery attenuates HFD-induced hypothalamic inflammation and microgliosis and restores leptin sensitivity, despite ongoing exposure to HFD.

Suspect Screening Analysis of Chemicals in Consumer Products.

A two-dimensional gas chromatography-time-of-flight/mass spectrometry (GC×GC-TOF/MS) suspect screening analysis method was used to rapidly characterize chemicals in 100 consumer products-which included formulations (e.g., shampoos, paints), articles (e.g., upholsteries, shower curtains), and foods (cereals)-and therefore supports broader efforts to prioritize chemicals based on potential human health risks. Analyses yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 tentatively identified chemicals, 1404 were not present in a public database of known consumer product chemicals. Reported data and model predictions of chemical functional use were applied to evaluate the tentative chemical identifications. Estimated chemical concentrations were compared to manufacturer-reported values and other measured data. Chemical presence and concentration data can now be used to improve estimates of chemical exposure, and refine estimates of risk posed to human health and the environment.

Microcollection of gases in a capillary tube: preservation of spatial and temporal resolution.

Researchers at the National Oceanic and Atmospheric Administration (NOAA) have recently developed and reported a novel air collection device for measuring vertical profiles of trace gases in the atmosphere, which has been applied to carbon dioxide and methane so far. The device consists of a long stainless steel tube that is prefilled with calibrated gas and allowed to ascend on a weather balloon. During ascent, the device is evacuated as it equilibrates with the decreasing atmospheric pressure. During descent, the tube is filled with atmospheric gas in an ordered manner. The diffusion rate inside the tube is slow enough that the collected gas remains ordered. Here is reported a miniaturization of the device described above, in which the stainless steel tube is replaced with Hydroguard fused silica tubing (0.53 mm × 30 m) and two lightweight valves having a total mass of less than 28 g. This micro-AirCore device was deployed on the SwRI-developed unmanned SkyWisp glider. Profiling of carbon dioxide in the atmosphere proceeded via mass spectrometric detection. A laboratory-based validation system was used for evaluating the micro-AirCore function, including accuracy and precision, for carbon dioxide. The diffusion profiles of carbon dioxide, argon, oxygen, and methane were also investigated. Overall, the micro-AirCore demonstrated an accuracy error of 2% ((12)CO(2)) and minimal diffusion over a period of 16 h (peak width increased by a factor of 1.6). Even after 63 h, mixing of the gases inside the tube was not complete. A triplet of micro-AirCores was deployed on the SkyWisp glider yielding a relative standard deviation of 0.08%, or 0.3 ppm, for CO(2). The profile collected resulted in observation of the boundary layer with elevated CO(2) levels, a region in the free troposphere with relatively constant CO(2) mole fraction, and a gradual decrease in CO(2) above 10,000 m. This microdevice has broad applications extending beyond vertical profiling. Fitting the device with a metering device could enable horizontal collection of gases.

Dust as a collection media for contaminant source attribution.

Dust was investigated for its ability to retain source attribution profiles (SAPs) after chemical exposure. Three distinct sources of the organophosphate pesticide acephate were investigated as a proof-of-concept model. In addition, attribution profiles were created and tested using compounds related to chemical warfare agents (CWAs), specifically VX and G-series agents: O-ethyl methylphosphonothioate (EMPTA), N,N-diisopropylmethylamine (DIPMA), N,N-diisopropylethylamine (DIEA), diisopropylamine (DIPA), diethyl aniline (DEA), diethyl ethyl phosphonate (DEEP), trimethyl phosphite (TMP), dimethyl hydrogen phosphite (DMHP), diethyl hydrogen phosphite (DEHP), triethyl phosphate (TEP), ethyl methylphosphonate (EMPA), and diisopropyl methylphosphonate (DIMP). Dust was collected from a storage shed, aliquots deposited on carpet and loaded with distinct chemical profiles using an exposure chamber and aerosolizer. After a given period of time (1h, 24h, or 72 h), the dust was extracted and its SAP analyzed by gas chromatography-mass spectrometry (GC-MS) and/or liquid chromatography-tandem mass spectrometry (LC-MS/MS). Principal components analysis (PCA) was used to determine the association of dust exposed to the same and different chemical sources. PCA results demonstrate that dust samples exposed to distinct chemical sources are clearly differentiated from one another across all collection times. Furthermore, dust aliquots exposed to the same source can be clearly associated with one another across all collection times. When the CWA-related compounds were subjected to elevated temperature (90°C) conditions, it was found that the signature was stable at the 1h and 24h collections. At 72 h and elevated temperature, larger deviations from the control were observed for some compounds. Elevated pH (10) affected the profile to a lesser degree than elevated temperature. Overall, dust is found to be an effective media for the in situ collection of source attribution profiles.