LC-HRMS based method for suspect/non-targeted screening for biomarkers of chemical exposure in human urine

Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids

Suspect and non-target screening (SNTS) methods are being promoted in order to decode the human exposome since a wide chemical space can be analysed in a diversity of human biofluids. However, SNTS approaches in the exposomics field are infra-studied in comparison to environmental or food monitoring studies. In this work, a comprehensive suspect screening workflow was developed to annotate exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Precisely, human urine, breast milk, saliva and ovarian follicular fluid were employed as samples and analysed by means of ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry (UHPLC-HRMS/MS). To automate the workflow, the "peak rating" parameter implemented in Compound Discoverer 3.3.2 was optimized to avoid time-consuming manual revision of chromatographic peaks. In addition, the presence of endogenous molecules that might interfere with the annotation of xenobiotics was carefully studied as the employment of inclusion and exclusion suspect lists. To evaluate the workflow, limits of identification (LOIs) and type I and II errors (i.e., false positives and negatives, respectively) were calculated in both standard solutions and spiked biofluids using 161 xenobiotics and 22 metabolites. For 80.3 % of the suspects, LOIs below 15 ng/mL were achieved. In terms of type I errors, only two cases were identified in standards and spiked samples. Regarding type II errors, the 7.7 % errors accounted in standards increased to 17.4 % in real samples. Lastly, the use of an inclusion list for endogens was favoured since it avoided 18.7 % of potential type I errors, while the exclusion list caused 7.2 % of type II errors despite making the annotation workflow less time-consuming.

Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome

In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.

Comprehensive profiling and semi-quantification of exogenous chemicals in human urine using HRMS-based strategies

Chemicals infiltrate our daily experiences through multiple exposure pathways. Human biomonitoring (HBM) is routinely used to comprehensively understand these chemical interactions. Historically, HBM depended on targeted screening methods limited to a relatively small set of chemicals with triple quadrupole instruments typically. However, recent advances in high-resolution mass spectrometry (HRMS) have facilitated the use of broad-scope target, suspect, and non-target strategies, enhancing chemical exposome characterization within acceptable detection limits. Despite these advancements, establishing robust and efficient sample treatment protocols is still essential for trustworthy broad-range chemical analysis. This study sought to validate a methodology leveraging HRMS-based strategies for accurate profiling of exogenous chemicals and related metabolites in urine samples. We evaluated five extraction protocols, each encompassing various chemical classes, such as pharmaceuticals, plastic additives, personal care products, and pesticides, in terms of their extraction recoveries, linearity, matrix effect, sensitivity, and reproducibility. The most effective protocol was extensively validated and subsequently applied to 10 real human urine samples using wide-scope target analysis encompassing over 2000 chemicals. We successfully identified and semi-quantified a total of 36 chemicals using an ionization efficiency-based model, affirming the methodology's robust performance. Notably, our results dismissed the need for a deconjugation step, a typically labor-intensive and time-consuming process.

The role of sample preparation in suspect and non-target screening for exposome analysis using human urine

The use of suspect and non-target screening (SNTS) for the characterization of the chemical exposome employing human biofluids is gaining attention. Among the biofluids, urine is one of the preferred matrices since organic xenobiotics are excreted through it after metabolization. However, achieving a consensus between selectivity (i.e. preserving as many compounds as possible) and sensitivity (i.e. minimizing matrix effects by removing interferences) at the sample preparation step is challenging. Within this context, several sample preparation approaches, including solid-phase extraction (SPE), liquid-liquid extraction (LLE), salt-assisted LLE (SALLE) and dilute-and-shoot (DS) were tested to screen not only exogenous compounds in human urine but also their phase II metabolites using liquid-chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS). Additionally, enzymatic hydrolysis of phase II metabolites was evaluated. Under optimal conditions, SPE resulted in the best sample preparation approach in terms of the number of detected xenobiotics and metabolites since 97.1% of the total annotated suspects were present in samples extracted by SPE. In LLE and SALLE, pure ethyl acetate turned out to be the best extractant but fewer suspects than with SPE (80.7%) were screened. Lastly, only 52.5% of the suspects were annotated in the DS approach, showing that it could only be used to detect compounds at high concentration levels. Using pure standards, the presence of diverse xenobiotics such as parabens, industrial chemicals (benzophenone-3, caprolactam and mono-2-ethyl-5-hydroxyhexyl phthalate) and chemicals related to daily habits (caffeine, cotinine or triclosan) was confirmed. Regarding enzymatic hydrolysis, only 10 parent compounds of the 44 glucuronides were successfully annotated in the hydrolysed samples. Therefore, the screening of metabolites in non-hydrolysed samples through SNTS is the most suitable approach for exposome characterization.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Tumoral and normal brain tissue extraction protocol for wide-scope screening of organic pollutants

Little is known about the presence of organic pollutants in human brain (and even less in brain tumors). In this regard, it is necessary to develop new analytical protocols capable of identifying a wide range of exogenous chemicals in this type of samples (by combining target, suspect and non-target strategies). These methodologies should be robust and simple. This is particularly challenging for solid samples, as reliable extraction and clean-up techniques should be combined to obtain an optimal result. Hence, the present study focuses on the development of an analytical methodology that allows the screening of a wide range of organic chemicals in brain and brain tumor samples. This protocol was based on a solid-liquid extraction based on bead beating, solid-phase extraction clean-up with multi-layer mixed-mode cartridges, reconstitution and LC-HRMS analysis. To evaluate the performance of the extraction methodology, a set of 66 chemicals (e.g., pharmaceuticals, biocides, or plasticizers, among others) with a wide range of physicochemical properties was employed. Quality control parameters (i.e., linear range, sensitivity, matrix effect (ME%), and recoveries (R%)) were calculated and satisfactory results were obtained for them (e.g., R% within 60-120% for 32 chemicals, or ME% higher than 50% (signal suppression) for 79% of the chemicals).

Metabolomic Strategy to Characterize the Profile of Secondary Metabolites in Aspergillus aculeatus DL1011 Regulated by Chemical Epigenetic Agents

Chemical epigenetic regulation (CER) is an effective method to activate the silent pathway of fungal secondary metabolite synthesis. However, conventional methods for CER study are laborious and time-consuming. In the meantime, the overall profile of the secondary metabolites in the fungi treated by the CER reagent is not well characterized. In this study, suberohydroxamic acid (SBHA), a histone deacetylase inhibitor, was added to a culture of Aspergillus aculeatus DL1011 and a new strategy based on LC-MS/MS analysis integrated with various metabolomic tools (MetaboAnalyst, MS-DIAL, SIRIUS and GNPS) was developed to characterize the profile of induced metabolites. As a result, 13.6%, 29.5% and 27.2% of metabolites were identified as newly biosynthesized, increasing and decreasing in abundance by CER, respectively. The structures of the 18 newly induced secondary metabolites were further identified by the new strategy to demonstrate that 72.2% of them (1 novel compound and 12 known compounds) were first discovered in A. aculeatus upon SBHA treatment. The accuracy of the new approach was confirmed by purification and NMR data analysis of major newly biosynthesized secondary metabolites. The bioassay showed that the newly biosynthesized compounds, roseopurpurin analogues, showed selective activities against DPPH scavenging, cytotoxicity and SHP1 inhibition. Our research demonstrated that CER was beneficial for changing the secondary metabolic profile of fungi and was an effective means of increasing the diversity of active metabolites. Our work also supplied a metabolomic strategy to characterize the profile changes and determine the newly induced compounds in the secondary metabolites of fungi treated with the chemical epigenetic regulator.

Nontargeted Screening in Environmental Analysis: An Example Showing Migration of Chemicals from Reusable Sports Plastic Bottles into Drinking Water

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.