Untargeted screening of unknown xenobiotics and potential toxins in plasma of poisoned patients using high-resolution mass spectrometry: Generation of xenobiotic fingerprint using background subtraction

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

DART-HRMS allows the detection of toxic alkaloids in animal autopsy specimens and guides the selection of confirmatory methods in accidental plant poisoning

BACKGROUND

In cases of suspected animal poisonings or intoxications, there is the need for high-throughput, rapid and accurate analytical tools capable of giving rapid answers and, thus, speeding up the early stages of investigations. Conventional analyses are very precise, but do not meet the need for rapid answers capable of orienting the decisions and the choice of appropriate countermeasures. In this context, the use of ambient mass spectrometry (AMS) screening methods in toxicology laboratories could satisfy the requests of forensic toxicology veterinarians in a timely manner.

RESULTS

As a proof of principle, direct analysis in real time high resolution mass spectrometry (DART-HRMS) was applied to a veterinary forensic case in which 12 of a group of 27 sheep and goats died with an acute neurological onset. Because of evidence in the rumen contents, the veterinarians hypothesized an accidental intoxication after ingestion of vegetable materials. The DART-HRMS results showed abundant signals of the alkaloids calycanthine, folicanthidine and calycanthidine, both in the rumen content and at the liver level. The DART-HRMS phytochemical fingerprinting of detached Chimonanthus praecox seeds was also compared with those acquired from the autopsy specimens. Liver, rumen content and seed extracts were then subjected to LC-HRMS/MS analysis to gather additional insights and confirm the putative assignment of calycanthine anticipated by DART-HRMS. HPLC-HRMS/MS confirmed the presence of calycanthine in both rumen contents and liver specimens and allowed its quantification, ranging from 21.3 to 46.9 mg kg^-1 in the latter. This is the first report detailing the quantification of calycanthine in liver after a deadly intoxication event.

SIGNIFICANCE AND NOVELTY

Our study illustrates the potential of DART-HRMS to offer a rapid and complementary alternative to guide the selection of confirmatory chromatography-MSⁿ strategies in the analysis of autopsy specimens from animals with suspected alkaloid intoxication. This method offers the consequent saving of time and resources over those needed for other methods.

Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility

Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden's largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.

Time-Series-Dependent Global Data Filtering Strategy for Mining and Profiling of Xenobiotic Metabolites in a Dynamic Complex Matrix: Application to Biotransformation of Flavonoids in the Extract of Ginkgo biloba by Gut Microbiota

Efficient characterization of xenobiotic metabolites and their dynamics in a changing complex matrix remains difficult. Herein, we proposed a time-series-dependent global data filtering strategy for the rapid and comprehensive characterization of xenobiotic metabolites and their dynamic variation based on metabolome data. A set of data preprocessing methods was used to screen potential xenobiotic metabolites, considering the differences between the treated and control groups and the fluctuations over time. To further identify metabolites of the target, an in-house accurate mass database was constructed by potential metabolic pathways and applied. Taking the extract of Ginkgo biloba (EGB) co-incubated with gut microbiota as an example, 107 compounds were identified as flavonoid-derived metabolites (including 67 original from EGB and 40 new) from 7468 ions. Their temporal metabolic profiles and regularities were also investigated. This study provided a systematic and feasible method to elucidate and profile xenobiotic metabolism.

From target analysis to suspect and non-target screening of endocrine-disrupting compounds in human urine

In the present work, a target analysis method for simultaneously determining 24 diverse endocrine-disrupting compounds (EDCs) in urine (benzophenones, bisphenols, parabens, phthalates and antibacterials) was developed. The target analysis approach (including enzymatic hydrolysis, clean-up by solid-phase extraction and analysis by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS)) was optimized, validated and applied to volunteers’ samples, in which 67% of the target EDCs were quantified. For instance, benzophenone-3 (0.2–13 ng g⁻¹), bisphenol A (7.7–13.7 ng g⁻¹), methyl 3,5-dihydroxybenzoate (8–254 ng g⁻¹), mono butyl phthalate (2–17 ng g⁻¹) and triclosan (0.3–9 ng g⁻¹) were found at the highest concentrations, but the presence of other analogues was detected as well. The developed target method was further extended to suspect and non-target screening (SNTS) by means of LC coupled to high-resolution MS/MS. First, well-defined workflows for SNTS were validated by applying the previously developed method to an extended list of compounds (83), and then, to the same real urine samples. From a list of approximately 4000 suspects, 33 were annotated at levels from 1 to 3, with food additives/ingredients and personal care products being the most abundant ones. In the non-target approach, the search was limited to molecules containing S, Cl and/or Br atoms, annotating 4 pharmaceuticals. The results from this study showed that the combination of the lower limits of detection of MS/MS and the identification power of high-resolution MS/MS is still compulsory for a more accurate definition of human exposome in urine samples.

Present-Day Practice of Non-Target Chemical Analysis

Abstract

We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.

HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats

In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.

Suspect screening and untargeted analysis of veterinary drugs in food by LC-HRMS: Application of background exclusion-dependent acquisition for retrospective analysis of unknown xenobiotics

The presence of veterinary drug and pesticide residues in food products pose considerable threats to human health. Monitoring of these residues in food is mainly carried out using targeted analysis by triple quadrupole mass spectrometry. However, these methods are not suitable for suspect screening and untargeted analysis of unknowns. The main objectives of this study were to develop a new high-resolution mass spectrometry (HRMS)-based analytical strategy for retrospective analysis of suspect and unknown xenobiotics and to evaluate its performance in the tentative identification of 48 veterinary drugs as "unknowns" spiked in a pork sample. In the analysis, a newly developed background exclusion data-dependent acquisition (BE-DDA) technique was employed to trigger the product ion (MS/MS) spectral acquisition of the "unknowns", and an in-house precise-and-thorough background-subtraction (PATBS) technique was applied to detect these "unknowns". Results showed that untargeted data mining of the acquired LC-MS dataset by PATBS was able to find all the 48 veterinary drugs and 46 of them were triggered by BE-DDA to generate accurate MS/MS spectra. The dataset of recorded accurate full-scan mass and MS/MS spectra of all the xenobiotics of the test pork sample is defined as the xenobiotics profile. Searching the xenobiotic profile of the test pork sample using mass spectral data of selected veterinary drugs (as suspects) from the mzCloud spectral library led to the correct hits. Searching against the mzCloud spectral library using the mass spectral data of selected individual veterinary drugs (as unknowns) from the xenobiotics profile tentatively confirmed their identities. In contrast, analysis of the same sample using ion intensity-data dependent acquisition only recorded the MS/MS spectra for 34 veterinary drugs. In addition, a data independent acquisition method enabled the acquisition of the fragment spectra for 44 veterinary drugs, but their spectral data displayed only one or a few true product ions of individual analytes of interest along with many fragments from coeluted biological components and background noises. This study demonstrates that this analytical strategy has a potential to become a practical tool for the retrospective suspect screening and untargeted analysis of unknown xenobiotics in a biological sample such as veterinary drugs and pesticides in food products.

Metabolomics-based parallel discovery of xenobiotics and induced endogenous metabolic dysregulation in clinical toxicology

Intoxication by xenobiotics triggers the perturbation of metabolic fingerprints in biofluids, including the accumulation of xenobiotic compounds and the dysregulation of endogenous metabolites. In this work, an untargeted metabolomics workflow was developed to simultaneously profile both xenobiotic and endogenous metabolites for the identification of the xenobiotic origin and an in-depth understanding of the intoxication mechanism. This workflow was demonstrated in a real-world clinical case. Plasma samples were collected from four intoxicated children and another three healthy children. Untargeted metabolomics analysis was performed using ultraperformance liquid chromatography (UPLC) coupled to a high-resolution mass spectrometer (HRMS) with data-independent MS^E acquisition. LC-MS^E data was processed using an untargeted metabolomics data interpretation workflow, in which the identities of xenobiotics and altered endogenous metabolic features were determined via database searching. Five xenobiotic chemicals and 19 endogenous metabolites were found to be dysregulated. Combined with the clinical evidence, penfluridol was confirmed as the xenobiotic toxin. Furthermore, a mechanistic hypothesis was developed to explain the dysregulation of the four endogenous acyl-carnitines. This workflow can be readily applied to a wide range of clinical toxicology cases, offering a powerful and convenient means of simultaneous discovery of intoxication source and the understanding of intoxication mechanisms.