Liquid Chromatography-high-resolution Mass Spectrometry Data Research Articles

Global xenobiotic profiling of rat plasma using untargeted metabolomics and background subtraction-based approaches: Method evaluation and comparison.

Global xenobiotic profiling (GXP) is to detect and structurally characterize all xenobiotics in biological samples using mainly liquid chromatography-high resolution mass spectrometry (LC-HRMS) based methods. GXP is highly needed in drug metabolism study, food safety testing, forensic chemical analysis, and exposome research. For detecting known or predictable xenobiotics, targeted LC-HRMS data processing methods based on molecular weights, mass defects and fragmentations of analytes are routinely employed. For profiling unknown xenobiotics, untargeted and LC-HRMS based metabolomics and background subtraction-based approaches are required. This study aimed to evaluate the effectiveness of untargeted metabolomics and the precise and thorough background subtraction (PATBS) in GXP of rat plasma. Rat plasma samples collected from an oral administration of nefazodone (NEF) or Glycyrrhizae Radix et Rhizoma (Gancao, GC) were analyzed by LC-HRMS. NEF metabolites and GC components in rat plasma were thoroughly searched and characterized via processing LC-HRMS datasets using targeted and untargeted methods. PATBS detected 68 NEF metabolites and 63 GC components, while the metabolomic approach (MS-DIAL) found 67 NEF metabolites and 60 GC components in rat plasma. The two methods found 79 NEF metabolites and 80 GC components with 96% and 91% successful rates, respectively. Metabolomics methods are capable of GXP and measuring alternations of endogenous metabolites in a group of biological samples, while PATBS is more suited for sensitive GXP of a single biological sample. A combination of metabolomics and PATBS approaches can generate better results in the untargeted profiling of unknown xenobiotics.

Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.

Stability and Chemical Conversion of the Purified Reference Material of Gymnodimine-A under Different Temperature and pH Conditions.

Gymnodimines (GYMs) are a group of fast-acting phycotoxins and their toxicological effects on human beings are still unclear due to the lack of sufficiently well-characterized large quantities of purified toxins for toxicology studies. In this study, a certified reference material (CRM) of GYM-A was prepared from the dinoflagellate Karenia selliformis, followed by multi-step chromatography separation and purification. Subsequently, the stability of GYM-A in methanolic media was evaluated at different temperature (-20, 4, and 20 °C) and pH (3, 5, and 7) conditions for 8 months, and the conversion products of GYM-A were explored by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The results show that the stability of GYM-A decreased with increasing temperature and pH values. The GYM-A was stable during storage at -20 °C regardless of pH, but it decreased rapidly (81.8% ± 9.3%) at 20 °C in pH 7 solution after 8 months. Moreover, the concentrations of GYM-A did not significantly change at all temperatures in solutions with pH 3 (p > 0.05). It is recommended that GYM-A should be stored at low temperature (≤-20 °C) and pH (≤3) conditions for long-term storage in aqueous methanolic media. In addition, two conversion products of GYM-A, tentatively named as GYM-K (m/z 540) and GYM-L (m/z 524), were identified in the samples stored at high levels of pH and temperature. Based on the LC-HRMS data, the hypothetical chemical structures of both converting derivatives were proposed. A useful strategy for long-term storage of GYM-A CRM in aqueous methanolic media was suggested and two hypothesized conversion products of GYM-A were discovered in this study.

Image classification combined with faster R–CNN for the peak detection of complex components and their metabolites in untargeted LC-HRMS data

Peak detection of untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) data is a key step to identify the metabolic status of the drugable chemicals and extracts from functional foods or herbs. Nevertheless, the existing approaches are difficult to obtain ideal results with low false positives and false negatives. In this paper, we proposed an automatic method based on convolutional neural network (CNN) for image classification and Faster R–CNN for peak location/classification in untargeted LC-HRMS data, and named it Peak_CF. It can achieve detection of target peaks with high accuracy and high recall (both >90%) as verified by an evaluation data-set. In terms of detecting the m/z peaks of known compounds, Peak_CF is better than Peakonly, and it can effectively have an overall peak shape judgment of split peaks. For the same evaluation data, the recall of MZmine2 (ADAP) is slightly higher than that of Peak_CF, however, the F1 score of Peak_CF is higher, indicating that it has higher accuracy. In addition, the Peak_ CF training model with strong generalization ability can be achieved and verified. At last, Peak_CF was applied in real metabolic fingerprints of total flavonoids from Glycyrrhiza uralensis Fisch, also a contrast was conducted based on 40 m/z peaks of 40 prototypes in serum data-set. The result showed that the recall rate of Peak_CF and Peakonly all reached 95%, higher than 70% of MZmine2 (ADAP), and Peak_CF is more accurate when detecting EIC that has serious drifts. In conclusion, Peak_CF provides a new route for data mining of LC-HRMS datasets of drug (or herbs, or functional foods) metabolites.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (tR), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The tR values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

Variable Data Independent Acquisition and Data Mining Exploring Feature-Based Molecular Networking Analysis for Untargeted Screening of Synthetic Cannabinoids in Oral Fluid.

Novel psychoactive substances (NPS) are constantly emerging in the drug market, and synthetic cannabinoids (SCs) are included in this NPS family. Forensic laboratories often struggle with these continually emerging SCs, forcing them to develop an untargeted workflow to incorporate these psychoactive drugs in their procedures. Usually, forensic laboratories select analytical methods based on targeted mass spectrometry (MS) technologies for strictly tracking already known NPS. The appropriate way to tackle unknown substances is to develop pipelines for untargeted analysis that include LC-HRMS analytical methods and data analysis. Once established, this strategy would allow drug testing laboratories to be always one step ahead of the new trends concerning the "designer drugs" market. To address this challenge an untargeted workflow based on mass spectrometry data acquisition and data analysis was developed to detect SCs in oral fluid (OF) samples at a low concentration range. The samples were extracted by mixed-mode solid-phase extraction and analyzed by Liquid Chromatography - High-Resolution Mass Spectrometry (LC-HRMS). Tandem mass spectra (MS2) were recorded performing a variable isolation width across a mass range of all theoretical precursor ions (vDIA) after the chromatographic separation. After raw data processing with the MSDial software, the deconvoluted features were sent to GNPS for Feature-Based Molecular Networking (FBMN) construction for nontargeted data mining. The FBMN analysis created a unique integrated network for most of the SCs assessed in the OF at a low level (20 ng/mL). These results demonstrate the potential of an untargeted approach to detect different derivatives of SCs at trace levels for forensic applications.

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.

Investigating the chemical impurity profiles of fentanyl preparations and precursors to identify chemical attribution signatures for synthetic method attribution

From an analytical chemistry standpoint, determining the chemical attribution signatures (CAS) of synthetic reaction mixtures is an impurity profiling exercise. Identifying and understanding the impurity profile and CAS of these chemical agents would allow them to be exploited for chemical forensic information, such as how a particular chemical agent was synthesised. Being able to determine the synthetic route used to make a chemical agent allows for the possibility of batches of the agent, and individual incidents using that agent, to be forensically linked. This information is of particular benefit to agencies investigating the nefarious and illicit use of chemical agents. One such chemical agent of interest to law enforcement and national security agencies is fentanyl. In this study two acylation methods for the final step of fentanyl production, herein termed the Janssen and Siegfried methods, were investigated by liquid chromatography- high resolution mass spectrometry (LC-HRMS) and multivariate statistical analysis (MVA). From these data, fifty-five chemical impurities were identified. Of these, ten were specific CAS for the Janssen method, and five for the Siegfried method. Additionally, analytical data from four different literature methods for production of the fentanyl precursor 4-anilino-N-phenethylpiperidine (ANPP), were compared to the results obtained from the method of production (Valdez) used in this study. Comparison of the LC-HRMS data for these five methods allowed for four Valdez specific impurities to be identified. These may be useful CAS for the Valdez method of ANPP production.

Quantification of Active Ingredient Losses from Formulating Pharmaceutical Industries and Contribution to Wastewater Treatment Plant Emissions.

In this work, emissions of active pharmaceutical ingredients (APIs) from formulating pharmaceutical industries (FPIs) were investigated for the first time based on detailed production information and compared to overall API emissions in wastewater treatment plant (WWTP) effluents. At two municipal WWTPs, both receiving wastewater from several FPIs, two months' daily effluent samples were collected and measured using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Thirty-three APIs formulated during the sampling period as well as >120 organic contaminants commonly present in WWTP effluents were quantified. On the basis of their time patterns and manufacturing data, industrial contributions were found for 22 of 26 APIs (85%) detected in the samples and processed by the FPIs. API emissions from FPIs led to daily concentration increases of up to 300-fold, despite pretreatment of the industrial wastewater. However, emissions from FPIs seemed to depend on the type of formulating activity, with granulation and mixing being most prone to API losses. Losses from FPIs were responsible for the highest concentrations and for up to 60% of the daily total API emissions measured. Furthermore, screening for suspects in LC-HRMS data resulted in the detection of unexpected emissions from FPIs, demonstrating the value of these data to comprehensively assess industrial API losses. Overall, this study showed that FPIs were relevant contributors of APIs emitted in the WWTP effluents, although only a minor fraction (<1%) of the total processed API quantity was lost to the wastewater, and despite the small percentage (<5%) of FPI wastewater compared to the total wastewater flow.

Liquid chromatography-mass spectrometry untargeted metabolomics reveals increased levels of tryptophan indole metabolites in urine of metabolic syndrome patients.

Metabolic syndrome (MetS) is a multifactor condition predisposing for diabetes, cardiovascular diseases and other degenerative disorders. Although several diagnostic criteria have been established, none of them is specific and there is a call for better pathophysiological explanation of MetS and for the discovery of molecular biomarkers. Phenotype characterization at metabolome level might be useful for both purposes. To this end, our aim was to perform comparative untargeted metabolomics of urines from MetS patients and from the control group. The study participants included 52 diagnosticated and 50 healthy individuals from Leon city in central Mexico; 23 anthropometric and clinical parameters were measured and submitted to Principal Component Analysis (PCA). The obtained PCA model allowed us for selection of 11 MetS patients and 13 control subjects, correspondingly representative for each of the two groups (clearly separated in PCA). The first morning urines from these subjects were ambulatory collected and, after methanol extraction and acidification, were submitted to capillary liquid chromatography-high resolution mass spectrometry (LC-HRMS). The obtained data were analyzed on MetaboScape® platform (Bruker Daltonics). Specifically, t-test applied to LC-HRMS data revealed several ions presenting at least 3-fold higher intensities in MetS with respect to the control samples (p < 0.05). Data analysis and complementary experiments yielded the identification of the following metabolites: indole-3-acetic acid, indole-3-acetic acid-O-glucuronide, N-(indol-3-ylacetyl) glutamine, indole-3-carbaldehyde and hydroxyhexanoycarnitine. Additionally, indole-3-carboxylic acid was annotated with 2.13-fold higher abundance in MetS patients. To assess the contribution of individual metabolites in the difference between two groups of subjects, partial least square discriminant analysis was performed for LC-HRMS data and the obtained values of variable importance in projection (VIP), confirmed the association of six above mentioned compounds with MetS. Overall, this study provides direct evidence on the disturbed catabolism of tryptophan in metabolic syndrome.

Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data.

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

Source-related smart suspect screening in the aqueous environment: search for tire-derived persistent and mobile trace organic contaminants in surface waters

A variant of suspect screening by liquid chromatography–high-resolution mass spectrometry (LC-HRMS) is proposed in this study: Samples of a potential source of contamination and of an environmental sample close to this source are first analyzed in a non-targeted manner to select source-related suspects and to identify them. The suspect list compiled from such an exercise is then applied to LC-HRMS data of environmental samples to ascribe and to identify persistent and mobile contaminants in the water cycle that may originate from the source under study. This approach was applied to tire crumb rubber (source) and road dust (close to source); by comparison of the two data sets, 88% of the features detected in tire leachate could be excluded. Of the 48 suspects remaining, a total of 41 could be tentatively identified as either related to hexamethoxymethyl melamine or cyclic amines, benzothiazoles, or glycols. Subsequently, environmental samples were searched for these suspects: 85% were determined in an urban creek after a combined sewer overflow and 67% in the influent of a municipal wastewater treatment plant (WWTP). These exceptionally high rates of positive findings prove that this source-related smart suspect screening effectively directs the effort of selecting and identifying unknown contaminants to those related to the source of interest. The WWTP effluent and the urban creek during dry weather also showed the presence of numerous contaminants that may stem from tire and road wear particles (TRWP) in road runoff. Contribution from other sources, however, cannot be ruled out.Graphical abstract

In Vitro Phase I Metabolism of the Recently Emerged Synthetic MDMB-4en-PINACA and Its Detection in Human Urine Samples.

MDMB-4en-PINACA (methyl (S)-3,3-dimethyl-2-(1-(pent-4-en-1-yl)-1H-indazole-3-carboxamido)butanoate) is a recently emerged synthetic cannabinoid in Turkey. MDMB-4en-PINACA was detected in herbal material investigated by the Council of Forensic Medicine, Istanbul Narcotics Department in Turkey in April 2019. MDMB-4en-PINACA was added to the drug abuse list and quickly reported in biological samples after its first detection. In this study, the in vitro metabolism of MDMB-4en-PINACA was investigated by using a pooled human liver microsomes (HLMs) assay and liquid chromatography-high-resolution mass spectrometry (LC-HRMS). MDMB-4en-PINACA (5μmol/L) was incubated with HLMs for up to 1h, and the metabolites were identified using LC-HRMS and software-assisted data mining. The in vivo metabolism was investigated by the analysis of 22 authentic urine samples and compared to the data received from the in vitro metabolism study. Less than 7.5% of the MDMB-4en-PINACA parent compound remained after the 1h incubation. We identified 14 metabolites, which were formed via double bond oxidation, ester hydrolysis, N-dealkylation, hydroxylation, dehydrogenation and further oxidation to N-pentanoic acid or a combination of these reactions in vitro. In 10 urine samples (total n=22), MDMB-4en-PINACA was detected as the parent drug. Three of the identified main metabolites, double bond oxidation in combination with ester hydrolysis and hydroxylation metabolite (M3), MDMB-4en-PINACA butanoic acid (M14) and monohydroxypentyl-MDMB-4en-PINACA (M12), were suggested as suitable urinary markers. In vitro screening of 2,150 authentic urine samples for these identified MDMB-4en-PINACA metabolites resulted in 56 cases of confirmed MDMB-4en-PINACA consumption (2.6%).

Comparison of Software Tools for Liquid Chromatography-High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples.

The field of high-resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as nontarget screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer, and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10%, and for each software between 40% and 55% of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of nontarget screening.

Metabolic profiling of synthetic cannabinoid 5F-ADB by human liver microsome incubations and urine samples using high-resolution mass spectrometry.

5F-ADB (methyl 2-{[1-(5-fluoropentyl)-1H-indazole-3-carbonyl] amino}-3,3-dimethylbutanoate) is a frequently abused new synthetic cannabinoid that has been sold since at least the end of 2014 on the drug market and has been classified as an illicit drug in most European countries, as well as Turkey, Japan, and the United States. In this study, the in vitro metabolism of 5F-ADB was investigated by using pooled human liver microsomes (HLMs) assay and liquid chromatography-high-resolution mass spectrometry (LC-HRMS). 5F-ADB (5μmol/L) was incubated with HLMs for up to 3hours, and the metabolites were identified using LC-HRMS and software-assisted data mining. The in vivo metabolism was investigated by the analysis of 30 authentic urine samples and was compared to the data received from the in vitro metabolism study. Less than 3.3% of the 5F-ADB parent compound remained after 1hour of incubation, and no parent drug was detected after 3hours. We identified 20 metabolites formed via ester hydrolysis, N-dealkylation, oxidative defluorination, hydroxylation, dehydrogenation, further oxidation to N-pentanoic acid and glucuronidation or a combination of these reactions in vitro. In 12 urine samples (n=30), 5F-ADB was detected as the parent drug. Three of the identified main metabolites 5F-ADB carboxylic acid (M20), monohydroxypentyl-5F-ADB (M17), and carboxypentyl ADB carboxylic acid (M8) were suggested as suitable urinary markers. The screening of 8235 authentic urine samples for identified 5F-ADB metabolites in vitro resulted in 3135 cases of confirmed 5F-ADB consumption (38%).

Elucidation of non-intentionally added substances migrating from polyester-polyurethane lacquers using automated LC-HRMS data processing.

An untargeted strategy aiming at identifying non-intentionally added substances (NIAS) migrating from coatings was developed. This innovative approach was applied to two polyester-polyurethane lacquers, for which suppliers previously provided the identity of the monomers involved. Lacquers were extracted with acetonitrile and analyzed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Data, acquired in the full scan mode, were processed using an open-source R-environment (xcms and CAMERA packages) to list the detected features and deconvolute them in groups related to individual compounds. The most intense groups, accounting for more than 85% of cumulated feature intensities, were then investigated. A homemade database, populated with predicted polyester oligomer combinations from a relevant selection of diols and diacids, enabled highlighting the presence of 14 and 17 cyclic predicted polyester oligomers in the two lacquers, including three mutual combinations explained by common known monomers. Combination hypotheses were strengthened by chromatographic considerations and by the investigation of fragmentation patterns. Regarding unpredicted migrating substances, four monomers were hypothesised to explain several polyester or caprolactam oligomer series. Finally, considering both predicted and tentatively elucidated unpredicted oligomers, it was possible to assign hypotheses to features representing up to 82% and 90% of the cumulated intensities in the two lacquers, plus 9% and 3% (respectively) originating from the procedural blank. Graphical abstract Elucidation of non-intentionally added substances.

MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics.

Stable isotope labeling (SIL) techniques have the potential to enhance different aspects of liquid chromatography–high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics methods including metabolite detection, annotation of unknown metabolites, and comparative quantification. In this work, we present MetExtract II, a software toolbox for detection of biologically derived compounds. It exploits SIL-specific isotope patterns and elution profiles in LC-HRMS(/MS) data. The toolbox consists of three complementary modules: M1 (AllExtract) uses mixtures of uniformly highly isotope-enriched and native biological samples for selective detection of the entire accessible metabolome. M2 (TracExtract) is particularly suited to probe the metabolism of endogenous or exogenous secondary metabolites and facilitates the untargeted screening of tracer derivatives from concurrently metabolized native and uniformly labeled tracer substances. With M3 (FragExtract), tandem mass spectrometry (MS/MS) fragments of corresponding native and uniformly labeled ions are evaluated and automatically assigned with putative sum formulas. Generated results can be graphically illustrated and exported as a comprehensive data matrix that contains all detected pairs of native and labeled metabolite ions that can be used for database queries, metabolome-wide internal standardization, and statistical analysis. The software, associated documentation, and sample data sets are freely available for noncommercial use at http://metabolomics-ifa.boku.ac.at/metextractII.

Construction of Prediction Models for the Transient Receptor Potential Vanilloid Subtype 1 (TRPV1)-Stimulating Activity of Ginger and Processed Ginger Based on LC-HRMS Data and PLS Regression Analyses.

To construct a model formula to evaluate the thermogenetic effect of ginger (Zingiber officinale Roscoe) from the ingredient information, we established transient receptor potential vanilloid subtype 1 (TRPV1)-stimulating activity prediction models by using a partial least-squares projections to latent structures (PLS) regression analysis in which the ingredient data from liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and the stimulating activity values for TRPV1 receptor were used as explanatory and objective variables, respectively. By optimizing the peak extraction condition of the LC-HRMS data and the data preprocessing parameters of the PLS regression analysis, we succeeded in the construction of a TRPV1-stimulating activity prediction model with high precision ability. We then searched for the components responsible for the TRPV1-stimulating activity by analyzing the loading plot and s-plot of the model, and we identified [6]-gingerol (1) and hexahydrocurcumin (3) as TRPV1-stimulating activity components.

Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments.

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS2 experiments is suggested for nontarget screening. MZmine 2 detected 75-100% of the peaks compared to manual peak detection at an intensity level of 105 in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ.

QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.

BackgroundMetabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process.ResultsThe software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection.ConclusionsQCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0783-x) contains supplementary material, which is available to authorized users.