Project description:Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.
Project description:Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.
Project description:This study aims to validate a fast method of simultaneous analysis of 365 LC-amenable and 142 GC-amenable pesticides in hen eggs by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS), respectively, operating in multiple reaction monitoring (MRM) acquisition modes. The sample preparation was based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction. Key method performance parameters investigated were specificity, linearity, limit of quantification (LOQ), accuracy, precision and measurement uncertainty. The method was validated at two spiking levels (10 and 50 μg/kg), and good recoveries (70%-120%) and relative standard deviations (RSDs) (≤20) were achieved for 92.9% of LC-amenable and 86.6% of GC-amenable pesticide residues. The LOQs were ≤10 μg/kg for 94.2% of LC-amenable and 92.3% of GC-amenable pesticides. The validated method was further applied to 100 egg samples from caged hens, and none of the pesticides was quantified.
Project description:IntroductionSince ancient times medicinal plants have been used as medicine in many parts of the world to promote human health and longevity. In recent years many novel secondary metabolites of plants have been isolated and reported to provide lead compounds for new drug discoveries. Solanum mauritianum Scopoli is native to South America. It is reported to be used by native South Americans during famine as a vegetable and as medicine to cure various diseases. In South Africa the plant is viewed as weed and is facing eradication, however, this plant is a valuable subject for research into its potential pharmaceutical and chemical uses. This study elucidated the metabolic profile of fungal endophytes that have promising bioactive secondary metabolites against pathogenic microorganisms, including mycobacterium species.Material and methodsFungal endophytes from a weed Solanum mauritianum Scop. were used to synthesize secondary metabolites. Gas chromatograph high-resolution time-of-flight mass spectrometry (GC-HRTOF-MS) was used to analyse volatile compounds to prove that potentially fungal endophytes could be extracted from this weed. Extracts obtained with ethyl acetate were screened for phytochemicals and analyzed using a gas chromatograph high-resolution time-of-flight mass spectrometry system. Principal component analysis was used to compare the gas chromatograph high-resolution time-of-flight mass spectrometry data for differences/similarities in their clustering. Phytochemical screening was conducted on the crude extracts of fungal endophytes obtained from different parts of Solanum mauritianum Scopoli (leaves, ripe fruit, unripe fruit and stems).ResultsPhytochemical screening indicated the presents of alkaloids, flavonoids, glycosides, phenols, quinones and saponins. Quinones were not present in the crude extracts of Fusarium sp. A total of 991 compounds were observed in the fungal endophytes, and Cladosporium sp. (23.8%) had the highest number of compounds, compared to Paracamarosporium leucadendri (1.7%) and Talaromyces sp. (1.5%). Some volatile compounds such as eicosane, 2-pentadecanone, 2-methyloctacosane, hexacosane and tridecanoic acid methyl ester with antibacterial activity were also observed.ConclusionCompositional variations between the plant and fungal endophyte phytochemicals were observed. The results of this study indicate that fungal endophytes from Solanum mauritianum Scop. contain compounds that can be exploited for numerous pharmaceutical and medicinal applications.
Project description:This study describes a methodology for evaluating regulatory levels of phthalate contamination. By collecting experimental data on short-term phthalate migration using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS), the migration of di(2-ethylhexyl) phthalate (DEHP) from polyvinyl chloride (PVC) to polyethylene (PE) was found to be expressed by the Fickian approximation model, which was originally proposed for solid (PVC)/liquid (solvent) migration of phthalates. Consequently, good data correlation was obtained using the Fickian approximation model with a diffusion coefficient of 4.2 × 10-12 cm²/s for solid (PVC)/ solid (PE) migration of DEHP at 25 °C. Results showed that temporary contact with plasticized polymers under a normal, foreseeable condition may not pose an immediate risk of being contaminated by phthalates at regulatory levels. However, as phthalates are small organic molecules designed to be dispersed in a variety of polymers as plasticizers at a high compounding ratio, the risk of migration-related contamination can be high in comparison with other additives, especially under high temperatures. With these considerations in mind, the methodology for examining regulatory levels of phthalate contamination using TD-GC-MS has been successfully demonstrated from the viewpoint of its applicability to solid (PVC)/solid (PE) migration of phthalates.
Project description:As seized drug casework becomes increasingly complex due to the continued prevalence of emerging drugs, laboratories are often looking for new analytical approaches including developing methods for the analysis of specific compounds classes. Recent efforts have focused on the development of targeted gas chromatography mass spectrometry (GC-MS) confirmation methods to compliment the information-rich screening results produced by techniques like direct analysis in real time mass spectrometry (DART-MS). In this work, a method for the confirmation of synthetic opioids and related compounds was developed and evaluated. An 11-component test solution was used to develop a method that focused on minimizing overlapping retention time acceptance windows and understanding the influence of instrument parameters on reproducibility and sensitivity. Investigated settings included column type, flow rate, temperature program, inlet temperature, source temperature, and tune type. Using a DB-200 column, a 35-min temperature ramped method was created. It was evaluated against a suite of 222 synthetic opioids and related compounds, and successfully differentiated all but four compound pairs based on nonoverlapping retention time acceptance windows or objectively different mass spectra. Compared to a general confirmatory method used in casework, the targeted method was up to 25 times more sensitive and provided at least a two-fold increase in retention time differences. Analysis of extracts from actual case samples successfully demonstrated utility of the method and showed no instance of carryover, although the high polarity column required wider retention time windows than other columns.
Project description:To address challenges associated with the increased prevalence of novel psychoactive substances (NPSs), laboratories often adopt new techniques or new methods with the goal of obtaining more detailed chemical information with a higher level of confidence. To demonstrate how new methods applied to existing techniques can be a viable approach, a targeted gas chromatography mass spectrometry (GC-MS) method for synthetic cathinones was developed. To create the method, a range of GC-MS parameters were first investigated using a seven-component test solution with the goal of minimizing compounds with overlapping acceptance windows by maximizing retention time differences within a reasonable runtime. Once developed, the targeted method was evaluated through several studies and was compared to a general GC-MS confirmatory method. The method produced a twofold increase in retention time differences of the test solution compounds with a 3.83-min shorter runtime than the general method. Limitations of the method were also studied by analyzing an additional forty-eight cathinones to identify instances where definitive compound identification may not be possible due to overlapping acceptance windows and mass spectra. Thirty-eight pairs of compounds had retention times differences of less than 2% and, of those thirty-eight, one pair had indistinguishable mass spectra. A set of case samples were also analyzed using the method to evaluate suitability for casework. An increase in split ratio was required to obtain acceptable sensitivity. The development of this method is part of a larger project to measure benefits and drawbacks of different drug chemistry workflows.
Project description:It has become increasingly important to qualitatively and quantitatively assess the volatile metabolites in a range of bodily fluids for use in monitoring health. There has been relatively little work on the quantitative analysis of compounds, particularly with respect to the effects of ethnicity or geographic location. A novel method for the quantification of compounds in stool using 13C labelled compounds as internal standards is presented. Using thermal desorption gas chromatography mass spectrometry, stool samples from 38 healthy volunteers were analysed. The 13C labelled compounds, acetone, ethyl butanoate, ethanoic acid, butanoic acid, 3-methylbutanoic acid, and indole, were added as internal standards. This process mimics the solubility characteristics of the compounds and thus the method was able to quantify the compounds within the solid stool. In total, 15 compounds were quantified: Dimethyl sulphide (26⁻25,626 ng/g), acetone (442⁻3006 ng/g), ethyl butanoate (39⁻2468 ng/g), ethyl 2-methylbutanoate (0.3⁻180 ng/g), dimethyl disulphide (35⁻1303 ng/g), 1-octen-3-one (12 ng/g), dimethyl trisulphide (10⁻410 ng/g), 1-octen-3-ol (0.4⁻58 ng/g), ethanoic acid (672⁻12,963 ng/g), butanoic acid (2493⁻11,553 ng/g), 3-methylbutanoic acid (64⁻8262 ng/g), pentanoic acid (88⁻21,886 ng/g), indole (290⁻5477 ng/g), and 3-methyl indole (37⁻3483 ng/g). Moreover, by altering the pH of the stool to pH 13 in conjunction with the addition of 13C trimethylamine, the method was successful in detecting and quantifying trimethylamine for the first time in stool samples (range 40⁻5312 ng/g). Statistical analysis revealed that samples from U.K. origin had five significantly different compounds (ethyl butanoate, 1-octen-3-ol, ethanoic acid, butanoic acid, pentanoic acid, and indole) from those of South American origin. However, there were no significant differences between vegetarian and omnivore samples. These findings are supported by pre-existing literature evidence. Moreover, we have tentatively identified 12 compounds previously not reported as having been found in stool.
Project description:With the increased presence of novel psychoactive substances (NPSs) in casework, drug analysis has become more challenging. To address these challenges, new screening technologies with improved specificity are being implemented, allowing for the creation and adoption of targeted confirmatory analyses that produce more conclusive results. This paper outlines a six-step, data-driven, framework to develop and evaluate gas chromatography mass spectrometry (GC-MS) methods for targeted classes of drugs. The process emphasizes maximizing retention time differences (to minimize the potential for retention time acceptance windows to overlap) and understanding the trade-offs between sensitivity and reproducibility using a test solution containing pairs of compounds that are difficult to distinguish. The method is then evaluated by expanding the panel of compounds analyzed, identifying limitations in compound discrimination, comparing to current methods, and analyzing representative casework to establish usability. To demonstrate this framework, a method for synthetic cannabinoids was created. The developed method utilizes a DB-200 column and an isothermal temperature program. It was found that sensitivity could be adjusted, without compromising reproducibility, by altering the split ratio and injection volume. The targeted method successfully differentiated 50 cannabinoids based on either retention time differences or mass spectral dissimilarity - determined using a newly developed spectral comparison test. Compared to a general method used for casework, the targeted method was an order of magnitude more sensitive, a minute shorter, and provided major increases in retention time differences. This framework can be implemented and adapted to develop targeted methods for other applications or compound classes.
Project description:This study evaluates the quality variation for twenty-seven capsicum fruit (CF) samples, in terms of their volatile oil composition and biological activities. The GCMS analysis revealed the presence of seventy one chemical compounds from different chemical classes with an average (%) composition of: 26.13 (alcohols) > 18.82 (hydrocarbons) > 14.97 (esters) > 3.08 (ketones) > 1.14 (others) > 1.07 (acids) > 0.72 (sugar) > 0.42 (aldehydes) > 0.15 (amino compounds). Alcohols and hydrocarbons were the most abundant in these CF samples with 1-Decanol, 2-octyl- and docosanoic acid, docosyl ester as the major components, respectively. The % inhibition in cytotoxicity assays was observed in the range of 9-47 (MCF7) and 4-41 (HCT116) whereas, the zone of inhibition (mm) for the antimicrobial activity was found to be 0.0-17 (P. aeruginosa) > 0.0-13 (E. coli and S. aureus). Moreover, the samples with the largest zone of inhibition in the agar-well-diffusion method (C16, C19, and C26) upon further evaluation presented the least MIC and MBC values against P. aeruginosa with an MIC and MBC (µg/mL) of 6.3 and 12.5, respectively. The outcome for GCMS and biological activities were further supported by statistical tools of PCA and K-mean cluster analysis which confirmed the C16 CF sample with the best activity followed by C5, C13 (the best cytotoxic), and C19, C26 (the best antimicrobial). The statistical analysis exhibited a high Chi-square value of 5931.68 (GCMS) and 32.19 (biological activities) with p = 0.00 for KMO and Bartlett's Test of Sphericity. The 27-CF samples were effectively distinguished based on quality variation, and the C16 CF sample exhibited significant potential for further study.