High-resolution Mass Spectrometry Platform Research Articles

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women.

Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Metabolite profile, antiproliferative, and antioxidant activity of Acacia auriculiformis and Acacia crassicarpa extracts

Acacia auriculiformis and A. crassicarpa can be used as non-timber forest products to produce therapeutic agents. To achieve this, it is essential to identify the metabolite profile and biological activity of these raw materials. In this study, a liquid chromatography high resolution mass spectrometry platform was used to identify metabolites in the leaf, bark, and heartwood extracts that were positively related to antioxidant and antiproliferative activity using in vitro and in silico studies. A. crassicarpa heartwood extract showed significantly higher antioxidant activity compared to the other extracts. Moreover, the leaf and heartwood extracts from A. auriculiformis, as well as the heartwood extracts from A. crassicarpa, showed significantly high antiproliferative activity. Multivariate analysis using an orthogonal partial least squares model identified several metabolites as significant marker and differentiator compounds between the two Acacia species, with a positive correlation to biological activity. The molecular docking technique predicted various positions of hydroxyl substitution on the flavonoids from A. crassicarpa and A. auriculiformis that alter their binding energy to target receptors, which are related to oxidative stress and cancer treatment targets. Moreover, the methylated flavonoid molecules provide less binding energy. These flavonoids and their derivatives were predominantly found in the heartwood extracts, which demonstrates their potential for further exploration using this part of A. crassicarpa and A. auriculiformis.

Evaluating the Generalizability of Predictive Classifiers Built from DESI Imaging Lipid Data across Mass Spectrometry Platforms.

In this study, we evaluate the generalizability of predictive classifiers built from DESI lipid data for thyroid fine needle aspiration (FNA) biopsy analysis and classification using two high-performance mass spectrometers (time-of-flight and orbitrap) suited with different DESI imaging sources operated by different users. The molecular profiles obtained from thyroid samples with the different platforms presented similar trends, although specific differences in ion abundances were observed. When using a previously published statistical model built to discriminate thyroid cancer from benign thyroid tissues to predict on a new independent data set obtained, agreement for 24 of the 30 samples across the imaging platforms was achieved. We also tested the classifier on six clinical FNAs and obtained agreement between the predictive results and clinical diagnosis for the different conditions. Altogether, our results provide evidence that statistical classifiers generated from DESI lipid data are applicable across different high-resolution mass spectrometry platforms for thyroid FNA classification.

The Metabolic Impact of Two Different Parenteral Nutrition Lipid Emulsions in Children after Hematopoietic Stem Cell Transplantation: A Lipidomics Investigation.

Hematopoietic stem cell transplantation (HSCT) involves the infusion of either bone marrow or blood cells preceded by toxic chemotherapy. However, there is little knowledge about the clinical benefits of parenteral nutrition (PN) in patients receiving high-dose chemotherapy during HSCT. We investigated the lipidomic profile of plasma and the targeted fatty acid profiles of plasma and erythrocytes in children after HSCT using PN with either a fish oil-based lipid emulsion or a classic soybean oil emulsion. An untargeted liquid chromatography high-resolution mass spectrometry platform connected with a novel in silico annotation algorithm was utilized to determine the most relevant chemical subclasses affected. In addition, we explored the interrelation between the lipidomics profile in plasma, the targeted fatty acid profile in plasma and erythrocytes, several biomarkers of inflammation, and antioxidant defense using an innovative data integration analysis based on Latent Components. We observed that the fish oil-based lipid emulsion had an impact in several lipid subclasses, mainly glycerophosphocholines (PC), glycerophosphoserines (PS), glycerophosphoethanolamines (PE), oxidized PE (O-PE), 1-alkyl,2-acyl PS, lysophosphatidylethanolamines (LPE), oxidized PS (O-PS) and dicarboxylic acids. In contrast, the classic soybean oil emulsion did not. Several connections across the different blocks of data were found and aid in interpreting the impact of the lipid emulsions on metabolic health.

Metabolomic profiles in relation to benchmark polycyclic aromatic compounds (PACs) and trace elements in two seabird species from Arctic Canada

While exposure of birds to oil-related contaminants has been documented, the related adverse effects this exposure has on Arctic marine birds remain unexplored. Metabolomics can play an important role to explore biologically relevant metabolite biomarkers in relation to different stressors, even at benchmark levels of contamination. The aim of this study was to characterize the metabolomics profiles in relation to polycyclic aromatic compounds (PACs) and trace elements in the liver of two seabird species in the Canadian Arctic. In July 2018, black guillemots (Cepphus grylle) and thick-billed murres (Uria lomvia) were collected by hunters from a region where natural oil seeps occur in the seabed near Qikiqtarjuaq, Nunavut, Canada. A total of 121 metabolites were identified in liver tissue samples using reversed phase and hydrophilic interaction liquid chromatography coupled to high resolution mass spectrometry platforms to detect non-polar and polar metabolites, respectively. Sixty-nine metabolites showed excellent repeatability and linearity and were used to examine possible effects of oil-related contaminants exposure (PACs and trace elements). Metabolites including 3-hydroxy anthranilic acid, adenine, adenosine, adenosine mono-phosphate, ascorbic acid, butyrylcarnitine, cholic acid, guanosine, guanosine mono-phosphate, inosine, norepinephrine and threonine showed significant differences (more than two fold) between the two species. Elevated adenine and adenosine, along with decreased reduced/oxidized glutathione ratio, highlighted the potential for oxidative stress in murres. Lipid peroxidation and superoxide dismutase activity assays also confirmed these metabolomic findings. These results will help to characterize the baseline metabolomic profiles of Arctic seabird species with different foraging behaviour and trace element burden.

Multi attribute method implementation using a High Resolution Mass Spectrometry platform: From sample preparation to batch analysis.

Quality control of biopharmaceuticals such as monoclonal antibodies (mAbs) has been evolving and becoming more challenging as the requirements of the regulatory agencies increase due to the demanding complexity of products under evaluation. Mass Spectrometry (MS)-based methods such as the multi-attribute method (MAM) are being explored to achieve a deeper understanding of the attributes critical for the safety, efficacy, and quality of these products. MAM uses high mass accuracy/high-resolution MS data that enables the direct and simultaneous monitoring of relevant product quality attributes (PQAs, in particular, chemical modifications) in a single workflow, replacing several orthogonal methods, reducing time and costs associated with these assays. Here we describe a MAM implementation process using a QTOF high resolution platform. Method implementation was accomplished using NIST (National Institute for Standards and Technology) mAb reference material and an in-process mAb sample. PQAs as glycosylation profiles, methionine oxidation, tryptophan dioxidation, asparagine deamidation, pyro-Glu at N-terminal and glycation were monitored. Focusing on applications that require batch analysis and high-throughput, sample preparation and LC-MS parameters troubleshooting are discussed. This MAM workflow was successfully explored as reference analytical tool for comprehensive characterization of a downstream processing (DSP) polishing platform and for a comparability study following technology transfer between different laboratories.

Plant Phosphoproteomics: Known Knowns, Known Unknowns, and Unknown Unknowns of an Emerging Systems Science Frontier.

Plant systems science research depends on the dynamic functional maps of the biological substrates of plant phenotypes and host/environment interactions in diverse ecologies. In this context, high-resolution mass spectrometry platforms offer comprehensive insights into the molecular pathways regulated by protein phosphorylation. Reversible protein phosphorylation is a ubiquitous reaction in signal transduction mechanisms in biological systems. In contrast to human and animal biology research, a plethora of experimental options for functional mapping and regulation of plant biology are, however, not currently available. Plant phosphoproteomics is an emerging field of research that aims at addressing this gap in systems science and plant omics, and thus has a large scope to empower fundamental discoveries. To date, large-scale data-intensive identification of phosphorylation events in plants remained technically challenging. In this expert review, we present a critical analysis and overview of phosphoproteomic studies performed in the model plant Arabidopsis thaliana. We discuss the technical strategies used for the enrichment of phosphopeptides and methods used for their quantitative assessment. Various types of mass spectrometry data acquisition and fragmentation methods are also discussed. The insights gathered here can allow plant biology and systems science researchers to design high-throughput function-oriented experimental workflows that elucidate the regulatory signaling mechanisms impacting plant physiology and plant diseases.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

Plasma biochemical signals associated with exposure to PM2.5 in an ethnically diverse aging population with and without dementia

BACKGROUND AND AIM: Advances in public health have increased the average human lifespan across the globe. As a result, we are observing a greater burden of age-related disorders. The elderly population is particularly vulnerable to the adverse effects of air pollution. Using data from participants of the Washington Heights and Inwood Community Aging Project (WHICAP) in New York City, we assessed the relationship between plasma biochemical signals and exposure to particulate matter less than 2.5 µm in diameter (PM2.5). METHODS: As a pilot study, for 107 participants, we generated plasma metabolomic profiles using an untargeted liquid chromatography coupled high-resolution mass spectrometry platform, operated in two modes, using a HILIC column under positive ESI (HILICpos) and a C18 column under negative ESI (C18neg). The annual average level of PM2.5 exposure was predicted based on residential address at the time of enrollment. We modeled the relationship between each plasma biochemical feature, that was present in at least 70% of the samples, and PM2.5 exposure in the year prior to plasma collection using a metabolome wide association framework. The model was adjusted to account for confounding by age, sex, race/ethnicity, the year of plasma collection, and whether or not they were diagnosed with dementia. RESULTS:From the HILICpos mode, 60 metabolic features were significantly associated (FDR 0.05) with PM2.5, including metabolites of cystine. Seventeen features from the C18neg mode, including metabolites of glutamic acid, were significantly associated with PM2.5. Pathway analysis performed using features associated with PM2.5 from both modes revealed changes in metabolism of amino acids, energy production, and oxidative stress response. CONCLUSIONS:Using an untargeted metabolomics approach, we found several plasma biochemical signals associated with annual PM2.5 levels in an ethnically diverse aging cohort. These signals could help understand the mechanisms through which PM2.5 exposure can lead to altered metabolic outcomes. KEYWORDS: Air pollution, Particulate matter, Metabolomics, Aging

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery.

Background:Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.Objectives:We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.Discussion:The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome—the totality of the biologically active exposures relevant to disease development—through coupling biochemical receptor-binding assays with affinity purification–mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.Conclusions:Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327

A New Workflow for Drug Metabolite Profiling by Utilizing Advanced Tribrid Mass Spectrometry and Data-Processing Techniques.

Drug metabolite profiling utilizes liquid chromatography with tandem mass spectrometry (LC/MS/MS) to acquire ample information for metabolite identification and structural elucidation. However, there are still challenges in detecting and characterizing all potential metabolites that can be masked by a high biological background, especially the unknown and uncommon ones. In this work, a novel metabolite profiling workflow was established on a platform using a state-of-the-art tribrid high-resolution mass spectrometry (HRMS) system. Primarily, an instrumental method was developed based on the novel design of the tribrid system that facilitates in-depth MSn scans with two fragmentation devices. Additionally, different advanced data acquisition techniques were assessed and compared, and automatic background exclusion and deep-scan approaches were adopted to promote assay efficiency and metabolite coverage. Finally, different data-analysis techniques were explored to fully extract metabolite data from the information-rich MS/MS data sets. Overall, a workflow combining tribrid mass spectrometry and advanced acquisition methodology has been developed for metabolite characterization in drug discovery and development. It maximizes the tribrid HRMS platform's utility and enhances the coverage, efficiency, quality, and speed of metabolite profiling assays.

Improving the simultaneous target and non-target analysis LC-amenable pesticide residues using high speed Orbitrap mass spectrometry with combined multiple acquisition modes

The use of high-resolution mass spectrometry (HRMS) for the simultaneous target and non-target analysis of pesticide residues in food control is a subject that has been studied over the last decade. However, proving its efficacy compared to the more established triple quadrupole mass spectrometers (QQQ-MS2) is challenging. Various HRMS platforms have been evaluated, seemingly showing this approach not to be as effective as QQQ-MS2 for quantitative analysis, especially in routine food testing laboratories. The two main reasons are (i) the lower sensitivity especially in the case of the fragment ions produced and (ii) the lack of familiarity and an understanding of the most appropriate combination of HRMS acquisition modes to use. In fact, the number of different acquisition modes can appear as a puzzle to inexperienced users. This work was therefore focused on obtaining experimental data to gain a better understanding of the extended acquisition capabilities of a new Q-Orbitrap platform. Experimental data were obtained for 244 pesticides and their degradation products in commodities of varying matrix complexity (tomato, onion, avocado, and orange) using various combinations of acquisition modes. The best results for targeted analysis were obtained with a combination of full scan (FS), all-ions fragmentation (AIF) and target MS2 (tMS2) modes, and for non-target analysis using full scan (FS) and data-dependent MS2 (ddMS2) modes. All these acquisition modes (FS, AIF, tMS2, and ddMS2) could be applied simultaneously with cycle times ≤ 1 s. The tMS2 especially, proved to be a very powerful approach to increase sensitivity for MS2 fragments and identification rates. Overall, the results for the various pesticide-commodity combinations were fully satisfactory in terms of limit of quantitation (LOQ) repeatability and identification when considered against the SANTE EU Guideline criteria. In addition, the screening capabilities were evaluated for a non-target survey with the use of spectral libraries, the presence of non-target compounds was detected, thus proving the efficacy of the proposed approach. Another issue often overlooked is the optimization of use of spectral libraries, but in our experiments the compounds present in these libraries were not blindly sought in the screening analyses. To minimize the potential for false positives detects in our study, the extractability of the compounds present in the libraries, was also taken into account. The extractability of compounds using a QuEChERS acetonitrile procedure was estimated based on the physicochemical properties of target compounds. By removing compounds that will not be extracted, reduces the occurrences of false detects, reducing the time required for data processing and thus improving the efficiency of the overall screening workflow.

Recent advancements in LC-MS based analysis of biotoxins: Present and future challenges.

There has been a rising concern regarding the harmful impact of biotoxins, source of origin, and the determination of the specific type of toxin. With numerous reports on their extensive spread, biotoxins pose a critical challenge to figure out their parent groups, metabolites, and concentration. In that aspect, liquid chromatography-mass spectrometry (LC-MS) based analysis paves the way for its accurate identification and quantification. The biotoxins are ideally categorized as phytotoxins, mycotoxins, shellfish-toxins, ciguatoxins, cyanotoxins, and bacterial toxins such as tetrodotoxins. Considering the diverse nature of biotoxins, both low-resolution mass spectrometry (LRMS) and high-resolution mass spectrometry (HRMS) methods have been implemented for their detection. The sample preparation strategy for complex matrix usually includes "QuEChERS" extraction or solid-phase extraction coupled with homogenization and centrifugation. For targeted analysis of biotoxins, the LRMS consisting of a tandem mass spectrometer operating in multiple reaction monitoring mode has been widely implemented. With the help of the reference standard, most of the toxins were accurately quantified. At the same time, the suspect screening and nontarget screening approach are facilitated by the HRMS platforms during the absence of reference standards. Significant progress has also been made in sampling device employment, utilizing novel sample preparation strategies, synthesizing toxin standards, employing hybrid MS platforms, and the associated data interpretation. This critical review attempts to elucidate the progress in LC-MS based analysis in the determination of biotoxins while pointing out major challenges and suggestions for future development.

Deep Proteome Profiling of Semen of Indian Indigenous Malnad Gidda (Bos indicus) Cattle.

Malnad Gidda is a dwarf indigenous cattle breed of India, which is known for its uniqueness of calving every year under a low input grazing system of rearing. Bulls of Malnad Gidda are known to be highly fertile even in stress conditions. However, the proteomic profiling of semen of this breed has not been investigated so far, which might provide a platform for a better understanding of its semen quality and male fertility. Therefore, we made an effort to characterize and quantify the proteome of seminal plasma and spermatozoa components of Malnad Gidda semen using a high-resolution mass spectrometry platform. We identified 2814 proteins from spermatozoa and 1974 proteins from the seminal plasma of this breed. Furthermore, >90% of proteins from each fraction were quantified using the intensity-based absolute quantification. We observed signal peptides in 33% of seminal plasma proteins, indicating their secretory nature. Gene Ontology analysis revealed their involvement in cytoskeletal assembly associated with sperm head, sperm motility, acrosome reaction, seminal plasma binding, and spermatogenesis-associated protein. An in-depth proteome profiling of semen of a unique indigenous cattle breed of India was carried out. Our findings could provide a reference for further studies on sperm functions, semen quality, and reproductive health of Bos indicus cattle. Mass spectrometry data generated in this study is deposited and publicly made available through ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014172.

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Comprehensive Examination of the Mouse Lung Metabolome Following Mycobacterium tuberculosis Infection Using a Multiplatform Mass Spectrometry Approach.

The mechanisms whereby Mycobacterium tuberculosis (Mtb) rewires the host metabolism in vivo are surprisingly unexplored. Here, we used three high-resolution mass spectrometry platforms to track altered lung metabolic changes associated with Mtb infection of mice. The multiplatform data sets were merged using consensus orthogonal partial least squares-discriminant analysis (cOPLS-DA), an algorithm that allows for the joint interpretation of the results from a single multivariate analysis. We show that Mtb infection triggers a temporal and progressive catabolic state to satisfy the continuously changing energy demand to control infection. This causes dysregulation of metabolic and oxido-reductive pathways culminating in Mtb-associated wasting. Notably, high abundances of trimethylamine-N-oxide (TMAO), produced by the host from the bacterial metabolite trimethylamine upon infection, suggest that Mtb could exploit TMAO as an electron acceptor under anaerobic conditions. Overall, these new pathway alterations advance our understanding of the link between Mtb pathogenesis and metabolic dysregulation and could serve as a foundation for new therapeutic intervention strategies. Mass spectrometry data has been deposited in the Metabolomics Workbench repository (data-set identifier: ST001328).

The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics.

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Syntheses and analytical characterizations of the research chemical 1-[1-(2-fluorophenyl)-2-phenylethyl]pyrrolidine (fluorolintane) and five of its isomers.

A number of substances based on the 1,2-diarylethylamine template have been investigated for various potential clinical applications whereas others have been encountered as research chemicals sold for non-medical use. Some of these substances have transpired to function as NMDA receptor antagonists that elicit dissociative effects in people who use these substances recreationally. 1-[1-(2-Fluorophenyl)-2-phenylethyl]pyrrolidine (fluorolintane, 2-F-DPPy) has recently appeared as a research chemical, which users report has dissociative effects. One common difficulty encountered by stakeholders confronting the appearance of new psychoactive substances is the presence of positional isomers. In the case of fluorolintane, the presence of the fluorine substituent on either the phenyl and benzyl moieties of the 1,2-diarylethylamine structure results in a total number of six possible racemic isomers, namely 2-F-, 3-F-, and 4-F-DPPy (phenyl ring substituents) and 2"-F-, 3"-F-, and 4"-F-DPPy (benzyl ring substituents). The present study reports the chemical syntheses and comprehensive analytical characterizations of the two sets of three positional isomers. These studies included various low- and high-resolution mass spectrometry platforms, gas- and liquid chromatography (GC and LC), nuclear magnetic resonance (NMR) spectroscopy and GC-condensed phase and attenuated total reflection infrared spectroscopy analyses. The differentiation between each set of three isomers was possible under a variety of experimental conditions including GC chemical ionization triple quadrupole tandem mass spectrometric analysis of the [M+H - HF]+ species. The latter MS method was particularly helpful as it revealed distinct formations of product ions for each of the six investigated substances.

Return of the lysergamides. Part V: Analytical and behavioural characterization of 1-butanoyl-d-lysergic acid diethylamide (1B-LSD).

The psychedelic properties of lysergic acid diethylamide (LSD) have captured the imagination of researchers for many years and its rediscovery as an important research tool is evidenced by its clinical use within neuroscientific and therapeutic settings. At the same time, a number of novel LSD analogs have recently emerged as recreational drugs, which makes it necessary to study their analytical and pharmacological properties. One recent addition to this series of LSD analogs is 1-butanoyl-LSD (1B-LSD), a constitutional isomer of 1-propanoyl-6-ethyl-6-nor-lysergic acid diethylamide (1P-ETH-LAD), another LSD analog that was described previously. This study presents a comprehensive analytical characterization of 1B-LSD employing nuclear magnetic resonance spectroscopy (NMR), low- and high-resolution mass spectrometry platforms, gas- and liquid chromatography (GC and LC), and GC-condensed phase and attenuated total reflection infrared spectroscopy analyses. Analytical differentiation of 1B-LSD from 1P-ETH-LAD was straightforward. LSD and other serotonergic hallucinogens induce the head-twitch response (HTR) in rats and mice, which is believed to be mediated largely by 5-HT2A receptor activation. HTR studies were conducted in C57BL/6J mice to assess whether 1B-LSD has LSD-like behavioral effects. 1B-LSD produced a dose-dependent increase in HTR counts, acting with ~14% (ED50 =976.7nmol/kg) of the potency of LSD (ED50 =132.8nmol/kg). This finding suggests that the behavioral effects of 1B-LSD are reminiscent of LSD and other serotonergic hallucinogens. The possibility exists that 1B-LSD serves as a pro-drug for LSD. Further investigations are warranted to confirm whether 1B-LSD produces LSD-like psychoactive effects in humans.

Multiplexing data independent untargeted workflows for mycotoxins screening on a quadrupole-Orbitrap high resolution mass spectrometry platform.

A new multi-analytical strategy was developed to screen mycotoxins and their transformation products in nutraceuticals from green tea. Seven class mycotoxins were determined using a fully automated on-line extraction procedure followed by ultrahigh-performance liquid chromatography coupled to high resolution mass spectrometry. This multi-analytical methodology has been explored providing an entire spectrum of fragment ion peaks for each mycotoxin, which allows unbiased high-throughput screening and quantitation of non-target mycotoxins. The validated mycotoxins exhibited satisfactory recoveries in the range of 82-108% in accordance with 2002/657/EC. The coefficients of linear determination were in the range of 0.9920-0.9999, with relative standard deviations were in the range of 3.2-14.9%. CCβ ranged between 0.022 μg kg-1 and 0.50 μg kg-1. The screening and confirmation method was applied to green tea nutraceuticals. Seven samples were found to be positive for mycotoxins at concentrations ranging from 0.23 μg kg-1 (3-acetyldeoxynivalenol) to 7.92 μg kg-1 (T-2 toxin).