Accurate mass measurements and their appropriate use for reliable analyte identification

An integrated strategy of MS-network-based offline 2DLC-QTOF-MS/MS coupled with UHPLC-QTRAP®-MS/MS for the characterization and quantification of the non-polysaccharides in Sijunzi decoction

Sijunzi decoction (SJZD), a classic recipe in traditional Chinese medicine (TCM), has been applied for the clinical treatment of gastrointestinal diseases. While there are reports on pharmaceutical substances of SJZD focusing on its polysaccharides, the composition of non-polysaccharides (NPSs) has not yet been holistically clarified. In the current study, offline two-dimensional liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (2DLC-QTOF-MS/MS) was used for comprehensive NPS chemical profiling of SJZD. In addition, the MS-network of SJZD was proposed, which led to the construction of a larger in-house chemical library and accelerated qualitative processing. Four hundred forty-nine components, among which 6 were potentially novel, and 32 were confirmed by standard substances, were identified or tentatively assigned. Furthermore, based on good method validation, 19 representative components were simultaneously quantified by ultra-high-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UHPLC-QTRAP®-MS/MS). They were selected for quantification on the account of their bioactive reports on in vivo or in vitro activities, the peak intensity in the mass spectrum, and characteristic structures, which have the potential to be qualitative or quantitative markers of SJZD. The present work furthers understanding of the pharmacological effects and action mechanism of NPSs in SJZD, and provides a useful analytical approach for complex composition research of TCMs.

Armed with the Facts: A Method for the Analysis of Smokeless Powders by Ambient Mass Spectrometry

The work presented here follows several others in investigating what capabilities, if any, ambient mass spectrometry might have toward the analysis of compounds commonly associated with smokeless propellant powders. This family of instrumental techniques has attracted curiosity from the field of forensic science due to its desirable properties such as rapid collection of information-rich data, combined with minimal requirements for sample mass and preparation. Experiments were conducted with a "Direct Sample Analysis" ion source integrated with a time-of-flight mass spectrometer. The ionization behaviors of nitroglycerin, methyl and ethyl centralite, akardite, diphenylamine, nitrosodiphenylamine, and nitrated diphenylamine derivatives were investigated specifically, with accurate-mass data presented for each. Diphenylamine standards were used to demonstrate the performance of this instrument, which exhibited good response linearity across 1 order of magnitude and sub-nanogram detection limits. Thirty smokeless powder extracts, recovered from ammunition potentially in circulation within Australia, were analyzed to determine whether the technique is appropriate for rapid analysis of smokeless powder particles. Results demonstrated that the technique might be applied to compare individual particles with each other or to a database. Such a capability may be of value in the examination of explosive devices containing smokeless powder, postblast residues therefrom, or muzzle discharge from a close-range shooting. However, when efforts were made to detect residues from the hands of a volunteer shooter, only some returned positive results, and a high background signal from the sample collection stub indicates that detection using this instrument is thus far insufficiently reliable.

A comparison of three liquid chromatography (LC) retention time prediction models

High-resolution mass spectrometry (HRMS) data has revolutionized the identification of environmental contaminants through non-targeted analysis (NTA). However, chemical identification remains challenging due to the vast number of unknown molecular features typically observed in environmental samples. Advanced data processing techniques are required to improve chemical identification workflows. The ideal workflow brings together a variety of data and tools to increase the certainty of identification. One such tool is chromatographic retention time (RT) prediction, which can be used to reduce the number of possible suspect chemicals within an observed RT window. This paper compares the relative predictive ability and applicability to NTA workflows of three RT prediction models: (1) a logP (octanol-water partition coefficient)-based model using EPI Suite™ logP predictions; (2) a commercially available ACD/ChromGenius model; and, (3) a newly developed Quantitative Structure Retention Relationship model called OPERA-RT. Models were developed using the same training set of 78 compounds with experimental RT data and evaluated for external predictivity on an identical test set of 19 compounds. Both the ACD/ChromGenius and OPERA-RT models outperformed the EPI Suite™ logP-based RT model (R² = 0.81-0.92, 0.86-0.83, 0.66-0.69 for training-test sets, respectively). Further, both OPERA-RT and ACD/ChromGenius predicted 95% of RTs within a ± 15% chromatographic time window of experimental RTs. Based on these results, we simulated an NTA workflow with a ten-fold larger list of candidate structures generated for formulae of the known test set chemicals using the U.S. EPA's CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard), RTs for all candidates were predicted using both ACD/ChromGenius and OPERA-RT, and RT screening windows were assessed for their ability to filter out unlikely candidate chemicals and enhance potential identification. Compared to ACD/ChromGenius, OPERA-RT screened out a greater percentage of candidate structures within a 3-min RT window (60% vs. 40%) but retained fewer of the known chemicals (42% vs. 83%). By several metrics, the OPERA-RT model, generated as a proof-of-concept using a limited set of open source data, performed as well as the commercial tool ACD/ChromGenius when constrained to the same small training and test sets. As the availability of RT data increases, we expect the OPERA-RT model's predictive ability will increase.

Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard

Chemical features observed using high-resolution mass spectrometry can be tentatively identified using online chemical reference databases by searching molecular formulae and monoisotopic masses and then rank-ordering of the hits using appropriate relevance criteria. The most likely candidate "known unknowns," which are those chemicals unknown to an investigator but contained within a reference database or literature source, rise to the top of a chemical list when rank-ordered by the number of associated data sources. The U.S. EPA's CompTox Chemistry Dashboard is a curated and freely available resource for chemistry and computational toxicology research, containing more than 720,000 chemicals of relevance to environmental health science. In this research, the performance of the Dashboard for identifying known unknowns was evaluated against that of the online ChemSpider database, one of the primary resources used by mass spectrometrists, using multiple previously studied datasets reported in the peer-reviewed literature totaling 162 chemicals. These chemicals were examined using both applications via molecular formula and monoisotopic mass searches followed by rank-ordering of candidate compounds by associated references or data sources. A greater percentage of chemicals ranked in the top position when using the Dashboard, indicating an advantage of this application over ChemSpider for identifying known unknowns using data source ranking. Additional approaches are being developed for inclusion into a non-targeted analysis workflow as part of the CompTox Chemistry Dashboard. This work shows the potential for use of the Dashboard in exposure assessment and risk decision-making through significant improvements in non-targeted chemical identification. Graphical abstract Identifying known unknowns in the US EPA's CompTox Chemistry Dashboard from molecular formula and monoisotopic mass inputs.

Status of hormones and painkillers in wastewater effluents across several European states-considerations for the EU watch list concerning estradiols and diclofenac

Present technologies for wastewater treatment do not sufficiently address the increasing pollution situation of receiving water bodies, especially with the growing use of personal care products and pharmaceuticals (PPCP) in the private household and health sector. The relevance of addressing this problem of organic pollutants was taken into account by the Directive 2013/39/EU that introduced (i) the quality evaluation of aquatic compartments, (ii) the polluter pays principle, (iii) the need for innovative and affordable wastewater treatment technologies, and (iv) the identification of pollution causes including a list of principal compounds to be monitored. In addition, a watch list of 10 other substances was recently defined by Decision 2015/495 on March 20, 2015. This list contains, among several recalcitrant chemicals, the painkiller diclofenac and the hormones 17β-estradiol and 17α-ethinylestradiol. Although some modern approaches for their removal exist, such as advanced oxidation processes (AOPs), retrofitting most wastewater treatment plants with AOPs will not be acceptable as consistent investment at reasonable operational cost. Additionally, by-product and transformation product formation has to be considered. The same is true for membrane-based technologies (nanofiltration, reversed osmosis) despite of the incredible progress that has been made during recent years, because these systems lead to higher operation costs (mainly due to higher energy consumption) so that the majority of communities will not easily accept them. Advanced technologies in wastewater treatment like membrane bioreactors (MBR) that integrate biological degradation of organic matter with membrane filtration have proven a more complete elimination of emerging pollutants in a rather cost- and labor-intensive technology. Still, most of the presently applied methods are incapable of removing critical compounds completely. In this opinion paper, the state of the art of European WWTPs is reflected, and capacities of single methods are described. Furthermore, the need for analytical standards, risk assessment, and economic planning is stressed. The survey results in the conclusion that combinations of different conventional and advanced technologies including biological and plant-based strategies seem to be most promising to solve the burning problem of polluting our environment with hazardous emerging xenobiotics.

Plasma metabolomics in human pulmonary tuberculosis disease: a pilot study

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution.

Correlated imaging--a grand challenge in chemical analysis

Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest.