Rapid screening of riot control agents using DART-TD-HRMS.

PURPOSE: Riot Control Agents (RCAs) are chemicals used in law enforcement for non-lethal riot control and use in conflicts between states that violates the Chemical Weapons Convention. OPCW's Scientific Advisory Board has identified sixteen potential RCAs including capsaicinoids, CS, and CR. RCAs may be misused for criminal purposes, so methods for detecting such misuse are needed. This study therefore evaluates the feasibility of a rapid, high throughput screening method of RCAs on surfaces (particularly clothing surfaces) by Direct Analysis in Real Time with a thermal desorption unit coupled to high-resolution mass spectrometry (DART-TD-HRMS). METHODS: A broadly applicable method for detecting potential RCAs was developed and tested on cotton fabric samples sprayed with self-defence sprays from an in-house reference stock. The feasibility of detecting RCAs by direct analysis of surface wipe samples placed in the DART source was also investigated. RESULTS: The method detected all sixteen RCAs and contaminated clothing were successfully screened for active agents in a reference collection of self-defence sprays. A pilot study also showed that RCAs can be detected by holding a sample directly in front of the DART source. CONCLUSION: DART-TD-HRMS enables rapid and simple screening of RCAs on fabric samples enabling a high sample throughput.

Potential exhaled breath biomarkers identified in chlorine-exposed mice.

Exhaled breath (EB) contains various volatile organic compounds (VOCs) that can indicate specific biological or pathological processes in the body. Analytical techniques like gas chromatography-mass spectrometry (GC-MS) can be used to detect and measure these exhaled biomarkers. In this study, the objective was to develop a non-invasive method of EB sampling in animals that were awake, as well as to analyze EB for volatile biomarkers specific for chlorine exposure and/or diagnostic biomarkers for chlorine-induced acute lung injury (ALI). To achieve this, a custom-made sampling device was used to collect EB samples from 19 female Balb/c mice. EB was sampled both pre-exposure (serving as internal control) and 30 min after exposure to chlorine. EB was collected on thermal desorption tubes and subsequently analyzed for VOCs by GC-MS. The following day, the extent of airway injury was assessed in the animals by examining neutrophils in the bronchoalveolar lavage fluid. VOC analysis revealed alterations in the EB biomarker pattern post-chlorine exposure, with eight biomarkers displaying increased levels and six exhibiting decreased levels following exposure. Four chlorinated compounds: trichloromethane, chloroacetone, 1,1-dichloroacetone and dichloroacetonitrile, were increased in chlorine-exposed mice, suggesting their specificity as chlorine EB biomarkers. Furthermore, chlorine-exposed mice displayed a neutrophilic inflammatory response and body weight loss 24 h following exposure. In conclusion, all animals developed an airway inflammation characterized by neutrophil infiltration and a specific EB pattern that could be extracted after chlorine exposure. Monitoring EB samples can readily and non-invasively provide valuable information on biomarkers for diagnosis of chlorine-induced ALI, confirming chlorine exposures.

The potential of thermal desorption-GC/MS-based analytical methods for the unambiguous identification and quantification of perfluoroisobutene and carbonyl fluoride in air samples.

The reactive gases perfluoroisobutene and carbonyl fluoride are highly toxic and difficult to analyze in air. For this paper, the available sampling and analysis methods involving gas chromatography/mass spectrometry were investigated for their potential to give unambiguous identification and quantification of perfluoroisobutene and carbonyl fluoride, for which no such methods exist. Although high concentrations of perfluoroisobutene could be analyzed directly by manual split injection, sorbent sampling followed by thermal desorption GC/MS allowed lower concentrations to be analyzed. However, a significant degradation of perfluoroisobutene observed after thermal desorption analysis inspired the use of derivatization of perfluoroisobutene with 3,4-dimercaptotoluene. The use of Tenax TA sorbent tubes spiked with 3,4-dimercaptotoluene and trimethylamine in a molar ratio of 1:8 proved successful for the quantification of a unique perfluoroisobutene derivative, and the method was validated for atmospheres in the range of 0.13-152 ppb with a relative standard deviation of less than 20% and an accuracy of 90%. Although carbonyl fluoride was less stable than perfluoroisobutene, direct analysis was possible at high concentrations but the response was not linear. The 3,4-dimercaptotoluene derivatization method developed was also applicable for quantification of carbonyl fluoride atmospheres.

Source Attribution of the Chemical Warfare Agent Soman Using Position-Specific Isotope Analysis by 2H NMR Spectroscopy: From Precursor to Degradation Product.

Position-specific isotope analysis (PSIA) by NMR spectroscopy is a technique that provides quantitative isotopic values for every site-a so-called isotopic fingerprint-of a compound of interest. The isotopic fingerprint can be used to link samples with a common origin or to attribute a synthetic chemical to its precursor source. Despite PSIA by NMR being a powerful tool in chemical forensics, it has not yet been applied on chemical warfare agents (CWAs). In this study, different batches of the CWA Soman were synthesized from three distinctive pinacolyl alcohols (PinOHs). Prior to NMR analysis, the Soman samples were hydrolyzed to the less toxic pinacolyl methylphosphonate (PMP), which is a common degradation product. The PinOHs and PMPs were applied to PSIA by 2H NMR experiments to measure the isotopic distribution of naturally abundant 2H within the pinacolyl moiety. By normalizing the 2H NMR peak areas, we show that the different PinOHs have unique intramolecular isotopic distributions. This normalization method makes the study independent of references and sample concentration. We also demonstrate, for the first time, that the isotopic fingerprint retrieved from PSIA by NMR remains stable during the production and degradation of the CWA. By comparing the intramolecular isotopic profiles of the precursor PinOH with the degradation product PMP, it is possible to attribute them to each other.

Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening.

Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large number of compounds were detected, and multivariate data analysis of the mass spectrometric results enabled the discovery of route-specific signature profiles. The performance of two supervised machine learning algorithms for retrospective synthetic route attribution, orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), were compared using external test sets. Complete classification accuracy was achieved for test set samples (2/2 and 9/9) by using classification models to resolve the one-step routes starting from ethylene and the thiodiglycol chlorination methods used in the two-step routes. Retrospective determination of initial thiodiglycol synthesis methods in sulfur mustard samples, following chlorination, was more difficult. Nevertheless, the large number of markers detected using the nontargeted methodology enabled correct assignment of 5/9 test set samples using OPLS-DA and 8/9 using RF. RF was also used to construct an 11-class model with a total classification accuracy of 10/11. The developed methods were further evaluated by classifying sulfur mustard spiked into soil and textile matrix samples. Due to matrix effects and the low spiking level (0.05% w/w), route determination was more challenging in these cases. Nevertheless, acceptable classification performance was achieved during external test set validation: chlorination methods were correctly classified for 12/18 and 11/15 in spiked soil and textile samples, respectively.

Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures.

Riot control agents such as pepper sprays can be misused for antagonistic and criminal purposes. Several web-pages and YouTube videos are available describing how to make homemade pepper spray. In this study, we investigated whether it was possible to identify the origin of homemade pepper sprays based on chemical attribution signatures from thirteen different types of chili acquired from six different vendors analyzed by GC-MS. The results showed that it was possible to differentiate chili based on species, chili type and vendor using OPLS-DA. Application of an external test set of chilies acquired and extracted one year later than development of the models resulted in correct classification in all models. The models displayed high predictability, suggesting their use for prediction of the identity and origin of seized homemade pepper sprays.

Attribution of fentanyl analogue synthesis routes by multivariate data analysis of orthogonal mass spectral data.

Chemical attribution signatures (CAS) can be used to obtain useful forensic information and evidence from illicit drug seizures. A CAS is typically generated using hyphenated chemical analysis techniques and consists of a fingerprint of the by-products and additives present in a sample. Among other things, it can provide information on the sample's origin, its method of production, and the sources of its precursors. This work investigates the possibility of using multivariate CAS analysis to identify the synthetic methods used to prepare seized fentanyl analogues, independently of the analogues' acyl derivatization. Three chemists working in two labs synthesized three different fentanyl analogues, preparing each one in duplicate by six different routes. The final collection of analogues (96 samples) and two intermediates (16 + 32 samples) were analysed by GC-MS and UHPLC-HRMS, and the resulting analytical data were used for multivariate modelling. Independently of analogue structure, the tested fentanyls could be classified based on the method used in the first step of their synthesis. The multivariate model's ability to classify unknown samples was then evaluated by applying it to six new fentanyl analogues. Additionally, seized fentanyl samples was analysed and classified by the model.

Correction to: Metabolomic profiling identifies distinct phenotypes for ASS1 positive and negative GBM.

In the original publication of this article [1], published on 8 February 2018, it was noticed that the acknowledgement of the source of the drug ADI-PEG20 was missing.

Metabolomic profiling identifies distinct phenotypes for ASS1 positive and negative GBM.

BACKGROUND: Tumour cells have a high demand for arginine. However, a subset of glioblastomas has a defect in the arginine biosynthetic pathway due to epigenetic silencing of the rate limiting enzyme argininosuccinate synthetase (ASS1). These tumours are auxotrophic for arginine and susceptible to the arginine degrading enzyme, pegylated arginine deiminase (ADI-PEG20). Moreover, ASS1 deficient GBM have a worse prognosis compared to ASS1 positive tumours. Since altered tumour metabolism is one of the hallmarks of cancer we were interested to determine if these two subtypes exhibited different metabolic profiles that could allow for their non-invasive detection as well as unveil additional novel therapeutic opportunities. METHODS: We looked for basal metabolic differences using one and two-dimensional gas chromatography-time-of-flight mass spectrometry (1D/2D GC-TOFMS) followed by targeted analysis of 29 amino acids using liquid chromatography-time-of-flight mass spectrometry (LC-TOFMS). We also looked for differences upon arginine deprivation in a single ASS1 negative and positive cell line (SNB19 and U87 respectively). The acquired data was evaluated by chemometric based bioinformatic methods. RESULTS: Orthogonal partial least squares-discriminant analysis (OPLS-DA) of both the 1D and 2D GC-TOFMS data revealed significant systematic difference in metabolites between the two subgroups with ASS1 positive cells generally exhibiting an overall elevation of identified metabolites, including those involved in the arginine biosynthetic pathway. Pathway and network analysis of the metabolite profile show that ASS1 negative cells have altered arginine and citrulline metabolism as well as altered amino acid metabolism. As expected, we observed significant metabolite perturbations in ASS negative cells in response to ADI-PEG20 treatment. CONCLUSIONS: This study has highlighted significant differences in the metabolome of ASS1 negative and positive GBM which warrants further study to determine their diagnostic and therapeutic potential for the treatment of this devastating disease.

Metabolomic screening of pre-diagnostic serum samples identifies association between α- and γ-tocopherols and glioblastoma risk.

Glioblastoma is associated with poor prognosis with a median survival of one year. High doses of ionizing radiation is the only established exogenous risk factor. To explore new potential biological risk factors for glioblastoma, we investigated alterations in metabolite concentrations in pre-diagnosed serum samples from glioblastoma patients diagnosed up to 22 years after sample collection, and undiseased controls. The study points out a latent biomarker for future glioblastoma consisting of nine metabolites (γ-tocopherol, α-tocopherol, erythritol, erythronic acid, myo-inositol, cystine, 2-keto-L-gluconic acid, hypoxanthine and xanthine) involved in antioxidant metabolism. We detected significantly higher serum concentrations of α-tocopherol (p=0.0018) and γ-tocopherol (p=0.0009) in future glioblastoma cases. Compared to their matched controls, the cases showed a significant average fold increase of α- and γ-tocopherol levels: 1.2 for α-T (p=0.018) and 1.6 for γ-T (p=0.003). These tocopherol levels were associated with a glioblastoma odds ratio of 1.7 (α-T, 95% CI:1.0-3.0) and 2.1 (γ-T, 95% CI:1.2-3.8). Our exploratory metabolomics study detected elevated serum levels of a panel of molecules with antioxidant properties as well as oxidative stress generated compounds. Additional studies are necessary to confirm the association between the observed serum metabolite pattern and future glioblastoma development.

Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas.

BACKGROUND: Glioblastomas progress rapidly making response evaluation using MRI insufficient since treatment effects are not detectable until months after initiation of treatment. Thus, there is a strong need for supplementary biomarkers that could provide reliable and early assessment of treatment efficacy. Analysis of alterations in the metabolome may be a source for identification of new biomarker patterns harboring predictive information. Ideally, the biomarkers should be found within an easily accessible compartment such as the blood. METHOD: Using gas-chromatographic- time-of-flight-mass spectroscopy we have analyzed serum samples from 11 patients with glioblastoma during the initial phase of radiotherapy. Fasting serum samples were collected at admittance, on the same day as, but before first treatment and in the morning after the second and fifth dose of radiation. The acquired data was analyzed and evaluated by chemometrics based bioinformatics methods. Our findings were compared and discussed in relation to previous data from microdialysis in tumor tissue, i.e. the extracellular compartment, from the same patients. RESULTS: We found a significant change in metabolite pattern in serum comparing samples taken before radiotherapy to samples taken during early radiotherapy. In all, 68 metabolites were lowered in concentration following treatment while 16 metabolites were elevated in concentration. All detected and identified amino acids and fatty acids together with myo-inositol, creatinine, and urea were among the metabolites that decreased in concentration during treatment, while citric acid was among the metabolites that increased in concentration. Furthermore, when comparing results from the serum analysis with findings in tumor extracellular fluid we found a common change in metabolite patterns in both compartments on an individual patient level. On an individual metabolite level similar changes in ornithine, tyrosine and urea were detected. However, in serum, glutamine and glutamate were lowered after treatment while being elevated in the tumor extracellular fluid. CONCLUSION: Cross-validated multivariate statistical models verified that the serum metabolome was significantly changed in relation to radiation in a similar pattern to earlier findings in tumor tissue. However, all individual changes in tissue did not translate into changes in serum. Our study indicates that serum metabolomics could be of value to investigate as a potential marker for assessing early response to radiotherapy in malignant glioma.

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information.

Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

Blood Metabolomic Predictors of 1-Year Outcome in Subarachnoid Hemorrhage.

BACKGROUND: Delayed neurological deficit (DND) is the most important cause of morbidity and mortality in patients with subarachnoid hemorrhage (SAH) whose aneurysms have been secured. However, the methods currently used to predict the development of DND, such as trans-cranial Doppler or levels biochemical markers in blood and cerebrospinal fluid are not very accurate. METHOD: Venous blood was drawn from 50 patients with SAH, admitted to the neurosurgical department Umeå University Hospital, at day 1-3 and day 7 after the bleed. The clinical status of the patients was followed up approximately 1 year after this episode and classified according to the Glasgow Outcome Score (GOS). RESULTS: Results showed considerable differences in blood metabolomic patterns between day 1-3 and 7 after the hemorrhage. Fifty-six out of 98 metabolites could be identified from our in-house library and 17 of these metabolites changed significantly from day 1-3 to 7 after the bleed. One of these, myo-inositol, was predictive of clinical outcome even after correction for multiple testing. An estimation of the diagnostic accuracy of high levels of this substance in predicting good outcome (GOS 4-5) yielded a sensitivity of .763 and a specificity of .5 at the optimal cut off point. CONCLUSIONS: SAH is an event with a profound effect on blood metabolomics profiles. Myo-inositol might be an interesting compound for future study to focus on in the search for metabolic markers in venous blood of delayed neurological deterioration in SAH patients.

Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study.

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype.

Meningiomas of WHO grade I can usually be cured by surgical resection. However, some tumors may, despite their benign appearance, display invasive growth behavior. These tumors constitute a difficult clinical problem to handle. By histology alone, bone invasive meningiomas may be indistinguishable from their noninvasive counterparts. In this study we have examined the protein spectra in a series of meningiomas in search of protein expression patterns that may distinguish between bone invasive and noninvasive meningiomas. Tumor tissue from 13 patients with fibrous (6 invasive and 7 noninvasive) and 29 with meningothelial (10 invasive and 19 noninvasive) grade I meningiomas were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI). Multivariate statistical methods were applied for data analyses. Comparing the protein spectra from invasive and noninvasive fibrous meningioma we found 22 peaks whose intensities were significantly different between the two groups (P < 0.001). Based on the expression pattern of these peaks we were able to perfectly separate the two entities (area under ROC curve = 1.0). In meningothelial meningioma the same comparison yielded six significantly differentially expressed peaks (P < 0.001), which to a large degree separated the invasive from noninvasive tissue (area under ROC curve = 0.873). By analyzing the protein spectra in benign meningiomas we could differentiate between invasive and noninvasive growth behavior in both fibrous and meningothelial meningiomas of grade I. A possibility for early identification of invasive grade I meningiomas may have a strong influence on the follow-up policy and the issue of early or late radiotherapy.