Identification of molecules from non-targeted analysis

Untargeted metabolomics identifies potential serum biomarkers associated with Crohn's disease

Crohn's disease (CD) is well characterized by chronic inflammation of the gastrointestinal tract. The diagnose of CD relays on the comprehensive evaluation of patient symptoms, laboratory examination, radiology, and endoscopy. There is lack of biomarkers or simple test for CD detection. Serum samples from healthy subjects (n = 16) and CD patients (n = 16) were collected and prepared for untargeted metabolomics analysis using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) method. The alterations of serum metabolites and the potential biomarkers were profiled by statistical analysis. And the associated metabolic pathway was analyzed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The performance of potential biomarkers was assessed by receiver operating characteristic (ROC) analysis. A complete separation between HS and CD groups was seen in OPLS-DA. A total of 108 and 131 significantly altered metabolites in positive and negative ion mode, respectively, were identified, and most of them belong to several pathways ranging from lipid metabolism to amino acid metabolism and energy homeostasis. KEGG analysis revealed that lipid metabolism enriched most significantly. Further, ceramide, phosphatidylethanolamine (PE), and taurochenodeoxycholic acid (TCDCA) presented the highest predictive accuracy of the patients with CD as analyzed by ROC. The current study demonstrated that lipid metabolism is mostly related to CD pathogenesis. Further investigations are indicated to examine the use of lipid-related metabolites of ceramide, PE, and TCDCA as potential biomarkers for CD diagnosis.

Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products

The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.