WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage of current untargeted and widely-targeted methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0. It enables the construction of a high-coverage library containing 2111 plant volatiles; the development of a comprehensive-selective ion monitoring (cSIM) acquisition method that contains the fewest but sufficient ions for most plant volatiles, including the selection of characteristic qualitative ions with minimal ions number for each compound and the optimized segmentation of acquisition method; and finally, the automatic qualitative and semi-quantitative analysis of cSIM data. Furthermore, the library and acquisition method can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. WTV 2.0 increased the median signal-to-noise ratio by 7.6-fold compared to the untargeted method, doubled the annotation coverage compared to the untargeted and WTV 1.0 methods in tomato fruit, and leading to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface, and is applicable to volatiles and primary metabolites profiling in any species.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

in eleven Chinese patent medicines by UHPLC/QTOF MS

Current China Pharmacopoeia standards for the Chinese patent medicines (CPMs) that contain one or several the same drug(s) employ case-dependent TLC or HPLC approaches to achieve qualitative identification. A qualitative "monomethod-heterotrait matrix" (MHM) strategy is thus proposed, by selective monitoring of multi-biomarkers, to achieve the identification of different CPMs. Carthamus tinctorius L. (safflower) is a reputable gynecological herbal medicine containing characteristic quinochalcone C-glycosides (QCGs) as the major bioactive components. Qualitative identification of safflower in diverse CPMs by selective monitoring of QCG markers was performed by use of the MHM strategy. Initially, 27 QCG analogs (involving 16 potentially new ones) were selectively characterized by product ion filtering (m/z 119.05) and integrated analysis of the negative mode MS(E) and Fast DDA data obtained on a UHPLC/QTOF mass spectrometer. Subsequently, by fingerprint analysis of 20 batches of safflower samples followed by a thermostable test, six QCGs (hydroxysafflor yellow A and its two isomers, anhydrosafflor yellow B, safflomin C, and isosafflomin C) were selected as the biomarkers for safflower. Then, a highly specific selective ion monitoring (SIM) method by recording centroided data was developed and applied to selectively profile six QCG biomarkers from 28 batches of CPM samples collected from versatile vendors. By reference to a standard SIM spectrum established using a home-made safflower reference extract, simultaneous identification of safflower in eleven different CPMs was accomplished with the unified sample preparation and a single UHPLC/QTOF-SIM method. The qualitative MHM strategy represents the novel methodology that facilitates the quality control of CPMs more efficiently.

Quantification of neurosteroids during pregnancy using selective ion monitoring mass spectrometry

Analytical techniques used to quantify neurosteroids in biological samples are often compromised by non-specificity and limited dynamic range which can result in erroneous results. A relatively rapid and inexpensive gas chromatography-mass spectrometry (GC-MS) was developed to simultaneously measure nine neurosteroids, including allopregnanolone, estradiol, and progesterone, as well as 25-hydroxy-vitamin D3 in plasma samples collected from adult women subjects during and after pregnancy. Sample preparation involved solid-phase extraction and derivatization, followed by automated injection on a GC equipped with a mass selective detector (MSD) operated in single ion monitoring (SIM) mode to yield a run time of less than 11min. Method detection limits for all neurosteroids ranged from 30 to 200pg/mL (parts per trillion), with coefficients of variation that ranged from 3% to 5% based on intra-assay comparisons run in triplicate. Although concentrations of estradiol measured by chemiluminescent immunoassay (CIA) were consistent with values determined by GC-MS values, CIA yielded considerable higher values of progesterone, suggesting antibody cross reactions resulting from low specificity. Mean neurosteroid levels and representative time-course data demonstrate the ability of the method to quantify changes in multiple neurosteroids during pregnancy, including rapid declines in neurosteroid levels associated with delivery. This simplified GC-MS method holds particular promise for research and clinical laboratories that require simultaneous quantification of multiple neurosteroids, but lack the resources and expertise to support advanced liquid chromatography-tandem mass spectrometry facilities.