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Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

The raw and steamed products of ginseng exhibit different efficacy, and elucidation of the underlying chemical transformation is significant for their rational application and quality control. This work was designed to holistically depict the chemical variation of Panax ginseng (PG), P. quinquefolius (PQ), and P. notoginseng (PN) induced by the steaming, and to unveil the steaming-associated markers diagnostic for differentiating between the raw and processed products, following a three-step strategy: 1) systematic ginsenosides characterization by hybrid scan (namely HDDIDDA) available on the Vion™ ion mobility-quadrupole time-of-flight mass spectrometer coupled with reversed-phase ultra-high performance liquid chromatography; 2) holistic depiction of the chemical variation and discovery of potential markers by the pattern recognition untargeted metabolomics analysis; and 3) construction of steaming-induced transformation network combined with desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Consequently, 542 ginsenosides were characterized from the raw and processed PG/PQ/PN. Steaming at 1–10 h could cause significant chemical variation, and separately 26, 28, and 18 potential markers were found for the steaming of PG, PQ, and PN. Steaming-induced transformation network mainly involved hydration of the malonyl group, hydrolysis of the glycosyl moiety, and dehydration at C-20. DESI-MSI further revealed spatial distribution of marker saponins in the cork layer, phloem, and xylem, and primarily confirmed the hydrolysis reactions occurring to ginseng steaming. Conclusively, the established strategy is practical to unveil the holistic ginsenosides variation of ginseng induced by the processing, which is useful in the quality control of both the herbal medicines and foods.

Abstract Background Panax japonicus var. major (PJM) belongs to the well-known ginseng species used in west China for hundreds of years, which has the effects of lung tonifying and yin nourishing, and exerts the analgesic, antitussive, and hemostatic activities. Compared with the other Panax species, the chemical composition and the spatial tissue distribution of the bioactive ginsenosides in PJM have seldom been investigated. Methods Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS) and desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) were integrated for the systematic characterization and spatial tissue distribution studies of ginsenosides in the rhizome of PJM. Considering the great difficulty in exposing the minor saponins, apart from the conventional Auto MS/MS (M1), two different precursor ions list-including data-dependent acquisition (PIL-DDA) approaches, involving the direct input of an in-house library containing 579 known ginsenosides (M2) and the inclusion of the target precursors screened from the MS1 data by mass defect filtering (M3), were developed. The in situ spatial distribution of various ginsenosides in PJM was profiled based on DESI-MSI with a mass range of m/z 100–1500 in the negative ion mode, with the imaging data processed by the High Definition Imaging (HDI) software. Results Under the optimized condition, 272 ginsenosides were identified or tentatively characterized, and 138 thereof were possibly not ever reported from the Panax genus. They were composed by 75 oleanolic acid type, 22 protopanaxadiol type, 52 protopanaxatriol type, 16 octillol type, 19 malonylated, 35 C-17 side-chain varied, and 53 others. In addition, the DESI-MSI experiment unveiled the differentiated distribution of saponins, but the main location in the cork layer and phloem of the rhizome. The abundance of the oleanolic acid ginsenosides was high in the rhizome slice of PJM, which was consistent with the results obtained by UHPLC/QTOF-MS. Conclusion Comprehensive characterization of the ginsenosides in the rhizome of PJM was achieved, with a large amount of unknown structures unveiled primarily. We, for the first time, reported the spatial tissue distribution of different subtypes of ginsenosides in the rhizome slice of PJM. These results can benefit the quality control and further development of PJM and the other ginseng species.

Xuebijing (XBJ) Injection is a reputable patent Chinese medicine widely used to cure sepsis, among the Chinese ″Three Medicines and Three Prescriptions″ solution to fight against COVID-19. We were aimed to achieve the comprehensive multicomponent characterization from the single drugs to traditional Chinese medicine (TCM) formula, by integrating powerful data acquisition and the in-house MS2 spectral database searching. By ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS), a hybrid scan approach (HDMSE-HDDDA) was developed, while the HDMSE data for five component drugs and 56 reference compounds were acquired and processed to establish an in-house MS2 spectral database of XBJ. Good resolution of the XBJ components was accomplished on a Zorbax Eclipse Plus C18 column within 24 min, while a fit-for-purpose HDMSE-HDDDA approach was elaborated in two ionization modes for enhanced MS2 data acquisition. XBJ MS2 spectral library was thus established on the UNIFITM platform involving rich structure-related information for the chemicals from five component drugs. We could identify or tentatively characterize 294 components from XBJ, involving 81 flavonoids, 51 terpenoids, 42 phthalides, 40 organic acids, 13 phenylpropanoids, seven phenanthrenequinones, six alkaloids, and 54 others. In contrast to the application of conventional MS1 library, this newly established strategy could demonstrate superiority in the accuracy of identification results and the characterization of isomers, due to the more restricted filtering/matching criteria. Conclusively, the integration of the HDMSE-HDDDA hybrid scan approach and the in-house MS2 spectral database can favor the efficient and more reliable multicomponent characterization from single drugs to the TCM formula.

Abstract Background Triple-negative breast cancer (TNBC) is a clinically aggressive disease with abundant variants that cause homologous recombination repair deficiency (HRD). Whether TNBC patients with HRD are sensitive to anthracycline, cyclophosphamide and taxane (ACT), and whether the combination of HRD and tumour immunity can improve the recognition of ACT responders are still unknown. Methods Data from 83 TNBC patients in The Cancer Genome Atlas (TCGA) was used as a discovery cohort to analyse the association between HRD and ACT chemotherapy benefits. The combined effects of HRD and immune activation on ACT chemotherapy were explored at both the genome and the transcriptome levels. Independent cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) were adopted to validate our findings. Results HRD was associated with a longer ACT chemotherapy failure-free interval (FFI) with a hazard ratio of 0.16 (P = 0.004) and improved patient prognosis (P = 0.0063). By analysing both HRD status and ACT response, we identified patients with a distinct TNBC subtype (ACT-S&HR-P) that showed higher tumour lymphocyte infiltration, IFN-γ activity and NK cell levels. Patients with ACT-S&HR-P had significantly elevated immune inhibitor levels and presented immune activation associated with the increased activities of both innate immune cells and adaptive immune cells, which suggested treatment with immune checkpoint blockade as an option for this subtype. Our analysis revealed that the combination of HRD and immune activation enhanced the efficiency of identifying responders to ACT chemotherapy (AUC = 0.91, P = 1.06e−04) and synergistically contributed to the clinical benefits of TNBC patients. A transcriptional HRD signature of ACT response-related prognostic factors was identified and independently validated to be significantly associated with improved survival in the GEO cohort (P = 0.0038) and the METABRIC dataset (P < 0.0001). Conclusions These findings highlight that HR deficiency prolongs FFI and predicts intensified responses in TNBC patients by combining HRD and immune activation, which provides a molecular basis for identifying ACT responders.

Severe interference from the endogenous substances is often encountered in characterizing the drug metabolites by liquid chromatography/mass spectrometry using data-dependent acquisition (DDA). To add a precursor ions list (PIL) by DDA or apply data-independent acquisition (DIA) coupled with post-acquisition data processing (such as mass defect filtering, MDF) may assist to target more metabolites from the complex biosamples. Dehydrocorynine (DHC) is a bioactive alkaloid compound rich in Corydalis yanhusuo. We integrated both PIL-DDA and DIA-MDF strategies to probe the metabolites of DHC simultaneously from the rat plasma, bile, urine, and feces. Chromatographic separation was performed on an HSS C18 SB column. The positive-mode collision-induced dissociation-MS2 data of DHC metabolites were recorded by PIL-DDA on both the QTrap 4500 and Vion IM-QTOF mass spectrometers, and by HDMSE on Vion IM-QTOF. Efficient workflows to process the high-definition DDA (HDDDA) and HDMSE data were elaborated. Totally 40 metabolites (orally administrated at the dose of 100 mg/kg) were identified or tentatively characterized, involving 30 from bile, 16 from feces, 7 from plasma, and 18 from urine. The methoxyls and C-5/C-6/C-8 were the main sites prone to be metabolized via demethylation and oxidation, and further glucuronic acid conjugation and sulfuric acid conjugation. Compared with literature, we can newly discover 17 metabolites in bile, and, for the first time, report the metabolites of DHC from rat urine and feces. Conclusively, the presented PIL-DDA and DIA-MDF strategies are powerful in elucidating the drug metabolites, which thus provides reference to characterizing the metabolic profiles of traditional Chinese medicine components.

Precise identification and differentiation among those congeneric Traditional Chinese Medicines (TCMs) or derived from the same plant trend to be more challenging, particularly in the absence of appearance characteristics. Three TCMs, involving Gleditsiae Sinensis Fructus (GSF), Gleditsiae Fructus Abnormalis (GFA), and Gleditsiae Spina (GS), recorded in Chinese Pharmacopoeia (2020 edition) are derived from Gleditsia sinensis, but prescribed for different clinical uses. The documents aimed to compare their chemical differences are rare, to date. An untargeted metabolomics approach, based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS), was elaborated to unveil the potential chemical markers to differentiate among GSF, GFA, and GS. Good chromatographic separation of all the GSF/GFA/GS components was achieved within 33 min by utilizing a BEH C18 column, while data-independent MSE in the positive mode was selected for profiling the metabolic features. Notably, the high-mass saponins (1300–2500 Da) gave unique protonated precursors ([M + H]+) in the positive ESI mode, compared with those complicated ion species occurring in the negative mode. Pattern recognition chemometrics analysis of 45 batches of G. sinensis samples could unveil 70 significantly altered ions assigned as 46 potentially differential components. The positive/negative high-accuracy MS2 data analysis, phytochemical isolation/NMR analysis, and searching of an in-house library of G. sinensis, were utilized for structural elucidation. Three compounds (saikachinoside A, locustoside A, and locustoside B) rich in GSF could be the markers to differentiate from GFA/GS, while four components were characteristic for GS. These results obtained can greatly benefit the quality control of TCMs derived from G. sinensis.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

The flower bud of Panax notoginseng (PNF) consumed as a tonic shows potential in the prevention and treatment of cardiovascular diseases. To identify the contained multi-components and, in particular, to clarify which components can be absorbed and what metabolites are transformed, unveiling the effective substances of PNF is of vital significance. A unique ultrahigh-performance liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) profiling approach and efficient data processing by the UNIFITM bioinformatics platform were employed to comprehensively identify the multi-components of PNF and the related metabolites in the plasma of rats after oral administration (at a dose of 3.6 g/kg). Two MS2 data acquisition modes operating in the negative electrospray ionization mode, involving high-definition MSE (HDMSE) and data-dependent acquisition (DDA), were utilized aimed to extend the coverage and simultaneously ensure the quality of the MS2 spectra. As a result, 219 components from PNF were identified or tentatively characterized, and 40 thereof could be absorbed. Moreover, 11 metabolites were characterized from the rat plasma. The metabolic pathways mainly included the phase I (deglycosylation and oxidation). To the best of our knowledge, this is the first report that systematically studies the in vivo metabolites of PNF, which can assist in better understanding its tonifying effects and benefit its further development.

The leaves of Panax species (e.g., Panax ginseng-PGL, P. quinquefolius-PQL, and P. notoginseng-PNL) can serve as a source for healthcare products. Comprehensive characterization and unveiling of the metabolomic difference among PGL, PQL, and PNL are critical to ensure their correct use. For this purpose, enhanced profiling and chemometrics were integrated to probe into the ginsenoside markers for PGL/PQL/PNL by ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS). A hybrid scan approach (HDMSE-HDDDA) was established achieving the dimension-enhanced metabolic profiling, with 342 saponins identified or tentatively characterized from PGL/PQL/PNL. Multivariate statistical analysis (33 batches of leaf samples) could unveil 42 marker saponins, and the characteristic ginsenosides diagnostic for differentiating among PGL/PQL/PNL were primarily established. Compared with the single DDA or DIA, the HDMSE-HDDDA hybrid scan approach could balance between the metabolome coverage and spectral reliability, leading to high-definition MS spectra and the additional collision-cross section (CCS) useful to differentiate isomers.

Wenxin granule (WXG) is a popular traditional Chinese medicine (TCM) preparation for the treatment of arrhythmia disease. Potent analytical technologies are needed to elucidate its chemical composition and assess the quality differences among multibatch samples. In this work, both a multicomponent characterization and quantitative assay of WXG were conducted using two liquid chromatography–mass spectrometry (LC-MS) approaches. An ultra-high performance liquid chromatography–ion mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) approach combined with intelligent peak annotation workflows was developed to characterize the multicomponents of WXG. A hybrid scan approach enabling alternative data-independent and data-dependent acquisitions was established. We characterized 205 components, including 92 ginsenosides, 53 steroidal saponins, 14 alkaloids, and 46 others. Moreover, an optimized scheduled multiple reaction monitoring (sMRM) method was elaborated, targeting 24 compounds of WXG via ultra-high performance liquid chromatography–triple quadrupole linear ion trap mass spectrometry (UHPLC/QTrap-MS), which was validated based on its selectivity, precision, stability, repeatability, linearity, sensitivity, recovery, and matrix effect. By applying this method to 27 batches of WXG samples, the content variations of multiple markers from Notoginseng Radix et Rhizoma (21) and Codonopsis Radix (3) were depicted. Conclusively, we achieved the comprehensive multicomponent characterization and holistic quality assessment of WXG by targeting the non-volatile components.

Cancer-associated thrombosis (CAT) studies have increased in recent years and the quality of guidelines to guide the clinical practice of CAT prevention and treatment becomes crucial. The therapy status of new oral anticoagulants (NOACs) has been established in some thrombotic diseases, but the evidence for CAT remains unconvincing. The aim of this research is to evaluate the quality of CAT guidelines and discuss the role of NOAC in CAT. A search of articles was performed using PubMed/Medline, Chinese National Knowledge Infrastructure, and other authoritative websites. Search terms included guideline or guidance, consensuses, cancer, and thrombosis. Appraisal of Guidelines for Research & Evaluation II (AGREE II) tool was used to evaluate the qualities of the guidelines. A total of 19 guidelines were screened out and evaluated, of which 8 were recommended, 5 were recommended after revision, and 6 were not recommended. For prevention and treatment of CAT, low-molecular-weight heparin is the most recommended, followed by vitamin K antagonist, unfractionated heparin, fondaparinux, and aspirin. New oral anticoagulant is optional in some cases of CAT treatment. Based on AGREE II assessment tool, the quality of CAT guidelines is inconsistent. Attention should be drawn to the quality of CAT guidelines during clinical practice. The role of NOAC in the treatment of CAT is gradually established but requires more supporting evidence from future clinical trials.

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