Functional annotation map of natural compounds in traditional Chinese medicines library: TCMs with myocardial protection as a case

Rapid processing of liquid chromatography mass spectrometry data for compound screening and characterization in complex matrix based on python: with Meconopsis quintuplinervia Regel as an example

Herbal extracts are time-consuming and laborious to analyse by traditional methods due to the complexity of their composition. Therefore, it is necessary to develop a rapid identification tool. In this study, a mass spectrometry data processing workstation was designed based on python language, which can rapidly screen and comprehensively characterize the components of herbal medicines. The main steps are as follows: (1) data acquisition by ultra-high-performance liquid chromatography coupled with Q-Exactive MS/MS (UPLC-QE-MS/MS); (2) conversion of mass spectrometry raw data file format and collection of key information; (3) enumeration of permutations and combinations of core structures and substituents based on the structural features of common flavonoids, and establishment of a database of precursor ions. (4) matching precursor ions from mass spectrometry data to a database and labeling mass spectrometry fragment ions based on structural information provided by the database;(5) establishment of a rating system for evaluating compound combinations based on mass spectrometry fragmentation information; (6) information on combinations of highly rated compounds was collected, analyzed for retention times and ionic behavior, and compared with online databases for further structural confirmation. Taking Meconopsis quintuplinervia Regel as an example, 140 flavonoids were finally preliminarily characterized. The results showed that the collection and identification of mass spectrometry information with the help of the mass spectrometry data processing workstation is fast and effective, and can be used for the rapid screening and characterization of compounds in traditional Chinese medicine or other complex matrix.

Mass spectrometry-based metabolomic as a powerful tool to unravel the component and mechanism in TCM

Mass spectrometry (MS)-based metabolomics has emerged as a transformative tool to unraveling components and their mechanisms in traditional Chinese medicine (TCM). The integration of advanced analytical platforms, such as LC-MS and GC-MS, coupled with metabolomics, has propelled the qualitative and quantitative characterization of TCM's complex components. This review comprehensively examines the applications of MS-based metabolomics in elucidating TCM efficacy, spanning chemical composition analysis, molecular target identification, mechanism-of-action studies, and syndrome differentiation. Recent innovations in functional metabolomics, spatial metabolomics, single-cell metabolomics, and metabolic flux analysis have further expanded TCM research horizons. Artificial intelligence (AI) and bioinformatics integration offer promising avenues for overcoming analytical bottlenecks, enhancing database standardization, and driving interdisciplinary breakthroughs. However, challenges remain, including the need for improved data processing standardization, database expansion, and understanding of metabolite-gene-protein interactions. By addressing these gaps, metabolomics can bridge traditional practices and modern biomedical research, fostering global acceptance of TCM. This review highlights the synergy of advanced MS techniques, computational tools, and TCM's holistic philosophy, presenting a forward-looking perspective on its clinical translation and internationalization.

Unveiling mitochondrial-targeting compounds in Qishenyiqi dropping pills for heart failure treatment: An integrative UHPLC-QTOF MS and high-content imaging strategy

Mitochondrial dysfunction, a central pathogenic driver of heart failure (HF), underscores the therapeutic imperative to preserve mitochondrial homeostasis. Qishenyiqi dropping pills (QSYQ), a clinically validated traditional Chinese formulation, exhibits cardioprotective efficacy in HF; however, its mitochondrial-targeting bioactive constituents and mechanisms remain uncharacterized. Here, we integrate untargeted UHPLC-QTOF MS chemical profiling with high-content phenotypic screening across three HF cellular models-isoproterenol-induced hypertrophy, TGF-β1-driven fibrosis, and LPS-triggered inflammation-to systematically identify mitochondrial-targeting active compounds in QSYQ. Multidimensional assessment of mitochondrial function (ATP synthesis, membrane potential, reactive oxygen species flux) combined with machine learning-aided chemophenotypic mapping revealed 74 bioactive candidates from 2385 m/z signals, including novel HF-associated compounds. Crucially, pratensein-7-O-β-D-glucopyranoside (PG), a previously unreported isoflavone in QSYQ, demonstrated potent antifibrotic activity in NIH/3T3 cells via mitochondrial optimization: restoring ATP production, stabilizing membrane potential, and suppressing mtROS. This study establishes PG as a first-in-class mitochondrial homeostatic regulator within QSYQ, while advancing a phenotype-driven discovery framework that bridges traditional medicine complexity with mechanistic cardiology.

Machine learning insights into the antioxidant and biomolecular shielding effects of polyphenol-rich 18 date palm pit extracts

This study investigated the antioxidant and biomolecular shielding effects of polyphenol-rich extracts from 18 date palm pit varieties. Phytochemical profiling revealed significant varietal differences, with catechin (358.48 mg/100 g in Maghool) and gallic acid (34.55 mg/100 g in Khalas) as dominant compounds. Antioxidant assays demonstrated robust activity, with Shikat alkahlas showing 64.10 % DPPH inhibition and Maghool exhibiting the highest FRAP value (190.57 mmol Fe2+/100 g). Enzyme inhibition varied widely: Maghool inhibited tyrosinase by 65.40 %, while Shabebe showed 42.21 % α-amylase inhibition. Protective effects against DNA and BSA damage were pronounced in Sagay (39.99 % supercoiled DNA retention) and Jabri (98.95 % BSA protection). An XGBoost model predicted antioxidant, enzyme inhibitory, and protective activities with up to 92.57 % accuracy, identifying catechin and cinnamic acid as key predictors. These findings underscore the potential of date pits as sustainable sources of bioactive compounds for nutraceutical and pharmaceutical applications.

Ginsenoside CK targets PHD2 to prevent platelet adhesion and enhance blood circulation by modifying the three-dimensional arrangement of collagen

Platelets are indispensable for physiological hemostasis and pathological thrombus formation, and platelet adhesion to endothelial collagen is a critical initial step in thrombus formation, often overlooked in current antiplatelet therapies. This study aims to elucidate how ginsenoside CK enhances hemodynamic circulation, alleviates stasis, and proposes therapeutic mechanisms. Inspired by the effects on improving microcirculatory disturbances in an acute soft tissue injury model, CK was identified as a PHD2 inhibitor, effectively suppressing platelet adhesion to collagen. It was proposed that targeting PHD2 regulates collagen hydroxylation modification, thereby influencing the formation of its three-dimensional structure, reducing the binding affinity between VWF and collagen, and ultimately suppressing thrombotic events. The efficacy of this mechanism was subsequently confirmed through a mouse DIC model, demonstrating the feasibility of CK in alleviating circulatory disorders. It is worth noting that when Phd2 was knocked down in mice's lungs, pulmonary embolism was significantly reduced. Additionally, PHD2 inhibitors approved for other diseases have exhibited similar anti-thrombotic effects. Moreover, when PHD2 inhibitors were combined with aspirin, they more effectively inhibited arterial thrombosis in rats. The findings offer valuable insights into potential targets for developing antiplatelet drugs or expanding therapeutic applications for existing PHD2 inhibitors in treating thrombotic diseases.

Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology

INTRODUCTION

Fibrosis can damage the normal function of many organs, such as cardiac function, for which no effective clinical therapies exist. However, traditional approaches to anti-fibrosis drug discovery have primarily focused on the final biological indicators, often overlooking the dynamic morphological changes during fibrosis progression. Here, we present a novel approach, deep-DPC, which integrates label-free, time-series digital phase contrast (DPC) imaging with cell morphology analysis and unsupervised machine learning to dynamically control and monitor cell morphology.

OBJECTIVES

This method enables discrimination between resting and activated fibrocytes and facilitates the discovery of non-invasive labeled anti-fibrotic lead compounds.

METHODS

The deep-DPC comprises two major steps: (1) preliminary analysis by Harmony 4.9 software and (2) image classification via a neural network. For the experiment dataset, label-free time-series imaging was acquired from each well at 10 × magnification using the high-content imaging system, equipped with a high-speed charge-coupled device (CCD) camera. Dual-channel output images were generated through the imaging system, with one channel for bright-field and the other for DPC imaging, captured at 30-minute intervals. Firstly, applying the anti-fibrotic cell model as a case, a label-free time-series DPC imaging was developed by combining cell morphological analysis and deep learning, and its stability was verified by training with 12,000 images. Furthermore, the application of deep-DPC in the discovery of anti-fibrotic lead compounds.

RESULTS

Using the deep-DPC platform, over 100,000 images generated from 1,400 compounds were processed, identifying Neo-Przewaquinone A as a potent anti-fibrosis agent. Neo-Przewaquinone A exerts its effects by inhibiting TGF-β receptor I, thereby maintaining cells in a resting state and arresting the cell cycle.

CONCLUSION

The deep-DPC offers a promising strategy for fibrosis assessment by combining deep learning with dynamic cell morphology analysis based on time-series DPC images. Additionally, the platform holds potential as a novel therapeutic approach for anti-myocardial fibrosis by regulating cell morphology.

Desorption Electrospray Ionization-Mass Spectrometry Imaging-Based Spatial Metabolomics for Visualizing and Comparing Ginsenosides and Lipids among Multiple Parts and Positions of the Panax ginseng Root

Ginsenosides and lipids are both bioactive ingredients for ginseng. Targeting these two categories of components, this study was designed to develop desorption electrospray ionization-mass spectrometry imaging approaches and spatial metabolomics strategies, achieving the visualization and differentiation among different parts of Panax ginseng root (e.g., rhizome, main root, lateral root, fibrous root, and adventitious root). Potential chemical markers were identified by searching an in-house ginsenoside library and online Lipid Maps database, together with high-resolution MS2 data analysis. Six ginsenosides and 11 lipids were diagnostic to differentiate five different parts of the P. ginseng root. Additionally, three ginsenosides and 20 lipids were identified as differential markers among the six positions of the main root of P. ginseng. High-abundance malonyl- and oleanolic acid-ginsenosides were observed in the rhizome. These results assist in understanding the accumulation of bioactive molecules all through the root of P. ginseng, which can benefit its quality control and rational use.

Online Comprehensive Two-Dimensional Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry-Based Metabolic Profiling and Comparison Enabling the Characterization of 1146 Ginsenosides and More Explicit Differentiation of Ginseng

This work was designed for the in-depth characterization and holistic comparison of up to 12 ginseng varieties, which can benefit the development of functional foods and ensure their authenticity in the food industry. An online comprehensive two-dimensional liquid chromatography/quadrupole time-of-flight mass spectrometry (2D-LC/QTOF-MS) approach was established by configurating the XCharge C18 and HSS Cyano columns. Under the optimal conditions, we characterized a total of 1146 ginsenosides (including 876 potentially new compounds) from 12 ginseng varieties by reference to an in-house library of 573 known ginsenosides and 70 reference compounds. The online 2D-LC/QTOF-MS-based untargeted metabolomics workflows were developed, by which 126 potential ginsenoside markers were unveiled and utilized to establish the key identification points for each ginseng species. Compared with the conventional liquid chromatography/mass spectrometry metabolomics, our multidimensional chromatography approach performed better in discriminating multiple ginseng varieties. This work demonstrates a potent and practical methodology to identify easily confused functional plants.

Fe3O4@MXene@PEI aerogel immobilized acetylcholinesterase for inhibitor screening from herbal plants

Screening of acetylcholinesterase (AChE) inhibitors is a common strategy in drug discovery for treating Alzheimer's disease. Herein, AChE was immobilized onto magnetic polyethyleneimine-based MXene aerogels through both electrostatic interactions and covalent bonds, enabling its application in AChE activity assays and inhibitor screening of herbal plants. The composite was analyzed using a range of techniques, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and Zeta potential analysis, to gain insight into its chemical and physical properties. The proposed Fe3O4@MXene@PEI-AChE composite exhibited enhanced temperature and pH stability, as evaluated by Ellman's method, along with good reusability. The Michaelis Menten constant (Km) of the immobilized AChE was calculated to be 0.68 mmol/L. Additionally, an inhibition kinetic study was conducted to verify the feasibility of utilizing the immobilized enzyme to screen for inhibitors, with huperzine A employed as a model inhibitor. The proposed strategy was employed to compare the AChE inhibitory activity of 18 commonly used herbal medicines for treating AD, revealing both the aqueous and alcoholic extracts of Coptis chinensis as exhibiting the highest AChE inhibitory activity. Finally, the screening of AChE inhibitors in Coptis chinensis extracts were conducted by combining the proposed strategy with UPLC-Q-Orbitrap high resolution mass spectrometry. This study presents a feasible strategy for monitoring AChE activity and holds considerable potential for further exploration of AChE-inhibiting active ingredients in herbal medicines.

Characterization and identification of the wide-polarity multicomponents from Prunella vulgaris by offline two-dimensional liquid chromatography and hydrophilic interaction chromatography coupled to ion mobility-quadrupole time-of-flight mass spectrometry

Metabolites identification is crucial to develop functional foods or perform quality control. Prunella vulgaris (Xia-Ku-Cao) is a medicinal and edible plant used as the herbal medicine or main additive in functional beverage. However, current analytical strategies can only on-line characterize tens of compounds, restricted by insufficient chromatographic resolution and low coverage of the mass spectrometric scan methods. This work was designed to characterize the wide-polarity components from the ear of P. vulgaris. The total extract was fractionated by semi-preparative high-performance liquid chromatography into the retained medium-polarity fraction and unretained polar fraction, which were further analyzed by offline two-dimensional liquid chromatography (2D-LC) and hydrophilic interaction chromatography, respectively. Data-independent high-definition MSE of the Vion™ ion mobility time-of-flight mass spectrometer was utilized enabling the high-coverage acquisition of collision-induced dissociation-MS2 data. The offline 2D-LC, configuring the XBridge Amide and HSS T3 columns, gave high orthogonality (0.81) and effective peak capacity (1555). Automatic peak annotation facilitated by the UNIFI™ bioinformatics platform and comparison with 62 reference compounds achieved the efficient and more reliable structural elucidation. We could characterize 255 compounds from P. vulgaris, with numerous phenylpropanoid phenolic acids and triterpenoid O-glycosides newly reported. Especially, collision cross section (CCS) prediction and targeted isolation of three compounds assisted in the identification of 39 groups of isomers. Additionally, 17 hydrophilic compounds, involving oligosaccharides and organic acids, were characterized from the unretained polar fraction. Conclusively, the in-depth metabolites identification of P. vulgaris was accomplished, and the results can benefit the development and better quality control of this valuable plant.

Evaluating the impact of ecological factors on the quality and habitat distribution of Lonicera japonica Flos using HPLC and the MaxEnt model

Introduction

The quality of traditional Chinese medicine is based on the content of their secondary metabolites, which vary with habitat adaptation and ecological factors. This study focuses on Lonicera japonica Flos (LJF), a key traditional herbal medicine, and aims to evaluate how ecological factors impact its quality.

Methods

We developed a new evaluation method combining high-performance liquid chromatography (HPLC) fingerprinting technology and MaxEnt models to assess the effects of ecological factors on LJF quality. The MaxEnt model was used to predict suitable habitats for current and future scenarios, while HPLC was employed to analyze the contents of key compounds. We also used ArcGIS for spatial analysis to create a quality zoning map.

Results

The analysis identified 21 common chromatographic peaks, with significant variations in the contents of Hyperoside, Rutin, Chlorogenic acid, Cynaroside, and Isochlorogenic acid A across different habitats. Key environmental variables influencing LJF distribution were identified, including temperature, precipitation, and elevation. The current suitable habitats primarily include regions south of the Yangtze River. Under future climate scenarios, suitable areas are expected to shift, with notable expansions in southern Gansu, southeastern Tibet, and southern Liaoning. The spatial distribution maps revealed that high-quality LJF is predominantly found in central and southern Hebei, northern Henan, central Shandong, central Sichuan, southern Guangdong, and Taiwan.

Discussion

The study indicates that suitable growth areas can promote the accumulation of certain secondary metabolites in plants, as the accumulation of these metabolites varies. The results underscore the necessity of optimizing quality based on cultivation practices. The integration of HPLC fingerprinting technology and the MaxEnt model provides valuable insights for the conservation and cultivation of herbal resources, offering a new perspective on evaluating the impact of ecological factors on the quality of traditional Chinese medicines.

Two Multidimensional Chromatography/High-Resolution Mass Spectrometry Approaches Enabling the In-Depth Metabolite Characterization Simultaneously from Three Glycyrrhiza Species: Method Development, Comparison, and Integration

Two offline multidimensional chromatography/high-resolution mass spectrometry systems (method 1: fractionation and online two-dimensional liquid chromatography, 2D-LC; method 2: fractionation and offline 2D-LC) were established to characterize the metabolites simultaneously from three Glycyrrhiza species. Ion exchange chromatography in the first-dimensional (1D) separation was well fractionated between the acidic (mainly triterpenoids) and weakly acidic components (flavonoids). These obtained subsamples got sophisticated separation by the second (2D) and third dimension (3D) of chromatography either by online reversed-phase chromatography × reversed-phase chromatography (RPC × RPC) or offline hydrophilic interaction chromatography × RPC (HILIC × RPC). Orthogonality for the 2D/3D separations reached 0.73 for method 1 and 0.81 for method 2, respectively. We could characterize 1097 compounds from three Glycyrrhiza species based on an in-house library and 33 reference standards, involving 618 by method 1 and 668 by method 2, respectively. They exhibited a differentiated performance and complementarity in identifying the multiple subclasses of Glycyrrhiza components.