Integration of multicomponent characterization, untargeted metabolomics and mass spectrometry imaging to unveil the holistic chemical transformations and key markers associated with wine steaming of Ligustri Lucidi Fructus

https://doi.org/10.1016/j.chroma.2020.461228Get rights and content

Highlights

  • We report an integral strategy to study chemical variation of TCM by processing.

  • Totally 158 components were characterized from Ligustri Lucidi Fructus (LLF).

  • Eight markers were discovered to differentiate between the raw and processed LLF.

  • Mass spectrometry imaging visualized the distribution of the discovered markers.

Abstract

Processing of traditional Chinese medicine (TCM) can enhance the efficacy and/or reduce the toxicity. Currently available approaches regarding TCM processing generally focus on a few markers, rendering a one-sided strategy that fail to unveil the involved global chemical transformation. We herein present a strategy, by integrating enhanced multicomponent characterization, untargeted metabolomics, and mass spectrometry imaging (MSI), to visualize the chemical transformation and identify the markers associated with the wine steaming of Ligustri Lucidi Fructus (LLF), as a case. An ultra-high-performance liquid chromatography/quadrupole-Orbitrap mass spectrometry-based polarity-switching (between the negative and positive modes), precursor ions list-including data-dependent acquisition approach was developed, which enabled the simultaneous targeted/untargeted characterization of 158 components from LLF via one injection analysis. Holistic, continuous, and time-dependent chemical variation trajectory, among different processing time (0–12 h) for LLF, was depicted by principle component analysis. Pattern recognition chemometrics could unveil 20 markers, among which the peak area ratios of eight components to oleuropein aglycone, used as an internal standard, were diagnostic to identify the processed (both the commercial and in-house prepared) from the raw LLF. Four markers (10-hydroxyoleoside dimethylester, 8-demethyl-7-ketoliganin, elenolic acid, and salidroside) showed an increasing trend, while another four (neonuezhenide/isomer, verbascoside/isomer, luteoline, and nuzhenal A) decreased in LLF after processing. MSI visualized the spatial distribution in the fruit and indicated consistent variation trends for four major markers deduced by the untargeted metabolomics approach. This integral strategy, in contrast to the conventional approaches, gives more convincing data supporting the processing mechanism investigations of TCM from a macroscopic perspective.

Introduction

Being one principle feature of the fundamental traditional Chinese medicine (TCM) theory, processing is occasionally performed for the raw TCM materials prior to the clinic use (by diverse technologies like stir-frying, roasting, carbonizing, calcining, steaming, boiling, and stewing, etc.), aiming to enhance the efficacy and(or) to reduce the toxicity. The changes in efficacy and toxicity can corelate to the holistic chemical variations under processing. Benefitting from the rapid advancements of the analytical technologies, depiction of the overall chemical transformations induced by processing of TCM can be achievable by means of various direct profiling methods (e.g. near infrared spectroscopy, NIR [1]; nuclear magnetic resonance, NMR [2]; and ambient ionization mass spectrometry such as direct analysis in real time-mass spectrometry, DART-MS [3], etc.) or by chromatography-based approaches (such as liquid chromatography/mass spectrometry, LC-MS [4]; gas chromatography/mass spectrometry, GC–MS [5]; and supercritical fluid chromatography/mass spectrometry, SFC-MS [6], etc.). However, the documents regarding the exploration of the chemical variations due to processing of TCM, in most cases, only focus on a few known markers by quantitative assays, which is incomplete and fail to characterize the induced holistic chemical variation.

MS, coupled to the on-line chromatographic separation, has become one of the most powerful and most preferable approaches appliable to the comprehensive profiling and characterization of the multicomponents from diverse natural products or biosamples [7]. MS-based untargeted profiling strategies are often utilized to primarily characterize the components involved in a given sample without the need of any pre-knowledge. Currently available untargeted MSn acquisition approaches, in general, can be divided into two types: (1) one-by-one selection of the scanned precursors; and (2) no selection or stepwise selection of fixed m/z ranges of the precursors. The first mode, including various DDA approaches, apply an intensity-ranking criterion to automatedly induce the MSn fragmentation of the precursor ions. Despite the widest applicability, its performance in respect of the coverage of the interested components can be largely restricted due to the rather complex chemical matrix or multiplexed ionization species (e.g. multiple adducts, trimers, dimers, doubly-charged precursors and doubly-charged adducts, etc.) [8]. Many efforts have been made to improve the coverage of DDA in untargeted profiling, one of which is the inclusion of a precursor ions list (PIL) that can be established by a summary of the phytochemistry information [9], molecular design [10,11], neutral loss filtering [12], and mass defect filtering (MDF) [13], etc. The second pattern, such as the MSE [14], All Ions and AIF (All Ions Fragmentation) [15], DIA (Data-Independent Acquisition) [16], and SWATH (Sequential Window Acquisition for all Theoretical Mass Spectra) [17]), in theory, are more powerful in untargeted metabolites characterization since the fragmentation information of all the m/z range of the precursors can be acquired. However, one deficiency for this strategy is the necessity of precursor-product ions matching prior to MS data interpretation, which inevitably introduces false positive results in structure elucidation [18,19].

Metabolomics, as an emerging “omics” science, aims to determine the content variations of all small molecules in given biological systems due to perturbations, Recent innovations in the analytical technologies and bioinformatics have advanced metabolomics to drug discovery and precision medicine [20]. Metabolomics also serves as a potent vehicle in TCM authentication and food science to ensure the safety, quality, and traceability [21], [22], [23], [24]. Metabolomics can be operated in either an untargeted or targeted mode. Untargeted metabolomics, typically by full-scan MS or MSE, can measure the variation of the whole metabolome from a system and thereby is suitable for the biomarkers discovery, while the targeted mode quantitatively assays the known metabolites by MRM and thus can be utilized to verify the biomarkers discovered by untargeted metabolomics [25,26]. To bridge these two different modes, pseudo-targeted metabolomics has been proposed and proven to be practical either by integrating quadrupole time-of-flight-MS (QTOF-MS) and triple quadrupole/linear ion-trap-MS (QTRAP-MS), or by single QTOF-MS [27,28].

Mass spectrometry imaging (MSI) is an effective, label-free, and multiplex tool that can map the distribution of small molecules and biological macromolecules in flat surgical tissues [29]. Drugs [30], proteins [31], peptides [32], and lipids [33], in biological tissues or plant slices, can be imaged by MS using matrix-assisted laser desorption ionization (MALDI) [29,31,32], desorption electrospray ionization (DESI) [30], secondary ion MS (SIMS) [33], or liquid extraction surface analysis (LESA) [34], etc. MSI is covering increasing attention in various research fields, which can be utilized alongside LC-MS providing timely, cost-effective insights into the interested components. However, this technique has been seldom applied to investigate the chemical transformations of TCM during processing.

Ligustri Lucidi Fructus (LLF; Nu-Zhen-Zi) is derived from the mature fruit of Ligustrum lucidum Ait. (Oleaceae) and currently serves as both herbal medicine and food material with significant tonifying effects. Multiple classes of bioactive natural ingredients have been isolated (e.g. triterpenoids, iridoids, flavonoids, phenylethanoid glycosides, and the others) and a variety of pharmacological effects (e.g. anti-tumor, hepatoprotective, immune regulating, antioxidative, anti-aging, anti-inflammation, and reducing hypercholesterolaemia) are reported [35]. In clinical practice, LLF is often processed prior to use to strength the tonifying function. Wine steaming is the most preferable approach and has been recorded in Chinese Pharmacopoeia (2015 edition) to process LLF [36], however, the underlying holistic chemical transformation remains unknown.

The aim of this work was to report an integral strategy to unveil the processing-induced holistic chemical transformations of TCM by combining the systematic multicomponent characterization, untargeted metabolomics, and MSI, which was validated using the wine steaming of LLF as a case (Fig. 1). By feat of the ultra-high performance liquid chromatography/quadrupole-Orbitrap mass spectrometry (UHPLC/Q-Orbitrap-MS) platform, a PIL-containing DDA approach was established with the rapid polarity switching enabled in the multicomponent characterization. MSI experiments were performed to testify the discovered markers and visualize their spatial distribution. To our knowledge, it's the first attempt to unveil the chemical variation of LLF under wine steaming by the holistic characterization, but not the determination of a few markers.

Section snippets

Chemicals and reagents

A total of 27 compounds, representative of five different classes of bioactive components (e.g. phenylethanols, iridoids, flavonoids, triterpenoids, and the others), ever-isolated from LLF by the authors or purchased from Shanghai Standard Biotech. Co., Ltd. (Shanghai, China) and National Institutes for Food and Drug Control (Beijing, China), were used as the reference compounds. Their structures are exhibited in Fig. S1 (Supporting Information), and the detailed information is offered in Table

Results and discussion

Aiming to expand the coverage of DDA in the multicomponent characterization of LLF, we set a PIL in the Full MS/dd-MS2 (top-N) method, aside from the enabling of dynamic exclusion. Moreover, due to the Frontier transform function of Orbitrap, we enabled polarity switching between ESI– and ESI+ within a single run analysis in both the multicomponent characterization and untargeted metabolomics experiments, to acquire more metabolite information and, simultaneously, to enhance the analysis

Conclusion

We integrated the systematic multicomponent characterization, untargeted metabolomics, and MSI, as a strategy to unveil the holistic chemical transformation and the associated markers for processing of TCM, exemplified by LLF in the current work. Enabling of rapid polarity switching and PIL-including DDA was proven as efficient and potent in the comprehensive metabolites characterization, with 158 components confirmatively identified or tentatively characterized from LLF. Time-dependent,

CRediT authorship contribution statement

Mengrong Li: Investigation, Writing - original draft, Formal analysis. Xiaoyan Wang: Investigation, Writing - original draft, Formal analysis. Lifeng Han: Software. Li Jia: Validation. Erwei Liu: Methodology, Writing - review & editing. Zheng Li: Validation. Heshui Yu: Data curation, Visualization. Yucheng Wang: Data curation, Visualization. Xiumei Gao: Supervision, Project administration. Wenzhi Yang: Funding acquisition, Conceptualization, Writing - review & editing.

Declaration of Competing Interest

None.

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 81630106 and 81872996) and National Science and Technology Major Project of China (Grant No. 2018ZX09201011, 2018ZX09711001-009-010, and 2018ZX09735-002). The authors also thank Jing Dong from Shimadzu, for the technical assistance in MSI experiments.

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