Spatial metabolomics method to reveal the differences in chemical composition of raw and honey-fried Stemona tuberosa Lour. by using UPLC-Orbitrap Fusion MS and desorption electrospray ionization mass spectrometry imaging.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Haixuan Xiong, Shuding Sun, Weiwei Zhang, Di Zhao, Xuefang Liu, Yange Tian, Suxiang Feng
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引用次数: 0

Abstract

Introduction: Stemona tuberosa Lour. (ST) is a significant traditional Chinese medicine (TCM) renowned for its antitussive and insecticidal properties. ST is commonly subjected to processing in clinical practice before being utilized as a medicinal substance. Currently, the customary technique for processing ST is honey-fried. Nevertheless, the specific variations in chemical constituents of ST before and after honey-fried remain unclear.

Objective: This work aimed to analyze the variations in chemical constituents of ST before and after honey-fried and to study the distribution of differential markers in the roots.

Methods: UPLC-Orbitrap Fusion MS combined with molecular network analysis was used to analyze the metabolome of ST and honey-fried ST (HST) and to screen the differential metabolites by multivariate statistical analysis. Spatial metabolomics was applied to study the distribution of differential metabolites by desorption electrospray ionization mass spectrometry imaging (DESI-MSI).

Results: The ST and HST exhibited notable disparities, with 56 and 61 chemical constituents found from each, respectively. After processing, the types of alkaloids decreased, and 12 differential metabolites were screened from the common compounds. The notable component variations were epibisdehydro-tuberostemonine J, neostenine, tuberostemonine, croomine, neotuberostemonine, and so forth. MSI visualized the spatial distribution of differential metabolites.

Conclusions: Our research provided a rapid and effective visualization method for the identification and spatial distribution of metabolites in ST. Compared with the traditional method, this method offered more convincing data supporting the processing mechanism investigations of Stemona tuberosa from a macroscopic perspective.

利用 UPLC-Orbitrap Fusion MS 和解吸电喷雾电离质谱成像技术,采用空间代谢组学方法揭示生食和蜜炒 Stemona tuberosa Lour.化学成分的差异。
介绍:Stemona tuberosa Lour.(ST) 是一种重要的传统中药,以其止咳和杀虫特性而闻名。在临床实践中,茎叶通常要经过加工才能用作药材。目前,加工 ST 的习惯技术是蜜炒。然而,蜜炒前后 ST 化学成分的具体变化仍不清楚:本研究旨在分析蜜炒前后 ST 化学成分的变化,并研究差异标记物在根中的分布:方法:采用 UPLC-Orbitrap Fusion MS 结合分子网络分析技术分析蜜炒 ST 和蜜炒 ST(HST)的代谢组,并通过多元统计分析筛选差异代谢物。通过解吸电喷雾电离质谱成像(DESI-MSI),应用空间代谢组学研究了差异代谢物的分布:结果:ST 和 HST 表现出明显的差异,分别发现了 56 和 61 种化学成分。经过处理后,生物碱的种类有所减少,从普通化合物中筛选出了 12 种不同的代谢物。值得注意的成分变化是表双脱氢柚木碱 J、新柚木碱、柚木碱、新柚木碱等。MSI 对不同代谢物的空间分布进行了可视化分析:我们的研究为 ST 中代谢物的鉴定和空间分布提供了一种快速有效的可视化方法。结论:我们的研究为 ST 代谢物的鉴定和空间分布提供了快速有效的可视化方法,与传统方法相比,该方法从宏观角度为 Stemona tuberosa 的加工机制研究提供了更有说服力的数据支持。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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