Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
J Chromatogr A. 2022 Aug 16;1678:463360. doi: 10.1016/j.chroma.2022.463360. Epub 2022 Jul 21.
Herbal medicines (HMs) are widely recognized as extremely complicated matrices, resulting in a great challenge for the existing analytical approaches to characterize the widely targeted metabolome. The primary obstacles include high-level structural diversity, broad concentration range, large polarity span, insufficient authentic compounds and frequent occurrences of isomers, even enantiomers. Here, we aimed to propose an integrated strategy being able to circumvent the technical barriers, and a well-known HM namely Peucedani Radix was employed to illustrate and justify the applicability. Regarding qualitative analysis, the hydrophilic metabolites were detected with HILIC-predictive multiple-reaction monitoring mode, and structurally identified by matching predefined identities with authentic compounds or information archived in relevant databases. After RPLC-MS/MS measurement, full collision energy ramp-MS spectrum in combination with quantum structural calculation was applied to confirmatively identify those less polar components, mainly angular-type pyranocoumarins (APs). For quantitative analysis, achiral-chiral RPLC/HILIC was configured for chromatographic separations because the analytes spanned a large polarity range and involved many enantiomers. A quasi-content concept was employed for comprehensively relative quantitation through constructing a so-called universal metabolome standard (UMS) sample and building calibration curves by assaying serial diluted UMS solutions. Consequently, high-confidence structural annotation and relatively quantitative analysis were achieved for 103 compounds, in total. After multivariate statistical analysis, some APs, e.g., (3'S)-praeruptorin A, (3'S)-praeruptorin B, (3'S)-praeruptorin E, as well as several primary metabolites were screened out as the prominent contributors for inter-batch variations. Together, current study shows a promising strategy enabling widely targeted metabolomics of, but not limited to, HMs.
草药（HMs）被广泛认为是极其复杂的基质，这对现有的分析方法提出了巨大的挑战，使其难以对广泛靶向的代谢组进行特征描述。主要障碍包括高水平的结构多样性、广泛的浓度范围、大的极性跨度、不足的真实化合物以及同系物甚至对映异构体的频繁出现。在这里，我们旨在提出一种能够规避技术障碍的综合策略，并以一种著名的草药白芷为例来说明和证明其适用性。关于定性分析，亲水代谢物采用亲水作用色谱-预测多重反应监测模式进行检测，并通过将预定义的特征与真实化合物或相关数据库中存档的信息相匹配来进行结构鉴定。在 RPLC-MS/MS 测量之后，采用全碰撞能量 ramp-MS 谱结合量子结构计算来确证性地鉴定那些极性较小的成分，主要是角型吡喃香豆素（APs）。对于定量分析，由于分析物跨越大的极性范围并涉及许多对映异构体，因此配置了非手性-手性 RPLC/HILIC 进行色谱分离。通过构建所谓的通用代谢物标准（UMS）样品并通过测定系列稀释的 UMS 溶液来构建校准曲线，采用准含量概念进行全面相对定量。因此，总共实现了 103 种化合物的高置信结构注释和相对定量分析。经过多元统计分析，筛选出一些 APs，例如（3'S）-前胡素 A、（3'S）-前胡素 B、（3'S）-前胡素 E 以及几种主要代谢物，作为批次间变化的主要贡献者。总的来说，目前的研究表明，这是一种有前途的策略，不仅可以用于草药，还可以用于广泛靶向的代谢组学研究。
