Qiao Xue, Li Ru, Song Wei, Miao Wen-juan, Liu Jia, Chen Hu-biao, Guo De-an, Ye Min
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.
Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China.
J Chromatogr A. 2016 Apr 8;1441:83-95. doi: 10.1016/j.chroma.2016.02.079. Epub 2016 Mar 3.
Structural identification of natural products by tandem mass spectrometry requires laborious spectral analysis. Herein, we report a targeted post-acquisition data processing strategy, key ion filtering (KIF), to analyze untargeted mass spectral data. This strategy includes four steps: (1) untargeted data acquisition by ultra-high performance liquid chromatography coupled with hybrid quadrupole orbitrap mass spectrometry (UHPLC/orbitrap-MS); (2) construction of a key ion database according to diagnostic MS/MS fragmentations and conservative substructures of natural compounds; (3) high-resolution key ion filtering of the acquired data to recognize substructures; and (4) structural identification of target compounds by analyzing their MS/MS spectra. The herbal medicine Huang-Qin (Scutellaria baicalensis Georgi) was used to illustrate this strategy. Its extract was separated within 20 min on a C18 column (1.8 μm, 2.1×150 mm) eluted with acetonitrile, methanol, and water containing 0.1% formic acid. The compounds were detected in the (-)-ESI mode, and their MS/MS spectra were recorded in the untargeted manner. Key ions were then filtered from the LC/MS data to recognize flavones, flavanones, O-/C-glycosides, and phenylethanoid glycosides. Finally, a total of 132 compounds were identified from Huang-Qin, and 59 of them were reported for the first time. This study provides an efficient data processing strategy to rapidly profile the chemical constituents of complicated herbal extracts.
通过串联质谱对天然产物进行结构鉴定需要繁琐的光谱分析。在此,我们报告了一种靶向的采集后数据处理策略——关键离子过滤(KIF),用于分析非靶向质谱数据。该策略包括四个步骤:(1)通过超高效液相色谱与混合四极杆轨道阱质谱联用(UHPLC/轨道阱-MS)进行非靶向数据采集;(2)根据天然化合物的诊断性二级质谱碎片和保守子结构构建关键离子数据库;(3)对采集的数据进行高分辨率关键离子过滤以识别子结构;(4)通过分析目标化合物的二级质谱对其进行结构鉴定。以中药材黄芩(Scutellaria baicalensis Georgi)为例来说明该策略。其提取物在C18柱(1.8μm,2.1×150mm)上于20分钟内分离,流动相为含0.1%甲酸的乙腈、甲醇和水。化合物在(-)-电喷雾电离(ESI)模式下检测,其二级质谱以非靶向方式记录。然后从液相色谱/质谱数据中过滤关键离子以识别黄酮类、黄烷酮类、O-/C-糖苷类和苯乙醇苷类。最终,从黄芩中总共鉴定出132种化合物,其中59种为首次报道。本研究提供了一种有效的数据处理策略,以快速剖析复杂草药提取物的化学成分。