ABSTRACT
Data-dependent acquisition (DDA) and data-independent acquisition (DIA)-based MSn strategies are extensively applied in metabolites characterization. DDA gives accurate MSn information, but receives low coverage, while DIA covers the entire mass range, but the precursor-product ions matching often yields false positives. Currently available MS scan approaches rarely integrate DIA and DDA within a dutycircle. Utilizing a Vion™IM-QTOF (ion mobility-quadrupole time-of-flight) mass spectrometer, we report a novel hybrid scan approach, namely HDDIDDA, which involves three scan events: 1) IM-enabled full scan (MS1), 2) high-definition MSE (HDMSE) of all precursor ions (MS2); and 3) high-definition DDA (HDDDA) of top N precursors (MS2).
As a proof-of-concept, the HDDIDDA approach combined with offline two-dimensional liquid chromatography (2D-LC) was applied to characterize the multiple ingredients from a reputable Chinese patent medicine, Compound Danshen Dripping Pill (CDDP) used for treating the cardiovascular diseases. An off-line 2D-LC system by configuring an XBridge Amide column and an HSS T3 column showed a measurable orthogonality of 0.92 and enhanced the separation of coeluting components. A fit-for-purpose HDDIDDA methodology was developed in the negative mode to characterize saponins and salvianolic acids, while tanshinones in the positive mode. Computational workflows to efficiently process the acquired HDMSE and HDDDA data were established, and the searching of an in-house CDDP library (recording 712 compounds) eventually characterized 403 components from CDDP, indicating approximate 12-fold improvement compared with the previous report. The HDDIDDA approach can measure collision cross section of each component, and merges the merits ofDIA and DDA in MS2 data acquisition.
Chemicals and reagents
Desite Biotechnology Co., Ltd. (Chengdu, China), were used as the reference compounds with their chemical structures and information detailed in Fig. S1 and Table S1, respectively.
原文链接:https://doi.org/10.1016/j.aca.2021.339320
Royal society of chemistry-12 Mar 2020
The development and
World Journal of Microbiology and Biotechnology-20 June 2018
期刊名:World Journal of
Nat Biotech| 北京大学谢正伟课题组与合作者创建基于基因指纹和深度学习的药效预测系统(DLEPS)
原文链接:htt
Journal of separation science-26 November 2019
Biosurfactant trehal
Journal of separation science-07 January 2020
Comparison of the ac
细辛水提物HPLC指纹图谱及化学模式识别的研究
摘要:[目的] 通过建立中药细辛水提物高