化学
串联质谱法
色谱法
质谱法
表征(材料科学)
整体式高效液相色谱柱
纳米技术
高效液相色谱法
材料科学
作者
Yan Cao,Wěi Li,Wei Chen,Xiaoya Niu,Nian Wu,Ying Wang,Jun Li,Pengfei Tu,Jiao Zheng,Yuelin Song
出处
期刊:Analytical Chemistry
[American Chemical Society]
日期:2022-10-26
卷期号:94 (44): 15395-15404
被引量:18
标识
DOI:10.1021/acs.analchem.2c03269
摘要
The bile acid (BA) submetabolome can partially reflect either physiological or pathological status of vertebrates. The structural diversity, however, extensively hinders BA submetabolome clarification. Here, efforts were primarily devoted to enhance structural annotation confidences of BAs, in particular the conjugated BAs, through fortifying a new technology, namely, squared energy-resolved mass spectrometry (ER2-MS), to traditional liquid chromatography with tandem mass spectrometry (LC–MS/MS). Because of possessing two tandem-in-space collision cells, namely, q2 and linear ion trap (LIT) chambers, Qtrap-MS was employed as the fit-for-purpose tool to conduct ER2-MS measurements. The first ER-MS was undertaken in a q2 cell to gain first-generation breakdown graphs to disclose conjugation sites via applying the multiple-reaction monitoring (MRM) program, and the second ER-MS was accomplished in a LIT chamber through programming MRM cubed to acquire second-generation breakdown graphs of concerned ions for scaffold characterization. An authentic BA library consisting of commercial BAs together with their in vitro metabolites was built to record a reference breakdown graph set. Moreover, the so-called universal metabolome standard sample that was prepared by pooling diverse BA-enriched matrices was applied for structural deciphering potential evaluation and quasi-quantitative analysis of all detected BAs as well, according to applying a well-defined quasi-content concept. High-confidence structural analysis was achieved for as many as 201 BAs, and significant impacts occurred for the BA submetabolome of HepG2 cells after lithocholic acid treatment. Together, ER2-MS provides a promising tool to promote, although not limited to, LC–MS/MS-based BA-targeted metabolomics.
科研通智能强力驱动
Strongly Powered by AbleSci AI