Simultaneous metabolomics and lipidomics analysis based on novel heart-cutting two-dimensional liquid chromatography-mass spectrometry.
Simultaneous metabolomics and lipidomics analysis based on novel heart-cutting two-dimensional liquid chromatography-mass spectrometry.
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DOI：
10.1016/j.aca.2017.03.004
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发表时间：
2017-05
影响因子：
6.2
通讯作者：
Shuangyuan Wang;Lina Zhou;Zhichao Wang;Xianzhe Shi;Guowang Xu
中科院分区：
文献类型：
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作者：
Shuangyuan Wang;Lina Zhou;Zhichao Wang;Xianzhe Shi;Guowang Xu
通过结合来自不同平台或方法的数据来增加代谢物覆盖率，可以提高对相关代谢机制的理解和生物标志物的识别。然而，没有一种方法可以在单一分析中获得代谢组学和脂质组学信息。为了在一次分析中收集代谢物组和脂质组的全面信息，我们建立了一种在线心切割二维液相色谱-质谱（2D-LC-MS）方法。生物样品中复杂的代谢物通过预柱被分成两个部分。直接转移第一级分并进行代谢组学分析。大多数脂质保留在预柱上，直到用于脂质组学的移动的相流过;然后对其进行脂质组学分析。在阳性模式和阴性模式下，在30 min内分别鉴定了血浆中多达447种和289种代谢物，包括氨基酸、肉毒碱、胆汁酸、游离脂肪酸、溶血磷脂、磷脂、鞘磷脂等。新开发的方法与传统的代谢组学和脂质组学方法的比较表明，约99%的功能，通过两种传统的方法可以覆盖这种2D-LC方法。分析特性评价表明，该方法线性范围宽，灵敏度高，回收率和重现性良好。这些结果表明，该方法是可靠的，稳定的和合格的代谢组学分析，特别是对于大规模的代谢组学研究与少量的样品。
Increasing metabolite coverage by combining data from different platforms or methods can improve understanding of related metabolic mechanisms and the identification of biomarkers. However, no one method can obtain metabolomic and lipidomic information in a single analysis. In this work, aiming at collecting comprehensive information on metabolome and lipidome in a single analytical run, we developed an on-line heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Complex metabolites from biological samples are divided into two fractions by using a precolumn. The first fraction is directly transferred and subjected to metabolomics analysis. Most lipids are retained on the precolumn until the mobile phases for lipidomics flow through; then they are subjected to lipidomics analysis. Up to 447 and 289 metabolites in plasma, including amino acids, carnitines, bile acids, free fatty acids, lyso-phospholipids, phospholipids, sphingomyelins etc. were identified within 30 min in the positive mode and negative mode, respectively. A comparison of the newly developed method with the conventional metabolomic and lipidomic approaches showed that approximately 99% features obtained by the two conventional methods can be covered with this 2D-LC method. Analytical characteristics evaluation showed the method had a wide linearity range, high sensitivity, satisfactory recovery and repeatability. These results demonstrate that this method is reliable, stable and well qualified in metabolomics analysis, particularly for large-scale metabolomics studies with small amount of samples.