Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time-Ion Mobility Mass Spectrometry
Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time-Ion Mobility Mass Spectrometry
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DOI：
10.1021/acs.analchem.8b01527
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发表时间：
2018-09-18
影响因子：
7.4
通讯作者：
Fiehn, Oliver
中科院分区：
文献类型：
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作者：
Blazenovic, Ivana;Shen, Tong;Fiehn, Oliver
未知代谢物是非靶向代谢组学研究的瓶颈。离子迁移率-质谱法（IM-MS）有助于脂质鉴定，因为它产生的碰撞截面（CCS）信息与质量或亲脂性无关。迄今为止，只有少数CCS值是公开的复杂脂质，如磷脂酰胆碱，鞘磷脂，或三酰甘油酯。这种数据的稀缺性限制了CCS值作为与质量、MS/MS或保留时间正交的鉴别参数的使用。使用脂质描述符的组合来训练五种不同的机器学习算法以用于自动脂质注释，组合准确的质量（m/z）、保留时间（RT）、CCS值、碳数和不饱和度水平。使用来自四种脂质类别的429个真阳性脂质注释的训练数据集，使用内部交叉验证实现了总体92.7%的正确注释。将训练的预测模型应用于未知的牛奶脂质组学数据集，并允许根据代谢组学标准倡议（MSI）报告指南对该应用集中检测到的大多数特征进行3类水平注释。
Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass (m/z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.