West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis , Davis, California 95616, United States.
Department of Food Science and Technology, University of California, Davis , Davis, California 95616, United States.
Anal Chem. 2017 Nov 21;89(22):12360-12368. doi: 10.1021/acs.analchem.7b03404. Epub 2017 Nov 7.
Liquid chromatography-mass spectrometry (LC-MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted "best practice" documents, and reports lack harmonization with respect to quantitative data that enable interstudy comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments (1 single TOF, 1 Q/orbital ion trap, and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimate absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match, and retention times. Quantitative results were highly comparable between the LC-MS platforms tested. Using partial least-squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples.
液相色谱-质谱联用（LC-MS）方法通常用于非靶向代谢组学和脂质组学。然而，由于缺乏被接受的“最佳实践”文件标准化方法，并且关于能够进行研究间比较的定量数据的报告缺乏协调，因此方法尚未标准化。研究人员在不同的操作条件下使用各种高分辨率质谱仪，并且尚不清楚结果是否会因仪器性能的不同而产生不同的生物学结论。为此，我们使用了来自营养干预研究的 126 个相同的人血浆样本和 29 个质控样本。我们研究了跨越 9 种不同 MS 仪器（1 种单 TOF、1 种 Q/轨道离子阱和 7 种 QTOF 仪器）的脂质组学数据采集。保留了相同的样品制备、色谱条件和数据处理方法。使用单点内标校准法来估算通过精确质量、MS/MS 光谱匹配和保留时间鉴定的 307 种独特脂质的绝对浓度。在所测试的 LC-MS 平台之间，定量结果具有高度可比性。使用偏最小二乘判别分析（PLS-DA）比较平台之间的结果，基于变量重要性投影（VIP）得分，对于至少两种仪器平台检测到的所有脂质，都可以实现基于最具区分性脂质的 92%重叠。重要的是，即使是个别样本在 PLS-DA 投影上的相对位置也是相同的。成功实现结果协调的关键是通过使用串行稀释法仔细评估线性动态范围，并且在运行实际研究样本之前调整重悬体积和/或进样体积，以避免离子饱和。
