University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, Studentská 573, 532 10, Pardubice, Czech Republic.
Palacký University, Medical School and Teaching Hospital, Department of Oncology, I.P. Pavlova 6, 775 20, Olomouc, Czech Republic.
Talanta. 2021 Aug 15;231:122367. doi: 10.1016/j.talanta.2021.122367. Epub 2021 Apr 2.
The lipidomic research is currently devoting considerable effort to the harmonization that should enable the generation of comparable and accurate quantitative lipidomic data across different laboratories and regardless of the mass spectrometry-based platform used. In the present study, we systematically investigate the effects of the experimental setup on quantitative lipidomics data obtained by two lipid class separation approaches, hydrophilic interaction liquid chromatography (HILIC) and ultrahigh-performance supercritical fluid chromatography (UHPSFC), coupled to two different quadrupole - time of flight (QTOF) mass spectrometers from the same vendor. This approach is applied for measurements of 268 human plasma samples of healthy volunteers and renal cell carcinoma patients resulting in four data sets. We investigate and visualize differences among these data sets by multivariate data analysis methods, such as principal component analysis (PCA), orthogonal partial least square discriminant analysis (OPLS-DA), box plots, and logarithmic correlations of molar concentrations of individual lipid species. The results indicate that even measurements in the same laboratory for the same samples using different analytical platforms may yield slight variations in the molar concentrations determined. The normalization to a reference sample with defined lipid concentrations can further diminish these small differences, resulting in highly homogenous molar concentrations of individual lipid species. This strategy indicates a potential approach towards the reporting of comparable quantitative results independent from the quantitative approach and mass spectrometer used, which is important for a wider acceptance of lipidomics data in various biomarker inter-laboratory studies and ring trials.
脂质组学研究目前正在努力实现协调，以便能够在不同的实验室中生成可比且准确的定量脂质组学数据，而不受所使用的基于质谱的平台的影响。在本研究中，我们系统地研究了实验设置对通过两种脂质类别分离方法（亲水相互作用液相色谱（HILIC）和超高性能超临界流体色谱（UHPSFC））获得的定量脂质组学数据的影响，这两种方法均与来自同一供应商的两种不同的四极杆-飞行时间（QTOF）质谱仪联用。该方法应用于 268 个健康志愿者和肾细胞癌患者的人血浆样本的测量，得到了四个数据集。我们通过多元数据分析方法（例如主成分分析（PCA）、正交偏最小二乘判别分析（OPLS-DA）、箱线图和单个脂质种类摩尔浓度的对数相关性）来研究和可视化这些数据集之间的差异。结果表明，即使在同一实验室中使用不同的分析平台对相同的样品进行测量，所确定的摩尔浓度也可能存在微小差异。将摩尔浓度归一化为具有定义脂质浓度的参考样品可以进一步减小这些小差异，从而导致单个脂质种类的摩尔浓度高度均匀。该策略表明，对于报告独立于所使用的定量方法和质谱仪的可比定量结果，这是各种生物标志物实验室间研究和环试验中广泛接受脂质组学数据的重要方法。
