González-Ruiz Víctor, Pezzatti Julian, Roux Adrien, Stoppini Luc, Boccard Julien, Rudaz Serge
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Swiss Centre for Applied Human Toxicology, Switzerland.
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
J Chromatogr A. 2017 Dec 8;1527:53-60. doi: 10.1016/j.chroma.2017.10.055. Epub 2017 Oct 25.
This work introduces a strategy for decomposing variable contributions within the data obtained from structured metabolomic studies. This approach was applied in the context of an in vitro human neural model to investigate biochemical changes related to neuroinflammation. Neural cells were exposed to the neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of an untargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected features were annotated at level 1 by comparison against a library of standards, and the 126 identified metabolites were analysed using a recently proposed chemometric tool dedicated to multifactorial Omics datasets, namely, ANOVA multiblock OPLS (AMOPLS). First, the total observed variability was decomposed to highlight the contribution of each effect related to the experimental factors. Both the dose of trimethyltin and the exposure time were found to have a statistically significant impact on the observed metabolic alterations. Cells that were exposed for a longer time exhibited a more mature and differentiated metabolome, whereas the dose of trimethyltin was linked to altered lipid pathways, which are known to participate in neurodegeneration. Then, these specific metabolic patterns were further characterised by analysing the individual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysing complex metabolomic data. The proposed strategy allowed the separation, quantitation and characterisation of the specific contribution of the different factors and the relative importance of every metabolite to each effect with respect to the total observed variability of the system.
这项工作介绍了一种策略，用于在从结构化代谢组学研究获得的数据中分解变量贡献。该方法应用于体外人类神经模型，以研究与神经炎症相关的生化变化。将神经细胞暴露于不同剂量和暴露时间的神经炎症毒物三甲基锡。在非靶向方法框架内，使用与高分辨质谱联用的亲水作用色谱法分析细胞内容物。通过与标准品库比较，在1级水平对检测到的特征进行注释，并使用最近提出的一种专门用于多因素组学数据集的化学计量学工具，即方差分析多块正交偏最小二乘法（AMOPLS），对鉴定出的126种代谢物进行分析。首先，分解观察到的总变异性，以突出与实验因素相关的每种效应的贡献。发现三甲基锡的剂量和暴露时间对观察到的代谢改变均有统计学显著影响。暴露时间较长的细胞表现出更成熟和分化的代谢组，而三甲基锡的剂量与脂质途径改变有关，已知脂质途径参与神经退行性变。然后，通过分析各个变量对每种效应的贡献，进一步表征这些特定的代谢模式。AMOPLS被认为是分析复杂代谢组学数据的有用工具。所提出的策略能够分离、定量和表征不同因素的特定贡献，以及每种代谢物相对于系统总观察变异性对每种效应的相对重要性。
