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分解结构化代谢组学研究数据中变量贡献的策略。这种方法是在体外应用的人体神经模型研究与神经炎症相关的生化变化。神经细胞以不同的剂量和暴露时间暴露于神经炎性毒物三甲基锡。在非靶向方法的框架内，使用与HRMS联用的HILIC分析细胞含量。通过与标准库进行比较，将检测到的特征标注为1级，并使用最近提出的专用于多因素Omics数据集的化学计量工具即ANOVA多嵌段OPLS（AMOPLS）对126种鉴定出的代谢物进行了分析。首先，将观察到的总变异性分解以突出显示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均对观察到的代谢改变具有统计学上显着的影响。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改变的脂质途径有关，脂质途径已知参与神经变性。然后，通过分析各个变量对每种作用的贡献，进一步表征这些特定的代谢模式。AMOPLS被强调为分析复杂的代谢组学数据的有用工具。所提出的策略允许对不同因素的特定贡献进行分离，定量和表征，以及每种代谢物对每种效应的相对重要性（相对于系统的总观测变异性）。已知参与神经变性。然后，通过分析各个变量对每种作用的贡献，进一步表征这些特定的代谢模式。AMOPLS被强调为分析复杂的代谢组学数据的有用工具。所提出的策略允许对不同因素的特定贡献进行分离，定量和表征，以及每种代谢物对每种效应的相对重要性（相对于系统的总观测变异性）。已知参与神经变性。然后，通过分析各个变量对每种作用的贡献，进一步表征这些特定的代谢模式。AMOPLS被强调为分析复杂的代谢组学数据的有用工具。所提出的策略允许对不同因素的特定贡献进行分离，定量和表征，以及每种代谢物对每种效应的相对重要性（相对于系统的总观测变异性）。