Normalization Approach by a Reference Material to Improve LC–MS-Based Metabolomic Data Comparability of Multibatch Samples
Normalization Approach by a Reference Material to Improve LC–MS-Based Metabolomic Data Comparability of Multibatch Samples
复制标题
采用参考物质标准化方法提高多批次样品基于 LC–MS 的代谢组数据的可比性
DOI：
10.1021/acs.analchem.2c04188
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发表时间：
2022
影响因子：
7.4
通讯作者：
Tiangang Luan
中科院分区：
文献类型：
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作者：
Yao Yao;Hui Zhang;Lanyin Tu;Tiantian Yu;Baowei Chen;Peng Huang;Yumin Hu;Tiangang Luan
全球代谢组学研究通常需要来自多个批次的大量样本来表征人类疾病的代谢状态。因此，消除系统差异并真正揭示与生物相关的变化是至关重要的。在这项研究中，我们提出了一种基于参考物质的方法(Ref-M)，用于液-质联用的数据校正，并以多批次的人血清样品的分析为代表。标准物质是通过混合来自健康供体的血清而产生的，并分发给每一批提取的受试者样本。然后，在每个采集批次中应用汇集的质量控制样品和同位素内标物，以进行数据质量控制。最后，受试者样本中的每个代谢物被参考血清中的相应代谢物标准化。我们证明，Ref-M显著增加了有效特征的数量，并有效地消除了522份健康人、良性肺结节和肺癌患者的血清样本的批量变异。在训练组中，20种不同的代谢物被用来区分肺癌和健康对照。在接收器工作特性曲线下面积为0.853的独立数据集中对判别模型进行了验证。用REF-M进一步检测40份血清样本，所建立的模型的AUC值为0.843。我们的结果表明，基于参考材料的方法具有提高大规模代谢组学研究中发现生物标记物的数据可比性和精确度的潜力。
Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography–mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.