Metabolornic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies
Metabolornic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies
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DOI：
10.1016/j.aca.2016.12.029
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发表时间：
2017-02-22
影响因子：
6.2
通讯作者：
Rudaz, Serge
中科院分区：
文献类型：
--
作者：
Gagnebin, Yoric;Tonoli, David;Rudaz, Serge
在代谢组学中使用的各种生物基质中，尿液是主要感兴趣的生物流体，因为其非侵入性收集和大量可用性。然而，基于UHPLC-MS的尿液代谢组学变异性的重要来源与分析漂移和样品浓度变化有关，因此需要归一化。开发了一种顺序归一化策略来消除这些不利影响，包括：（i）通过单个稀释因子对采集前样品进行归一化，以缩小浓度范围并标准化分析条件，（ii）通过基于质量控制的稳健LOESS信号校正（QC-RLSC）对采集后数据进行归一化，以校正潜在的分析漂移，和（iii）通过MS总有用信号（MSTUS）或概率商归一化（PQN）进行采集后数据归一化，以防止浓度变化的影响。使用健康个体的尿液样本进行了该通用策略，并在临床研究中进一步实施，以检测肾衰竭引起的尿液代谢组学特征的改变。在肾衰竭的情况下，肌酐/渗透压与样品浓度之间的关系被修改，并且仅依赖于这些测量值进行标准化可能是非常有害的。顺序标准化策略被证明可以通过减少不必要的变异性来显著改善患者分层，从而提高数据质量。(C)2016爱思唯尔B. V.保留所有权利。
Among the various biological matrices used in metabolomics, urine is a biofluid of major interest because of its non-invasive collection and its availability in large quantities. However, significant sources of variability in urine metabolomics based on UHPLC-MS are related to the analytical drift and variation of the sample concentration, thus requiring normalization. A sequential normalization strategy was developed to remove these detrimental effects, including: (i) pre-acquisition sample normalization by individual dilution factors to narrow the concentration range and to standardize the analytical conditions, (ii) post-acquisition data normalization by quality control based robust LOESS signal correction (QC-RLSC) to correct for potential analytical drift, and (iii) post-acquisition data normalization by MS total useful signal (MSTUS) or probabilistic quotient normalization (PQN) to prevent the impact of concentration variability. This generic strategy was performed with urine samples from healthy individuals and was further implemented in the context of a clinical study to detect alterations in urine metabolomic profiles due to kidney failure. In the case of kidney failure, the relation between creatinine/osmolality and the sample concentration is modified, and relying only on these measurements for normalization could be highly detrimental. The sequential normalization strategy was demonstrated to significantly improve patient stratification by decreasing the unwanted variability and thus enhancing data quality. (C) 2016 Elsevier B.V. All rights reserved.