标准化
代谢组学
可靠性(半导体)
样本量测定
生物标志物发现
数据科学
色谱法
化学
风险分析(工程)
生化工程
样品(材料)
数据挖掘
计算机科学
生物
生物信息学
医学
统计
工程类
数学
蛋白质组学
功率(物理)
物理
生物化学
量子力学
基因
操作系统
作者
Vinicius Veri Hernandes,Coral Barbas,Danuta Dudzik
标识
DOI:10.1002/elps.201700086
摘要
Abstract Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre‐analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre‐processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre‐analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre‐processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.
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