代谢组学
协议(科学)
生命银行
医学实验室
数据收集
样品(材料)
数据科学
医学
计算机科学
医学物理学
风险分析(工程)
生物信息学
病理
统计
生物
替代医学
化学
色谱法
数学
作者
Jennifer Kirwan,Lorraine Brennan,David Broadhurst,Oliver Fiehn,Marta Cascante,Warwick B. Dunn,Michael A. Schmidt,Vidya Velagapudi
出处
期刊:Clinical Chemistry
[Oxford University Press]
日期:2018-06-19
卷期号:64 (8): 1158-1182
被引量:160
标识
DOI:10.1373/clinchem.2018.287045
摘要
Abstract BACKGROUND The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data. CONTENT This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices. SUMMARY Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, “time to freezer,” sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.
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