Abstract
BackgroundQuality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.Aim of reviewThis tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.Key scientific concepts of reviewSystem suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.
Highlights
Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of any research study, and in the context of metabolomics, they are critical for the acquisition of high quality data in any high-throughput analytical chemistry laboratory
We have discussed different types of system suitability and QC samples that can be used in untargeted MS-based metabolomics
We have argued the unique importance, and applicability of each type of system suitability and QC sample; described the metrics that can be used to enable confidence in both the ongoing reliability of a given analytical platform and provided advice on how to ensure the collection of high quality data
Summary
Clinical metabolomics (otherwise known as metabonomics or metabolic phenotyping) is a rapidly growing field of research, primarily focused on the investigation of human health (Dunn et al 2015), disease (Xie et al 2014) and ageing (Menni et al 2013), with diverse clinical application in areas such as prognostic biomarkers (Rhee et al 2016; Shah et al 2012; O’Gorman and Brennan 2017), pathophysiological mechanisms (Kirpich et al 2016; Terunuma et al 2014; Drenos et al 2016), and stratified medicine (Kaddurah-Daouk and Weinshilboum 2015). Further guidance has been developed for the measurement of biomarkers (usually proteins) with slightly different acceptance criteria (Lowes and Ackermann 2016) Whilst these guidelines provide a good practical foundation for metabolomics system suitability and QA/QC processes they were not designed with metabolomics in mind, and readily adaptable to (semi-) targeted methods, they are not translated into a form usable for untargeted metabolomics. In 2006, the introduction of a pragmatic approach to the use of pooled QC samples for within-study reporting of data quality helped drive the QC processes forward in this area (Sangster et al 2006) This initial work was further developed with recommendations as to how the data from such QCs could be analysed, (Gika et al 2007) and numerous papers and reviews have emerged from this early introduction (for example see, Dunn et al 2011b, 2012; Godzien et al 2015). We will discuss the motivation for each sample type, followed by recommendations on how to prepare the samples, how the resulting data are assessed to report quality, and how these samples can be integrated in to single or multi-batch analytical experiments
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