Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Metabolomics Platform, Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
Max Delbrück Center, Robert-Rössle Strasse 10, 13125, Berlin, Germany.
Metabolomics. 2022 Aug 27;18(9):70. doi: 10.1007/s11306-022-01926-3.
Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work.
The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources.
Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.
越来越多的人认为，证明代谢表型研究（代谢组学/代谢组学）中产生的数据质量良好是此类研究结果获得认可的关键因素。使用既定的质量控制 (QC) 协议，包括适当的 QC 样本，是这一过程的一个重要且不断发展的方面。然而，研究中遵循的 QA/QC 程序报告不足或不正确可能导致对研究结果的误解或过分强调，并阻止对该工作主体的未来荟萃分析。
本指南的目的是为研究人员提供一个框架，鼓励他们描述基于质谱和核磁共振光谱的非靶向代谢组学中质量评估和质量控制程序和结果，并重点详细报告 QC 样本，以便于理解、信任和复制。本指南无意对分析最佳实践进行规定；而是建议报告 QA/QC 程序的指南。提供了一个可在研究过程中完成的模板，以确保收集到相关数据，并提供了更多的文件作为在线资源。
在报告代谢表型数据的 QA/QC 协议和结果时，应考虑多个主题。涵盖范围应包括 QC 材料和样本的一般作用、来源、类型、制备和用途，这些材料和样本通常用于代谢组学数据的生成。详细信息，如样品基质和样品制备、测试混合物和系统适用性测试、空白和技术特定因素的使用等，均进行了考虑，并讨论了报告方法，包括报告 QC 接受标准的重要性。为此，QC 样本和结果的报告考虑了两个详细级别：“最低”和“最佳报告实践”级别。
