Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

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.

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

“…Quality assurance (QA) and quality control (QC) are important processes to ensure robust data acquisition and to maintain confidence in analysis results. Their importance gains increasing recognition in the metabolomics community 5,21,22 and they are required for specific applications by governmental institutions such as the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) who provide specific guidelines 23,24 . Put simply, while QA defines processes planned and performed to fulfill defined quality requirements, QC comprises measures to report whether these quality requirements have been met.…”

“…While standardized methods such as the Targeted Metabolomics Kits of Biocrates 8 promise consistent, reproducible and comparable measurements, they are not fully resistant to external influences. These include sample to sample concentration differences, sample handling and processing errors, contamination, sample carryover, batch effects, intra-batch drift, edge effects, missing values of unknown origin and instrument condition 5,7,[9][10][11] . Consequently, multiple checks and controls are required to verify data quality, consistency and reproducibility (i.e.…”

Challenges and Opportunities in Cancer Metabolomics

“…Newer data‐oriented efforts such as deep learning methods (rather than machine learning tools) was demonstrated to help successfully discriminate positive estrogen receptor (ER+) and negative estrogen receptor (ER−) in breast cancer types, and they remain promising for future efforts in the field of cancer metabolomics . With newer efforts to revitalize the existing MSI and data harmonization alongside QA and QC measures in untargeted clinical metabolomics, more robust efforts in cancer and tumor tissue sampling, collection, and analysis would be needed to match the analytical and bioinformatics progress in metabolomics.…”

Tools and resources for metabolomics research community: A 2017–2018 update

“…The data preprocessing tools, which define the quality of data generated from a specific platform for further interpretation, are of interest to the community as well. Given that QA and QC as well as system suitability are critical for the data generation and reproducibility thereof, an excellent tutorial underlining the best practices has been delineated for the research community . For instance, a recent study that investigated patterns of missing data in an MS‐based metabolomics experiment of serum samples from the German Cooperative Health Research in the region of Augsburg (KORA) S4/F4 cohort ( n = 1750) using 31 imputation methods revealed that k‐nearest neighbors (KNNs) imputation on observations with variable preselection showed robust performance across all evaluation schemes and was computationally more tractable .…”