Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC)
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Published:2022-04
Issue:4
Volume:18
Page:
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ISSN:1573-3890
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Container-title:Metabolomics
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language:en
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Short-container-title:Metabolomics
Author:
Lippa Katrice A., Aristizabal-Henao Juan J., Beger Richard D., Bowden John A., Broeckling Corey, Beecher Chris, Clay Davis W., Dunn Warwick B., Flores Roberto, Goodacre Royston, Gouveia Gonçalo J., Harms Amy C., Hartung Thomas, Jones Christina M., Lewis Matthew R., Ntai Ioanna, Percy Andrew J., Raftery Dan, Schock Tracey B., Sun Jinchun, Theodoridis Georgios, Tayyari Fariba, Torta Federico, Ulmer Candice Z., Wilson Ian, Ubhi Baljit K.ORCID
Abstract
Abstract
Introduction
The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research.
Objectives
This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other ‘omics areas that generate high dimensional data.
Results
The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities.
Conclusions
The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
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