11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC)

      review-article
      1 , 2 , 3 , 4 , 2 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 1 , 14 , 15 , 16 , 17 , 7 , 4 , 18 , 19 , 20 , 21 , 22 , 23 ,
      Metabolomics
      Springer US
      Reference materials, Certified reference materials, Internal standards, Untargeted analysis, Mass spectrometry, Metabolomics, Lipidomics, Metabolomics quality assurance and quality control consortium (mQACC)

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references79

          • Record: found
          • Abstract: found
          • Article: not found

          Membrane lipids: where they are and how they behave.

          Throughout the biological world, a 30 A hydrophobic film typically delimits the environments that serve as the margin between life and death for individual cells. Biochemical and biophysical findings have provided a detailed model of the composition and structure of membranes, which includes levels of dynamic organization both across the lipid bilayer (lipid asymmetry) and in the lateral dimension (lipid domains) of membranes. How do cells apply anabolic and catabolic enzymes, translocases and transporters, plus the intrinsic physical phase behaviour of lipids and their interactions with membrane proteins, to create the unique compositions and multiple functionalities of their individual membranes?
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.

            Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Untargeted Metabolomics Strategies—Challenges and Emerging Directions

              Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes, and as such the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating, workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS based untargeted metabolomics studies – specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.
                Bookmark

                Author and article information

                Contributors
                drbkubhi@gmail.com
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                9 April 2022
                9 April 2022
                2022
                : 18
                : 4
                : 24
                Affiliations
                [1 ]GRID grid.94225.38, ISNI 000000012158463X, Chemical Sciences Division, , National Institute of Standards and Technology (NIST), ; Gaithersburg, MD 20899 USA
                [2 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Physiological Sciences, Center for Environmental and Human Toxicology, College of Veterinary Medicine, , University of Florida, ; Gainesville, FL 32610 USA
                [3 ]GRID grid.510404.4, ISNI 0000 0004 6006 3126, BERG LLC, ; 500 Old Connecticut Path, Building B, 3rd Floor, Framingham, MA 01710 USA
                [4 ]GRID grid.417587.8, ISNI 0000 0001 2243 3366, Division of Systems Biology, National Center for Toxicological Research, , U.S. Food and Drug Administration (FDA), ; Jefferson, AR 72079 USA
                [5 ]GRID grid.47894.36, ISNI 0000 0004 1936 8083, Analytical Resources Core: Bioanalysis and Omics Center, , Colorado State University, ; Fort Collins, CO 80523 USA
                [6 ]IROA Technologies, Chapel Hill, NC 27517 USA
                [7 ]GRID grid.94225.38, ISNI 000000012158463X, Chemical Sciences Division, , National Institute of Standards and Technology (NIST), ; Charleston, SC 29412 USA
                [8 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, School of Biosciences, Institute of Metabolism and Systems Research and Phenome Centre Birmingham, , University of Birmingham, ; Birmingham, B15, 2TT UK
                [9 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Division of Program Coordination, Planning and Strategic Initiatives, Office of Nutrition Research, Office of the Director, , National Institutes of Health (NIH), ; Bethesda, MD 20892 USA
                [10 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, , University of Liverpool, ; BioSciences Building, Crown St., Liverpool, L69 7ZB UK
                [11 ]GRID grid.213876.9, ISNI 0000 0004 1936 738X, Department of Biochemistry and Molecular Biology, , University of Georgia, ; Athens, GA 30602 USA
                [12 ]GRID grid.5132.5, ISNI 0000 0001 2312 1970, Biomedical Metabolomics Facility Leiden, , Leiden University, ; Einsteinweg 55, 2333 CC Leiden, The Netherlands
                [13 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Bloomberg School of Public Health, Environmental Health and Engineering, , Johns Hopkins University, ; Baltimore, MD 21205 USA
                [14 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, National Phenome Centre, , Imperial College London, ; London, SW7 2AZ UK
                [15 ]GRID grid.418190.5, ISNI 0000 0001 2187 0556, Thermo Fisher Scientific, ; San Jose, CA 95134 USA
                [16 ]Cambridge Isotope Laboratories, Inc., Tewksbury, MA 01876 USA
                [17 ]GRID grid.34477.33, ISNI 0000000122986657, Northwest Metabolomics Research Center, , University of Washington, ; Seattle, WA 98109 USA
                [18 ]GRID grid.4793.9, ISNI 0000000109457005, Department of Chemistry, , Aristotle University, ; 54124 Thessaloniki, Greece
                [19 ]GRID grid.214572.7, ISNI 0000 0004 1936 8294, Department of Internal Medicine, , University of Iowa, ; Iowa City, IA 52242 USA
                [20 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Centre for Life Sciences, , National University of Singapore, ; 28 Medical Drive, Singapore, 117456 Singapore
                [21 ]GRID grid.416738.f, ISNI 0000 0001 2163 0069, Centers for Disease Control and Prevention (CDC), ; Atlanta, GA 30341 USA
                [22 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Computational & Systems Medicine, , Imperial College, ; Exhibition Rd, London, SW7 2AZ UK
                [23 ]MOBILion Systems Inc., 4 Hillman Drive Suite 130, Chadds Ford, PA 19317 USA
                Author information
                http://orcid.org/0000-0003-3010-7135
                Article
                1848
                10.1007/s11306-021-01848-6
                8994740
                35397018
                8a25b365-5c04-4150-8395-aabeac78d85a
                © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 June 2021
                : 7 October 2021
                Categories
                Review Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2022

                Molecular biology
                reference materials,certified reference materials,internal standards,untargeted analysis,mass spectrometry,metabolomics,lipidomics,metabolomics quality assurance and quality control consortium (mqacc)

                Comments

                Comment on this article