title: The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans.
abstract: The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics; to provide a statistically robust investigation, the study of hundreds to thousands of individuals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC samples to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.
authors.affiliation: Centre for Advanced Discovery & Experimental Therapeutics, Institute of Human Development, University of Manchester & Manchester Academic Health Sciences Centre, Central Manchester NHS Foundation Trust, York Place, Oxford Road, Manchester, M13 9WL, UK. warwick.dunn@manchester.ac.uk
authors.forename: Warwick B
authors.initials: WB
authors.lastname: Dunn
authors.forename: Ian D
authors.initials: ID
authors.lastname: Wilson
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Nicholls
authors.forename: David
authors.initials: D
authors.lastname: Broadhurst
mesh_terms: D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D011786:Quality Control; D012015:Reference Standards; D015203:Reproducibility of Results; D012107:Research Design