Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow

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Item Type:	Article
Title:	Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow
Creators Name:	Kirwan, J.A., Broadhurst, D.I., Davidson, R.L. and Viant, M.R.
Abstract:	Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate samples, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9%, measured as the median relative standard deviation (RSD) for the technical replicates of the biological samples, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20% as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.
Keywords:	Batch Effect, Block Effects, QC-RSC, Relative Standard Deviation, Reproducibility, Animals, Cattle, Sheep
Source:	Analytical and Bioanalytical Chemistry
ISSN:	1618-2642
Publisher:	Springer
Volume:	405
Number:	15
Page Range:	5147-5157
Date:	June 2013
Additional Information:	Erratum in: Anal Bioanal Chem 406(24):6075-6076.
Official Publication:	https://doi.org/10.1007/s00216-013-6856-7
PubMed:	View item in PubMed

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