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

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A Protocol for this article was published on 12 January 2017

An Erratum to this article was published on 30 July 2014

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.

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Acknowledgments

This work was in part supported by the UK Natural Environmental Research Council (NERC) Biomolecular Analysis Facility at the University of Birmingham (R8-H10-61) and by the British Heart Foundation. The FT-ICR used in this research was obtained through the Birmingham Science City Translational Medicine: Experimental Medicine Network of Excellence project, with support from Advantage West Midlands (AWM). David Broadhurst holds salary support from Pfizer Canada. We thank Leansale Ltd. Abattoir, Birmingham, for donating the hearts and Tristan Payne for providing MATLAB programming support.

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Authors and Affiliations

  1. School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    J. A. Kirwan & M. R. Viant

  2. Department of Medicine, University of Alberta, Edmonton, AB, Canada, T6G 2EI

    D. I. Broadhurst

  3. NERC Biomolecular Analysis Facility—Metabolomics Node (NBAF-B), University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    R. L. Davidson & M. R. Viant

Authors
  1. J. A. Kirwan
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  2. D. I. Broadhurst
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  4. M. R. Viant
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Correspondence to M. R. Viant.

Additional information

Published in the topical collection Metabolomics and Metabolite Profiling with guest editors Rainer Schuhmacher, Rudolf Krska, Roy Goodacre, and Wolfram Weckwerth.

J. A. Kirwan and D. I. Broadhurst contributed equally to this work.

An erratum to this article is available at http://dx.doi.org/10.1007/s00216-014-8047-6.

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Kirwan, J.A., Broadhurst, D.I., Davidson, R.L. et al. Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Anal Bioanal Chem 405, 5147–5157 (2013). https://doi.org/10.1007/s00216-013-6856-7

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  • DOI: https://doi.org/10.1007/s00216-013-6856-7

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