This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/rcm.8010 This article is protected by copyright. All rights reserved. Rapid Communication in Mass Spectrometry Research Article Mass Spectrometry in Untargeted LC-MS Metabolomics: ESI parameters and global coverage of the metabolome Fidele Tugizimana 1 , Paul A. Steenkamp 1, 2 , Lizelle A. Piater 1 and Ian A. Dubery 1, * 1 Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa 2 Drug Discovery and Development, CSIR Biosciences, Pretoria, South Africa * Author to whom correspondence should be addressed; E-Mail: idubery@uj.ac.za; Tel.: +27-011-559-2401; Fax: +27-011-559-2370. RATIONALE: Liquid chromatography coupled to mass spectrometry (LC-MS) is a dominant analytical platform in metabolomics, because of high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC-MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC-ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations. METHODS: Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC-ESI-MS system. Here the capillary and sample cone voltages of the ZSpray TM ESI ion source were varied between 1.5–3.0 kV and 10.0–40.0 V, respectively. The acquired data were processed with MarkerLynx TM software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis). RESULTS: The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome. CONCLUSIONS: The observations postulate in that in untargeted-LC-MS metabolomics, ‘what you see is what you ionise’. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.