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Utilisation of Ambient Laser Desorption Ionisation Mass Spectrometry (ALDI-MS) improves lipid-based microbial species level identification

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Title: Utilisation of Ambient Laser Desorption Ionisation Mass Spectrometry (ALDI-MS) improves lipid-based microbial species level identification
Authors: Cameron, SJS
Bodai, Z
Temelkuran, B
Perdones-Montero, A
Bolt, F
Burke, A
Alexander-Hardiman, K
Salzet, M
Fournier, I
Rebec, M
Takáts, Z
Item Type: Journal Article
Abstract: The accurate and timely identification of the causative organism of infection is important in ensuring the optimum treatment regimen is prescribed for a patient. Rapid evaporative ionisation mass spectrometry (REIMS), using electrical diathermy for the thermal disruption of a sample, has been shown to provide fast and accurate identification of microorganisms directly from culture. However, this method requires contact to be made between the REIMS probe and microbial biomass; resulting in the necessity to clean or replace the probes between analyses. Here, optimisation and utilisation of ambient laser desorption ionisation (ALDI) for improved speciation accuracy and analytical throughput is shown. Optimisation was completed on 15 isolates of Escherichia coli, showing 5 W in pulsatile mode produced the highest signal-to-noise ratio. These parameters were used in the analysis of 150 clinical isolates from ten microbial species, resulting in a speciation accuracy of 99.4% - higher than all previously reported REIMS modalities. Comparison of spectral data showed high levels of similarity between previously published electrical diathermy REIMS data. ALDI does not require contact to be made with the sample during analysis, meaning analytical throughput can be substantially improved, and further, increases the range of sample types which can be analysed in potential direct-from-sample pathogen detection.
Issue Date: 28-Feb-2019
Date of Acceptance: 30-Jan-2019
URI: http://hdl.handle.net/10044/1/67928
DOI: https://dx.doi.org/10.1038/s41598-019-39815-w
ISSN: 2045-2322
Publisher: Nature Publishing Group
Journal / Book Title: Scientific Reports
Volume: 9
Issue: 1
Copyright Statement: © The Author(s) 2019. This 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 Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted 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 license, visit http://creativecommons.org/licenses/by/4.0/.
Publication Status: Published
Conference Place: England
Open Access location: https://www.nature.com/articles/s41598-019-39815-w
Article Number: ARTN 3006
Appears in Collections:Division of Surgery
Computing
Faculty of Medicine



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