Utilisation of Ambient Laser Desorption Ionisation Mass Spectrometry (ALDI-MS) Improves Lipid-Based Microbial Species Level Identification.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
28 02 2019
Historique:
received: 13 08 2018
accepted: 30 01 2019
entrez: 1 3 2019
pubmed: 1 3 2019
medline: 17 9 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 30816263
doi: 10.1038/s41598-019-39815-w
pii: 10.1038/s41598-019-39815-w
pmc: PMC6395639
doi:

Substances chimiques

Lipids 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3006

Subventions

Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L020858/1
Pays : United Kingdom

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Auteurs

Simon J S Cameron (SJS)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Zsolt Bodai (Z)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Burak Temelkuran (B)

The Hamlyn Centre, Department of Computing, Faculty of Engineering, Imperial College London, South Kensington Campus, London, UK.

Alvaro Perdones-Montero (A)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Frances Bolt (F)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Adam Burke (A)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Kate Alexander-Hardiman (K)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.

Michel Salzet (M)

Laboratoire Proteomique, Reponse Inflammatoire et Spectrometrie de Mass (PRISM), Universite de Lille, Lille, France.

Isabelle Fournier (I)

Laboratoire Proteomique, Reponse Inflammatoire et Spectrometrie de Mass (PRISM), Universite de Lille, Lille, France.

Monica Rebec (M)

Department of Microbiology, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London, UK.

Zoltán Takáts (Z)

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK. z.takats@imperial.ac.uk.

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Classifications MeSH