title: Automated High-Throughput Identification and Characterization of Clinically Important Bacteria and Fungi using Rapid Evaporative Ionization Mass Spectrometry.
abstract: Rapid evaporative ionization mass spectrometry (REIMS) has been shown to quickly and accurately speciate microorganisms based upon their species-specific lipid profile. Previous work by members of this group showed that the use of a hand-held bipolar probe allowed REIMS to analyze microbial cultures directly from culture plates without any prior preparation. However, this method of analysis would likely be unsuitable for a high-throughput clinical microbiology laboratory. Here, we report the creation of a customized platform that enables automated, high-throughput REIMS analysis that requires minimal user input and operation and is suitable for use in clinical microbiology laboratories. The ability of this high-throughput platform to speciate clinically important microorganisms was tested through the analysis of 375 different clinical isolates collected from distinct patient samples from 25 microbial species. After optimization of our data analysis approach, we achieved substantially similar results between the two REIMS approaches. For hand-held bipolar probe REIMS, a speciation accuracy of 96.3% was achieved, whereas for high-throughput REIMS, an accuracy of 93.9% was achieved. Thus, high-throughput REIMS offers an alternative mass spectrometry based method for the rapid and accurate identification of clinically important microorganisms in clinical laboratories without any preanalysis preparative steps.
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Frances
authors: F
authors: Bolt
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Simon J S
authors: SJ
authors: Cameron
authors: Waters Research Centre, 7 Zahony Street, Budapest, 1031, Hungary.
authors: Tamas
authors: T
authors: Karancsi
authors: Waters Research Centre, 7 Zahony Street, Budapest, 1031, Hungary.
authors: Daniel
authors: D
authors: Simon
authors: Waters Research Centre, 7 Zahony Street, Budapest, 1031, Hungary.
authors: Richard
authors: R
authors: Schaffer
authors: Department of Microbiology, Imperial College Healthcare NHS Trust , Charing Cross Hospital, London W6 8RF, United Kingdom.
authors: Tony
authors: T
authors: Rickards
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Kate
authors: K
authors: Hardiman
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Adam
authors: A
authors: Burke
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Zsolt
authors: Z
authors: Bodai
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Alvaro
authors: A
authors: Perdones-Montero
authors: Department of Microbiology, Imperial College Healthcare NHS Trust , Charing Cross Hospital, London W6 8RF, United Kingdom.
authors: Monica
authors: M
authors: Rebec
authors: Waters Research Centre, 7 Zahony Street, Budapest, 1031, Hungary.
authors: Julia
authors: J
authors: Balog
authors: Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London, SW7 2AZ, United Kingdom.
authors: Zoltan
authors: Z
authors: Takats
mesh_terms: D001419:Bacteria / Q000302:isolation & purification*; D005658:Fungi / Q000302:isolation & purification*; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D025341:Principal Component Analysis; D013269:Stochastic Processes