27Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has been shown to quickly and accurately 28 speciate microorganisms based upon their species-specific lipid profile. Previous work by members of 29 this group showed that the use of a handheld bipolar probe allowed REIMS to analyse microbial 30 cultures directly from culture plates, without any prior preparation. However, this method of analysis 31 would likely be unsuitable for a high-throughput clinical microbiology laboratory. Here, we report on 32 the creation of a customised platform which enables automated, high-throughput REIMS analysis, 33 which requires minimal user input and operation; and suitable for use in clinical microbiology 34 laboratories. The ability of this high-throughput platform to speciate clinically important 35 microorganisms was tested through the analysis of 375 different clinical isolates, collected from 36 distinct patient samples, from 25 microbial species. After optimisation of our data analysis approach, 37we achieved substantially similar results between the two REIMS approaches. For handheld bipolar 38 probe REIMS a speciation accuracy of 96.3% was achieved, whilst for high-throughput REIMS, an 39 accuracy of 93.9% was achieved. Thus, high-throughput REIMS offers an alternative mass 40 spectrometry based method for the rapid and accurate identification of clinically important 41 microorganisms in clinical laboratories without any pre-analysis preparative steps.

Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value < 0.05) lipids within the 600 to 1000 m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities.

The recently developed automated, high-throughput monopolar REIMS platform is suited for the identification of clinically important microorganisms. Although already comparable to the previously reported bipolar forceps method, optimization of the geometry of monopolar electrodes, at the heart of the system, holds the most scope for further improvements to be made. For this, sharp tip and round shaped electrodes were optimized to maximize species-level classification accuracy. Following optimization of the distance between the sample contact point and tube inlet with the sharp tip electrodes, the overall cross-validation accuracy improved from 77% to 93% in negative and from 33% to 63% in positive ion detection modes, compared with the original 4 mm distance electrode. As an alternative geometry, round tube shaped electrodes were developed. Geometry optimization of these included hole size, number, and position, which were also required to prevent plate pick-up due to vacuum formation. Additional features, namely a metal “X”-shaped insert and a pin in the middle were included to increase the contact surface with a microbial biomass to maximize aerosol production. Following optimization, cross-validation scores showed improvement in classification accuracy from 77% to 93% in negative and from 33% to 91% in positive ion detection modes. Supervised models were also built, and after the leave 20% out cross-validation, the overall classification accuracy was 98.5% in negative and 99% in positive ion detection modes. This suggests that the new generation of monopolar REIMS electrodes could provide substantially improved species level identification accuracies in both polarity detection modes. Graphical abstract Electronic supplementary materialThe online version of this article (10.1007/s13361-017-1818-5) contains supplementary material, which is available to authorized users.

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO2) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO2 laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.

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