Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes.

BACKGROUND: Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network. METHODS: We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events. RESULTS: Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations. CONCLUSIONS: Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.

Rapid, Portable, and Electricity-free Sample Extraction Method for Enhanced Molecular Diagnostics in Resource-Limited Settings.

The COVID-19 pandemic has highlighted the need for rapid and reliable diagnostics that are accessible in resource-limited settings. To address this pressing issue, we have developed a rapid, portable, and electricity-free method for extracting nucleic acids from respiratory swabs (i.e. nasal, nasopharyngeal and buccal swabs), successfully demonstrating its effectiveness for the detection of SARS-CoV-2 in residual clinical specimens. Unlike traditional approaches, our solution eliminates the need for micropipettes or electrical equipment, making it user-friendly and requiring little to no training. Our method builds upon the principles of magnetic bead extraction and revolves around a low-cost plastic magnetic lid, called SmartLid, in combination with a simple disposable kit containing all required reagents conveniently prealiquoted. Here, we clinically validated the SmartLid sample preparation method in comparison to the gold standard QIAamp Viral RNA Mini Kit from QIAGEN, using 406 clinical isolates, including 161 SARS-CoV-2 positives, using the SARS-CoV-2 RT-qPCR assays developed by the US Centers for Disease Control and Prevention (CDC). The SmartLid method showed an overall sensitivity of 95.03% (95% CI: 90.44-97.83%) and a specificity of 99.59% (95% CI: 97.76-99.99%), with a positive agreement of 97.79% (95% CI: 95.84-98.98%) when compared to QIAGEN's column-based extraction method. There are clear benefits to using the SmartLid sample preparation kit: it enables swift extraction of viral nucleic acids, taking less than 5 min, without sacrificing significant accuracy when compared to more expensive and time-consuming alternatives currently available on the market. Moreover, its simplicity makes it particularly well-suited for the point-of-care where rapid results and portability are crucial. By providing an efficient and accessible means of nucleic acid extraction, our approach aims to introduce a step-change in diagnostic capabilities for resource-limited settings.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends.

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

Investigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Surface and Air Contamination in an Acute Healthcare Setting During the Peak of the Coronavirus Disease 2019 (COVID-19) Pandemic in London.

BACKGROUND: We evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface and air contamination during the coronavirus disease 2019 (COVID-19) pandemic in London. METHODS: Prospective, cross-sectional, observational study in a multisite London hospital. Air and surface samples were collected from 7 clinical areas occupied by patients with COVID-19 and a public area of the hospital. Three or four 1.0-m3 air samples were collected in each area using an active air sampler. Surface samples were collected by swabbing items in the immediate vicinity of each air sample. SARS-CoV-2 was detected using reverse-transcription quantitative polymerase chain reaction (PCR) and viral culture; the limit of detection for culturing SARS-CoV-2 from surfaces was determined. RESULTS: Viral RNA was detected on 114 of 218 (52.3%) surfaces and in 14 of 31 (38.7%) air samples, but no virus was cultured. Viral RNA was more likely to be found in areas immediately occupied by COVID-19 patients than in other areas (67 of 105 [63.8%] vs 29 of 64 [45.3%]; odds ratio, 0.5; 95% confidence interval, 0.2-0.9; P = .025, χ2 test). The high PCR cycle threshold value for all samples (>30) indicated that the virus would not be culturable. CONCLUSIONS: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from environmental contamination in managing COVID-19 and the need for effective use of personal protective equipment, physical distancing, and hand/surface hygiene.

Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis.

BACKGROUND: Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (COVID-19) in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions. METHODS: The study took place in a large teaching hospital group in London, United Kingdom. All patients tested for SARS-CoV-2 between 4 March and 14 April 2020 were included. Utilizing data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation and generated geotemporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users. RESULTS: Real-time surveillance reports revealed changing rates of HOCI throughout the course of the COVID-19 epidemic, key wards fueling probable transmission events, HOCIs overrepresented in particular specialties managing high-risk patients, the importance of integrating analysis of individual prior pathways, and the value of co-design in producing data visualization. Our surveillance system can effectively support national surveillance. CONCLUSIONS: Through early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterizing risk of transmission and targeting infection-control interventions.

A Multispecies Cluster of GES-5 Carbapenemase-Producing Enterobacterales Linked by a Geographically Disseminated Plasmid.

BACKGROUND: Early and accurate treatment of infections due to carbapenem-resistant organisms is facilitated by rapid diagnostics, but rare resistance mechanisms can compromise detection. One year after a Guiana Extended-Spectrum (GES)-5 carbapenemase-positive Klebsiella oxytoca infection was identified by whole-genome sequencing (WGS; later found to be part of a cluster of 3 cases), a cluster of 11 patients with GES-5-positive K. oxytoca was identified over 18 weeks in the same hospital. METHODS: Bacteria were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry, antimicrobial susceptibility testing followed European Committee on Antimicrobial Susceptibility Testing guidelines. Ertapenem-resistant isolates were referred to Public Health England for characterization using polymerase chain reaction (PCR) detection of GES, pulsed-field gel electrophoresis (PFGE), and WGS for the second cluster. RESULTS: The identification of the first GES-5 K. oxytoca isolate was delayed, being identified by WGS. Implementation of a GES-gene PCR informed the occurrence of the second cluster in real time. In contrast to PFGE, WGS phylogenetic analysis refuted an epidemiological link between the 2 clusters; it also suggested a cascade of patient-to-patient transmission in the later cluster. A novel GES-5-encoding plasmid was present in K. oxytoca, Escherichia coli, and Enterobacter cloacae isolates from unlinked patients within the same hospital group and in human and wastewater isolates from 3 hospitals elsewhere in the United Kingdom. CONCLUSIONS: Genomic sequencing revolutionized the epidemiological understanding of the clusters; it also underlined the risk of covert plasmid propagation in healthcare settings and revealed the national distribution of the resistance-encoding plasmid. Sequencing results also informed and led to the ongoing use of enhanced diagnostic tests for detecting carbapenemases locally and nationally.

Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care.

OBJECTIVES: The transmission of carbapenemase-producing Enterobacterales (CPE) poses an increasing healthcare challenge. A range of infection prevention activities, including screening and contact precautions, are recommended by international and national guidelines. We evaluated the introduction of an enhanced screening programme in a multisite London hospital group. METHODS: In June 2015, an enhanced CPE policy was launched in response to a local rise in CPE detection. This increased infection prevention measures beyond the national recommendations, with enhanced admission screening, contact tracing and environmental disinfection, improved laboratory protocols and staff/patient education. We report the CPE incidence and trends of CPE in screening and clinical cultures and the adoption of enhanced CPE screening. All non-duplicate CPE isolates identified between April 2014 and March 2018 were included. RESULTS: The number of CPE screens increased progressively, from 4530 in July 2015 to 10 589 in March 2018. CPE detection increased from 18 patients in July 2015 (1.0 per 1000 admissions) to 50 patients in March 2018 (2.7 per 1000 admissions). The proportion of CPE-positive screening cultures remained at approximately 0.4% throughout, suggesting that whilst the CPE carriage rate was unchanged, carrier identification increased. Also, 123 patients were identified through positive CPE clinical cultures over the study period; there was no significant change in the rate of CPE from clinical cultures per 1000 admissions (P = 0.07). CONCLUSIONS: Our findings suggest that whilst the enhanced screening programme identified a previously undetected reservoir of CPE colonization in our patient population, the rate of detection of CPE in clinical cultures did not increase.

Evaluation of Direct from Sample Metabolomics of Human Feces Using Rapid Evaporative Ionization Mass Spectrometry.

Mass spectrometry is a powerful tool in the investigation of the human fecal metabolome. However, current approaches require time-consuming sample preparation, chromatographic separations, and consequently long analytical run times. Rapid evaporative ionization mass spectrometry (REIMS) is a method of ambient ionization mass spectrometry and has been utilized in the metabolic profiling of a diverse range of biological materials, including human tissue, cell culture lines, and microorganisms. Here, we describe the use of an automated, high-throughput REIMS robotic platform for direct analysis of human feces. Through the analysis of fecal samples from five healthy male participants, REIMS analytical parameters were optimized and used to assess the chemical information obtainable using REIMS. Within the fecal samples analyzed, bile acids, including primary, secondary, and conjugate species, were identified, and phospholipids of possible bacterial origin were detected. In addition, the effect of storage conditions and consecutive freeze/thaw cycles was determined. Within the REIMS mass spectra, the lower molecular weight metabolites, such as fatty acids, were shown to be significantly affected by storage conditions for prolonged periods at temperatures above -80 °C and consecutive freeze/thaw cycles. However, the complex lipid region was shown to be unaffected by these conditions. A further cohort of 50 fecal samples, collected from patients undergoing bariatric surgery, were analyzed using the optimized REIMS parameters and the complex lipid region mass spectra used for multivariate modeling. This analysis showed a predicted separation between pre- and post-surgery specimens, suggesting that REIMS analysis can detect biological differences, such as microbiome-level differences, which have traditionally been reliant upon methods utilizing extensive sample preparations and chromatographic separations and/or DNA sequencing.

Automated High-Throughput Identification and Characterization of Clinically Important Bacteria and Fungi using Rapid Evaporative Ionization Mass Spectrometry.

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.

Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates.

Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48, and blaVIM. Combining the recently reported ML method "Amplification and Melting Curve Analysis" (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single-fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p-value <0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additional costs, thus potentially providing substantial clinical utility on screening patients for CPO carriage.

Handheld Point-of-Care System for Rapid Detection of SARS-CoV-2 Extracted RNA in under 20 min.

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

Novel Corynebacterium diphtheriae in domestic cats.

Novel nontoxigenic Corynebacterium diphtheriae was isolated from a domestic cat with severe otitis. Contact investigation and carrier study of human and animal contacts yielded 3 additional, identical isolates from cats, although no evidence of zoonotic transmission was identified. Molecular methods distinguished the feline isolates from known C. diphtheriae.

Multilocus sequence typing identifies evidence for recombination and two distinct lineages of Corynebacterium diphtheriae.

We describe the development of a multilocus sequence typing (MLST) scheme for Corynebacterium diphtheriae, the causative agent of the potentially fatal upper respiratory disease diphtheria. Global changes in diphtheria epidemiology are highlighted by the recent epidemic in the former Soviet Union (FSU) and also by the emergence of nontoxigenic strains causing atypical disease. Although numerous techniques have been developed to characterize C. diphtheriae, their use is hindered by limited portability and, in some instances, poor reproducibility. One hundred fifty isolates from 18 countries and encompassing a period of 50 years were analyzed by multilocus sequence typing (MLST). Strain discrimination was in accordance with previous ribotyping data, and clonal complexes associated with disease outbreaks were clearly identified by MLST. The data produced are portable, reproducible, and unambiguous. The MLST scheme described provides a valuable tool for monitoring and characterizing endemic and epidemic C. diphtheriae strains. Furthermore, multilocus sequence analysis of the nucleotide data reveals two distinct lineages within the population of C. diphtheriae examined, one of which is composed exclusively of biotype belfanti isolates and the other of multiple biotypes.

Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System.

The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of colistin-resistant organisms. This work demonstrates a rapid response for detecting the most recently reported mcr gene, mcr-9, using a portable and affordable lab-on-a-chip (LoC) platform, offering a promising alternative to conventional laboratory-based instruments such as real-time PCR (qPCR). The platform combines semiconductor technology, for non-optical real-time DNA sensing, with a smartphone application for data acquisition, visualization and cloud connectivity. This technology is enabled by using loop-mediated isothermal amplification (LAMP) as the chemistry for targeted DNA detection, by virtue of its high sensitivity, specificity, yield, and manageable temperature requirements. Here, we have developed the first LAMP assay for mcr-9 - showing high sensitivity (down to 100 genomic copies/reaction) and high specificity (no cross-reactivity with other mcr variants). This assay is demonstrated through supporting a hospital investigation where we analyzed nucleic acids extracted from 128 carbapenemase-producing bacteria isolated from clinical and screening samples and found that 41 carried mcr-9 (validated using whole genome sequencing). Average positive detection times were 6.58 ± 0.42 min when performing the experiments on a conventional qPCR instrument (n = 41). For validating the translation of the LAMP assay onto a LoC platform, a subset of the samples were tested (n = 20), showing average detection times of 6.83 ± 0.92 min for positive isolates (n = 14). All experiments detected mcr-9 in under 10 min, and both platforms showed no statistically significant difference (p-value > 0.05). When sample preparation and throughput capabilities are integrated within this LoC platform, the adoption of this technology for the rapid detection and surveillance of antimicrobial resistance genes will decrease the turnaround time for DNA detection and resistotyping, improving diagnostic capabilities, patient outcomes, and the management of infectious diseases.

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

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.

Metabolic Phenotyping and Strain Characterisation of Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients Using Rapid Evaporative Ionisation Mass Spectrometry.

Rapid evaporative ionisation mass spectrometry (REIMS) is a novel technique for the real-time analysis of biological material. It works by conducting an electrical current through a sample, causing it to rapidly heat and evaporate, with the analyte containing vapour channelled to a mass spectrometer. It was used to characterise the metabolome of 45 Pseudomonas aeruginosa (P. aeruginosa) isolates from cystic fibrosis (CF) patients and compared to 80 non-CF P. aeruginosa. Phospholipids gave the highest signal intensity; 17 rhamnolipids and 18 quorum sensing molecules were detected, demonstrating that REIMS has potential for the study of virulence-related metabolites. P. aeruginosa isolates obtained from respiratory samples showed a higher diversity, which was attributed to the chronic nature of most respiratory infections. The analytical sensitivity of REIMS allowed the detection of a metabolome that could be used to classify individual P. aeruginosa isolates after repeated culturing with 81% accuracy, and an average 83% concordance with multilocus sequence typing. This study underpins the capacities of REIMS as a tool with clinical applications, such as metabolic phenotyping of the important CF pathogen P. aeruginosa, and highlights the potential of metabolic fingerprinting for fine scale characterisation at a sub-species level.

Effect of Electrode Geometry on the Classification Performance of Rapid Evaporative Ionization Mass Spectrometric (REIMS) Bacterial Identification.

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.

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) Provides Accurate Direct from Culture Species Identification within the Genus Candida.

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.