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Automated High-Throughput Identification and Characterisation of Clinically Important Bacteria and Fungi using Rapid Evaporative Ionisation Mass Spectrometry (REIMS) Frances Bolt, Simon J S Cameron, Tamas Karancsi, Daniel Simon, Richard Schaffer, Tony Rickards, Kate Hardiman, Adam Burke, Zsolt Bodai, Alvaro Perdones-Montero, Monica Rebec, Julia Balog, and Zoltan Takats Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01016 • Publication Date (Web): 25 Aug 2016 Downloaded from http://pubs.acs.org on August 30, 2016

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Analytical Chemistry

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Automated High-Throughput Identification and Characterisation of Clinically Important Bacteria

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and Fungi using Rapid Evaporative Ionisation Mass Spectrometry (REIMS)

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Frances Bolt (1)†; Simon JS Cameron (1)†; Tamas Karancsi (2); Daniel Simon (2); Richard Schaffer (2);

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Tony Rickards (3); Kate Hardiman (1); Adam Burke (1); Zsolt Bodai (1); Alvaro Perdones-Montero (1);

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Monica Rebec (3); Julia Balog (2); Zoltan Takats (1)*.

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(1) Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial

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College London, London, SW7 2AZ, United Kingdom; (2) Waters Research Centre, 7 Zahony Street,

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Budapest, 1031, Hungary; (3) Department of Microbiology, Imperial College Healthcare NHS Trust,

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Charing Cross Hospital, London, W6 8RF, United Kingdom

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† Joint first authors / Contributed equally

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* Corresponding Author: Professor Zoltan Takats, Division of Computational and Systems Medicine,

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Department of Surgery and Cancer, Imperial College London, South Kensington Campus, Sir

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Alexander

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[email protected]

Fleming

Building,

London,

SW7

2AZ.

Telephone:

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1 Environment ACS Paragon Plus

020

7594

2760.

Email:


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Abstract

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Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has been shown to quickly and accurately

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speciate microorganisms based upon their species-specific lipid profile. Previous work by members

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of this group showed that the use of a handheld bipolar probe allowed REIMS to analyse microbial

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cultures directly from culture plates, without any prior preparation. However, this method of

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analysis would likely be unsuitable for a high-throughput clinical microbiology laboratory. Here, we

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report on the creation of a customised platform which enables automated, high-throughput REIMS

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analysis, which requires minimal user input and operation; and suitable for use in clinical

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microbiology laboratories. The ability of this high-throughput platform to speciate clinically

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important microorganisms was tested through the analysis of 375 different clinical isolates, collected

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from distinct patient samples, from 25 microbial species. After optimisation of our data analysis

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approach, we achieved substantially similar results between the two REIMS approaches. For

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handheld bipolar probe REIMS a speciation accuracy of 96.3% was achieved, whilst for high-

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throughput REIMS, an accuracy of 93.9% was achieved. Thus, high-throughput REIMS offers an

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alternative mass spectrometry based method for the rapid and accurate identification of clinically

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important microorganisms in clinical laboratories without any pre-analysis preparative steps.

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Introduction

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The introduction of mass spectrometry based microbial identification has revolutionised clinical

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microbiology laboratories. Historically, species delineations were often reliant on phenotypic and

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biochemical characteristics, which were laborious, costly, and often extended the time required for

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results by days

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VITEK MS (bioMérieux) have reduced the time to identification, they do require the addition of a

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matrix to assist in lysis and ionisation and, in some instances, additional extraction steps are

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required for accurate species classification 2,4.

1-3

. Whilst mass spectrometry platforms including the MALDI Biotyper (Bruker) and

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Rapid evaporative ionisation mass spectrometry (REIMS) provides accurate speciation of bacteria

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and yeasts directly from colonies without the need for additional preparative steps 5,6. In contrast to

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many of the commercially available instruments, which are based upon the fingerprinting of proteins

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within the mass range 2−20 kDa 7, REIMS allows the lipidomic profile of bacteria and fungi to be

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determined. The use of lipid analysis for microbial taxonomic classifications is well established and,

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gas chromatography fatty acid profiling, has been widely used for over fifty years

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also important in differentiating bacteria using Pyrolysis MS; however, in early studies this technique

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was limited as only small molecules and fragments of larger molecules were observed due to the

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high energy ionisation process 13-16. Fast atom bombardment (FAB-MS) based upon the desorption

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of phospholipid molecules also allowed the differentiation of bacterial species

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desorption electrospray ionization (DESI) provided strain level resolution on intact bacteria based

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upon analysis within the lipid region 18.

17

8-12

. Lipids were

. More recently,

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REIMS works by applying a radiofrequency electrical current to the microorganism, which causes

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rapid heating of microbial biomass. The resulting vapour, containing gas phase ions of metabolites

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and structural lipids, is channelled to a mass spectrometer for MS analysis. We have previously


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demonstrated that lipidomic spectral profiles obtained using REIMS could be used for the accurate

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speciation of yeast and 28 clinically important bacteria 6.

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To increase throughput and reduce costs, clinical microbiology laboratories have undergone rapid

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automation. This has been the result of a range of factors, including increased financial pressures,

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centralisation of laboratory diagnostics, and substantially increased number of specimens 19. Current

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methods of automation are increasingly being paired with mass spectrometry platforms for

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microbial identification and have been shown to reduce the time to identification by approximately

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24 hours and improve antibiotic prescribing practices

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clinical microbiology laboratories

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customised TECAN Freedom EVO instrument integrated with a Pickolo system (SciRobotics, Israel) to

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enable automated analysis. This provides a single platform for automated colony picking, REIMS

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analysis, and molecular processing thereby minimising user related errors and thus making it highly

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applicable to a modern clinical microbiology laboratory’s workflow. As described here, the method is

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simple, requires minimal user input, and can easily be adapted to fit into a full laboratory automated

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workflow

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further reduce analysis times and improve patient outcomes.

20

21,22

20

. Due to the high-throughput nature of

, the REIMS platform has since been modified using a

. Furthermore, because the technique requires no pre-analysis preparation, it could

96 97 98 99 100 101 102 103 104



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Experimental Section

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Culturing of Microbial Isolates

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A total of 375 isolates comprising 25 bacterial and fungal species, shown in Supplementary Table S1,

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were collected from clinical samples routinely processed at the Imperial College Healthcare Trust

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(London, UK) microbiology diagnostic laboratory. Prior to REIMS analysis, each isolate was cultivated

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according to the conditions detailed in Table S1. Fifteen isolates were examined for each of the 25

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species.

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MALDI Biotyper Identification

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Microbial species identifications were verified for all isolates using matrix assisted laser desorption

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ionisation time of flight (MALDI-TOF) mass spectrometry on the Bruker Microflex LT (Bruker

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Daltonics, Coventry, UK) system, according to the manufacturer’s instructions. For bacteria, a single

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colony of culture was spotted onto a 24-spot steel plate (Bruker Daltonics). After drying, 1 µl of α-

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cyano-4-hydroxycinnamic acid (HCCA) matrix, (Bruker Daltonics) dissolved in 50% acetonitrile, 47.5%

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water, and 2.5% trifluoroacetic acid was applied. Once dried, the samples were analysed following

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manufacturer’s instructions. For yeasts, one colony was suspended in 1 mL of 70% ethanol and

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homogenised. The sample then underwent centrifugation at 15,000 x g for 2 minutes and the

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supernatant was removed. After drying at room temperature for 30 minutes, between 10 µL and 60

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µL of 70% formic acid, depending on the size of the culture pellet, was added and mixed through

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pipetting. Pure acetonitrile was then added in an equal amount to formic acid and incubated at

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room temperature for 3 minutes. The sample underwent centrifugation at 15,000 x g for 3 minutes

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and 1 µL of supernatant was spotted onto an analysis plate. After drying, the spot was overlaid with

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1 µL of HCCA matrix as previously described and allowed to dry before analysis following

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manufacturer’s instructions. Both bacterial and yeast isolates were analysed using the Bruker

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Biotyper software (version 3.0) and library (version 5.0) for taxonomic classification.


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REIMS Analysis using Handheld Bipolar Probe

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REIMS analysis with a handheld bipolar probe was performed using a similar protocol to that

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described earlier6. In brief, handheld bipolar probes (Erbe Elektromedizin, Tübingen, Germany) were

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combined with an ERBE IC 300 electrosurgical generator (Erbe Elektromedizin) used at a 70 W power

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setting in biopolar mode as a radio frequency alternating current power supply (470 kHz sinusoid). A

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ca. 1.5 m long (inner diameter 1.5 mm, outer diameter 3.2 mm) PTFE tube (Sigma-Aldrich, UK) was

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used to connect the handheld bipolar probe to the inlet capillary of a Xevo G2-XS Q-ToF mass

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spectrometer instrument (Waters Corporation, Wilmslow, UK), allowing transfer of the generated

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aerosol. The vacuum of the mass spectrometer was used for aspiration of the aerosol and it was co-

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aspirated, at a flow rate of 0.2 mL/s, with 2-propanol (HPLC Chromasolv grade, Sigma Aldrich)

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containing Leu-enkephaline (VWR) at a concentration of 10 ng/µL. The aerosol and 2-propanol/leu-

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enkephaline solution was introduced using a T-piece as shown in Supplementary Figure S1. For each

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analytical repeat, between one and five colonies, was removed from the culture plate using one side

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of the handheld bipolar probe. The handheld bipolar probe was then closed and the power supply

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was triggered using a manual foot switch, which caused the biomass to rapidly evaporate and

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produce an analyte-containing aerosol. Five individual measurements were performed for each

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isolate.

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High-Throughput REIMS Analysis

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To allow for high-throughput REIMS analysis, a commercially available colony picker robot (Freedom

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EVO 75 (TECAN, Switzerland) equipped with a Pickolo platform (SciRobotics, Israel)) was used. As

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shown in Figure 1, the user places the agar plates in the rack, (a), and loads the probes in position,

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(b). High-throughput REIMS uses a modified command script whereby the user initially selects the

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number of culture plates to be analysed. The robotic arm of the instrument then picks up the first

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plate and places it on the imaging platform, Figure 1c. The operator is then able to see microbial



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colonies and select ones of interest, or the software is able to automatically detect individual

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colonies for analysis. In this study, only the operator select mode was used for colony identification.

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After the selection of colonies, a second arm picks up a stainless steel electrode probe as

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demonstrated by Figure 1d. The bespoke stainless steel probe is a modified version of a 200 µL pure

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tip (TECAN). Once the probe touches the microbial colony, an ERBE IC 300 electrosurgical generator,

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set at a 17 W power setting in monopolar mode, applies an electrical current. The rapid heating of

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the microbial biomass results in the formation of aerosol which is channelled from the probe

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through a ca. 1.5 M long (inner diameter 1.5 mm, outer diameter 3.2 mm) PTFE tube (Sigma-Aldrich)

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to a Xevo G2-XS Q-ToF instrument (Waters Corporation). The analyte aerosol is co-aspirated with a

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10 ng/µL solution of leu-enkephaline in isopropanol as described for handheld bipolar probe REIMS

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analysis. For each isolate, a total of five individual measurements, were performed with one

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microbial colony used for each.

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Operation of Xevo G2-XS Q-ToF Mass Spectrometer

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The Xevo G2-XS Q-ToF instrument was operated under the instrument settings detailed in

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Supplementary Table S2 for both handheld bipolar probe REIMS and high-throughput REIMS

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analysis. Prior to its use each day, the mass spectrometer was calibrated using sodium formate in

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negative electrospray ionisation mode, following the manufacturer’s standard instructions. All mass

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spectrometry analysis was performed in negative ionisation mode across the 50 to 2500 m/z range.

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Data Analysis

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All acquired raw mass spectrometry files were imported into the Offline Model Builder (OMB)

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software (Waters Research Center, Budapest, Hungary) for preprocessing and multivariate analysis.

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First the data scans were combined into spectra, resulting in five individual replicate spectra of each

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strain. The spectra were background subtracted, and lockmass corrected on external compound

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Leucin-encephalin (negative ionisation = m/z 554.2516). After lockmass correction, spectra were TIC


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normalised and rebinned to 0.1 Da. After preprocessing, the data was subjected to multiple

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multivariate algorithms. First a principle component analysis (PCA) was calculated, and using the first

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50 principle components, a supervised linear discriminant analysis (LDA) was calculated for

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classification keeping the first 15 discriminant components. Both PCA and LDA attempt to identify

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linear combinations of variables within a data set. PCA is an unsupervised data analysis approach

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which does not take into account class seperation of samples

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approach which explicity looks to identify linear relationships between variables taking into account

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class groupings of samples 24. Here, we initially used PCA modelling to reduce the dimensionality of

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our data set, and then used LDA to identify relationships between variables to form the basis of

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classification models. The resulting mutivariate ’models’ were validated with leave-one isolate out

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cross-validation, i.e. each data file containing spectra acquired from one isolate was left out of the

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training set, a model was built on all other datasets and the data file left out was classified using the

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training model. Each spectrum was classified to the closest class in the LDA space based on the

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Mahalanobis distances between objects 25. The correct classification rate was calculated based on

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the number of spectra classified correctly divided by all spectra of the full dataset. For all species

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analysis, a model built using all isolates from 25 species was created and used in cross-validation. For

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taxonomic groupings analysis, individual models were constructed for distinct groups at each

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taxonomic hierarchy, namely phylum, class, order, family, genus, and species. A probability matrix

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was then constructed to calculate the likely probability that an isolate would be correctly identified

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to its taxonomic grouping at each level, shown on Supplementary Figure S2. For Random Forest

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analyses, a total of 400 decision trees were constructed for each model based on a mass spectral

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matrix exported from the OMB software after data normalisation, background removal, and lock-

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massing to leu-enkaphaline. For each model, a mass range of 600 to 900 m/z was used, with mass

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binning to 0.1.

23

. LDA however, is a supervised

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Safety considerations



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In this work, all microorganisms were treated as Hazard Group 2 organisms and were therefore

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manipulated within containment level 2 facilities. All cultures and REIMS analyses were performed

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within a class 2 biological safety cabinet. All solvents, such as 2-propanol and methanol, were

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handled according to the material safety data sheet provided by their respective manufacturer.

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Results and Discussion

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The use of REIMS for the successful classification and characterisation of bacteria and yeasts has

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previously been reported

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(electrosurgical bipolar forceps) for analysis and thus may not be suitable for use in a high-

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throughput, clinical microbiology laboratory. Here, an automated instrument incorporating a colony

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picker robot and a mass spectrometer was developed to allow for high-throughput REIMS

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processing. This study explored its use for the identification of microbes by analysing a taxonomically

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diverse collection of bacteria and fungi. A total of 15 isolates from 25 species were analysed using

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both REIMS approaches. This represents a total of 375 distinct clinical isolates of which 8% were

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fungi, 52% were Gram-positive bacteria, and 40% Gram-negative bacteria. All of the species

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examined are commonly encountered within a clinical diagnostic laboratory. Some species, including

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Lactobacillus jensenii, are frequently isolated but rarely cause disease whilst others, including

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Escherichia coli and Staphylococcus aureus, more frequently result in severe infections.

5,6

. However, this approach employed a handheld surgical tool

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Comparison Between Handheld Bipolar and High-Throughput Monopolar Probe REIMS

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Similar classification accuracies were obtained at Gram-stain, genus, and species level using both

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manual and high-throughput automated REIMS approaches. However, high-throughput REIMS

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showed a slightly lower (94% to 96%), genus and species level accuracy compared to handheld

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bipolar probe REIMS. As Figure 2 demonstrates, this may result from differences in the temperature

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gradient during the rapid heating of the bipolar (handheld) and monopolar (high-throughput) probe.

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Because the handheld bipolar probe generates a radiofrequency electrical current between two

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probe surfaces, the temperature is lower, but more consistent through the microbial biomass,

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compared to the monopolar probe. The latter may result in reduced ionisation of some microbial

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metabolites due to the higher localised temperature at the point of contact between the electrical

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probe and microbial biomass. This may in part, explain the slightly reduced level of genus and

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species level classification accuracy observed. Optimisation of the high-throughput REIMS’s


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monopolar probe will be pursued to modify the temperature at the point of contact to improve

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biomass ionisation and thus species-level accuracy.

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Although the high-throughput REIMS approach has a slightly reduced level of genus and species-

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level accuracy, it is capable of analysing substantially higher numbers of microbial colonies, with

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reduced variability, and user input. At present, this method is potentially capable of analysing

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approximately 3,000 to 4,000 microbial colonies over a 24 hour period. The increased analysis

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throughput, reduced variability, and user input makes the high-throughput REIMS approach,

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reported here, well suited to implementation in a clinical microbiology laboratory.

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Effect of Mixing Analyte Aerosol with Solvent Matrix

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The previously reported handheld bipolar REIMS method did not incorporate a solvent matrix within

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the analyte aerosol. Here, we used 2-propanol as a solvent matrix based upon preliminary data

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which revealed that it improves detectability (i.e. by the incremental improvement of MS response

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factor) of spectral features. Furthermore, it reduces the contamination of the mass spectrometer by

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removing all solid deposits from the capillary inlet of the instrument and allows an external lock

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mass compound to be used so that mass drift correction can be applied. This ability would be

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essential for a platform introduced into a clinical microbiology laboratory, since there is no universal

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microbial marker compound which could be used as a lock mass reference similarly to human tissue

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REIMS analysis 26.

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Supplementary Figure S3 shows the Gram-stain, genus, and species-level cross-validation scores for

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both handheld bipolar probe REIMS and high-throughput REIMS with and without 2-propanol

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infusion. Although Gram-stain accuracy is comparable, there is a marked reduction in genus and

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species-level accuracy when isolates are analysed using both handheld bipolar probe REIMS and

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high-throughput REIMS without 2-propanol infusion.



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Initial All Species Analysis

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Initial data analysis evaluated the entire acquisition range of 50 to 2500 m/z, using a mass bin of 1.

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An LDA model was created using all 375 isolates and CV scores were determined from this for both

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REIMS approaches (shown in Figure 3). For handheld bipolar probe REIMS, cross-validation scores to

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Gram-stain, genus, and species levels were 99.2%, 94.1%, and 88.0% respectively. Cross-validation

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scores were 98.7%, 91.5%, and 85.6% for each taxonomic level for high-throughput REIMS.

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Supplementary Table S2 shows the individual species cross-validation scores and suggests that the

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overall cross-validation score is being reduced substantially by poor scores associated with closely

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related species, such as members of the Staphylococcus genus, and members of the

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Enterobacteriaceae family.

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Comparison of Mass Spectral Ranges

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The species-level accuracy of both handheld bipolar probe REIMS and high-throughput monopolar

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REIMS using an all species analysis model was substantially lower than previously reported

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accuracies for bipolar REIMS 5,6. To explore possible optimisations to our data analysis approach, we

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created eight different all species models using restricted mass ranges of 50 to 2500 m/z, 150 to

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2500 m/z, 250 to 2500 m/z, 500 to 2500 m/z, 600 to 900 m/z, 1000 to 2000 m/z, and 1200 to 1800

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m/z. Figure 4 shows that through a reduction in mass range from 50 to 2500 m/z to 600 to 900 m/z,

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species-level accuracy increases from 85% to 90% for handheld bipolar probe REIMS and from 86%

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to 90% for high-throughput REIMS. This mass range commonly contains ionised lipids

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prior to the introduction of advanced biochemical, genetic, and mass spectrometric analyses, have

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historically been used for species-level identification

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accuracy, analysis using a reduced mass range could also increase the method’s throughput rate. The

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reduced mass range of 600 to 900 m/z equals approximately 12% of the entire acquisition mass

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range of the Xevo G2-XS Q-ToF. This would correspond to a similar level of reduction in the

29-31

26-28

, which

. In addition to the improved species-level


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computational requirements of data analysis and allow for real-time species-level identification, as

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has been shown in other applications of REIMS including intra-operative tissue assessment 26.

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Effect of Mass Bin on Species Cross-Validation

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We also assessed the effect of bin size on the classification performance of the statistical models.

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For both REIMS approaches, the mass bin size appeared to have minimal effect on species-level

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cross-validation accuracy, Figure 5. At mass resolutions below 1, namely 0.01, 0.05, 0.1, and 0.5, no

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consistent result was visible between the two REIMS approaches. For example, the 0.05 mass bin

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achieved the highest species accuracy for high-throughput REIMS out of all six bin sizes evaluated,

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whilst for handheld bipolar probe REIMS it achieved the lowest. In agreement with our earlier

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studies

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good species level accuracy and computational time. For each tenfold decrease in bin size (for

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example from 1 to 0.1) the number of variables within each spectrum is increased ten-fold, along

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with the computational time required for analysis. To minimise the time required for bioinformatic

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analysis and species-level identification, using a mass bin of one will substantially reduce the

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computational time and resources required; allowing near real-time species-level identification in a

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clinical laboratory.

5,6

, we utilised unit resolution for all subsequent analyses as it achieves a balance between

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Species Cross-Validation Using Taxonomy Grouped Models

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Members of closely-related taxonomic groups, such as the Enterobacteriaceae family or

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Streptococcus genera, are likely to share substantial similarities in their mass spectral profiles

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because of the lower level of evolutionary divergence. Therefore, determining species-level cross-

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validation accuracy using an approach which combines all taxonomic species into one classification

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model, as described above, may suffer from misclassifications between closely related species. Here,

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we have explored an alternative approach whereby individual models are created for each

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taxonomic group at the domain, phylum, class, order, family, genus, and species levels. As



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Supplementary Figures S4 and S5 demonstrate, this allows for a probability matrix to be constructed

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to calculate the likelihood that an isolate from each of the 25 species analysed in this study, would

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be correctly classified within each model of its relevant taxonomic nodes. Using this approach,

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species-level accuracy was increased from 90.40% to 96.17% for handheld bipolar probe REIMS and

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from 89.87% to 94.03% for high-throughput REIMS. It has to be noted that hierarchical classification

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systems have also been employed for species-level identification of bacterial mass spectrometry

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fingerprints from MALDI-ToF based identification platforms 32.

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Species Cross-Validation Using Random Forest Models

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To this point, REIMS spectral fingerprints have been analysed using a combined PCA/LDA approach.

356

This approach is beneficial as it reduces the dimensionality of data sets and provides good predictive

357

power. However, it is more susceptible than other methods (e.g. Random Forest) to over training

358

and over fitting and has a poor ability to rank variables according to their discriminatory importance

359

33

360

was originally designed to handle high-dimensional, non-linear ecological data

361

considerable application to mass spectrometry data sets as well 35. Here. Random Forest constructs

362

decision making trees using mass spectral bins, at 0.1 Da, to construct classification models which

363

are internally validated through the splitting of samples into training and test data sets. Through the

364

construction of hundreds of decision making trees, the importance of individual variables is

365

determined; allowing for a final model using the most discriminatory variables to be constructed.

366

The classification accuracy of this model is then determined using leave one sample out cross-

367

validation.

. The Random Forest algorithm is a machine learning algorithm based on decision making trees and 34

, but has found

368 369

In the present study we used Random Forest to construct a total of 400 decision making trees to

370

assign species-level taxonomy to all 15 isolates from 25 species. Through this approach, we were

371

able to achieved species-level accuracy of 96.27% and 93.87% to handheld bipolar probe REIMS and


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372

monopolar high-throughput REIMS respectively. These results were comparable to those achieved

373

using taxonomy grouped models, albeit in substantially reduced computational time. The confusion

374

matrices for handheld bipolar probe REIMS, Figure 6a, and for high-throughput REIMS, Figure 6b,

375

show limited misclassification, predominately within the Enterobacteriaceae family and

376

Staphylococcus genus. Whilst the classification accuracies for some members of the Staphylococcus

377

genus and Enterobacteriaceae group are lower than those described using the Bruker Biotyper

378

system, further platform optimisations, targeting these taxonomic groups, will likely improve

379

classification accuracies substantially

380

throughput diagnostic environment due to its rapid computational ability. Table 1 gives classification

381

accuracy rates for each of the 25 microbial species analysed using both REIMS approaches using

382

three bioinformatic approaches.

36-38

. Random Forest may have particular utility in a high-

383 384

Intra-Sample Variation

385

To assess the intra-sample variability in handheld bipolar probe REIMS and monopolar high-

386

throughput REIMS analysis, we completed species-level cross-validation using each of the five

387

analysis points from each isolate for each REIMS approach. Results were compared against the

388

species-level cross-validation for the combined mass spectra for the five analysis points, as shown in

389

Supplementary Figure S6. Theoretically, the difference between the species-level accuracy of these

390

two approaches would be positively correlated to the level of intra-sample variation. Thus, the

391

greater the difference between the two cross-validation figures, the greater the level of intra-sample

392

variation. As shown in Supplementary Figure S7, minimal differences were observed between the

393

species-level accuracy of the cross-validation. Within the 50 to 2500 m/z range a difference of two

394

percentage points was observed for the handheld bipolar probe REIMS approach, and less than one

395

percentage point for the high-throughput REIMS approach. In regards to the 600 to 900 m/z range, a

396

difference of two percentage points was observed for handheld bipolar probe REIMS, and one

397

percentage point for high-throughput REIMS. Although not measured directly, the use of leucine



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

398

enkaphline as an external lock mass compound, allowing for correction of the mass drift associated

399

with time of flight MS, will minimise the extent of variation between analysis periods. In addition,

400

this work was completed over multiple analysis days, using two different instrument operators. As

401

evident with the reduced intra-sample variation of the high-throughput REIMS platform, the nature

402

of the analytical set up will also likely reduce any potential inter-sample variation introduced as a

403

result of different analysis day or operator.

404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427


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428



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

429

Conclusions

430

Here, we have shown that the initial species-level identification and characterisation previously

431

reported by members of this group using bipolar REIMS technology can be comparably achieved

432

using an automated, high-throughput REIMS approach. Both bacterial and fungal isolates are

433

analysed directly from culture, without any prior preparative steps. The method described requires

434

minimal user input and can easily be integrated with automated clinical microbiology workflows.

435

Although not employed here, based on colony analysis time of 15 seconds, this high-throughput

436

workflows allows approximately 3,000 to 4,000 microbial colonies to be analysed over a 24 hour

437

period, including time required for instrument calibration, maintenance, and cleaning, with species-

438

level identifications given in near real-time. Although not directly comparable as the bioinformatic

439

pipeline for clinical microbiology laboratories is in development, this time period is substantially

440

lower than that required for commercial MALDI-ToF platforms, which take approximately six

441

minutes to analyse each isolate 3.

442 443

Here, we have discussed commercially available MS-based platforms which are currently used in

444

clinical microbiology laboratories, such as the Bruker BioTyper MALDI-ToF system. These references

445

were used to contextualise our REIMS based platform against familiar MS platforms. Although we

446

used the Bruker BioTyper platform for species level identification of microbial isolates, in line with

447

the standard operation procedures of our collaborating clinical laboratory, we have not drawn direct

448

comparisons between its identification accuracy and that of high-throughput REIMS. The Gram-stain,

449

genus, and species level classifications accuracies reported are used primarily as a tool to compare

450

the handheld and high-throughput REIMS approaches. They could however, be considered a

451

percentage conformity to the Bruker BioTyper platform. To determine the true comparable

452

classification accuracies of the two platforms, and other commercially available ones, a non-MS

453

species identification method, such as 16S/18S/ITS rRNA gene sequencing would be required; which

454

was not conducted.


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455 456

Although we have focussed on the lipid region of the mass spectra, additional microbial metabolites

457

identified using the REIMS procedure could have utility in defining phenotypic features including

458

virulence and antibiotic resistance. Furthermore, work is currently underway to extend high-

459

throughput REIMS analysis from pure microbial cultures to analysis of mixed microbial cultures and

460

communities, and complex human bio-fluids including urine and blood using species-specific

461

features for identifications.

462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480



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482

Supporting Information

483

Detailed information regarding bacterial and fungal species analysed in this study, and on additional

484

data analyses completed as referenced within this text is given within the supporting information

485

accompanying this manuscript. This material is available free of charge via the Internet at

486

http://pubs.acs.org.

487 488

Author Contributions

489

Study was planned by ZT, FB, and SJSC. Experiments were conducted by AB, TR, KH, FB, and SJSC.

490

Data was analysed and interpreted by SJSC, FB, ZT, and APM. Technical assistance and method

491

optimisation was provided by ZB, MR, JB, TK, DS, RS, and ZT. The manuscript was written by FB, SJSC,

492

and ZT with input from all authors. All authors have given approval to the final version of the

493

manuscript.

494 495

Conflict of Interest Statement

496

This work was funded by a Biotechnology and Biological Sciences Research Council (BBSRC) grant

497

(reference number BB/L020858/1). Additional support, both technical and financial, was provided by

498

Waters Corporation. TK, DS, RS, and JB are employed by the Waters Corporation. ZT consults for

499

Waters Corporation. The work detailed in this manuscript does not promote any available

500

commercial product from Waters Corporation.

501 502

Acknowledgments

503

The authors would like to thank Imperial College Healthcare Trust for providing isolates and their

504

technical input. We thank the anonymous reviewers for their constructive comments which have

505

made this a better manuscript.

506 507



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508 509

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(17) Heller, D. N.; Cotter, R. J.; Fenselau, C.; Uy, O. M. Anal. Chem. 1987, 59, 2806-2809.

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(18) Song, Y.; Talaty, N.; Tao, W. A.; Pan, Z.; Cooks, R. G. Chem. Commun. 2007, 61-63.

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(20) Mutters, N. T.; Hodiamont, C. J.; de Jong, M. D.; Overmeijer, H. P.; van den Boogaard, M.; Visser,

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C. E. Ann. Lab. Med. 2014, 34, 111-117.

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(21) Greub, G.; Prod’Hom, G. Clin. Microbiol. Infect. 2011, 17, 655-660.

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(22) Novak, S. M.; Marlowe, E. M. Clin. Lab. Med. 2013, 33, 567-588.

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(23) Wold, S.; Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 37-52.

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(24) Fisher, R. A. Ann. Hum. Genet. 1938, 8, 376-386.

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(25) Mahalanobis, P. C. Proc. Natl. Inst. Sci. India 1936, 2, 49-55.

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Dezső, B.; Damjanovich, L.; Darzi, A. Sci. Transl. Med. 2013, 5, 194ra193-194ra193.

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(27) Pulfer, M.; Murphy, R. C. Mass Spectrom. Rev. 2003, 22, 332-364.

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(28) Gibson, B.; Melaugh, W.; Phillips, N. J.; Apicella, M.; Campagnari, A.; Griffiss, J. J. Bacteriol. 1993,

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175, 2702-2712.

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(29) Komagata, K.; Suzuki, K.-I. Methods Microbiol. 1987, 19, 162-207.

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(30) Lüderitz, O.; Galanos, C.; Lehmann, V.; Nurminen, M.; Rietschel, E. T.; Rosenfelder, G.; Simon,

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M.; Westphal, O. J. Infect. Dis. 1973, 128, S17-S29.

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(31) van Baar, B. L. FEMS Microbiol. Rev. 2000, 24, 193-219.

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(32) Simmuteit, S.; Schleif, F.-M.; Villmann, T.; Hammer, B. Knowl. Inf. Syst. 2010, 25, 327-343.

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(33) Gromski, P. S.; Muhamadali, H.; Ellis, D. I.; Xu, Y.; Correa, E.; Turner, M. L.; Goodacre, R. Anal.

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Chim. Acta 2015, 879, 10-23.

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(34) Cutler, D. R.; Edwards Jr, T. C.; Beard, K. H.; Cutler, A.; Hess, K. T.; Gibson, J.; Lawler, J. J. Ecology

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2007, 88, 2783-2792.

558

(35) Patterson, A. D.; Bonzo, J. A.; Li, F.; Krausz, K. W.; Eichler, G. S.; Aslam, S.; Tigno, X.; Weinstein, J.

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560

(36) Dubois, D.; Leyssene, D.; Chacornac, J. P.; Kostrzewa, M.; Schmit, P. O.; Talon, R.; Bonnet, R.;

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Delmas, J. J. Clin. Microbiol. 2010, 48, 941-945.

562

(37) Saffert, R. T.; Cunningham, S. A.; Ihde, S. M.; Jobe, K. E. M.; Mandrekar, J.; Patel, R. J. Clin.

563

Microbiol. 2011, 49, 887-892.

564

(38) Zhu, W.; Sieradzki, K.; Albrecht, V.; McAllister, S.; Lin, W.; Stuchlik, O.; Limbago, B.; Pohl, J.;

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Rasheed, J. K. J. Microbiol. Methods 2015, 117, 14-17.

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590


Figure Captions

591 592

Figure 1. Customised TECAN Freedom EVO automated sample handling machine and sampling

593

probe

594

A customised TECAN Freedom EVO automated sample handling machine is contained within a class

595

2 microbiological safety cabinet. Before use, agar culture plates to be placed into a plate rack (a),

596

sampling probes to be loaded into a 96 rack (b). Plates are then moved from the plate rack to the

597

Pickolo visualisation platform (c), which also acts as the return electrode, where the user or the

598

automated system can select colonies for analysis. A technical drawing of the stainless steel

599

sampling probe is given in (d).

600 601

Figure 2. Temperature gradient differences between REIMS approaches

602

The handheld bipolar probe (a) employed in this study generates a radiofrequency electrical current

603

between two probes, and thus has a lower, but more even, heating temperature throughout the

604

microbial biomass. However, the monopolar probe used in high-throughput REIMS (b) generates a

605

higher, localised heating temperature at the point of contact between probe and microbial biomass.

606

Counter electrode shown by grey bar at base of microbial biomass. Colour gradient of microbial

607

biomass indicates heating temperature, with red showing highest temperatures and green lowest

608

temperatures. The mass spectra beside each representative diagram is of the 600 to 900 m/z range

609

of the same Lactobacillus jensenii isolate after background removal for each REIMS approach. Figure

610

is not constructed to scale.

611 612

Figure 3. Gram-stain, genus, and species-level cross-validation accuracy for both REIMS

613

approaches

614

Cross-validation using a PCA/LDA model over the 50 to 2500 m/z range with a mass bin of 1 gives

615

similar Gram-stain level accuracy for both handheld bipolar probe REIMS and high-throughput



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

616

REIMS. However, at genus and species-level accuracy, handheld bipolar probe REIMS gives slightly

617

higher accuracy levels, by three and two percentage points respectively.

618 619

Figure 4. Species-level classification accuracy using restricted mass ranges

620

A total of eight PCA/LDA models were constructed using a mass bin of 1 for both handheld bipolar

621

probe REIMS and high-throughput REIMS using restricted mass ranges. For handheld bipolar probe

622

REIMS, the highest species-level accuracy was achieved through using the 500 to 1500 m/z range

623

and 600 to 900 m/z range, whilst for high-throughput REIMS the highest accuracy was achieved with

624

the 600 to 900 m/z range.

625 626

Figure 5. Effect of mass bin size on species-level accuracy

627

Species-level accuracy was measured through cross-validation of PCA/LDA models constructed using

628

the 600 to 900 m/z range for mass bins of 0.01, 0.05, 0.1, 0.5, 1.0, and 5.0. For both handheld bipolar

629

probe REIMS and high-throughput REIMS, minimal differences were evident between different mass

630

bin sizes. To achieve a balance between good species-level accuracy and reduced computational

631

time for further analyses, a mass bin size of 1 was subsequently used.

632 633

Figure 6. Species-Level confusion matrices from Random Forest analysis

634

Random Forest analysis was conducted on (a) handheld bipolar probe REIMS and (b) high-

635

throughput REIMS datasets within the 600 to 900 m/z range, using a mass bin of 0.1. Random Forest

636

models were constructed using 400 decision making trees. Confusion matrices suggest a marginally

637

higher level of misclassification using the high-throughput REIMS approach than with handheld

638

bipolar probe REIMS, with misclassifications occurring predominately within the Enterobacteriaceae

639

family. Colour scale gradient indicates strength of species-level accuracy with darker gradients

640

showing higher species-level accuracy. Abbreviations used within the True Class and Predicted Class

641

labels are given alongside full species names in Table 1.


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642 643

Table 1. Individual species-level accuracy for all species, taxonomic groupings, and Random Forest

644

models

645

The species-level accuracy for each of the 25 microbial species analysed using both REIMS approach

646

is given for each of the three bioinformatic data analysis methods used in this study. For handheld

647

bipolar probe REIMS, the highest overall species-level accuracy was achieved using the Random

648

Forest approach, whilst for high-throughput REIMS the highest was achieved using the taxonomic

649

groupings approach. Although, in both cases, the differences between the taxonomic groupings and

650

Random Forest approaches is less than 0.2 percentage points.

651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670



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671


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672


Table 1 Handheld Bipolar Probe REIMS (% Accuracy)

High-Throughput REIMS (% Accuracy)

Isolate Number

All Species

Taxonomic Groupings

Random Forest

All Species

Taxonomic Groupings

Random Forest

Average

∑ 375

90.4

96.2

96.3

89.9

94.0

93.9

Candida albicans (CALB)

15

100.0

100.0

100.0

100.0

100.0

100.0

Candida parapsilosis (CALP)

15

100.0

100.0

100.0

93.3

100.0

100.0

Corynebacterium amycolatum (CAMY)

15

100.0

100.0

100.0

100.0

97.0

100.0

Micrococcus luteus (MLUT)

15

100.0

100.0

100.0

86.7

97.0

100.0

Enterococcus faecalis (EFAC)

15

60.0

91.2

100.0

86.7

77.6

87.0

Enterococcus faecium (EFAM)

15

66.7

91.2

83.0

86.7

84.4

80.0

Lactobacillus jensenii (LACJ)

15

86.7

98.0

100.0

100.0

93.0

86.0

Streptococcus agalactiae(SAGA)

15

93.3

91.2

100.0

93.3

86.5

87.0

Streptococcus pneumoniae (SPNE)

15

100.0

98.0

97.0

93.3

93.0

97.0

Streptococcus pyogenes (SPYO)

15

100.0

98.0

97.0

93.3

93.0

93.0

Staphylococcus aureus (SAUR)

15

100.0

98.0

93.0

93.3

93.0

85.0

Staphylococcus epidermidis (SEPI)

15

80.0

91.2

100.0

73.3

100.0

97.0

Staphylococcus haemolyticus (SHAM)

15

73.3

78.4

100.0

73.3

80.0

100.0

Staphylococcus hominis (SHOM)

15

86.7

91.2

97.0

73.3

100.0

97.0

Clostridium difficile (CDIF)

15

100.0

99.0

100.0

100.0

100.0

100.0

Enterobacter cloacae (ECLO)

15

100.0

99.0

93.0

86.7

92.1

100.0

Escherichia coli (ECOL)

15

46.7

92.1

97.0

60.0

79.2

93.0

Klebsiella oxytoca (KOXY)

15

100.0

99.0

97.0

86.7

99.0

97.0

Klebsiella pneumoniae (KPNE)

15

86.7

99.0

80.0

86.7

99.0

84.0 90.0

Morganella morganii (MMORG)

15

86.7

99.0

97.0

93.3

99.0

Proteus mirabilis (PMIR)

15

100.0

92.1

87.0

93.3

99.0

83.0

Serratia marcescens(SMAR)

15

93.3

99.0

100.0

93.3

92.1

100.0 100.0

Haemophilus influenza (HINF)

15

100.0

100.0

100.0

100.0

99.0

Pseudomonas aeruginosa (PAER)

15

100.0

100.0

93.0

100.0

99.0

93.0

Stenotrophomonas maltophilia (SMAL)

15

100.0

100.0

97.0

100.0

99.0

97.0

673

31 ACS Paragon Plus Environment


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674

Table of Contents Graphic (Actual Size)

675 676


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Figure 1. Customised TECAN Freedom EVO automated sample handling machine and sampling probe. A customised TECAN Freedom EVO automated sample handling machine is contained within a class 2 microbiological safety cabinet. Before use, agar culture plates to be placed into a plate rack (a), sampling probes to be loaded into a 96 rack (b). Plates are then moved from the plate rack to the Pickolo visualisation platform (c), which also acts as the return electrode, where the user or the automated system can select colonies for analysis. A technical drawing of the stainless steel sampling probe is given in (d). 525x500mm (150 x 150 DPI)

ACS Paragon Plus Environment


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Figure 2. Temperature gradient differences between REIMS approaches. The handheld bipolar probe (a) employed in this study generates a radiofrequency electrical current between two probes, and thus has a lower, but more even, heating temperature throughout the microbial biomass. However, the monopolar probe used in high-throughput REIMS (b) generates a higher, localised heating temperature at the point of contact between probe and microbial biomass. Counter electrode shown by grey bar at base of microbial biomass. Colour gradient of microbial biomass indicates heating temperature, with red showing highest temperatures and green lowest temperatures. The mass spectra beside each representative diagram is of the 600 to 900 m/z range of the same Lactobacillus jensenii isolate after background removal for each REIMS approach. Figure is not constructed to scale. 1109x499mm (115 x 115 DPI)


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Figure 3. Gram-stain, genus, and species-level cross-validation accuracy for both REIMS approaches. Crossvalidation using a PCA/LDA model over the 50 to 2500 m/z range with a mass bin of 1 gives similar Gramstain level accuracy for both handheld bipolar probe REIMS and high-throughput REIMS. However, at genus and species-level accuracy, handheld bipolar probe REIMS gives slightly higher accuracy levels, by three and two percentage points respectively. 397x500mm (150 x 150 DPI)



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Figure 4. Species-level classification accuracy using restricted mass ranges. A total of eight PCA/LDA models were constructed using a mass bin of 1 for both handheld bipolar probe REIMS and high-throughput REIMS using restricted mass ranges. For handheld bipolar probe REIMS, the highest species-level accuracy was achieved through using the 500 to 1500 m/z range and 600 to 900 m/z range, whilst for high-throughput REIMS the highest accuracy was achieved with the 600 to 900 m/z range.

842x500mm (150 x 150 DPI)


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Figure 5. Effect of mass bin size on species-level accuracy. Species-level accuracy was measured through cross-validation of PCA/LDA models constructed using the 600 to 900 m/z range for mass bins of 0.01, 0.05, 0.1, 0.5, 1.0, and 5.0. For both handheld bipolar probe REIMS and high-throughput REIMS, minimal differences were evident between different mass bin sizes. To achieve a balance between good species-level accuracy and reduced computational time for further analyses, a mass bin size of 1 was subsequently used. 871x500mm (150 x 150 DPI)



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Figure 6. Species-Level confusion matrices from Random Forest analysis. Random Forest analysis was conducted on (a) handheld bipolar probe REIMS and (b) high-throughput REIMS datasets within the 600 to 900 m/z range, using a mass bin of 0.1. Random Forest models were constructed using 400 decision making trees. Confusion matrices suggest a marginally higher level of misclassification using the high-throughput REIMS approach than with handheld bipolar probe REIMS, with misclassifications occurring predominately within the Enterobacteriaceae family. Colour scale gradient indicates strength of species-level accuracy with darker gradients showing higher species-level accuracy. Abbreviations used within the True Class and Predicted Class labels are given alongside full species names in Table 1. 312x155mm (150 x 150 DPI)


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Automated High-Throughput Identification and Characterization of

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