Article pubs.acs.org/ac
Characterization and Identification of Clinically Relevant Microorganisms Using Rapid Evaporative Ionization Mass Spectrometry Nicole Strittmatter,† Monica Rebec,‡ Emrys A. Jones,† Ottmar Golf,† Alireza Abdolrasouli,‡ Julia Balog,† Volker Behrends,† Kirill A. Veselkov,† and Zoltan Takats*,† †
Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, United Kingdom ‡ Department of Microbiology, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London W6 8RF, United Kingdom S Supporting Information *
ABSTRACT: Rapid evaporative ionization mass spectrometry (REIMS) was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode, and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.9%, 97.8%, and 100% on species, genus, and Gramstain level, respectively. These results were not affected by the resolution of the mass spectral data. Blind identification tests were performed for strains cultured on different culture media and analyzed using different instrumental platforms which led to 97.8−100% correct identification. Seven different Escherichia coli strains were subjected to different culture conditions and were distinguishable with 88% accuracy. In addition, the technique proved suitable to distinguish five pathogenic Candida species with 98.8% accuracy without any further modification to the experimental workflow. These results prove that REIMS is sufficiently specific to serve as a culture condition-independent tool for the identification and characterization of microorganisms. clinical settings but mostly find application in reference laboratories. Mass spectrometry (MS) gained attention for microbial identification more than 4 decades ago due to its intrinsic advantages of fast data acquisition, high sensitivity, and specificity.5−7 Early studies were mostly applying pyrolysis followed by electron impact or chemical ionization. Due to the destructive nature of pyrolysis methods, only small molecules and fragments of larger molecules were detected using these approaches.5 A decade later, the introduction of fast atom bombardment (FAB-MS) allowed monitoring of larger biomolecules as intact complex phospholipid species desorbed directly from intact bacterial cells.8 Eventually, the advent of the soft ionization techniques, especially matrix-assisted laser desorption ionization (MALDI), gave significant momentum to mass spectrometric microbial identification.1,9 Following the initial reports, MALDI found widespread use for the ionization of proteins desorbed from intact bacterial cells.1 Nowadays analysis is focused on the mass range of 2−20 kDa resulting in the detection of various protein signals, half of which are of ribosomal origin.10,11 Commercialized systems were demon-
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evelopment of a fast and reliable identification system for microorganisms is a rapidly developing field.1 Although a number of concepts have been proposed, none have proven to be specific, universal, fast, and at the same time cheap enough to find widespread application. Until today, in routine clinical microbiology settings, identification of an isolate is mostly accomplished by observing phenotypic characteristics such as colonial morphology, Gram-stain behavior, and different enzymatic properties or carbon source utilization patterns. However, these techniques are time-consuming, need experienced personnel, and often lack specificity. Microbial species are defined by their 16S rRNA sequence; thus, sequencing of the 16S rRNA encoding gene serves as the gold standard for bacterial identification and classification. Partial or full 16S rRNA sequencing has the advantage of being culture-independent and thus is especially valuable for fastidious microorganisms. However, the sensitivity and specificity for direct sample applications varies considerably.2 Despite of its role as gold standard, in some cases bacterial species cannot be identified confidently by 16S rRNA which can still make the application of additional techniques necessary.3,4 In addition, genotypic methods generally need extensive sample preparation, are comparably expensive, and still need at least several hours for identification. Due to these reasons, sequencing methods are rarely applied in routine © 2014 American Chemical Society
Received: March 25, 2014 Accepted: June 4, 2014 Published: June 4, 2014 6555
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methodological resemblance to pyrolysis approaches; however, unlike pyrolysis systems, REIMS provides soft ionization yielding predominantly molecular ions.30,31 We have previously described a proof-of-principle study using REIMS for microbial identification.31 Our current study provides a comprehensive and critical assessment of REIMSbased microbial identification.
strated to give comparable or even superior results to conventional identification systems.12−15 Although MALDI can be used directly on intact cells, it was shown that matrix addition itself leads to lysis of the bacterial cells and thus release of the intracellular proteins. However, short additional extraction steps significantly increase the identification accuracy, especially in case of yeasts and Gram-positive bacteria.12,16 With individual analysis times of 20−90 s per precultured isolate and reporting times as low as 6 min, these systems are able to reduce the average turnover time in clinical microbiology laboratories by about 1 day. In addition, analysis costs are a fraction of those of conventional techniques.15,17 Besides the generally applied intact protein profiling methodology for MALDI-based microbial identification systems, peptide mixtures resulting from tryptically digested proteins can be analyzed to gain further specificity in a bottom-up approach termed shotgun mass mapping (SMM).18 The inherent speed of analysis, high sensitivity, and specificity combined with the good agreement with 16S rRNA sequencing led to the widespread use of MALDI-MS for the identification of microorganisms, both in research and in clinical microbiology laboratories.1 Commercial MALDI timeof-flight (TOF) microbial identification systems are now available for clinical microbiology routine use in the Europe Union, as well as in many countries around the world, and most recently gained approval by the U.S. Food and Drug Administration. A growing interest in lipidomics, and the advent of ambient ionization techniques as an easy means to generate lipid profiles, gave new momentum to lipid profile-based identification of microorganisms. Traditionally the only general identification system based on bacterial lipid composition has been fatty acid profiling using gas chromatography and flame ionization detector (GC-FID). However, bacterial fatty acid profiles are strongly affected by culturing conditions, whereas the composition of intact membrane phospholipids proved more robust. Since the first application of desorption ionization methods to obtain lipid spectra from intact bacterial cells in 1987,19 many different ionization techniques, including fast atom bombardment (FAB), 8 electrospray ionization (ESI),20−22 MALDI,23,24 and desorption electrospray ionization (DESI)25,26 have been used to demonstrate that different bacteria have species-specific phospholipid profiles. However, none of these methods have been shown to possess the specificity and robustness required to serve as the basis for a general lipid-based identification system. Further interesting developments include mass spectrometric techniques to analyze bacterial metabolites and phospholipids in vivo and directly from the Petri dish using ambient ionization techniques as DESI,27 nanospray desorption electrospray ionization,28 and other liquid microjunction−electrospray setups.29 The recently developed technique rapid evaporative ionization mass spectrometry (REIMS) yields highly specific phospholipid profiles of different biological tissue types and also offers a new opportunity for the development of a lipidbased, sample-preparation-free microbial identification system.30,31 In case of REIMS analysis, species-specific mass spectral fingerprints are generated by subjecting the cellular biomass to radiofrequency alternating electric current. Thermal disintegration of cells produces an aerosol comprising lipidcovered droplets containing intracellular and extracellular metabolites, which is introduced into the mass spectrometer for subsequent analysis. The REIMS method shows high
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EXPERIMENTAL SECTION Culturing of Bacterial Strains. Isolated strains of various microorganisms were grown on a range of solid agar-based media commonly used in clinical microbiology settings. Media were purchased from Oxoid (Basingstoke, U.K.). The bacteria were incubated under various atmospheric conditions at 37 °C overnight before analysis. For more information on culture conditions refer to Supporting Information Tables S-4 and S-6. Microorganisms were isolated during routine clinical microbiological workflow and identified using conventional workflows and a Microflex LT MALDI TOF instrument (Bruker Daltonics, Bremen, Germany). REIMS Analysis. For REIMS analysis, two hand-held electrodes in form of a forceps were used as the sampling probe (bipolar forceps, obtained from Erbe Elektromedizin, Tübingen, Germany). A Valleylab Force EZc power-controlled electrosurgical unit (Covidien, Dublin, Ireland) was used at 60 W power setting in bipolar mode as rf alternating current power supply (470 kHz, sinusoid). An approximately 1.5 m long 1/8 in. outer diameter, 1/16 in. inner diameter PTFE tubing (Fluidflon PTFE tubing; LIQUID-scan GmbH Co. KG, Ü berlingen, Germany) was applied to connect the embedded fluid line of the bipolar forceps and the inlet capillary of either an LTQ Orbitrap Discovery instrument (Thermo Scientific GmbH, Bremen, Germany), a Thermo Exactive instrument (Thermo Scientific GmbH), or a Xevo G2-S Q-TOF instrument (Waters Micromass, Manchester, U.K.). In each case the inherent vacuum system of the mass spectrometer was used for aspiration of the aerosol. This setup is shown in Figure 1, while instrumental settings are given in Supporting Information Table S-1. Mass spectrometric analysis of the microorganisms was performed directly from the solid culture medium (Figure 1). An amount of 0.1−1.5 mg of microbial biomass was scraped off the agar surface using one of the electrodes of the bipolar forceps. The two electrodes were subsequently brought into close proximity (i.e., by pinching the biomass between the tips of the forceps), and the rf power supply was triggered using a foot switch. The microbial biomass is rapidly heated up due to its nonzero impedance, and an aerosol containing the analytes is produced and transferred directly into the mass spectrometer. Five individual measurements were performed for each strain and averaged as a database entry. Data Analysis. Raw mass spectrometric files were imported as imzML format32 into MATLAB (Mathworks, Natick, MA; http://www.mathworks.co.uk/) for data preprocessing, pattern recognition analysis, and visualization. All REIMS spectra were linearly interpolated to a common sampling interval of 0.01 Da. Recursive segmentwise peak alignment was then used to remove small mass shifts in peak positions across spectral profiles.33 The aligned data were subjected to data normalization (median, mean, or TIC normalization) and log-based transformation to ensure that the noise structure was consistent with the downstream application of multivariate statistical techniques.34 Principal component analysis (PCA) and 6556
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Figure 2. REIMS spectral profiles obtained for Staphylococcus aureus ATCC 25923, P. aeruginosa ATCC 27853, and E. coli ATCC 25922, each grown on five different solid growth media (from front to back: brain-heart infusion agar, columbia horse blood agar, chocolate agar, Mueller−Hinton agar, Trypticase soy agar); acquired using Exactive instrument.
Figure 1. (A) Setup used for analysis of bacteria by REIMS. (B) Scheme of analysis. Microbial biomass is held between the two electrodes of the irrigated bipolar forceps, electrical current is applied, sample is evaporated thermally, and the produced aerosol is aspirated into the opening of the embedded fluid line.
Supporting Information Figure S-2) are dominated by intact phospholipids in the mass range of m/z = 600−900. These signals are mostly derived from phosphatidylglycerols (PGs), phosphatidylethanolamines (PEs), and phosphatidic acids (PAs). Signals with lower mass-to-charge ratio were associated with fatty acids (C12−C20), monorhamno- and dirhamnolipids, and a range hydroxyalkylquinolines-derived quorum sensing molecules (including PQS) for Pseudomonas aeruginosa,36,37 ceramides Cer(34:0), Cer(35:0), Cer(36:0) for Bacteroides fragilis,38 and short-chain mycolic acids with C26− C36, including corynomycolic acid (C32H64O3), for Corynebacterium species.39 Cardiolipins were identified in the higher mass range for all bacterial species analyzed. The signals with highest mass-to-charge ratio so far were tentatively identified as intact lipid A species in case of Helicobacter pylori (m/z = 1547) and Escherichia coli (m/z = 1796). Besides these lipid species and lipid-related species, bacterial polyhydroxybutyrate polymers were identified for Bacillus cereus and Burkholderia cepacia complex strains.40,41 For more details on the nature of identified compounds see the Supporting Information (Figures S-3 and S-4, Tables S-2 and S-3). In addition, no signals attributed to the growth medium were observed using REIMS, neither when the agar surface is left intact during analysis nor when agar is deliberately analyzed. This is tentatively associated with the nature of the
hierarchical cluster analysis (HCA) were used for unsupervised analysis of the data set. A recursive maximum margin criterion (RMMC)35 algorithm and linear discriminant analysis (LDA) were used as supervised classification algorithms. A more detailed description of the data analysis workflow is given in the Supporting Information. Ionic species in the mass spectra were identified based on exact mass measurements (mass deviation 2.0). While the recommended sample pretreatment for MALDI TOF MS comprises the complete extraction of the fungal material using formic acid and acetonitrile,16 intact yeast species can directly be analyzed by REIMS without any modification in experimental setup or analysis workflow. Supporting Information Figure S-11 shows the PCA and RMMC plots for 87 6560
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Figure 6. PCA-LDA model of seven different E. coli strains. Data was mean normalized and reduced to m/z = 600−900. CV = 87.3%.
different bacterial species and 16 different fungal (yeast) species. No overlap between bacteria and fungi was observed in the PCA data space. This was attributed to the markedly different phospholipid composition between bacteria and fungi. Whereas REIMS spectra obtained from bacteria mostly feature phosphatidylglycerols, phosphatidylethanolamines, and lowabundance phosphatidic acids, the spectra obtained from fungi mainly consist of high-abundance phosphatidic acids, phosphatidylethanolamines, and phosphatidylinositols, with particularly the last group being very rare among bacteria. Figure 7 shows the separation of five clinically relevant pathogenic Candida species both in supervised and unsupervised analysis. C. glabrata is clearly separated from the three other species along the first principal component while C. lusitaniae, C. parapsilosis, C. tropicalis, and C. albicans are largely separated along the second principal component. This separation into two groups is tentatively attributed to the fact that C. glabrata was found to belong to a separate phylogenetic clade than the other Candida species based on DNA sequences.45 Two misclassifications were observed when performing leave-one-out cross-validation, resulting in 98.8% correct classification.
Figure 7. PCA (A) and RMMC (B) plot of C. albicans (n = 20), C. glabrata (n = 19), C. lusitaniae (n = 12), C. parapsilosis (n = 19), and C. tropicalis (n = 16). CV = 98.8%.
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ASSOCIATED CONTENT
S Supporting Information *
Detailed information on sample sets analyzed in this study, further plots of multivariate statistical analysis of the presented data, detailed information about identified spectral features, and mass spectra resulting from different experimental setups and settings. This material is available free of charge via the Internet at http://pubs.acs.org.
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CONCLUSION The results shown in this study clearly demonstrate that REIMS is a suitable method for the identification of microorganisms in clinical microbiology settings. Both bacteria and yeasts can be analyzed using the same experimental setup and workflow, which lacks the sample preparation step and thus allows analysis times as short as 3−5 s, using mass analyzers with arbitrary resolution. The demonstrated subspecies specificity and the detection of various secondary metabolites including rhamnolipids or lipid A suggest that the technique provides information well beyond the taxonomical classification of identified species pertaining to phenotypic factors including virulence, antibiotic resistance, serotype, or ribotype of the microorganisms. The number of conserved and genus-specific spectral features gives strong implications on the feasibility of culture-free applications by the direct analysis of bacterial cells within human biological fluids; however, these types of applications remain to be developed in the future.
AUTHOR INFORMATION
Corresponding Author
*Phone: +44 0-207 5942760. E-mail:
[email protected]. Author Contributions
Study was planned by Z.T., N.S., and M.R. Experiments were conducted by N.S., M.R., A.A., and V.B. Data was analyzed and interpreted by N.S., E.A.J., O.G., Z.T., K.A.V., and J.B. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The work was funded by the European Research Council under the Starting Grant scheme (contract no. 210356) and the European Commission FP7 Intelligent Surgical Device project (contract no. 3054940). We acknowledge Medimass Ltd. 6561
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(Budapest, Hungary) for the technical support. K.A.V. acknowledges his Imperial College Junior Research Fellowship funding.
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