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DOI: 10.1111/jdv.12324
ORIGINAL ARTICLE
Detection and differentiation of causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy T. G. Smijs,1,* J. W. Jachtenberg,2 S. Pavel,3 T. C. Bakker-Schut,4 D. Willemse-Erix,5 E. R. M. de Haas,4 H. Sterenborg1 1
Centre for Optical Diagnostics and Therapy, Department of Radiotherapy, Erasmus Medical Centre, Rotterdam, The Netherlands Department of Neurosurgery, Erasmus Medical Centre, Rotterdam, The Netherlands 3 Department of Dermatology, Charles University, Pilsen, Czech Republic 4 Department of Dermatology and Venereology, Erasmus Medical Centre, Rotterdam, The Netherlands 5 Department of Medical Microbiology and Infectious Diseases, Erasmus Medical Centre, Rotterdam, The Netherlands *Correspondence: T. G. Smijs. E-mail: [email protected] 2
Abstract Background Onychomycosis is worldwide the most prevalent infection of the nail. It is mainly caused by the dermatophytes Trichophyton rubrum and Trichophyton mentagrophytes and to a lesser extent Trichophyton tonsurans. The yeast Candida albicans and the mould Scopulariopsis brevicaulis can also cause onychomycosis. Management of these nail conditions may require appropriate treatment methods and therefore the identification of the causative species can be of importance. However, the determination of agents causing onychomycosis is still not optimal. Objectives To detect and differentiate causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy. The work focusses is on the discriminative power of Raman spectroscopy for detection of differences between T. rubrum, T. mentagrophytus and T. tonsurans on human nail and distinguishing these dermatophytic from the non-dermatophytic species S. brevicaulis and C. albicans. Methods Raman spectra (200/sample) were taken from 50-lm human nail slices infected with T. rubrum, T. mentagrophytus, T. tonsurans, S. brevicaulis or C. albicans using a 2500 High-Performance Raman Module and 785-nm diode laser. Processed spectra were analysed by sorting the correlation matrix and presented as dendrogram and heat map. Raman spectra from suspended dermatophytic microconidia were taken for mutual comparisons. Results Spectral differences between the dermatophytes T. rubrum, T. mentagrophytus and T. tonsurans (635–795, 840–894, 1018–1112, 1206–1372, 1566–1700/cm) and the non-dermatophytes S. brevicaulis and C. albicans (442–610, 692–758, 866–914, 1020–1100, 1138–1380,1492–1602/cm) growing on nail were confirmed by clustering correlation showing two main clusters. Dissimilarities between tested dermatophytes were also found with T. rubrum being most different. Raman spectra of the dermatophytic microconidia varied over the whole tested 400–1800/cm range. Conclusion Important dermatophytic and non-dermatophytic agents of onychomycosis growing on ex vivo human nail can be distinguished specifically and non-invasively by Raman spectroscopy. Received: 19 July 2013; Accepted: 28 October 2013
Conflicts of interest None declared.
Funding sources This study was supported by the Dutch Technology Foundation, STW project 11622.
Introduction Onychomycosis is the most prevalent infection of the nail and the incidence of this disease is still growing worldwide.1,2 This increased prevalence may be due to unsatisfactory therapeutic efficacy and a growing number of patients with a compromised immune system3 or diabetes mellitus.4 Moreover, dermatophytic onychomycosis increases with age and since the population of
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elderly people is increasing, this clinical condition may become a significant medical problem.5 Onychomycosis is approximately three times more prevalent among people with diabetes mellitus than in those without this condition.6 Dermatophytes are the fungi that are most commonly responsible for various clinical types of onychomycosis. The strains Trichophyton rubrum and Trichophyton mentagrophytes are most frequently isolated from
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hands or feet. Less frequently diagnosed organism is Trichophyton tonsurans. The yeast Candida is the second important cause of onychomycosis, whereas nail infections by moulds (non-dermatophytic fungi) are currently rising worldwide.7 A saprophytic mould that is typically associated with onychomycosis is Scopulariopsis brevicaulis.8 Current therapeutics still have important restrictions. As a consequence, the recurrence of the infection and therefore an increased duration of the treatment is frequently seen. The efficacy of available drugs differs between Candida onychomycosis when compared to onychomycosis caused by dermatophytes and management of these nail conditions requires identification of the causative species.9,10 In addition, the in vitro antifungal drug susceptibility of microconidia and arthroconidia isolated from dermatophytes has been shown to be both drug and strain dependent. Especially T. rubrum conidia appeared to be less susceptible to various antifungal agents.11 This dermatophyte can be very persistent and consequently difficult to treat which is partly due to a decreased efficiency of the host’s immune responses.12–14 Differentiation between the causative dermatophytes may therefore be relevant for treatment choices. Moreover, differentiation between dermatophytic vs. non-dermatophytic causes of onychomycosis is important and determines treatment options as well as longterm prognosis for the patient. Knowledge of causative species of onychomycosis may also attribute to a better comprehension of the epidemiology of this disease. The diagnostic techniques that are commonly applied in clinics are mycological examination (both microscopic and culture investigations) of nail samples and histological analysis.1 In case of mycological examination, a correct diagnosis is highly depended on the sample’s quality, whereas microscopic identification of fungal elements in the nail sample requires some experience. Plating out the samples on Sabouraud dextrose agar (SDA) on the other hand is a useful but time-consuming identification method which takes for dermatophytes approximately 2–6 weeks.15 The periodic acid-Schiff (PAS) staining is currently used in many centres and proposed to be the single investigation which proves pathogenicity with highest sensitivity and specificity.16 Newer diagnostic methods comprise in vivo confocal microscopy, polymerase chain reaction (PCR), optical coherence tomography, nail digital onychoscopy and matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS).17–21 Nevertheless, determination of onychomycotic agents is still not optimal and a quick, specific and non-invasive tool with highly distinctive character is desired. Raman spectroscopy may be such a diagnostic tool. This method is a vibrational spectroscopic technique that can be used in various biomedical applications, specifically in the field of clinical evaluation.22,23 It requires a minimum sample quantity and allows the investigation of the molecular composition of samples based on molecular specificity of spectral bands in a vibrational spectrum.24 Biomedical developments of Raman
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spectroscopic microbiological applications involve bacterial identification and typing of different species.23,25 A detailed Raman spectroscopic study of fungi of the genus Lactarius and their conidia has been reported as well.26 Keratin-containing materials, particularly nails, have also been studied with Raman spectroscopic techniques.27–29 Widjaja et al.30,31 have frequently applied Raman spectroscopy to human nail clippings to analyse the bio-constitutes of this keratin material and as a novel method for human gender classification. To our knowledge, however, there are no reports on Raman spectroscopic studies of dermatophytes alone or in combination with nail or other keratin-containing material. Our present study concerns the detection and differentiation of causative microorganisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy. We focused primarily on differences between T. rubrum, T. mentagrophytus and T. tonsurans in the presence of human nail material. To investigate the discriminative power of Raman spectroscopy for dermatophytic compared to non-dermatophytic causes of onychomycosis, spectral properties of S. brevicaulis and C. albicans were analysed. For mutual comparisons separate spectra were taken from suspended dermatophytic microconidia that were used to develop the nail infections.
Materials and methods Strains
Clinical (onychomycosis) isolates of T. mentagrophytus (no. 88), T. tonsurans (no. 305), Candida albicans (no. 8) and S. brevicaulis (no. 358) were kindly provided by the Department of Medical Microbiology and Infectious Diseases of the Erasmus Medical Center (Rotterdam, the Netherlands). T. rubrum was obtained from American Type Culture Collection (ATCC, no. 28188). All strains were cultures on Sabouraud dextrose agar (SDA, Sigma Aldrich Chemie GmbH, Schnelldorf, Germany) at 28°C. Nails
Human healthy nail clippings were collected and provided by the Leids Cytologisch Pathologisch Laboratorium (Leiden, the Netherlands) from anonymous sources. Clippings were cleaned in 70% ethanol for 3 min in an ultrasonic water bath, cut to 3 9 3 mm pieces, and subsequently sectioned parallel to the surface into 50 lm slices (CM 1950 cryostat, Leica Biosystems Nussloch GmbH, Germany). Layers from the ventral zone were subsequently disinfected (twice) by 1-min immersion in 70% isopropanol (Sigma-Aldrich Chemie, Zwijndrecht, the Netherlands) and sterile Milli-Q (Millipore B.V., Amsterdam, the Netherlands). Preparation of microconidia and culture suspensions
The protocol for obtaining a suspension of microconidia produced by dermatophytes cultured on SDA was performed according to a method described previously.32,33 Microconidia
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suspensions thus obtained were stored in liquid nitrogen for no longer than 6 months. S. brevicaulis and C. albicans suspensions were prepared in sterile Milli-Q from a 14-day-old SDA culture.
(Eigenvector Research, Wenatchee, WA, USA). Raw data were first calibrated and corrected for the wavelength-dependent signal detection efficiency of the Raman setup as described earlier.23
Ex vivo nail model
Signal processing Potential interfering spectral contributions like signals from the fused silica glass, nail material and broadband biomass background were obtained and subtracted in the final analysis. The Extended Multiplicative Scatter Correction with Spectral Interference Subtraction (EMSC-SIS)35 scaling procedure was used to eliminate the influence of these interfering signals and to scale the spectra on basis of informative biochemical content. This method uses a fitting procedure to estimate the contributions of known sources to a measured spectrum. In this study, this procedure was used to estimate contributions of (1) the microorganisms, (2) the non-informative interfering signals from the fused silica glass, (3) the nail, and (4) the background fluorescence. The fit coefficients for contributions (2) to (4) were used to eliminate interfering signals, whereas the fit coefficient of (1) was used to scale the resulting spectrum.
Dermatophytic (T. rubrum 28188, T. mentagrophytus 88, T. tonsurans 305) microconidia, S. brevicaulis and C. albicans suspensions were adjusted to a 0.5 McFarland turbidity standard (Oxoid turbidimeter, Scientific Device laboratory. Inc., des Plaines, IL, USA). The 0.5 McFarland suspensions were 10 times diluted in Milli-Q and inoculated (4 lL) on the nail plate layers. Slices were then placed into 35-mm Petri dishes which were subsequently (per two pieces) transferred to 96-mm Petri dishes. To create a moist environment 25 mL of sterile water was added to the larger dishes34 and the nail pieces were incubated at 28°C for 2 weeks. Growth of the inoculates was checked microscopically before usage. Raman spectroscopy Instrument The instrument consisted of a Model 2500 High-
Performance Raman Module (RiverD International BV, Rotterdam, the Netherlands) coupled to a custom-built measurement compartment equipped with an automated x- and y-axis-positioning stage. A fused silica slide (Tower Optical Corporation, Boynton Beach, FL, USA) containing the biomass samples was positioned on the stage. A custom-designed near-infrared-optimized microscope objective (numerical aperture, 0.7) was used to focus the laser light emitted by the Raman Module into the samples and collect Raman scattered light from the samples. A 785-nm diode laser (Sacher Tiger, Sacher Lasertechnik, Marburg, Germany) was used to illuminate the samples (delivery 220 mW). The spectrometer was calibrated according to the manufacturer’s guidelines. In each sample, 200 spectra (1 second collection time) were measured at different positions. Sample preparation Nail samples were transferred from the Petri dishes to the fused silica glass and measured directly. To remove air bubbles and concentrate the specimens, spore suspensions were centrifuged at 16000 g for 1 min. The pellet was subsequently resuspended, transferred to the fused silica slide which was then covered with a 24 wells containing removable silicone isolator (Sigma–Aldrich, Zwijndrecht, the Netherlands). Samples were allowed to dry at 35°C for approximately 20 min.
Data analysis
The similarity between the processed spectra was calculated using the squared Pearson correlation coefficient (R2). This procedure results for our 18 samples (three for every strain/blank) in a 18 9 18 correlation matrix. The relationship between the samples in this correlation matrix was visualized using a colour index (heat map). Rows and columns were sorted in such a way that samples with the most similar correlation patterns are presented next to each other. In this way, high correlation values are grouped close to the diagonal (indicated red to orange), whereas lower correlation values can be found further away from the diagonal. Potentially related samples are marked yellow to orange. In addition to this, the sorted correlation matrix was used to generate a dendrogram providing a more classical illustration of cluster arrangements.23 The vertical axis of such a dendrogram represents the samples and clusters and the horizontal axis represents the distance or dissimilarity between clusters. Each node in a dendrogram defines a cluster and represents the lowest correlation coefficient (or similarity) between samples in that cluster.
Results and discussion Raman spectra
Data analysis Data processing The Raman data were processed and analysed with software that was developed in-house. This software operates in a MATLAB version 7.1 environment (MathWorks, natick, MA, USA) and uses the multivariate toolbox PLS-toolbox 7.0.0c
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Figure 1 shows the mean processed spectra of human nail plate slices (a), human nails infected with T. rubrum, T. mentagrophytus and T. tonsurans (b) and S. brevicaulis and C. albicans (c). Figure 2 presents the mean spectra from samples containing T. rubrum, T. mentagrophytus and T. tonsurans microconidia suspensions.
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Figure 1 Mean processed Raman spectra from three independent experiments of ex vivo (a) human nail material, (b) human nail infected with the dermatophytes Trichophyton rubrum (blue, ns 28188), T. tonsurans (red, ns 305) and T. mentagrophytus (green, ns 88), and (c) human nail infected with the nondermatophyte S. brevicaulis (brown, ns 358) and the yeast Candida albicans (purple, ns 8). Spectra in (b) and (c) are artificially offset from each other for reason of clarity. Two hundred spectra were taken from each sample at different positions and a 1-s collection time. Excitation laser power was 220 mW at 785 nm. Grey-shaded areas and arrows indicate wavelength regions and numbers where spectral differences can be noticed between the dermatophytes (b) and between S. brevicaulis and C. albicans (c).
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Human nail Important regions in this spectrum can be found
between 500–800, 850–1200, 1200–1500 and 1500–1800/cm. These regions are consistent with those that have been described and identified before.31,36 In this manuscript, we would like to emphasize the 500–800/cm spectral region that represents nail characteristics based on -S-S- disulphide bonds that are found in human nail plates and responsible for the nails rigidity and hardness.37,38 Most intense representative is the cysteine S–S stretching mode at 510/ cm. Other cysteine C–S stretching modes can be identified in our spectrum at wavenumbers 620 and 644/cm. According to Widjaja et al.31 a Raman 1156/cm signal derived from human nail clippings could be associated with a carotene component. This explanation, however, will be unlikely in this case because the larger 1527/cm carotene peak is lacking in our nail spectrum. Dermatophytes on human nail Figure 1b shows that when growing on human nail slices T. rubrum, T. mentagrophytus and T. tonsurans displayed different Raman spectra. These spectral differences can be found in the grey-shaded regions with wavenumbers between 635–795, 840–894, 1018–1112, 1206–1372 and 1566–1700/cm. Most distinct were the spectral differences observed for T. rubrum compared to the other tested dermatophytes in the 1240–1278/cm region and at 644, 666, 700, 740, 754, 884, 1080, 1100, 1158, 1174, 1246, 1270 and 1338/cm. Interesting is the peak at 1338/cm clearly present in the T. rubrum spectrum, to a lesser extent in the nail (Fig. 1a) and T. mentagrophytus spectrum while seen as a shoulder only in the spectrum of T. tonsurans. This 1338/cm signal may be assigned to the CH2/ CH3 twisting and bending mode of lipids. Lipid components play an important role in fungal physiological processes involving growth, sporulation and germination. The type and amount of (phospho) lipids has been reported to vary for different dermatophytic strains in particular T. rubrum39 but these lipid
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Figure 2 Mean processed Raman spectra from three independent experiments of T. rubrum (blue, sp 28188), T. tonsurans (red, sp 305) and T. mentagrophytus (green, sp 88) microconidia samples. Two hundred spectra were taken from each sample at different positions and a 1-s collection time. For reason of clarity, spectra are artificially offset from each other. Excitation laser power was 220 mW at 785 nm. Greyshaded areas and arrows indicate wavelength regions and numbers where spectral differences between the samples are noticed.
compositions depend on the fungal growth stage as well.40 Other spectral differences may partly be caused by differences in the galactomannan structure (glycoproteins consisting of a-mannopyranose, mannofuranose and galactofuranose attached to a peptide backbone) of T. rubrum compared to that of other Trichophyton species.41 In general, these species have two kinds of galactomannans, i.e. galactomanan I and II. In T. rubrum, however, galactofuranose units are missing in galactomanan I, whereas galactomannan II consists of a-1,2- and a-1,6-linked mannopyranose and mannofuranose units.42 Moreover, according to literature signals within 840–868 and 890–900/cm regions (largely corresponding to the 840–894/cm grey-shaded area in Fig. 1b) have been associated with polysaccharide structures.24 Investigations of Raman spectral properties of dermatophytic galactomannans would be most interesting as they have, to our opinion, not been reported in literature. Differences observed in other regions may also be due to differences in fungal pigmentation.43 Mutual differences between T. mentagrophytus and T. tonsurans on nail were noticed between 1244 and 1282 and at 1158/cm. S. brevicaulis and C. albicans on human nail Causative organisms of onychomycosis other than dermatophytes, like the mould S. brevicaulis and the yeast C. albicans were found to have their own characteristic Raman spectra on ex vivo nail. Important spectral differences between these species were noticed between 442–610, 758–692, 866–914, 1020–1100, 1138–1380 and 1492–1602/cm (Fig. 1c, grey-shaded regions). Notice the 600– 700 and 1100–1200 regions in Fig. 1b compared to 1c where grey areas do not match and spectral differences between dermatophytes compared to other important causes of onychomycosis, S. brevicaulis and C.albicans, are most distinct. Remarkable are furthermore the peaks found for C. albicans in the 1338/cm lipid
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region showing resemblance to the T. rubrum signals in the same area. Both samples showed the previously discussed 1156 nm peak (C. albicans to a lesser extent). Molecular identification of the Raman spectral difference between causative onyhomycosis organisms is beyond the scope of this manuscript, but remains an interesting topic for further research. Dermatophytic microconidia Figure 2 shows Raman spectra of the microconidia from the dermatophytes that have been used to induce the dermatophytic ex vivo human nail infection. The large spectral differences appeared to be related to both the signal’s shape and intensity as indicated with grey-shaded regions: 484–672, 704–968, 1000–1454 and 1548–1660/cm. Moreover, at 672 and 1586/cm both T. rubrum and T. mentagrophytus microconidia showed distinct signals while these were lacking in the T. tonsurans spectrum. Other distinct differences between T. rubrum and T. mentagrophytus and T. tonsurans are marked in Figure 2 at 1104, 1138, 1338 and 1370/cm. Indicated
differences need further investigation which is beyond the scope of this report but may contribute to the scarcely reported data regarding chemical (wall) architecture and pigmentation of dermatophytic conidia.44 Apparently, the Raman spectral variations observed between these microconidia, with T. tonsurans showing the most obvious differences, diminished after adherence and germination on nail material. T. rubrum then gave the most distinct Raman spectral differences when compared with the other two dermatophytes. Dendrogram and heat map
Figure 3 shows the multiple Raman spectral patterns observed for nails infected with various dermatophytes, S. brevicaulis and C. albicans, as sorted dendrogram (a) and heat plot (b). Based on similarity and clustering, the dendrogram contains two main clusters representing the dermatophytic (cluster 1) and nondematophytic/yeast (cluster 2) nail infections. Notice these dissimilarities between dermatophytic nail infections and those caused by S. brevicaulis or C. albicans indicated in the heat map
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(b) Figure 3 Graphical representation of the correlation matrix for Raman spectra taken from ex vivo blank human nail, the dermatophytes (T. rubrum: ns 28188, T. mentagrophytus: ns 88 and T. tonsurans: ns 305) and non-dermatophytes (C. albicans: ns 8 and S. brevicaulis: ns 358) on human nail as Sorted dendrogram (a) and heat map (b). Main clusters in the dendrogram are indicated as cluster 1 and 2. In the heat map, highly correlated samples are located on or close to the diagonal (red and orange colour indices) whereas samples with a lower correlation value are positioned further away from the diagonal. Blue areas represent lowly correlated samples.
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(Fig. 3b) as black to blue areas for C. albicans and light blue to green colouring for S. brevicaulis. Within cluster one (dermatophytes and blank nails), different samples from the same strain showed indeed high similarity (red colour index in the heat map), whereas within cluster two (non-dermatophytes) lower similarities can be seen for at least one of the samples compared with the other two samples of the same strain (green indication in the heat map for S. brevicaulis and green to blue for C. albicans). As already discussed T. rubrum showed indeed less similarity to the other Trichophyton strains (indicated green to yellow in the heat map for comparison with T. mentagrophytus and green to dark blue for T. tonsurans). Higher mutual similarity was found between T. mentagrophytus and T. tonsurans, but differences were still large enough to discriminate between the two dermatophytes (see for comparison the subclusters in Fig. 3a given for these strains).
Conclusion It can be concluded that the most important causative organisms of onychomycosis when growing on ex vivo human nail may be distinguished by easily performed, short-lasting Raman spectroscopic measurements. Presented research may thus be the basis for large-scale investigations on onychomycosis diagnostics. Since in vivo Raman studies have already been described for characterization of some other dermatological conditions, like certain types of skin cancer,45 presented results may provide input for an additional (and thus more cost effective) in vivo application of Raman spectroscopy within dermatology. In case of onychomycosis diagnostics, an optical fibre can be directly positioned on the infected nail thereby making a nail biopsy dispensable. Reported possibility to discriminate between inactivated and viable bacteria with Raman spectroscopy may offer additional possibilities to establish in vivo antimicrobial and/or antifungal agents efficacy and to detect remaining fungal conidia.49 Limitations of the study
Raman measurements of clinical onychomycosis may also record spectral properties of contaminating bacteria, like present in case of paronychia (a nail bed infection mainly caused by Pseudomonas aeruginosa or Staphylococcus aureus).46 Both types of bacteria have been studied with Raman spectroscopy and these investigations showed that S. aureus can be clearly distinguished from our tested causatives of onychomycosis by for instance its characteristic carotene peak at 1578/cm.23,47,48 Raman spectral differences between P. aeruginosa and our tested organisms, can be found mainly in the 1400–1600/ cm.47 Moreover, Cheng et al. demonstrated effective Raman spectral differences between P. aeruginosa or S. aureus. As coinfection with these organisms commonly affects different nail areas, it will not lead to confusing false-negative or false-positive results.
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Acknowledgements The authors thank Ing. Cindy Groenendijk-Kok (Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, the Netherlands) for providing the clinical isolates of dermatophytes, C. albicans and S. brevicaulis, and Moniek Suijkerbuijk and Jasper Visser for their technical assistance.
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22 Tu Q, Chang C. Diagnostic applications of Raman spectroscopy. Nanomedicine 2012; 8: 545–558. 23 Willemse-Erix DF, Scholtes-Timmerman MJ, Jachtenberg JW et al. Optical fingerprinting in bacterial epidemiology: raman spectroscopy as a real-time typing method. J Clin Microbiol 2009; 47: 652–659. 24 Movasaghi Z, Rehman S, Rehman IU. Raman spectroscopy of biological tissues. Appl Spectrosc Rev 2007; 42: 493–541. 25 Maquelin K, Dijkshoorn L, van der Reijden TJ et al. Rapid epidemiological analysis of Acinetobacter strains by Raman spectroscopy. J Microbiol Methods 2006; 64: 126–131. 26 De Gussem K, Vandenabeele P, Verbeken A et al. Raman spectroscopic study of Lactarius spores (Russulales, Fungi). Spectrochim Acta A Mol Biomol Spectrosc 2005; 61: 2896–2908. 27 Towler MR, Wren A, Rushe N et al. Raman spectroscopy of the human nail: a potential tool for evaluating bone health? J Mater Sci Mater Med 2007; 18: 759–763. 28 Zhang G, Senak L, Moore DJ. Measuring changes in chemistry, composition, and molecular structure within hair fibers by infrared and Raman spectroscopic imaging. J Biomed Opt 2011; 16: 056009. 29 Edwards HG, Hunt DE, Sibley MG. FT-Raman spectroscopic study of keratotic materials: horn, hoof and tortoiseshell. Spectrochim Acta A Mol Biomol Spectrosc 1998; 54A: 745–757. 30 Widjaja E, Garland M. Detection of bio-constituents in complex biological tissue using Raman microscopy. Application to human nail clippings. Talanta 2010; 80: 1665–1671. 31 Widjaja E, Lim GH, An A. A novel method for human gender classification using Raman spectroscopy of fingernail clippings. Analyst 2008; 133: 493–498. 32 Smijs TG, Bouwstra JA, Schuitmaker HJ et al. A novel ex vivo skin model to study the susceptibility of the dermatophyte Trichophyton rubrum to photodynamic treatment in different growth phases. J Antimicrob Chemother 2007; 59: 433–440. 33 Zurita J, Hay RJ. Adherence of dermatophyte microconidia and arthroconidia to human keratinocytes in vitro. J Invest Dermatol 1987; 89: 529–534. 34 Dai T, Tegos GP, Rolz-Cruz G et al. Ultraviolet C inactivation of dermatophytes: implications for treatment of onychomycosis. Br J Dermatol 2008; 158: 1239–1246.
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35 Martens H, Stark E. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. J Pharm Biomed Anal 1991; 9: 625–635. 36 Gniadecka M, Faurskov NO, Christensen DH et al. Structure of water, proteins, and lipids in intact human skin, hair, and nail. J Invest Dermatol 1998; 110: 393–398. 37 Brown MB, Khengar RH, Turner RB et al. Overcoming the nail barrier: a systematic investigation of ungual chemical penetration enhancement. Int J Pharm 2009; 370: 61–67. 38 Malhotra GG, Zatz JL. Investigation of nail permeation enhancement by chemical modification using water as a probe. J Pharm Sci 2002; 91: 312–323. 39 Khuller GK, Sharma S, Deo D. Dermatophyte lipids-Composition and regulation of phospholipids. Indian J Clin Biochem 2000; 15: 51–59. 40 Swanson R, Stock JJ. Biochemical alterations of dermatophytes during growth. Appl Microbiol 1966; 14: 438–444. 41 Blake JS, Dahl MV, Herron MJ et al. An immunoinhibitory cell wall glycoprotein (mannan) from Trichophyton rubrum. J Invest Dermatol 1991; 96: 657–661. 42 San Blas G. The cell wall of fungal human pathogens: its possible role in host-parasite relationships. Mycopathologia 1982; 79: 159–184. 43 Youngchim S, Pornsuwan S, Nosanchuk JD et al. Melanogenesis in dermatophyte species in vitro and during infection. Microbiology 2011; 157: 2348–2356. 44 Wu-Yuan CD, Hashimoto T. Architecture and chemistry of microconidial walls of Trichophyton mentagrophytes. J Bacteriol 1977; 129: 1584–1592. 45 Wang H, Zhao J, Lee AM et al. Improving skin Raman spectral quality by fluorescence photobleaching. Photodiagnosis Photodyn Ther 2012; 9: 299– 302. 46 Nakamura R, Broce AA, Palencia DP et al. Dermatoscopy of nail lichen planus. Int J Dermatol 2013; 52: 684–687. 47 Willemse-Erix DF, Jachtenberg JW, Schut TB, et al. Towards Ramanbased epidemiological typing of Pseudomonas aeruginosa. J Biophotonics 2010; 3: 506–511. 48 Cheng IF, Lin CC, Lin DY et al. A dielectrophoretic chip with a roughened metal surface for on-chip surface-enhanced Raman scattering analysis of bacteria. Biomicrofluidics 2010; 4: 034104-1–034104-11. 49 Buijtels PC, Willemse-Erix HF, Petit PL et al. Rapid identification of mycobacteria by Raman spectroscopy. J Clin Microbiol 2008; 46: 961–965.
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DOI: 10.1111/jdv.12324
ORIGINAL ARTICLE
Detection and differentiation of causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy T. G. Smijs,1,* J. W. Jachtenberg,2 S. Pavel,3 T. C. Bakker-Schut,4 D. Willemse-Erix,5 E. R. M. de Haas,4 H. Sterenborg1 1
Centre for Optical Diagnostics and Therapy, Department of Radiotherapy, Erasmus Medical Centre, Rotterdam, The Netherlands Department of Neurosurgery, Erasmus Medical Centre, Rotterdam, The Netherlands 3 Department of Dermatology, Charles University, Pilsen, Czech Republic 4 Department of Dermatology and Venereology, Erasmus Medical Centre, Rotterdam, The Netherlands 5 Department of Medical Microbiology and Infectious Diseases, Erasmus Medical Centre, Rotterdam, The Netherlands *Correspondence: T. G. Smijs. E-mail: [email protected] 2
Abstract Background Onychomycosis is worldwide the most prevalent infection of the nail. It is mainly caused by the dermatophytes Trichophyton rubrum and Trichophyton mentagrophytes and to a lesser extent Trichophyton tonsurans. The yeast Candida albicans and the mould Scopulariopsis brevicaulis can also cause onychomycosis. Management of these nail conditions may require appropriate treatment methods and therefore the identification of the causative species can be of importance. However, the determination of agents causing onychomycosis is still not optimal. Objectives To detect and differentiate causative organisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy. The work focusses is on the discriminative power of Raman spectroscopy for detection of differences between T. rubrum, T. mentagrophytus and T. tonsurans on human nail and distinguishing these dermatophytic from the non-dermatophytic species S. brevicaulis and C. albicans. Methods Raman spectra (200/sample) were taken from 50-lm human nail slices infected with T. rubrum, T. mentagrophytus, T. tonsurans, S. brevicaulis or C. albicans using a 2500 High-Performance Raman Module and 785-nm diode laser. Processed spectra were analysed by sorting the correlation matrix and presented as dendrogram and heat map. Raman spectra from suspended dermatophytic microconidia were taken for mutual comparisons. Results Spectral differences between the dermatophytes T. rubrum, T. mentagrophytus and T. tonsurans (635–795, 840–894, 1018–1112, 1206–1372, 1566–1700/cm) and the non-dermatophytes S. brevicaulis and C. albicans (442–610, 692–758, 866–914, 1020–1100, 1138–1380,1492–1602/cm) growing on nail were confirmed by clustering correlation showing two main clusters. Dissimilarities between tested dermatophytes were also found with T. rubrum being most different. Raman spectra of the dermatophytic microconidia varied over the whole tested 400–1800/cm range. Conclusion Important dermatophytic and non-dermatophytic agents of onychomycosis growing on ex vivo human nail can be distinguished specifically and non-invasively by Raman spectroscopy. Received: 19 July 2013; Accepted: 28 October 2013
Conflicts of interest None declared.
Funding sources This study was supported by the Dutch Technology Foundation, STW project 11622.
Introduction Onychomycosis is the most prevalent infection of the nail and the incidence of this disease is still growing worldwide.1,2 This increased prevalence may be due to unsatisfactory therapeutic efficacy and a growing number of patients with a compromised immune system3 or diabetes mellitus.4 Moreover, dermatophytic onychomycosis increases with age and since the population of
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elderly people is increasing, this clinical condition may become a significant medical problem.5 Onychomycosis is approximately three times more prevalent among people with diabetes mellitus than in those without this condition.6 Dermatophytes are the fungi that are most commonly responsible for various clinical types of onychomycosis. The strains Trichophyton rubrum and Trichophyton mentagrophytes are most frequently isolated from
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hands or feet. Less frequently diagnosed organism is Trichophyton tonsurans. The yeast Candida is the second important cause of onychomycosis, whereas nail infections by moulds (non-dermatophytic fungi) are currently rising worldwide.7 A saprophytic mould that is typically associated with onychomycosis is Scopulariopsis brevicaulis.8 Current therapeutics still have important restrictions. As a consequence, the recurrence of the infection and therefore an increased duration of the treatment is frequently seen. The efficacy of available drugs differs between Candida onychomycosis when compared to onychomycosis caused by dermatophytes and management of these nail conditions requires identification of the causative species.9,10 In addition, the in vitro antifungal drug susceptibility of microconidia and arthroconidia isolated from dermatophytes has been shown to be both drug and strain dependent. Especially T. rubrum conidia appeared to be less susceptible to various antifungal agents.11 This dermatophyte can be very persistent and consequently difficult to treat which is partly due to a decreased efficiency of the host’s immune responses.12–14 Differentiation between the causative dermatophytes may therefore be relevant for treatment choices. Moreover, differentiation between dermatophytic vs. non-dermatophytic causes of onychomycosis is important and determines treatment options as well as longterm prognosis for the patient. Knowledge of causative species of onychomycosis may also attribute to a better comprehension of the epidemiology of this disease. The diagnostic techniques that are commonly applied in clinics are mycological examination (both microscopic and culture investigations) of nail samples and histological analysis.1 In case of mycological examination, a correct diagnosis is highly depended on the sample’s quality, whereas microscopic identification of fungal elements in the nail sample requires some experience. Plating out the samples on Sabouraud dextrose agar (SDA) on the other hand is a useful but time-consuming identification method which takes for dermatophytes approximately 2–6 weeks.15 The periodic acid-Schiff (PAS) staining is currently used in many centres and proposed to be the single investigation which proves pathogenicity with highest sensitivity and specificity.16 Newer diagnostic methods comprise in vivo confocal microscopy, polymerase chain reaction (PCR), optical coherence tomography, nail digital onychoscopy and matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS).17–21 Nevertheless, determination of onychomycotic agents is still not optimal and a quick, specific and non-invasive tool with highly distinctive character is desired. Raman spectroscopy may be such a diagnostic tool. This method is a vibrational spectroscopic technique that can be used in various biomedical applications, specifically in the field of clinical evaluation.22,23 It requires a minimum sample quantity and allows the investigation of the molecular composition of samples based on molecular specificity of spectral bands in a vibrational spectrum.24 Biomedical developments of Raman
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spectroscopic microbiological applications involve bacterial identification and typing of different species.23,25 A detailed Raman spectroscopic study of fungi of the genus Lactarius and their conidia has been reported as well.26 Keratin-containing materials, particularly nails, have also been studied with Raman spectroscopic techniques.27–29 Widjaja et al.30,31 have frequently applied Raman spectroscopy to human nail clippings to analyse the bio-constitutes of this keratin material and as a novel method for human gender classification. To our knowledge, however, there are no reports on Raman spectroscopic studies of dermatophytes alone or in combination with nail or other keratin-containing material. Our present study concerns the detection and differentiation of causative microorganisms of onychomycosis in an ex vivo nail model by means of Raman spectroscopy. We focused primarily on differences between T. rubrum, T. mentagrophytus and T. tonsurans in the presence of human nail material. To investigate the discriminative power of Raman spectroscopy for dermatophytic compared to non-dermatophytic causes of onychomycosis, spectral properties of S. brevicaulis and C. albicans were analysed. For mutual comparisons separate spectra were taken from suspended dermatophytic microconidia that were used to develop the nail infections.
Materials and methods Strains
Clinical (onychomycosis) isolates of T. mentagrophytus (no. 88), T. tonsurans (no. 305), Candida albicans (no. 8) and S. brevicaulis (no. 358) were kindly provided by the Department of Medical Microbiology and Infectious Diseases of the Erasmus Medical Center (Rotterdam, the Netherlands). T. rubrum was obtained from American Type Culture Collection (ATCC, no. 28188). All strains were cultures on Sabouraud dextrose agar (SDA, Sigma Aldrich Chemie GmbH, Schnelldorf, Germany) at 28°C. Nails
Human healthy nail clippings were collected and provided by the Leids Cytologisch Pathologisch Laboratorium (Leiden, the Netherlands) from anonymous sources. Clippings were cleaned in 70% ethanol for 3 min in an ultrasonic water bath, cut to 3 9 3 mm pieces, and subsequently sectioned parallel to the surface into 50 lm slices (CM 1950 cryostat, Leica Biosystems Nussloch GmbH, Germany). Layers from the ventral zone were subsequently disinfected (twice) by 1-min immersion in 70% isopropanol (Sigma-Aldrich Chemie, Zwijndrecht, the Netherlands) and sterile Milli-Q (Millipore B.V., Amsterdam, the Netherlands). Preparation of microconidia and culture suspensions
The protocol for obtaining a suspension of microconidia produced by dermatophytes cultured on SDA was performed according to a method described previously.32,33 Microconidia
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suspensions thus obtained were stored in liquid nitrogen for no longer than 6 months. S. brevicaulis and C. albicans suspensions were prepared in sterile Milli-Q from a 14-day-old SDA culture.
(Eigenvector Research, Wenatchee, WA, USA). Raw data were first calibrated and corrected for the wavelength-dependent signal detection efficiency of the Raman setup as described earlier.23
Ex vivo nail model
Signal processing Potential interfering spectral contributions like signals from the fused silica glass, nail material and broadband biomass background were obtained and subtracted in the final analysis. The Extended Multiplicative Scatter Correction with Spectral Interference Subtraction (EMSC-SIS)35 scaling procedure was used to eliminate the influence of these interfering signals and to scale the spectra on basis of informative biochemical content. This method uses a fitting procedure to estimate the contributions of known sources to a measured spectrum. In this study, this procedure was used to estimate contributions of (1) the microorganisms, (2) the non-informative interfering signals from the fused silica glass, (3) the nail, and (4) the background fluorescence. The fit coefficients for contributions (2) to (4) were used to eliminate interfering signals, whereas the fit coefficient of (1) was used to scale the resulting spectrum.
Dermatophytic (T. rubrum 28188, T. mentagrophytus 88, T. tonsurans 305) microconidia, S. brevicaulis and C. albicans suspensions were adjusted to a 0.5 McFarland turbidity standard (Oxoid turbidimeter, Scientific Device laboratory. Inc., des Plaines, IL, USA). The 0.5 McFarland suspensions were 10 times diluted in Milli-Q and inoculated (4 lL) on the nail plate layers. Slices were then placed into 35-mm Petri dishes which were subsequently (per two pieces) transferred to 96-mm Petri dishes. To create a moist environment 25 mL of sterile water was added to the larger dishes34 and the nail pieces were incubated at 28°C for 2 weeks. Growth of the inoculates was checked microscopically before usage. Raman spectroscopy Instrument The instrument consisted of a Model 2500 High-
Performance Raman Module (RiverD International BV, Rotterdam, the Netherlands) coupled to a custom-built measurement compartment equipped with an automated x- and y-axis-positioning stage. A fused silica slide (Tower Optical Corporation, Boynton Beach, FL, USA) containing the biomass samples was positioned on the stage. A custom-designed near-infrared-optimized microscope objective (numerical aperture, 0.7) was used to focus the laser light emitted by the Raman Module into the samples and collect Raman scattered light from the samples. A 785-nm diode laser (Sacher Tiger, Sacher Lasertechnik, Marburg, Germany) was used to illuminate the samples (delivery 220 mW). The spectrometer was calibrated according to the manufacturer’s guidelines. In each sample, 200 spectra (1 second collection time) were measured at different positions. Sample preparation Nail samples were transferred from the Petri dishes to the fused silica glass and measured directly. To remove air bubbles and concentrate the specimens, spore suspensions were centrifuged at 16000 g for 1 min. The pellet was subsequently resuspended, transferred to the fused silica slide which was then covered with a 24 wells containing removable silicone isolator (Sigma–Aldrich, Zwijndrecht, the Netherlands). Samples were allowed to dry at 35°C for approximately 20 min.
Data analysis
The similarity between the processed spectra was calculated using the squared Pearson correlation coefficient (R2). This procedure results for our 18 samples (three for every strain/blank) in a 18 9 18 correlation matrix. The relationship between the samples in this correlation matrix was visualized using a colour index (heat map). Rows and columns were sorted in such a way that samples with the most similar correlation patterns are presented next to each other. In this way, high correlation values are grouped close to the diagonal (indicated red to orange), whereas lower correlation values can be found further away from the diagonal. Potentially related samples are marked yellow to orange. In addition to this, the sorted correlation matrix was used to generate a dendrogram providing a more classical illustration of cluster arrangements.23 The vertical axis of such a dendrogram represents the samples and clusters and the horizontal axis represents the distance or dissimilarity between clusters. Each node in a dendrogram defines a cluster and represents the lowest correlation coefficient (or similarity) between samples in that cluster.
Results and discussion Raman spectra
Data analysis Data processing The Raman data were processed and analysed with software that was developed in-house. This software operates in a MATLAB version 7.1 environment (MathWorks, natick, MA, USA) and uses the multivariate toolbox PLS-toolbox 7.0.0c
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Figure 1 shows the mean processed spectra of human nail plate slices (a), human nails infected with T. rubrum, T. mentagrophytus and T. tonsurans (b) and S. brevicaulis and C. albicans (c). Figure 2 presents the mean spectra from samples containing T. rubrum, T. mentagrophytus and T. tonsurans microconidia suspensions.
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Figure 1 Mean processed Raman spectra from three independent experiments of ex vivo (a) human nail material, (b) human nail infected with the dermatophytes Trichophyton rubrum (blue, ns 28188), T. tonsurans (red, ns 305) and T. mentagrophytus (green, ns 88), and (c) human nail infected with the nondermatophyte S. brevicaulis (brown, ns 358) and the yeast Candida albicans (purple, ns 8). Spectra in (b) and (c) are artificially offset from each other for reason of clarity. Two hundred spectra were taken from each sample at different positions and a 1-s collection time. Excitation laser power was 220 mW at 785 nm. Grey-shaded areas and arrows indicate wavelength regions and numbers where spectral differences can be noticed between the dermatophytes (b) and between S. brevicaulis and C. albicans (c).
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between 500–800, 850–1200, 1200–1500 and 1500–1800/cm. These regions are consistent with those that have been described and identified before.31,36 In this manuscript, we would like to emphasize the 500–800/cm spectral region that represents nail characteristics based on -S-S- disulphide bonds that are found in human nail plates and responsible for the nails rigidity and hardness.37,38 Most intense representative is the cysteine S–S stretching mode at 510/ cm. Other cysteine C–S stretching modes can be identified in our spectrum at wavenumbers 620 and 644/cm. According to Widjaja et al.31 a Raman 1156/cm signal derived from human nail clippings could be associated with a carotene component. This explanation, however, will be unlikely in this case because the larger 1527/cm carotene peak is lacking in our nail spectrum. Dermatophytes on human nail Figure 1b shows that when growing on human nail slices T. rubrum, T. mentagrophytus and T. tonsurans displayed different Raman spectra. These spectral differences can be found in the grey-shaded regions with wavenumbers between 635–795, 840–894, 1018–1112, 1206–1372 and 1566–1700/cm. Most distinct were the spectral differences observed for T. rubrum compared to the other tested dermatophytes in the 1240–1278/cm region and at 644, 666, 700, 740, 754, 884, 1080, 1100, 1158, 1174, 1246, 1270 and 1338/cm. Interesting is the peak at 1338/cm clearly present in the T. rubrum spectrum, to a lesser extent in the nail (Fig. 1a) and T. mentagrophytus spectrum while seen as a shoulder only in the spectrum of T. tonsurans. This 1338/cm signal may be assigned to the CH2/ CH3 twisting and bending mode of lipids. Lipid components play an important role in fungal physiological processes involving growth, sporulation and germination. The type and amount of (phospho) lipids has been reported to vary for different dermatophytic strains in particular T. rubrum39 but these lipid
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Figure 2 Mean processed Raman spectra from three independent experiments of T. rubrum (blue, sp 28188), T. tonsurans (red, sp 305) and T. mentagrophytus (green, sp 88) microconidia samples. Two hundred spectra were taken from each sample at different positions and a 1-s collection time. For reason of clarity, spectra are artificially offset from each other. Excitation laser power was 220 mW at 785 nm. Greyshaded areas and arrows indicate wavelength regions and numbers where spectral differences between the samples are noticed.
compositions depend on the fungal growth stage as well.40 Other spectral differences may partly be caused by differences in the galactomannan structure (glycoproteins consisting of a-mannopyranose, mannofuranose and galactofuranose attached to a peptide backbone) of T. rubrum compared to that of other Trichophyton species.41 In general, these species have two kinds of galactomannans, i.e. galactomanan I and II. In T. rubrum, however, galactofuranose units are missing in galactomanan I, whereas galactomannan II consists of a-1,2- and a-1,6-linked mannopyranose and mannofuranose units.42 Moreover, according to literature signals within 840–868 and 890–900/cm regions (largely corresponding to the 840–894/cm grey-shaded area in Fig. 1b) have been associated with polysaccharide structures.24 Investigations of Raman spectral properties of dermatophytic galactomannans would be most interesting as they have, to our opinion, not been reported in literature. Differences observed in other regions may also be due to differences in fungal pigmentation.43 Mutual differences between T. mentagrophytus and T. tonsurans on nail were noticed between 1244 and 1282 and at 1158/cm. S. brevicaulis and C. albicans on human nail Causative organisms of onychomycosis other than dermatophytes, like the mould S. brevicaulis and the yeast C. albicans were found to have their own characteristic Raman spectra on ex vivo nail. Important spectral differences between these species were noticed between 442–610, 758–692, 866–914, 1020–1100, 1138–1380 and 1492–1602/cm (Fig. 1c, grey-shaded regions). Notice the 600– 700 and 1100–1200 regions in Fig. 1b compared to 1c where grey areas do not match and spectral differences between dermatophytes compared to other important causes of onychomycosis, S. brevicaulis and C.albicans, are most distinct. Remarkable are furthermore the peaks found for C. albicans in the 1338/cm lipid
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region showing resemblance to the T. rubrum signals in the same area. Both samples showed the previously discussed 1156 nm peak (C. albicans to a lesser extent). Molecular identification of the Raman spectral difference between causative onyhomycosis organisms is beyond the scope of this manuscript, but remains an interesting topic for further research. Dermatophytic microconidia Figure 2 shows Raman spectra of the microconidia from the dermatophytes that have been used to induce the dermatophytic ex vivo human nail infection. The large spectral differences appeared to be related to both the signal’s shape and intensity as indicated with grey-shaded regions: 484–672, 704–968, 1000–1454 and 1548–1660/cm. Moreover, at 672 and 1586/cm both T. rubrum and T. mentagrophytus microconidia showed distinct signals while these were lacking in the T. tonsurans spectrum. Other distinct differences between T. rubrum and T. mentagrophytus and T. tonsurans are marked in Figure 2 at 1104, 1138, 1338 and 1370/cm. Indicated
differences need further investigation which is beyond the scope of this report but may contribute to the scarcely reported data regarding chemical (wall) architecture and pigmentation of dermatophytic conidia.44 Apparently, the Raman spectral variations observed between these microconidia, with T. tonsurans showing the most obvious differences, diminished after adherence and germination on nail material. T. rubrum then gave the most distinct Raman spectral differences when compared with the other two dermatophytes. Dendrogram and heat map
Figure 3 shows the multiple Raman spectral patterns observed for nails infected with various dermatophytes, S. brevicaulis and C. albicans, as sorted dendrogram (a) and heat plot (b). Based on similarity and clustering, the dendrogram contains two main clusters representing the dermatophytic (cluster 1) and nondematophytic/yeast (cluster 2) nail infections. Notice these dissimilarities between dermatophytic nail infections and those caused by S. brevicaulis or C. albicans indicated in the heat map
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(Fig. 3b) as black to blue areas for C. albicans and light blue to green colouring for S. brevicaulis. Within cluster one (dermatophytes and blank nails), different samples from the same strain showed indeed high similarity (red colour index in the heat map), whereas within cluster two (non-dermatophytes) lower similarities can be seen for at least one of the samples compared with the other two samples of the same strain (green indication in the heat map for S. brevicaulis and green to blue for C. albicans). As already discussed T. rubrum showed indeed less similarity to the other Trichophyton strains (indicated green to yellow in the heat map for comparison with T. mentagrophytus and green to dark blue for T. tonsurans). Higher mutual similarity was found between T. mentagrophytus and T. tonsurans, but differences were still large enough to discriminate between the two dermatophytes (see for comparison the subclusters in Fig. 3a given for these strains).
Conclusion It can be concluded that the most important causative organisms of onychomycosis when growing on ex vivo human nail may be distinguished by easily performed, short-lasting Raman spectroscopic measurements. Presented research may thus be the basis for large-scale investigations on onychomycosis diagnostics. Since in vivo Raman studies have already been described for characterization of some other dermatological conditions, like certain types of skin cancer,45 presented results may provide input for an additional (and thus more cost effective) in vivo application of Raman spectroscopy within dermatology. In case of onychomycosis diagnostics, an optical fibre can be directly positioned on the infected nail thereby making a nail biopsy dispensable. Reported possibility to discriminate between inactivated and viable bacteria with Raman spectroscopy may offer additional possibilities to establish in vivo antimicrobial and/or antifungal agents efficacy and to detect remaining fungal conidia.49 Limitations of the study
Raman measurements of clinical onychomycosis may also record spectral properties of contaminating bacteria, like present in case of paronychia (a nail bed infection mainly caused by Pseudomonas aeruginosa or Staphylococcus aureus).46 Both types of bacteria have been studied with Raman spectroscopy and these investigations showed that S. aureus can be clearly distinguished from our tested causatives of onychomycosis by for instance its characteristic carotene peak at 1578/cm.23,47,48 Raman spectral differences between P. aeruginosa and our tested organisms, can be found mainly in the 1400–1600/ cm.47 Moreover, Cheng et al. demonstrated effective Raman spectral differences between P. aeruginosa or S. aureus. As coinfection with these organisms commonly affects different nail areas, it will not lead to confusing false-negative or false-positive results.
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Acknowledgements The authors thank Ing. Cindy Groenendijk-Kok (Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, the Netherlands) for providing the clinical isolates of dermatophytes, C. albicans and S. brevicaulis, and Moniek Suijkerbuijk and Jasper Visser for their technical assistance.
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