Anal Bioanal Chem (2014) 406:3051–3058 DOI 10.1007/s00216-014-7761-4
RESEARCH PAPER
Towards a receptor-free immobilization and SERS detection of urinary tract infections causative pathogens Nicoleta E. Mircescu & Haibo Zhou & Nicolae Leopold & Vasile Chiş & Natalia P. Ivleva & Reinhard Niessner & Andreas Wieser & Christoph Haisch
Received: 30 November 2013 / Revised: 6 March 2014 / Accepted: 10 March 2014 / Published online: 6 April 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Based on molecular-specific surface-enhanced Raman scattering (SERS) spectroscopy we were able to discriminate between rough and smooth strains of Escherichia coli and Proteus mirabilis bacteria. For this purpose, bacteria have been immobilized through electrostatic forces by inducing a positive charge on the glass slide. This way, SERS spectra on bacterial biomass and also on single bacteria could be recorded in less than 2 h, by using concentrated silver nanoparticles as SERS-active substrate. Single-bacterium SERS spectral fingerprints showed to be sensitive to the presence of the Oantigen at strain level and to the microorganisms growth phase. By using principal component analysis (PCA) on the SERS spectra recorded from E. coli and P. mirabilis, these two uropathogens could be fairly discriminated.
Keywords SERS . Uropathogen . Receptor free . Principal component analysis
Electronic supplementary material The online version of this article (doi:10.1007/s00216-014-7761-4) contains supplementary material, which is available to authorized users. N. E. Mircescu : N. Leopold : V. Chiş Faculty of Physics, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania H. Zhou : N. P. Ivleva : R. Niessner : C. Haisch (*) Chair for Analytical Chemistry, Institute of Hydrochemistry, Technische Universität München, Marchioninistrasse 17, 81377 Munich, Germany e-mail: [email protected] A. Wieser Max von Pettenkofer-Institut für Hygiene und Medizinische Mikrobiologie, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
Introduction Urinary tract infections (UTI) are the most common bacterial infection encountered in the western world. They are responsible for considerable morbidity as well as mortality. Furthermore, they have been in the focus of recent attention due to their association with low-weight birth, premature labor, and fetal mortality [1]. Particularly, sexually active women have high prevalence rates of UTIs. Almost 50 % will experience at least one episode of UTIs during their lifetime, many cases will be chronic and recurrent [2]. In addition to women, children of both sexes and older men are at high risk of developing UTIs. Currently, UTIs are diagnosed by using urinalysis detecting the presence of leukocyte esterase, a marker for inflammatory cells, pH, as well as nitrite which is produced by the most prevalent uropathogenic organisms. However, some bacterial infections either have too low bacterial loads or do not elicit sufficient leukocyte influx into the urinary compartment. So, the sensitivity of urinalysis to detect UTI can be as low as 68– 88 % [3]. Alternative test methods directly from urine may apply molecular techniques such as polymerase chain reaction (PCR) or MALDI-TOF and are quite complicated as well as expensive. Most common techniques are time-consuming and culture based. This means a certain amount of urine will be spread on culture media and bacteria will be grown in vitro [4, 5]. Bacterial cultivation is costly and time consuming. Considering the low sensitivity and specificity, the urine dipstick, which is relatively cheap and fast, should be confirmed by an alternative testing method. PCR and antibody-based sandwich immunoassay [6] have several potential problems, such as high rate of false negatives and false positives. Different recent approaches that possess a high sensitivity are molecular techniques, such as fluorescence in situ hybridization (FISH) [7] or loop-mediated isothermal amplification (LAMP) [8], being also used to identify UTI causative pathogens. In these cases,
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the sample preparation and post-processing fluorescence imaging make them difficult and rely on highly skilled personnel. Therefore, there is a growing need to ensure appropriate, fast, and automated diagnosis, succeeded by further investigation to allow for targeted antimicrobial therapy where indicated. Vibrational spectroscopy is a powerful tool considering the high and well-defined spectral information and the simple, mobile and affordable equipment required. Surface-enhanced Raman scattering (SERS), by its nature of enhancing the Raman cross-sections, provides intense spectra, allowing the detection down to the single (bio)molecule level [9–11]. Moreover, SERS provides reduced acquisition time and therefore allows recording of significant numbers of spectra for a reliable statistic model in a short time. When pathogenic bacteria were cultured under conditions known to affect virulence, their SERS fingerprints changed significantly [12]. The pathogens can be individually identified by SERS; SERS fingerprint may even reflect the physiological state of a bacterial cell. Bacteria respond to environmental triggers, such as temperature, pH, and nutrient concentrations, by switching to different physiological states. The switch between different physiological states or phenotypes involves numerous changes in the bacteria’s metabolic profile, including the number and composition of its outer membrane proteins, lipopolysaccharides (LPS), and cellular fatty acids [12]. Both Gram-positive and Gram-negative bacteria contain a cell membrane and a sacculus surrounding the cell made of disaccharide polymers, the peptidoglycan. However, Gramnegative bacteria exhibit another phospholipid bilayer outside of their peptidoglycan sacculus called the outer membrane. This membrane is actually in close proximity to the SERS substrate, so we will focus on its components. A main component of the outer leaflet of the outer membrane is LPS. These large molecules consist of a lipid part anchored in the membrane (lipid A), the core structures as linkers and polysaccharide motifs joined by a covalent bond. The O-antigen or O-polysaccharide is the repetitive glycan polymer attached to the outer core structure. Strains with O-antigens are called smooth, strains lacking the O-chains are referred to as semi rough and strains bearing an LPS structure that stops at the inner core are considered rough. Most laboratory Escherichia coli strains have rough LPS, whereas pathogenic E. coli tend to have various O-antigens which can be considered to be a virulence determinant. Several groups used Fourier-transform infrared spectroscopy (FTIR) to obtain useful information for genus, species, or even strain level discrimination [13–17]. The purpose of these studies was to group the E. coli isolates according to their Oantigenic properties which reside in the structure of O-specific side chains of LPS. By investigating the spectral range where the vibrational modes of cellular carbohydrates dominate
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(900–1,200 cm−1 in IR) and using the data as input for cluster analysis, the obtained dendrogram had revealed distinct clustering suggesting different O serotypes. SERS detection of bacteria is assessed either by simply analyzing a mixture of nanoparticles (NPs) and bacterial biomass after dehydrating the sample [18], or by immobilization of the bacteria by using, for instance, antibodies [19, 20]. The SERS detection of microorganisms at single-bacterium level [21] was already reported but the common bacteria fixation procedure includes sandwich immunoassays [22], positively charged polyamino acids (poly-L-lysine) [23] or other specific receptors, like siderophores [24]. In a more recent approach, the feasibility of SERS bacteria identification at strain level in microfluidic devices was shown [25]. The bacterial biomass was busted by ultrasonication and then mixed with the colloidal suspension and the SERS spectra acquired were averaged. The novelty of the setup presented here lays in the reliable SERS detection of single bacteria from a monolayer of bacteria adsorbed on a modified glass substrate, as well as the avoidance of sample dehydration prior to the SERS measurement. The aim of this study is to prove the possibility to reproducibly identify E. coli, independently of O-type antigen, strain and growth phase by using SERS. The first approach developed provides highly specific vibrational information within some hours from the immobilization of the biomass on the modified glass slide by simply using electrostatic forces. In order to acquire SERS signals from a singlebacterium and also to reduce the analysis time required, we further proposed another approach. The immobilization principle is similar, but the SERS substrate enhancement factor is improved by preconcentration of the silver NPs. Unlike the other studies carried out, we did not dehydrate the sample prior to SERS investigation and moreover, immobilization of bacteria was achieved without employing a specific receptor. By using the here introduced SERS detection approach, E. coli DH5α and Proteus mirabilis were fairly discriminated by PCA of their corresponding SERS spectra. Experimental details Chemicals Hydroxylamine hydrochloride (NH2OH·HCl), sodium hydroxide (NaOH), silver nitrate (AgNO3), sodium chloride (NaCl), 3-glycidyloxypropyltrimethoxysilane (GOPTS), and 37 % hydrochloric acid (HCl) were purchased from Sigma Aldrich (Taufkirchen, Germany). Diamino-PEG 2000 (2,000 g/mol) was a gift from Huntsman Holland (Rozenburg, The Netherlands). The glass slides (26 mm× 76 mm×1.25 mm) were purchased from Roth (Karlsruhe, Germany). Luria Broth (LB) growth medium was prepared in our lab (Max-von-Pettenkofer-Inst., LMU). Unless otherwise specified, other chemicals were reagent grade. High purity water with resistivity higher than 18 MΩ·cm−1 (MilliQ System, Millipore, USA) was used as solvent.
Towards a receptor-free immobilization and SERS detection of UTI
Slide preparation The surface chemistry performed on the glass slide is described elsewhere [26]. Noteworthy, we added an extra step after the silanization and diamino-PEG-ylation. The slides were rinsed thoroughly 1 min by using a 65-mM HCl aqueous solution and dried under a stream of nitrogen. This protonation step provides a permanent positive charge on the glass surface that was used to capture the negatively charged bacteria and improve the single event ratio necessary for single-bacterium detection. Preparation of bacteria samples E. coli and P. mirabilis were obtained from the strain collection of the Max-vonPettenkofer-Institute. Pure bacterial cultures were grown in 5-ml glass tubes at 37 °C under aeration and agitation for 3 or 12 h to achieve exponential or stationary growth phases respectively. For each strain or growth phase studied, the five samples were taken from each culture. From each sample, at least 20 single spectra were recorded. Between 10 and 30 % of these spectra were sorted out which did not exhibit any specific peaks, which can be explained by imprecise focusing. Cultivations were repeated several times. The biomass of 2 ml cultural suspension was harvested by centrifugation (5,000×g, 5 min, 4 °C), the supernatant was discarded and the pellet was washed twice with normal saline buffer (0.9 % NaCl, pH 7.4), whereas in the last step the biomass was resuspended in 0.5ml saline solution by pipetting up and down. The saline buffer was used to reduce osmotic stress on the bacterial cell wall thus leaving an undamaged cell wall for further analysis. The bacterial suspension was then applied on the glass surface. Preparation of concentrated silver NPs Stable, affordable and highly SERS-active substrates were obtained by reducing a 10−3-M silver nitrate solution with hydroxylamine hydrochloride by using the method reported by Leopold and Lendl [27] and modified by Knauer et al. [28]. The obtained colloidal solution was then concentrated four times by centrifugation (4,500 rpm, 30 min, 4 °C). Thus, the NPs synthesis and the preparation step proceeded fast to completion (in less than 2 h) and offered the advantage of ambient conditions, fast completion, and minimal number of reactants, being economical, and resulting in a ready-to-use product. As shown by Kahraman et al. [29], the four times concentration of the silver NPs can be successfully used in bacteria detection, this concentration being the most appropriate in terms of preparation time and the obtained SERS enhancement. Raman instrumentation The SERS spectra were recorded using a Raman microscope (LabRAM HR, HORIBA Jobin Yvon, Japan). The holographic grating with 600 lines/mm yields a spectral resolution of 2 cm−1; the wavelength calibration was performed by using a silicon wafer. The 633-nm line of a HeNe laser was used as the excitation source. The emitting laser power was set to 14 mW, but used only at
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1 %, i.e., 0.14 mW at the target. The SERS spectra were recorded with ×50 or ×100 objective and acquisition time of 5 s. Each measurement was repeated at least five times, from independent cultures of the same strain. Computational details The software Unscrambler X 10.0 (CAMO Software, Norway) was used to perform the PCA analysis. In order to eliminate scattering effects and because all the spectra should appear at the same baseline, the singlebacterium SERS spectra (650–1,650 cm−1 region) were handled by the multiplicative scatter correction (MSC) tool of the software.
Results and discussion In this study, bacteria were immobilized by electrostatic forces on the glass surface, prior to SERS measurements. As mentioned in the experimental section (the slides preparation subsection), due to the surface chemistry used, the glass slides exhibit a permanent positive charge (protonation of the terminal amine groups, induced by HCl). It is well-known that the surface membrane of E. coli is negatively charged due to phosphate and carboxylate groups composing the core of the LPS molecules. Therefore, once the glass slide is exposed to the sample and these biomolecules are captured simply by electrostatic forces, a Raman microscope system is used to collect SERS fingerprints from the bacterial spots present on the glass slide. SERS investigation of bacterial biomass First of all we proved the bacteria fixation on the unspecific glass substrate for further recording SERS spectra of bacterial biomass. For this purpose, the sample was applied on the positively charged glass surface, followed by a washing step with Millipore water, to ensure that only a bacterial monolayer is immobilized on the surface. Next, a two-step synthesis of the SERS-active substrate, as reported earlier [30, 31] was carried out. Briefly, a silver nitrate solution was added and let to adhere (1 h), and then the hydroxylamine hydrochloride reducing agent was added (1 h action time). After several hours, coalescence of silver clusters was observed, as silver islands (dark spots), still attached on the glass surface after another washing step. These silver islands provided the most enhanced Raman spectra, thus acting as SERS substrate. Figure 1 shows SERS spectra recorded by focusing the 633nm laser on such silver clusters which are in the close proximity of the bacterial biomass. The inset in Fig. 1 shows the biomass of one rough E. coli strain (DSM 1116) captured on the glass substrate. In addition, several silver clusters can be observed in the inset image (indicated by arrows).
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N.E. Mircescu et al. Table 1 Tentative assignments of the SERS spectra of the characterized bacteria
Fig. 1 SERS spectra collected from the E. coli DSM 1116 biomass after electrostatic immobilization and in situ silver substrate preparation. Inset, microscopic ×50 objective image of the tested E. coli DSM 1116 biomass. The arrows indicate silver clusters
The reproducibility of the unprocessed spectra is exemplary illustrated for one rough E. coli strain (DSM 1116), harvested in the stationary growth phase (overnight culture). A reasonable reproducibility for band position and relative intensity can be observed. Variations in spectra background are due to scattering effects, common in SERS experiments. The SERS spectra of E. coli strain (DSM 1116) recorded following the described procedure are in good agreement with the data reported in the literature [25, 32–34]. Mainly, the bands are assigned to the cell wall fingerprint: the glycosidic ring deformation vibration at 554 and 749 cm−1, the phospholipids C–C skeletal vibration at 889 cm−1, the amide III band at 1,242 cm−1 and, the CH2 deformation at 1,465 cm−1. In the higher wavenumbers region, the most intense Raman bands are at 1,287 cm−1 ascribed to the CH deformations in proteins, and 1,358 cm−1 assigned to the COO− stretching vibrations. These putative assignments rely heavily on previous studies and are included in Table 1. The approach developed by electrostatic immobilization of bacteria and in situ silver substrate preparation provided an appreciable reproducibility of the recorded SERS spectra. However, a major shortcoming of this approach is the time required to obtain the specific SERS fingerprint, the SERS spectra reaching the desired quality (in terms of Raman enhancement and signal-to-noise ratio) only after several hours since the in situ silver NPs were prepared. Moreover, because the reducing reaction takes place in the presence of the bacteria, the exposure of the bacteria to the silver nitrate solution and to the reducing agent, separately, could compromise the intact viable bacterial cells. For these reasons, we improved the approach by making it faster and more sensitive at a single-bacterium level.
Wavenumbers(cm−1)
Band assignments
References
364–474 500–586 643–688 721–749
Carbohydrates Carbohydrates, glycosidic ring δ(COO−), guanine Glycosidic ring, adenine
[33] [34] [30, 32] [25, 33, 35]
801–819 867–889 921–953 1,100–1,130 1,149–1,209 1,242–1,245 1,266–1,287 1,346–1,368 1,422–1,465 1,523–1,543 1,589–1,668 1,690–1,702
ν(CN) tyrosin ν(C–C) skeletal protein Ring breathing vibration δ(CC, CO, –COH) carbohydrates =C–C=lipid Amide III δ(CH) protein δ(CH) protein, ν(COO−) sym. δ(CH2) saturated lipids Ring stretching vibration ν(DNA) ν(C=C)
[25, 32] [18, 30] [36] [37] [33] [30] [30] [33, 38] [25] [36] [25] [36]
SERS investigation of single bacteria Using the following approach we were able to reduce the sample preparation time to less than 2 h and also to record single-bacterium SERS spectra. By using four times concentrated solution of silver NPs (described in the preparation of concentrated silver NPs section), SERS spectra were collected after several minutes of incubation. In this experimental procedure, the biomass harvested from an overnight culture was applied on the glass surface and left to adsorb for 30 min, then the glass slide was washed. The four times concentrated colloidal suspension was added on the freshly washed glass slide and after another 30 min the bacteria attached on the glass surface were Raman tested. This way we considerably reduced the analysis time to less than 2 h. By using the ×100 objective, we could confirm the immobilization of single bacterial cells, isolated, as well as in silver islands. The acquired SERS spectra were collected only from single cells and some representative results for each strain in stationary phase are shown in Fig. 2. The inset in Fig. 2 shows a microscopic view of isolated single E. coli bacteria tested by the aforementioned procedure. The spectra were normalized and baseline subtracted and then shifted for better visualization. The reproducibility of the recorded single cell SERS spectra and also the small fluctuations in the recorded spectral features can be appreciated by Fig. S1 in the Electronic supplementary material. For single-bacterium SERS investigation, we tested two types of rough E. coli strains (DH5α and TOP10) and two smooth E. coli strains (UTI89; O-6, 536; O-18). The selected E. coli strains can be seen as representative for rough strains,
Towards a receptor-free immobilization and SERS detection of UTI
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interact with the silver metal surface are amino acids, proteins, lipids, and sugars. SERS investigation of single-bacterium in different growth phases
Fig. 2 Single-bacterium SERS spectra of rough strains (without Oantigen: DH5α and TOP10) and of smooth strains (with O-antigen: UTI89, 536), respectively. Inset, microscopic image of the tested E. coli DH5α rough strain
and the two smooth strains can be seen representative for the uropathogenic O-types 6 and 18, respectively. The SERS spectra show significant qualitative differences comparing the two smooth and rough strains. The presence of the Oantigen obviously significantly influences the SERS spectral features. The SERS spectra resulting from the use of concentrated silver NPs show in the case of rough strains (without the O-antigen) enhanced Raman bands at 661 and 674 cm−1, respectively, ascribed to the adenine and guanine nucleobases. Other dominant spectral features are the bands at 801, 867, and 953 cm−1, respectively, assigned to the in plane ring breathing vibration. For the smooth strains with O-antigen, the prominent spectral features are due to the adenine, at 740 and 747 cm−1, respectively, the ν(=C–C=) lipids between 1,157 and 1,216 cm−1, δ(CH2) in saturated lipids between 1,422 and 1,459 cm−1, and the δ(CH) in proteins as well as COO− sym. str. 1,266 and 1,359 cm−1. The O-antigen of Gram-negative bacteria is surface exposed and shows variations resulting from differences in the composition of the monosaccharide units and sugar linkages. This makes it one of the most variable cell constituents, which play an important role in bacterial evasion of host defense systems. However, the most prevalent molecules that could
As in the case of an infection, one can expect bacteria in different growth phases in the urine sample, we decided to test the developed SERS detection approach for singlebacterium in two growth phases. For this purpose, we used a stationary phase culture (overnight cultivation time) and an exponential phase culture (3 h cultivation time). The tested samples had bacterial biomass in concentrations between 105 and 107cells/ml, thus comparable to concentrations expected in urine of infected patients. By using the approach optimized for single-bacterium detection, SERS spectra were recorded from two different strains of E. coli, as shown in Fig. 3. By comparing the repetitive single-bacterium SERS spectra acquired for each E. coli strain in mid-log growth phase (Fig. 3), slight modifications in band positions and relative intensities can be observed. For instance, the band at 723 cm−1, ascribed to the glycosidic ring vibration, is shifted to 726 cm−1 or even to 738 cm−1. However, the single-bacterium SERS spectra collected from E. coli in the stationary growth phase (Fig. 2) and the single-bacterium SERS spectra collected from E. coli in midlog growth phase (Fig. 3) exhibit clear spectral differences. Thus, in the case of E. coli DH5α rough strain for the stationary phase (Fig. 2) the marker band with the highest intensity is the specific guanine band at 661 cm−1, while for the exponential phase, the most intense band is the one specific to the glycosidic ring vibration in the range of 723–738 cm−1. In the case of E. coli 536 smooth strain, the most prevalent band is at 1,167 cm−1 for the stationary phase, whereas for the mid-log phase the SERS spectrum is dominated by the band at 580 cm−1, specific to carbohydrates. Bacteria discrimination based on the SERS spectra For obtaining the configuration and the composition of the outer membrane layer, an analysis that combines chromatographic and spectroscopic techniques is required. The essential information from complex spectroscopic patterns is usually extracted by using pattern recognition techniques, like chemometrics. To assess the identification potential of the single-bacterium SERS spectra, principal component analysis (PCA) was performed on the unprocessed single-bacterium SERS spectra collected from three different microorganisms (E. coli and P. mirabilis), harvested in the stationary phase. PCA is a common multicomponent analysis method used to find the main variance sources in data sets. By defining a number of principal components, less than the number of original variables (bacterial samples in our case), a
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Fig. 3 SERS spectra collected from three different singlebacteria of DH5α rough strain (a) and of 536 smooth strain (b) in mid-log growth phase, respectively
transformation is applied to those variables, so that the first principal component has the largest possible variance and the succeeding component is constraint to be orthogonal with the first one and still have the highest variance as possible, and so on. The highest variance in the spectral features was shown by the first two components scores. These results plotted explain 50 % of the spectral differences present in the 650–1,650 wavenumbers region, considered as the spectral bacterial fingerprint. From the loadings chart (see Fig. S2 in the Electronic supplementary material), it can be observed that the highest contribution in discriminating E. coli and P. mirabilis samples is correlated with the marker Raman bands, at 727 and 650– 675 cm−1, present with different relative intensities in all SERS spectra. Figure 4 fairly discriminates between three groups of SERS spectra: one group corresponds to the E. coli DH5α, representing rough strains, one to the E. coli UTI89, representing uropathogenic smooth strains, and one to the P. mirabilis. SERS spectra for P. mirabilis
Fig. 4 PCA scores showing the three separated groups: E. coli DH5α (rough strain), E. coli UTI89 (smooth strain, uropathogenic), and P. mirabilis, all stationary phase
are included in the Electronic supplementary material, Fig. S3. The data points shown in this figure represent arbitrarily selected spectra of the different organisms, comprising five different cultivations, thus representing independent samples.
Conclusions The electrostatic bacterial immobilization approach enabled SERS spectra recording of the bacterial biomass and also of the single-bacteria of the investigated microorganisms. Distinct spectral marker bands were identified for rough and smooth strains of E. coli but also for the two main growth phases of these strains. The proposed SERS-based methodology is promising due to the receptor-free immobilization step that ensures the single-bacterium SERS detection and the identification of the bacteria. Moreover, by using unbiased computational resources, by means of
Towards a receptor-free immobilization and SERS detection of UTI
PCA, two of the most common UTI pathogens were discriminated with satisfying selectivity. Obtaining reproducible SERS spectra is crucial for accurate bacterial identification at strain level in real-life applications. The common labor-intensive procedure of detecting the type of microorganism that causes an infection at strain level and also which could be the effective antibiotic against it can last 1–3 days for a full result. By using a SERS-based approach, a valuable input can be provided to the clinician much faster, within several hours, or the same shift. The results presented here open interesting venues for the implementation of NP-based biochemical analysis schemes. Therefore, we believe that this is a promising technique for further applications in relevant problems arising whenever reliable bacterial identification is required. We are confident that these results motivate a SERS-based potential routine practice for a large number of samples due to the straightforward sample preparation and detection procedure and the minimum reagent usage and costs.
Acknowledgments This work was possible with the financial support of the Sectoral Operational Programme for Human Resources Development 2007–2013, co-financed by the European Social Fund, under the project number POSDRU/107/1.5/S/76841 with the title “Modern Doctoral Studies: Internationalization and Interdisciplinarity”. N.L. acknowledges support from National Research Council-Executive Unit for Funding Education Higher, R&D and Innovation (CNCS-UEFISCDI), project number PN-II-RU-TE-2012-3-0227.
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3057 10. Kneipp K et al (1998) Surface-enhanced Raman scattering (SERS)— a new tool for single molecule detection and identification. Bioimaging 6:104–110 11. Kneipp J, Kneipp H, Kneipp K (2008) SERS—a single-molecule and nanoscale tool for bioanalytics. Chem Soc Rev 37:1052– 1060 12. Grow AE et al (2003) New biochip technology for label-free detection of pathogens and their toxins. J Microbiol Methods 53:221–233 13. Helm D et al (1991) Classification and identification of bacteria by Fourier-transform infrared spectroscopy. J Gen Microbiol 137: 69–79 14. Horbach I, Naumann D, Fehrenbach FJ (1988) Simultaneous infections with different serogroups of Legionella pneumophila investigated by routine methods and Fourier transform infrared spectroscopy. J Clin Microbiol 26:1106–1110 15. Kirschner C et al (2001) Classification and identification of enterococci: a comparative phenotypic, genotypic, and vibrational spectroscopy study. J Clin Microbiol 39:1763–1770 16. Maquelin K et al (2003) Prospective study of the performance of vibrational spectroscopies for rapid identification of bacterial and fungal pathogens recovered from blood cultures. J Clin Microbiol 41:324–329 17. Tintelnot K et al (2000) Evaluation of phenotypic markers for selection and identification of Candida dubliniensis. J Clin Microbiol 38: 1599–1608 18. Zeiri L et al (2004) Surface-enhanced Raman spectroscopy as a tool for probing specific biochemical components in bacteria. Appl Spectrosc 58:33–40 19. Naja G et al (2007) Raman-based detection of bacteria using silver nanoparticles conjugated with antibodies. Analyst 132:679–686 20. Knauer M et al (2012) A flow-through microarray cell for the online SERS detection of antibody-captured E. coli bacteria. Anal Bioanal Chem 402:2663–2667 21. Harz M, Rösch P, Popp J (2009) Vibrational spectroscopy—a powerful tool for the rapid identification of microbial cells at the singlecell level. Cytometry Part A 75A:104–113 22. Guven B et al (2011) SERS-based sandwich immunoassay using antibody coated magnetic nanoparticles for Escherichia coli enumeration. Analyst 136:740–748 23. Huang J, Yamaji H, Fukuda H (2007) Immobilization of Escherichia coli cells using porous support particles coated with cationic polymers. J Biosci Bioeng 104:98–103 24. Kim Y et al (2012) Label-free detection of a bacterial pathogen using an immobilized siderophore, deferoxamine. Lab Chip 12:971–976 25. Walter A et al (2011) Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device. Lab Chip 11:1013–1021 26. Wolter A, Niessner R, Seidel M (2007) Preparation and characterization of functional poly(ethylene glycol) surfaces for the use of antibody microarrays. Anal Chem 79:4529–4537 27. Leopold N, Lendl B (2003) A new method for fast preparation of highly surface-enhanced Raman scattering (SERS) active silver colloids at room temperature by reduction of silver nitrate with hydroxylamine hydrochloride. J Phys Chem B 107:5723–5727 28. Kahraman M et al (2007) Reproducible surface-enhanced Raman scattering spectra of bacteria on aggregated silver nanoparticles. Appl Spectrosc 61:479–485 29. Efrima S, Bronk BV (1998) Silver colloids impregnating or coating bacteria. J Phys Chem B 102:5947–5950 30. Kahraman M et al (2008) Convective assembly of bacteria for surface-enhanced Raman scattering. Langmuir 24:894–901 31. Zhou H, Yang DT, Ivleva N, Mirescu NE, Niessner R, Haisch C, (2014) SERS detection of bacteria in water by in situ coating with Ag nanoparticles. Anal Chem 86:1525–1533 32. Knauer M et al (2010) Surface-enhanced Raman scattering-based label-free microarray readout for the detection of microorganisms. Anal Chem 82:2766–2772
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RESEARCH PAPER
Towards a receptor-free immobilization and SERS detection of urinary tract infections causative pathogens Nicoleta E. Mircescu & Haibo Zhou & Nicolae Leopold & Vasile Chiş & Natalia P. Ivleva & Reinhard Niessner & Andreas Wieser & Christoph Haisch
Received: 30 November 2013 / Revised: 6 March 2014 / Accepted: 10 March 2014 / Published online: 6 April 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Based on molecular-specific surface-enhanced Raman scattering (SERS) spectroscopy we were able to discriminate between rough and smooth strains of Escherichia coli and Proteus mirabilis bacteria. For this purpose, bacteria have been immobilized through electrostatic forces by inducing a positive charge on the glass slide. This way, SERS spectra on bacterial biomass and also on single bacteria could be recorded in less than 2 h, by using concentrated silver nanoparticles as SERS-active substrate. Single-bacterium SERS spectral fingerprints showed to be sensitive to the presence of the Oantigen at strain level and to the microorganisms growth phase. By using principal component analysis (PCA) on the SERS spectra recorded from E. coli and P. mirabilis, these two uropathogens could be fairly discriminated.
Keywords SERS . Uropathogen . Receptor free . Principal component analysis
Electronic supplementary material The online version of this article (doi:10.1007/s00216-014-7761-4) contains supplementary material, which is available to authorized users. N. E. Mircescu : N. Leopold : V. Chiş Faculty of Physics, Babeş-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania H. Zhou : N. P. Ivleva : R. Niessner : C. Haisch (*) Chair for Analytical Chemistry, Institute of Hydrochemistry, Technische Universität München, Marchioninistrasse 17, 81377 Munich, Germany e-mail: [email protected] A. Wieser Max von Pettenkofer-Institut für Hygiene und Medizinische Mikrobiologie, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
Introduction Urinary tract infections (UTI) are the most common bacterial infection encountered in the western world. They are responsible for considerable morbidity as well as mortality. Furthermore, they have been in the focus of recent attention due to their association with low-weight birth, premature labor, and fetal mortality [1]. Particularly, sexually active women have high prevalence rates of UTIs. Almost 50 % will experience at least one episode of UTIs during their lifetime, many cases will be chronic and recurrent [2]. In addition to women, children of both sexes and older men are at high risk of developing UTIs. Currently, UTIs are diagnosed by using urinalysis detecting the presence of leukocyte esterase, a marker for inflammatory cells, pH, as well as nitrite which is produced by the most prevalent uropathogenic organisms. However, some bacterial infections either have too low bacterial loads or do not elicit sufficient leukocyte influx into the urinary compartment. So, the sensitivity of urinalysis to detect UTI can be as low as 68– 88 % [3]. Alternative test methods directly from urine may apply molecular techniques such as polymerase chain reaction (PCR) or MALDI-TOF and are quite complicated as well as expensive. Most common techniques are time-consuming and culture based. This means a certain amount of urine will be spread on culture media and bacteria will be grown in vitro [4, 5]. Bacterial cultivation is costly and time consuming. Considering the low sensitivity and specificity, the urine dipstick, which is relatively cheap and fast, should be confirmed by an alternative testing method. PCR and antibody-based sandwich immunoassay [6] have several potential problems, such as high rate of false negatives and false positives. Different recent approaches that possess a high sensitivity are molecular techniques, such as fluorescence in situ hybridization (FISH) [7] or loop-mediated isothermal amplification (LAMP) [8], being also used to identify UTI causative pathogens. In these cases,
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the sample preparation and post-processing fluorescence imaging make them difficult and rely on highly skilled personnel. Therefore, there is a growing need to ensure appropriate, fast, and automated diagnosis, succeeded by further investigation to allow for targeted antimicrobial therapy where indicated. Vibrational spectroscopy is a powerful tool considering the high and well-defined spectral information and the simple, mobile and affordable equipment required. Surface-enhanced Raman scattering (SERS), by its nature of enhancing the Raman cross-sections, provides intense spectra, allowing the detection down to the single (bio)molecule level [9–11]. Moreover, SERS provides reduced acquisition time and therefore allows recording of significant numbers of spectra for a reliable statistic model in a short time. When pathogenic bacteria were cultured under conditions known to affect virulence, their SERS fingerprints changed significantly [12]. The pathogens can be individually identified by SERS; SERS fingerprint may even reflect the physiological state of a bacterial cell. Bacteria respond to environmental triggers, such as temperature, pH, and nutrient concentrations, by switching to different physiological states. The switch between different physiological states or phenotypes involves numerous changes in the bacteria’s metabolic profile, including the number and composition of its outer membrane proteins, lipopolysaccharides (LPS), and cellular fatty acids [12]. Both Gram-positive and Gram-negative bacteria contain a cell membrane and a sacculus surrounding the cell made of disaccharide polymers, the peptidoglycan. However, Gramnegative bacteria exhibit another phospholipid bilayer outside of their peptidoglycan sacculus called the outer membrane. This membrane is actually in close proximity to the SERS substrate, so we will focus on its components. A main component of the outer leaflet of the outer membrane is LPS. These large molecules consist of a lipid part anchored in the membrane (lipid A), the core structures as linkers and polysaccharide motifs joined by a covalent bond. The O-antigen or O-polysaccharide is the repetitive glycan polymer attached to the outer core structure. Strains with O-antigens are called smooth, strains lacking the O-chains are referred to as semi rough and strains bearing an LPS structure that stops at the inner core are considered rough. Most laboratory Escherichia coli strains have rough LPS, whereas pathogenic E. coli tend to have various O-antigens which can be considered to be a virulence determinant. Several groups used Fourier-transform infrared spectroscopy (FTIR) to obtain useful information for genus, species, or even strain level discrimination [13–17]. The purpose of these studies was to group the E. coli isolates according to their Oantigenic properties which reside in the structure of O-specific side chains of LPS. By investigating the spectral range where the vibrational modes of cellular carbohydrates dominate
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(900–1,200 cm−1 in IR) and using the data as input for cluster analysis, the obtained dendrogram had revealed distinct clustering suggesting different O serotypes. SERS detection of bacteria is assessed either by simply analyzing a mixture of nanoparticles (NPs) and bacterial biomass after dehydrating the sample [18], or by immobilization of the bacteria by using, for instance, antibodies [19, 20]. The SERS detection of microorganisms at single-bacterium level [21] was already reported but the common bacteria fixation procedure includes sandwich immunoassays [22], positively charged polyamino acids (poly-L-lysine) [23] or other specific receptors, like siderophores [24]. In a more recent approach, the feasibility of SERS bacteria identification at strain level in microfluidic devices was shown [25]. The bacterial biomass was busted by ultrasonication and then mixed with the colloidal suspension and the SERS spectra acquired were averaged. The novelty of the setup presented here lays in the reliable SERS detection of single bacteria from a monolayer of bacteria adsorbed on a modified glass substrate, as well as the avoidance of sample dehydration prior to the SERS measurement. The aim of this study is to prove the possibility to reproducibly identify E. coli, independently of O-type antigen, strain and growth phase by using SERS. The first approach developed provides highly specific vibrational information within some hours from the immobilization of the biomass on the modified glass slide by simply using electrostatic forces. In order to acquire SERS signals from a singlebacterium and also to reduce the analysis time required, we further proposed another approach. The immobilization principle is similar, but the SERS substrate enhancement factor is improved by preconcentration of the silver NPs. Unlike the other studies carried out, we did not dehydrate the sample prior to SERS investigation and moreover, immobilization of bacteria was achieved without employing a specific receptor. By using the here introduced SERS detection approach, E. coli DH5α and Proteus mirabilis were fairly discriminated by PCA of their corresponding SERS spectra. Experimental details Chemicals Hydroxylamine hydrochloride (NH2OH·HCl), sodium hydroxide (NaOH), silver nitrate (AgNO3), sodium chloride (NaCl), 3-glycidyloxypropyltrimethoxysilane (GOPTS), and 37 % hydrochloric acid (HCl) were purchased from Sigma Aldrich (Taufkirchen, Germany). Diamino-PEG 2000 (2,000 g/mol) was a gift from Huntsman Holland (Rozenburg, The Netherlands). The glass slides (26 mm× 76 mm×1.25 mm) were purchased from Roth (Karlsruhe, Germany). Luria Broth (LB) growth medium was prepared in our lab (Max-von-Pettenkofer-Inst., LMU). Unless otherwise specified, other chemicals were reagent grade. High purity water with resistivity higher than 18 MΩ·cm−1 (MilliQ System, Millipore, USA) was used as solvent.
Towards a receptor-free immobilization and SERS detection of UTI
Slide preparation The surface chemistry performed on the glass slide is described elsewhere [26]. Noteworthy, we added an extra step after the silanization and diamino-PEG-ylation. The slides were rinsed thoroughly 1 min by using a 65-mM HCl aqueous solution and dried under a stream of nitrogen. This protonation step provides a permanent positive charge on the glass surface that was used to capture the negatively charged bacteria and improve the single event ratio necessary for single-bacterium detection. Preparation of bacteria samples E. coli and P. mirabilis were obtained from the strain collection of the Max-vonPettenkofer-Institute. Pure bacterial cultures were grown in 5-ml glass tubes at 37 °C under aeration and agitation for 3 or 12 h to achieve exponential or stationary growth phases respectively. For each strain or growth phase studied, the five samples were taken from each culture. From each sample, at least 20 single spectra were recorded. Between 10 and 30 % of these spectra were sorted out which did not exhibit any specific peaks, which can be explained by imprecise focusing. Cultivations were repeated several times. The biomass of 2 ml cultural suspension was harvested by centrifugation (5,000×g, 5 min, 4 °C), the supernatant was discarded and the pellet was washed twice with normal saline buffer (0.9 % NaCl, pH 7.4), whereas in the last step the biomass was resuspended in 0.5ml saline solution by pipetting up and down. The saline buffer was used to reduce osmotic stress on the bacterial cell wall thus leaving an undamaged cell wall for further analysis. The bacterial suspension was then applied on the glass surface. Preparation of concentrated silver NPs Stable, affordable and highly SERS-active substrates were obtained by reducing a 10−3-M silver nitrate solution with hydroxylamine hydrochloride by using the method reported by Leopold and Lendl [27] and modified by Knauer et al. [28]. The obtained colloidal solution was then concentrated four times by centrifugation (4,500 rpm, 30 min, 4 °C). Thus, the NPs synthesis and the preparation step proceeded fast to completion (in less than 2 h) and offered the advantage of ambient conditions, fast completion, and minimal number of reactants, being economical, and resulting in a ready-to-use product. As shown by Kahraman et al. [29], the four times concentration of the silver NPs can be successfully used in bacteria detection, this concentration being the most appropriate in terms of preparation time and the obtained SERS enhancement. Raman instrumentation The SERS spectra were recorded using a Raman microscope (LabRAM HR, HORIBA Jobin Yvon, Japan). The holographic grating with 600 lines/mm yields a spectral resolution of 2 cm−1; the wavelength calibration was performed by using a silicon wafer. The 633-nm line of a HeNe laser was used as the excitation source. The emitting laser power was set to 14 mW, but used only at
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1 %, i.e., 0.14 mW at the target. The SERS spectra were recorded with ×50 or ×100 objective and acquisition time of 5 s. Each measurement was repeated at least five times, from independent cultures of the same strain. Computational details The software Unscrambler X 10.0 (CAMO Software, Norway) was used to perform the PCA analysis. In order to eliminate scattering effects and because all the spectra should appear at the same baseline, the singlebacterium SERS spectra (650–1,650 cm−1 region) were handled by the multiplicative scatter correction (MSC) tool of the software.
Results and discussion In this study, bacteria were immobilized by electrostatic forces on the glass surface, prior to SERS measurements. As mentioned in the experimental section (the slides preparation subsection), due to the surface chemistry used, the glass slides exhibit a permanent positive charge (protonation of the terminal amine groups, induced by HCl). It is well-known that the surface membrane of E. coli is negatively charged due to phosphate and carboxylate groups composing the core of the LPS molecules. Therefore, once the glass slide is exposed to the sample and these biomolecules are captured simply by electrostatic forces, a Raman microscope system is used to collect SERS fingerprints from the bacterial spots present on the glass slide. SERS investigation of bacterial biomass First of all we proved the bacteria fixation on the unspecific glass substrate for further recording SERS spectra of bacterial biomass. For this purpose, the sample was applied on the positively charged glass surface, followed by a washing step with Millipore water, to ensure that only a bacterial monolayer is immobilized on the surface. Next, a two-step synthesis of the SERS-active substrate, as reported earlier [30, 31] was carried out. Briefly, a silver nitrate solution was added and let to adhere (1 h), and then the hydroxylamine hydrochloride reducing agent was added (1 h action time). After several hours, coalescence of silver clusters was observed, as silver islands (dark spots), still attached on the glass surface after another washing step. These silver islands provided the most enhanced Raman spectra, thus acting as SERS substrate. Figure 1 shows SERS spectra recorded by focusing the 633nm laser on such silver clusters which are in the close proximity of the bacterial biomass. The inset in Fig. 1 shows the biomass of one rough E. coli strain (DSM 1116) captured on the glass substrate. In addition, several silver clusters can be observed in the inset image (indicated by arrows).
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N.E. Mircescu et al. Table 1 Tentative assignments of the SERS spectra of the characterized bacteria
Fig. 1 SERS spectra collected from the E. coli DSM 1116 biomass after electrostatic immobilization and in situ silver substrate preparation. Inset, microscopic ×50 objective image of the tested E. coli DSM 1116 biomass. The arrows indicate silver clusters
The reproducibility of the unprocessed spectra is exemplary illustrated for one rough E. coli strain (DSM 1116), harvested in the stationary growth phase (overnight culture). A reasonable reproducibility for band position and relative intensity can be observed. Variations in spectra background are due to scattering effects, common in SERS experiments. The SERS spectra of E. coli strain (DSM 1116) recorded following the described procedure are in good agreement with the data reported in the literature [25, 32–34]. Mainly, the bands are assigned to the cell wall fingerprint: the glycosidic ring deformation vibration at 554 and 749 cm−1, the phospholipids C–C skeletal vibration at 889 cm−1, the amide III band at 1,242 cm−1 and, the CH2 deformation at 1,465 cm−1. In the higher wavenumbers region, the most intense Raman bands are at 1,287 cm−1 ascribed to the CH deformations in proteins, and 1,358 cm−1 assigned to the COO− stretching vibrations. These putative assignments rely heavily on previous studies and are included in Table 1. The approach developed by electrostatic immobilization of bacteria and in situ silver substrate preparation provided an appreciable reproducibility of the recorded SERS spectra. However, a major shortcoming of this approach is the time required to obtain the specific SERS fingerprint, the SERS spectra reaching the desired quality (in terms of Raman enhancement and signal-to-noise ratio) only after several hours since the in situ silver NPs were prepared. Moreover, because the reducing reaction takes place in the presence of the bacteria, the exposure of the bacteria to the silver nitrate solution and to the reducing agent, separately, could compromise the intact viable bacterial cells. For these reasons, we improved the approach by making it faster and more sensitive at a single-bacterium level.
Wavenumbers(cm−1)
Band assignments
References
364–474 500–586 643–688 721–749
Carbohydrates Carbohydrates, glycosidic ring δ(COO−), guanine Glycosidic ring, adenine
[33] [34] [30, 32] [25, 33, 35]
801–819 867–889 921–953 1,100–1,130 1,149–1,209 1,242–1,245 1,266–1,287 1,346–1,368 1,422–1,465 1,523–1,543 1,589–1,668 1,690–1,702
ν(CN) tyrosin ν(C–C) skeletal protein Ring breathing vibration δ(CC, CO, –COH) carbohydrates =C–C=lipid Amide III δ(CH) protein δ(CH) protein, ν(COO−) sym. δ(CH2) saturated lipids Ring stretching vibration ν(DNA) ν(C=C)
[25, 32] [18, 30] [36] [37] [33] [30] [30] [33, 38] [25] [36] [25] [36]
SERS investigation of single bacteria Using the following approach we were able to reduce the sample preparation time to less than 2 h and also to record single-bacterium SERS spectra. By using four times concentrated solution of silver NPs (described in the preparation of concentrated silver NPs section), SERS spectra were collected after several minutes of incubation. In this experimental procedure, the biomass harvested from an overnight culture was applied on the glass surface and left to adsorb for 30 min, then the glass slide was washed. The four times concentrated colloidal suspension was added on the freshly washed glass slide and after another 30 min the bacteria attached on the glass surface were Raman tested. This way we considerably reduced the analysis time to less than 2 h. By using the ×100 objective, we could confirm the immobilization of single bacterial cells, isolated, as well as in silver islands. The acquired SERS spectra were collected only from single cells and some representative results for each strain in stationary phase are shown in Fig. 2. The inset in Fig. 2 shows a microscopic view of isolated single E. coli bacteria tested by the aforementioned procedure. The spectra were normalized and baseline subtracted and then shifted for better visualization. The reproducibility of the recorded single cell SERS spectra and also the small fluctuations in the recorded spectral features can be appreciated by Fig. S1 in the Electronic supplementary material. For single-bacterium SERS investigation, we tested two types of rough E. coli strains (DH5α and TOP10) and two smooth E. coli strains (UTI89; O-6, 536; O-18). The selected E. coli strains can be seen as representative for rough strains,
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interact with the silver metal surface are amino acids, proteins, lipids, and sugars. SERS investigation of single-bacterium in different growth phases
Fig. 2 Single-bacterium SERS spectra of rough strains (without Oantigen: DH5α and TOP10) and of smooth strains (with O-antigen: UTI89, 536), respectively. Inset, microscopic image of the tested E. coli DH5α rough strain
and the two smooth strains can be seen representative for the uropathogenic O-types 6 and 18, respectively. The SERS spectra show significant qualitative differences comparing the two smooth and rough strains. The presence of the Oantigen obviously significantly influences the SERS spectral features. The SERS spectra resulting from the use of concentrated silver NPs show in the case of rough strains (without the O-antigen) enhanced Raman bands at 661 and 674 cm−1, respectively, ascribed to the adenine and guanine nucleobases. Other dominant spectral features are the bands at 801, 867, and 953 cm−1, respectively, assigned to the in plane ring breathing vibration. For the smooth strains with O-antigen, the prominent spectral features are due to the adenine, at 740 and 747 cm−1, respectively, the ν(=C–C=) lipids between 1,157 and 1,216 cm−1, δ(CH2) in saturated lipids between 1,422 and 1,459 cm−1, and the δ(CH) in proteins as well as COO− sym. str. 1,266 and 1,359 cm−1. The O-antigen of Gram-negative bacteria is surface exposed and shows variations resulting from differences in the composition of the monosaccharide units and sugar linkages. This makes it one of the most variable cell constituents, which play an important role in bacterial evasion of host defense systems. However, the most prevalent molecules that could
As in the case of an infection, one can expect bacteria in different growth phases in the urine sample, we decided to test the developed SERS detection approach for singlebacterium in two growth phases. For this purpose, we used a stationary phase culture (overnight cultivation time) and an exponential phase culture (3 h cultivation time). The tested samples had bacterial biomass in concentrations between 105 and 107cells/ml, thus comparable to concentrations expected in urine of infected patients. By using the approach optimized for single-bacterium detection, SERS spectra were recorded from two different strains of E. coli, as shown in Fig. 3. By comparing the repetitive single-bacterium SERS spectra acquired for each E. coli strain in mid-log growth phase (Fig. 3), slight modifications in band positions and relative intensities can be observed. For instance, the band at 723 cm−1, ascribed to the glycosidic ring vibration, is shifted to 726 cm−1 or even to 738 cm−1. However, the single-bacterium SERS spectra collected from E. coli in the stationary growth phase (Fig. 2) and the single-bacterium SERS spectra collected from E. coli in midlog growth phase (Fig. 3) exhibit clear spectral differences. Thus, in the case of E. coli DH5α rough strain for the stationary phase (Fig. 2) the marker band with the highest intensity is the specific guanine band at 661 cm−1, while for the exponential phase, the most intense band is the one specific to the glycosidic ring vibration in the range of 723–738 cm−1. In the case of E. coli 536 smooth strain, the most prevalent band is at 1,167 cm−1 for the stationary phase, whereas for the mid-log phase the SERS spectrum is dominated by the band at 580 cm−1, specific to carbohydrates. Bacteria discrimination based on the SERS spectra For obtaining the configuration and the composition of the outer membrane layer, an analysis that combines chromatographic and spectroscopic techniques is required. The essential information from complex spectroscopic patterns is usually extracted by using pattern recognition techniques, like chemometrics. To assess the identification potential of the single-bacterium SERS spectra, principal component analysis (PCA) was performed on the unprocessed single-bacterium SERS spectra collected from three different microorganisms (E. coli and P. mirabilis), harvested in the stationary phase. PCA is a common multicomponent analysis method used to find the main variance sources in data sets. By defining a number of principal components, less than the number of original variables (bacterial samples in our case), a
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Fig. 3 SERS spectra collected from three different singlebacteria of DH5α rough strain (a) and of 536 smooth strain (b) in mid-log growth phase, respectively
transformation is applied to those variables, so that the first principal component has the largest possible variance and the succeeding component is constraint to be orthogonal with the first one and still have the highest variance as possible, and so on. The highest variance in the spectral features was shown by the first two components scores. These results plotted explain 50 % of the spectral differences present in the 650–1,650 wavenumbers region, considered as the spectral bacterial fingerprint. From the loadings chart (see Fig. S2 in the Electronic supplementary material), it can be observed that the highest contribution in discriminating E. coli and P. mirabilis samples is correlated with the marker Raman bands, at 727 and 650– 675 cm−1, present with different relative intensities in all SERS spectra. Figure 4 fairly discriminates between three groups of SERS spectra: one group corresponds to the E. coli DH5α, representing rough strains, one to the E. coli UTI89, representing uropathogenic smooth strains, and one to the P. mirabilis. SERS spectra for P. mirabilis
Fig. 4 PCA scores showing the three separated groups: E. coli DH5α (rough strain), E. coli UTI89 (smooth strain, uropathogenic), and P. mirabilis, all stationary phase
are included in the Electronic supplementary material, Fig. S3. The data points shown in this figure represent arbitrarily selected spectra of the different organisms, comprising five different cultivations, thus representing independent samples.
Conclusions The electrostatic bacterial immobilization approach enabled SERS spectra recording of the bacterial biomass and also of the single-bacteria of the investigated microorganisms. Distinct spectral marker bands were identified for rough and smooth strains of E. coli but also for the two main growth phases of these strains. The proposed SERS-based methodology is promising due to the receptor-free immobilization step that ensures the single-bacterium SERS detection and the identification of the bacteria. Moreover, by using unbiased computational resources, by means of
Towards a receptor-free immobilization and SERS detection of UTI
PCA, two of the most common UTI pathogens were discriminated with satisfying selectivity. Obtaining reproducible SERS spectra is crucial for accurate bacterial identification at strain level in real-life applications. The common labor-intensive procedure of detecting the type of microorganism that causes an infection at strain level and also which could be the effective antibiotic against it can last 1–3 days for a full result. By using a SERS-based approach, a valuable input can be provided to the clinician much faster, within several hours, or the same shift. The results presented here open interesting venues for the implementation of NP-based biochemical analysis schemes. Therefore, we believe that this is a promising technique for further applications in relevant problems arising whenever reliable bacterial identification is required. We are confident that these results motivate a SERS-based potential routine practice for a large number of samples due to the straightforward sample preparation and detection procedure and the minimum reagent usage and costs.
Acknowledgments This work was possible with the financial support of the Sectoral Operational Programme for Human Resources Development 2007–2013, co-financed by the European Social Fund, under the project number POSDRU/107/1.5/S/76841 with the title “Modern Doctoral Studies: Internationalization and Interdisciplinarity”. N.L. acknowledges support from National Research Council-Executive Unit for Funding Education Higher, R&D and Innovation (CNCS-UEFISCDI), project number PN-II-RU-TE-2012-3-0227.
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