Highly efficient SERS-based detection of cerebrospinal fluid neopterin as a diagnostic marker of bacterial infection
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A highly efficient recognition unit based on surface-enhanced Raman spectroscopy (SERS) was developed as a promising, fast, and sensitive tool for detection of meningococcal meningitis, which is an extremely serious and often fatal disease of the nervous system (an inflammation of the lining around the brain and spinal cord). The results of this study confirmed that there were specific differences in SERS spectra between cerebrospinal fluid (CSF) samples infected by Neisseria meningitidis and the normal CSF, suggesting a potential role for neopterin in meningococcal meningitis detection and screening applications. To estimate the best performance of neopterin as a marker of bacterial infection, principal component analysis (PCA) was performed in a selected region (640–720 cm−1) where the most prominent SERS peak at 695 cm−1 arising from neopterin was observed. The calculated specificity of 95 % and sensitivity of 98 % clearly indicate the effective diagnostic efficiency for differentiation between infected and control samples. Additionally, the limit of detection (LOD) of neopterin in CSF clinical samples was estimated. The level of neopterin was significantly higher in CSF samples infected by N. meningitidis (48 nmol/L), compared to the normal (control) group (4.3 nmol/L). Additionally, this work presents a new type of SERS-active nanostructure, based on polymer mats, that allows simultaneous filtration, immobilization, and enhancement of the Raman signal, enabling detection of spectra from single bacterial cells of N. meningitidis present in CSF samples. This provides a new possibility for fast and easy detection of bacteria in CSF and other clinical body fluids on a time scale of seconds. This method of detection produces consistent results faster and cheaper than traditional laboratory techniques, demonstrates the powerful potential of SERS for detection of disease, and shows the viability of future development in healthcare applications.
KeywordsSurface-enhanced Raman spectroscopy (SERS) Neopterin Cerebrospinal fluid Bacterial infections Neisseria meningitidis
Neopterin (2-amino-4-hydroxy-6-(d-erythro-1′,2′,3′-trihydroxypropyl)pteridine) is produced from guanosine triphosphate by human monocytes and macrophages after stimulation by interferon gamma (IFN-γ) derived from antigen-activated T lymphocytes [1, 2]. After activation of the immune system the level of neopterin in human body fluids is significantly increased. Thus, determination of neopterin may indicate the state of activation of the cellular immune system during subsequent stages of various diseases, such as rheumatoid arthritis (RA) , neuropsychiatric abnormalities , cardiovascular disease , insulin resistance , allograft rejection, and some tumors . Elevated neopterin levels were also observed in viral infections [8, 9, 10, 11] (hepatitis A, B, and C, cytomegalovirus, measles, rubella, influenza), and bacterial infections . In patients with sepsis (a consequence of metabolic and hemodynamic events caused by microbial invasion), plasma concentrations of neopterin are increased compared with healthy controls .
Bacterial meningitis might be associated with both, elevated serum and cerebrospinal fluid (CSF) neopterin levels compared to controls [10, 14, 15]. In brucellosis, neopterin levels were a mean of 52.5 mmol/mL, significantly higher than for healthy controls (<5 nmol/L) and patients with tuberculosis . In leprosy caused by Mycobacterium leprae, 75 % of patients with tuberculoid and lepromatous leprosy presented elevated urinary neopterin excretion .
In the context of rising drug resistance and difficulties in monitoring drug compliance, a new diagnostics marker needs to be explored. It may also be useful to distinguish active forms of disease from latent ones. Within the group of bacterial infections it was shown that patients with symptoms lasting for at least 5 days had significantly higher neopterin concentrations than patients with acute illness. Investigations on critically ill patients in intensive care units evaluated neopterin levels as a tool to discriminate patients with systemic inflammatory response syndrome with and without infectious etiology. Neopterin levels were found to have a specificity of 78 % for discriminating infectious and noninfectious etiology of critical illness .
The measurement of the neopterin levels can provide reliable information regarding the disease diagnosis, stage, prognosis, and is also important for monitoring the response to therapy. Screening of neopterin concentrations in blood donations allows one to detect acute infections in a nonspecific way and improves safety of blood transfusions. Up to now several analytical procedures have been applied for evaluation of the neopterin level in blood using mainly high pressure liquid chromatography (HPLC)  and enzyme-linked immunosorbent assay (ELISA)  techniques.
However, both of these methods are effortful and expensive, and moreover they require technical equipment and highly qualified personnel. Additionally, the difficulties and inaccuracy of existing neopterin assays have been presented, and a number of factors have been shown to affect the validity and quality of such measurements [21, 22, 23]. To the best of our knowledge, all immunoassays which are used for this immune marker are at or near their limit of measurement. Therefore, there is a need to develop a more sensitive, selective, stable, and durable method for the specified biomarkers.
As was mentioned, the main method for determination of the selected immune markers relies heavily on various ELISA kits. Alternative approaches during recent decades have usually used fluorescent antibody assays, evanescent wave interference, and electrochemical methods. Current successes in nanotechnology and instrumentation development have led to recognition of biomolecular systems based on surface-enhanced Raman spectroscopy (SERS) with a higher sensitivity and a more clear visualization of bioanalytes. In brief, surface-enhanced Raman scattering is a vibrational spectroscopy that relies on enhancement of the electromagnetic field due to resonance between the excitation light and surface plasmons of the SERS nanostructures [24, 25]. This electromagnetic effect is the main contributor in SERS enhancement and may increase the Raman signal up to 1011-fold. A chemical mechanism is also believed to take place in SERS enhancement owing to the charge transfer between adsorbed molecules and the metal conduction band of metallic nanostructures. This chemical mechanism may provide an enhancement factor (EF) of less than 4 orders of magnitude [26, 27]. Both these mechanisms ensure enhancement of Raman signal with single molecule resolution . Another interesting point about SERS is the linear dependence of SERS intensity on the power of incident light despite the extraordinary nonlinear effect of signal enhancement. Therefore, SERS technology can be used for the quantitative measurement of analytes with ultrahigh sensitivity . The SERS technique therefore extends the range of Raman applications to more sensitive, specific, and fast detection of a wide range of analytes, e.g., nucleic acids and proteins , therapeutic agents , drugs and trace materials , microorganisms , and cells .
In this study we present our research efforts aimed at detection of specific bacterial infection caused by Neisseria meningitidis using surface-enhanced Raman spectroscopy. Anton Weichselbaum first isolated N. meningitidis from the CSF of a patient . This bacterium is a Gram-negative diplococcus and belongs to pathogenic members of the Neisseriaceae family . N. meningitidis is one of the three main bacteria that cause acute bacterial meningitis, along with Streptococcus pneumoniae and Haemophilus influenzae .
N. meningitidis only infects humans and the average incubation period is 4 days, but it can range between 2 and 10 days. Meningitis caused by this bacteria is usually very serious (5 % to 10 % of patients die, typically within 24 to 48 h after the onset of symptoms) and requires rapid detection and urgent medical attention with appropriate antibiotic therapy. Taking into account the high mortality rates, rapid detection of bacteria in CSF and subsequent effective treatment are essential.
In this work we present also, for the first time, the possibility of using neopterin levels for diagnosis of meningococcal meningitis disease in CSF clinical samples already diagnosed by microbiological techniques. Additionally, the obtained results were compared with ELISA as the reference method. We use SERS combined with a multivariate statistical method (principal component analysis, PCA) to differentiate between CSF control clinical samples (from healthy patients) and CSF clinical samples infected by N. meningitidis. Moreover, besides the Si/ZnO/Au platform used for neopterin level calculation, a new SERS substrate based on a polymer mat was applied for simultaneous filtration, immobilization, and enhancement of the Raman signal for the detection of single bacterial cells.
Materials and methods
Chemicals and materials
Neopterin was purchased from Tocris Bioscience (Bristol, UK). Water (resistivity over 18 MΩ cm) was purified using a Milli-Q plus 185 system and used in all experiments. The CSF and N. meningitidis strain were obtained from the National Reference Centre for Bacterial Meningitis (NRCBM) in the National Medicines Institute (NMI) in Warsaw. The neopterin levels in CSF were estimated by a commercial ELISA test (IBL International GmbH, Hamburg).
Instrumentation and data collection
Raman and SERS spectra of analyzed samples were recorded using the Renishaw inVia Raman system with 1024 × 256 pixel Peltier-cooled RenCam CCD detector. All measurement were performed using ×20 microscope objective (numerical aperture = 0.25), focusing the 785-nm laser to a spot size of approximately 5 μm. SERS spectra were acquired from less than 5 mW of incident laser power at ambient conditions using a back-scattering geometry. The SERS spectra were recorded between 400 and 1600 cm−1 at resolution of ca. 2 cm−1. The typical acquisition time was 10 s for a single SERS measurement. The obtained spectra were processed with OPUS software provided by Bruker. All spectra were smoothed, baseline corrected, and normalized.
SEM measurements were conducted using the FEI Nova NanoSEM 450 with an accelerating voltage of 10 kV under high vacuum.
SERS nanostructures fabrication
Atomic layer deposition (ALD) was used for zinc oxide layer deposition on Si(100) at 100 °C. Diethylzinc and deionized water were used as precursors, and nitrogen was used as purging gas . The process was conducted in the Savannah-100 reactor. Typically the ZnO layers were grown with 10,000 ALD cycles, which lead to approximately 1.4 μm thickness.
Electrospun polymer mats
The poly(l-lactide) (PLA) mats were purchased from MECC Co., Ltd., Japan and cut into squares (area of 0.25 cm2).
Procedure for SERS nanostructure gold sputtering
Si/ZnO layers and electrospun polymer mats were covered with a thin layer of sputtered gold (ca. 90 nm) using PVD equipment from Leica (model EM MED020). Au target was obtained from Mennica Metale Szlachetne, Warsaw, Poland. During this procedure a vacuum level of 10−2 mbar and current of 25 mA were applied.
Bacterial sample preparation for microbiological and SERS experiments
Clinical CSF samples were obtained as a part of routine activity of the NRCBM and were analyzed anonymously. All the data were collected in accordance with the European Parliament and Council decision for the epidemiological surveillance and control of communicable disease in the European Community [38, 39]. Thus ethical approval and informed consent were not required. N. meningitidis of serogroup B (603/2011) used during the study serves as a reference strain in polymerase chain reaction (PCR) in the NRCBM.
Microbiological confirmation of N. meningitidis
In the case of negative culture, the NRCBM has been receiving clinical materials, including CSF, from patients with suspected invasive meningococcal disease. The DNA isolated from these samples was used for PCR to identify N. meningitidis [40, 41].
The strain was identified on the basis of typical morphology of colonies, Gram stain, oxidase test, and API NH test (bioMerieux, Marcy-l’Etoile, France) according to the manufacturer’s instructions. Serogroup was determined by slide agglutination tests using commercial antisera (Remel).
Bacterial culture and SERS sample preparation
Neisseria meningitidis used in the experiment was obtained from the NRCBM in Warsaw, Poland. To multiply microbial organisms, we cultivated them on solid BHI (brain heart infusion) growth medium at 37 °C for 24 h. After that, some bacterial colonies were redispersed in saline solution (sterile 0.9 % NaCl solution) and centrifuged for 5 min at 4000 rpm (so as not to destroy the cell membrane). The centrifugation process in the fresh saline solution was repeated four times to obtain a solution of clean bacterial cells, at a concentration of N. meningitidis of 106 CFU/mL. The density of bacterial cells was determined by counting the amount of colonies which had grown on the Petri dish from a known amount of medium. The count was taken after 1 day of cultivation at 37 °C. Before carrying out Raman measurements, 10 μL of an aqueous bacterial solution was placed over the SERS substrate. Measurements were taken after 5 min.
The SERS spectra were prepared for PCA using a two-step approach. First, OPUS software (Bruker Optic GmbH 2012 version) was used to smooth the spectra with the Savitsky–Golay filter, the background was removed using baseline correction, and then the spectra were normalized using a so-called Min-Max normalization (the area of band around 963 cm−1). All the data were transferred to the Unscrambler software (CAMO software AS, version 10.3, Norway) where PCA was performed. PCA is a multivariate technique that reduces the dimensionality of complex spectroscopic data from many wavenumber assignments to a few principal components (PCs), making it easier to identify the majority of variations within the spectra .
PCA reduces the complexity of high-dimensional SERS data from many wavenumber assignments to several PCs. Each PC represents a linear combination of the original variables (e.g., Raman wavenumber). The first component (in the horizontal direction) is the most important one and accounts for as much variation in the data as possible. In the PCA model, the big spectral set matrix (X) is transformed into two smaller matrices according to the formula X = TP T + E where T is the matrix of scores, P is the matrix of loadings, and E is the error matrix. The PCA method enables one to understand the sources of variation in the obtained SERS data, e.g., the plot of loadings vs. the wavenumber indicates the most important diagnostic vibrations in the spectra.
Results and discussion
SERS analysis of CSF and neopterin: comparison between normal healthy control and N. meningitidis-infected clinical samples
Although significant spectral differences between normal and infected CSF have been clearly observed in SERS spectra, this simplistic analysis of the experimental observations uses only limited number of SERS peaks or SERS regions. Therefore, multivariate statistical analysis in the form of PCA has been exploited to utilize the whole spectra and to automatically determine the diagnostic marker bands for improving the efficiency of CSF diagnosis and also to find out the statistical significance of the proposed method. This analysis method is known as an excellent approach towards reducing dimensionality of Raman data and is widely used by researchers in discriminating cancer tissues from that of a control subject , for identification of pathogens in body fluids , and for blood analysis .
The loadings of the PCs provide information on the variables (wavenumber of the spectrum) that are important for group separation. Figure 3c displays the loadings plots of PC1 and PC2 for the whole wavenumber region 500–1600 cm−1. By analysing these plots one can indicate the most important diagnostic variables in the analyzed data set. Variables with high loading values are the most important for diagnostic purposes. The loadings of both PC1 and PC2 exhibit a positive peak attributed to the main marker bands of the neopterin (Fig. 3c). That is why, for the best classification among the two groups of analyzed samples, the PCA was performed for the selected narrow region (640–720 cm−1) where the most prominent SERS peak at 695 cm−1 arising from neopterin is observed (see Fig. 1b and inset in Fig. 1).
Measurement of neopterin concentration in CSF
CSF neopterin concentration obtained using two methods, SERS and ELISA
SERS (new method)
ELISA (reference method)
Control CSF (nmol/L)
3.8 ± 0.7
4.0 ± 1.3
Infected CSF (nmol/L) (with Neisseria meningitidis)
30.0 ± 4.1
36.0 ± 5.2
It should be highlighted that for normal healthy control samples of CSF the level of neopterin was below 4.3 nmol/L. A value below 5 nmol/L is a typical CSF neopterin concentration calculated for patients without a modified immune system . In contrast, CSF neopterin concentrations determined by radioimmunoassay (RIA) were 63.0 nmol/L for patients with acute bacterial meningitis, 32.5 nmol/L for patients with Lyme neuroborreliosis, and 130.9 nmol/L in individuals with viral meningitis .
These results clearly demonstrate that neopterin may be used as a marker in the meningococcal meningitis diagnosis and monitoring of this infection.
Detection of N. meningitidis in CSF
The SERS spectrum of this bacterium also indicates peaks assigned to amide III (1252 cm−1) and CH2 vibrations (1450 cm−1) . Table S1 (see ESM) shows band assignments for N. meningitidis. The most intense bands at 736, 1002, 1330, and 1450 cm−1 were also observed in the remaining part of a filtrated solution where the presence of, inter alia, bacteria is expected (Fig. 6b). As exemplified by Fig. 6, we are able to detect and identify the bacterial cell from CSF samples using our novel SERS substrates.
This work presents a new label-free method for neopterin detection based on the SERS technique and the possibility of using this method for determination of the neopterin levels in CSF. It was also shown that CSF neopterin evaluation may be used in determining the bacterial meningitis infections caused by N. meningitidis. Additionally, a new class of SERS substrates based on a polymer mat was developed for simultaneous filtration, immobilization, and enhancement of the Raman signal, which allows the detection of single bacterial cells of N. meningitidis present in CSF samples. This opens a new route for simple identification of bacteria in CSF and other clinical body fluids on a time scale of seconds. In conclusion, neopterin emerges as an important biomarker and a strong predictor of bacterial meningitis infections. In the very near future, this study will be extended to a larger number of clinical samples to improve the diagnostic sensitivity and selectivity.
We would like to thank Tomasz Szymborski for the preparation of fiber-based SERS platform.
The research was supported by the European Union within European Regional Development Fund, through Innovative Economy grant (POIG.01.01.02-00-008/08). AK acknowledges the support from NCBiR under grant PBS2/A1/8/2013.
Compliance with ethical standards
Clinical CSF samples were obtained as a part of routine activity of the NRCBM and were analyzed anonymously. All the data were collected in accordance with the European Parliament and Council decision for the epidemiological surveillance and control of communicable disease in the European Community [38, 39].
Conflict of interest
The authors declare that they have no conflict of interests.
- 7.Murr C, Bergant A, Widschwendter M, Heim K, Schröcksnadel H, Fuchs D. Clin Chem. 1999;45:1998–2004.Google Scholar
- 8.Werner-Felmayer G, Werner ER, Fuchs D, Hausen A, Reibnegger G, Wachter H. Cancer Res. 1990;50:2863–7.Google Scholar
- 11.Dukes CS, Matthews TJ, Rivadeneira ED, Weinberg JB. J Leukoc Biol. 1994;56:650–3.Google Scholar
- 15.Gasse T, Murr C, Meyersbach P, Schmutzhard E, Wachter H, Fuchs D. Eur J Clin Chem Clin Biochem. 1994;32:685–9.Google Scholar
- 16.Cakan G, Bezirci FB, Kacka A, Cesur S, Aksaray S, Tezeren D, et al. Jpn J Infect Dis. 2008;61:366–70.Google Scholar
- 17.Schmutzhard E, Fuchs D, Hausen A, Reibnegger G, Wachter H. East Afr Med J. 1986;63:577–80.Google Scholar
- 19.Werner ER, Bichier A, Daxenbichier G, Fuchs D, Fuith LC, Hausen A, et al. Clin Chem. 1987;33:62–6.Google Scholar
- 21.Cannon JG, van der Meer JWM, Poutsiaka DD, Dinarello CA. J Appl Physiol. 1993;75:1897–902.Google Scholar
- 35.Weichselbaum A. Fortschr Med. 1887;5:573–83.Google Scholar
- 38.The European Commission of the European Communities, European Union website. Commission decision of 22 December 1999 on the communicable diseases to be progressively covered by the community network under decision number 2119/98/EC of the Parliament and of the Council. Off J Eur Commun L28/50; 2000. Available: http://europa.eu.int/eur-lex/pri/en/oj/dat/2000/l_028/l_02820000203en00500053.pdf.
- 39.The European Parliament, the Council of the EU, European Union website. Decision number 2119/98/EC of the European Parliament and of the Council of 24 September 1998: setting up a network for the epidemiological surveillance and control of communicable diseases in the community. Off J Eur Commun L268/1; 1998. Available: http://eur-lex.europa.eu/LexUriServ/LexUriServ.douri=CELEX:31998D2119:EN:NOT.
- 41.Taha MK. J Clin Microbiol. 2000;38:855–7.Google Scholar
- 56.Handin RI, Lux SE, Stossel TP, editors. Blood: principles and practice of hematology. vol. 2. Philadelphia: Lippincott Williams and Wilkins; 2003. p. 471.Google Scholar
- 59.Luna-Pineda T, Soto-Feliciano K, De la Cruz-Montoya E, Londono LCP, Rios-Velazquez C, Hernandez-Rivera SP. In: Fountain AW, editor. Chemical and biological sensing VIII. Bellingham: SPIE - The International Society of Optical Engineering; 2007.Google Scholar
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