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Raman spectroscopy towards clinical application: drug monitoring and pathogen identification
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ARTICLE IN PRESS International Journal of Antimicrobial Agents xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
International Journal of Antimicrobial Agents journal homepage: http://www.elsevier.com/locate/ijantimicag
Review
Raman spectroscopy towards clinical application: drug monitoring and pathogen identification Ute Neugebauer a,b,c , Petra Rösch c,d , Jürgen Popp a,b,c,d,∗ a
Center for Sepsis Control and Care (CSCC), Jena University Hospital, Erlanger Allee 101, D-07747 Jena, Germany Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany InfectoGnostics Forschungscampus Jena, Philosophenweg 7, D-07743 Jena, Germany d Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany b c
a r t i c l e
i n f o
Keywords: Raman spectroscopy Therapeutic drug monitoring Infection detection Antibiotic resistance Lab-on-a-chip (LOC) Fibre-enhanced Raman spectroscopy
a b s t r a c t Raman spectroscopy is a label-free method that measures quickly and contactlessly, providing detailed information from the sample, and has proved to be an ideal tool for medical and life science research. In this review, recent advances of the technique towards drug monitoring and pathogen identification by the Jena Research Groups are reviewed. Surface-enhanced Raman spectroscopy (SERS) and ultraviolet resonance Raman spectroscopy in hollow-core optical fibres enable the detection of drugs at low concentrations as shown for the metabolites of the immunosuppressive drug 6-mercaptopurine as well as antimalarial agents. Furthermore, Raman spectroscopy can be used to characterise pathogenic bacteria in infectious diseases directly from body fluids, making time-consuming cultivation processes dispensable. Using the example of urinary tract infection, it is shown how bacteria can be identified from patients’ urine samples within <1 h. The methods cover both single-cell analysis and dielectrophoretic capturing of bacteria in suspension. The latter method could also be used for fast (<3.5 h) identification of antibiotic resistance as shown exemplarily for vancomycin-resistant enterococci. © 2015 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.
1. Introduction Raman spectroscopy has proved to be an ideal tool for medical and life science research, as Raman measures without contact, providing label-free information on processes within living cells without disturbing them. Furthermore, Raman spectroscopy measures quickly, overcoming the need for complex and timeconsuming laboratory analyses in many cases. Raman spectroscopy also measures precisely, providing the ultrasensitive detection capabilities needed for clinical applications. Last but not least, Raman spectroscopy can easily be combined with other optical and non-optical methods to enable convenient sample handling and processing of clinical patient samples. In combination with a microscope, high spatial resolution (<1 m) can be achieved, enabling the analysis of single bacterial cells. As water yields only a very weak Raman spectrum, it is the ideal solvent for Raman spectroscopic analysis. Thus, analysis of body fluids can be carried out by means of Raman spectroscopy [1–3].
∗ Corresponding author. Present address: Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany. Tel.: +49 3641 948 320; fax: +49 3641 948 302. E-mail address: [email protected] (J. Popp).
In the following, the results presented at the 6th European Conference on Bloodstream Infections, on 6–7 June 2015 in Vravrona, Greece, will be summarised. They cover recent research highlights from the Jena Research Group dealing with Raman spectroscopybased concepts for the detection of drugs and the technological developments towards therapeutic drug monitoring, the detection and identification of bacteria from body fluids with a special focus on urine from patients suffering from urinary tract infections (UTIs), as well as spectroscopic approaches for fast antibiotic susceptibility testing. 2. Therapeutic drug monitoring For the detection of low concentrations of drugs, enhancement methods need to be applied. Here especially, surface-enhanced Raman spectroscopy (SERS) enables monitoring of substances at low concentrations [4]. Combining SERS with a lab-on-a-chip (LOC) microfluidic device enables enhancement of the reproducibility of the analysis [5,6]. By means of LOC-SERS it is possible to detect and quantify antibiotics at micromolar concentrations, which are in the therapeutically important range [7,8]. An alternative approach to detect analytes that cannot be directly monitored by means of LOCSERS is an indirect detection of a fluorescence dye that can react with the analyte [9].
http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014 0924-8579/© 2015 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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Fig. 1. Schematics of the fibre sensing setup. The analyte is injected into the fibre with a syringe pump. Laser light is coupled into the same fibre to interact with the analyte over a large distance (fibre length) enabling the detection of low concentrations. The backscattered Raman signal is collected through the same objective lens and is further analysed in a spectrometer with a charge-coupled device (CCD) camera. Adapted with permission from [11]. Copyright (2013) American Chemical Society.
LOC-SERS is not only suitable for identifying and quantifying pharmacologically active substances. It can also be used to monitor the therapeutic efficacy of drugs with respect to enzyme activity, which may inactivate the drug too fast or not at all. An example is thiopurine methyltransferase (TPMT) activity in red blood cells. Since the concentration of toxic and active metabolites of the immunosuppressive drug 6-mercaptopurine is rather high in patients, therapy using this drug can result in serious toxicity as well as failure of efficacy owing to genetic differences in metabolising enzymes. A huge variety of TPMT genotypes exhibiting different activities for the methylation of thiopurines impede determination of a therapeutic dosage, since high enzyme activity results in the inactivation of thiopurines, whereas low activity can lead to toxic effects. Here, LOC-SERS was used successfully to determine the TPMT activity in blood samples [10]. An alternative method to detect therapeutic agents is fibreenhanced Raman spectroscopy (FERS). Here, a hollow-core optical fibre is used to enhance the Raman signal significantly in order to detect even lower concentrations. Application of an ultraviolet excitation wavelength in combination with a hollow-core optical fibre can even detect chloroquine and mefloquine at concentrations <100 M in an aqueous environment (Fig. 1) [11]. Applying visible excitation wavelengths enables direct monitoring of human breath, which is a mixture of different major compounds including N2 , O2 , CO2 and H2 O as well as traces of volatile organic compounds. In addition, there are important gaseous markers for the detection of different diseases such as, e.g., acetone (C3 H6 O) and methane (CH4 ) for lung cancer, or NH3 and 12 CO for Helicobacter pylori infection. Application of FERS with a 2 hollow-core optical fibre allows the detection of all sorts of gaseous components in human breath. Even the differentiation between isotope-labelled substances is possible in routine measurements [12]. Applying a microstructured hollow-core photonic crystal fibre for FERS even allows simultaneous monitoring of H2 in the presence of all other gases such as, e.g., CH4 , N2 , O2 and CO2 . This was achieved by a combination of rotational Raman spectroscopy for H2 and vibrational Raman spectroscopy for the other gases. With this approach, it was possible to detect H2 down to a limit of detection of 4.7 ppm besides other gases (CH4 , N2 , O2 , 12 CO2 and 13 CO2 ) [13].
Fig. 2. Mean Raman spectra of major pathogens in urinary tract infections used to construct a support vector machine classification model for the identification of unknown patient samples. Adapted with permission from [16]. Copyright (2013) American Chemical Society.
3. Diagnosis of infectious diseases from body fluids The prevalent causes of death in non-cardiology intensive care units are pathogen-induced sepsis and its most extreme form, septic shock. Before arriving at the state of septic shock, the patient has undergone several stages in the continuum of sepsis, starting with a local infection where the pathogen invades the host and releases toxic products, passing the stage where an overwhelming immune response is not only targeted towards the pathogen but also induces morphological damage to cells and tissue leading to organ failure [14]. A faster and more detailed diagnosis could help to save lives in the future. However, currently established microbiological diagnosis involves time-consuming cultivation steps. Thus, a detailed microbiological analysis is only available after 1 day up to several days. Raman spectroscopy is a non-invasive, label-free optical technology that can record in real time the spectroscopic fingerprint of single bacteria, offering a high potential for faster bacterial characterisation directly from patient samples [15–21]. Promising results have been obtained for the diagnosis of UTIs, which account for >150 million infections/year with a spectrum ranging from uncomplicated UTI to life-threatening healthcareassociated sepsis. They are the most frequent infections in women, with >60% of all women having a UTI during their lives. UTIs are also the most common cause of nosocomial infections causing healthcare costs of ca. US$6 billion. The current gold-standard method for pathogen identification from urine culture takes longer than 24 h to give a result [22–24]. To evaluate the potential of Raman spectroscopy for fast and reliable identification of pathogens directly from urine samples, a reference database has been built including the most common causative pathogens, which are Escherichia coli (50%), Klebsiella spp. (14%), enterococci (10%), staphylococci (6%), Pseudomonas aeruginosa (3%) and rarely other bacteria, viruses and fungi. In total, 11 different species have been measured in more than four independent batches per species yielding more than 200 spectra from single cells per species. Fig. 2 displays the averaged Raman spectra per included species. The resulting 2952 spectra were used to train a classification model based on support vector machines. This model was tested with independently measured Raman spectra (in total 514) from the same 11 species yielding a prediction accuracy of 95% [16]. This high accuracy makes the model suitable to be evaluated with real-world patient samples. However, when analysing urine samples from patients, several unknown factors will complicate
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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Fig. 3. Evaluation of patient urine specimens: assignment of measured single-cell Raman spectra based on the previously built support vector machine model. * Samples with a positive inhibitor test. Adapted with permission from [16]. Copyright (2013) American Chemical Society.
the analysis, including the unknown composition of the urine, the unknown dwell time of the bacteria in the urine and, most likely, the urine will not contain a bacterial strain that is already known to the database. Two Raman-based approaches have been tested, one focused on single bacterial analysis [16] and the second optimised to perform the analysis directly in suspension [25]. For single-cell analysis, the urine sample is centrifuged and the washed bacteria are placed on a Nickel foil where they are allowed to dry. Raman spectra are recoded from single bacteria cells of the patient’s sample. For each single-cell measurement, the database predicts the identity of the pathogen. For each specimen, between 66% and 98% of measured cells were classified as one species. In Fig. 3, the first seven patient samples were determined to contain E. coli and the following three samples to contain Enterococcus faecalis as the predominate species. These results agree with the results of the gold standard of urine culture. When using a threshold of ≥60% abundance in the single-cell Raman spectra, a correct assignment of all investigated patient samples can be achieved. Noticeably, five of the ten analysed samples showed a positive inhibitor test. Thus, Raman spectroscopic analysis can identify pathogens from urine despite the presence of antibiotics or other growth-inhibitory substances. This can bring up very promising applications for the identification
of non-cultivatable pathogens directly from body fluids of patients [16–18]. The second approach, in which the Raman spectroscopic analysis is performed directly in suspension, uses dielectrophoretic forces to capture bacteria in defined locations in space. Only minimal sample preparation is required for analysis of a conventional urine sample. One droplet of the filtered and washed urine is placed on a dielectrophoresis chip with four gold electrodes (Fig. 4). An alternating electric field between the electrodes forces the bacteria into the middle where they are analysed by means of Raman spectroscopy. As in the first approach, with the help of a previously established database the Raman spectra from the patient’s sample are used to identify the pathogen in the urine sample. High prediction accuracies have been reported for the differentiation of E. coli and E. faecalis [25]. Furthermore, it was shown that the electric field does not influence the viability of the captured bacteria, which is important if the method is going to be further developed to be implemented in fast, spectroscopy-based antibiotic susceptibility testing (see Section 4). Instead of dielectrophoresis, centrifugal forces can also be utilised to directly capture bacteria from suspension and subsequently analyse them by means of Raman spectroscopy [26].
Fig. 4. Visualisation of the workflow for the spectroscopic analysis of urine samples in suspension based on results in [25]. The patient sample arrives in the laboratory and can be placed after a short pre-treatment step onto the dielectrophoresis chip. An alternating electric field is applied on the gold electrodes (depicted in black) and the bacteria experience a force towards the centre (as indicated with yellow arrows in the middle image). The bacteria collect in a cloud in a well-defined region where they are analysed by means of Raman spectroscopy. With the help of a database, the bacteria can be identified using the information contained in the Raman spectra. The whole procedure takes ca. 35 min. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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Fig. 5. (a) Averaged Raman spectra in the fingerprint region of a vancomycin-sensitive strain (Enterococcus faecalis ATCC 29212) and a vancomycin-resistant strain (Enterococcus faecalis ATCC 51299) at four different time points, both with and without addition of 10 g/mL vancomycin. (b) Statistical analysis of the Raman data shown in (a) (top) and for the model of two unknown E. faecium strains (bottom). After 120 min, a clear separation of the resistant and sensitive strains can be achieved using the vancomycin score. Whilst the treated sensitive strains show up with positive vancomycin effect score values, the treated resistant strains can be found together with the untreated controls at negative vancomycin effect scores. Adapted from [28].
4. Spectroscopic characterisation of antibiotic resistance In times of rising antibiotic resistance, timely characterisation of antibiotic susceptibility is of utmost importance to administer appropriate tailored antibiotic therapy. An increase in the prevalence of resistant pathogens is seen for many antibiotics. In the following, vancomycin-resistant enterococci (VRE) will be used as a model to demonstrate the power of Raman spectroscopy as a fast method to differentiate sensitive and resistant strains. Currently, the proportion of vancomycin-resistant Enterococcus faecium isolates in European hospitals ranges from <1% in Finland and Sweden to 25–50% in Ireland [27]. In the USA, VRE are so widespread that vancomycin is almost removed from the guideline recommendations for the treatment of severe enterococcal infections. Whilst established microbiological analysis requires a minimum of 24 h and often even several days owing to time-consuming cultivation steps, a fast Raman-based method has been developed that can distinguish VRE from vancomycin-sensitive enterococci within only 3.5 h [28]. The method is based on the molecular changes that occur within the bacteria owing to the action of the drug and that are detected in the Raman spectra. In vancomycin-sensitive bacteria, the antibiotic binds to the d-alanyl-d-alanine terminus of peptidoglycan pentapeptide precursors in the nascent cell wall via five hydrogen bonds and by this prevents further cell wall synthesis, finally leading to cell death [29]. In the Raman spectra, druginduced changes can be detected as early as 30 min when using antibiotic concentrations above the minimum inhibitory concentration (MIC) of the bacteria. These spectral changes can be used to train a robust classification model that can reliably distinguish treated and untreated bacteria based on their Raman spectra [30]. For vancomycin-resistant bacteria, several resistance mechanisms are known. Most common nowadays are VanA and VanB resistances, which are inducible. This means that initially the drug binds to the growing cell wall. However, unlike in sensitive bacteria, this does not lead to cell death but triggers the activation of a cascade of gene clusters that ultimately lead to the synthesis of a d-alanyl-d-lactate terminus instead of the previous d-alanyl-dalanine. Vancomycin shows a reduced binding to this new group
and the bacteria can continue to grow despite the presence of vancomycin. This inducible resistance is also reflected in the Raman spectra (Fig. 5a) and can be clearly visualised when analysing the Raman data with partial least squares (PLS) regression and subsequent linear discriminant analysis (LDA) (Fig. 5b) [28]. Fig. 5a shows the Raman spectra of a sensitive strain (E. faecalis ATCC 29212) and a resistant strain (E. faecalis ATCC 51299) at different time points of interaction with vancomycin as well as the untreated control samples of both strains for comparison. Immediately after vancomycin addition (0 min) all Raman spectra appear very similar. At 30 min, already the effect of the drug on the bacteria can be detected and the Raman spectra of the treated bacteria show characteristic differences compared with the untreated controls. However, at that time point, both the sensitive and resistant strains exhibit similar spectral changes. After 60 min, the spectral features of the treated resistant strain start to resemble those of the untreated controls, and after 120 min differentiation of the resistant and sensitive E. faecalis strains can be achieved with high sensitivity and specificity. This is visualised in Fig. 5b where the Raman data are projected into a PLS-LDA model. A positive vancomycin effect score indicates efficient binding of vancomycin to the bacteria, whilst the untreated controls are found at negative vancomycin effect score values. Raman spectra of treated bacteria with an inducible vancomycin resistance and an MIC in the range of the applied drug concentration can be found at positive vancomycin effect score values at short vancomycin–bacteria interaction times (30 min; Fig. 5b, top). However, as time progresses, the resistance fully develops and after 120 min the Raman spectra of the treated resistant strain can be found at the same negative vancomycin effect score values as the untreated controls, evidencing unobstructed growth of the resistant strain. For a resistant enterococcal strain with an MIC ca. 10-fold higher than the administered drug concentration, the effect of an inducible resistance is not so pronounced (Fig. 5b, bottom). Notably, the presented Raman-based algorithm was developed for two E. faecalis strains and also could successfully differentiate a resistant E. faecium strain from a sensitive one, demonstrating the high potential of the method for the analysis of real-world patient’s samples where each patient is likely to carry a different strain [28].
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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5. Conclusion Raman spectroscopy is a powerful tool both for drug monitoring at therapeutically significant concentrations as well as label-free biophotonic characterisation of infections. It yields highly specific molecular fingerprint information from the observed pathogens enabling differentiation of various species as well as characterisation of antibiotic interactions and identification of resistant bacteria. The spatial resolution when combined with microscopy enables the analysis of single bacterial cells. However, the major advantage for medical diagnosis lies in the short times after which reliable results can be obtained, such as the identification of pathogens from the urinary tract after only 35 min and information about antibiotic susceptibility after <3.5 h. Funding: Financial support was received from the German Research Foundation (DFG) within FOR 1738 as well as the collaborative research centre ChemBioSys [SFB 1127], from the European Union via the EU project ‘HemoSpec’ [CN 611682], from the Federal Ministry of Education and Research (BMBF), Germany, via the Integrated Research and Treatment Center ‘Center for Sepsis Control and Care’ (CSCC) [FKZ 01EO1002 and FKZ 01EO1502], and from the research projects RiMaTH [02WRS1276E] Fast Diagnosis [13N11350] as well as funding from the research projects Fast-TB [2013FE9057] and BioInter [13022-15] of the Free State of Thuringia and the European Union (EFRE). Competing interests: None declared. Ethical approval: Not required. References [1] Krafft C, Popp J. The many facets of Raman spectroscopy for biomedical analysis. Anal Bioanal Chem 2015;407:699–717. [2] Galler K, Bräutigam K, Große C, Popp J, Neugebauer U. Making a big thing of a small cell—recent advances in single cell analysis. Analyst 2014;139:1237–73. [3] Neugebauer U, Trenkmann S, Bocklitz T, Schmerler D, Kiehntopf M, Popp J. Fast differentiation of SIRS and sepsis from blood plasma of ICU patients using Raman spectroscopy. J Biophotonics 2014;7:232–40. [4] Pahlow S, März A, Seise B, Hartmann K, Freitag I, Kämmer E, et al. Bioanalytical application of surface- and tip-enhanced Raman spectroscopy. Eng Life Sci 2012;12:131–43. [5] März A, Henkel T, Cialla D, Schmitt M, Popp J. Droplet formation via flow-through microdevices in Raman and surface enhanced Raman spectroscopy—concepts and applications. Lab Chip 2011;11:3584–92. [6] Kämmer E, Olschewski K, Bocklitz T, Rösch P, Weber K, Cialla D, et al. A new calibration concept for a reproducible quantitative detection based on SERS measurements in a microfluidic device demonstrated on the model analyte adenine. Phys Chem Chem Phys 2014;16:9056–63. [7] Hidi IJ, Mühlig A, Jahn M, Liebold F, Cialla D, Weber K, et al. LOC-SERS: towards point-of-care diagnostic of methotrexate. Anal Methods 2014;6:3943–7. [8] Hidi IJ, Jahn M, Weber K, Cialla-May D, Popp J. Droplet based microfluidics: spectroscopic characterization of levofloxacin and its SERS detection. Phys Chem Chem Phys 2015;17:21236–42. [9] März A, Trupp S, Rösch P, Mohr GJ, Popp J. Fluorescence dye as novel label molecule for quantitative SERS investigations of an antibiotic. Anal Bioanal Chem 2012;402:2625–31.
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[10] März A, Mönch B, Rösch P, Kiehntopf M, Henkel T, Popp J. Detection of thiopurine methyltransferase activity in red blood cells by the means of lab-ona-chip surface enhanced Raman spectroscopy (LOC-SERS). Anal Bioanal Chem 2011;400:2755–61. [11] Frosch T, Yan D, Popp J. Ultrasensitive fiber enhanced UV resonance Raman sensing of drugs. Anal Chem 2013;85:6264–71. [12] Hanf S, Keiner R, Yan D, Popp J, Frosch T. Fiber-enhanced Raman multigas spectroscopy: a versatile tool for environmental gas sensing and breath analysis. Anal Chem 2014;86:5278–85. [13] Hanf S, Bögözi T, Keiner R, Frosch T, Popp J. Fast and highly sensitive fiberenhanced Raman spectroscopic monitoring of molecular H2 and CH4 for pointof-care diagnosis of malabsorption disorders in exhaled human breath. Anal Chem 2015;87:982–8. [14] World Sepsis Day. http://www.world-sepsis-day.org [accessed 24.10.15]. [15] Pahlow S, Meisel S, Cialla-May D, Weber K, Rösch P, Popp J. Isolation and identification of bacteria by means of Raman spectroscopy. Adv Drug Deliv Rev 2015;89:105–20. [16] Kloß S, Kampe B, Sachse S, Rösch P, Straube E, Pfister W, et al. Culture independent Raman spectroscopic identification of urinary tract infection pathogens—a proof of principle study. Anal Chem 2013;85:697–708. [17] Kloß S, Lorenz B, Dees S, Labugger I, Rösch P, Popp J. Destruction-free procedure for the isolation of bacteria from sputum samples for Raman spectroscopic analysis. Anal Bioanal Chem 2015;407:8333–41. [18] Kloß S, Rösch P, Pfister W, Kiehntopf M, Popp J. Towards culture-free Raman spectroscopic identification of pathogens in ascitic fluid. Anal Chem 2015;87:937–43. [19] Stöckel S, Meisel S, Elschner M, Rösch P, Popp J. Raman spectroscopic detection of anthrax endospores in powder samples. Angew Chem Int Ed Engl 2012;51:5339–42. [20] Kusic´ D, Kampe B, Ramoji A, Neugebauer U, Rösch P, Popp J. Raman spectroscopic differentiation of planktonic bacteria and biofilms. Anal Bioanal Chem 2015;407:6803–13. [21] Silge A, Abdou E, Schneider K, Meisel S, Bocklitz T, Lu-Walther H-W, et al. Shedding light on host niches: label-free in situ detection of Mycobacterium gordonae via carotenoids in macrophages by Raman microspectroscopy. Cell Microbiol 2015;17:832–42. [22] Goodacre R, Timmins EM, Burton R, Kaderbhai N, Woodward AM, Kell DB, et al. Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 1998;144:1157–70. [23] Foxman B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am J Med 2002;113:5–13. [24] Gastmeier P, Kampf G, Wischnewski N, Hauer T, Schulgen G, Schumacher M, et al. Prevalence of nosocomial infections in representative German hospitals. J Hosp Infect 1998;38:37–49. [25] Schröder UC, Ramoji A, Glaser U, Sachse S, Leiterer C, Cszaki A, et al. Combined dielectrophoresis-Raman setup for the classification of pathogens recovered from the urinary tract. Anal Chem 2013;85:10717–24. [26] Schröder UC, Bokeloh F, O’Sullivan M, Glaser U, Wolf K, Pfister W, et al. Rapid, culture-independent, optical diagnostics of centrifugally captured bacteria from urine samples. Biomicrofluidics 2015;9:044118. [27] European Centre for Disease Prevention and Control. Antimicrobial resistance surveillance in Europe 2013. Stockholm, Sweden: ECDC; 2013. [28] Schröder UC, Beleites C, Assmann C, Glaser U, Hübner U, Pfister W, et al. Detection of vancomycin resistances in enterococci within 3½ hours. Sci Rep 2015;5:8217. [29] Kahne D, Leimkuhler C, Lu W, Walsh C. Glycopeptide and lipoglycopeptide antibiotics. Chem Rev 2005;105:425–48. [30] Assmann C, Kirchhoff J, Beleites C, Hey J, Kostudis S, Pfister W, et al. Identification of vancomycin interaction with Enterococcus faecalis within 30 min of interaction time using Raman spectroscopy. Anal Bioanal Chem 2015;407:8343–52.
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
ARTICLE IN PRESS International Journal of Antimicrobial Agents xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
International Journal of Antimicrobial Agents journal homepage: http://www.elsevier.com/locate/ijantimicag
Review
Raman spectroscopy towards clinical application: drug monitoring and pathogen identification Ute Neugebauer a,b,c , Petra Rösch c,d , Jürgen Popp a,b,c,d,∗ a
Center for Sepsis Control and Care (CSCC), Jena University Hospital, Erlanger Allee 101, D-07747 Jena, Germany Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany InfectoGnostics Forschungscampus Jena, Philosophenweg 7, D-07743 Jena, Germany d Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany b c
a r t i c l e
i n f o
Keywords: Raman spectroscopy Therapeutic drug monitoring Infection detection Antibiotic resistance Lab-on-a-chip (LOC) Fibre-enhanced Raman spectroscopy
a b s t r a c t Raman spectroscopy is a label-free method that measures quickly and contactlessly, providing detailed information from the sample, and has proved to be an ideal tool for medical and life science research. In this review, recent advances of the technique towards drug monitoring and pathogen identification by the Jena Research Groups are reviewed. Surface-enhanced Raman spectroscopy (SERS) and ultraviolet resonance Raman spectroscopy in hollow-core optical fibres enable the detection of drugs at low concentrations as shown for the metabolites of the immunosuppressive drug 6-mercaptopurine as well as antimalarial agents. Furthermore, Raman spectroscopy can be used to characterise pathogenic bacteria in infectious diseases directly from body fluids, making time-consuming cultivation processes dispensable. Using the example of urinary tract infection, it is shown how bacteria can be identified from patients’ urine samples within <1 h. The methods cover both single-cell analysis and dielectrophoretic capturing of bacteria in suspension. The latter method could also be used for fast (<3.5 h) identification of antibiotic resistance as shown exemplarily for vancomycin-resistant enterococci. © 2015 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.
1. Introduction Raman spectroscopy has proved to be an ideal tool for medical and life science research, as Raman measures without contact, providing label-free information on processes within living cells without disturbing them. Furthermore, Raman spectroscopy measures quickly, overcoming the need for complex and timeconsuming laboratory analyses in many cases. Raman spectroscopy also measures precisely, providing the ultrasensitive detection capabilities needed for clinical applications. Last but not least, Raman spectroscopy can easily be combined with other optical and non-optical methods to enable convenient sample handling and processing of clinical patient samples. In combination with a microscope, high spatial resolution (<1 m) can be achieved, enabling the analysis of single bacterial cells. As water yields only a very weak Raman spectrum, it is the ideal solvent for Raman spectroscopic analysis. Thus, analysis of body fluids can be carried out by means of Raman spectroscopy [1–3].
∗ Corresponding author. Present address: Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany. Tel.: +49 3641 948 320; fax: +49 3641 948 302. E-mail address: [email protected] (J. Popp).
In the following, the results presented at the 6th European Conference on Bloodstream Infections, on 6–7 June 2015 in Vravrona, Greece, will be summarised. They cover recent research highlights from the Jena Research Group dealing with Raman spectroscopybased concepts for the detection of drugs and the technological developments towards therapeutic drug monitoring, the detection and identification of bacteria from body fluids with a special focus on urine from patients suffering from urinary tract infections (UTIs), as well as spectroscopic approaches for fast antibiotic susceptibility testing. 2. Therapeutic drug monitoring For the detection of low concentrations of drugs, enhancement methods need to be applied. Here especially, surface-enhanced Raman spectroscopy (SERS) enables monitoring of substances at low concentrations [4]. Combining SERS with a lab-on-a-chip (LOC) microfluidic device enables enhancement of the reproducibility of the analysis [5,6]. By means of LOC-SERS it is possible to detect and quantify antibiotics at micromolar concentrations, which are in the therapeutically important range [7,8]. An alternative approach to detect analytes that cannot be directly monitored by means of LOCSERS is an indirect detection of a fluorescence dye that can react with the analyte [9].
http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014 0924-8579/© 2015 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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Fig. 1. Schematics of the fibre sensing setup. The analyte is injected into the fibre with a syringe pump. Laser light is coupled into the same fibre to interact with the analyte over a large distance (fibre length) enabling the detection of low concentrations. The backscattered Raman signal is collected through the same objective lens and is further analysed in a spectrometer with a charge-coupled device (CCD) camera. Adapted with permission from [11]. Copyright (2013) American Chemical Society.
LOC-SERS is not only suitable for identifying and quantifying pharmacologically active substances. It can also be used to monitor the therapeutic efficacy of drugs with respect to enzyme activity, which may inactivate the drug too fast or not at all. An example is thiopurine methyltransferase (TPMT) activity in red blood cells. Since the concentration of toxic and active metabolites of the immunosuppressive drug 6-mercaptopurine is rather high in patients, therapy using this drug can result in serious toxicity as well as failure of efficacy owing to genetic differences in metabolising enzymes. A huge variety of TPMT genotypes exhibiting different activities for the methylation of thiopurines impede determination of a therapeutic dosage, since high enzyme activity results in the inactivation of thiopurines, whereas low activity can lead to toxic effects. Here, LOC-SERS was used successfully to determine the TPMT activity in blood samples [10]. An alternative method to detect therapeutic agents is fibreenhanced Raman spectroscopy (FERS). Here, a hollow-core optical fibre is used to enhance the Raman signal significantly in order to detect even lower concentrations. Application of an ultraviolet excitation wavelength in combination with a hollow-core optical fibre can even detect chloroquine and mefloquine at concentrations <100 M in an aqueous environment (Fig. 1) [11]. Applying visible excitation wavelengths enables direct monitoring of human breath, which is a mixture of different major compounds including N2 , O2 , CO2 and H2 O as well as traces of volatile organic compounds. In addition, there are important gaseous markers for the detection of different diseases such as, e.g., acetone (C3 H6 O) and methane (CH4 ) for lung cancer, or NH3 and 12 CO for Helicobacter pylori infection. Application of FERS with a 2 hollow-core optical fibre allows the detection of all sorts of gaseous components in human breath. Even the differentiation between isotope-labelled substances is possible in routine measurements [12]. Applying a microstructured hollow-core photonic crystal fibre for FERS even allows simultaneous monitoring of H2 in the presence of all other gases such as, e.g., CH4 , N2 , O2 and CO2 . This was achieved by a combination of rotational Raman spectroscopy for H2 and vibrational Raman spectroscopy for the other gases. With this approach, it was possible to detect H2 down to a limit of detection of 4.7 ppm besides other gases (CH4 , N2 , O2 , 12 CO2 and 13 CO2 ) [13].
Fig. 2. Mean Raman spectra of major pathogens in urinary tract infections used to construct a support vector machine classification model for the identification of unknown patient samples. Adapted with permission from [16]. Copyright (2013) American Chemical Society.
3. Diagnosis of infectious diseases from body fluids The prevalent causes of death in non-cardiology intensive care units are pathogen-induced sepsis and its most extreme form, septic shock. Before arriving at the state of septic shock, the patient has undergone several stages in the continuum of sepsis, starting with a local infection where the pathogen invades the host and releases toxic products, passing the stage where an overwhelming immune response is not only targeted towards the pathogen but also induces morphological damage to cells and tissue leading to organ failure [14]. A faster and more detailed diagnosis could help to save lives in the future. However, currently established microbiological diagnosis involves time-consuming cultivation steps. Thus, a detailed microbiological analysis is only available after 1 day up to several days. Raman spectroscopy is a non-invasive, label-free optical technology that can record in real time the spectroscopic fingerprint of single bacteria, offering a high potential for faster bacterial characterisation directly from patient samples [15–21]. Promising results have been obtained for the diagnosis of UTIs, which account for >150 million infections/year with a spectrum ranging from uncomplicated UTI to life-threatening healthcareassociated sepsis. They are the most frequent infections in women, with >60% of all women having a UTI during their lives. UTIs are also the most common cause of nosocomial infections causing healthcare costs of ca. US$6 billion. The current gold-standard method for pathogen identification from urine culture takes longer than 24 h to give a result [22–24]. To evaluate the potential of Raman spectroscopy for fast and reliable identification of pathogens directly from urine samples, a reference database has been built including the most common causative pathogens, which are Escherichia coli (50%), Klebsiella spp. (14%), enterococci (10%), staphylococci (6%), Pseudomonas aeruginosa (3%) and rarely other bacteria, viruses and fungi. In total, 11 different species have been measured in more than four independent batches per species yielding more than 200 spectra from single cells per species. Fig. 2 displays the averaged Raman spectra per included species. The resulting 2952 spectra were used to train a classification model based on support vector machines. This model was tested with independently measured Raman spectra (in total 514) from the same 11 species yielding a prediction accuracy of 95% [16]. This high accuracy makes the model suitable to be evaluated with real-world patient samples. However, when analysing urine samples from patients, several unknown factors will complicate
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Fig. 3. Evaluation of patient urine specimens: assignment of measured single-cell Raman spectra based on the previously built support vector machine model. * Samples with a positive inhibitor test. Adapted with permission from [16]. Copyright (2013) American Chemical Society.
the analysis, including the unknown composition of the urine, the unknown dwell time of the bacteria in the urine and, most likely, the urine will not contain a bacterial strain that is already known to the database. Two Raman-based approaches have been tested, one focused on single bacterial analysis [16] and the second optimised to perform the analysis directly in suspension [25]. For single-cell analysis, the urine sample is centrifuged and the washed bacteria are placed on a Nickel foil where they are allowed to dry. Raman spectra are recoded from single bacteria cells of the patient’s sample. For each single-cell measurement, the database predicts the identity of the pathogen. For each specimen, between 66% and 98% of measured cells were classified as one species. In Fig. 3, the first seven patient samples were determined to contain E. coli and the following three samples to contain Enterococcus faecalis as the predominate species. These results agree with the results of the gold standard of urine culture. When using a threshold of ≥60% abundance in the single-cell Raman spectra, a correct assignment of all investigated patient samples can be achieved. Noticeably, five of the ten analysed samples showed a positive inhibitor test. Thus, Raman spectroscopic analysis can identify pathogens from urine despite the presence of antibiotics or other growth-inhibitory substances. This can bring up very promising applications for the identification
of non-cultivatable pathogens directly from body fluids of patients [16–18]. The second approach, in which the Raman spectroscopic analysis is performed directly in suspension, uses dielectrophoretic forces to capture bacteria in defined locations in space. Only minimal sample preparation is required for analysis of a conventional urine sample. One droplet of the filtered and washed urine is placed on a dielectrophoresis chip with four gold electrodes (Fig. 4). An alternating electric field between the electrodes forces the bacteria into the middle where they are analysed by means of Raman spectroscopy. As in the first approach, with the help of a previously established database the Raman spectra from the patient’s sample are used to identify the pathogen in the urine sample. High prediction accuracies have been reported for the differentiation of E. coli and E. faecalis [25]. Furthermore, it was shown that the electric field does not influence the viability of the captured bacteria, which is important if the method is going to be further developed to be implemented in fast, spectroscopy-based antibiotic susceptibility testing (see Section 4). Instead of dielectrophoresis, centrifugal forces can also be utilised to directly capture bacteria from suspension and subsequently analyse them by means of Raman spectroscopy [26].
Fig. 4. Visualisation of the workflow for the spectroscopic analysis of urine samples in suspension based on results in [25]. The patient sample arrives in the laboratory and can be placed after a short pre-treatment step onto the dielectrophoresis chip. An alternating electric field is applied on the gold electrodes (depicted in black) and the bacteria experience a force towards the centre (as indicated with yellow arrows in the middle image). The bacteria collect in a cloud in a well-defined region where they are analysed by means of Raman spectroscopy. With the help of a database, the bacteria can be identified using the information contained in the Raman spectra. The whole procedure takes ca. 35 min. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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Fig. 5. (a) Averaged Raman spectra in the fingerprint region of a vancomycin-sensitive strain (Enterococcus faecalis ATCC 29212) and a vancomycin-resistant strain (Enterococcus faecalis ATCC 51299) at four different time points, both with and without addition of 10 g/mL vancomycin. (b) Statistical analysis of the Raman data shown in (a) (top) and for the model of two unknown E. faecium strains (bottom). After 120 min, a clear separation of the resistant and sensitive strains can be achieved using the vancomycin score. Whilst the treated sensitive strains show up with positive vancomycin effect score values, the treated resistant strains can be found together with the untreated controls at negative vancomycin effect scores. Adapted from [28].
4. Spectroscopic characterisation of antibiotic resistance In times of rising antibiotic resistance, timely characterisation of antibiotic susceptibility is of utmost importance to administer appropriate tailored antibiotic therapy. An increase in the prevalence of resistant pathogens is seen for many antibiotics. In the following, vancomycin-resistant enterococci (VRE) will be used as a model to demonstrate the power of Raman spectroscopy as a fast method to differentiate sensitive and resistant strains. Currently, the proportion of vancomycin-resistant Enterococcus faecium isolates in European hospitals ranges from <1% in Finland and Sweden to 25–50% in Ireland [27]. In the USA, VRE are so widespread that vancomycin is almost removed from the guideline recommendations for the treatment of severe enterococcal infections. Whilst established microbiological analysis requires a minimum of 24 h and often even several days owing to time-consuming cultivation steps, a fast Raman-based method has been developed that can distinguish VRE from vancomycin-sensitive enterococci within only 3.5 h [28]. The method is based on the molecular changes that occur within the bacteria owing to the action of the drug and that are detected in the Raman spectra. In vancomycin-sensitive bacteria, the antibiotic binds to the d-alanyl-d-alanine terminus of peptidoglycan pentapeptide precursors in the nascent cell wall via five hydrogen bonds and by this prevents further cell wall synthesis, finally leading to cell death [29]. In the Raman spectra, druginduced changes can be detected as early as 30 min when using antibiotic concentrations above the minimum inhibitory concentration (MIC) of the bacteria. These spectral changes can be used to train a robust classification model that can reliably distinguish treated and untreated bacteria based on their Raman spectra [30]. For vancomycin-resistant bacteria, several resistance mechanisms are known. Most common nowadays are VanA and VanB resistances, which are inducible. This means that initially the drug binds to the growing cell wall. However, unlike in sensitive bacteria, this does not lead to cell death but triggers the activation of a cascade of gene clusters that ultimately lead to the synthesis of a d-alanyl-d-lactate terminus instead of the previous d-alanyl-dalanine. Vancomycin shows a reduced binding to this new group
and the bacteria can continue to grow despite the presence of vancomycin. This inducible resistance is also reflected in the Raman spectra (Fig. 5a) and can be clearly visualised when analysing the Raman data with partial least squares (PLS) regression and subsequent linear discriminant analysis (LDA) (Fig. 5b) [28]. Fig. 5a shows the Raman spectra of a sensitive strain (E. faecalis ATCC 29212) and a resistant strain (E. faecalis ATCC 51299) at different time points of interaction with vancomycin as well as the untreated control samples of both strains for comparison. Immediately after vancomycin addition (0 min) all Raman spectra appear very similar. At 30 min, already the effect of the drug on the bacteria can be detected and the Raman spectra of the treated bacteria show characteristic differences compared with the untreated controls. However, at that time point, both the sensitive and resistant strains exhibit similar spectral changes. After 60 min, the spectral features of the treated resistant strain start to resemble those of the untreated controls, and after 120 min differentiation of the resistant and sensitive E. faecalis strains can be achieved with high sensitivity and specificity. This is visualised in Fig. 5b where the Raman data are projected into a PLS-LDA model. A positive vancomycin effect score indicates efficient binding of vancomycin to the bacteria, whilst the untreated controls are found at negative vancomycin effect score values. Raman spectra of treated bacteria with an inducible vancomycin resistance and an MIC in the range of the applied drug concentration can be found at positive vancomycin effect score values at short vancomycin–bacteria interaction times (30 min; Fig. 5b, top). However, as time progresses, the resistance fully develops and after 120 min the Raman spectra of the treated resistant strain can be found at the same negative vancomycin effect score values as the untreated controls, evidencing unobstructed growth of the resistant strain. For a resistant enterococcal strain with an MIC ca. 10-fold higher than the administered drug concentration, the effect of an inducible resistance is not so pronounced (Fig. 5b, bottom). Notably, the presented Raman-based algorithm was developed for two E. faecalis strains and also could successfully differentiate a resistant E. faecium strain from a sensitive one, demonstrating the high potential of the method for the analysis of real-world patient’s samples where each patient is likely to carry a different strain [28].
Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014
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5. Conclusion Raman spectroscopy is a powerful tool both for drug monitoring at therapeutically significant concentrations as well as label-free biophotonic characterisation of infections. It yields highly specific molecular fingerprint information from the observed pathogens enabling differentiation of various species as well as characterisation of antibiotic interactions and identification of resistant bacteria. The spatial resolution when combined with microscopy enables the analysis of single bacterial cells. However, the major advantage for medical diagnosis lies in the short times after which reliable results can be obtained, such as the identification of pathogens from the urinary tract after only 35 min and information about antibiotic susceptibility after <3.5 h. Funding: Financial support was received from the German Research Foundation (DFG) within FOR 1738 as well as the collaborative research centre ChemBioSys [SFB 1127], from the European Union via the EU project ‘HemoSpec’ [CN 611682], from the Federal Ministry of Education and Research (BMBF), Germany, via the Integrated Research and Treatment Center ‘Center for Sepsis Control and Care’ (CSCC) [FKZ 01EO1002 and FKZ 01EO1502], and from the research projects RiMaTH [02WRS1276E] Fast Diagnosis [13N11350] as well as funding from the research projects Fast-TB [2013FE9057] and BioInter [13022-15] of the Free State of Thuringia and the European Union (EFRE). Competing interests: None declared. Ethical approval: Not required. References [1] Krafft C, Popp J. The many facets of Raman spectroscopy for biomedical analysis. Anal Bioanal Chem 2015;407:699–717. [2] Galler K, Bräutigam K, Große C, Popp J, Neugebauer U. Making a big thing of a small cell—recent advances in single cell analysis. Analyst 2014;139:1237–73. [3] Neugebauer U, Trenkmann S, Bocklitz T, Schmerler D, Kiehntopf M, Popp J. Fast differentiation of SIRS and sepsis from blood plasma of ICU patients using Raman spectroscopy. J Biophotonics 2014;7:232–40. [4] Pahlow S, März A, Seise B, Hartmann K, Freitag I, Kämmer E, et al. Bioanalytical application of surface- and tip-enhanced Raman spectroscopy. Eng Life Sci 2012;12:131–43. [5] März A, Henkel T, Cialla D, Schmitt M, Popp J. Droplet formation via flow-through microdevices in Raman and surface enhanced Raman spectroscopy—concepts and applications. Lab Chip 2011;11:3584–92. [6] Kämmer E, Olschewski K, Bocklitz T, Rösch P, Weber K, Cialla D, et al. A new calibration concept for a reproducible quantitative detection based on SERS measurements in a microfluidic device demonstrated on the model analyte adenine. Phys Chem Chem Phys 2014;16:9056–63. [7] Hidi IJ, Mühlig A, Jahn M, Liebold F, Cialla D, Weber K, et al. LOC-SERS: towards point-of-care diagnostic of methotrexate. Anal Methods 2014;6:3943–7. [8] Hidi IJ, Jahn M, Weber K, Cialla-May D, Popp J. Droplet based microfluidics: spectroscopic characterization of levofloxacin and its SERS detection. Phys Chem Chem Phys 2015;17:21236–42. [9] März A, Trupp S, Rösch P, Mohr GJ, Popp J. Fluorescence dye as novel label molecule for quantitative SERS investigations of an antibiotic. Anal Bioanal Chem 2012;402:2625–31.
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Please cite this article in press as: Neugebauer U, et al. Raman spectroscopy towards clinical application: drug monitoring and pathogen identification. Int J Antimicrob Agents (2015), http://dx.doi.org/10.1016/j.ijantimicag.2015.10.014