The current state of the art on bacterial classification using Raman and Surface Enhanced Raman Spectroscopy (SERS) for the purpose of developing a rapid and more accurate method for urinary tract infection (UTI) diagnosis is presented. SERS, an enhanced version of Raman offering much increased sensitivity, provides complex biochemical information which, in conjunction with advanced analysis and classification techniques, can become a valuable diagnostic tool. The variety of metal substrates used for SERS, including silver and gold colloids, as well as nanostructured metal surfaces, is reviewed. The challenges in preprocessing noisy and complicated spectra and the various methods used for feature creation as well as a novel method using spectral band ratios are described. The various unsupervised and supervised classification methods commonly used for SERS spectra of bacteria are evaluated. Current research on transforming SERS into a valuable clinical tool for the diagnosis of UTIs is presented. Specifically, the classification of bacterial spectra (a) as positive or negative for an infection, (b) as belonging to a particular species of bacteria, and (c) as sensitive or resistant to an antibiotic are described. This work can lead to the development of novel technology with extremely important benefits for public health. 1. Introduction Urinary tract infections (UTIs) are some of the most common types of infections in humans with an estimated 34 percent of adults aged 20 or older reported as having had at least one occurrence of a UTI or cystitis. Specifically, over 50% of women and over 13% of men will have a UTI at least once in their lifetime [1, 2]. This results in millions of doctors’ visits and a cost of several billion USD every year just in the USA alone [3, 4]. UTI diagnosis is a multistep process which includes the determination of the concentration of pathogens, and the identification of the responsible bacteria, as well as their susceptibility to various antibiotics, the so-called antibiogram. Such assays require repeated culturing of a sample and take over 48 hours in order for bacterial colonies to be grown, counted, and exposed to antibiotics using conventional clinical methods. Since the patient cannot remain untreated during this rather prolonged period before definitive diagnosis is obtained, physicians prescribe broad spectrum antibiotics prior to antibiogram results. This practice has many undesirable consequences, both short term and long term: (i) unsuccessful treatments leading to chronic infections, (ii) increased health care
References
[1]
T. L. Griebling, “Urinary tract infection in women,” in Urologic Diseases in America, pp. 587–619, DHHS, PHS, NIH, NIDDK, NIH, Washington, DC, USA, 2007.
[2]
T. L. Griebling, “Urinary tract infection in women,” in Urologic Diseases in America, pp. 621–645, DHHS, PHS, NIH, NIDDK, NIH, Washington, DC, USA, 2007.
[3]
National Center for Health Statistics, National Hospital Discharge Survey: 2004 Annual Summary With Detailed Diagnosis and Procedure Data, DHHS, Centers for Disease Control and Prevention, DHHS, Hyattsville, Md, USA, 2006, 1733.
[4]
M. S. Litwin and C. S. Saigal, “Introduction,” in Urologic Diseases in America, pp. 3–7, DHHS, PHS, NIH, NIDDK, NIH, Washington, DC, USA, 2007.
[5]
R. Gruneberg, “Changes in urinary pathogens and their antibiotic sensitivities, 1971–1992,” Journal of Antimicrobial Chemotherapy, vol. 33, supplement A, pp. 1–8, 1994.
[6]
A. Casadevall, “Crisis in infectious diseases: time for a new paradigm?” Clinical Infectious Diseases, vol. 23, no. 4, pp. 790–794, 1996.
[7]
S. Cosgrove and Y. Carmeli, “The impact of antimicrobial resistance on health and economic outcomes,” Clinical Infectious Diseases, vol. 36, no. 11, pp. 1433–1437, 2003.
[8]
A. Alanis, “Resistance to antibiotics: are we in the post-antibiotic era?” Archives of Medical Research, vol. 36, no. 6, pp. 697–705, 2005.
[9]
E. Mothershed and A. Whitney, “Nucleic acid-based methods for the detection of bacterial pathogens: present and future considerations for the clinical laboratory,” Clinica Chimica Acta, vol. 363, no. 1-2, pp. 206–220, 2006.
[10]
J. Rolain, M. Mallet, P. Fournier, and D. Raoult, “Real-time PCR for universal antibiotic susceptibility testing,” Journal of Antimicrobial Chemotherapy, vol. 54, no. 2, pp. 538–541, 2004.
[11]
W. Chen, P. Tsai, and Y. Chen, “Functional nanoparticle-based proteomic strategies for characterization of pathogenic bacteria,” Analytical Chemistry, vol. 80, no. 24, pp. 9612–9621, 2008.
[12]
K. Maquelin, L. Choo-Smith, T. Vreeswijk et al., “Raman spectroscopic method for identification of clinically relevant microorganisms growing on solid culture medium,” Analytical Chemistry, vol. 72, no. 1, pp. 12–19, 2000.
[13]
K. C. Schuster, I. Reese, E. Urlaub, J. R. Gapes, and B. Lendl, “Multidimensional information on the chemical composition of single bacterial cells by confocal Raman microspectroscopy,” Analytical Chemistry, vol. 72, no. 22, pp. 5529–5534, 2000.
[14]
K. C. Schuster, E. Urlaub, and J. R. Gapes, “Single-cell analysis of bacteria by Raman microscopy: spectral information on the chemical composition of cells and on the heterogeneity in a culture,” Journal of Microbiological Methods, vol. 42, no. 1, pp. 29–38, 2000.
[15]
K. Kneipp, M. Moskovits, and H. Kneip, “Surface-enhanced Raman scattering,” Physics and Applications Topics in Applied Physics, vol. 103, pp. 1–464, 2006.
[16]
T. T. Liu, Y. H. Lin, C. S. Hung et al., “A high speed detection platform based on surface-enhanced Raman scattering for monitoring antibiotic-induced chemical changes in bacteria cell wall,” Plos One, vol. 4, no. 5, Article ID e5470, 2009.
[17]
E. Kastanos, A. Kyriakides, K. Hadjigeorgiou, and C. Pitris, “A novel method for urinary tract infection diagnosis and antibiogram using Raman spectroscopy,” Journal of Raman Spectroscopy, vol. 41, no. 9, pp. 958–963, 2010.
[18]
E. Kastanos, K. Hadjigeorgiou, A. Kyriakides, and C. Pitris, “Classification of bacterial samples as negative or positive for a UTI and antibiogram using surface enhanced Raman spectroscopy,” in Proceedings of the SPIE, vol. 7911, 2011.
[19]
J. Magee, “Whole-organism fingerprinting,” in Handbook of New Bacterial Systematics, M. Goodfellow and A. O'Donnell, Eds., pp. 383–427, Academic Press, London, UK, 1993.
[20]
A. Mahadevan-Jansen, “Raman spectroscopy from benchtop to bedside,” in Biomedical Photonics Handbook, T. Vo-Dinh, Ed., CRC Press, London, USA, 2002.
[21]
O. Preisner, J. Lopes, and J. Menezes, “Uncertainty assessment in FT-IR spectroscopy based bacteria classification models,” Chemometrics and Intelligent Laboratory Systems, vol. 94, no. 1, pp. 33–42, 2008.
[22]
J. Forrester, N. Valentine, Y. Su, and T. Johnson, “Chemometric analysis of multiple species of Bacillus bacterial endospores using infrared spectroscopy: discrimination to the strain level,” Analytica Chimica Acta, vol. 651, no. 1, pp. 24–30, 2009.
[23]
R. Jarvis and R. Goodacre, “Ultra-violet resonance Raman spectroscopy for the rapid discrimination of urinary tract infection bacteria,” FEMS Microbiology Letters, vol. 232, no. 2, pp. 127–132, 2004.
[24]
D. Hutsebaut, K. Maquelin, P. De Vos, P. Vandenabeele, L. Moens, and G. Puppels, “Effect of culture conditions on the achievable taxonomic resolution of Raman spectroscopy disclosed by three Bacillus species,” Analytical Chemistry, vol. 76, no. 21, pp. 6274–6281, 2004.
[25]
C. Xie, J. Mace, M. Dinno et al., “Identification of single bacterial cells in aqueous solution using confocal laser tweezers Raman spectroscopy,” Analytical Chemistry, vol. 77, no. 14, pp. 4390–4397, 2005.
[26]
M. Harz, P. R？sch, K. Peschke, O. Ronneberger, H. Burkhardt, and J. Popp, “Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions,” Analyst, vol. 130, no. 11, pp. 1543–1550, 2005.
[27]
U. Schmid, P. Rosch, M. Krause, M. Harz, J. Popp, and K. Baumann, “Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy,” Chemometrics and Intelligent Laboratory Systems, vol. 96, no. 2, pp. 159–171, 2009.
[28]
K. Kneipp, Y. Wang, H. Kneipp et al., “Single molecule detection using surface-enhanced Raman scattering (SERS),” Physical Review Letters, vol. 78, no. 9, pp. 1667–1670, 1997.
[29]
S. Nie and S. Emory, “Probing single molecules and single nanoparticles by surface-enhanced Raman scattering,” Science, vol. 275, no. 5303, pp. 1102–1106, 1997.
[30]
M. Moskovits, “Surface-enhanced spectroscopy,” Reviews of Modern Physics, vol. 57, no. 3, pp. 783–826, 1985.
[31]
T. Vo-Dinh, F. Yan, and D. Stokes, “Plasmonics-based nanostructures for surface-enhanced Raman scattering bioanalysis,” Methods in Molecular Biology, vol. 300, pp. 255–283, 2005.
[32]
R. Jarvis, N. Law, I. Shadi, P. O'Brien, J. Lloyd, and R. Goodacre, “Surface-enhanced Raman scattering from intracellular and extracellular bacterial locations,” Analytical Chemistry, vol. 80, no. 17, pp. 6741–6746, 2008.
[33]
S. Efrima and B. Bronk, “Silver colloids impregnating or coating bacteria,” Journal of Physical Chemistry B, vol. 102, no. 31, pp. 5947–5950, 1998.
[34]
L. Zeiri, B. V. Bronk, Y. Shabtai, J. Czege, and S. Efrima, “Silver metal induced surface enhanced Raman of bacteria,” Colloids and Surfaces A, vol. 208, no. 1–3, pp. 357–362, 2002.
[35]
L. Zeiri, B. Bronk, Y. Shabtai, J. Eichler, and S. Efrima, “Surface-enhanced raman spectroscopy as a tool for probing specific biochemical components in bacteria,” Applied Spectroscopy, vol. 58, no. 1, pp. 33–40, 2004.
[36]
S. Efrima and L. Zeiri, “Understanding SERS of bacteria,” Journal of Raman Spectroscopy, vol. 40, no. 3, pp. 277–288, 2009.
[37]
R. Jarvis and R. Goodacre, “Discrimination of bacteria using surface-enhanced Raman spectroscopy,” Analytical Chemistry, vol. 76, no. 1, pp. 40–47, 2004.
[38]
R. Jarvis, A. Brooker, and R. Goodacre, “Surface-enhanced Raman spectroscopy for bacterial discrimination utilizing a scanning electron microscope with a Raman spectroscopy interface,” Analytical Chemistry, vol. 76, no. 17, pp. 5198–5202, 2004.
[39]
R. Jarvis, A. Brooker, and R. Goodacre, “Surface-enhanced Raman scattering for the rapid discrimination of bacteria,” Faraday Discussions, vol. 132, pp. 281–292, 2006.
[40]
R. Jarvis and R. Goodacre, “Characterisation and identification of bacteria using SERS,” Chemical Society Reviews, vol. 37, no. 5, pp. 931–936, 2008.
[41]
W. Premasiri, D. Moir, M. Klempner, N. Krieger, G. Jones, and L. Ziegler, “Characterization of the surface enhanced Raman scattering (SERS) of bacteria,” Journal of Physical Chemistry B, vol. 109, no. 1, pp. 312–320, 2005.
[42]
I. Patel, W. Premasiri, D. Moir, and L. Ziegler, “Barcoding bacterial cells: a SERS-based methodology for pathogen identification,” Journal of Raman Spectroscopy, vol. 39, no. 11, pp. 1660–1672, 2008.
[43]
S. Hennigan, J. Driskell, R. Dluhy et al., “Detection of Mycoplasma pneumoniaein simulated and true clinical throat swab specimens by nanorod array-surface-enhanced raman spectroscopy,” Plos One, vol. 5, no. 10, Article ID e13633, 2010.
[44]
A. Walter, A. Marz, W. Schumacher, P. Rosch, and J. Popp, “Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device,” Lab on a Chip, vol. 11, no. 6, pp. 1013–1021, 2011.
[45]
A. Kyriakides, E. Kastanos, K. Hadjigeorgiou, and C. Pitris, “Classification of Raman spectra using the correlation kernel,” Journal of Raman Spectroscopy, vol. 42, no. 5, pp. 904–909, 2011.
[46]
F. Ehrentreich and L. Summchen, “Spike removal and denoising of Raman spectra by wavelet transform methods,” Analytical Chemistry, vol. 73, no. 17, pp. 4364–4373, 2001.
[47]
T. D？rfer, W. Schumacher, N. Tarcea, M. Schmitt, and J. Popp, “Quantitative mineral analysis using Raman spectroscopy and chemometric techniques,” Journal of Raman Spectroscopy, vol. 41, no. 6, pp. 684–689, 2010.
[48]
I. T. Jollife, Principal Component Analysis, Springer, New York, NY, USA, 2nd edition, 2002.
[49]
I. T. Jolliffe, “A note on the use of principal components in regression,” Journal of the Royal Statistical Society C, vol. 31, no. 3, pp. 300–303, 1982.
[50]
J. R. Ferraro, K. Nakamoto, and C. W. Brown, Introductory Raman Spectroscopy, Elsevier Science, New York, NY, USA, 2nd edition, 2003.
[51]
Y. L. Loethen, D. Zhang, R. N. Favors, S. B. G. Basiaga, and D. Ben-Amotz, “Second-derivative variance minimization method for automated spectral subtraction,” Applied Spectroscopy, vol. 58, no. 3, pp. 272–278, 2004.
[52]
N. Navas, J. Romero-Pastor, E. Manzano, and C. Cardell, “Raman spectroscopic discrimination of pigments and tempera paint model samples by principal component analysis on first-derivative spectra,” Journal of Raman Spectroscopy, vol. 41, no. 11, pp. 1196–1203, 2010.
[53]
F. B. Baker and L. J. Hubert, “Measuring the power of hierarchical cluster analysis,” Journal of the American Statistical Association, vol. 70, no. 349, pp. 31–38, 1975.
[54]
W. J. Krzanowski, Principles of Multivariate Analysis: a User's Perspective, Oxford University Press, New York, NY, USA, 2000.
[55]
M. Barker and W. Rayens, “Partial least squares for discrimination,” Journal of Chemometrics, vol. 17, no. 3, pp. 166–173, 2003.
[56]
J. Weston and Watkins, “Support vector machines for multi-class pattern recognition,” in Proceedings of the Seventh European Symposium on Artificial Neural Networks, Brussels, Belgium, April 1999.
[57]
J. A. Westerhuis, H. C. J. Hoefsloot, S. Smit et al., “Assessment of PLSDA cross validation,” Metabolomics, vol. 4, no. 1, pp. 81–89, 2008.
[58]
V. N. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, NY, USA, 1998.
[59]
B. Sch？lkopf, C. J. C Burges, and A. J. Smola, Eds., Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge, Mass, USA, 1998.
[60]
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines: and other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, UK, 2004.
[61]
M. Escoriza, J. VanBriesen, S. Stewart, J. Maier, and P. Treado, “Raman spectroscopy and chemical imaging for quantification of filtered waterborne bacteria,” Journal of Microbiological Methods, vol. 66, no. 1, pp. 63–72, 2006.
[62]
C. Fan, Z. Hu, L. Riley, G. Purdy, A. Mustapha, and M. Lin, “Detecting food—and waterborne viruses by surface-enhanced raman spectroscopy,” Journal of Food Science, vol. 75, no. 5, pp. M302–M307, 2010.
[63]
X. Wang, Y. Li, H. Wang et al., “Gold nanorod-based localized surface plasmon resonance biosensor for sensitive detection of hepatitis B virus in buffer, blood serum and plasma,” Biosensors and Bioelectronics, vol. 26, no. 2, pp. 404–410, 2010.
[64]
M. Baek, W. Nelson, D. Britt, and J. Sperry, “UV-excited resonance raman spectra of heat denatured lysozyme and staphylococcus epidermidis,” Applied Spectroscopy, vol. 42, no. 7, pp. 1312–1314, 1988.
[65]
A. Grow, L. Wood, J. Claycomb, and P. Thompson, “New biochip technology for label-free detection of pathogens and their toxins,” Journal of Microbiological Methods, vol. 53, no. 2, pp. 221–233, 2003.
[66]
L. Robertson, A. Campbell, and H. Smith, “Survival of Cryptosporidium parvum oocysts under various environmental pressures,” Applied and Environmental Microbiology, vol. 58, no. 11, pp. 3494–3500, 1992.
[67]
M. Deng and D. Cliver, “Cryptosporidium parvum studies with dairy products,” International Journal of Food Microbiology, vol. 46, no. 2, pp. 113–121, 1999.
[68]
M. Lorenzo-Lorenzo, M. Ares-Mazas, I. Villacorta-Martinez de Maturana, and D. Duran-Oreiro, “Effect of ultraviolet disinfection of drinking water on the viability of Cryptosporidium parvum oocysts,” Journal of Parasitology, vol. 79, no. 1, pp. 67–70, 1993.
[69]
M. Belosevic, S. A. Craik, J. L. Stafford, N. F. Neumann, J. Kruithof, and D. W. Smith, “Studies on the resistance/reactivation of Giardia muris cysts and Cryptosporidium parvum oocysts exposed to medium-pressure ultraviolet radiation,” FEMS Microbiology Letters, vol. 204, no. 1, pp. 197–203, 2001.
