Development of Methodology Based on Commercialized SERS-Active

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Anal. Chem. 2008, 80, 2817-2825

Development of Methodology Based on Commercialized SERS-Active Substrates for Rapid Discrimination of Poxviridae Virions Troy A. Alexander*

U.S. Army Research Laboratory Sensors and Electron Devices Directorate, Photonics Integration BranchsRadiometric Sensor Development and Applications Team, AMSRD-ARL-SE-EE, 2800 Powder Mill Road, Adelphi, Maryland 20783

Surface-enhanced Raman spectroscopy (SERS) can be made an attractive approach for identification of Ramanactive compounds and biological materials (i.e., toxins, viruses, or intact bacterial cells/spores) through development of reproducible, spatially uniform SERS-active substrates. Recently, reproducible (from substrate-to-substrate), spatially homogeneous (over large areas) SERSactive substrates have been commercialized and are now available in the marketplace. We have utilized these patterned surfaces to acquire SERS spectral signatures of intact bovine papular stomatitis, pseudocowpox, and Yaba monkey tumor viruses. Salient spectral signature features make it possible to discriminate among these genetically distinct Poxviridae-Chordopoxvirinae virions. In addition, partial least-squares, a multivariate calibration method, has been used to develop personal computerborne algorithms useful for classification of unknown Parapoxvirus (e.g., bovine papular stomatitis virus and pseudocowpox virus) samples based solely on SERS spectral signatures. To our knowledge, this is the first report detailing application of these commercial-off-theshelf (COTS) SERS-active substrates to identification of intact poxviruses. As early as the 18th century, Variola major (e.g., smallpox) was used as a biologically driven armament.1 More recently, an increase in the apparent number of worldwide bioterrorist acts combined with information suggesting that smallpox was weaponized by the former Soviet Union highlights the fact that biological attacks are a plausible if not imminent reality.2,3 In addition to this etiological agent’s relative environmental robustness and thus its utility as a biological threat agent, it has been difficult to develop reliable, sensitive, and rapid techniques for its unambiguous identification for field-deployable applications. Further, clinical presentation of smallpox postexposure poses a challenge in diagnosis since it is indistinguishable from several * Phone: 301-394-2303. Fax: 301-394-0310. E-mail: troy.a.alexander1@ us.army.mil. (1) Fenner, F.; Henderson; D. A.; Arita, I.; Jezek, Z.; Ladnyi, I. D. Smallpox and its Eradication; World Health Organization: Geneva, Switzerland, 1988. (2) Tucker, J. B. Emerging Infect. Dis. 1999, 5 (4), 498-504. (3) Alibek, K.; Handelman, S. Biohazard: The Chilling True Story of the Largest Covert Biological Weapons Program in the WorldsTold from Inside by the Man Who Ran It; Delta, 1999. 10.1021/ac702464w Not subject to U.S. Copyright. Publ. 2008 Am. Chem. Soc.

Published on Web 02/28/2008

other (less important) external exanthems.4,5 For example, most modern-day medical practitioners lack the experience to quickly distinguish smallpox, human monkeypox, and chickenpox. Differential diagnosis of these conditions is especially critical since confirmation of a single case of smallpox represents a national security concern and is evidence of likely clandestine (e.g., bioterrorism) activity.6 Conversely, (a) diagnosis of human monkeypox (monkeypox virus) in disease naı¨ve regions (as in the United States during the summer of 2003) without additional indicators of biowarfare activity or (b) diagnosis of isolated cases of chickenpox (Varicella-Zoster virus) do not necessarily represent a cause for alarm.7,8 Similarly, and of equal importance, veterinarians face challenges in differential diagnosis of foot-andmouth disease (aphthovirus), bovine papular stomatitis (bovine papular stomatitis virus), and pseudocowpox (pseudocowpox virus) in ruminant livestock herds.9 Since foot-and-mouth disease (FMD) has potentially grave clinical as well as economic consequences (e.g., estimated U.S. losses could total more than $1 B/year), methods for rapid discrimination of bovine papular stomatitis virus and pseudocowpox virus from FMD are severely needed. Moreover, the possible use of FMD through agroterrorism underscores the urgency for approaches which rule out clinically similar diseases such as bovine papular stomatitis and pseudocowpox.10 To date, several discrimination methods, based primarily on polymerase chain reaction (PCR), have been developed which focus on identification/discrimination of poxviruses.11-15 These (4) Henderson, D. A.; Inglesby, T. V.; Bartlett, J. G.; Ascher, M. S.; Eitzen, E.; Jahrling, P. B.; Hauer, J.; Layton, M.; McDade, J; Osterholm, M. T.; O’Toole, T.; Parker, G; Perl, T.; Russell, P. K.; Tonat, K. JAMA, J. Am Med. Assoc. 1999, 281 (22), 2127-2137. (5) Breman, J. G.; Henderson, D. A. N. Engl. J. Med. 2002, 346 (17), 13001308. (6) Mercer, A. A., Schmidt, A., Weber, O., Eds. Poxviruses; Birkhauser Verlag: Berlin, Germany, 2007. (7) Bernard, S. M.; Anderson, S. A. Emerging Infect. Dis. 2006, 12 (12), 18271833. (8) Reed, K. D.; Melski, J. W.; Graham, M. B.; Regnery, R. L.; Sotir, M. J.; Wegner, M. V.; Kazmierczak, J. J.; Stratman, E. J.; Li, Y.; Fairley, J. A.; Swain, G. R.; Olson, V. A.; Sargent, E. K.; Kehl, S. C.; Frace, M. A.; Kline, R.; Foldy, S. L.; Davis, J. P.; Damon, I. K. N. Engl. J. Med. 2004, 350 (4), 342-350. (9) Musser, J. M. B. J. Am. Vet. Med. Assoc. 2004, 224 (8), 1261-1268. (10) Monke, J. Agroterrorism: Threats and Preparedness, CRS Report for Congress/Order Code RL32521, 2004. CRS Web site. (11) Ibrahim, M. S.; Kulesh, D. A.; Saleh, S. S.; Damon, I. K.; Esposito, J. J.; Schmaljohn, A. L.; Jahrling, P. B. J. Clin. Microbiol. 2003, 41 (8), 38353839.

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methods have limited utility in field-deployed scenarios since they are reagent-intensive, relatively slow (hours), and require significant sample preparation and highly trained personnel for efficient operation. Many of these technical hurdles can be addressed through development of viral discrimination approaches based on the surface-enhanced Raman spectroscopy (SERS) phenomenon.16 Availability of spatially uniform, high-reproducibility SERS-active substrates through commercial sources makes development of rapid, sensitive, and specific poxvirus discrimination approaches now possible.17,18 Additionally, these approaches are expected to be easily deployed for in-the-field operation, require little to no sample pretreatment, and can be managed by operators with minimal technical training. Raman spectroscopy, the photophysical foundation of SERS, has been an invaluable tool in the study of various chemical and biochemical systems and has become widely accepted as a routine analytical characterization methodology.16 Interest in this technique stems from (1) its narrow spectral band structure (e.g., high analyte discrimination power), (2) its lack of interference from water (i.e., amenability to aqueous media), and (3) its ease of initiation with most available laser wavelengths (i.e., agility). However, for most analytes Raman signals are considerably weak thus requiring high (hundreds of milliwatts to watts) laser powers and relatively lengthy acquisition times to generate good signalto-noise (S/N) spectra. Through exploitation of Raman signal amplification techniques such as SERS, high-quality spectra may be acquired in shortened acquisition times with more vibrational information content.16,19,20 A plausible explanation for a significant portion of the signal amplification achieved through SERS can be attributed to an increase in the electromagnetic field strength encountered by analytes adjacent to the metal surface.16,20 Briefly, this intensified electromagnetic field is generated when noble metal surfaces (typically, Au, Ag, Pt, Pd, or Cu) are irradiated with the requisite wavelength of light and metal conduction band electrons are excited to collective oscillation through a localized surface plasmon resonance (LSPR). Most significantly, with certain metal-analyte systems enhancements between 4 and 14 orders magnitude above unenhanced Raman spectra are readily achieved.16,19,20 Moreover, several published reports indicate that in certain situations this method can be made sufficiently sensitive to facilitate detection of single molecules.21-23 Most recently, several groups have (12) Olson, V. A.; Laue, T.; Laker, M. T.; Babkin, I. V.; Drosten, C.; Shchelkunov, S. N.; Niedrig, M.; Damon, I. K.; Meyer, H. J. Clin. Microbiol. 2004, 42 (5), 1940-1946. (13) Niedrig, M.; Meyer, H.; Panning, M.; Drosten, C. J. Clin. Microbiol. 2006, 44 (4), 1283-1287. (14) Kulesh, D. A.; Loveless, B. M.; Norwood, D.; Garrison, J.; Whitehouse, C. A.; Hartmann, C.; Mucker, E.; Miller, D.; Wasieloski, L. P.; Huggins, J.; Huhn, G.; Miser, L. L.; Imig, C.; Martinez, M.; Larsen, T.; Rossi, C. A.; Ludwig, G. V. Lab. Invest. 2004, 84 (9), 1200-1208. (15) Fenner, F.; Henderson; D. A.; Arita, I.; Jezek, Z.; Ladnyi, I. D. USAMRIID’s Medical Management of Biological Casualties Handbook; U.S. Army Medical Research Institute of Infectious Diseases: Fort Detrick, MD, 2005. (16) Ferraro J. R.; Nakamoto, K. Introductory Raman Spectroscopy; Academic Press: San Diego, CA, 1994. (17) Perney, N. M. B.; Baumberg, J. J.; Zoorob, M. E.; Charlton, M. D. B.; Mahnkopf, S.; Netti, C. M. Opt. Express 2006, 14 (2), 847-857. (18) Alexander, T. A.; Le, D. M. Appl. Opt. 2007, 46 (18), 3878-3890. (19) Campion, A.; Kambhampati, P. Chem. Soc. Rev. 1998, 27 (4), 241-250. (20) Moskovits, M. Rev. Mod. Phys. 1985, 57 (3), 783-826. (21) Kneipp, K.; Kneipp, H.; Deinum, G.; Itzkan, I.; Dasari, R. R.; Feld, M. S. Appl. Spectrosc. 1998, 52 (2), 175-178.

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conducted studies devoted to unraveling fundamental plasmonic behavior and its influence on the SERS process.24-27 This new understanding of the photophysics of SERS makes it possible to develop methodologies, based on the SERS phenomenon, toward challenging applications such as biosensing. We report here preliminary results related to a novel methodology, based on the SERS phenomenon combined with chemometric pattern recognition, for rapid (minutes) discrimination of Poxviridae virions. Specifically, we have studied the utility of this approach to distinguish bovine papular stomatitis virus (ATCC VR-801), pseudocowpox virus (ATCC VR-634), and Yaba monkey tumor virus (ATCC VR-587) as well as nonbiological, Raman-active particulates. To our knowledge, this is the first reported study devoted to development of a rapid, potentially field-deployable, and sensitive methodology for the discrimination of intact poxviruses. EXPERIMENTAL SECTION Instrumentation. Raman Microscope. All SERS measurements were acquired using a Renishaw inVia Reflex Raman microscope (Hoffman Estates, IL) equipped with a near-infrared (NIR) diode laser excitation source. Light from the high-power (300 mW) diode (λ ) 785 nm) was focused onto the sample at the microscope stage through a 20× (N.A. 0.40) objective. Prior to coupling into the microscope the diode laser beam was circularized, by inserting a pinhole into the optical beam path with concomitant reduction of the maximum available laser power to ∼20 mW. Samples at the microscope stage were positioned remotely from a joystick by an encoded, motorized XYZ translation stage (0.3 µm repeatability, 0.1 µm step size) managed by a Prior Scientific (Rockland, MA) ProScan II controller. The instrument was controlled and data were collected by WiRE 2.0 software operating on a personal computer. Before all measurements, the instrument was wavelength calibrated using an internal Si standard. Each SERS spectrum was collected from 200 to 1600 cm-1 in the Extended SynchroScan mode (10.00 s exposure time) with a spectral resolution better than 2 cm-1 and is the result of a single accumulation unless otherwise stated. All spectra were saved in ASCII format for subsequent analysis using other software packages. Materials. SERS-Active Substrates. Gold-coated Klarite SERSactive substrates were purchased from D3 Technologies Ltd. (Southampton, Hampshire, United Kingdom). To facilitate easy handling/positioning, the 6 mm × 10 mm chip (consisting of a 4 mm × 4 mm patterned region and an unpatterned Au reference area) is adhered to a standard microscope slide (25 mm × 75 mm) at the foundry. As a safeguard against environmental and shipping hazards, each substrate is placed in a microscope slide holder (one substrate/slide holder) enclosed within an opaque, vacuum-sealed pouch before shipping. Just prior to use, each (22) Doering, W. E.; Nie, S. J. Phys. Chem. B 2002, 106 (2), 311-317. (23) Mandal, M.; Kundu, S.; Ghosh, S. K.; Jana, N. R.; Panigrahi, M.; Pal, T. Curr. Sci. 2004, 86 (4), 556-559. (24) Grand, J.; Lamy de la Chapelle, M.; Bijeon, J.-L.; Adam, P.-M.; Vial, A.; Royer, P. Phys. Rev. B 2005, 72 (3), 033407-1-033407-4. (25) Felidj, N.; Aubard, J.; Levi, G.; Krenn, J. R.; Schider, G.; Leitner, A.; Aussenegg, F. R. Phys. Rev. B 2002, 66 (24), 245407-1-245407-7. (26) Kalkbrenner, T.; Hakanson, U.; Sandoghdar, V. Nano Lett. 2004, 4 (12), 2309-2314. (27) Reilly, T. H.; Chang, S. H.; Corbman, J. D.; Schatz, G. C.; Rowlen, K. L. J. Phys. Chem. C 2007, 111 (4), 1689-1694.

substrate was removed from the vacuum-sealed shipping container and slide holder. The substrates were used as received without further modification. Poxviridae Virions. Bovine papular stomatitis virus (ATCC VR801), pseudocowpox virus (ATCC VR-634), and Yaba monkey tumor virus (ATCC VR-587) biological safety level 2 (BSL-2), intracellular mature virions (IMV) were purchased from American Type Culture Collection (Manassass, VA). According to the manufacturer, bovine papular stomatitis virions were prepared by propagation in bovine embryonic kidney cells for 4 days, pseudocowpox virions were prepared by propagation in bovine embryonic kidney cells for 2-3 days, and Yaba monkey tumor virions were prepared by propagation in Cercopithecus monkey kidney cells for 18 days. Following growth, mature virions of each species were purified by the CsCl gradient centrifugation method.28 Viral titers of 1.0 × 102, 1.0 × 103, 1.0 × 104, 1.0 × 105, and 1.0 × 106 virions/ mL for each virus were prepared by serial dilution of each viral stock suspension in deionized water. Prior to SERS spectra acquisition, 1.00 µL of each viral suspension was deposited onto the substrate and the sample was dried at room temperature leaving intact viral particles on the substrate surface with estimated mean densities of 0.1, 0.9, 9.0, 94, and 943 particles/mm2. Polystyrene Microspheres. Uniform, 0.304 µm diameter (2.2% C.V.) polystyrene microspheres (cat. no. 5030A) were purchased from Duke Scientific Corporation (Palo Alto, CA). Dilute suspensions of the microspheres were prepared by diluting the received stock suspension in ethanol. To form a uniform layer of microspheres on the substrate surface, prior to SERS spectra acquisition, 8.50 µL of each microsphere suspension was spin-coated onto the substrate (at ca. 1000 rpm) using a home-built spin-coater leaving dried polystyrene microspheres on the substrate surface with mean densities of 29.8, 44.7, 59.6, 74.5, and 89.5 particles/mm2. Software. Chemometric Software. The Unscrambler (version 7.6) chemometric development environment was purchased from Applied Chemometrics (Sharon, MA) and used to develop partial least-squares (PLS2) regression algorithms for classification of unknown samples. Training spectra and data for classification were imported into this software package as ASCII format. RESULTS AND DISCUSSION Through maturation of micromachining techniques and highresolution analysis tools, it is currently possible to routinely fabricate devices with precisely placed building blocks or components.29-31 These efficient fabrication techniques and analysis tools have been leveraged to develop SERS-active substrates which are both highly sensitive and reproducibly manufacturable.18 Relatively recently, photon-active surfaces manufactured using these techniques and tools have been introduced into the marketplace and are now readily available to technologists in a range of diverse fields (e.g., medicine, agriculture, biology) outside of the core SERS community. Previously, we have characterized (28) Neurath, A. R.; Wiktor, T. J.; Koprowski, H. J. Bacteriol. 1966, 92 (1), 102106. (29) Whang, D.; Jin, S.; Wu, Y.; Lieber, C. M. Nano Lett. 2003, 3 (9), 12551259. (30) Vieu, C.; Carcenac, F.; Pepin, A.; Chen, Y.; Mejias, M.; Lebib, A.; ManinFerlazzo, L.; Couraud, L.; Launois, H. Appl. Surf. Sci. 2000, 164 (1-4), 111-117. (31) Kahl, M.; Voges, E.; Kostrewa, S.; Viets, C.; Hill, W. Sens. Actuators, B 1998, 51 (1-3), 285-291.

Figure 1. SERS spectral signature of 304 nm diameter polystyrene (PS) microspheres, Yaba monkey tumor virus (YMTV), pseudocowpox virus (PCPV), and bovine papular stomatitis virus (BPSV). The surface density of the PS microspheres is 89.5 particles/mm2, and the surface density of all virions is 94 virions/mm2. All spectra were acquired using COTS SERS-active substrates and are the result of 1 accumulation (10.00 s integration). Spectra are offset for clarity.

these substrates by atomic force microscopy (AFM), scanning electron microscopy (SEM), and microreflectance spectrometry. Results of these analyses reveal that these surfaces are composed of C4v symmetric depressions positioned at a periodicity of ∼2 µm with reflectance coefficient of variance (CV) better than 6% in the NIR region. More, the utility of these surfaces for SERS applications has been assessed (data unpublished) using trans1,2-bis(4-pyridyl) ethylene (BPE) as the target molecule. Analyses based on the BPE 1200 cm-1 band illustrate that the run-to-run and sample-to-sample CV are 18.4% and 17.3%, respectively. Therefore, we have selected these commercial-off-the-shelf (COTS) surfaces as the foundation to develop a novel methodology for rapid detection and discrimination of intact Poxviridae viruses. This viral discrimination approach is based on the intrinsic surface-enhanced Raman spectra acquired from whole poxviruses deposited on these surfaces and irradiated with NIR (λ ) 785 nm) light. Further, this method does not rely on either molecular recognition elements or receptor molecules to achieve selectivity. Significantly, high antigenic cross-reactivity within most Poxviridae genera limits the utility of antibodies in applications which require species-level specificity.4,6,15 Accordingly, immunoglobulins are of little benefit to aid in analyte enrichment from complex viral samples. Plotted in Figure 1 are intensity-normalized spectra of submicrometer-sized particulates (both biological and synthetic) investigated during this study. Specifically, shown in the figure are the SERS spectra of 89.5 particles/mm2 polystyrene (PS) nanospheres, 94 virions/mm2 Yaba monkey tumor virus (YMTV), 94 virions/ mm2 pseudocowpox virus (PCPV), and 94 virions/mm2 bovine papular stomatitis virus (BPSV) over the Raman shift range from 200 to 1600 cm-1. As illustrated, this spectral region is interesting since it contains features representative of Raman-active compounds present in these particles. In general the viral spectra (e.g., YMTV, PCPV, and BPSV) displayed are dominated by features centered near 276, 455, 853, 1003, 1168, and 1448 cm-1. These Analytical Chemistry, Vol. 80, No. 8, April 15, 2008

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bands are most likely due to aliphatic chain C-C bending [δ(CC)], protein S-S stretching [v(SS)], protein C-C-H bending [δ(CCH)], protein phenyl ring breathing [v(CC)], protein C-N stretching [v(CN)], and protein/lipid CH2 bending [δ(CH2)] modes, respectively. Although it is difficult to unambiguously identify biomarker molecules responsible for these shifts, they are most likely attributable to species-specific compounds (i.e., epitopes) present on the virion surface. This can be understood in light that SERS is primarily an interfacial analysis technique and can be utilized to investigate compounds held on the virus surface. Comparison of BPSV and PCPV (the two Parapoxviruses studied) shows strong similarity between their spectral signatures. However, the BPSV spectrum contains spectral features at 539, 853, 1168, and 1515 cm-1 which are absent in the PCPV spectrum. These spectral differences are especially interesting since recent phylogenetic studies, based on structural and core peptides, have established that these virions are in fact genetically distinct and that their molecular compositions are insensitive to animal host or geographical region of origin.32,33 In addition, empirical data suggests that the host ranges (e.g., susceptible vertebrate species) of BPSV and PCPV are different. Specifically, BPSV infects cattle, sheep, goats, and humans, whereas PCPV more selectively infects cattle and humans.34 Further, these data imply that it is possible to distinguish BPSV and PCPV based on their molecular composition and hence SERS spectral signatures. Although several SERS-based methods have previously been developed for detection of intact viruses, most of these methodologies are founded on exploitation of monoclonal antibodies or difficult-to-manufacture SERS-active substrate architectures.35-37 By contrast, the current methodology is based solely on the intrinsic virion spectral signatures acquired using COTS SERSactive surfaces. In addition to BPSV and PCPV, we have used these SERSactive surfaces to study YMTV, a Yatapoxvirus. As plotted in the figure, the YMTV spectral signature is similar in shape to BPSV and PCPV. This similarity is expected since most genera (e.g., Parapoxvirus, Yatapoxvirus) within the Poxviridae-Chordopoxvirinae subfamily (with exception of the Orthopoxvirus genus) are morphologically identical by high-specificity analysis techniques such as electron microscopy.6 Comparative analyses of these spectra (BPSV and PCPV with YMTV) show a relative intensity decrease in the 1003 cm-1 band with the simultaneous growth of a band at 1518 cm-1 in the YMTV spectrum. Additionally, the shape of the 1168 cm-1 band in the YMTV spectrum is different than bands located at the same Raman shift frequency in the PCPV and BPSV spectra. (32) Gubser, C.; Hue, S.; Kellam, P.; Smith, G. L. J. Gen. Virol. 2004, 85 (1), 105-117. (33) Tikkanen, M. K.; McInnes, C. J.; Mercer, A. A.; Buttner, M.; Tuimala, J.; Hirvela-Koski, V.; Neuvonen, E.; Huovilainen, A. J. Gen. Virol. 2004, 85 (6), 1413-1418. (34) Carter, G. R.; Wise, D. J. Poxviridae. In A Concise Review of Veterinary Virology; Carter, G. R., Wise, D. J., Flores, E. F., Eds.; International Veterinary Information Service (www.ivis.org): Ithaca, New York, 2005; Document No. A3410.1005. (35) Driskell, J. D.; Kwarta, K. M.; Lipert, R. J.; Porter, M. D.; Neill, J. D.; Ridpath, J. F. Anal. Chem. 2005, 77 (19), 6147-6154. (36) Shanmukh, S.; Jones, L.; Driskell, J.; Zhao, Y.; Dluhy, R.; Tripp, R. A. Nano Lett. 2006, 6 (11), 2630-2636. (37) Bao, P.-D.; Huang, T.-Q.; Liu, X.-M.; Wu, T.-Q. J. Raman Spectrosc. 2001, 32 (4), 227-230.

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Also plotted in Figure 1 is the SERS spectral signature of PS nanospheres acquired using substrates identical to those used to acquire the viral signatures. It is instructive to compare the selected Poxviridae viral particles to PS nanospheres since the PS spheres employed during this study are comparable in size (∼300 nm) to many Poxviridae viruses, have been well-characterized by Raman spectroscopy, and have a large Raman cross section (e.g., large S/N).38 As illustrated, the PS spectrum contains bands (located at slightly different Raman shift frequencies) within the same spectral window as the Poxviridae virions studied and is in good agreement with previously published data. In principle, differences in band shape and spectral band frequency position apparent among the BPSV, PCPV, YMTV, and PS SERS spectral signatures make it possible to distinguish these analytes by univariate (i.e., dependent on a single Raman shift frequency) calibration approaches. However, using most univariate calibration approaches, it is not possible to simultaneously identify multiple analytes (i.e., viruses). An alternative approach to identification is to leverage the strengths of multivariate (i.e., full spectrum) calibration, namely, (1) ease of simultaneous identification of multiple analytes, (2) highly correlated adjacent Raman shift frequencies help to improve classification precision as well as fault (i.e., unknown interferences) detection, and (3) developed classification algorithms can be automated to aid in identification of unknown viral samples by nonspectroscopists (e.g., first responders, military personnel, veterinarians, physicians).39-41 We have utilized the partial least-squares 2 (PLS2) method to develop a personal computer addressable model useful for simultaneous speciation of intact Parapoxviruses (e.g., BPSV and PCPV) based on their intrinsic SERS spectral signatures (from 200 to 1600 cm-1) acquired with these substrates. We have used BPSV and PCPV training samples over a broad viral titer range (e.g., logarithm of viral titer from 2 to 6) as the basis to develop this model. In concept, this model simultaneously utilizes two enclave algorithms which are devoted to identification of either BPSV or PCPV. Tandem application of these statistical algorithms facilitates the rapid classification (i.e., identification) of unknown SERS spectral signatures. Model diagnostic analyses show that the classification model performs optimally with 10 principal components (PCs). Additionally, these analyses show root-meansquare error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) for BPSV and PCPV of 0.3 and 1.8, and 0.4 and 1.5, respectively. Equivalently, the RMSEC and RMSEP based on viral titer for BPSV and PCPV are estimated to be 1.9 and 56.8, and 2.3 and 29.5 virions/mL, respectively. Plotted in Figure 2A are scores (e.g., sum of residuals in the selected spectral window) of the training spectral signatures projected onto the first three PCs for this model. As illustrated in the plot, BPSV and PCPV training spectral signatures are delineated into two distinct groups. Closer inspection of this plot reveals separation of the BPSV signatures from PCPV signatures using PC1, PC2, and PC3. These data affirm that the BPSV and PCPV spectral signatures and hence their virion molecular (38) Drumm, C. A.; Morris, M. D. Appl. Spectrosc. 1995, 49 (9), 1331-1337. (39) Martens, H.; Naes, T. Multivariate Calibration; John Wiley and Sons: New York, 1989. (40) Kramer, R. Chemometric Techniques for Quantitative Analysis; Marcel Dekker, Inc.: New York, 1998. (41) Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide; John Wiley and Sons: New York, 1998.

Figure 2. (A) Scores plot of the PLS2 model, using the first three calculated PCs. BPSV and PCPV data points are encircled as a guide to the eye. (B) Classification of training samples, using the BPSV-specific algorithm based on 10 PCs. Linear fit of data: slope ) 0.9873 and R2 ) 0.9823. (C) Classification of training samples, using the PCPV-specific algorithm based on 10 PCs. Linear fit of data: slope ) 0.9889 and R2 ) 0.9750.

compositions are different. Moreover, segregation of the training spectra into two lobes implies that this model can be used to

distinguish BPSV from PCPV viral samples. As a preliminary validation, we have used this model to predict (e.g., classify and Analytical Chemistry, Vol. 80, No. 8, April 15, 2008

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quantify) the training spectral signatures used to develop the model. Results of these predictions are shown in Figure 2, parts B and C. As depicted in Figure 2B, the training samples are, expectedly, partitioned into two groups, namely, BPSV and PCPV. Samples which contain BPSV are properly identified, based on the predicted log(BPSV viral titer), by the BPSV-specific algorithm, whereas low (e.g., log(BPSV viral titer) < 2) values are predicted for samples devoid of BPSV. Specifically, BPSV-containing training samples with log(viral titer) ranging from 2 to 6 are predicted to have viral titers approximately equivalent to the training sample actual viral titer. Applying the PCPV-specific algorithm to an identical training sample set produces a similar calibration plot (Figure 2C) in which PCPV samples are accurately quantified as containing PCPV, whereas training samples containing BPSV are predicted to have low PCPV viral titers. The results shown in Figure 2, parts B and C, illustrate that it is possible, using this model, to (1) classify viral samples (i.e., SERS spectra) as either BPSV or PCPV and (2) accurately quantify the sample viral titer. These results are especially interesting since they demonstrate that this model can be employed to rapidly identify as well as quantify Parapoxviridae samples. To better assess the performance of this model for rapid discrimination of Poxviridae virions, we have used it to predict a set of unknown SERS viral spectra acquired using COTS SERSactive substrates. Specifically, this validation set was composed of both Parapoxviridae viruses (e.g., BCPV and PCPV) as well as a Yatapoxviridae virus (e.g., YMTV). In contrast to the training spectra predicted in Figure 2, these spectra were not used to develop the classification algorithm and hence can be used to challenge the model in a manner similar to real-world viral samples. Results related to these predictions are plotted in Figure 3. As shown in Figure 3A, the BPSV-specific algorithm applied to this sample set predicts log(BPSV viral titer) > 2 in all cases for the unknown BPSV samples. Integral to classification, we establish a threshold at the predicted log(BPSV viral titer) ) 2 level. This demarcation represents the minimum viral titer used for calibration and thus the lower boundary of the modeled range. In principle, values above this threshold are indicative of viral species identification, whereas values below this threshold signify that the sample (i.e., SERS signature) is not characteristic of the presumed virion. Applying this logic filter, the model correctly classifies all unknown BPSV samples as containing BPSV virions, whereas the PCPV and YMTV (excluding actual log(viral titer) ) 5 and 6) validation samples are inconsistent with BPSV. Additionally, these results illustrate that viral titers predicted by the model are in good agreement with actual BPSV viral titers. Plotted in Figure 3B are results for predictions made using the PCPV-specific algorithm applied to the same validation sample set. Applying a filter equivalent to that used in Figure 3A, these results demonstrate that this algorithm correctly identifies the PCPV validation samples as containing PCPV virions, whereas the BPSV and YMTV (excluding actual log(viral titer) ) 2 and 3) validation samples do not contain PCPV. Data presented in Figure 3, parts A and B, illustrate that this model can be used to accurately discriminate Poxviridae virions. The BPSV-specific and PCPV-specific algorithms can be utilized to identify viral samples which contain either of these Parapoxviruses. Concomitantly, results generated from these algorithms 2822 Analytical Chemistry, Vol. 80, No. 8, April 15, 2008

Figure 3. (A) Classification of unknown Poxviridae viral samples, using the BPSV-specific algorithm based on 10 PCs. (B) Classification of unknown Poxviridae viral samples, using the PCPV-specific algorithm based on 10 PCs. Error bars represent the 75% confidence interval.

examined with the logic filter (applied at the lower limit of the calibration range) can be used to indicate viral samples which are deficient of the presumed virion of interest. To further estimate the performance of this approach, we have used this model to classify submicrometer-sized PS particles. As illustrated in Figure 1, these synthetic (i.e., nonbiological) nanospheres have analytespecific spectral features within the same window used to study the Poxviridae virions. Additionally, these particles can be used as a negative control to better understand the classification model’s behavior considering unknown, nonbiological samples. Specifically, we have used the model to classify SERS spectra of 0.304 µm diameter PS sphere deposited on the substrate surface at mean densities of 29.8, 44.7, 59.6, 74.5, and 89.5 particles/mm2. It should be noted that the latter of these densities is comparable to the density of virions deposited on the substrate surface at viral titer ) 1.0 × 105 virions/mL. Results of predictions made using the classification model and considering PS nanospheres are depicted in Figure 4. As shown, at all PS densities both the BPSV-specific and the PCPV-specific algorithms predict values below the model’s

Figure 4. Classification of unknown PS samples, using the BPSVspecific and PCPV-specific algorithms based on 10 PCs. Error bars represent the 75% confidence interval.

lower limit (i.e., predicted log(viral titer) ) 2). Consequently, these samples are classified as containing neither BPSV nor PCPV. Consolidated in Table 1 are results from Figures 3 and 4. Specifically, shown are predictions made by the BPSV-specific and PCPV-specific algorithms for unknown samples containing BPSV, PCPV, YMTV, or PS. For each unknown sample, tabulated are the actual concentration, predicted BPSV concentrations and predicted PCPV concentrations. We have applied the logic filter described above to the values predicted by the model to categorize the predicted results as either positive (+), negative (-), false positive ([+]), or false negative ([-]) identifications. On the basis of values predicted by the species-specific algorithms and applying the log(titer) g 2 logic filter, the BPSV-specific algorithm positively identifies the BPSV test samples, whereas all other test samples are estimated not to contain BPSV. Similarly, the PCPV-specific algorithm positively identifies the PCPV test samples, whereas all other samples are estimated not to contain PCPV. It should be noted that these results show no false negative identifications and a small (∼10%) false positive rate. The tabulated results are especially encouraging since they indicate that easily applicable algorithms may be developed which facilitate the rapid (minutes) discrimination of Poxviridae-containing samples. During the course of this work we have focused on development of a PLS2 model which can be used to quickly and accurately classify CsCl-purified poxvirus samples. However, viral samples encountered outside of the laboratory are likely to contain the bioanalyte (i.e., virion) of interest contaminated with other Ramanactive material (i.e., one or several spectral interferences). Since it is impossible to fully model, a priori, all possible spectral interferences which may appear in real-world samples, it is imperative to develop approaches which accommodate unpurified samples while maintaining or surpassing the classification performance achieved in the present work. Additionally, forthright analysis of contaminated samples (without purification) in the field would greatly increase the utility of this method for many applications. It is noteworthy that the work presented in this report is a requisite first step in realization of such computer-driven,

chemometric classification algorithms for rapid identification of contaminated viral samples. In addition to the notable strengths of the first-order PLS2 method discussed above, multivariate calibration methods have been developed which can be extended to operate on secondorder data sets. These second-order calibration approaches are critical in realization of field-deployable biosensors since they exploit the so-called second-order advantage and hence can be used to identify modeled (pure) analytes in the presence of unmodeled contaminants.42 In contrast to the first-order PLS2 method which relies on a data vector (i.e., digitized intensities measured at a number of Raman shift frequencies) per training sample, secondorder calibration methods rely on a data matrix per training sample. In general, two-dimensional data matrices are generated from hyphenated (e.g., GC/MS, 2D-NMR) or simultaneous scan instruments such as excitation/emission (EEM) fluorometers. However, most available Raman spectrometers are not inherently second-order instruments and thus produce first-order data representations. Recently, Alm et al. have proposed a method which can be used to transform first-order data into a format suitable for second-order modeling and analysis.43 Their work in principle illustrates that a spectrum comprised of a fundamental infrared absorption and its subsequent first and second overtone transitions can be rearranged into a second-order tensor matrix. The resulting m × k matrix can be visualized to contain m wavelength columns and k rows. In this representation, successive rows in the matrix contain the fundamental transition and each of its consecutive overtone transitions. Models based on several different second-order multivariate calibration methods demonstrate that this data transformation procedure benefits from the second-order advantage and can be used to develop functional algorithms which yield accurate analyte predictions even in the presence of unmodeled interferences. The foundation of this procedure stems from the fact that fundamental, first overtone, and second overtone transitions represent identical chemical information. This data transformation procedure is, in general, applicable to any first-order data representation (i.e., spectrum) composed of contiguous regions which contain redundant chemical information. In general, Raman and SERS spectra fulfill this caveat since the stretching and bending vibrational bands for a given functionality are spectrally resolved and can be realigned using this method. Of particular interest, molecular vibration bands relevant to the analysis of many biological samples are present in both the fingerprint region (below 2000 cm-1) as well as the highwavenumber Raman shift spectral region (above 2000 cm-1). As, for example, C-H, O-H, and N-H vibrations appear at ∼1450, ∼1300, and ∼1620 cm-1 and ∼2960, ∼3630, and ∼3330 cm-1 in the fingerprint (bending) and high-wavenumber (stretching) regions, respectively. More, Koljenovic et al.44 and Santos et al.45 have used comparative Raman microspectroscopic mapping of biological samples to show that highly correlated spectral features (42) Booksh, K. S.; Kowalski, B. R. Anal. Chem. 1994, 66 (15), 782A-791A. (43) Alm, E.; Bro, R.; Engelsen, S. B.; Karlberg, B.; Torgrip, R. J. O. Anal. Bioanal. Chem. 2007, 388 (1), 179-188. (44) Koljenovic, S.; Bakker Schut, T. C.; Wolthuis, R.; de Jong, B.; Santos, L.; Caspers, P. J.; Kros, J. M.; Puppels, G. J. J. Biomed. Opt. 2005, 10 (3), 031116-1-031116-11. (45) Santos, L. F.; Wolthuis, R.; Koljenovic, S.; Alemeida, R. M.; Puppels, G. J. Anal. Chem. 2005, 77 (20), 6747-6752.

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Table 1. Predictions of BPSV, PCPV, YMTV, and PS Validation Samples Using the PLS2 Model Based on 10 PCs BPSV algorithm prediction

PCPV algorithm prediction

actual concn log(titer)a

concn log(titer)

BPSV (5 replicates)

2.0 3.0 4.0 5.0 6.0

2.1 4.1 3.2 4.0 5.6

+ + + + +

0.5 0.0 0.3 0.6 0.2

-

PCPV (5 replicates)

2.0 3.0 4.0 5.0 6.0

0.0 0.6 0.0 1.5 1.5

-

2.5 2.0 3.0 3.1 4.0

+ + + + +

YMTV (10 replicates)

2.0 3.0 4.0 5.0 6.0

0.9 1.7 1.5 2.5 2.3

[+] [+]

2.1 2.4 1.1 1.6 1.7

[+] [+] -

PS (45 replicates)

29.8 44.7 59.6 74.5 89.5

0.0 0.8 0.0 0.0 0.4

-

1.6 1.1 1.1 1.0 0.7

-

sample

identificationb

concn log(titer)

identificationb

a Units for polystyrene (PS) samples are particles/mm2. b + positive identification by model; - negative identification by model; [+] false positive identification by model; [-] false negative identification by model.

present in the fingerprint and high-wavenumber regions represent essentially the same chemical information. Additionally, our previous characterization of the substrates used in the present study (by microreflectance spectrometry) illustrates that these surfaces support LSPRs up to Raman shift frequencies of ∼3800 cm-1 (λEX ) 785 nm).18 Significantly, this implies that these surfaces can be used to conduct spectral analyses spanning both the fingerprint and high-wavenumber regions without modification. Accordingly, extending our SERS spectral analyses to also encompass the high-wavenumber shift region would facilitate straightforward transformation of first-order spectra into secondorder data matrices. Since the second-order advantage is linked to second-order data representations, functional second-order multivariate calibrations may be developed which can be used for the identification of viral analytes in the presence of Ramanactive contaminants (e.g., real-world samples). A variant of PLS which is designed to operate on second-order data, unfolded-partial least-squares (U-PLS), has been developed. Relatively recently, U-PLS has been coupled with the residual bilinearization (RBL) technique to formulate a multivariate calibration method which employs second-order data inputs and generates algorithms insensitive to the presence of unmodeled sample components.46 U-PLS/RBL has been studied both theoretically as well as experimentally and has been shown to produce relative errors of prediction (REP) less than 6% for contaminated samples. These studies indicate that robust calibrations can be developed using the U-PLS/RBL method which can be employed for quantitative analyte identification in the presence of unmodeled spectral interferences. In essence, the methodology reported here can be made sufficiently robust to facilitate identification of Poxviridae virions (46) Bohoyo Gil, D.; Munoz de la Pena, A.; Arancibia, J. A.; Escandar, G. M.; Olivieri, A. C. Anal. Chem. 2006, 78 (23), 8051-8058.

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in the presence of Raman-active contaminants by (1) extending the spectral tuning range to include both the fingerprint and highwavenumber Raman shift ranges, (2) transformation of this firstorder SERS spectral signature into a second-order representation of the viral data, and (3) classification of the second-order data representation with previously trained U-PLS/RBL algorithms. The chemometric second-order advantage or “mathematical chromatography” is expected to make it possible to rapidly discriminate Poxviridae analytes contaminated by Raman-active spectral interferences with little to no sample pretreatment. Experiments to explore this analysis strategy are in progress in our laboratory. CONCLUSIONS In summary, we have developed a novel methodology for rapid discrimination of Poxviridae virions based on the SERS phenomenon utilizing COTS substrates combined with a PLS2 chemometrics identification model. Unlike work published previously, the current approach does not require reagents, labels, or difficultto-manufacture SERS-active substrates.35-37 Our results illustrate that this method can be used to distinguish among Poxviridae species such as BPSV, PCPV, and YMTV, based on the intrinsic SERS spectra of intact virions acquired using COTS SERS-active substrates. We have validated the developed identification algorithm using unknown poxvirus samples as well as synthetic Raman-active particulates. These validations show that this approach can be made to have high specificity for selected poxviruses over even taxonomically similar poxvirus samples or nonbiological (i.e., synthetic, highly Raman-active) particulates. Moreover, our results illustrate that this methodology predicts no false negative identifications and only a small (∼10%) false positive rate. To our knowledge, this is the first reported study focused on development of a rapid (minutes) and sensitive approach which can be used to distinguish potentially highconsequence, enveloped virions such as Variola major or mon-

keypox virus. Further, the availability of high-sensitivity COTS substrates makes this an attractive approach for a diverse range of fields including medicine, agriculture, military operations, or homeland security. In addition, we have proposed an approach which can be leveraged to develop field-deployable biosensors based on the work presented here. The foundation of this rationale is to harness the power of the second-order advantage through development of U-PLS/RBL algorithms which have been shown to be useful

for analyte quantitative identification in the presence of unmodeled spectral interferences. Further, we have discussed a novel spectral rearrangement procedure which facilitates access to the secondorder advantage using first-order data.

Received for review December 3, 2007. Accepted January 26, 2008. AC702464W

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