Journal ofMaterials Chemistry B

PAPER

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article OnlineView Journal

aAmrita Centre for Nanosciences & Molecula

University, Cochin, 682041, India. E-mail: m

2802030; Tel: +91 484 4008750bDepartment of Head and Neck Surgery, Am

682041, India

† Electronic supplementary informatiocomparison, EDAX spectra and deta10.1039/c3tb21398f

Cite this: DOI: 10.1039/c3tb21398f

Received 8th October 2013Accepted 20th November 2013

DOI: 10.1039/c3tb21398f

www.rsc.org/MaterialsB

This journal is © The Royal Society of

Rapid detection of oral cancer using Ag–TiO2

nanostructured surface-enhanced Ramanspectroscopic substrates†

Chundayil Madathil Girish,a Subramania Iyer,b Krishnakumar Thankappan,b

V. V. Divya Rani,a G. Siddaramana Gowd,a Deepthy Menon,a Shantikumar Naira

and Manzoor Koyakutty*a

The unique vibrational signatures of the biochemical changes in tissue samples may enable the Raman

spectroscopic detection of diseases, like cancer. However, the Raman scattering cross-section of tissues

is relatively low and hence the clinical translation of such methods faces serious challenges. In this study,

we report a simple and efficient surface-enhanced Raman scattering (SERS) substrate, for the rapid and

label-free detection of oral cancer. Raman active silver (Ag) surfaces were created on three distinct

titania (TiO2) hierarchical nanostructures (needular, bipyramidal and leaf-like) by a process involving a

hydrothermal reaction, followed by the sputter deposition of Ag nanoparticles (average size: 30 nm). The

resulting SERS substrate efficiencies, measured using crystal violet (CV) as an analyte molecule, showed a

highest analytical enhancement factor of �106, a detection limit �1 nM and a relative standard deviation

of the Raman peak maximum of �13% for the nano-leafy structure. This substrate was used to analyze

tissue sections of 8 oral cancer patients (squamous cell carcinoma of tongue) comprising a total of 24

normal and 32 tumor tissue sections and the recorded spectra were analyzed by principal component

analysis and discriminant analysis. The tissue spectra were correctly classified into tumor and normal

groups, with a diagnostic sensitivity of 100%, a specificity of 95.83% and the average processing time per

patient of 15–20 min. This indicates the potential translation of the SERS method for the rapid and

accurate detection of cancer.

Introduction

Raman spectroscopy has been a very effective analytical tool forthe ngerprinting of molecules based on their vibrationalenergy levels.1 Particularly, in biology, biomolecular nger-printing can provide plenteous information about the structuralconformation of biomolecules as well as the patho-physiolog-ical state of cells and tissues, based on their biochemicalcomposition.2–4 Recent studies about the ex vivo Raman spec-troscopic characterization and classication of tissues frombiopsy samples reveals the immense potential of Ramanmethods for the real time analysis of diseased tissues.5,6 Theworking principle relies on the detection of unique Ramanngerprints of biological components in various disease

r Medicine, Amrita Vishwa Vidyapeetham

anzoork@aims.amrita.edu; Fax: +91 484

rita Institute of Medical Sciences, Cochin,

n (ESI) available: Raman spectrails of PCA-DA results. See DOI:

Chemistry 2014

conditions. Especially, in cancer, its’ pathogenesis is alwaysassociated with alterations in the expression levels of cellularproteins, lipids, nucleic acids and their conformational states.7

Diagnostics approaches, based on such direct changes inmolecular signatures can improve the detection specicity andsensitivity. However, several factors such as the low scatteringcross-section and tissue auto-uorescence (while using visiblewavelength lasers) resulting in larger spectral integration timeand the requirement of expensive, background-free opticalsubstrates like quartz, CaF2 etc. (non-usability of glass slides,due to interfering low wave number peaks) limit the clinicaltranslation of this technique.

With the emergence of the SERS phenomena, limitationsdue to the low sensitivity of Raman scattering have beenovercome, to a great extent. SERS has been widely utilized forthe detection of the vibrational signatures of molecules, due toits extreme sensitivity and enhancement capability to deliverwell-resolved spectral information, when the analyte moleculelies near to a plasmonic metal surface (Ag, Au, Cu).8 Severalstudies have been reported so far on SERS based approaches todetect protein phosphorylation,9 DNA hybridization,10 molec-ular and live cell imaging,11,12 immuno-assay13 and pathogendetection.14 The development of SERS platforms for the

J. Mater. Chem. B

Journal of Materials Chemistry B Paper

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

acquisition of enhanced spectral features, from canceroustissue sections, can improve the diagnostic reliability. A fewstudies have demonstrated the potential of the enhancedspectral characterization of tumor and normal tissues byincorporating Ag nanoparticles (NP), either on the top of tissuesections or by crushing the tissues and mixing with the NPs.15,16

For any SERS detection method, even though the primaryrequisite is the high enhancement factors, for practical appli-cations and routine analysis, the system should also possess anease of experimental procedure and handling, be an inexpen-sive method for large scale production, stability, and goodspectral reproducibility.

Large area nanostructured SERS substrates have overcomethe limitations of the simple, nanoparticle mediated SERStechnique, which involves a lack of reproducibility, due to thenon-uniform distribution of NPs. Various methods, such aselectron beam lithography (EBL),17 oblique angle deposition(OAD),18 nanosphere lithography,19 template methods etc.,were able to generate an array of different plasmonic nano-structured morphologies over large area platforms that wereutilized for a wide range of applications, from the charac-terization of a cellular surface to the ultrasensitive detectionof molecules, down to the single molecule level.20,21 Out ofthese, the template methods are oen the easiest andcheapest method, that require nanostructured templates likeporous anodic aluminium oxide (AAO) or self-grown nano-structures like nanorods, nanowires, or 3D nanostructures,on to which the plasmonic metals are deposited by varioustechniques.22–24

In this study, we have demonstrated a simple and easyfabrication method for the development of Ag–TiO2 SERSsubstrates for the rapid detection of oral squamous cellcarcinoma. The fabrication of the SERS substrate involves atwo-step process: the hydrothermal synthesis of TiO2 nano-structures on a Ti plate and sputter deposition of Ag NPs on tothese nanostructures, to generate Raman ‘hot spots’. Metaloxides like ZnO, SiO2, and TiO2 receive wide attention in SERSsubstrate development, because of their ease in growing acontrolled array of nanostructures.25–27 Three different TiO2

nanostructures, named as nano-needular (NN), nano-bipyra-midal (NB) and nano-leafy (NL) were synthesized by alteringthe hydrothermal conditions and their enhancement effi-ciencies on Ag deposition were studied using a standardRaman analyte molecule, the CV dye. Even though a fewreports are available on 1D and 2D Ag–TiO2 based SERSsystems,27–29 this is the rst report on the successful fabrica-tion of a large area Ag–TiO2 nanostructured (with threedifferent morphologies) SERS substrate and its utility incancer detection, using clinical samples. As the variations inthe overall spectral pattern among the tissue types is minimal,in order to understand, evaluate and classify the tissue, basedon the most relevant spectral features from a large collectionof spectral dataset, a multivariate statistical analysis techniquewas employed.5,30,31 Diagnostically signicant spectral featuresfrom normal and tumor tissues were evaluated and classiedusing the principal component analysis-discriminant analysis(PCA-DA) method.

J. Mater. Chem. B

Experimental section

Sodium hydroxide (NaOH), hexamine (C6H12N4), CV andacetone were purchased from Sigma-Aldrich. Commerciallyavailable pure titanium plates (Surgical Grade II, ASTMF-67)were procured from Jayon Surgicals, India. All the polishingsilicon carbide grits (Carbimet Paper Discs) were purchasedfrom Buehler Co. The silver (99.99%, 57 mm diameter, 0.3 mmthickness) target for sputtering was obtained from Ted PellaInc. The commercial, silver SERS substrates were purchasedfrom Yash Nanotech Pvt. Ltd., India.

SERS substrate fabrication

In the rst step, three different nanostructures were synthesizedby a hydrothermal process, as described in our previous work,by Rani et al.,32 with some modications. Briey, titaniumplates (1.5 � 1.5 � 0.2 cm) were polished using silicon carbidegrits, of various sizes, ranging from 600, 1200 and 2400, to makethe surface smooth. The polished plates were degreased byultra-sonicating in acetone, followed by washing with distilledwater. The hydrothermal process was carried out in a Teon®lined stainless steel chamber (70 ml volume capacity) bycontrolling the concentration of solvent, temperature and timeduration of the experiment. Three experimental conditionswere performed for the synthesis of different nanostructures, byimmersing the cleaned Ti plates in the Teon® chamber con-taining (i) a mixture of 20 ml 0.5 M NaOH and 20 ml of 0.5 Mhexamine for 5 h at 250 �C, (ii) 40 ml of 0.5 M NaOH for 5 h at250 �C, (iii) 40 ml of 0.5 M NaOH for 5 h at 175 �C, whichresulted in a formation of the NB, NN and NL structures,respectively. For each experimental set up, the 04 polished Tiplates were immersed in the Teon® chamber, without over-lapping. Aer the reaction, the plates were washed with distilledwater several times and dried at 60 �C. The second step involvesthe deposition of Ag NPs onto the nanostructured substrates bymagnetron sputtering (Emitech, Peltier-K575X). For all thesputtering deposition, the pressure, sputtering current andcoating time were maintained at 10�3 mbar, 40 mA and 100 s,respectively. The distance from the target to the sample insidethe sputter coater was maintained at 40 mm. All the AFMmeasurements (JEOLJSPM5200) were carried out in the tappingmode, using super sharp silicon probes (resonance frequency:330 kHz, spring constant: 42 N m�1).

Sample preparation

For the SERS substrate efficiency studies, CV solutions withserial concentrations, ranging from 100 mM to 1 nM wereprepared in distilled water and 10 ml of the solution was dropcast on the substrates, followed by air drying. The tissue sampleswere acquired during the surgical excision of suspicious lesionsfrom the oral cavity of patients, with their proper consent and bythe full approval from the Institute Ethical Committee (IEC) atthe Amrita Institute of Medical Sciences & Research Centre,Cochin. Eight patients were included in the study, diagnosedwith oral squamous cell carcinoma of the tongue. The excisedtissues were washed in cold PBS (to remove the blood content),

This journal is © The Royal Society of Chemistry 2014

Paper Journal of Materials Chemistry B

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

snap frozen immediately aer excision and transferred to�80 �C, until use. Alternate cryosections were obtained for theRaman studies (7–9 mm thickness) in freshly prepared Ag-NLsubstrates, as well as for hematoxylin and eosin staining (H&Estaining; 4 mm thick) in poly-lysine coated glass slides.

Characterization

The morphology and composition of the nanostructures werecharacterized using SEM (JEOLJSM 6420), FESEM (HitachiSU660), TEM (JEOL 3010) and energy dispersive analysis (EDS)respectively. X-ray powder diffraction analysis was carried out bya PANalytical X'pert Pro-diffractometer, which revealed thecrystalline phase of the nanostructured materials. The SERSmeasurements were carried out with a WITec alpha 300RAconfocal Raman microscope system, comprising of a UHTS 300spectrometer (600 lines per mm grating) coupled with a Peltiercooled CCD detector. A laser wavelength of 488 nm from a DPSSsource was used to excite the sample, guided via an optical berof diameter 100 mm, which was focused on to the sample by a50� objective lens (NA – 0.8). For all the CV experiments, thelaser power was maintained at approximately 50 mW, with anintegration time and number of acquisitions of 0.5 s and 10,respectively. All the tissue spectral measurements were carriedout at a laser power of nearly 100 mW, with an integration timeand number of accumulations of 3 s and 10, respectively. Theinstrument was calibrated using a standard silicon samplepeak, at 520 cm�1.

Raman spectral analysis

Spectral data acquired from the tissue sections in the range500–1800 cm�1 were selected for our studies and subjected toauto-polynomial background correction and normalization,cosmic ray removal and 3rd order averaging, using the WITecProject Plus soware. Further, the spectral data were analyzedand classied by the multivariate analysis (PCA-DA) method,using XLSTAT Pro (Addinso). PCA is used to reduce thecomplexity of the data and to identify the most relevant spectralfeatures related to normal and tumor tissues, from a largenumber of spectral datasets. By utilizing PCA, it reduces thedimension of the large spectral dataset to a few principalcomponents (PC), that represents the total dataset withoutlosing any meaningful information. PCs are formed in such away that the rst principal component (PC1) shows the spectralfeatures that shows the maximum variance in the total dataset,where as the succeeding components shows the spectralfeatures in the order of decreasing variance. Each spectralloading of a particular PC is related to the individual spectrum,by a PC score value. Diagnostically signicant PCs were identi-ed using the Student's t-test (p < 0.05) and their correspondingPC score values were considered for the classication of normaland tumor spectra using DA. DA uses statistical functions togenerate discriminant scores, based on the spectral attributesthat denes each group and predicts each observation in totheir corresponding groups. The entire spectral acquisitionfrom the tissues and its classication were carried out within atime duration of 2 h.

This journal is © The Royal Society of Chemistry 2014

Results and discussionSERS substrate characterization

The SEM, XRD and EDS analysis reveal the morphology andcomposition of three different nanostructures, as shown inFig. 1. Fig. 1a shows the smooth surface of the polished Ti plateon which different nanostructures are grown (inset shows thephotographic image of an unpolished Ti plate). The NB struc-tures (Fig. 1b) have an effective width around 400–500 nm at thecenter, which tapers towards the two ends, with an end diameterof �100 nm. For the NN structures (Fig. 1c), the diameter wasaround 50–120 nm and length ranged from 1–3 mm. In the caseof the NL structures (Fig. 1d), the nanostructures appear as un-oriented leaets, with an inter-leaf spacing of 20–150 nm. Fig. 1eshows the XRD patterns of the nanostructures, indicating amajor rutile phase (JCPDS-01-076-0323) with traces of someanatase content (JCPDS-01-071-1169). In the case of NL, the peakat 2q value 25� is completely absent, compared to the NN and NBstructures. In the NN structure, the presence of an impurity peakof sodium titanate (S) is detected at 28�, whereas all the otherpeaks were not seen. Fig. 1f shows a typical EDS spectrum of theTiO2 nanostructure, that mainly constitutes Ti, O and traces ofNa. The formation of different nanostructures on polished Tiplates can be explained on the basis of dissolution and re-precipitation mechanisms.32 Ti dissolves into the solution asTi(OH)3+ ions and re-precipitates back onto the plates as TiO2, inthe presence of aqueous alkali, under hydrothermal conditions.The morphology of the different structures could be tuned bychanging various reaction conditions, such as temperature andthe concentration of NaOH and hexamine. At 175 �C, pH �12, aunique leaf-like structure was formed. By increasing thetemperature to 250 �C, the precipitation rate increases, resultingin a needular morphology. At the same temperature, whenhexamine was used as a chelating agent, which restricts thereaction kinetics and availability of metal ions from the solution,a bipyramidal nanostructure was obtained.32

To make the above nanostructures Raman active, Ag NPswere sputter deposited on these nanostructures, under opti-mized conditions and the SERS enhancements in addition tothe deposited nanoparticle size were characterized. Fourdifferent sputtering durations, of 25, 50, 75 and 100 s, at aconstant sputtering current 40 mA were studied. The Ramanmeasurements were carried out on Ag sputtered NB substrates,coated at 40 mA, for the above mentioned sputtering durations.It was observed that sputtering for 100 s provided a maximumSERS intensity and uniform spectrum from various spots(Fig. S1, ESI†). In order to understand the pattern of the nano-particle formation, atomically at mica sheets were also placedalong with the NB substrates during sputtering. Fig. 2a–c showthe AFM topographic images of a Ag sputtered mica sheet,coated at 40 mA, for durations of 25, 50 and 100 s. These imagesshow the increasing trend in the formation of bigger nano-particles (200–500 nm) when the sputtering time increasedfrom 25 to 100 s, which may be explained by the coalescenceeffect.33 Fig. 2d–f show the AFM topographic images of the NBsubstrate before (Fig. 2d) and aer (Fig. 2e and f) sputtering

J. Mater. Chem. B

Fig. 1 SEM images of (a) polished Ti plate (inset: photographic image of Ti plate), (b) NB, (c) NN, (d) NL structures. (e) shows the XRD pattern ofhydrothermally synthesized NL, NN and NB nanostructures (A – anatase, R – rutile, Ti – titanium, S – sodium titanate). (f) corresponds to the EDSpattern of NL structures. Inset (b–d) shows the magnified view of NB, NN and NL structures (inset scale bar-200 mm).

Fig. 2 AFMmeasurements: 3D view of topographic images of Ag sputteredmica sheets at different sputtering durations of (a) 25 s, (b) 75 s and (c)100 s. Topographic image of NB structure before sputtering (scan size: 1 � 1 mm) (d), after sputtering at 40 mA for 100 s imaged at scan size of2.5 � 2.5 mm (e) and scan size of 1 � 1 mm (f).

J. Mater. Chem. B This journal is © The Royal Society of Chemistry 2014

Journal of Materials Chemistry B Paper

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

Paper Journal of Materials Chemistry B

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

with Ag for 100 s. The AFM topographic image from Ag-NBreveals that the average size of the deposited NPs is around30 nm (Fig. 2f). Since the nanostructures increase the surfacearea for Ag deposition enormously, the coalescence observed athigher sputtering durations from the mica sheet is very muchreduced from the nanostructured substrate.34 In a similarmanner, all the other nanostructures were coated with Ag NPsof the same size and sputtering conditions. Fig. 3 shows theFESEM images (Fig. 3a–d), TEM image (Fig. 3e) and XRD(Fig. 3f) pattern of the Ag sputtered nanostructures. Although,we could not obtain a high resolution image for the depositedNPs on the NL structure, the FESEM images shows that aersputter deposition, the effective wall thickness of the NL wasincreased from 20 � 3 nm to 44 � 4 nm (Fig. 3b and d). TEManalysis of the Ag-NN structure (scratched from the Ti platesand dispersed in acetone) reveals that the deposited Ag nano-particles have an average size of nearly 30 nm (Fig. 3e). Thepresence of Ag was shown in the XRD spectra of NL structures at2q value 44.2� and 64.42�, while the Ag peak at 38.1� overlapswith the Ti peak (Fig. 3f). In the Ag-NL, the elemental compo-sitions (atomic percentage) constituted by Ti, O, Ag were 29.71,60.04 and 3.30, respectively (Fig. S2, ESI†).

We used the above synthesized substrates for the SERSinvestigations. To quantify the enhancement effect, we calcu-lated the enhancement factor of the substrates by measuringthe ratio between the Raman scattering intensities of the CVpeak with, and without SERS. CV shows major characteristicpeaks at 914 cm�1 (ring skeletal vibration of radical orienta-tion), 1175 cm�1, (ring C–H bend, in-plane vibration) 1371 cm�1

Fig. 3 FESEM images of NB (a and c) and NL (b and d) structures before Afor 100 s (c and d), respectively, (e) TEM image of single needular structursputtered NL nanostructures (A – anatase, R – rutile, Ti – titanium, Ag –

This journal is © The Royal Society of Chemistry 2014

(N-phenyl stretching), 1588 cm�1 (ring C–C stretching),1619 cm�1 (ring C–C stretching).35 Even though there are manyapproaches to determine the enhancement factor,36 we calcu-lated the analytical enhancement factor, which determines theenhancement for a given analytical volume of a sample with,and without SERS, based on the equation37,38

EF ¼ (ISERS/INR) � (CNR/CSERS)

where ISERS and INR correspond to the Raman intensities of the1588 cm�1 peak with a SERS effect from the Ag–TiO2 substratesand normal scattering recorded from CaF2 slides, respectively,while CSERS and CNR represent the corresponding concentrationof CV in these substrates, respectively (reference concentrationstaken as 10 mM). The averaged analytical enhancement factorcalculated for the Ag–TiO2 substrates were estimated as nearly1 � 106 for the NL and 9 � 105 for the NB and NN structures.

To know the enhancement effect generated by the nano-structures, the CV spectrum was also measured from the pol-ished Ti plates sputtered with Ag, at the same conditions, aswell as from a commercial silver SERS substrate. Fig. 4 showsthe averaged SERS spectra of CV (10 mM) obtained from threedifferent TiO2 nanostructures (NB, NN, NL) sputtered with Ag.Of the three structures, NL showed the highest enhancement,whereas the NN and NB structures showed almost a similarenhancement. The spectral intensity at 1588 cm�1 from thecommercial Ag-SERS substrate, NN, NB and NL substrates werenearly 10, 90, 94, 130 times higher, respectively, compared tothe Ag coated polished Ti plates. Generally, the enhancement

g sputter deposition (a and b) and after Ag sputter deposition at 40 mAe deposited with Ag (scale bar: 50 nm). (f) shows the XRD pattern of Agsilver).

J. Mater. Chem. B

Fig. 4 Averaged SERS spectra of 10 mM CV solution from NB, NN andNL substrates sputtered with Ag. The spectra were also compared withcommercial Ag SERS substrate and Ag coated polished Ti substrates(integration time: 0.5 s, no of accumulations: 10).

Fig. 5 Formation of hot spots in Ag coated NN, NB and NL structures.(Left panel) SEM images of three nanostructures substrates showing aregion within an area of 1 mm2. (Right panel) Schematic representingintra-structure (a) and inter-structure (b) hot spot formation withinnanostructures.

Journal of Materials Chemistry B Paper

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

effects from the nanostructured plasmonic surfaces are mainlycontributed by favorable surface geometries that facilitatelocalized plasmonic resonances between the incident photonsand plasmons.8,39 Typical geometric features that promote ahigh local eld enhancement include surface roughness, sharpedges, tips and junctions or gaps between the plasmonic NPsthat serve as ‘hot spots’ for eld enhancement, compared to theindividual NPs.40,41 In the case of the Ag NP coated nano-structures (TiO2), these hot spots may form either between twonearby nanoparticles on the same nanostructure (intra-struc-ture contribution) and/or the nanoparticles on adjacent nano-structures (inter-structure contribution). The intra-structurecontribution may vary with the NPs concentration, while theinter-structure contribution will depend on the morphology(needular, bipyramidal, leaf-like, etc.), inter-structure distance(structure density per unit area) and nanoparticle size.39

Considering these aspects, we have examined the possiblecontributions leading to a SERS enhancement in our NN, NBand NL structures.

Fig. 5, le panel, shows the SEM images of the NN, NB, NLstructures within an area of 1 mm2 and the right panel shows theschematic representation of the possible hot spot formation inthese structures. For the Ag-NN, the sites of enhancement canbe between the Ag NPs on the surface of the needles (intra-structure, denoted as ‘a’ in Fig. 5 schematic) and/or betweenneedles (inter-structures, denoted as ‘b’ in Fig. 5 schematic).However, recently, Deng et al., reported that the inter-structureenhancement is prominent only if the size of the NPs is rela-tively large ($150 nm).37 In the case of smaller size particles(�10 nm), the intra-structure contribution is prominent. In ourcase of NN, the average size of the Ag nanoparticle was �30 nmand the averaged inter-structure distance between the needleswas relatively high (�350 nm). Hence, probably, the possibilityof inter-needle enhancement is relatively low. In the case of NB,the enhancement can be contributed to by the NPs on the faces,edges, tips and between the bipyramids. However, from theSEM images, it is clear that 2 to 5 pyramidal structures are seen

J. Mater. Chem. B

within a 1 mm2 area. Whereas in the NL structure, a largenumber of closely packed, leaf-like nanostructures, with anaverage inter-structure spacing of 20–150 nm can be seen. Inaddition, the leaf-like morphology provides more adjacentparallel surfaces than the unaligned needles or bipyramids. TheAg NPs on each of these closely packed adjacent leaf-likesurfaces can contribute to the formation of more inter-structurehot spots than any other structure. This may explain theobserved higher SERS enhancement in NL, compared to NB andNN. However, detailed simulation studies may be needed toquantify the exact eld enhancement prevailing in thesedistinct nanostructures.

Further, to study the limit of detection in the NL structure,we measured the CV spectrum for a wide range of molarconcentrations, ranging from 100 mM to 1 nM. Fig. 6A shows theSERS spectrum of different CV concentrations from the Ag-NLsubstrate. From the plot, it is clear that 100 mM shows themaximum intensity and all ve CV characteristic peaks werevisible up to 10 nM. Even though the noise levels were high inthe 1 nM CV spectrum, the main characteristic peaks of CV at1175, 1371, 1588 and 1619 cm�1 were clearly visible, as shown inthe inset. To assess the reproducibility, the relative standarddeviations (RSD) of the major peak intensities were measured.From recently reported studies, it is understood that the RSDvalue for an efficient SERS substrate should be within 20%.26,42

For calculating the RSD value, we measured the SERS signalintensity of four major characteristic peaks of CV (10 mM) fromthe Ag-NL substrates, obtained from 10 randomly selectedpoints (Fig. 6B). The RSD value of the peaks at 1175, 1371, 1588and 1619 cm�1 were measured as 10%, 16%, 12% and 10%,respectively (average: 13%). From Fig. 6B, it is clear that thespectra were highly uniform, with insignicant variations in the

This journal is © The Royal Society of Chemistry 2014

Fig. 6 (A) SERS spectra of CV recorded from the Ag-NL substrate at different concentrations; 1 nM, 10 nm, 100 nm, 1 mM, 10 mM, 100 mM. Insetshows the spectra obtained at 1 nM showing the characteristic CV peaks at 1175, 1371, 1588 and 1619 cm�1. (B) SERS spectrum of CV (10 mM)obtained from 10 random locations showing the reproducibility and uniformity of SERS signals from Ag-NL substrates. Inset shows the brightfield image of NL substrate and the spots from which the spectrum was recorded (integration time: 0.5 s, no of accumulations: 10).

Paper Journal of Materials Chemistry B

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

intensities, probably due to some non-uniformity in the Agdeposition.

Oral cancer detection using SERS substrates

As the Ag-NL substrate showed the highest SERS enhancement,we used this substrate for all the further studies on the detec-tion and classication of tumor versus normal tissues from oralcancer patients. For the SERS phenomena to occur, one of themain criteria is the close proximity of the tissue with the SERSsubstrate. In this case, it was achieved by the quick adsorptionof cryosections on to a Ag-NL substrate, due to prevailingtemperature difference between the substrate (normal roomtemperature) and tissue cryosections (nearly �20 �C). Spectrawere acquired from the tissue–substrate interface, which wasidentied by the sudden increase in spectral intensity and areduction of uorescence, upon depth proling. Fig. 7 showsthe averaged SERS spectra of tumor and normal tissues,obtained from Ag-NL substrates, as well as H&E stained imagesof tissue sections. The H&E stained images conrmed themalignant and normal pathology of the tissue sections. Thecharacteristic features of a tumor showed high cellular andnuclear pleomorphism, more mitotic cells and highly invadedconnective tissue regions, whereas normal sections showedlongitudinally and transversely arranged connective tissue, withfew cells. Similar to the previously reported studies, the majorchanges associated with recorded spectral signatures relates tothe characteristic peak assignments of proteins, amino acids,nucleotides and lipids.31,43–47 The major intense peaks, commonin both normal and tumor spectra are around 728 (C–Nstretching), 870 (C–C stretching), 946 (C–C stretching), 1012(symmetric ring breathing, phenylalanine), 1100 (C–C/C–Nstretching), 1260 (amide III/C–H deformation), 1460 (CH2

bending), 1672 (amide I) and 1750 cm�1 (C]O stretching). Inaddition, the tumor spectra showed more intense peak

This journal is © The Royal Society of Chemistry 2014

positions contributed especially by aromatic amino acids(phenylalanine, tryptophan, and tyrosine) and nucleotides at645, 680, 794, 825, 1189, 1326, 1585, and 1618 cm�1. Thenormal tissue spectra were characterized by an intense peakposition, assigned by the collagen from connective tissue, at 870and 950 cm�1, and the less intense peaks of phenylalanine andprotein/nucleotide at 1012 and 1326 cm�1, respectively. Theseunique tissue specic spectral characteristics can be used toclassify normal and tumor tissue types, which in turn wereidentied using PCA, from a large spectral dataset.

Diagnostically signicant spectral features, related totumor and normal tissue types, were represented by the rstve PCs (estimated by the Student's t-test, p < 0.05; Fig. S3,ESI†). Fig. 8A shows the contribution of spectral peak posi-tions in the rst ve signicant PCs that represents all themajor peak assignments related to proteins, lipids, nucleo-tides and aromatic amino acids, discussed above. Further,these PCs were used for classication of tumor and normaltissues using DA. Fig. 8B shows the discriminant analysisbiplot, showing the classication of each spectrum (normal ascircle, tumor as square) to the respective tumor or normalgroups, based on the discriminant scores. Out of 32 tumorspectra, all were classied correctly into the tumor group,whereas one out of 24 normal spectra was wrongly classied into the tumor group. This refers to a sensitivity of 100%, whichdenes the test's ability to correctly identify positive results(tumor, 32/32) and a specicity of 95.83%, which denes thetest's ability to correctly identify negative results (normal,23/24) (Table T1, ESI†). The enhanced spectral signatures fromthe tissue sections, acquired using the Ag–TiO2 SERSsubstrate, provided an overall accuracy (55/56) of about98.21% towards the detection of normal and tumor tissues.This is achieved because of the presence of enhanced spectraldiscriminating features related to tumor and normal tissuecomposition, carried by each individual spectrum. This

J. Mater. Chem. B

Fig. 7 Averaged SERS spectra of tumor and normal tissue obtained from Ag-NL substrates. Inset shows the H&E stained images of tumor andnormal tissues. All the spectrums were acquired with an integration time of 3 s. (A – adenine, G – guanine, T – thymine, Phe – phenylalanine, Tyr– tyrosine, Try – tryptophan, P–O – phosphate, AA – aromatic amino acids, Pro – proteins, Lip – lipids, Coll – collagen, Am – amide; str –stretching, wag – wagging, bend – bending, twi – twisting vibrations).

Journal of Materials Chemistry B Paper

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

method allows for an improved accuracy rate on a tumor andits sub-type detection in the context of prevailing studiesreported on Raman spectroscopy based cancer detection.5,41 As

Fig. 8 Enhanced spectral features that contribute to the first five significanalysis bi-plot using discriminant scores (LD1, LD2) shows the classificclassification estimated by cross-validation methods were 95.83% (23/2

J. Mater. Chem. B

the entire procedure takes only a few minutes for the spectralacquisition and classication, our study projects the potentialof SERS mediated faster diagnosis of cancer.

ant PCs (PC1 to PC5) of normal and tumor tissue spectra. Discriminantation of normal and tumor spectra. The specificity and sensitivity of4) and 100% (32/32), respectively.

This journal is © The Royal Society of Chemistry 2014

Paper Journal of Materials Chemistry B

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

Conclusions

In summary, we have demonstrated the potential application ofa Ag–TiO2 SERS substrate for the rapid detection of oral cancer,with a 100% sensitivity. Three unique TiO2 nanostructures(needular, bipyramidal and leaf-like) were grown by a hydro-thermal method and a uniform coating of Ag nanoparticles wasdone by magnetron sputtering. The substrate efficiencies weremonitored by measuring the peak intensities of the CV spec-trum. A leaf-like Ag–TiO2 nanostructure showed the highestanalytical enhancement factor, of �106, a minimum detectableconcentration of �1 nM and a RSD of the Raman peakmaximum of �13%. The enhanced spectral signatures of tissuesections from 8 patients (consisting of 24 normal and 32 tumorspectra) were obtained using the Ag-NL substrate and classiedwith the help of the PCA-DA method, to successfully detectcancer tissue with a specicity and sensitivity of 95.83% and100%, respectively. Thus, we show the potential of the Ag–TiO2

SERS substrate for the diagnosis of oral squamous cell carci-noma by Raman histopathology. These substrates may also beused for other tumor types and further experiments are inprogress for the staging of tumor grades.

Acknowledgements

We thank the Department of Biotechnology, Govt of India fornancial support, under the project “Oncogene sensing andFinger-printing from saliva using Nanotechnology enabledRaman Spectroscopy” (project no. BT/PR11796/MED/32/102/2009). One of the authors, C. M. Girish is thankful to theCouncil of Scientic and Industrial Research, Govt of India, forproviding a Senior Research Fellowship (ref. no. 9/963(0015)2K11-EMR1). We thank the School of Nanosciences, NIT Calicutand DST Unit of Nanosciences, IIT Chennai for helping to carryout the FESEM and TEM measurements, respectively. We alsothank Mr Sajin P. Ravi for his support on the electron micros-copy analysis.

Notes and references

1 J. De Gelder, K. De Gussem, P. Vandenabeele and L. Moens,J. Raman Spectrosc., 2007, 38, 1133.

2 C. J. Frank, R. L. McCreery and D. C. B. Redd, Anal. Chem.,1995, 67, 777.

3 A. Downes, R. Mouras, P. Bagnaninchi and A. Elck, J. RamanSpectrosc., 2011, 42, 1864.

4 N. C. Maiti, M. M. Apetri, M. G. Zagorski, P. R. Carey andV. E. Anderson, J. Am. Chem. Soc., 2004, 126, 2399.

5 N. Stone, C. Kendall, N. Sheperd, P. Crow and H. Barr,J. Raman Spectrosc., 2002, 33, 564.

6 N. D. Magee, J. S. Villaumie, E. T. Marple, M. Ennis,J. S. Elborn and J. J. McGarvey, J. Phys. Chem. B, 2009, 113,8137.

7 J. R. Mourant, K. W. Short, S. Carpenter, N. Kunapareddy,L. Coburn, T. M. Powers and J. M. Freyer, J. Biomed. Opt.,2005, 10, 031106.

This journal is © The Royal Society of Chemistry 2014

8 P. L. Stiles, J. A. Dieringer, N. C. Shah and R. P. Van Duyne,Annu. Rev. Anal. Chem., 2008, 1, 601.

9 S. Siddhanta, D. Karthigeyan, P. P. Kundu, T. K. Kundu andC. Narayana, RSC Adv., 2013, 3, 4221.

10 R. P. Johnson, J. A. Richardson, T. Brown and P. N. Barlett,J. Am. Chem. Soc., 2012, 134, 14099.

11 C. L. Zavaleta, E. Garai, J. T. C. Liu, S. Sensarn,M. J. Mandella, D. V. Sompel, S. Friedland, J. V. Dam,C. H. Contag and S. S. Gambhir, Proc. Natl. Acad. Sci. U. S.A., 2013, 110, E2288.

12 G. Zhang, G. Qu, Y. Chen, A. Shen, W. Xie, X. Zhou and J. Hu,J. Mater. Chem. B, 2013, 1, 4364.

13 Y. Pei, Z. Wang, S. Zhong and Y. Cui, J. Mater. Chem. B, 2013,1, 3992.

14 X. Zhang, M. A. Young, O. Lyandres and R. P. Van Duyne,J. Am. Chem. Soc., 2005, 127, 4484.

15 S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang,R. Chen and H. Zeng, Appl. Phys. Lett., 2013, 102, 043702.

16 O. Aydin, M. Altas, M. Kahraman, O. F. Bayarak andM. Culha, Appl. Spectrosc., 2009, 63, 1095.

17 A. Nahla, A. Hatab, J. M. Oran and M. J. Sepaniak, ACS Nano,2008, 2, 377.

18 S. B. Chaney, S. Shanmukh, R. A. Dluhy and Y. P. Zhao, Appl.Phys. Lett., 2005, 87, 031908.

19 A. V. Whitney, J. W. Elam, P. C. Stair and R. P. Van Duyne,J. Phys. Chem. C, 2007, 111, 16827.

20 T. T. Liu, Y. H. Lin, C. S. Hung, T. J. Liu, Y. Chen,Y. C. Huang, T. H. Tsai, H. H. Wang, D. W. Wang,J. K. Wang, Y. L. Wang and C. H. Lin, PLoS One, 2009, 4,e5470.

21 H. Liu, L. Zhang, X. Lang, Y. Yamaguchi, H. Iwasaki,Y. Inouye, Q. Xue and M. Chen, Sci. Rep., 2011, 1, 112.

22 G. Sinha, L. E. Depero and I. Alessandri, ACS Appl. Mater.Interfaces, 2011, 3, 2557.

23 E. Galopin, J. Barbillat, Y. Coffinier, S. Szunerits,G. Patriarche and R. Boukherroub, ACS Appl. Mater.Interfaces, 2009, 1, 1396.

24 C. Cheng, B. Yan, S. M. Wong, X. Li, W. Zhou, T. Yu, Z. Shen,H. Yu and H. J. Fan, ACS Appl. Mater. Interfaces, 2010, 2, 1824.

25 H. Y. Wu and B. T. Cunningham, Appl. Phys. Lett., 2011, 98,153103.

26 F. Xu, Y. Zhang, Y. Sun, Y. Shi, Z. Wen and Z. Li, J. Phys.Chem. C, 2011, 115, 9977.

27 X. Li, G. Chen, L. Yang, Z. Jin and J. Liu, Adv. Funct. Mater.,2010, 20, 2815.

28 L. Yang, X. Jiang, W. Ruan, J. Yang, B. Zhao, W. Xu andJ. R. Lombardi, J. Phys. Chem. C, 2009, 113, 16226.

29 A. Roguska, A. Kudelski, M. Pisarek, M. Opara andM. J. Czachor, Vib. Spectrosc., 2011, 55, 38.

30 S. K. Teh, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh andZ. Huang, Br. J. Cancer, 2008, 98, 457.

31 S. P. Singh, A. Sahu, A. Deshmukh, P. Chaturvedi andC. Murali Krishna, Analyst, 2013, 138, 4175.

32 V. V. Divya Rani, K. Manzoor, D. Menon, N. Selvamuruganand S. V. Nair, Nanotechnology, 2009, 20, 195101.

33 A. Merlen, V. Gadenne, J. Romann, V. Chevallier, L. Patroneand J. C. Valmalette, Nanotechnology, 2009, 20, 215705.

J. Mater. Chem. B

Journal of Materials Chemistry B Paper

Publ

ishe

d on

21

Nov

embe

r 20

13. D

ownl

oade

d by

Ast

on U

nive

rsity

on

21/0

1/20

14 0

8:48

:10.

View Article Online

34 N. Nuntawong, M. Horprathum, P. Eiamchai, K. Wong-ek,V. Patthanasettakul and P. Chindaudom, Vacuum, 2010, 84,1415.

35 M. Sackmann, S. Bom, T. Balster and A. Materny, J. RamanSpectrosc., 2007, 38, 277.

36 E. C. Le Ru, E. Blackie, M. Meyer and P. G. Etchegoin, J. Phys.Chem. C, 2007, 111, 13794.

37 S. Deng, H. M. Fan, X. Zhang, K. P. Loh, C.-L. Cheng,C. H. Sow and Y. L. Foo, Nanotechnology, 2009, 20,175705.

38 W. H. Hsiao, H. Y. Chen, Y. C. Yang, Y. L. Chen, C. Y. Lee andH. T. Chiu, ACS Appl. Mater. Interfaces, 2011, 3, 3280.

39 R. J. C. Brown, J. Wang and M. J. T. Milton, J. Nanomater.,2007, 2007, 12086.

40 T. Y. Jeon, S. G. Park, S. Y. Lee, H. C. Jeon and S. M. Yang,ACS Appl. Mater. Interfaces, 2013, 5, 243.

J. Mater. Chem. B

41 H. Ko, S. Singamaneni and V. V. Tsukruk, Small, 2008, 4,1576.

42 M. Fan and A. G. Brolo, Phys. Chem. Chem. Phys., 2009, 11,7381.

43 Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang,S. Xie, H. Zeng and R. Chen, J. Biomed. Opt., 2013, 18, 27003.

44 S. K. Teh, W. Zheng, D. P. Lau and Z. Huang, Analyst, 2009,134, 1232.

45 M. S. Bergholt, K. Lin, W. Zheng, D. P. C. Lau and Z. Huang,J. Biomed. Opt., 2012, 17, 077002.

46 L. E. Kamemoto, A. K. Misra, S. K. Sharma, M. T. Goodman,H. Luk, A. C. Dykes and T. Acosta, Appl. Spectrosc., 2010, 64,255.

47 K. Venkatakrishna, J. Kurien, K. M. Pai, M. Valiathan,N. N. Kumar, C. Murali Krishna, G. Ullas and V. B. Kartha,Curr. Sci., 2001, 80, 665.

This journal is © The Royal Society of Chemistry 2014