Multivariate analysis of ToF-SIMS data for biological applications

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ReviewReceived: 30 September 2008 Revised: 2 January 2009 Accepted: 3 February 2009 Published online in Wiley Interscience: 13 May 2009

(www.interscience.wiley.com) DOI 10.1002/sia.3049

Multivariate analysis of ToF-SIMS data forbiological applicationsJi-Won Park,a,b Hyegeun Min,a,b Young-Pil Kim,c Hyun Kyong Shon,a

Jinmo Kim,d Dae Won Moona,b∗ and Tae Geol Leea,b∗

Applications of time-of-flight secondary ion mass spectrometry (ToF-SIMS) were demonstrated, focusing on multivariate analysis(MVA) such as principal component analysis (PCA), principal component regression (PCR), and maximum autocorrelation factors(MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surfacecharacterization of chemical composition, and image-based analysis in disease-related tissues. The use of MVA in a ToF-SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled thestraightforward analysis of ToF-SIMS data. Even on biological samples with high complexity, the MVA method effectivelycontributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA withToF-SIMS data will be widely used for exploring biological studies in a reliable and simple way. Copyright c© 2009 John Wiley &Sons, Ltd.

Keywords: ToF-SIMS; multivariate analysis; principal component analysis; image analysis

Introduction

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is apowerful tool that is used to analyze surface properties of organic-and biomaterials due to its surface sensitivity, chemical specificity,and imaging capability.[1,2] However, the large number of peaksin ToF-SIMS spectra makes a straightforward and systematic in-terpretation difficult; therefore, significant information could behidden.[3] To enhance the data interpretation, multivariate analy-ses (MVAs), such as principal component analysis (PCA), principalcomponent regression (PCR), or maximum autocorrelation factors(MAFs), have been of great interest in decoding the complexity ofthe ToF-SIMS spectra.[3 – 9] In particular, PCA has been most widelyused for the ToF-SIMS analysis of organic- and biosurfaces. Indeed,the PCA method, representing the data set by scores and loadingsplots,[10] has provided useful information on surface composition,orientation, conformation, and distribution of organic- and bio-materials such as polymers,[11 – 14] proteins,[15 – 17] self-assembledmonolayers (SAMs),[18,19] and other biological samples.[20 – 22] Inparticular, the use of PCA and MAF in ToF-SIMS imaging hasshown great potential in pinpointing the mass peaks that arethe most relevant from a myriad of secondary ion signals fromthe area of interest.[6,13,23 – 25] MAF analysis is especially useful formaximizing autocorrelation between neighboring pixels withoutpreprocessing,[26,27] which general users of MVA would find advan-tageous since there is no need to choose the proper preprocessingmethod.

This report is a review of our past ToF-SIMS studies on biologicalapplications that were aided with multivariate analyses. Thefirst application was the quantitative analyses of surface amineand protein densities, both of which need to be quantifiedand controlled for superior performance and quality control ofbiochips.[28 – 34] The PCA method was mainly used to interpretthe ToF-SIMS spectra, while the PCR method was used toobtain a correlation curve of the principal component (PC)

scores with the surface amine density or surface proteindensity. These densities were determined by independentmethods such as UV–visible absorption spectroscopy,[28 – 31]

surface plasmon resonance (SPR),[32,33] and ellipsometry.[34]

Although the partial least square (PLS) method is robust andadequate for quantification modeling,[3,6] the PCR method is alsouseful for quantitative analysis of ToF-SIMS spectrum data.[35,36]

We used the PCA and PCR methods mainly because theyare simple and easy to use for the general user. Scheme1 shows our basic idea for quantitative analysis of surfaceamine functional groups on plasma-polymerized ethylenediamine(PPEDA) thin film and surface immobilized streptavidin (SA).On the basis of this idea, the effects of protein orientationand trehalose additive on a quantitative analysis of surface-immobilized protein by ToF-SIMS were carefully and systematicallystudied. The second application was the ToF-SIMS study ofself-assembled monolayers on a gold surface that was treatedwith different cleaning methods.[37] PCA results were used tojudge the reproducibility and quality of the self-assembledmonolayers. The final application was the ToF-SIMS studyof human skin and colon tissues.[38,39] In the first case, the

∗ Correspondence to: Dae Won Moon and Tae Geol Lee, Nanobio Fusion ResearchCenter, Korea Research Institute of Standards and Science, Daejeon 305-600,Korea. E-mail: [email protected]; [email protected]

a Nanobio Fusion Research Center, Korea Research Institute of Standards andScience, Daejeon 305-600, Korea

b Department of Nano Surface Science, University of Science and Technology,Daejeon 305-600, Korea

c Department of Biological Sciences, Korea Advanced Institute of Science andTechnology, Daejeon 305-701, Korea

d Department of Physics, Brain Korea 21 Physics Research Division and Instituteof Basic Science, Sungkyunkwan University, Suwon 440-746, Korea

Surf. Interface Anal. 2009, 41, 694–703 Copyright c© 2009 John Wiley & Sons, Ltd.

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Multivariate analysis of ToF-SIMS data

Scheme 1. Schematic diagram illustrating the basic concept for quantita-tive analysis of surface amine functional groups and surface-immobilizedstreptavidin using the correlation between ToF-SIMS analysis along withPCA and independent techniques such as UV–visible spectroscopy, surfaceplasmon resonance, and ellipsometry.

contaminated top layer of human skin tissue was cleaned usingdifferent cluster ion beams with different sputtering times. TheToF-SIMS spectra of each cleaned tissue were analyzed by PCA todetermine the optimum condition for tissue cleaning.[38] In thesecond case, image analyses of PCA and MAF were performedto differentiate between human normal colon tissue and cancercolon tissue. Previously, ToF-SIMS images of normal and cancercolon tissues were distinguishable by using only PCA analysis withautoscaling, without normalization.[39] In this report, PCA withvarious preprocessing and MAF analysis are newly performed andcompared in terms of image contrast.

Experimental

Sample preparation

Fabrication of amine-functionalized PPEDA and its chemical deriva-tives

A high-quality amine-functionalized PPEDA glass surface wasmade by using inductively coupled plasma chemical vapor de-position and ethylenediamine as a precursor. The details of PPEDAfilms deposited on glass slides are reported elsewhere.[28 – 31]

The surface amine density was controlled as a function ofdeposition plasma power and quantified using UV–visible ab-sorption spectrometry. In a nitrogen atmosphere, a PPEDA-coatedslide glass (1.5 × 2.5 cm2) was allowed to react with excess4-nitrobenzaldehyde (10 mg) anhydrous ethanol solution (25 ml)overnight at 50 ◦C. The aqueous solution of hydrolyzed 4-NBA(εmax = 1.45 × 104 M−1 cm−1) was measured with a HP 8453UV–visible spectrophotometer (Hewlett–Packard). All spectrawere recorded after baseline correction and converted to surfaceamine density in accordance with Beer’s law.

Fabrication of surface-immobilized streptavidin

SA, as a model protein, was immobilized by a poly(amidoamine)(PAMAM) G3 dendrimer-activated surface[32,33] or by two differentmethods: random immobilization onto SAMs of AUT on agold surface or oriented immobilization by using a biotinylatedsurface.[34] The details of immobilization procedures are reportedelsewhere.[32 – 34] Surfaces with various densities of SA were

treated with a 0.1% trehalose solution in PBS buffer for 20 minand subjected to a TOF-SIMS analysis. The resulting data werecompared with SPR data, which were obtained with a BIAcore-300 instrument and gold sensor chips (BIAcore). Ellipsometricmeasurements were used to determine the optical thicknessesof the SAMs and the dendrimer-activated surface of proteins.[34]

The optical thickness of each sample was the average of 2–3measurements performed at different locations on each substrateby using a spectroscopic ellipsometer (VU-302, J.A. Woollam Co.,USA) at a wavelength of 633 nm and an angle of incidence of 70◦.

SAM fabrication

1-dodecanethiol (DDT), self-assembled monolayer was producedon a gold substrate cleaned using different methods such as‘super piranha’, ‘piranha’ and ‘ethanol’.[37] Five pieces of goldsubstrates were treated by super-piranha cleaning in a solution (1:10: 6) 61% HNO3: 30% H2O2: 95% H2SO4 (v/v/v). Sulfuric acid wasadded slowly until the solution came to a boil. When the boilingstopped, the gold substrates were taken out from the cleaningsolution. Another five pieces of gold substrates were cleaned ina piranha solution (1 : 4) 30% H2O2: 98% H2SO4 (v/v) at roomtemperature for 5 min. (Caution: the super-piranha and piranhasolutions react violently with most organic materials and must behandled with extreme care.) The gold substrates that had beendipped in the super-piranha and piranha solutions were washedsufficiently with DI water. The other five substrates were sonicatedin ethanol solution for 5 min and washed sufficiently with DI wateras reference samples. For the formation of a SAM, each goldsubstrate was immersed in a 2 mM ethanol solution of 98% DDT,Aldrich for 12 h or more. They were then rinsed sequentially withethanol and dried by nitrogen gas.

Tissues

Korean adult volunteers without current or prior skin diseasewere studied.[38] Four millimeter punch biopsies were taken. Thespecimens were coated with optimal cutting temperature (OCT)Tissue-Tek compound (Miles, Elkhart, Ind) and snap-frozen in liquidnitrogen and preserved at −80 ◦C. After cryosection, the 20-µm-thick frozen sections were mounted onto silane coated slides(Dako, Glostrup, Denmark) and air dried before measurement. Thepreparation of the skin samples was approved by the InstitutionalReview Board at the Seoul National University Hospital, and allsubjects signed written consents. Fresh tissues of colon cancerand normal colon mucosa were obtained from the resectedspecimen.[38,39] These tissues were immediately embedded inTissue Tek OCT compound (Sakura, Tokyo, Japan), frozen at−40 ◦C,and sliced into 5-µm-thick sections. The preparation of the colonsamples was approved by the Institutional Review Board at theNational Cancer Center, and all subjects signed written consents.

ToF-SIMS

ToF-SIMS measurements were obtained on a ToF-SIMS V instru-ment (ION-TOF GmbH, Germany) with 25-keV Bin

+, 25-keV Aun+

primary ions. The Au primary ion source was operated with anaverage current of 0.8 pA, a pulse width of 16.8 ns, and a repetitionrate of 5 kHz at high-current bunched mode.[28 – 34] The bismuthprimary ion source was operated with an average current of 0.36,0.2 pA, a pulse width of 19.5, 16.8 ns, repetition rate of 5 kHz.[37 – 39]

To clean the surface of the human skin tissues, 25 keV Bin+,

10 keV C60+, 20 keV C60

++, and 30 keV C60+++ cluster ion beams

Surf. Interface Anal. 2009, 41, 694–703 Copyright c© 2009 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/sia

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Scheme 2. Schematic diagram illustrating the chemical derivatization process of surface amine functional groups by using chemical tags of 4-NBA forquantification study: (a) PPEDA-coated glass surface and (b) UV absorbable imine-formed glass surface. Adapted from Ref. [29].

Scheme 3. Schematic diagram depicting immobilization procedures of streptavidin on a SAM of AUT: (a) Random immobilization by amine-coupling viabis(sulfosuccinimidyl) suberate (BS3) and (b) oriented immobilization by SA-biotin affinity. Adapted from Ref. [34].

were used to sputter away the contamination layers on a500×500 µm2 sample area. The amount of ion dose was controlledby the sputtering time for each cluster ion beam. The ion dosedensity for the surface cleaning experiment was estimated to be3.6×1014 ions cm−2 (Bin

+), 2.7×1013 ions cm−2 (C60+), 9.6×1013

ions cm−2 (C60++), 8.3 × 1011 ions cm−2 (C60

+++, 40 s), 2.1 × 1012

ions cm−2 (C60+++, 100 s), and 4.2 × 1012 ions cm−2 (C60

+++,200 s). For the colon tissues, Bin

+/C60++ cluster ion beams were

used to sputter away the contamination layers on the samplearea of 500 × 500 µm2. The ion dose density was 4.4 × 1014 ionscm−2 (Bin

+) and 8.5 × 1012 ions cm−2 (C60++).

After surface cleaning, all ToF-SIMS measurements wereobtained using 50-keV Bi3

++ primary ions (average current of0.1 ∼ 0.18 pA, pulse width of 19.1 ns and repetition rate of5 kHz) in the high-current bunched mode. The analysis areasof 500 × 500 µm2 for skin samples and 300 × 300 µm2 forcolon samples were rastered by primary ions and were chargecompensated for glass-slide samples by low-energy electronflooding. The primary ion dose was kept at the same amountof 3.0 × 1011 ions cm−2 for skin samples and 1.0 × 1012 ionscm−2 for colon samples to ensure static SIMS conditions. The massresolution (M/�M) at C7H7

+ (m/z = 91) was usually more than5000 in the positive ion spectra.

The mass calibrations of the positive and negative ion spectraof TOF-SIMS data were performed internally using H+, H2

+, CH3+,

C2H3+, C3H5

+ and C7H7+ peaks and H−, C−, CH−, C2H− and C4H−

peaks, respectively.

Multivariate methods

PCA and PCR were performed on ToF-SIMS spectrum data using aPLS Toolbox (version 3.5, Eigenvector Research, Manson, WA) forMATLAB (MathWorks, Inc., Natick, MA). For the analysis of ToF-SIMS

image data, PCA and MAF were performed using PLS Toolbox 4.0and MIA Toolbox 1.0 (Eigenvector Research, Inc.), respectively. Toquantitatively compare the images obtained from each analysismethod, we calculated the image contrast using Tyler’s method,which defines the equation for contrast between two regions inan image.[24,25]

Principal Components Analysis

PCA can reduce the dimensionality of a data set in which there area large number of interrelated variables, while retaining as muchas possible of the variation present in the data set. This reduction isachieved by transforming to a new set of variables, the PCs, whichare uncorrelated, and which are ordered so that the first few retainmost of the variation present in all of the original variables.[9]

PCA results reflect the scores and loadings of the PCs and canbe used to establish a PCR between sample scores and the surfacedensity of amine functional groups or proteins. ToF-SIMS has beendemonstrated to accurately determine the quantitative molecularconcentration on the surfaces of unknown samples by PCR.[28 – 34]

Maximum Autocorrelation Factors

Paul Switzer originally put forth MAF analysis as an alterna-tive transformation of multivariate spatial imagery, instead ofthe PCA transform.[6] The MAF algorithm is equivalent to theMolgedey–Schuster algorithm for independent component anal-ysis (ICA).[27] The basic assumption behind the MAF analysis isthat interesting signals exhibit high autocorrelation, whereasnoise exhibits low autocorrelation. Particularly, MAF is a scaling-independent technique because scaling factors applied to eachpeak are cancelled out during factor analysis, so identical loadingsvectors can be calculated without any prescaling of the data.[25]

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Figure 1. Plot of surface amine densities of PPEDA film vs scores on PC 1 from PCA of ToF-SIMS data for PPEDA and their chemical-derivatized surfaces(a and c) and their corresponding plots of m/z vs loadings on PC 1 (b and d). Adapted from Ref. [30].

Figure 2. Quantitative PCR between scores of PC 1 from ToF-SIMS spectra of streptavidin and surface protein density from (a) SPR method and(b) ellipsometric thickness. Adapted from Refs [32,33].

Tyler et al. recently demonstrated that MAF could be successfullyapplied to SIMS image analysis.[24,25]

Data preprocessing

Various preprocessing techniques are commonly applied to TOF-SIMS data prior to MVA.[3,7,25] Data preprocessing is often necessaryto obtain useful information from MVA and must be consideredin conjunction with the selection of the appropriate MVA method.The most common of these are normalization, autoscaling andmean centering. Under these conditions, each variable is scaled sothat its useful signal is on an equal footing with the signal of othervariables.[7,25]

Spectral data were normalized to the total secondary ioncounts or the sum of the selected characteristic peaks toeliminate systematic differences between spectra. The mean-centered method was used as a preprocessing method beforethe PCA process on ToF-SIMS spectra.[28 – 34,37 – 39] Before imagePCA, several preprocessing methods were applied to the ToF-SIMS

image data of the colon tissues, while no preprocessing methodswere necessary for the image MAF analysis.

Quantitative Analyses of Surface AmineGroups and Surface-Immobilized Proteins

In this section, we report on applications of PCA and PCR forquantitative analysis of surface amine groups on PPEDA thin filmand for quantitative analysis of surface-immobilized SA. As shownin Scheme 1, the basic concept of quantitative analysis is based onthe correlation between PCA results from ToF-SIMS spectra anddensity information obtained from independent techniques suchas UV–visible spectroscopy, SPR, and ellipsometry by using PCR.

Quantitative analysis of surface amine groups on PPEDA thinfilm

Plasma-deposited films (PDFs) are better than conventionalpolymer thin films for biochip fabrication because they are pinhole


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Figure 3. Quantitative PCR for SA immobilized by amine-coupling (a) before and (b) after trehalose treatment, and biotin-coupling (c) before and (d) aftertrehalose treatment; Scores plots are represented as a function of surface density on PC 1 from PCA of the positive ion spectra of SA. Adapted fromRef. [34].

Figure 4. (a) Scores plot from PCA of negative ion spectra of 1-dodecanethiol SAM produced on gold substrate after each surface cleaning. Note theexcellent grouping and separation of the super-piranha spectra from the other groups; (b) corresponding loadings plot on PC 1 (94.03%). Adapted fromRef. [37]. This figure is available in colour online at www.interscience.wiley.com/journal/sia.

free, mechanically and chemically stable, and strongly adhere tonumerous substrates due to their cross-linked network structures.In addition, we can control their thickness and functional groupdensity to a high degree, as well as their uniformity duringdeposition within relatively short periods of time. However,surface characterization and quantification of specific functionalgroups on these PDFs are difficult since the mechanism of plasmapolymerization and deposition is complicated.

To quantify surface amine functional groups on an amine-functionalized glass surface, we developed a quantitative chemicalderivatization technique using ToF-SIMS. Scheme 2 shows thebasic idea of the ‘derivatization ToF-SIMS’ technique. A largeexcess of the 4-NBA chemical tag was hybridized with an aminegroup to form an imine, which was hydrolyzed in a known volumeof water to reproduce 4-NBA. Subsequently, the absorbance ofthe reproduced 4-NBA molecules was measured to determine thesurface density of the reactive amine groups by using UV–visible

spectroscopy. Hybridized 4-NBA molecules were also subject toToF-SIMS analysis for correlation with UV–visible spectroscopyresults. Initially, characteristic peaks (m/z 177, 191, 207, 221 forpositive ions, m/z 163 for negative ion) of hybridized iminemolecules were correlated with the surface amine densitiesobtained from UV–visible spectroscopy. Every peak except m/z191 correlated well with the surface amine densities. The goodlinear correlation was explained as being due to the insignificantgeometry change as a function of amine density, i.e. standing-upgeometry after the imine double bond formation. On the contrary,the poor correlation of m/z 191 was explained as being due to thedifferent secondary ion formation mechanism.

Rather than a manual selection of peaks and individualcorrelation of the selected peaks for quantitative analysis, PCAand PCR were performed on ToF-SIMS data of both surfaces, thePPEDA itself, and chemical tagged PPEDA with 4-NBA. Raw datawere normalized to the total secondary ion counts and mean


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Figure 5. (a) Scores plot of PC 1 from PCA of positive ion spectra obtained from skin tissues before and after surface cleaning by Bin+/C60

+/C60++/C60

+++cluster ion beams. There is a good separation of scores between as-received samples and precleaned samples. (b) Corresponding loadings plot on PC 1(77.22%). Adapted from Ref. [38].

Figure 6. (a) Scores plot and (b) corresponding loadings plot of PC 1 from PCA of positive ion spectra of normal and cancer colon tissues. The loadingsare labelled by the m/z value with their corresponding amino acid. Adapted from Ref. [39].

centered before the PCA process. There was a poor correlation inthe PCR curve, in which the scores on PC 1 for PPEDA were plottedagainst the surface amine densities as shown in Fig. 1(a). This poorcorrelation was attributed to the complex plasma-polymerizationmechanism as a function of plasma deposition power. On thecontrary, there was a good linear correlation in the PCR curve, inwhich the scores on PC 1 for 4-NBA-tagged PPEDA were plottedagainst the surface amine densities as shown in Fig. 1(c).

Despite the failure of quantitative analysis for pure PPEDA thinfilm, our PCA results (Fig. 1(a) and (b)) can be used to understandthe surface chemical compositions of PPEDA thin film. The negativescores on PC 1 for PPEDA film corresponded well to the peaks ofm/z > 109, while the positive scores on PC 1 for PPEDA filmcorresponded well to the peaks of m/z < 67. This shows thatmolecular cross-linking occurred more frequently at a higherdeposition plasma power, while the formation of small speciescontaining intact amine groups occurred more frequently at alower deposition plasma power.

Quantitative analysis of surface-immobilized streptavidin

Compared to quantification of surface amine functional groups,quantification of surface-immobilized protein is more importantbecause it determines the performance of biochips and is essentialto various fields of biosensors and biomaterial engineering.As a model system, interaction between SA and biotin onpoly(amidoamine) (PAMAM) dendrimer-activated surfaces andon SAMs was quantitatively studied using ToF-SIMS, PCA, and

PCR. The surface protein density was systematically varied as afunction of protein concentration and independently quantifiedusing SPR or ellipsometry techniques. The ellipsometric thicknesscan be converted into the surface density by using the equationproposed by DeFeijter et al.[40] For PCR with SPR experiments, sixcharacteristic signals of SA (m/z 110 (C5H8N3

+, His), 120 (C8H10N+,Phe), 130 (C9H8N+, Trp), 136 (C8H10NO+, Tyr), 159 (C10H11N2

+,Trp), and 170 (C11H8NO+, Trp)) were normalized to the totalsecondary ion counts and mean centered before the PCA process.For PCR with ellipsometry experiments, characteristic peaks ofSA (m/z 59 (CH5N3

+, Arg), 110, 120, 130, 136, 159, and 170) andcharacteristic peaks of biotin (m/z 76 (C2H6NS+), 97 (C4H7N3

+),114 (C5H12N3

+), 227 (C10H15O2N2S+), and 272 (C12H22O2N3S+))were normalized to the sum of the selected characteristic peaks toeliminate systematic differences between the spectra and meancentered before the PCA process.[34] Fig. 2 shows a good linearcorrelation between the scores of PC 1 from the ToF-SIMS spectraof surface-immobilized SA and the surface protein densities fromSPR measurements (Fig. 2(a)) or the surface protein densitiesfrom ellipsometric thickness (Fig. 2(b)). This study shows thatvarious surface protein densities can be easily quantified with highsensitivity in a label-free manner by ToF-SIMS together with PCAand PCR methods.

On the basis of this quantification scheme, we have studiedthe effects of protein orientation and trehalose on a quantitativeanalysis of surface-immobilized SA, which was immobilized on asolid surface at different configurations by random or orientedimmobilization as shown in Scheme 3 and subsequently treated


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Figure 7. (a) CCD image, (b) RGB overlay of normal colon tissue; (c) CCD image, (d) RGB overlay of cancer colon tissue (Red: C4H8N+, Green: C3H7N+ ,Blue: 41K+). Adapted from Ref. [39]. This figure is available in colour online at www.interscience.wiley.com/journal/sia.

with trehalose. The surface density of the SA was independentlydetermined by using SPR for PCR analysis.

Figure 3 shows PCR curves for randomly immobilized SAbefore and after trehalose treatment (Fig. 3(a) and (b)) and fororiented SA before and after trehalose treatment (Fig. 3(c) and(d)), respectively. Interestingly, ion peak intensities measured byToF-SIMS correlated well with the SPR data, regardless of thepresence of trehalose. On the contrary, trehalose significantlyincreased the correlation between ToF-SIMS and SPR data for therandomly immobilized SA. We suggested that a trehalose-treatedsurface was less vulnerable to denaturation, thus leading to areliable quantification of surface-immobilized proteins by ToF-SIMS. These results suggest that ToF-SIMS along with PCA and PCRmethods can be useful for understanding biophysical states suchas orientation and denaturation of surface-immobilized proteinsas well as for quantifying proteins within the field of biosensorsand biochips.

Application of PCA for ToF-SIMS Study on SAM

We recently developed a new method of cleaning gold surface foralkanethiol SAMs since the poor reproducibility of SAMs formationhas limited the popularity of SAMs-based biochips for industrialand medical applications. The reproducibility and quality of eachSAM formed on differently cleaned gold surfaces was examined byToF-SIMS analysis along with PCA. Each peak was normalized bythe summation of selected peaks and the data was mean centeredbefore the PCA process. Figure 4(a) shows the scores plot (scoreson PC 1 vs scores on PC 2) from negative ToF-SIMS spectra of theDDT SAM with three different gold cleaning methods, i.e. super-piranha, piranha, and ethanol cleaning methods. Interestingly,scores for the super-piranha cleaning method were well grouped

around the score of −0.065 on PC 1, while scores for the othertwo cleaning methods deviated from each other on the positivescores side of PC 1. In addition to this superior reproducibility, thesuper-piranha group was characterized by a molecular ion peakof DDT SAM and their gold adducts with negative loadings asshown in Fig. 4(b). It was suggested by Graham and Ratner thatmore molecular ion species could be emitted from better orderedSAMs by an impact of the primary ion in ToF-SIMS.[10] Our studyshows that a ToF-SIMS study together with PCA can be usefulfor checking the quality of SAM and the reproducibility of SAMformation as a function of experimental factors.

Application of MVA for Tissue Studies

TOF-SIMS with MVA were used to study two kinds of human tissues:skin tissues for understanding the effects of surface cleaning andcolon cancer tissues to gather insight into the development of newcancer diagnostics.[38,39] Because we obtained all of the humantissues from hospitals, surface cleaning of the tissue samples wasessential to obtain valuable information. To determine whichcluster ion beam would be better for removing the surfacecontamination layers on the skin tissues, a PCA was performed onthe ToF-SIMS data obtained before and after surface cleaning byeach cluster ion beam.[38] As shown in Fig. 5, the scores on PC 1for data obtained before cleaning were positive values and theirpositive loadings were OCT-related peaks (m/z 58, 86, 184, 332,etc.). On the contrary, the scores on PC 1 for data obtained aftercleaning with the exception of 40s-C60

+++ cleaning conditionwere negative values and their negative loadings were skin-related peaks (m/z 30 (CH4N+, Gly), 41 (41K), 72 (C3H6NO+, Gly),81 (C4H5N2

+, His), 88 (C3H6NO2+, Asp), 104 (C5H14NO+, choline),

etc.). Because the positive scores corresponded to the positive


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Figure 8. The scores plot on PC1 and PC2 obtained from PCA with several preprocessing and MAF analysis on ToF-SIMS images of human normal andcancer colon tissues. This figure is available in colour online at www.interscience.wiley.com/journal/sia.

loadings and vice versa, these PCA results indicate that sputteringon skin samples by cluster ion beams were effective in removingthe surface contamination layer (i.e. OCT residue) on the samplesurface to reveal the surface of the skin. Regarding effectiveremoval of the surface contamination layers (i.e., OCT compounds)and preservation of chemical distribution for each ion, C60

++ (20 s,ion dose density of 9.6×1013 ions cm−2) or C60

+++ cleaning (200 s,ion dose density of 4.2 × 1012 ions cm−2) seemed to provide thebest surface cleaning for skin samples.[38]

After using the C60++ (20 s, ion dose density of 9.6 × 1013 ions

cm−2) cleaning condition on human colon tissues, ToF-SIMS datawere obtained and analyzed with the PCA and MAF methods.Initially, a PCA from TOF-SIMS data with amino acid peaks wasperformed to see whether it was possible to differentiate cancertissues from normal ones. Figure 6(a) shows that the scores onPC 1 for cancer tissues were positive scores and were distinctfrom the negative scores for normal tissues. In addition, thePCA results show that the amino acid fragments of C3H7N+

(lysine), C7H7+ (tyrosine), C5H9OS+ (methionine), and C10H11N2

+

(tryptophan) are associated with cancer tissues, while the aminoacid fragments of CH4N+ (glysine), C2H6N+ (alanine), C2H6NO+

(L-serine), and C4H8N+ (proline) are associated with normal tissues,

as the positive scores correspond to the positive loadings and viceversa (Fig. 6(b)). On the basis of these PCA results, we reconstructedRGB images of normal and cancer colon tissues with the majorloadings (Red: C4H8N (proline), Green: C3H7N (lysine), Blue: 41K)of PC 1. Figure 7(a) and (b) clearly shows the major structures ina normal colon mucosa, i.e., crypt region (black circular region),lamina propria (the pink region between the crypts identified withan arrow), gland structure (red circular region at the center of eachcrypt), and epithelium cells (region between black circular andred circular regions, Green (C3H7N, Lysine) image in RGB images).On the contrary, the major structures including crypt and laminapropria regions were distorted in a colon cancer tissue due to theproliferation of cancerous epithelium cells in the crypt region intothe lamina propria region as shown in Fig. 7(c) and (d).[39]

While a manual analysis would use several peaks obtained froma PCA analysis of ToF-SIMS spectrum data, MVA of ToF-SIMS imagedata can be useful in identifying features or patterns from rawimage data. To obtain representative images for normal and cancercolon tissues, we initially applied the PCA method to a normalcolon tissue with four different data preprocessing methods suchas no scaling, mean-centered with normalization, autoscaling withnormalization, and autoscaling without normalization. The scores


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Table 1. Estimated values of image contrast for PC 1 score imagesobtained from PCA with various preprocessing and MAF for the ToF-SIMS images of normal and cancer colon tissues

Preprocessing Normal Cancer

No scaling 2.127 1.971

Mean centered with normalization 1.65 1.274

Autoscaling with normalization 1.787 2.221

Autoscaling without normalization 2.37 2.47

MAF 2.376 2.481

images of PC 1 and PC 2 for each data preprocessing methods areshown Fig. 8. Clearly, PC 1 and PC 2 scores images obtained withautoscaling without normalization (Fig. 8(d)) show better contrastfor the major structures (crypt, lamina propria, and epitheliumcells regions) in a normal and cancer colon tissues than thoseobtained with other preprocessing methods. The preprocessingof autoscaling has been popularly used in many studies forinterpretation of the ToF-SIMS image data.[41,42] Recently, the MAFmethod has been suggested as a better image analysis methodthan the PCA method as it does not require any preprocessingprocess to obtain representative images from ToF-SIMS imagedata.[24,25,43,44] We performed a MAF analysis on a normal andcancer colon tissues and compared scores images from MAFwith those from PCA. Figure 8(e) shows scores images of PC 1 andPC 2 obtained from MAF analysis. These scores images are similar tothose obtained from PCA with autoscaling without normalization(Fig. 8(d)) with improved contrast.

To quantitatively compare PCA images with MAF images, imagecontrasts were estimated for each PC 1 score image based onTyler’s method[25] and summarized in Table 1. Contrast valuesof PCA with autoscaling without normalization and MAF aresignificantly higher than others. Contrast values from MAF analysisshow the best results.

Conclusions

Our previous applications of MVA-based ToF-SIMS studies toanalyze various organic and/or biological materials were reviewedin this report. To assist in the effective analysis of ToF-SIMS data,the use of MVA was successfully demonstrated in quantitativestudies, surface characterization, and image-based analyses ofchemicals, biomolecules, and human tissues. The MVA methodsincluding PCR and PCA made the quantitative analysis andchemical identification possible from ToF-SIMS data of a surfaceof chemical or biological samples; that is, surface density andspecific functional group (e.g. amino acid or chemical group) ofproteins and organic materials were identified and correlatedbetween ToF-SIMS data and other independent measurements(spectrometry, SPR and ellipsometry). Spectrum MVA such as PCAand PCR methods are simple and easy to use for general usersand can be much more convenient than manual data analysis,allowing an easy-to-use, simple ToF-SIMS data analysis for studiesof complex biological systems. In the case of imaging analysis,it was revealed that abnormality of : tissue samples could beclearly distinguished with the assistant of PCA and MAF. The MAFmethod especially has the great advantage of no preprocessingfor raw image data and also delivers better image contrast thanthe PCA method. Our results show that ToF-SIMS with MVA is both

reliable as well as simple, and we are excited about its potential inbiological applications.

Acknowledgements

This work was supported by the Bio-Signal Analysis Technol-ogy Innovation Program (M106450100002-06N4501-00210) ofMEST/KOSEF, the Next-Generation New-Technology DevelopmentProgram for MKE, the Development of Characterization Tech-niques for Nano-materials Safety Project of KRCF and the grant(08162KFDA550) from Korea Food & Drug Administration in 2008.

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Multivariate analysis of ToF-SIMS data for biological applications

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