pubs.acs.org/Langmuir © 2010 American Chemical Society
Surface Characterization of Carbohydrate Microarrays )
David J. Scurr,† Tim Horlacher,‡ Matthias A. Oberli,‡ Daniel B. Werz,‡,§ Lenz Kroeck,‡ Simone Bufali,‡ Peter H. Seeberger,‡, Alexander G. Shard,^ and Morgan R. Alexander*,† †
)
University of Nottingham, School of Pharmacy, Boots Science Building, NG7 2RD, United Kingdom, ‡ Max-Planck Institute of Colloids and Interfaces, Am M€ uhlenberg 1, D-14476 Potsdam, Germany, § Institut f€ ur Organische und Biomolekulare Chemie der Georg-August-Universit€ at G€ ottingen, Tammannstr. 2, D-37077 G€ ottingen, Germany, Freie Universit€ at Berlin, Arnimallee 22, 14195 Berlin, Germany, and ^ National Physical Laboratory, Hampton Road, Teddington, Middlesex, TW11 0LW, United Kingdom Received July 28, 2010. Revised Manuscript Received September 17, 2010 Carbohydrate microarrays are essential tools to determine the biological function of glycans. Here, we analyze a glycan array by time-of-flight secondary ion mass spectrometry (ToF-SIMS) to gain a better understanding of the physicochemical properties of the individual spots and to improve carbohydrate microarray quality. The carbohydrate microarray is prepared by piezo printing of thiol-terminated sugars onto a maleimide functionalized glass slide. The hyperspectral ToF-SIMS imaging data are analyzed by multivariate curve resolution (MCR) to discern secondary ions from regions of the array containing saccharide, linker, salts from the printing buffer, and the background linker chemistry. Analysis of secondary ions from the linker common to all of the sugar molecules employed reveals a relatively uniform distribution of the sugars within the spots formed from solutions with saccharide concentration of 0.4 mM and less, whereas a doughnut shape is often formed at higher-concentration solutions. A detailed analysis of individual spots reveals that in the larger spots the phosphate buffered saline (PBS) salts are heterogeneously distributed, apparently resulting in saccharide concentrated at the rim of the spots. A model of spot formation from the evaporating sessile drop is proposed to explain these observations. Saccharide spot diameters increase with saccharide concentration due to a reduction in surface tension of the saccharide solution compared to PBS. The multivariate analytical partial least squares (PLS) technique identifies ions from the sugars that in the complex ToF-SIMS spectra correlate with the binding of galectin proteins.
Introduction Carbohydrate interactions are involved in most extracellular biological processes and are important in a large number of diseases.1-4 Most specific glycan functions are mediated by interactions with proteins. Due to glycan complexity and microheterogeneity, relatively little is known about carbohydrate function within organisms.5 Carbohydrate microarrays are very useful to address the challenges in carbohydrate research. Glycan arrays allow for hundreds of glycan interactions to be screened in parallel.6-8 Only very small amounts of carbohydrate are needed to produce glycan microarrays, an important consideration when complex sugars have to be synthesized or purified. The surface display of the molecules on the array mimics the situation on the cell surface. For these reasons, carbohydrate microarrays are indispensable tools to study glycan interactions, including the identification of novel glycan ligands, new glycan binding proteins, *To whom correspondence should be addressed. E-mail: Morgan.
[email protected] (MRA). (1) Adams, E. W.; Ratner, D. M.; Bokesch, H. R.; McMahon, J. B.; O’Keefe, B. R.; Seeberger, P. H. Chem. Biol. 2004, 11, 875–881. (2) Schofield, L.; Hewitt, M. C.; Evans, K.; Siomos, M. A.; Seeberger, P. H. Nature 2002, 418, 785–789. (3) Barth, K. A.; Coullerez, G.; Nilsson, L. M.; Castelli, R.; Seeberger, P. H.; Vogel, V.; Textor, M. Adv. Funct. Mater. 2008, 18, 1459–1469. (4) Seeberger, P. H.; Werz, D. B. Nature 2007, 446, 1046–1051. (5) Werz, D. B.; Ranzinger, R.; Herget, S.; Adibekian, A.; Lieth, C.-W. v. d.; Seeberger, P. H. ACS Chem. Biol. 2007, 2, 685–691. (6) Fukui, S.; Feizi, T.; Galustian, C.; Lawson, A. M.; Chai, W. Nat. Biotechnol. 2002, 20, 1011–7. (7) Blixt, O.; Head, S.; Mondala, T.; Scanlan, C.; Huflejt, M. E.; Alvarez, R.; Bryan, M. C.; Fazio, F.; Calarese, D.; Stevens, J.; Razi, N.; Stevens, D. J.; Skehel, J. J.; van Die, I.; Burton, D. R.; Wilson, I. A.; Cummings, R.; Bovin, N.; Wong, C. H.; Paulson, J. C. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 17033–17038. (8) Horlacher, T.; Seeberger, P. H. Chem. Soc. Rev. 2008, 37, 1414–1422.
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characterization of glycan interactions, analysis of structureactivity relationships, inhibition studies, and the determination of kinetic constants. Glycan arrays have been used to assess glycan immunogenicity and helped to identify malarial glycosylphosphatidylinositol (GPI) substructures that are recognized by anti-GPI antibodies.9,10 Discrimination of Salmonella infection subtypes was enabled by glycan arrays of Salmonella antigens.10 Hence, carbohydrate microarrays may become diagnostic tools. In such a diagnostic glycan array, the sugar probe molecule is attached to a surface and selectively captures target molecules in solution, which are generally antibodies that are present in serum samples and constitute a biomarker for a specific disease. For instance, using carbohydrate microarrays, anticarbohydrate antibodies that recognize strain-specific glycans may be detected to reveal the pathogen causing an infection. Rapid identification of diseases with improved accuracy will result in the identification of the correct treatment and greater patient benefit.8 The development of glycan arrays is an essential step in unraveling important carbohydrate antigens and finding new approaches for diagnosis and monitoring of diseases. Most microarrays are produced by covalent coupling of sugars to functionalized slides. Generally, carbohydrates equipped with a linker that guarantees flexibility and bears a chemical group (e.g., amine, thiol, azide) for coupling are synthesized. Microarray glass slides are functionalized with a corresponding reactive chemical group (e.g., epoxide, NHS-ester, maleimide), and the (9) Kamena, F.; Tamborrini, M.; Liu, X.; Kwon, Y. U.; Thompson, F.; Pluschke, G.; Seeberger, P. H. Nat. Chem. Biol. 2008, 4, 238–40. (10) Blixt, O.; Hoffmann, J.; Svenson, S.; Norberg, T. Glycoconj. J. 2008, 25, 27-36.
Published on Web 10/18/2010
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carbohydrates are printed onto these slides using automatic arraying robots. For our investigation, saccharides equipped with a thiol-bearing linker have been attached to aminosilane functionalized slides terminated with a maleimide linker. Even though glycan arrays are now widely used to address biological issues,8,11,12 their physicochemical characterization has been lacking. It has been noted that the relationships among carbohydrate structure, density, and recognition are highly complex, unpredictable, and generally determined empirically.13 There is good reason to characterize the arrays since there is general agreement that the application of oligonucleotide and protein microarrays has been hindered by several problems. Poor quantification and reproducibility result from inconsistencies in the printed area, reagent density at the surface, uneven distribution, variable chemical composition, molecular orientation, and immobilization state.14-17 Model arrays have been employed to provide detailed information on the surface ligand density, saccharide distribution, and various linker chemistries.18-20 We aim to understand the factors that contribute to the production of saccharide spots to identify and address any underlying array fabrication issues for this class of biomolecules. The characterization of saccharides in solution is not routine, and analyzing carbohydrates that are immobilized on slides as monolayers or submonolayers is even more challenging. Time of flight secondary ion mass spectrometry (ToF-SIMS), a surface chemical analytical technique, can determine the molecular distribution within a spot. A primary ion beam is used to generate secondary ions from the surface which, when analyzed by measuring their time-of-flight, produce a mass spectrum from the uppermost surface monolayer. Since this is a highly energetic process, fragment ions are dominant, making the interpretation challenging compared to mass spectrometry of solutions where the molecular ion is normally dominant. Consequently, a multivariate analysis approach is required to sift and maximize the information extracted from the complex ToF-SIMS spectral data.21 The presence of non-covalently immobilized galactose derivatives on the surface of latex particles and diamond has previously been analyzed by SIMS.22,23 In these two studies, SIMS was used to confirm the presence of the saccharide at the surface. More recently, Berman et al. reported a ToF-SIMS study of seven isomeric monosaccharides analyzed individually as thick samples (11) Seeberger, P. H. Nat. Chem. Biol. 2009, 5, 368–372. (12) Paulson, J. C.; Blixt, O.; Collins, B. E. Nat. Chem. Biol. 2006, 2, 238–248. (13) Oyelaran, O.; Li, Q.; Farnsworth, D.; Gildersleeve, J. C. J. Proteome Res. 2009, 8, 3529–3538. (14) Sherlock, G. Nat. Methods 2005, 2, 329–330. (15) Grainger, D. W. G., C. H.; Gong, P.; Lochhead, M. J. In Microarrays: Methods and Protocols (Methods in Molecular Biology), 2nd ed.; Rampal, J., Ed.; Humana Press: Totowa, NJ, 2006. (16) Gong, P.; Lee, C.-Y.; Gamble, L. J.; Castner, D. G.; Grainger, D. W. Anal. Chem. 2006, 78, 3326–3334. (17) Lee, C.-Y.; Gong, P.; Harbers, G. M.; Grainger, D. W.; Castner, D. G.; Gamble, L. J. Anal. Chem. 2006, 78, 3316–3325. (18) Dhayal, M.; Ratner, D. A. Langmuir 2009, 25, 2181–2187. (19) Wendeln, C.; Heile, A.; Arlinghaus, H. F.; Ravoo, B. J. Langmuir 2010, 26, 4933–4940. (20) Dietrich, P. M.; Horlacher, T.; Gross, T.; Wirth, T.; Castelli, R.; Shard, A. G.; Alexander, M.; Seeberger, P. H.; Unger, W. E. S. Surf. Interface Anal. 2010, 42, 1188–1192. (21) Lee, J. L. S.; Gilmore, I. S., The Application of Multivariate Data Analysis Techniques in Surface Analysis. In Surface Analysis: The Principal Techniques, 2nd ed.; Vickerman, J. C.; Gilmore, I. S., Eds.; John Wiley & Sons Ltd: Chichester, 2009; pp 563-612. (22) Davies, M. C.; Lynn, R. A. P.; Davis, S. S.; Hearn, M. J.; Watts, J. F.; Vickerman, J. C.; Paul, A. J. Langmuir 1993, 9, 1637–1645. (23) Leonard, D.; Chevolot, Y.; Heger, F.; Martins, J.; Crout, D. H. G.; Sigrist, H.; Mathieu, H. J. Surf. Interface Anal. 2001, 31, 457–464. (24) Berman, E.; Kulp, K.; Knize, M.; Wu, L.; Nelson, E.; DO, N.; Wu, K. Anal. Chem. 2006, 78, 6497–6503.
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deposited from purified water, a sample format chosen to obtain the best-quality ToF-SIMS spectra.24 Despite their equivalent molecular weights, the multivariate statistical technique of principle component analysis (PCA) allowed the isomeric monosaccharides of mannose, glucose, galactose, fructose, sorbose, tagatose, and psicose, to be distinguished. Each isomer displayed a unique relative abundance of fragment ions that the PCA analysis of the SIMS results was able to use as a “fingerprint” for each isomer. ToF-SIMS may constitute a powerful tool with great potential in the analysis of glycan arrays, but it has not previously been determined whether this technique can provide useful information from carbohydrate microarrays since the previous studies utilized idealized thick films of noncovalently attached sugars. Unavoidable contaminants, such as constituents of the PBS buffer which is employed in virtually all biological protocols, may also hinder microarray analysis. The analysis of arrays presents two major challenges: data acquisition from many spots and the scale of data processing. These obstacles can be overcome using the multivariate statistical methods of partial least-squares (PLS) and multivariate curve resolution (MCR): For example, surface chemical analysis has previously been successfully applied to the analysis of hundreds of different polymer samples in a microarray format for materials discovery using combinatorial libraries.25 To handle this volume of acquisition and processing, automated methods for X-ray photoelectron spectroscopy (XPS), ToF-SIMS, and water contact angle analysis (WCA) were developed.26 Partial least-squares (PLS) and multivariate curve resolution (MCR) are means of processing hyperspectral ToF-SIMS images that have spectra with many thousands of channels and hundreds of peaks at each pixel.21 To detect relationships between the complex multivariate ToF-SIMS spectra with a univariate data set, such as WCA data, PLS regression analysis was applied, which extracted the role of surface molecular surface structure in controlling the WCA.27 This earlier work forms the basis for the analysis of carbohydrate microarrays reported here, since the data capture and analysis issues are similar. We have analyzed carbohydrate microarrays using imaging ToF-SIMS to understand the relationship between glycan solution concentration, printed spot size, saccharide density, and glycan distribution on the slide and within the spots. Advanced imaging multivariate analysis techniques are employed to extract the information from the hyperspectral SIMS images, with MCR proving to be the most suitable. The ToF-SIMS analysis provides the basis for a model to be proposed of sessile drop evaporation on glycan microarray spots which can explain the structures observed. The multivariate statistical technique of PLS regression analysis helped identify the important surface molecular saccharide fragment ions in the ToF-SIMS data and revealed tentative correlations between protein binding and these.
Experimental Section Synthesis of the Mono- and Oligosaccharides. A modular approach was used for the chemical synthesis of carbohydrates 1-17 (Figure 1). Key building blocks equipped with permanent (Bn, Piv) and temporary protecting groups (mainly Fmoc or Ac) for the hydroxyl groups were chosen.5 Linear solution-phase (25) Anderson, D.; Levenberg, S.; Langer, R. Nat. Biotechnol. Lett. 2004, 22, 863–866. (26) Urquhart, A. J.; Anderson, D. G.; Taylor, M.; Alexander, M. R.; Langer, R.; Davies, M. C. Adv. Mater. 2007, 19, 2486–2491. (27) Urquhart, A.; Taylor, M.; Anderson, D.; Langer, R.; Davies, M.; Alexander, M. Anal. Chem. 2008, 80, 135–142.
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Figure 1. Chemical structures and layout of the microarray of 17 different synthetic oligosaccharides 1-17 printed at four concentrations (2 mM and 400, 80, and 10 μM) in PBS.
synthesis, installation of the reactive thiol moiety at the linker and global deprotection by Birch reduction furnished the desired oligosaccharides. Langmuir 2010, 26(22), 17143–17155
Fabrication of Carbohydrate Microarrays. Carbohydrate microarrays were produced as described previously.28,29 In brief, carbohydrate compounds were diluted to concentrations of DOI: 10.1021/la1029933
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Article 2 mM, 400 μM, 80 μM, and 16 μM in PBS (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.76 mM KH2PO4) with 1 mol equiv tris(2-carboxyethyl)phosphine (TCEP). The maleimide slides used in this series of experiments are produced using commercially sourced amine-functionalized slides, an approach adopted to maximize batch to batch reproducibility. Maleimide-functionalized microarrays were produced by submerging amine-coated slides (GAPS II slides, Corning) in 2 mM 6-maleimidohexanoic acid N-hydroxysuccinimide ester in DMF with 2.5% (v/v) diisopropylethylamine for 24 h at room temperature. Slides were washed three times with water and three times with ethanol, centrifuged until dry, and stored under argon atmosphere until spotting. Diluted and reduced compounds were printed onto the functionalized microarray slides at 1 nL per spot by an automatic arraying robot (Scienion). For completion of the immobilization reaction, the printed slides were stored for 24 h in a humidified chamber at ∼70%. The structures of the oligosaccharides included structural isomers (e.g., 2/3 and 12/13), sulfated galactosides (12 and 13), and linear and branched oligosaccharides (e.g., 7 and 9, respectively); these are presented in Figure 1. A thiol moiety on the carbohydrate molecules was used to link them to an aminosilane surface functionalized with maleimide. These carbohydrates were printed in a PBS solution (1 nL per spot) with the intention of covalently linking them to a surface of the slide. PBS maintains the correct pH for the maleimide/thiol reaction to take place while suppressing others. A PBS control was included on the slide of the same total drop volume as for the spots including the saccharides. Galectin Biotinylation. Galectins (R&D Systems) at a concentration of 0.5 mg/mL in PBS were incubated with 2.5 mM biotin 3-sulfo-N-hydroxysuccinimide ester firstly for 30 min at room temperature and then for 1 h at 4 C. Unreacted biotin 3-sulfo-N-hydroxysuccinimide ester was quenched by the addition of 1 vol equiv 100 mM glycine in PBS and incubation for 30 min at room temperature. Galectin Binding Experiments. Carbohydrate microarray slides were washed three times with water. Nonreacted maleimide was quenched by submerging the slides in 0.1% (v/v) β-mercaptoethanol in PBS for 1 h at room temperature. Slides were washed three times with water and with ethanol, centrifuged to dryness, and blocked with 2.5% BSA in PBS for 1 h at room temperature. Blocked slides were washed twice with PBS, centrifuged, and incubated with 10 μg/mL biotinylated galectins (human galectin-3, human galectin-4, and human galectin-8; R&D Systems) in PBS with 1% BSA and 0.1% Tween-20 for 1 h at room temperature. Incubated slides were washed with PBS, centrifuged, and overlaid with 10 μg/mL Cy3-streptavidin in PBS with 1% BSA and 0.1% Tween-20 for 1 h at room temperature. Slides were washed twice with PBS and centrifuged to dryness. For detection, slides were scanned with a fluorescence microarray scanner (Tecan). Spot intensities were evaluated using the Genespotter software (MicroDiscovery). SIMS Data Acquisition. ToF-SIMS data was acquired from the slide as printed and after an extensive rinsing procedure using deionized ultrapure water. Hyperspectral image data acquisition was employed to acquire an image with a full spectrum at each pixel. From this type of image, the full spectrum can be selected from any region of interest and images may be reconstructed from selected ions. To optimize the quality of the data, three 9 9 mm2 fields of view were used to cover the 18 sample areas, thereby capturing the data from each material printed in 10 replicates at 4 concentrations. The ToF-SIMS data were acquired using a ToF-SIMS IV instrument (ION-TOF GmbH., M€ unster, Germany) equipped (28) Crouch, E.; Hartshorn, K.; Horlacher, T.; McDonald, B.; Smith, K.; Cafarella, T.; Seaton, B.; Seeberger, P. H.; Head, J. Biochemistry 2009, 48, 3335–45. (29) de Paz, J. L.; Horlacher, T.; Seeberger, P. H. Methods Enzymol. 2006, 415, 269–92.
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Scurr et al. with a bismuth liquid metal ion gun and a single-stage reflectron analyzer. Typical operating conditions utilized a Bi3þ primary ion energy of 25 kV and a pulsed target current of approximately 1.0 pA. Low-energy electrons (20 eV) were used to compensate for surface charging caused by the positively charged primary ion beam on the insulating surfaces. Macroscale rastered areas of 9 9 mm2 were analyzed at a resolution of 256 256 pixels, with 64 pulses per pixel. In order to analyze all 18 samples, the analysis of the array was divided into 3 sections, covering 6 saccharides for both the positive and negative secondary ion spectra: samples 1-6, 7-12, and 13-18. The total primary ion beam dose for each analyzed area was kept below 1 1012 ions cm-2, ensuring static conditions. Preliminary and preparative data analysis was carried out using IonSpec and IonImage (version 4.1) software. ToF-SIMS Multivariate Analysis Data Processing. The ToF-SIMS data sets were analyzed by PCA, MCR, and PLS using PLS_Toolbox (version 5.2, eigenvector Research, Manson, WA) for Matlab (Mathsworks, Inc., Natick, MA). PCA was performed on the 17 saccharide samples after rinsing (Supporting Information) and also as a precursor to MCR. Here, PCA is used to produce an eigenvalue plot, from which the suitable number of components are derived for the MCR analysis. In this instance, for both the PCA and MCR analysis, no mean centering was carried out, because for MCR, mean centering is not carried out as the non-negativity constraint requires the data to have positive values throughout. Two separate peak lists were created, each specific to the positive and negative secondary ion spectra; these were constructed using the data collected from the hyperspectral image data sets of the 17 saccharides analyzed. The integration of these peaks was obtained (using Ionspec v 4.1), subsequently exported and normalized to the total ion count, thereby accounting for any normal variation in secondary ion yield between the spots. The peak integrations for the positive and negative secondary ion data were then concatenated into a single data matrix from which PLS was carried out. As a prerequisite to the final data acquisition, PLS was first calculated using the leave one out cross-validation method applying 17 latent variables. From an analysis of the resulting rootmean-square error of cross-validation (RMSECV) and an assessment of the results using a range of latent variables, a suitable latent variable number was selected. A more detailed analysis of how the number of latent variables was established can be found in the Supporting Information.
Results A series of thiol-functionalized saccharide molecules (1-17) were synthesized using established protocols (Figure 1).30 Structural isomers (e.g., 2/3 and 12/13), sulfated galactosides (12 and 13), and linear and branched oligosaccharides (e.g., 7 and 9) were included. Blank PBS printing buffer (18) served as a control. The seventeen different synthetic oligosaccharides and the control were printed onto a maleimide functionalized glass slide as phosphate buffered aqueous solutions in ten replicates of four concentrations (2 mM and 400, 80, and 10 μM). ToF-SIMS analysis of the microarray was performed both just after printing and after rinsing with deionized water. The conditions before rinsing reflect the situation after carbohydrate surface reaction, while the rinsed slides reflect the situation in which the protein binding experiment occurs. Hyperspectral images of the array which included a full SIMS spectrum at each pixel were acquired from 0 to 800 m/z. Post-processing allowed for the reconstruction of spectra from certain areas, and maps can be constructed using ions of interest. To accommodate the full area of the array, three (30) Horlacher, T.; Oberli, M. A.; Werz, D. B.; Krock, L.; Bufali, S.; Mishra, R.; Sobek, J.; Simons, K.; Hirashima, M.; Niki, T.; Seeberger, P. H. ChemBioChem 2010, 11, 1563–73.
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Figure 2. 9 9 mm2 field of view showing scores images for selected components from (a) the unrinsed array and (b) the rinsed array, where the highest 13 loaded ions are shown below each image. MCR analysis is performed separately for the unrinsed (u) and rinsed (r) arrays and the positive and negative secondary ion data, where each analysis generates a number of components, as denoted here by a number and þ or -. The sample number is denoted in white text on component u1þ, where the spots are printed in replicates of 10, with 2 columns of each of the 4 concentrations stated in Figure 1.
such areas (9 9 mm2) were acquired for both positively and negatively charged secondary ions. Identification of Characteristic Ions. The ToF-SIMS spectra from the rinsed and unrinsed microarrays are composed of many ions. For instance, a spot of coupled linker-equipped lactose (1) contained more than 327 secondary ion peaks in the positive ion spectrum and more than 307 fragment ions peaks in the negative secondary ion spectrum, with up to 5 separate components at some unit masses. Data processing presents a considerable challenge for the analyst in terms of not only volume, but also chemical complexity, since the ions must first be assigned with a stoichiometric formula based upon the mass and chemical structural considerations and then is allocated to the parent Langmuir 2010, 26(22), 17143–17155
compound. To aid in this process, multivariate analysis approaches were adopted to handle the many ToF-SIMS images generated from each secondary ion.21 It is reasonable to suppose that the surface consists of a finite number of distinct chemical phases, which are arranged over the surface at different concentrations in different areas. If we then assume that each of these phases generates a unique SIMS spectrum in which the relative intensities of secondary ions do not change with concentration, then the SIMS spectrum from a particular area of the surface will be a sum of the individual spectra from the chemical phases. By examining the manner in which the SIMS spectra vary across the surface, it is often possible to deduce the spectra of those phases; this deductive task is carried DOI: 10.1021/la1029933
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out by multivariate curve resolution (MCR). MCR groups each distinct surface chemical phase in the image as a principal component. These are then expressed as scores images showing the distribution of the chemical phase and a loadings plot which describes the chemistry in terms of the ToF-SIMS ions associated with this principal component. To do this, it uses an iterative leastsquares algorithm to extract solutions to the factor analysis equation with a non-negativity constraint. The factors identified by MCR in the ToF-SIMS spectra are the scores images which are accompanied by the 13 most intense ions from the loadings plot in Figure 2. The loadings plots contain the ions used to construct these images. This approach allows the variance within the ToFSIMS hyperspectral images to be identified without the need for user intervention. To ensure that the data are not over-fit, the number of components is determined for each area using the scree plot procedure described in the Experimental Section.21 This procedure determined that six and eight components are required to model the main features in the positive secondary ion data presented in Figure 2 for the unrinsed and rinsed arrays, respectively. For the negative secondary ion data presented in Figure 2 for the rinsed array, five principal components were determined. Peaks can be assigned to ion structures that are consistent with their location in the image and measured mass, i.e., sugars, linker, or buffer salts by comparison with the known chemical constituents of the microarray. The array section containing samples 13-18 presented in Figure 2 reveals aspects of the surface chemistry determined by MCR for both unrinsed and rinsed samples. The scores plots for the unrinsed slides (Figure 2a) shows secondary ions that are indicative of salts such as potassium, sodium, and phosphate ions from the PBS printing buffer (component 1 of the positive secondary ion image (component u1þ) in Figure 2a). Analysis of the unrinsed sample revealed the maleimide-amino silane background clearly as component u5þ by the multitude of hydrocarbon ions and characteristic nitrogen and oxygen containing ions. Component u6þ revealed a ringshaped surface structure containing sodium and potassium as well as organic ions from saccharide and maleimide. This ring is thought likely to result from a thin layer of salt at the rim of the spot where an increase in the ionization probability of the local organic fragments (such as C3H5þ, C2H3Oþ, C2H5Oþ) is induced. Component u4þ is thought to be representative of the distinct amino group present in sample 15. After rinsing, the MCR scores image for component r2þ showed that organic fragment secondary ions were observed from all printed spots in the field of view except the PBS control (Figure 2b). Thus, we conclude that these ions are associated with saccharide molecules. Potassium (Kþ) was also present in this principal component suggesting that it may be retained after rinsing, although at a far lower relative intensity than that prior to washing. Consistent with the retention of lower levels of PBS, component r2- contains the negative secondary ion PO3H-. The linker fragment HS- and the saccharide fragment ions C2H3O2-, CHO2-, C2HO-, OH-, and C2H- were also identified in this principal component from all saccharide spots in this field of view. The intensity of component r2- in the PBS control area is very low, indicating that no saccharide contribution comes from these spots. These observations suggest that when the PBS is retained despite the rinsing process it is most probably by association with the saccharide material. Component r8þ is indicative of the maleimide-amino silane background with the peaks in the loadings assigned to hydrocarbons similar to the MCR component u5þ of the unrinsed slide. The negative secondary ion component 17148 DOI: 10.1021/la1029933
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r1- was dominated in the loadings by sulfate species SO4H-, SO3-, and SO4-, as well as oxygen (O-) and hydroxyl (OH-) ions. The image indicates these are primarily from sample 13 (Gal-6-SO4) indicative of the sulfate moiety. Imaging of Carbohydrate Microarray using Representative Ions. Using the assignments generated from the MCR analysis discussed above, secondary ions were selected to represent the surface species present on the array before and after rinsing. The Kþ secondary ion was selected to represent the PBS salts, S- and HS- the linker, C4H7þ the background maleimide slide, and the C2H3Oþ and the C5H7O3þ secondary ions were observed to be associated with certain saccharides. From the three ion intensity images covering the whole array, it is apparent that the spots were dominated by PBS salts prior to rinsing (Figure 3). After rinsing, the potassium ion was retained in the spotted areas but at a much reduced intensity. Some spots displayed organic fragment ions indicative of saccharides both before and after rinsing, including C2H3Oþ (Figure 3), which may be a nonspecific fragmentation generated from most saccharide monomers. The maleimide-functionalized amino silane background was clearly defined by the C4H7þ ion for both the unrinsed and rinsed array (Figure 3). Rinsing increased the intensity of many saccharide ion fragments indicating that the saccharide is obscured by salt deposits; for example, prior to rinsing the secondary ion image suggests that many spotted areas had little or no C2H3Oþ intensity, whereas after rinsing, this intensity increased significantly. The exception was sample 15 for which the intensity of C2H3Oþ decreased after rinsing. This suggests unbound saccharide before rinsing, possibly on salt residues, that was removed after rinsing of the salts. The S- þ SH- ions, associated with the sugar linker molecule can be used to estimate its distribution. Within the spots formed from solutions of concentration less than 2 mM, the S- þ SH- is relatively uniform (Figure 3), whereas for the spots formed from the 2 mM concentration solutions, a ring structure was often observed; this phenomenon is investigated in more detail later by acquisition of higher-resolution images. The relatively uniform S- þ SH- ion intensity between the spots suggests similar surface concentrations for the different printed sugar molecules, although this is only a first-order approximation since SIMS is not a readily quantitative technique. Interestingly, the relative intensity of sulfur ions from spot to spot was very different from the relative intensity of the C2H3Oþ fragment ions seen from many of the sugars. For example, spots of sugars 3 and 4 resulted in high-intensity C2H3Oþ signals, but the S- þ SH- intensity from these spots was similar to that of the other spots on the array (Figure 3). Furthermore, the C2H3Oþ secondary ion intensity was far more strongly emitted from spot 3 than 2 (structural isomers). This wide variety of intensity from different sugars was also observed for the C5H7O3þ ion, which was only detected above the background slide level for 3 and 11 before rinsing and only for sugar 11 after rinsing (Figure 3). These observations indicate that subtle changes in the structures of the sugars have a significant effect on the intensity of the secondary ion fragments such as the C2H3Oþ and C5H7O3þ ions that arise from the sugars. Such a wide range of fragmentation/ionization efficiency in the SIMS process makes quantification difficult, but should provide sensitivity to the exact sugar identity from similar environments. This effect is particularly notable for the isomers 10 and 11, where 11 has a high intensity of C3H7O3þ, yet neither sugar 10 nor any of the other saccharides show this ion at a significant intensity. The relative uniformity of the sulfur linker fragments suggests similar sugar immobilization density; thus, the stark difference in intensity of C3H7O3þ between Langmuir 2010, 26(22), 17143–17155
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Figure 3. Secondary ion images of unrinsed and rinsed array made up of 3 images to show the full array of 17 printed saccharides and a control, where the spots are printed in replicates of 10, with 2 columns of each of the 4 concentrations stated in Figure 1.
10 and 11 indicates that fragmentation and ionization effects result in significant ion intensity enhancements, such as for the Man(R1-3) environment in saccharide 11. The ability of the information contained in the ToF-SIMS data to detect these subtle differences in structure is further demonstrated by the PCA results obtained from all 17 rinsed saccharide materials. The formation of distinct clusters of some saccharide samples when plotted as PC1 versus PC2 and PC3 versus PC4 (Figure 4 and Supporting Information Figure S-1, respectively) demonstrates that, using the ToF-SIMS data, these samples Langmuir 2010, 26(22), 17143–17155
can be distinguished from one another. Importantly, this includes differentiating among structural isomers (e.g., 2/3, 10/11, and 12/13). The ions which contribute to these principal components are listed as the 15 strongest positively and negatively loaded values in Table 1. The majority of the secondary ions can be rationalized as fragments from the saccharides. Several of these secondary ions have been previously identified as being derived from the structure of saccharides in ToF-SIMS analysis; these include C2H3Oþ, CH3Oþ, C3H5O2þ, and C5H7O3þ.24 There are a small number DOI: 10.1021/la1029933
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Article
Scurr et al.
Figure 4. Scores plot from PCA data reduction of ToF-SIMS spectra showing principle components 1 and 2 for 17 saccharide samples and the PBS control (18) taken from the rinsed saccharide array, where the sample numbers correspond with the chemical structures outlined in Figure 1. Table 1. The 15 Strongest Loaded Secondary Ions and Their Corresponding Mass/Charge (m/z) for PC 1 and PC 2 Identified from PCA Analysis of the 17 Saccharide Materials and the PBS Control, Obtained from the Rinsed Array
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
PC 1
PC 1
PC 2
PC 2
maximum 15 loadings
strongest negatively loaded ions
maximum 15 loadings
strongest negatively loaded ions
ion
m/z
ion
m/z
ion
m/z
ion
m/z
OOHCNCHSO2CHCNO CSOC2HCSO2CHSOAlþ C2HSO5CSiþ C2-
15.99 17.00 26.00 76.97 13.01 42.00 59.97 25.01 75.97 60.98 26.98 136.94 12.00 27.98 24.00
PO3COClSO3C2H3O2C3H7O3þ CHO2SO3HC3H3O2unknownC8H7O5C4H9þ SO4HHSC6HC3H5Oþ
78.96 62.97 79.96 59.02 91.06 45.00 80.97 71.02 447.15 183.02 57.07 96.96 32.98 73.01 57.04
PO3COClOC4H9þ C2H3Oþ OHC3H8Nþ C2H5Oþ SO4HC3H3Oþ CHO2UnknownC5H9þ C5H7Oþ CH3Oþ
78.96 62.97 15.99 57.07 43.02 17.00 58.07 45.04 96.96 55.02 45.00 447.15 69.07 83.06 31.02
C3H5þ C3H7þ C2HC4H7þ CNCHCNOKþ C4HC2H3þ Naþ C8H7O5SiCH3CHSO2NO2-
41.04 43.06 25.01 55.06 26.00 13.01 42.00 38.96 49.01 27.02 22.99 183.02 43.00 76.97 45.99
of secondary ions identified in the loadings of PC1-4 that are representative of the PBS constituents, specifically Naþ, Kþ, PO3-, and COCl-. It is thought that the presence of these ions in the loadings may be reflective of the different propensity of each of the saccharide materials to retain PBS. Of these 15 strongest positively and negatively loaded data, the PBS constituent ions account for
