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Nanoarray-Based Biomolecular Detection Using Individual Au Nanoparticles with Minimized Localized Surface Plasmon Resonance Variations Longhua Guo, Abdul Rahim Ferhan, Kijoon Lee, and Dong-Hwan Kim* School of Chemical and Biomedical Engineering, Nanyang Technological University, 637457, Singapore
bS Supporting Information ABSTRACT: Here, we present a mean to expand the use of individual metallic nanoparticles to two-dimensional plasmonic nanoarrays. An optical detection platform to track down localized surface plasmon resonance (LSPR) signals of individual nanoparticles on substrates was built for the application of plasmonic nanoarrays. A pseudoimage of nanoparticles on a substrate was reconstructed from their scattering spectra obtained by scanning a user-defined area. The spectral and spatial resolutions of the system were also discussed in detail. Most importantly, we present a method to normalize the localized surface plasmon resonance from geometrically different nanoparticles. After normalization, plasmonic responses from different particles become highly consistent, creating well-fitted dose-response curves of both surrounding refractive index changes and receptoranalyte binding to the surface of individual nanoparticles. Finally, the proof-of-concept system for plasmonic nanoarray detection is demonstrated by the measurement of the aptamer-thrombin binding event.
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ince the success of array-based biomolecular detection (protein/DNA microarrays), nanometer-scaled sensing arrays (nanoarrays) have drawn great attention due to their significant advantages over the conventional microarrays in aspects of larger areal density (∼104-105-fold improvement),1,2 smaller sample volumes (thousands of times smaller),3 and greater sensitivity (orders of magnitude higher from a copy number standpoint).4 In addition, recent progress in dip-pen nanolithography (DPN),5-7 capable of generating biomolecular arrays with resolution below 50 nm, has opened the possibility of biomolecular nanoarrays.1,2 However, biomolecular sensing on nanostructured arrays often involves practical difficulties due to the lack of effective readout schemes. Although a few methods (e.g., fluorescence microscopy8,9 and atomic force microscopy (AFM))4,9,10 are currently being adopted to obtain nanoarray readouts, constraints still impede the use of these approaches. For example, detection based on fluorescence requires additional fluorophore labeling, while AFM-based methods carry the risk of protein degradation from tip-sample interactions. Therefore, there is a clear need for the development of simple and effective detection schemes for biomolecular nanoarrays. Label-free sensing based on localized surface plasmon resonance (LSPR) is a promising option for nanoarray readouts because LSPR is essentially derived from metal nanoparticles,11-13 the size of which is typically 10-100 nm.14 Despite the great potential of this technique,15-20 the example of nanoarray-based biomolecular detection in which wavelength shift of single nanoparticles is used for detection is currently unavailable. In fact, there are a number of fundamental issues which need to be addressed before early stage r 2011 American Chemical Society
single-nanoparticle sensor designs can be realized in miniaturized and high-throughput biomolecular nanoarrays. One principal concern is the variations in the peak-wavelength location (λmax) and corresponding localized surface plasmon resonance (LSPR) shifts (Δλmax) among nanoparticles due to practical, physical, and technical constraints in fabricating monodispersed nanoparticles. As a result, LSPR signals from nanoparticles with different sizes and shapes, even ones that are synthesized in the same batch, must be tuned to obtain consistent plasmonic responses between nanoparticles in the case of surrounding refractive index (RI) change (Δn). Another technical limitation for plasmonic nanoarrays lies in the lack of detection platform. Due to the fact that only one dimension of a spectral imaging camera can be used for spatial detection (the other dimension is required for spectral capture), current optical setups for single-nanoparticle plasmonic sensors are limited to the detection of nanoparticle(s) present in a single line, yielding averaged spectral information of a single line (Scheme 1A)21-26 or several nanoparticles with spatially resolved spectral information (an advanced platform, Scheme 1B).14,18,27 The above-mentioned setups employ either anchored or nonsynchronized sample stages so that the maximum detection area of samples is limited to a narrow spatial window that is determined by the size of the charge-coupled device (CCD) of the spectral camera (0.2 20 μm2 in our system, see Supporting Information, SI1). Received: November 11, 2010 Accepted: February 20, 2011 Published: March 09, 2011 2605
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Recently developed hyperspectral imaging, often equipped with liquid crystal variable filters,28,29 has enabled the determination of LSPR spectra of multiple plasmonic nanoparticles in parallel.30 However, these techniques are limited for molecular imaging where nanoparticles serve as visualization labels. Here, we demonstrate a practical optical setup that enables high-resolution LSPR wavelength-shift measurements (down to 0.16 nm) synchronously from 2D nanoarrays and identify individual nanoparticles with their corresponding spectra, allowing us to realize Scheme 1C into bioassay applications (Scheme 1D). Herein, we demonstrate two distinct but interconnected developments toward the application of single nanoparticles as sensing units for plasmonic nanoarrays, as follows: (1) a strategy for the identification and extraction of discrete LSPR wavelength shifts of individual nanoparticles from two-dimensional (2D) nanoarrays and (2) a method to minimize the diverse LSPR responses from nanoparticles with potentially inhomogeneous structural geometry.
’ EXPERIMENTAL SECTION System Setup. The foundation of the optical darkfield microscopy system was an Olympus IX71 inverted microscope (Figure S1A, Supporting Information). The system employed an oil immersion ultradarkfield condenser (IX-ADUCD U-DCW, NA 1.2-1.4, Olympus) and a 100 Plan Semi Apochromat objective (UPLFLN100OI, oil, iris, NA 1.30-0.6, WD 0.2 mm, Olympus). Illumination was provided by an integrated 100 W halogen source (IX2-ILL100, transmitted light illuminator, Olympus). A two-way TV adapter (U-TV1C, Olympus) with a selectable output was connected to the microscope’s camera port; this allowed the field of view to be imaged by either a color digital camera (DS-Fi1-U2 with NIS Element D software, 2560 1920 pixels, Nikon) or a line-imaging spectrometer (Acton Research SpectraPro2150i with a dual turret holding gratings of 1200 lines/mm for high spectral resolution and 300 lines/mm for low spectral resolution, Princeton). A PIXIS 100F cooled CCD camera with 1340 100 pixels and pixel size of 20 μm was in the detection plane of the spectrometer. It is worth noting that the maximum sample area capturable by a single snapshot was 0.2 20 μm2 (in the case in which a 100 objective was used). A shutter with a programmable
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slit ranging from 0 to 3 mm in width was internally mounted on the spectrometer entrance to control the field of view of the spectrometer. For imaging reconstruction, the slit was set to 20 μm to match the size of a single pixel of the CCD camera. A motorized stage with 50 nm step size linear encoders (Ludl Flat-top Inverted Stage 96S106-N3-LE2, BioVision Technologies) coupled with an automation controller (MAC6000 XY stage controller with joystick) was used as the sample stage, to provide a programmable scanning platform. Customized integrated software developed by Princeton Instruments was used to synchronously control the motorized stage, the spectrograph, and the CCD camera, to coordinate the scanned objects and the spectra for reconstructing a pseudoimage of the sample with high precision and accuracy. The maximum scanning range of the motorized stage was 100 120 mm. A customized flow cell was utilized in the setup for biomolecular detection (Figure S1B, Supporting Information). The design was adapted from a commercially available flow cell (FCS2, Bioptechs). Components were directly purchased from Bioptechs and were unaltered except for the upper and lower bases. Modifications of these two components were necessary to allow both our oilimmersion darkfield condenser and oil-immersion objective to be in direct contact with the flow cell. A 1.0 mL syringe was used to inject solutions into the flow cell. Spatial Resolution Investigation. The color image obtained from the darkfield platform was converted into a grayscale image by removing hue and saturation. Impurity removal was carried out by creating a mask from threshold and dilation manipulations. After impurity removal, particle positions were obtained by threshold analysis and distance transformation and local maxima were identified using eight particle neighbors. More than 150 particle positions were identified by this method, and their position and peak intensity values were used in two-dimensional Gaussian fitting. Full-width at half-maximum (fwhm) and 95% intensity diameter were calculated from the Gaussian width. Average and standard deviation were calculated using most of the selected points, excluding only those positioned too near to the image boundary. Au Nanoparticle Preparation. Au nanorods (AuNRs) were chemically synthesized by a seed-mediated growth procedure.31,32 The average dimensions of the synthesized AuNRs were 55 ( 6 nm in length and 23 ( 4 nm in diameter (n = 150) (Figure S4, Supporting Information). The AuNRs were immobilized on 40 mm diameter round microscope coverslips (No. 1.5, Bioptechs) for darkfield microscopic observation via the following steps. (1) The coverslips were cleaned in a “piranha” bath (30% H2O2 mixed at a 1:4 ratio with 98% H2SO4) at 60 °C for 15 min and then thoroughly rinsed with water and ethanol. (Warning: Piranha solution is very corrosive and must be handled with extreme caution; it reacts violently with organic materials.) (2) Before immobilization, 1 mL of AuNR suspension was centrifuged twice at 8500 rpm for 15 min to remove excess cetyltrimethylammonium bromide (CTAB). The AuNRs were resuspended in DI water to a total volume of 20 mL. (3) The cleaned coverslip was immersed into the diluted AuNR suspension for 20 s and then washed thoroughly with DI water and ethanol. Au nanospheres with diameters of ∼50 nm were prepared by sodium citrate reduction of HAuCl4.33 Before Au nanosphere modification, 40 mm diameter round microscope coverslips (No. 1.5, Bioptechs) were cleaned in a “piranha” bath (30% H2O2 mixed in a 1:4 ratio with 98% H2SO4) at 60 °C for 15 min and then thoroughly rinsed with water and ethanol. The slides were 2606
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Figure 1. Overall strategies for single nanoparticle traces. (a) Darkfield-microscopic image of AuNRs chemically immobilized on a cover glass. (b) Spectrum library of the fractional area of a (7 out of 103 strips; the size of each strip is 0.2 20 μm2 when a 100 objective was used). (c) Reconstructed image from a spectrum library containing spectral and spatial information for each nanoparticle. (d) Four typical spectra of AuNRs (labeled 1-4) taken from c. The numbers in a, c, and d indicate the different nanoparticles presenting discrete LSPR scattering peaks.
incubated in a 10% solution of 3-aminopropyltriethoxysilane (APTES, Sigma) in ethanol for 2 h, resulting in the formation of an amino-terminated, self-assembled silane monolayer (SAM) on the glass surface. The cover glass was then washed with ethanol, sonicated in ethanol three times for 3 min each, and then dried at 120 °C for 2 h. One hundred microliters of 100-times diluted Au nanospheres solution was dropped onto the silanized glass coverslips and incubated for 30 s, resulting in the formation of a sparsely populated monolayer of Au nanospheres on the glass. We also explored various shapes of Au nanoparticles (i.e., triangular Au nanoplates and Au nanooctahedrons) to confirm our normalization method can be independently applied to other shapes. Triangular Au nanoplates were synthesized by a thermal aqueous solution approach.34,35 The average width of the triangular nanoplates was 47 ( 10 nm (Figure S5, Supporting Information). Truncated Au nanooctahedrons were synthesized by an overgrowth (on AuNR seeds) approach as previously reported.36 The averaged diameter of the Au nanooctahedrons was 115 ( 7 nm (Figure S6, Supporting Information). Aptamer-Thrombin Binding Investigation. Thrombin was detected using a DNA-aptamer-thrombin recognition. The thrombin aptamer (50 -C6-S-S-(T)20-GGT TGG TGT GGT TGG-30 ) previously reported,37 with a dissociation constant of 25 nM, was used. Prior to the functionalization of AuNRs with aptamer, thiol groups in the aptamer were activated according to the method described in a previous report.38 Briefly, the 50 -thiol aptamer (0.1 mM) was deprotected with 0.l mM tris(2-carboxyethyl)phosphine (TCEP) in 50 mM acetate buffer (pH 5.2) for 1 h at room temperature. The deprotected aptamer was then diluted to 1 μM with deionized water and dropped onto the surface of a glass chip modified with AuNRs. The glass chip was then kept in
a dark drawer at room temperature for 24 h. After aptamer functionalization, the chip was washed with water and 100 μM 6-mercaptohexanol was applied to its surface; the chip was then incubated for 3 h to ensure full surface coverage of AuNRs. The aptamer-modified glass chip was rinsed thoroughly with deionized water and mounted onto the flow cell using the procedures shown in Figure S1, Supporting Information. The binding buffer (10 mM Na2HPO4-NaH2PO4, pH 7.0, 0.1 M KCl, 0.1% BSA) was then injected into the flow cell and incubated for 24 h to facilitate formation of the aptamer G-quartet structure for thrombin binding. Finally, thrombin solutions at specific concentrations were injected into the flow cell and incubated for 2 h. The LSPR scattering spectra before and after protein binding were recorded. A peak fitting based on Lorentzian algorithm was used for data processing using OriginPro 8.0 for accurate and efficient determination of λmax, and the corresponding wavelength shift (Δλmax) was calculated.
’ RESULTS AND DISCUSSION Our strategy for the identification of discrete LSPR signals is illustrated in Figure 1. The spectrum library of Au nanorods (AuNR; Figure 1b) was captured by scanning a user-defined area of a AuNR-immobilized glass slide, using a motorized stage and a spectrometer coupled to a CCD camera. Combined with a systematically assembled darkfield platform (see Supporting Information, Figure S1a), our customized synchronization software could map spectrum intensities of nanoparticles onto a reconstructed image. On the basis of one-to-one correspondence between a color image (Figure 1a) and a black-and-white, reconstructed pseudoimage (Figure 1c), the position of each particle could be accurately identified from the reconstructed image, and the corresponding 2607
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Figure 2. Spatial resolution of the proposed plasmonic nanoarray detection system. Darkfield images of AuNRs (a) and AuNSs (b) obtained from the color CCD camera. Scale bars represent 20 and 2.5 μm (insets), respectively. Reconstructed image of a single AuNR (c) and AuNS (d) based on spectra obtained by the spectral CCD camera. The white circle represents spectral intensities at fwhm, and the yellow circle represents spectral intensities at the 95% confidence interval. LSPR scattering spectra of AuNR (e) and AuNS (f) obtained by collecting signals from pixels located in different areas of the reconstructed images shown in (c) and (d) (the pixels marked with green spots). From top to bottom are spectra collected from the central region, the 95% confidence interval, the fwhm and the background.
LSPR spectrum could be simply retraced, as shown in Figure 1d, 1-4. Although the two images in Figure 1a,c appear quite similar, they are not a perfect match. First, there are subtle differences in particle brightness between the two images. This difference is partially due to different levels of light sensitivity of the two CCD cameras in our optical setup. The color image of the nanoparticles (Figure 1a) was captured with the color CCD camera, which is sensitive to light emission in the visible range, whereas the reconstructed image (Figure 1c) was taken with the blackand-white spectral CCD camera, which is sensitive to light emission both in the visible and the near-infrared region (450-950 nm). In addition, the spectral image shown in Figure 1c is the image of the peak spectral intensity of each pixel. Therefore, the reconstructed image based on intensity of pixels shows different behavior than its darkfield counterpart. Second, the positions of each particle in the two images are slightly mismatched. The image reconstruction process of the pseudoimage was based on the assumption that each translational step of the motorized stage is equal and that light is not distorted during transmission in the optical system. However, due to the limitations of the current technique, the above conditions could not be perfectly satisfied. To distinguish individual nanoparticles in the microscopic field of view and to avoid spectral overlap among neighboring particles, it is crucial to understand the scattering size of a single nanoparticle. To this end, two common plasmonic nanoparticles, AuNRs (aspect ratio of ∼2.3) and Au nanospheres (AuNS, diameter of ∼50 nm), were investigated to define spatial resolution of the proposed optical setup (Figure 2). The average scattering diameter of AuNRs and AuNSs on darkfield images (Figure 2a,b) were calculated to be 0.49 ( 0.11 μm and 0.48 ( 0.03 μm at full width at half-maximum (fwhm) and 0.75 ( 0.16 μm and 0.73 ( 0.04 μm at the 95% confidence interval, respectively, on the basis of the calculation by 2-dimensional Gaussian fitting (n > 150).
Figure 2e,f shows the corresponding spectra from pixels located in the central region, at the fwhm (white circle), at the 95% confidence interval (yellow circle), and in the background (far from the center), as indicated by green spots in Figure 2c,d. While the scattering intensity of a single nanoparticle dramatically decreases with increasing radius from the center, spectral interference occurs when the interparticle distance of adjacent particles is smaller than their scattering diameter. Therefore, for LSPR wavelength-shift assays, an interparticle distance equal to or larger than the scattering size of the 95% confidence interval is recommended to avoid interference among neighboring particles. The LSPR scattering radius of nanoparticles is directly related to the particle size, shape, and composition.39,40 In addition, the numerical aperture (NA) of the microscope objective can also affect the spatial resolution (the larger the NA, the better the resolution). The spatial resolution shown above, therefore, applies only to particles investigated herein. Nevertheless, this information will provide guidance on proper interparticle separation for plasmonic nanoarrays. The bandwidth of the LSPR scattering spectrum of a single nanoparticle is substantially narrower than the averaged extinction spectrum of an ensemble of nanoparticles obtained by UV spectrophotometry as shown in the case of AuNRs (Figure 3a) and in other studies.41,42 Narrow bandwidth is greatly desirable for LSPR peak-shift-based sensing applications because more confined peak bandwidths allow better discrimination of the peak shift upon binding of a target. However, different nanoparticles, even from the same batch, exhibit significant variations in LSPR peak wavelength (λmax; Figure 1d). Figure 3b shows the statistical distribution of λmax for 300 individual AuNRs from the same batch. The λmax values of AuNRs range from 600 to 850 nm. It is laborious to modulate polydispersity in the structural geometry of particles on a nanometer scale. Moreover, the extent of shift of LSPR peak wavelength (Δλmax) of a nanoparticle will be disrupted by the variation of λmax.43,44 Such deviation in the Δλmax among 2608
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Figure 3. (a) UV absorbance of an ensemble of AuNRs in solution and LSPR scattering of a single AuNR. (b) Peak wavelength distribution of AuNRs obtained from the same synthetic batch (n = 300). (c) Sensitivity of peak wavelength to bulk refractive index as a function of peak wavelength [(λmax(1) þ λmax(2))/2] for 100 individual AuNRs. (d,e) The LSPR responses of AuNRs before (d) and after (e) normalization. (f,g) LSPR responses as a function of the refractive index of surrounding medium before (f) and after (g) normalization of five typical AuNRs with λmax between 680 and 780 nm.
different nanoparticles produces undesirably scattered mean values of LSPR signals. This represents the main obstacle for the practical use of single nanoparticles as sensing units (i.e., large standard deviation during the generation of a dose-response curve where, typically, 5-7 nanoparticles are required to be employed). As a result, current approaches to single nanoparticle-based sensing aim either to obtain averaged values of Δλmax from a large quantity of nanoparticles, on the basis of the assumption that variation in the LSPR scattering of each nanoparticle is negligible,45 or to demonstrate the operational feasibility of a sensor at a single nanoparticle limit.18 Hence, there is an urgent need for methods to minimize variations in LSPR signals from geometrically different nanoparticles. There are a number of reports46-49 describing the relationship between the λmax of nanoparticles and the corresponding sensitivity to the surrounding refractive index change (Δn). Inspired by these studies, we hypothesized that λmax could be assigned to normalize the Δλmax of the polydispersed nanoparticles. When the LSPR spectra of 100 individual AuNRs were experimentally analyzed (Figure 3c), a linear fit to a plot of
bulk refractive index sensitivity (Δλmax/Δn) against λmax is obtained as follows: Δλmax =Δn ¼ 1:816λmax - 1009
ð1Þ
On the basis of the fact that Δλmax and λmax can be readily obtained by a darkfield microscope equipped with a spectrograph and that Δn is independent of the structural details of the nanoparticles, here, we propose to use eq 1 to normalize the spectral signals of the nanoparticles for deduction of comparable LSPR responses among potentially nonuniform nanoparticles. Thus, the normalization formula for the case of the AuNRs studied here is as follows: ΔN ¼ Δλmax =ð1:816λmax - 1009Þ
ð2Þ
where ΔN represents the normalized LSPR response of nanoparticles, allowing discrimination from the real refractive index (RI) shift for each particle, which is indeed Δn. The LSPR responses of 15 randomly selected AuNRs to Δn of 0.096 RI units (RIU) were investigated as shown in Figure 3d,e. Prior to 2609
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Figure 4. Thrombin detection on AuNR nanoarrays. (a) Darkfield image of a user-defined area (20 20 μm2) on aptamer- functionalized AuNR nanoarrays. The AuNRs were intentionally isolated from each other using electrostatic assembly to avoid any spectral interference between adjacent particles; pseudoimages of the same area as shown in (a), before (b) and after (c) thrombin binding (1.0 10-5 g/mL). The pseudo color in (b) and (c) represents the relative light intensity (blue: weak; green: moderate; red: strong). (d) LSPR spectra of a typical AuNR (white arrow) before (black line) and after (red dash) thrombin binding. LSPR responses of six discrete AuNRs as a function of thrombin concentrations of 10-8, 10-7, 10-6, 10-5, 10-4, and 10-3 g/mL before (e) and after (f) normalization. The error bars in (e) and (f) show standard deviations of five replicates.
normalization, the relative standard deviation (RSD) of LSPR responses, i.e., Δλmax, from different particles is 28.08%, whereas, after normalization by eq 2, the RSD of LSPR responses, i.e., ΔN, drops to less than 7%, showing improvement by more than a factor of 4. The normalization method presented at fixed RI was further explored by varying RIs from 1.333 to 1.495 RIU. Figure 3f shows the plot of Δλmax for five typical particles with λmax located at 680-780 nm as a function of RI. Despite high linearity of the Δλmax of each particle in relation to Δn, different slopes among particles made it infeasible to directly use the Δλmax for the measurement of a series of analyte bindings, because several nanoparticles are required for the dose-response curve as mentioned previously. Figure 3g shows plots of the normalized LSPR responses of the same five particles presented in Figure 3f. The deviation of ΔN of the five particles was small, exhibiting little discrepancy across linear fits from different particles. Minimized particle-to-particle variation of the LSPR response via the experimentally determined equation (eq 2) strongly supports the feasibility of the proposed normalization method for translating single nanoparticle-based biosensors into nanoarrays. In order to demonstrate the broad applicability of our normalization method (eq 2) for other nonspherical nanoparticles, we investigated the normalized LSPR response of triangular Au nanoplates and Au nanooctahedrons. The LSPR peak wavelengths of individual Au nanoparticles were recorded in deionized water and ethanol. Table S1 (Supporting Information) shows the LSPR responses before and after normalization. The relative standard deviation (RSD) of LSPR responses are improved by a factor of 3.1 for the triangular Au nanoplates and 1.5 for the Au nanooctahedrons. After normalization, the RSD among 30 randomly selected Au nanoparticles is less than 10%, indicating that the proposed normalization method can be applied to various shapes of nanoparticles. On the other hand, the Au nanooctahedrons reveal relatively smaller improvement
in RSD than the AuNRs and the triangular Au nanoplates, which can be ascribed to the smaller LSPR variations of Au nanooctahedrons compared with the other Au nanoparticles. These results strongly support our hypothesis that the proposed normalization approach is effective to minimize the LSPR variations among individual nanoparticles and can be independently applied to any shapes of nanoparticles. Finally, on the basis of the ability to extract highly sensitive, discrete LSPR signals from an ensemble of nanoparticles and the method for obtaining corresponding LSPR responses with significantly reduced particle-to-particle variation, we successfully demonstrated a biomolecular assay based on LSPR wavelength shift in a nanoarray format that employed individual nanoparticles as the sensing units (Figure 4). Briefly, randomly distributed AuNRs with average interparticle distances of 2.6 μm were fabricated using previously reported, electrostatic surfacial assembly.50 After functionalizing the AuNRs with nucleic acid aptamer, the chip was mounted onto our homemade flow cell (Supporting Information, Figure S1b) for investigation of protein binding. Figure 4a,b shows LSPR scattering and reconstructed images that were taken from the same user-defined area before thrombin binding. After thrombin binding to the aptamer-modified AuNRs, another spectral scan was conducted on the same area, and a resultant pseudoimage was constructed (Figure 4c). The LSPR response, i.e., Δλmax, after analyte binding to each AuNR could be calculated, as an example shown in Figure 4d. The Δλmax as well as ΔN of the rest of 30 individual AuNRs in this 20 20 μm2 area upon analyte binding are listed in Table S2, Supporting Information. Figure 4e,f shows dose-response curves of thrombin binding. Steady-state LSPR responses of six discrete AuNRs with their (λmax(1) þ λmax(2))/2 values at 766, 681, 723, 799, 756, and 677 nm were plotted against a series of concentrations of thrombin. Before normalization, the Δλmax interfered with the corresponding λmax, resulting in widely scattered LSPR responses as shown in 2610
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Analytical Chemistry Figure 4e. After normalization, however, a well-fitted, sigmoidal dose-response curve for thrombin binding was obtained (Figure 4f). Notably, each data point in the dose-response curve was generated by a single nanoparticle, in contrast to previously reported data obtained from averaged data points from dozens of nanoparticles.14,50
’ CONCLUSIONS In summary, we have addressed two fundamental issues concerning the practical application of single nanoparticles as sensing units for multiplexed plasmonic nanoarrays. First, a conventional darkfield microscope coupled with an imaging spectrograph and synchronization software was developed to track the LSPR shifts of individual nanoparticles on two-dimensional arrays with high sensitivity. Second, a normalization method to minimize the particle-to-particle variations in LSPR response upon analyte binding was exploited to accomplish a real single nanoparticle sensor on nanoarray. Because the RI sensitivity of single-component particles was theoretically predicated to be solely determined by their band location,46 the normalization method presented herein can be used for other plasmonic nanoarchitectures as well. These miniaturized plasmonic nanoarrays should find utility in parallel screening of nucleic acid and protein profiles,51,52 which will compete with the current state-of-the-art microarrays. ’ ASSOCIATED CONTENT
bS
Supporting Information. Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. Fax: 65-67911761.
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