Automatic Enumeration of Gold Nanomaterials at the Single-Particle

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Automatic Enumeration of Gold Nanomaterials at Single Particle Level Xiao Xu, Tian Li, Zhongxing Xu, Hejia Wei, Ruoyun Lin, Bin Xia, Feng Liu, and Na Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ac503756f • Publication Date (Web): 04 Feb 2015 Downloaded from http://pubs.acs.org on February 8, 2015

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Analytical Chemistry

Automatic Enumeration of Gold Nanomaterials at Single Particle Level Xiao Xu‡,¶,†, Tian Li‡,†, Zhongxing Xu‡, Hejia Wei§,ǁ, Ruoyun Lin‡, Bin Xia‡,§,ǁ, Feng Liu‡, Na Li‡* ‡

Beijing National Laboratory for Molecular Sciences (BNLMS), Key Laboratory of Bioorganic Chemistry

and Molecular Engineering of Ministry of Education, Institute of Analytical Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China. §Beijing NMR Center, Peking University, Beijing 100871, China. ǁSchool of Life Sciences, Peking University, Beijing 100871, China. Corresponding Author * Tel: +86 10 62761187; fax: +86 10 62751708. E-mail address: [email protected] (N. Li) Present Addresses ¶ Division of Nano Metrology and Materials Measurement, National Institute of Metrology, Beijing 100029, China Author Contributions †These authors contributed equally.

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ABSTRACT: In this study, we developed a highly sensitive automatic counting method for gold nanomaterials at single particle level, which can serve as a general sensing platform based on counting of gold nanomaterials. This method substantially improved the sensitivity and accuracy for AuNP counting by adopting the color image processing based on the distinctive localized plasmonic light-scattering of gold nanomaterials. The 60-nm AuNPs with concentration down to 4 fM can be detected with our method. As a universal counting approach for gold nanomaterials, such as gold nanospheres, nanorods and aggregates from particles under detectable size, this quantification method should be versatile to a breadth of applications.

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Gold nanomaterials, such as gold nanoparticles (AuNPs) and gold nanorods (AuNRs), have been widely used in the bioanalytical and diagnostic realm as the optical transducer due to their unique and superior optical properties.1-4 Accurate, sensitive, rapid and low cost quantification of gold nanomaterials is most desirable for chemical and biological sensing as well as clinical diagnostics, thus, localized surface plasmon resonance (LSPR) light-scattering related methods are favorable in these applications because LSPR endows light-scattering with a featured band to be easily distinguished from other particle scattering.5-8 Among these light-scattering methods, scanometric and microarray based imaging methods have been proven to be excellent approaches because of the simplicity of experimental design and less expensive and sophisticated requirements of instrumentation.9-13 However, the sensitivity is modest with most of the methods because ensemble signals have been measured over large amount of AuNPs or silver enhanced AuNPs.1, 14-15 Instead, enumeration of AuNPs is a promising approach to ultimately improve the signal transduction sensitivity.16-20 By integrating AuNP counting with microarray-based approaches, sensitivity and simplicity could be achieved simultaneously, and the automatic counting can speed up the assay to better facilitate high throughput detection.21-22 However, current automatic counting methods used a single criterion, e.g. the area range, for recognition of nanoparticles,22-23 making methods susceptible to interferences from the sample matrix or the surface of the substrate. Therefore, the accuracy of automatic counting has still been a major problem for quantification of nanoparticles, impeding the breadth of applications. The color is one featured parameter that reflects optical spectral characteristics of targets, thus can be used for image recognition.24-28 Gold nanomaterials present the unique LSPR band in the visible to near infrared region associated with the particle size or the morphology,29-31 thus exhibit distinctive colors under the dark-field microscope, providing the unique spectral parameter for refinement of the criterion in AuNP recognition. Furthermore, geometric parameters, including the area range and axial ratio, are also helpful in fine-tuning the criterion to ensure the counting method a better recognition performance. In the light of the above considerations as well as our previously work on bacterial recognition and counting,24 we proposed a universal quantification method for gold nanomaterials based on dark field light-scattering imaging and color image processing. The color characteristics of gold nanomaterials could

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be automatically retrieved from acquired images, thus our proposed method was capable to handle multiple types of gold nanomaterials including aggregates. EXPERIMENTAL SECTION Materials.1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide hydrochloride (EDC), auric chloride and succinic anhydride were obtained from Sinopharm Chemical Reagent Co., Ltd. N-hydroxy succinimide (NHS) was obtained from J&K Chemical Ltd. (3-trimethoxysilypropyl) diethylenetriamine (DETA) was obtained from Fluorochem Ltd. Kanamycin was obtained from Beijing Dingguo Changsheng Biotech Co., Ltd. Human alpha thrombin was obtained from Enzyme Research. The two thrombin-binding aptamers: Apt15

(5’H2N-A10-GGTTG-GTGTG-GTTGG

HS-A10-AGTCC-GTGGT-AGGGC-AGGTT-GGGGT-GACT

3’) 3’),

and and

Apt29

(5’

tris(2-carboxyethyl)phosphine

(TCEP), were purchased from Sangon Biotech (Shanghai) Co., Ltd. The E. coli strain DH5α was purchased from Beijing TransGen Biotech Co., Ltd. and was cultured to the concentration of ~109 CFU/mL. Instrumentation. UV-Visible extinction spectra were measured with a Hitachi U-3010 spectrophotometer. The shape and size of AuNPs and AuNRs were characterized using a Hitachi H-9000 transmission electron microscope. The dark-field images were captured using an Olympus BX-53 microscope with an Olympus DP-72 true color CCD and with the Olympus cellSense software. The image processing software was developed in the C# programming language based on our previous works.24 Modification of glass microscope coverslips. Coverslips were first cleaned in piranha solution for 30 min, and washed with purified water. For amino group modification, coverslips were treated with the methanol-DETA solution for 1 h.32 Then the coverslips were washed with absolute ethanol and baked at 120 ºC for 10 min prior to use. For carboxyl group modification, amino group modified coverslips (glass-NH2) were treated with the succinic anhydride DMF solution (20 g/L) at room temperature for 24 h, then washed with absolute ethanol. For thrombin-binding aptamer immobilization, the EDC-NHS treatment was applied. The carboxyl group modified coverslips were placed in the EDC-NHS solution for 1 h, then washed with purified water and treated with the Apt 15 PBS solution (1 µM) for 8 h. Preparation of nanomaterials. The 13-nm and 20-nm AuNPs were synthesized by reducing HAuCl4 with sodium citrate as described in our previous work.33-35 The 60-nm AuNPs were prepared by a two-step,

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seed-mediated growth method using 13-nm AuNPs as seeds.36 TEM images and extinction spectra of AuNPs are shown in Figure S-1. The AuNRs were prepared by the seed-mediated growth method.37 The size of AuNRs was about 13×44 nm and the maximum extinction peak was at 750 nm (Figure S-1F). The thrombin-binding aptamer Apt 29 was immobilized on the surface of 60-nm AuNPs through direct reaction in solution based on the protocol by Liu & Lu.38 The silica nanoparticles (SiO2NPs) were synthesized by reverse micro-emulsion method.39 The average size was 54 nm as characterized with TEM (Figure S-2). Gold nanomaterials involved reaction systems. For 60-nm AuNPs and AuNRs involved reactions, coverslips were placed vertically in the solution to prevent the adsorption induced by sedimentation. The solution was kept stirring during the reaction. For 20-nm AuNPs involved reactions, the solution was spotted on the coverslips. In all measurements, purified water was used as the blank to provide background images. Detailed procedures of each system are described as follows. Glass-NH2-AuNPs.The 60-nm AuNPs were diluted with purified water to 1/100 ~ 1/20000 of the original concentration. Glass-NH2 were placed in the solution for 5 min, and washed 3 times with purified water. SiO2NPs interference. In order to cover major concentration range of the 60-nm AuNPs in the glass-NH2-AuNPs system, three concentration levels (1/250, 1/1000, 1/4000 of the original concentration) of AuNPs were used in SiO2NPs and E. coli interference experiments. SiO2NPs were redispersed with purified water to the concentration of 5 µg/mL. AuNPs were diluted with SiO2NPs suspension to the three concentrations. Glass-NH2 were placed in the solution for 10 min, and washed 3 times with purified water. E. coli interference. E. coli cells were collected by centrifuging at 3000 rpm for 5 min and washed 3 times with purified water, and redispersed with purified water to 1/10 of the original volume. The 60-nm AuNPs were diluted with purified water to the three concentrations. Then for each concentration, 25 µL of E. coli suspension was added into 5 mL of AuNPs colloids, respectively. Glass-NH2 were placed in the solution for 10 min, and washed 3 times with purified water. AuNRs detection. AuNRs were diluted with purified water to 1/500 of the original concentration. The carboxyl group modified coverslips were placed in the solutions for 5 min, and washed 3 times with purified water.

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Thrombin detection. As K+ is required for the binding of Apt 29 with thrombin, the PBS buffer containing 1.50 g/L of KCl was used in this detection. Aptamer modified 60-nm AuNPs were diluted with PBS buffer to 1/50 of the original concentration. Aptamer modified coverslip was incubated with the thrombin PBS solution for 30 min. Then the coverslip was washed 3 times with PBS buffer and placed in the aptamer modified AuNP solution for 30 min. After the reaction was complete, the coverslip was washed sequentially with PBS buffer and purified water. Kanamycin detection. The 20-nm AuNPs were diluted with purified water to 1/10 of the original concentration and mixed with Kanamycin solution by the volume ratio of 49:1. Then 5 µL of the mixture was spotted on the glass-NH2. After 1 min, the coverslip was washed 3 times with purified water. Dark-field microscopic imaging. All dark-field images were acquired with the 40× object lens using “pixel-shifting” mode of Olympus cellSens software with a resolution of 4140×3096 pixels. The imaging condition was kept consistent during the experimental process of each reaction system. As mentioned previously, background images of the reaction systems were the corresponding images of purified water. The image of the highest sample concentration in each reaction system was used as the reference gold material image for color characteristics calibration. Each count number was the average of 8 images (2 slides with 4 images of each), other than indicated. Color image processing. The general idea of the proposed approach was to select the AuNP object refereed sequentially by shape and color characteristics with the color characteristics calibrated using reagent blank image and reference AuNP image (Scheme S-2~S-4). Specifically, the sample image was cropped to retain the center area (2500×2500 square pixels, 375×375 µm2) to avoid optical aberrations at the edge. Then a high-pass filtering step was applied to eliminate off-focus light scattering signals interfering with the recognition (Figure S-5). The shape-based segmentation was then used to divide the image into subimages with each containing single object to be identified. The shape (area and axial ratio) and color judgments were sequentially applied to each subimage to identify AuNPs, and the number of AuNPs was counted (Scheme S-5). Amongst the shape and color criteria, when the magnification factor was fixed, shape criteria were relatively stable under different image acquisition conditions and could be easily determined based on

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theoretical calculations as well as statistics of reference AuNP images (Figure S-8, S-9). In contrast, color characteristics were closely related to the imaging acquisition condition, thus were impossible to be described with a predefined value. Furthermore, due to the subjectivity on color judgment, manual determination of the color criteria would produce the inconsistent judgment amongst different operators even if they are well acquainted with the color characteristics of gold nanomaterials. Therefore, in order to improve the robustness and minimize biased judgment from manual intervention in the nanoparticle recognition, color characteristics criteria were automatically obtained by calibration using the reagent blank image and the reference AuNP image. In the calibration step, the reagent blank image and the reference AuNP image were treated, respectively, with the same procedure as that for the sample detection from cropping to shape judgment steps (Scheme S-3). Then color characteristics of each image were calculated and interfering colors were excluded (Scheme S-4) so that the calibrated color characteristics can genuinely represent gold nanomaterials to be analyzed (Figure S-10). The recognition rate was defined as the percent recognition with manual counting value as 100%. More details of the automatic counting procedure were described in Supporting Information. RESUTLS AND DISCUSSION To keep the detection simple, 60-nm gold nanospheres which were generally considered as the appropriate size for dark-field imaging, were used for method validation and the evaluation of anti-interference performance. AuNRs were used to illustrate the automatic color characteristics calibration. Two quantification systems, including the sandwich-based assay and the AuNPs aggregation based assay, were employed to demonstrate the feasibility on the analytical application of the proposed method. Negatively charged 60-nm AuNPs were able to be evenly adsorbed onto the surface of amino-group modified microscope coverslip, providing a simple and relatively stable system for high quality image acquisition (Figure 1). Amongst 16 images from 4 batches (4 images in each) of experiments, 85.5% of the manually recognized AuNPs (6771 out of 7923) were identified by the automatic counting procedure, while the false positive events were 49 in all 16 images, i.e., about 3.1 false positive results per image. The receiver operation characteristic (ROC) curve, which is commonly used to evaluate a classifier algorithm, plots the true positive rate (TPR) against false positive rate (FPR) at all discrimination threshold values,

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thus reflects overall recognition performance of the algorithm. As can be seen in Figure 1C, the glass-NH2-AuNP system already reached the high TPR, prior to the significant appearance of the FPR, indicative of good performance of the automatic recognition approach. The calibration curve showed a linear dynamic range based on the log-log scale plot, and could respond to AuNPs with the concentration down to 4 fM (Figure 1D). As a microscopic imaging and counting method, the limit of detection can be simply improved through expanding the observation area to collect more events. However, the upper limit of dynamic range is restricted by the density of AuNPs in the image. For very high AuNP density, the probability that adjacent particle image spots are connected will be significant. In such case, lower TPR value will be obtained because shape criteria fails to judge and discriminate connected particles. Consequently, the high density of AuNPs in the image may cause failure in particle counting, thus reducing the upper limit of the dynamic range.

Figure 1. Automatic recognition results of glass-NH2-AuNPs system. (A) Original image. (B) Recognized AuNPs. (C) ROC curve. (D) Automatic counting result of 60-nm AuNPs.

Introduction of the color criteria substantially improved the anti-interference power of the automatic nanoparticle recognition, thus endowed the method with high counting accuracy. Two major types of interferences in the counting-based and the intensity-based methods were introduced to evaluate the anti-interference performance. Specifically, aggregates of 60-nm SiO2NPs were used to provide interference image similar to AuNPs in shape and size (Figure 2A) and E. coli DH5α cells were used to

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produce intense background light-scattering (Figure 2B). Recognized AuNP images in the presence of the above interfering entities (Figure 2 C & D) demonstrated that the automatic counting method could effectively eliminate both types of interference signals. By comparing with manual counting, a reasonable true positive rate could still be achieved even at high interference-to-gold ratio and the false positive rate remained at a low level (Figure 3), demonstrating the excellent anti-interference performance. Therefore, this automatic counting method presented the potential in sensing complex samples without the need of sample pretreatment.

Figure 2.Images of interference systems. (A) Original image of SiO2NPs interference system. (B) Original image of E. coli interference system. (C) Recognized AuNPs of SiO2NPs system. (D) Recognized AuNPs of E. coli system.

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Figure 3. The recognition performance with interferences. (A) Total to gold lightness ratio of SiO2NPs system. (B) Non-AuNPs to AuNPs count ratio of SiO2NPs system. (C) Total to gold lightness ratio of E. coli system. (D) Non-AuNP to AuNP count ratio of E. coli system.

With the color judgment and automatic color characteristics calibration, the method was able to be easily extended to gold nanomaterials with other morphologies, using AuNRs (13×44 nm) for demonstration. From the dark-field light-scattering images, it was clear that 60-nm AuNPs presented green and yellow colors (Figure 1A) and AuNRs exhibited reddish color (Figure 4B). With the automatic calibration, visual characteristic colors were transformed as quantitative standards for recognition. Plots of B* against A*, the two components of the CIE LAB color space, intelligibly depicted the chroma difference between AuNPs and AuNRs (Figure 4A). Consequently, AuNRs were properly identified with a recognition rate of 85%.

Figure 4. Automatic counting results of AuNRs. (A) Automatic calibrated color characteristics (A* and B*) of AuNPs and AuNRs, where A* depicts the green (negative values) and red (positive values) colors, and B* depicts the blue (negative values) and yellow (positive values) colors. (B) Original AuNR image. (C) Image of recognized AuNRs.

In order to demonstrating the applicability, the automatic counting method was first integrated with a sandwich detection system. As one of the most frequently studied proteins in sandwich assays, thrombin was chosen as the target using the two well-known aptamers for recognition (Scheme S-1).40-41 Automatic counting results showed reasonable signal response to the change of the concentration of thrombin with a

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limit of detection of 0.1 pM and the linear range from 0.1 pM to 1 nM (Figure 5). Successful determination of thrombin proved that the proposed method was feasible for quantitative detection and more sensitive than reported optical detection methods with the limit of detection at subnanomolar level.42-43

Figure 5. Automatic counting results for thrombin detection with 60-nm AuNPs.

The feasibility of the automatic counting modality was further demonstrated using the AuNP-aggregation based assay. Usually, the AuNP-aggregation based method detects targets according to the change of plasmonic absorption or the light-scattering intensity,44 not the amount of AuNP aggregates. The irregularity of the aggregate produces light-scattering signals with diversified intensity and spectral characters, making it hard to perform consistent recognition with defined criteria by manual counting. In contrast, it is easy for the automatic counting method to perform definite and consistent judgment with defined shape and color criteria, thus quantification becomes feasible. In this illustration, the detection of Kanamycin with 20-nm AuNPs were used based on our previous studies.45 Kanamycin, as an aminoglycoside compound, could induce formation of AuNP aggregates with submicron or larger sizes (Figure S-4). The single 20-nm AuNP was nearly invisible under the common dark-field microscope, but the aggregates presented bright yellow or red colors (Figure 6 A, B & C). Thus, the automatic counting could be performed with specified shape parameters and automatically calibrated color characteristics. Preliminary results showed that Kanamycin with the concentration range from 2 pM to 200 nM was able to be quantified (Figure 6D), with the limit of detection of 3 orders of magnitude lower than our previous

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light-scattering intensity based method.45 Thus, the automatic counting method provided a feasible way for signal transduction in AuNP-aggregation detection systems.

Figure 6. Kanamycin detection results. Original image (A) and image of recognized AuNP aggregates (B) with 200 nM Kanamycin. (C) Image of 20-nm AuNPs. (D)Automatic counting results of AuNP aggregates for Kanamycin detection.

CONCLUSION In conclusion, based on the gold nanoparticle dark-field imaging modality, we proposed and demonstrated an accurate and highly sensitive automatic counting method for the quantification of gold nanomaterials. This proposed method presented the simplicity in effectively eliminating interferences from detection substrates by applying the high-pass filtering operation as well as introducing color criteria. The bias-free automatic color calibration procedure avoided manual adjustments on color characteristics, which endowed robustness of the method and greatly accelerated measurements by markedly reducing labor work in manual counting. Furthermore, this proposed method in principle could be easily extended as a general counting method for noble metal nanomaterials and was anticipated to have more applications.

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ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No. 21275011, 21475004, 21035005), China Postdoctoral Science Foundation (2013M530463) and Merieux Research Grant.

ASSOCIATED CONTENT Supporting Information Details of characterization of materials and image processing algorithm. This material is available free of charge via the Internet at http://pubs.acs.org.

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Automatic Enumeration of Gold Nanomaterials at the Single-Particle

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