Tuning the Gold Nanoparticle Colorimetric Assay by Nanoparticle Size

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Tuning the gold nanoparticle colorimetric assay by nanoparticle size, concentration, and size combinations for oligonucleotide detection Varsha S Godakhindi, Peiyuan Kang, Maud Serre, Naga Aravind Revuru, Jesse Minghao Zou, Michael Robert Roner, Ruth Levitz, Jeffrey Kahn, Jaona Randrianalisoa, and Zhenpeng Qin ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00482 • Publication Date (Web): 10 Oct 2017 Downloaded from http://pubs.acs.org on October 11, 2017

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Tuning the gold nanoparticle colorimetric assay by nanoparticle size, concentration, and size combinations for oligonucleotide detection

Varsha Sanjay Godakhindi1†, Peiyuan Kang2 †, Maud Serre3†, Naga Aravind Revuru2, Jesse Minghao Zou2, Michael Roner4, Ruth Levitz5, Jeffrey Kahn5, Jaona Randrianalisoa6 and Zhenpeng Qin1,2,7 * 1

Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080

2

Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080

3

Ecole nationale Supérieure d'Ingénieurs de Reims (ESIReims), University of Reims Champagne Ardenne, 3 Espl. Roland Garros, 51100 Reims, France 4

Department of Biology, University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX 76019

5

Departments of Pediatrics & Microbiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Children’s Medical Center, 1935 Medical District Dr, Dallas, TX 75235 6

Groupe de Recherche en Sciences pour l’Ingénieur (GRESPI) - EA 4694, University of Reims Champagne - Ardenne, 51687 Reims Cedex 2, France 7

Department of Surgery, The University of Texas at Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 * Corresponding author, [email protected]

† co-first author

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Abstract Gold nanoparticle (GNP)-based aggregation assay is simple, fast, and employs a colorimetric detection method. Although previous studies have reported using GNP-based colorimetric assay to detect biological and chemical targets, a mechanistic and quantitative understanding of the assay and effects of GNP parameters on the assay performance is lacking. In this work, we investigated this important aspect of the GNP aggregation assay including effects of GNP concentration and size, on the assay performance to detect malarial DNA. Our findings lead us to propose three major competing factors that determine the final assay performance including the nanoparticle aggregation rate, plasmonic coupling strength, and background signal. Firstly, increasing nanoparticle size reduces the Brownian motion and thus aggregation rate, but significantly increase plasmonic coupling strength. We found that larger GNP leads to stronger signal and improved limit of detection (LOD), suggesting a dominating effect of plasmonic coupling strength. Secondly, higher nanoparticle concentration increases the probability of nanoparticle interactions and thus aggregation rate, but also increases the background extinction signal. We observed that higher GNP concentration leads to stronger signal at high target concentrations due to higher aggregation rate. However, the fact the optimal LOD was found at intermediate GNP concentrations suggests a balance of two competing mechanisms between aggregation rate and signal/background ratio. In summary, our work provides new guidelines to design GNP aggregationbased POC devices to meet the signal and sensitivity needs for infectious disease diagnosis and other applications. Keywords: nanoparticle aggregation, gold nanoparticle, point-of-care (POC) diagnosis, cross-linking aggregation, infectious diseases


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Infectious diseases such as malaria cause high mortality and there is a significant need for point-of-care (POC) diagnostic assays for infectious disease screening and early detection.[1, 2] The ASSURED criteria (affordable, sensitive, specific, user-friendly, rapid & robust, equipment-free, and deliverable) recommended by the World Health Organizations (WHO) is considered to be a general framework for developing POC systems.[3] Various bioassay platforms have been developed and modified towards POC settings, such as paper-based diagnostics [4], isothermal nucleic acid amplification, [5, 6] and microfluidic ELISA.[7] Despite such progress, one of the major scientific challenges for the biomedical research community is to develop, validate, and apply low cost, rapid and ultrasensitive diagnostic tests for infectious diseases. Gold nanoparticle (GNP) colorimetric assay is a simple and fast detection method and is based on the aggregation of GNPs that results in a color change due to the plasmonic coupling.[8] First developed using latex micro particles (i.e., latex agglutination assay[9]), the GNP-based colorimetric assay has advantages over micro particles including less nonspecific aggregation[10] and better colorimetric visualization due to plasmon resonance and coupling.[11] Upon aggregation, monodispersed GNPs change color from red to light purple. Due to its simplicity, GNP colorimetric assay has found broad applications including the detection of proteins,[12, 13] nucleic acids, [6, 14, 15] viruses,[16, 17] heavy metals,[18, 19] aptamers,[20, 21] and bacteria[22, 23]. Nucleic acid detection using GNP colorimetric assay is frequently reported and has enabled detection of single base pair mismatch [24, 25]. Recent innovations have been attempted to improve the sensitivity and to simplify experimental conditions for nucleic acid detection using GNP aggregation assay. Firstly, various readout methods have been tested to detect GNP aggregates including colorimetric scattering, [11] linear scattering, [26] surface enhanced Raman spectroscopy (SERS), [27] and surface plasmon resonance microscopy (SPRM).[28] These methods have been shown to improve the analytical sensitivity by two to three orders of magnitude. Secondly, the fact that hybridization reaction using GNPs can be performed in 30 minutes at room temperature using an optimized buffer with dextran sulfate has improved its potential for integration in a POC system [11, 29]. In terms of GNP parameters, it has been suggested that larger nanoparticles lead to better sensitivity.[14] GNP parameters have been studied to understand various assays for detection of proteins [30, 31], aptamers [20], amino acids [32], metal ions [33, 34] and other molecular agents [35-37]. However, it is unclear how GNP design parameters affect the colorimetric assay performance for nucleic acid detection. Furthermore, there is a lack of attempts to incorporate and test findings from the field of colloidal science to further improve the assay performance for nucleic acid detection. In this report, we systematically investigated the effects of GNP design parameters, including GNP size, concentration, and size combinations, with the goal of further improving the assay performance. Specifically, we evaluated the assay performance by colorimetric detection of malarial DNA and reported the limit of detection (LOD) and signal strength (magnitude of colorimetric peak shift). Our result confirms that larger GNP gives more sensitive detection and stronger signal. Furthermore, GNP concentration has a profound effect on the assay performance, where higher GNP probe concentrations lead to stronger overall signal and an optimal intermediate GNP concentration gives best LOD Studies from the field of colloidal science suggest that nanoparticle aggregation is accelerated using mixture of small and large nanoparticles. Surprisingly, we did not observe any improvement in the assay 3 ACS Paragon Plus Environment

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performance when using GNP size combinations compared with using a homogeneous GNP size. Finally, we attempt to explain observed results and summarize key factors including nanoparticle aggregation rate, plasmonic coupling strength, and background signal that jointly determine the resulting colorimetric signal. Our study provides new knowledge on GNP parameter based design guidelines for the colorimetric assay.

Materials and Methods Materials Analytical grade chemical reagents including gold (III) chloride trihydrate, sodium citrate, hydrochloric acid, nitric acid, hydroquinone, formamide, dextran sulfate and sodium chloride were purchased from Sigma-Aldrich (St. Louis, MO, USA). The procured chemicals were used as received. HPLC purified oligonucleotides were purchased from Bio Basic Inc (Markham, ON, Canada). 384 well plates were purchased from Thermo Scientific (Rochester, NY, USA). 2-mercaptoethanol was procured from Fisher Scientific. Quant-iT Assay kit for GNP conjugated oligonucleotide quantification was purchased from Invitrogen. All experiments were performed with ultrapure water (Millipore, Billerica, MA, USA). GNP synthesis and characterization All the glassware used in the GNP synthesis was cleaned using aqua regia (3:1 ratio of hydrochloric acid and nitric acid). 15 nm GNP was synthesized using Fren’s method with slight modifications.[38, 39] Briefly, gold chloride (III) solution was brought to boil under continuous stirring and then sodium citrate was added. After a prominent color change from purple to pink, the solution was cooled to room temperature. 30 nm and 50 nm particles were synthesized by hydroquinone reduction.[40] Specifically, hydroquinone was added to a solution of gold chloride, sodium citrate and the 15 nm GNPs (used as seed particles) at room temperature. This solution was stirred continually overnight to allow for GNP growth. The size and distribution of GNPs were characterized by dynamic light scattering (DLS) using Malvern Zetasizer Nano (Malvern Instruments Ltd., UK) and JEOL JEM 2100 transmission electron microscopy (TEM). GNP sample was spotted onto a thin carbon film coated Cu grids (300 mesh, Pacific Grid Tech) and air-dried for TEM characterization. TEM images were analyzed using Image J (National Institute of Health). GNP-oligonucleotide conjugation Malaria (genus Plasmodium) was chosen as a clinically relevant target and the oligonucleotides sequences were designed complementary to the malarial sequence (Table S1).[29] A poly A-tail was included in the oligonucleotides to increase flexibility of the sequence. The oligonucleotides were resuspended in ultrapure water to obtain required concentrations. The thiol-capped oligonucleotides A and B were conjugated separately to GNPs by a pH-assisted and surfactant-free method[41] and the oligonucleotide: GNP ratios used was used (Table S2) to create stable GNP-oligonucleotide conjugates (Probe A and Probe B). Briefly, the pH of the mixture was adjusted to 3.0 ± 0.1 by the addition of citrate-HCl buffer (50mM) in a 1:1 volumetric ratio. After 30 minutes of incubation, the GNP-oligo conjugates were centrifuged and washed with ultrapure water. The size and distribution of probes were characterized by DLS. The oligonucleotide loading density on the GNP surface was quantified using 4 ACS Paragon Plus Environment

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Oli-Green fluorescence assay by reducing the thiol bond following 18 hour incubation with 1M βmercaptoethanol. The oligonucleotides A and B with known concentration were used as the calibration standard [42, 43]. The average loading density per GNP was measured and calculated for oligonucleotides A and B. Target detection The target sample was serially diluted to desired concentrations in terms of moles. The samples used in the assay ranged from 250 amoles to 25 pmoles for a target volume of 25uL and the hybridization buffer was used as a blank. The freshly prepared probes were mixed with the hybridization buffer (20% formamide, 16% dextran sulfate and 0.6 M sodium chloride) in a volume ratio of 3:3:4 (Probe A: Probe B: Buffer) to obtain the working solution.[29] This working solution was then incubated with the target in a 2:1 volume ratio (working solution: target). UV-Vis spectrum was measured in 384 well plates using a microplate reader (Biotek Synergy 2). All data was plotted using OriginLab. The LOD for each case was calculated using the IUPAC method [44], as detailed in the Supplemental Information and Figure S1. Optical Properties Calculation of GNP and aggregates using DDA Discrete Dipole Approximation (DDA) is a discrete solution method of the integral form of Maxwell’s equations and allows calculating nanostructure optical properties with complex geometries. Briefly, the target structure is discretized into a finite array of dipoles (N). After solving a large number (3N) of complex linear equations with unknown dipole moments, the extinction, absorption and scattering crosssections can be predicted. In this study, the DDA package DDSCAT 7.3 developed by Draine and coworkers [45] was implemented to solve the scattering and absorption properties of individual and aggregated GNPs. The dipole size effect on extinction efficiency factor in visible and near-infrared wavelength ranges was investigated for 15 nm GNP. Comparison with Mie theory and experimental data showed that a dipole size of 1 nm provides accurate response over the considered spectrum. The effect of nanoparticle size on the dielectric function was previously accounted and adapted here. The dipole structures were visualized in an open-source software package ParaView. The extinction coefficient was calculated by using the following formula: Qext =

Cext M

∑ πri

2

i =1

where Qext is the extinction efficiency factor, Cext is the extinction cross-section, and ri is the radius of individual GNP, and M is the number of GNPs in the aggregate (M ≥ 1). The denominator of the above equation refers to the summation of geometric cross-sectional areas for nanoparticles that form the aggregate, and is used here for convenient comparison with individual GNP.

Results GNP- oligonucleotide conjugation and GNP aggregation assay Conjugation of the oligonucleotide sequence on the GNP was confirmed by an increase in the GNP hydrodynamic size (10 – 20 nm, Figure S2) and plasmonic resonance peak shift (5 – 20 nm, Figure S2). 5 ACS Paragon Plus Environment

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The conjugation was performed using the molar ratios (oligonucleotide: GNP ratio) to obtain stable conjugates, as shown in Table S2. Furthermore, the number of oligonucleotide per GNP was measured using the Oli-Green fluorescence assay andwas determined to be approximately 62±14, 379±112, and 1467±480 for 15 nm, 30 nm, and 50 nm GNPs, respectively. GNP aggregation due to DNA hybridization was confirmed by the plasmonic peak shift using the UV-Vis spectrum, a visible color change and TEM (Figure 1, more TEM images in Figures S3 and S4). No peak shift was observed in the absence of target sequence or with control target sequence (Table S1). TEM images were obtained before and after the introduction of target DNA (Figure 1c). TEM image analysis shows an inter-particle distance c ≈ 5 nm (Figure S5). This distance was further used as a reference for computational simulation by DDA. Effect of nanoparticle Size Three GNP sizes were studied including 15 nm, 30 nm and 50 nm with peak absorbance at 519 nm, 525 nm and 535 nm respectively (Figure S2). We systematically investigated the effect of nanoparticle size by separately keeping the following parameters constant: (1) GNP concentration, (2) UV-Vis peak absorbance of GNP-oligonucleotide conjugate, and (3) the probe oligonucleotide concentration. The corresponding molar concentration and absorbance values are detailed in Table 1. To quantitatively compare the colorimetric assay performance, we evaluated the plasmonic peak shift at 30 min and calculated the peak shift by evaluating the peak absorbance of the target and comparing with that of the control. The difference between these two peak wavelengths was considered to be the peak shift (Figure1b). Changing the nanoparticle size has a significant impact on the colorimetric assay performance including the limit of detection and signal strength (i.e. magnitude of the peak shift). Firstly, with constant nanoparticle concentration (150 pM, Figure 2b), increasing nanoparticle size leads to higher signal strength and improved (lower) LOD. Specifically, LOD decreases from 54.2 fmoles for 30 nm GNP, to 22.5 fmoles for 50 nm GNP. The peak shift for 15 nm GNP was very small and did not yield a reliable estimation of LOD. It should be noted that the GNP absorbance values are very different when the nanoparticle concentration are kept constant, ranging from 2.5 for 50 nm, 0.5 for 30 nm, to 0.055 for 15 nm. Next, when keeping the absorbance of the nanoparticles consistent, 30 nm and 50 nm GNPs have similar magnitude in the peak shift (Figure 2c). Larger GNP size leads to improved LOD values as detailed in Table 1. Lastly, under the same probe oligonucleotide concentration (Figure 2d), 30 nm and 50 nm GNP have higher peak shift and an improved LOD for 50 nm GNP. In all cases, larger GNP size leads to stronger signal strength and improved LOD (Figure 7, case 1). All LOD values for Figure 2 are also listed in Table 1. Effect of GNP size combinations We then investigated the effect of combining two different GNP sizes for the colorimetric assay. The absorbance of GNP probe was kept constant (abs = 0.75). The size combinations include 15/30 nm, 15/50 nm, and 30/50 nm (Figure 3a). Overall, GNP size combinations did not improve the sensitivity or signal strength of the assay when compared to homogenous GNP sizes (Figure 3b). 15/30 nm combinations gave slightly better signal strength when compared to homogenous 15nm GNPs. 30/50 nm combination gave slightly higher peak shift than 15/50 nm and 15/30 nm combinations, but did not 6 ACS Paragon Plus Environment

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outperform the homogeneous GNP sizes (30 nm and 50 nm). LOD values for GNP size combinations are significantly higher than homogeneous GNP size groups (Figure 7, case 2) and are listed in Table S3. Effect of nanoparticle concentration We next investigated the effect of GNP concentration on the colorimetric assay performance (Figure 4). We chose 30 nm and 50 nm GNP since 15 nm did not yield significant peak shift. For both 30 and 50 nm GNPs, higher GNP probe concentration leads to larger peak shift. For LOD, it appears that there is an optimal GNP concentration that lead to the lowest LOD, specifically 220 pM for 30 nm GNP (Table 1, Figure 7, case 3) and 32 pM for 50 nm GNP (Figure S6). Simulation of the plasmonic coupling-induced peak shift We used discrete dipole approximation (DDA) to calculate the extinction spectrum of GNP aggregates. To illustrate the underlying physics and to keep a reasonable simulation domain, we studied the plasmonic coupling of two GNPs (Figure 5) and eight GNPs (Figure 6). GNPs were positioned at different clearances (c = 0, 1, 3, and 5 nm) and the extinction spectrum was calculated by averaging different incident light directions. Plasmonic coupling of two GNPs generates a second peak at longer wavelength (650 nm to 750 nm) in addition to the primary peak (500 nm to 550 nm). Comparing the plasmonic coupling of two identical sized GNPs, the magnitude of the second peak for 50 nm is much larger than 15 nm and 30 nm GNPs, and persists when increasing the clearance distance (Figure 5a-c). This observation was also validated while simulating eight GNPs which were positioned on a simple cubic lattice cell (Figure 6). Furthermore, the plasmonic coupling for size combinations is weaker than the two homogenous GNPs and is limited by the smaller GNP size in the size mixture. For instance, the 15/30 nm combination gives a smaller second peak when compared with both homogenous 15 nm and 30 nm cases (Figure 5d versus 5a and 5b). The 15/50 nm combination gives a very small second peak even when the two GNPs are in contact (c = 0 nm, Figure 5e).

Discussion Our study focuses on effects of GNP size, concentration, and size combinations on the colorimetric assay performance. Analysis of the results leads us to elucidate three key factors that determine the colorimetric assay performance, including the aggregation rate, the strength of plasmonic coupling between aggregated GNPs, and the background signal (Figure 7a). To generate a colorimetric peak shift, nanoparticles need to diffuse and interact with other particles for the hybridization and thus aggregation. The aggregation rate may increase with higher nanoparticle concentration due to higher possibility of nanoparticle interactions, decrease with nanoparticle size due to slower diffusion, and increase by mixing different nanoparticle sizes [46]. After aggregation, nanoparticle plasmons couple between aggregated nanoparticles by Coulomb interactions of opposite charges [47] , and leads to a shifted UVVis absorption, or colorimetric peak shift. Increasing nanoparticle size leads to much stronger plasmonic coupling (Figures 5 and 6). Lastly, the colorimetric peak shift among the aggregated nanoparticle population has to overcome the background absorption signal from non-aggregated nanoparticles to generate a measurable color shift. Firstly, we systematically examined the effect of GNP size and found that increasing nanoparticle size leads to higher overall signal strength, i.e. the magnitude of the peak shift, and improved LOD (Figure 7b and case 1). From a physical point of view, larger particles diffuse 7 ACS Paragon Plus Environment

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slower and may give a slower aggregation rate (Figure 7b, i) [46]. However, the plasmonic coupling strength is much higher for larger nanoparticles (Figures 5 and 6, Figure 7b ii)). The fact that larger GNP gives higher colorimetric peak shift suggests a dominant contribution of the plasmonic coupling strength over aggregation rate. Secondly, previous studies from the field of colloidal science suggest that nanoparticle aggregation is accelerated using mixture of small and large nanoparticles, i.e. a binary system.[46] The smaller particles move towards single large particle in the binary system to form stable aggregates. We tested using two GNP sizes for each probe (A and B, Figure 4) and evaluated the assay performance. In comparison to homogenous GNP sizes, the size combinations fail to improve the assay performance. This led us to hypothesize that while the size combination may accelerate the aggregation rate, the plasmonic coupling between two different GNP sizes is weaker based on our numerical analysis (Figure 5; Figure 7b and case 2). As a result, the size combination does not lead to improvement in the overall assay performance. This again suggests the important role of plasmonic coupling strength over the aggregation rate for nanoparticle size mixtures. Thirdly, as compared to GNP size, less is known on how the GNP probe concentration affects the target detection. Our findings suggest that GNP probe concentration has a profound impact on the colorimetric assay performance, with higher GNP probe concentrations leading to stronger overall signal. Furthermore, an optimal intermediate GNP concentration gives best LOD (Figure 7, case 3 for 30 nm GNP; Figure S6 for 50 nm GNP). Varying GNP concentration involves two competing physical mechanisms: (1) higher probability of GNP interaction and thus accelerated GNP aggregation rate with higher GNP concentrations (Figure 7c, i); and (2) higher background signal from excess non-aggregated GNP at high GNP concentrations (thus lower signal/background ratio, Figure 7c, ii). The fact that higher GNP concentration gives higher peak shift suggests a dominating effect of GNP aggregation rate at high target concentrations. On the other hand, the optimal LOD at intermediate GNP concentration suggests the balance of the two competing effects at low target concentrations. Lastly, we observed the signal drop at very high target concentrations (Figures 2 to 4) due to a phenomenon similar to the hook effect or prozone effect, previously observed in immunoassays [48, 49]. This is due to the saturation of the GNP probe caused by the excess of target DNA available in the solution. This saturation effect makes them unavailable for crosslinking (Figure 1) and thus reduces aggregation and signal. The onset of this effect depends on the total number of probe oligonucleotides available. For instance, at the same GNP concentration (Figure 2b), this effect takes place at a much lower target concentration for 30 nm GNP than 50 nm GNP, due to less probe oligo number on the 30 nm GNP. In conclusion, our study elucidates the three contributing factors that determine the colorimetric assay performance including the nanoparticle aggregation rate, the strength of plasmonic coupling between nanoparticles, and background signal. Larger GNP size leads to stronger signal and improved LOD, suggesting a dominating role of plasmonic coupling strength. Higher GNP concentration leads to stronger signal at high target concentrations as a result of higher aggregation rate; however, the fact the optimal LOD was found at intermediate GNP concentrations suggests a balance of two competing mechanisms including aggregation rate and signal/background ratio. This work contributes new


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knowledge to better design colorimetric assays for infectious disease POC diagnosis and other applications.

Acknowledgements The authors thank Kyryl Zagorovsky from Dr. Warren Chan’s group at University of Toronto for helpful discussions and suggesting the pH-assisted conjugation approach. This study is supported by a Texas Medical Research Collaborative (TxMRC) grant and startup fund from The University of Texas at Dallas (UTD). The authors acknowledge HPC resources from the Texas Advanced Computing Center (TACC) at The University of Texas at Austin, the ROMEO HPC Center hosted by the University of Reims Champagne-Ardenne, and the Extreme Science and Engineering Discovery Environment (XSEDE) which is supported by National Science Foundation grant number ACI-1053575. J.R. thanks the University of Texas at Dallas for supporting his visit at the Z.Q.’s group at UTD.

ASSOCIATED CONTENT Supporting Information The supporting information (SI) is available free of charge. Calculations for GNP molar concentration and Limit of Detection (LOD); Details on the oligonucleotides and target sequences, conjugation criteria and LOD values; UV-Vis characterization of synthesized GNP, their conjugation, aggregation and reaction kinetics for 15nm, 30nm and 50nm GNPs; TEM images indicating presence and absence of 30nm and 50nm GNP aggregation; Histogram plot for inter-particle clearance distance; Summarized effect of GNP concentration on the peak shift and LOD for 50nm GNP.

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16. Saleh, M.; El-Matbouli, M. Rapid detection of Cyprinid herpesvirus-3 (CyHV-3) using a gold nanoparticle-based hybridization assay. J. Virol. Methods 2015, 217, 50-54. 17. Dharanivasan, G.; Riyaz, S. U. M.; Jesse, D. M. I.; Muthuramalingam, T. R.; Rajendran, G.; Kathiravan, K. DNA templated self-assembly of gold nanoparticle clusters in the colorimetric detection of plant viral DNA using a gold nanoparticle conjugated bifunctional oligonucleotide probe. RSC Advances 2016, 6, 11773-11785. 18. Lin, Y. W.; Huang, C. C.; Chang, H. T. Gold nanoparticle probes for the detection of mercury, lead and copper ions. Analyst 2011, 136, 863-871. 19. Kanayama, N.; Takarada, T.; Maeda, M. Rapid naked-eye detection of mercury ions based on noncrosslinking aggregation of double-stranded DNA-carrying gold nanoparticles. Chem. Commun. (Camb) 2011, 47, 2077-2079. 20. Gopinath, S. C. B.; Lakshmipriya, T.; Awazu, K. Colorimetric detection of controlled assembly and disassembly of aptamers on unmodified gold nanoparticles. Biosens. Bioelectron. 2014, 51, 115-123. 21. Du, G.; Zhang, D.; Xia, B.; Xu, L.; Wu, S.; Zhan, S.; Ni, X.; Zhou, X.; Wang, L. A label-free colorimetric progesterone aptasensor based on the aggregation of gold nanoparticles. Microchimica Acta 2016, 183, 2251-2258. 22. Khan, S. A.; DeGrasse, J. A.; Yakes, B. J.; Croley, T. R. Rapid and sensitive detection of cholera toxin using gold nanoparticle-based simple colorimetric and dynamic light scattering assay. Anal. Chim. Acta 2015, 892, 167-174. 23. Verma, M. S.; Rogowski, J. L.; Jones, L.; Gu, F. X. Colorimetric biosensing of pathogens using gold nanoparticles. Biotechnol. Adv. 2015, 33, 666-680. 24. Fang, W.; Chen, W.; Yang, J. Colorimetric determination of DNA concentration and mismatches using hybridization-mediated growth of gold nanoparticle probes. Sens. Actuator B-Chem 2014, 192, 77-82. 25. Zhan, Z.; Ma, X.; Cao, C.; Sim, S. J. Gold-based optical biosensor for single-mismatched DNA detection using salt-induced hybridization. Biosens. Bioelectron. 2012, 32, 127-132. 26. Du, B.; Li, Z.; Liu, C. One-step homogeneous detection of DNA hybridization with gold nanoparticle probes by using a linear light-scattering technique. Angew. Chem. Int. Ed. 2006, 45, 8022-8025. 27. Frost, M. S.; Dempsey, M. J.; Whitehead, D. E. Highly sensitive SERS detection of Pb2+ ions in aqueous media using citrate functionalised gold nanoparticles. Sens. Actuator B-Chem 2015, 221, 1003-1008.


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28. Yuan, L.; Wang, X.; Fang, Y.; Liu, C.; Jiang, D.; Wo, X.; Wang, W.; Chen, H. Digitizing Gold Nanoparticle-Based Colorimetric Assay by Imaging and Counting Single Nanoparticles. Anal. Chem. 2016, 88, 2321-2326. 29. Cordray, M. S.; Amdahl, M.; Richards-Kortum, R. R. Gold nanoparticle aggregation for quantification of oligonucleotides: optimization and increased dynamic range. Anal. Biochem. 2012, 431, 99-105. 30. Thanh, N.; Rosenzweig, Z. Development of an aggregation based immunoassay for anti protein a using gold nanoparticles. Abstracts of Papers of the American Chemical Society 2002, 223, U74U74. 31. Liu, X.; Wang, Y.; Chen, P.; McCadden, A.; Palaniappan, A.; Zhang, J.; Liedberg, B. Peptide Functionalized Gold Nanoparticles with Optimized Particle Size and Concentration for Colorimetric Assay Development: Detection of Cardiac Troponin I. Acs Sensors 2016, 1, 1416-1422. 32. Li, L.; Li, B. Sensitive and selective detection of cysteine using gold nanoparticles as colorimetric probes. Analyst 2009, 134, 1361-1365. 33. Liu, J.; Lu, Y. Accelerated color change of gold nanoparticles assembled by DNAzymes for simple and fast colorimetric Pb2+ detection. J. Am. Chem. Soc. 2004, 126, 12298-12305. 34. Krpetic, Z.; Guerrini, L.; Larmour, I. A.; Reglinski, J.; Faulds, K.; Graham, D. Importance of Nanoparticle Size in Colorimetric and SERS-Based Multimodal Trace Detection of Ni(II) Ions with Functional Gold Nanoparticles. Small 2012, 8, 707-714. 35. Medley, C. D.; Smith, J. E.; Tang, Z.; Wu, Y.; Bamrungsap, S.; Tan, W. Gold nanoparticle-based colorimetric assay for the direct detection of cancerous cells. Anal. Chem. 2008, 80, 1067-1072. 36. Chen, Z.; Wang, Z.; Chen, J.; Wang, S.; Huang, X. Sensitive and selective detection of glutathione based on resonance light scattering using sensitive gold nanoparticles as colorimetric probes. Analyst 2012, 137, 3132-3137. 37. Poonthiyil, V.; Golovko, V. B.; Fairbanks, A. J. Size-optimized galactose-capped gold nanoparticles for the colorimetric detection of heat-labile enterotoxin at nanomolar concentrations. Organic & Biomolecular Chemistry 2015, 13, 5215-5223. 38. Kimling, J.; Maier, M.; Okenve, B.; Kotaidis, V.; Ballot, H.; Plech, A. Turkevich method for gold nanoparticle synthesis revisited. J Phys Chem B 2006, 110, 15700-15707. 39. Frens, G. Controlled Nucleation for Regulation of Particle-Size in Monodisperse Gold Suspensions. Nature-Phys. Sci. 1973, 241, 20-22. 40. Perrault, S. D.; Chan, W. C. Synthesis and surface modification of highly monodispersed, spherical gold nanoparticles of 50-200 nm. J. Am. Chem. Soc. 2009, 131, 17042-17043.


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41. Zhang, X.; Servos, M. R.; Liu, J. Instantaneous and quantitative functionalization of gold nanoparticles with thiolated DNA using a pH-assisted and surfactant-free route. J. Am. Chem. Soc. 2012, 134, 7266-7269. 42. Demers, L.; Mirkin, C.; Mucic, R.; Reynolds, R.; Letsinger, R.; Elghanian, R.; Viswanadham, G. A fluorescence-based method for determining the surface coverage and hybridization efficiency of thiol-capped oligonucleotides bound to gold thin films and nanoparticles. Anal. Chem. 2000, 72, 5535-5541. 43. Hill, H. D.; Millstone, J. E.; Banholzer, M. J.; Mirkin, C. A. The Role Radius of Curvature Plays in Thiolated Oligonucleotide Loading on Gold Nanoparticles. Acs Nano 2009, 3, 418-424. 44. Long, G. L.; Winefordner, J. D. Limit of detection. A closer look at the IUPAC definition. Anal. Chem. 1983, 55, 712A-724A. 45. Draine, B.; Flatau, P. Discrete-Dipole Approximation for Scattering Calculations. J. Opt. Soc. Am. A 1994, 11, 1491-1499. 46. Costanzo, P.; Patten, T.; Seery, T. Nanoparticle agglutination: Acceleration of aggregation rates and broadening of the analyte concentration range using mixtures of various-sized nanoparticles. Langmuir 2006, 22, 2788-2794. 47. Nordlander, P.; Oubre, C.; Prodan, E.; Li, K.; Stockman, M. Plasmon hybridizaton in nanoparticle dimers. Nano Letters 2004, 4, 899-903. 48. Gillet, P.; Mori, M.; Van Esbroeck, M.; Van den Ende, J.; Jacobs, J. Assessment of the prozone effect in malaria rapid diagnostic tests. Malaria Journal 2009, 8, 271. 49. Schiettecatte, J.; Anckaert, E.; Smitz, J. In Interferences in Immunoassays; Chiu, N.,H. L., Ed.; Advances in Immunoassay Technology; InTech: 2012; Vol. 1, pp 45.


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List of Figures

Figure 1. Gold nanoparticle aggregation-based colorimetric assay. (a) A schematic of the DNA induced gold nanoparticle aggregation (at rate k) and its subsequent colorimetric detection (peak shift ∆λ). The oligonucleotide conjugated gold nanoparticles (probe A and B) undergo hybridization in the presence of the target DNA to cause a plasmonic red shift and subsequent color change. The target sequence leads to head-to-tail clustering of GNPs. “c” refers the inter-particle clearance distance. (b) UV-Vis spectrum of the mixture of probes in the absence (control, black) and presence (target, red) of the target. Visual conformation of the target induced aggregation i.e. color change (Inlet). (c) TEM images confirm the formation of nanoparticle aggregates (left: no target; right: with target). The TEM images were prepared using 30nm GNP probes (peak absorbance = 0.75).


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Figure 2. Effect of gold nanoparticle size on the GNP-based aggregation assay performance. (a) Schematic of the change in nanoparticle size. The effect of GNP size was compared by keeping the GNP concentration (b, 150pM), peak absorbance (c, abs = 0.75) and the probe oligonucleotide concentration constant (d, 48 nM). Details calculations are available in Table S3).


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Figure 3. Effect of gold nanoparticle size combinations on the aggregation assay performance. (a) Schematic of GNP size combinations in the study (all GNP have the same absorbance, abs = 0.75). (b) Peak shift for probes consisting of size combinations in comparison with homogenous GNP sizes. (Solid lines: size combinations; dotted lines: homogenous GNP probe size)


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Figure 4. Effect of gold nanoparticle concentration on the aggregation assay performance. (a) The effect of GNP concentration was investigated for 30 and 50nm size gold nanoparticles. (b) Peak shift for 30nm GNP probes at different GNP concentrations (45pM, 120pM, 150pM, 220pM, 550pM). (c) Peak shift for 50nm probes at different GNP concentrations (8.7pM, 32pM, 44pM, 110pM, 150pM).


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Figure 5. Numerical simulation of the plasmonic coupling between two gold nanoparticles. Two GNPs are separated by distance “c” for different size combinations including (a) 15/15 nm; (b) 30/30 nm; (c) 50/50 nm; (d) 15/30 nm; (e) 15/50 nm; and (f) 30/50 nm. (a-c) also include spectrum for single GNP as represented by the dotted blue line.


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Figure 6. Numerical simulation of the plasmonic coupling between eight gold nanoparticles. Eight GNPs positioned in a simple cubic lattice. (a) Schematic of GNP positions for c = 1 nm (top) and c = 5 nm (bottom); Spectrum for 15 nm (b), 30 nm (c), and 50nm (d) GNP aggregates at inter-particle distance of 1 nm (orange) and 5 nm (blue), in comparison with single GNP (solid black line).


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Figure 7. Physical mechanisms that determine the colorimetric assay performance. (a) Schematic of key factors that determine the colorimetric assay peak shift, including the aggregation rate (k), plasmonic coupling strength (g), and signal versus background absorption. (b) Effect of nanoparticle size: summary of the contributing factors including (i) aggregation rate and (ii) plasmonic coupling strength as a function of nanoparticle size, and effect of nanoparticle size (iii, case 1) and size combinations (iv, case 2) on the peak shift and LOD. (c) Effect of nanoparticle concentration: summary of contributing factors including (i) aggregation rate and (ii) signal versus background absorption as a function of nanoparticle concentration, and effect of nanoparticle concentration on the peak shift (iii, case 3). Case 1: peak shift was plotted for 790 fmol from Figure 2b; Case 2: peak shift was plotted for 790 fmol from Figure 3b; Case 3: peak shift was plotted for 790 fmol from Figure 4a, 30 nm GNP.


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Table 1. Limit of detection (LOD) values for this study Figure

2b

2c,3b

2d

4b

4c

d (nm)

Absorbance

15 30 50 15 30 50 15 30 50 30 30 30 30 30 50 50 50 50 50

0.055 0.5 2.5 0.75 0.75 0.75 0.3 0.4 0.55 0.15 0.4 0.5 0.75 1.85 0.15 0.55 0.75 1.85 2.5

GNP concentration (pM)

Probe LOD Oligonucleotide (fmoles) Concentration (nM)

150 150 150 2000 220 44 820 120 32 45 120 150 220 550 8.7 32 44 110 150

9.3 56 213 126 85 64 51 45 47 17 45 56 85 208 13 47 64 158 213

** 54.2 22.5* 34 23.3 11.5 ** 47.6 10.4 93.7 47.6 54.2 23.3 48.6 225* 10.4 11.5 23.6* 22.5*

* This case has a very small or no dynamic range as shown in Figure 2c. ** This data does not have a linear range to allow calculation of the limit of detection (LOD).


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For TOC only


Tuning the Gold Nanoparticle Colorimetric Assay by Nanoparticle Size

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