Protein discrimination using a colorimetric sensor array based on gold

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Protein discrimination using a colorimetric sensor array based on gold nanoparticle aggregation induced by cationic polymer Hongyan Xi, Weiwei He, Qing-Yun Liu, and Zhengbo Chen ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/acssuschemeng.8b02063 • Publication Date (Web): 09 Jul 2018 Downloaded from http://pubs.acs.org on July 9, 2018

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ACS Sustainable Chemistry & Engineering

Protein discrimination using a colorimetric sensor array based on gold nanoparticle aggregation induced by cationic polymer Hongyan Xi,1† Weiwei He,1† Qingyun Liu,2 Zhengbo Chen1* 1

Department of Chemistry, Capital Normal University, Beijing, 100048, China

2

College of Chemical and Environmental Engineering, Shandong University of Science and Technology,

Qingdao, 266590, China * Corresponding author. Tel.: 010-68903047 †

These authors contributed equally to this work E-mail: [email protected]

ABSTRACT: In view of the charge characteristic of cationic polymers, herein, we put forward a sensitive and unique colorimetric sensor array strategy for discrimination of proteins. We used gold nanoparticles (AuNPs) as colorimetric probes and three nonspecific DNA strands (15A, 15C, and 15T) as nonspecific receptors. Upon the addition of the proteins, the diverse interactions between DNA and proteins result in the difference in the number of the remaining DNA strands. As we know, cationic polymer, such as poly(diallyldimethylammonium chloride) (PDDA), can interact with DNA and AuNPs with negative charges. Therefore, these remaining DNA can interact with PDDA. Superfluous PDDA molecules bind to AuNPs, leading to different AuNP aggregation. On the basis of this phenomenon, linear discriminant analysis (LDA) is employed to quantitatively discriminate the colorimetric responses (A620/A520) of proteins. 11 proteins at the 20 nM level were completely distinguished with 100% accuracy. Remarkably, the feasibility of the method was confirmed by the discrimination of proteins in ACS Paragon Plus Environment

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serum. KEYWORDS ： Sensor array; Gold nanoparticles; Cationic polymer; Colorimetry; Protein discrimination

INTRODUCTION Protein is related to different forms of life activities close together, such as immunity, cell growth, metabolic action, and differentiation). The existence of abnormal level of protein may show the state of the diseases. Therefore, the rapid and efficient discrimination of proteins is very important in proteomics and diagnostics in medicine.1,2 Electrophoresis and enzyme-linked immunosorbent assay-based technology is the most generally used traditional methods for medical protein detection.3-5 The excellent sensitivity can be achieved by ELISA and a large number of proteins can be discriminated through electrophoresis based approaches. However, the disadvantages of these strategies lie in the fact that high costs, instability, and intensive labor hinder their application, and simultaneous multianalyte assays can not be realized owing to the limitation of the specific binding affinities between the receptor and the target.6-8 Considering the burgeoning needs of proteomics, clinical diagnosis, and pathogen detection, it is an urgent need to develop efficient various protein discrimination methods. To overcome the above challenge, sensor arrays provide an ideal platform for detection and identification of a variety of chemically similar substances by simulating the smell or taste system of mammals.9,10 The sensor array is usually made up of a list of nonspecific receptors that create a unique response to each target through differential receptor-analyte binding interactions,11 these unique responses can be identified by linear discriminant analysis (LDA), offering general-purpose system that can not only discriminate diverse targets both individually and the mixtures,12,13 but also realize quantitative detection.14 Therefore, in recent years, the sensor arrays have attracted extensive attention and have been used for discriminating proteins. For example, many fluorescent sensor arrays based on various fluorescent materials were designed for discrimination of proteins.15-21 However, they suffer

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from complex data acquisition process and tedious nanomaterial synthesis. Thus, developing facile and sensitive approachs or small-number sensor arrays is highly demanded. Owing to the optical characteristics, gold nanoparticles (AuNPs) are the powerful candidates for colorimetric sensor arrays and have been utilized for discrimination of organic molecules, proteins, heavy metal ions, etc.22-26 However, AuNP-based colorimetric sensor arrays for protein discrimination are very few.27-29 Although great progress has been made on the development of AuNP-based colorimetric sensor arrays for identification of proteins, AuNP surface functionalization with receptors is essential, which largely hamper their further utilization. Herein, we develop a sensor array for sensitive colorimetric discrimination of diverse proteins. The key feature of this sensor array is the cationic polymer (here poly(diallyldimethylammonium chloride) (PDDA)) -mediated generation of diverse colorimetric signals. Thanks to the charge characteristic of water-soluble PDDA, negtively charged DNA strands that do not bind to proteins form DNA-PDDA complexes with charge complementary PDDA, the remaining PDDA links the negtively charged AuNPs together to make the different aggregation, giving various color change. We completely identify and differentiate 11 proteins based on the change of the colorimetric intensity as fingerprint at 20 nM level. Moreover, quantitative detection of protein (e.g., TRF) and protein discrimination in human serum, as well as blind samples with the sensor array were also elaborated. Notably, such a sensing strategy offers an excellent scaffold for other targets with similar properties or chemical structures.

EXPERIMENTAL SECTION Materials. trisodium citrate, HAuCl4, trypsin (Try), 11 proteins (lysozyme (Lys), bovine serum albumin (BSA), horseradish peroxidase (HRP), concanavalin (Con), egg white albumin (EA), urate oxidase (UOA), pepsin (Pep), hemoglobin (Hem), lipase(Lip), and transferrin (TRF)) were obtained from Sigma-Aldrich. PDDA was obtained from Aladdin reagent (Shanghai) Co. Ltd. All of the oligonucleotides: 5’-TTT TTT TTT TTT TTT-3’ (15T), 5’-CCC CCC CCC CCC CCC-3’ (15C), 5’AAA AAA AAA AAA AAA-3’ (15A) and were obtained from Sangon Biotechnology Co. Ltd. (Shanghai, China). Other reagents were at least analytical reagent grade. ACS Paragon Plus Environment

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Instrumentation. UV-vis spectra were measured with an Agilent Cary 2550 UV-vis spectrophotometer. The Discrimination of Proteins. Initially, 20 µL of DNA strands (15A, 15C, and 15T) with the same concentration (1 µM) were individually mixed with 20 µL of 20 nM 11 proteins in Tris-HCl buffer (pH 7.40) at 37°C for incubation of 3 h. Subsequently, 20 µL of PDDA (0.035 µM) was injected into the mixture to keep reaction for 30 min, and then 500 µL of 4 nM AuNP solution was added to the above solution. After that, the colrimetric intensity, K/K0, where K and K0 are the absorbance (K= OD620 nm/OD520 nm)

of the AuNPs before and after the addition of the analyte proteins, respectively, was

recorded. The obtained data were processed with LDA in SPSS (version 11.03).

RESULTS AND DISCUSSION Sensing Principle. The schematic of fabricating the colorimetric sensor array for protein discrimination is illustrated in Scheme 1. First, the three nonspecific oligonucleotides were added to the different protein solutions, respectively. Differential binding of these DNA strands with these proteins resulted in the difference in the amount of the remaining DNA strands. Then, PDDA molecules with the same amount were added to the above DNA-protein solutions, respectively. The surplus DNA strands with negative charges interacted with PDDA molecules with positive charges through electrostatic interaction. The remaining PDDA could link the negative charged AuNPs. Different amount of PDDA caused alteration in AuNP aggregation behaviors, and different AuNP aggregation behaviors caused various absorbance ratio (A620/A520) change of the solutions. Identification mode was then produced with colorimetric signals of the AuNPs at 520 and 620 nm. (Scheme 1) Optimization of the Experiment Parameters. To obtain the optimal discrimination performance, experiment Parameters were first optimized. Because the sensing scheme of the sensor array depends on diverse interactions between DNA and proteins, and between PDDA and AuNPs, in the case of DNA with fixed concentration, the PDDA concentrations for making AuNPs exhibit different aggregation behaviors are critical. As shown in Fig. S1(A,B), we noted that when PDDA concentration was lower ACS Paragon Plus Environment

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than 0.035 µM, the A520/A620 values obtained from A15, C15, and T15 were all higher than the absorbance at 520 nm of AuNP solution (blank), no obvious color change was observed. Whereas when PDDA concentration was higher than 0.04 µM, the difference between the A520/A620 values obtained from A15, C15, and T15 and the blank was negligible. In view of solution color change (Fig. S1A), 0.035 µM was chosen as the optimal PDDA concentration in the following experiment. In addition, the reasons for selecting three single bases (15A, 15C and 15T) instead of mixed bases as sensing elements are as follows: Owing to the difference in structure, the binding capacity between different DNA and different structural proteins is bound to be different, this difference is related to the different base sequences on DNA. If the DNA with the mixed bases is selected, the effect of protein discrimination may be affected. If a certain base has a better effect on some proteins with similar structures than other bases, it is necessary to avoid or reduce the effect of other base mixtures. In view of the previous literature and comparison with the single base DNA of other chains, we found that the degree of binding between DNA with 15 single bases and protein is the best. Meanwhile, the cost of DNA is taken into account. The influence of incubation time of target proteins on the sensing performance also was investigated (Fig. S2), the colrimetric responses (K/K0) almost reached the maximum when the incubation time was 3 h. Therefore, 3 h was taken as an optimal incubation time of proteins. Protein Discrimination. The sensing strategy was first used for the 11 protein discrimination with various molecular weight and isoelectric point (Table S1) at the 20 nM level. Due to the driving forces between three receptors (A15, T15, and C15) and proteins associated with the interaction between DNA and PDDA, as well as the interaction between the AuNPs and PDDA, the differential colorimetric responses were acquired when 3 sensing elements bound to the 11 proteins. Representative photographs of the colorimetric responses of the 11 proteins at 20 nM are shown in Fig. 1(A,B) and Fig. S3. Before and after the addition of proteins, the visible color variances were observed. The differential absorbance ratio from individual protein against the sensor array, i.e., K/K0, generates a unique pattern for protein discrimination from other proteins through statistical analysis. As displayed in Fig. 1C, the K/K0 values of proteins possessing lower pIs (EA, BSA, Con, Hem, HRP, UOA, and Lip) were lower than 1.0, these ACS Paragon Plus Environment

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protein solutions exhibited more serious aggregation behavior than those with higher pIs (Try and Lys), indicating surface charges of the proteins play a crucial role in discrimination. It was presumed that negatively charged proteins with lower pIs interacted with PDDA with positive charges, which caused that less PDDA linked with AuNPs. Moreover, some proteins with similar pIs (TRF and UOA; EA, BSA, and Lip) also generated diverse signal patterns, which indicates that size and surface hydrophobicity of proteins also play a role in protein discrimination. Five replicates were performed for each protein, and 165 data (3 sensing elements × 11 target proteins × 5 replicates) were subject to LDA to produce two discrimination factors (84.6 and 15.4% of the variation). The two discrimination factors were utilized to form a two-dimensional plot (Fig. 1D), where the 55 cases (11 targets × 5 replicates) were clearly gathered in 11 different groups with accuracy of 100%. LDA result was further confirmed by hierarchical cluster analysis (HCA) (Fig. 1E). When 11 target proteins existed, the UV-vis spectra of the sensor array are shown in Fig. 2 to confirm the different behavior of the sensor elements binding to different proteins. The training matrix of the colorimetric responses of the sensor array against the 11 proteins is shown in Table S2. In addition, the jackknifed classification matrix shows that the classification accuracy of proteins was enhanced from 81.8% for T15 to 96.4% for A15+C15, to 100% for A15+C15+T15 (Fig. S4B, Table S3). Of note, individual sensor elements can identify single protein with an accuracy of 100% (Fig. S4A), however, only the three sensing elements (A15+C15+T15) worked together to achieve the classification accuracy of 100% for the 11 proteins. These results mean that our sensing system can render diverse colorimetric response fingerprints for individual target proteins, and thus can successfully discriminate proteins. Furthermore, the selectivity of the sensor array was also explored (Fig. 3). Some ions, such as Cl-, SO42-, K+, and Na+, and some amino acids, including L-cysteine, L-histidine, and L-valine, even at a relative high concentration (0.1 µM), were introduced as interferent. These interfering substances can also generate colorimetric responses, however, they were distinctly separated from all target proteins. The result indicates that our sensor array is highly selective to the 11 proteins. (Figure 1) ACS Paragon Plus Environment

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(Figure 2) (Figure 3) After successful discrimination of the eleven proteins at 20 nM, detecting a single protein (here BSA and TRF as an example) with different concentrations faces a challenge. Fig. 4, Table S4, and Table S5 show that the sensor array is very sensitive for discrimination of BSA and TRF at nM in the range of 301000 nM and 20-90 nM, respectively. The linear relationships between other protein concentration and the factor 1 are shown in Fig. S5. The above linear relationships indictate that the binding affinities between oligonucleotides and proteins are stable and homogeneous, and that the sensor array had high repeatability. (Figure 4) To investigate the robustness of the proposed sensor array, blind samples were randomly extracted from the protein samples and then tested. Out of the 55 samples, 46, 51, and 45 samples were identified correctly with 83.64%, 92.73%, and 81.82% identification accuracy only using A15, C15, and T15, respectively (Table S6-S8). The recognition accuracy of the same protein samples was enhanced from 83.64% for A15 to 96.36% for C15+A15 (Table S9, Table S10). Notably, all unknown proteins were successfully identified using the three receptors (A15+T15+C15) with 100% accuracy at the 20 nM level (Table S11). Discrimination of Protein Mixtures. Identification of the mixtures of proteins is more challenging than the identification of individual proteins, mainly due to the need for higher resolution. The colorimetric responses of the sensor array on 7 mixtures of Hem and BSA (i.e., Hem 100%, BSA 10%Hem 90%, BSA 30%-Hem 70%, BSA 50%-Hem 50%, BSA 70%-Hem 30%, BSA 90%-Hem 10%, and BSA 100%), and 7 mixtures of Lys and TRF (i.e., Lys 100%, Lys 90%-TRF 10%, Lys 70%-TRF 30%, Lys 50%-TRF 50%, Lys 30%-TRF 70%, Lys 10%-TRF 90%, and TRF 100%) with 20 nM total protein, respectively, were analyzed (Table S12). The obtained absorbance was subjected to LDA to generate the score plot (Fig. 5 (A,C)), these mixtures, as well as pure Hem and BSA, Lys and TRF, were distinctly separated from each other in the LDA chart. Dendrograms formed by HCA showed in Fig. 5 (B,D) ACS Paragon Plus Environment

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further confirm the validity of LDA results. These results clearly demonstrate that the sensor array can not only be used for the discrimination of the individual proteins but discrimination of protein mixture. (Figure 5) Quantitative Detection of TRF. In order to demonstrate the quantitative determination characteristics of the sensor array, we took TRF detection as an example to illustrate the point. The colorimetric signals (expressed by the total Euclidean distances (EDs), which is the value of square root of the sum of the square of the K/K0 value) generated by TRF with various concentrations were tested. Notably, there is a linear relationship between EDs and log value of TRF concentration from 20 to 90 nM (Fig. 6). Moreover, we further calculated the detection limit to be 18.3 nM using the wellknown 3σ (signal-to-noise) criteria. The linear relationships between the logarithm of other protein concentration and the EDs are shown in Fig. S6. (Figure 6) Real Sample Assay. The real blood serum samples were utilized to evaluate the ability of the sensor array for protein discrimination. The serum was obtained from the General Hospital of the People's Liberation Army (Beijing, China) and filtered with nitrocellulose membranes (0.22 µM). Then, the serum was diluted 50 times with 20 mM Tris-HCl buffer (pH=7.40), which were then used as the assay medium. Subsequently, the proteins spiked in blood serum were measured (Table S13) and then analyzed using LDA (Fig. 7). As expected, the colorimetric signals of the sensor array to the eleven proteins at 20 nM in the serum samples are diverse, and diverse colorimetric responses offered unique patterns to successfully divide the eleven proteins into eleven different clusters through LDA. To further demonstrate the practicalities of the sensor array to blind samples, an unknown set of all eleven proteins (11 proteins × 5 replicates = 55 unknown cases) in serum were used as challenges. As shown in Table S17, all samples (11 of 11) were accurately discriminated with 100% correct unknown identification using the three DNA as receptors, yielding no false negative. Whereas when there are only one or two sensing elements, the % correct unknown identification decreases drastically (Table S14-Table S16). The results implied that the sensor array had potential for protein discrimination in real serum samples. ACS Paragon Plus Environment

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(Figure 7)

CONCLUSION In summary, we have designed a sensor array for colorimetric discrimination of proteins, where diverse colorimetric profiles were based on PDDA-triggered distinct AuNP aggregation behaviors due to the binding affinities between PDDA with positive charges and AuNPs with negative charges. The fingerprint K/K0 ratio patterns obtained from diverse proteins at 20 nM were evaluated through LDA and HCA. As a result, the sensor array was outstanding not only in the discrimination of eleven proteins with 100% identification accuracy but also in quantitative detection. Compared with other protein sensor arrays (Table S18),9,20,24,28,30-32 our sensor array shows comparable or higher sensitivity. In addition, the sensor array can efficiently identify among individual proteins and the protein mixtures. Particularly, the high identification accuracy for blind samples of the sensor array to, as well as real serum samples both validated its practicalities. All the reagents used in this work are non-toxic and cheap, therefore, the present sensor array will widen the application area of AuNP aggregation-based colorimetric sensors and provides a new idea for the development of the sensor array for nonspecific biological detection and medical diagnosis purpose.

ACKNOWLEDGEMENTS This work was supported by Scientific Research Project of Beijing Educational Committee (Grant No. KM201710028009), Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Funds (025185305000/195), and Youth Innovative Research Team of Capital Normal University.

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Scheme Captions Scheme 1 Schematic of the cationic polymer and AuNPs-based colorimetric sensor array for discrimination of proteins. Fig. 1 (A) The color change patterns of AuNPs at different DNA strands (A15, C15, and T15) against different proteins at 20 nM. (B) The color map extracting from photograph of the solution color change corresponding to (A). (C) Fingerprints of the 11 proteins at 20 nM based on the patterns of the corresponding values of K/K0 obtained from the colorimetric responses of the sensor array. (D) Canonical score plot for the discrimination of the 11 proteins at 20 nM based on the sensor array. (E) HCA analysis of the 11 proteins at 20 nM with five parallel measurements. Fig. 2 Colorimetric responses and corresponding UV-vis spectra of AuNP solution containing various proteins at 20 nM in the presence of 15A, 15C, and 15T, respectively. Fig. 3 Canonical score plots for the colorimetric response patterns obtained with the sensor array against proteins and other interfering substances at 0.1 µM. Fig. 4 Canonical score plots for the colorimetric response patterns obtained with the sensor array against (A) BSA, and (C) TRF at various concentrations. Plot of the discriminant factor 1 versus the logarithm of (B) the BSA concentration, and (D) TRF concentration. Fig. 5 Canonical score plot for colorimetric response patterns against (A) the mixture of Hem and BSA, as well as pure Hem and BSA, and (C) the mixture of Lys and TRF, as well as pure Lys and TRF. Hierarchical cluster analysis for (B) the mixture of Hem and BSA, as well as pure Hem and BSA, and (D) the mixture of Lys and TRF, as well as pure Lys and TRF. All of the experiments were performed in quintuplicate. Fig. 6 The linear relationship between the EDs and the logarithm of TRF concentration from 20 to 90 nM. Error bars represent standard deviations from five replicates. Fig. 7 Canonical score plots for the discrimination of the 11 proteins at the 20 nM level in human serum samples analyzed by LDA.


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Scheme 1


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Fig. 1


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Fig. 2


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Fig.3


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Fig.4


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Fig. 5


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Fig. 6


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Fig. 7


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For Table of Contents Use Only A sensor array for colorimetric discrimination of proteinsbased on PDDA-triggered distinct AuNP aggregation behaviors


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Protein discrimination using a colorimetric sensor array based on gold

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