Article pubs.acs.org/ac
Exciton Energy Transfer-Based Quantum Dot Fluorescence Sensing Array: “Chemical Noses” for Discrimination of Different Nucleobases Jianbo Liu,† Gui Li,† Xiaohai Yang, Kemin Wang,* Li Li, Wei Liu, Xing Shi, and Yali Guo State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Key Laboratory for Bio-Nanotechnology and Molecular Engineering of Hunan Province, Hunan University, Changsha 410082, P. R. China. S Supporting Information *
ABSTRACT: A novel exciton energy transfer-based fluorescence sensing array for the discrimination of different nucleobases was developed through target nucleobase-triggered self-assembly of quantum dots (QDs). Four QD nanoprobes with different ligand receptors, including mercaptoethylamine, N-acetyl-L-cysteine, 2dimethyl-aminethanethiol, and thioglycolic acid, were created to detect and identify nucleobase targets. These QDs served as both selective recognition scaffolds and signal transduction elements for a biomolecule target. The extent of particle assembly, induced by the analyte-triggered self-assembly of QDs, led to an exciton energy transfer effect between interparticles that gave a readily detectable fluorescence quenching and distinct fluorescence response patterns. These patterns are characteristic for each nucleobase and can be quantitatively differentiated by linear discriminate analysis. Furthermore, a fingerprint-based barcode was established to conveniently discriminate the nucleobases. This pattern sensing was successfully used to identify nucleobase samples at unknown concentrations and five rare bases. In this “chemical noses” strategy, the robust characteristics of QD nanoprobes, coupled with the diversity of surface functionality that can be readily obtained using nanoparticles, provides a simple and label-free biosensing approach that shows great promise for biomedical applications.
S
individual analytes or mixtures thereof.19 Currently, functional nanomaterials emerged as novel significant candidates for the development of pattern-sensing technology.20−22 In a pioneering study, Rotello et al. used gold nanoparticles for the development of a protein-sensing array and further selected gold nanoparticles with green fluorescent protein or fluorescent polymers as a signal transducer for a sensing array of protein, cells, tissues, bacteria, and glycosaminoglycan.22−27 Furthermore, graphene oxide and magnetic nanoparticles have also been proposed as excellent nanocarriers for pattern-sensing arrays.28−30 These sensing arrays usually require a fluorescence emitter and quencher pair, which introduces difficulty regarding the choice of the pair and limits the sensing diversity.31 QDs offer many advantages in terms of target recognition and signal transduction. Their high surface area and unique physicochemical properties, readily modulated by core materials and ligand structure, render a sensing-array response with high selectivity and sensitivity.32,33 In general, the aggregation or self-assembly of QDs usually arises from noncovalent interactions between interparticles, for example, hydrogen-bonding interactions, van der Waals forces, and electrostatic forces, which are determined by the ligand on the
emiconductor quantum dots (QDs), with their unique sizedependent properties and flexible processability, are widely used in various fields, such as nanosensors,1,2 bioimaging,3,4 catalysis,5,6 and optoelectronics7 and particularly as building blocks in the assembly of functional nanostructures.8,9 When QDs come close together, their exciton energy transfer effect between interparticles gives a readily detectable fluorescence quenching accompanied by a red shift of the emission peak.10 This distance-dependent exciton effect facilitates a simple and distinguishable signal readout for fluorescence sensing.11,12 Analyte-guide self-assembly of fluorescent QDs has been widely reported and used for sensitive detection of ions, proteins, DNA, and so on.13−15 For example, our group developed an exciton energy transfer-based fluorescence nanoprobe through target-triggered assembly of functional QDs.13 However, most studies have relied on the lock-and-key-based specific recognition mechanism, which usually involves complicated surface functionalization or chemical modification. Pattern array-based sensing approaches, called “chemical noses,” that use differential receptor−analyte binding interactions provide an alternative to lock-and-key approaches that use specific recognition processes.16−18 Rather than specifically designed binding interactions between receptors and analytes, “chemical noses” are dependent on each of the arrayed receptors’ response to a differing degree to each analyte. Composite fingerprints made up from multiple differential binding interactions provide unique sensing patterns for the © XXXX American Chemical Society
Received: June 11, 2014 Accepted: December 11, 2014
A
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 1. Schematic of the exciton energy transfer-based QD fluorescence sensing array for discrimination of nucleobases. (A) Target-guided selfassembly of QDs results in the exciton energy transfer between assembled QDs, which is accompanied by a fluorescence quenching. (B) Fluorescence pattern generation through distinct assembly behavior and the exciton energy transfer effect. The wells on the microplate contain functionalized QDs with different ligands, and the addition of nucleobase analytes produces a fingerprint for a given nucleobase. (C) Chemical structure of the acceptor ligands on the surface of QDs. The terminal mercapto group coordinates with QDs, and the other terminal group determines the QDs with different features. (D) Chemical structure of the five nucleobase analytes.
nanoparticle surface.34,35 For the“chemical noses” chemical sensor, differential arrays do not have to rely on specific binding sites, but multiple interactions may be pertinent. This is to be expected when modifying the QD surfaces with different functionalities, where several surface interactions may be possible. Therefore, we reason that by diversification of the surface ligands on nanoparticles and modulation of the colloid self-assembly behavior, QDs may display distinct dynamic assemblies and produce a differential fluorescence response, which would enable us to produce a fingerprint for the given analytes. As a proof of concept, we report the biomolecule-mediated self-assembly of QDs for a pattern-sensing array that triggers an exciton energy transfer effect. Nanomaterial-based pattern arrays usually require multiple different acceptor ligands decorated on the surface of nanoparticles for differential binding of target analytes. We designed four arrays of QDs with different acceptor ligands on their surface for pattern sensing. Five primary nucleobases were chosen as discrimination targets (Figure 1D). Nucleobases were nitrogen-containing biological compounds. Their ability to form base pairs and stack upon one another leads directly to the helical structure of nucleic acids, which are the carriers of genetic information and the material basis of genetic expression.36,37 Developing simple, selective, and inexpensive methods of identifying different nucleic acid bases is critical to biomedical research, disease diagnosis, physiological metabolism, and drug identification.38,39 The pattern-sensing strategy of our proposed approach is illustrated in Figure 1. Hydrophilic fluorescent QDs were capped with four kinds of acceptor ligands, mercaptoethylamine (MA), Nacetyl-L-cysteine (NAC), 2-dimethyl-aminethanethiol hydrochloride (DMAE), and thioglycolic acid (TGA). We found that the different functional fluorescent QDs exhibited different
assembly behaviors when mixed with target nucleobases, producing a differential energy transfer effect and giving various fluorescence responses. Thus, the variation in the fluorescence change could be used as a fingerprint to accurately discriminate nucleobases. Furthermore, a fingerprint-based barcode was established and facilitated a simple and convenient approach to discriminate nucleobases. In this sensing platform, the assembled QDs with different functionalities assume both the target recognition and signal transduction elements, which provide an alternative approach for the chemical nose sensing and may be expanded for the design of other nanoparticle pattern sensors.
■
EXPERIMENTS Materials and Instruments. 2-Dimethylaminethanethiol (DMAE, 95%) and thioglycolic acid (TGA, 99%) were obtained from Acros. Mercaptoethylamine (MA, 95%), cadmium perchlorate hexahydrate (Cd(ClO4)2·6H2O, 99%), and tellurium powder (Te, 99.8%) were purchased from SigmaAldrich. N-acetyl-L-cysteine (NAC, 99%), guanine (G, 99%), cytosine (C, 99%), thymine (T, 99%), uracil (U, 99%), adenine (A, 99%), N4-acetylcytidine (ac4C, 98%), 5-hydroxymethyluracil (hm5U, 98%), 5,6-dihydrouracil (DHU, 99%), xanthine (Xan, 99%), and hypoxanthine (HPX, 98%) were purchased from J&K. Nucleobase solutions with 10 mM concentrations were prepared in a phosphate buffer solution (pH = 8.4, 10 mM). A small amount of sodium hydroxide solution was added to improve the solubility of the nucleobases. All chemicals used were of analytical grade or of the highest purity available. Ultrapure water (18.0 MΩ·cm at 25 °C) was obtained from a Millipore Elix3 purification system. UV−vis spectroscopy measurements were conducted using a Shimadzu UV-1601 spectrophotometer in transmission mode B
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 2. (A) Zeta potentials of the four kinds of functional QDs. (B) Absorption and fluorescence spectra of the four kinds of functional QDs. The samples were determined in a phosphate buffer solution (pH = 8.4, 10 mM).
and a quartz cuvette (10 mm). The photoluminescence spectra were recorded on a Hitachi F7000 fluorometer, and the excitation wavelength was set at 480 nm with a recording emission range of 520−680 nm; the excitation and emission slit widths were 5 nm each. The fluorescence response pattern array was conducted on a plate reader (Tecan Infinite M1000) in clear-bottom white 96-well plates with excitation and emission fixed at 480 and 600 nm, respectively. The hydrodynamic sizes and zeta potentials were measured in a Nano-ZS analyzer (Malvern). Transmission electron microscopy (TEM) was performed on a JEOL-1230 microscope. The samples for TEM were obtained by drying sample droplets from water dispersion onto a Cu grid coated with a lacey carbon film, which was then allowed to dry prior to imaging. All measurements were performed at room temperature. Synthesis of QDs with Different Acceptor Ligands. Four kinds of QDs functionalized with different acceptor ligands were prepared via a modified method adopted from the literature.40 In brief, 2.35 mmol of Cd(ClO4)2·6H2O and 5.7 mmol TGA were dissolved in 125 mL of ultrapure water. The pH was adjusted to 11.2−11.6 with 2 M NaOH. Meanwhile, 0.1 g of Te powder and 0.25 g of NaBH4 were added into 3 mL of ultrapure water. The Te powder was gradually dissolved, and 4 M H2SO4 was injected swiftly. The generated H2Te gas was passed to the Cd solution with a N2 flow. The solution was heated and refluxed for about 20 h, resulting in TGA-capped CdTe QDs (TGA-QDs) with red fluorescence. MA-QDs, NAC-QDs, and DMAE-QDs were prepared using a similar procedure, except with different thiol compounds and pH. pHs of 5.6−5.9, 11.2−11.8, and 5.0−6.0 were used for the synthesis of MA-QD, NAC-QDs, and DMAE-QDs, respectively. Pattern Sensing Array. In a 96-well plate, 190 μL of QDs, diluted in a phosphate buffer solution (pH = 8.4, 10 mM), was pipetted into each well, and 10 μL of the nucleobases was added to each well, with final concentrations of 0.5 mM. After incubation for 60 min, the fluorescence intensity of QDs at 600 nm was recorded. This process was repeated for the five nucleobase targets to generate six replicates of each. Thus, the five nucleobases were tested against the four kinds of QD arrays six times to give a 5 nucleobases × 4 QDs × 6 replicates training data matrix. The raw data matrix was processed using classical linear discriminate analysis (LDA) in SPSS 11. LDA is a well-known statistical method for recognizing the linear combination of features that characterizes or separates two or more classes of objects or events. It can easily handle the case
where the within-class frequencies are unequal, and their performances have been examined on randomly generated test data. This method maximizes the ratio of between-class and within-class variances in any particular data set, allowing response patterns to be quantitatively differentiated.
■
RESULTS AND DISCUSSION Characterization of Different Ligands Functionalized QDs. We used four readily fabricated, structurally different fluorescent QD nanoparticles to create nucleobase pattern sensors. These QDs served as both selective recognition scaffolds and signal transduction elements for a biomolecule target. For our study, we chose mercapto compounds to cap the aqueous hydrophilic QDs because of their flexible processability, in particular, their convenience for synthesis.40 As presented in Figure 1C, four different mercapto compounds, MA, DMAE, NAC, and TGA, were chosen for the synthesis of CdTe QDs, and their terminal moieties featured the QDs’ different functional surface moieties, that is, amino, dimethyl amine, formamide, and carboxyl groups, respectively. Most of the biomolecules contain the functional groups, such as amino, hydroxyl, carboxyl, methyl, amide, and so on. Here, the QDs with different functionalities provide a biomimetic macromolecule nanostructure, which causes them to produce a differential interaction with the target. The different ligand anchored on the surface of QDs was identified by Fourier transform infrared (FTIR) spectroscopy. As illustrated in Figure S1, characteristic peaks at 3437 and 1592 cm−1 can be clearly assigned to the stretching mode of the NH2 group, indicating the presence of MA on the MA-QDs. The peaks at 2955 cm−1 st(CH3), 1371 cm−1 δs(CH3), and 1160 cm−1 st(N−C), assigned to the structure of DMAE, indicated the presence of DMAE-QDs (Figure S1). The peaks at 1396 cm−1 st(COO−), 1656 cm−1 st(CO), and 1091 cm−1 st(N−C), assigned to the structure of NAC on the NAC-QDs, and the peaks at 1578 and 1392 cm−1 st(COO−) indicated the presence of TGA on the TGA-QDs (Figure S1).41−43 Therefore, the FTIR data indicated that different QDs had been prepared and that the acceptor ligands were successfully capped on QDs. The functional groups also endowed the fluorescent QDs with different surface charges and hydrophilic properties. The zeta potentials of different QDs were determined using a Malvern zeta-potential analyzer. MA-QDs, DMAE-QDs, NACQDs, and TGA-QDs had zeta potentials of 33.9 mV, 26.9 mV, −29.9 mV, and −35.3 mV, respectively (Figure 2A). The C
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 3. (A) TEM imaging of NAC-QDs before and after the addition of nucleobase G. Inset: the variety of hydrodynamic diameters. (B) Fluorescence spectra of NAC-QDs before and after the addition of 4.5 mM nucleobase G.
the addition of a nucleobase, only a slight enhancement of resonance rayleigh scattering appeared (Figure S2). Thus, it is presumed that the scattering of QD aggregates showed a slight influence on the fluorescence of QDs. In addition, the scattering usually causes fluorescence quenching, but no bandshift of the emission peak. In our strategy, an obvious red-shift of the fluorescence peak can be observed. Here, the aggregation and fluorescence response clearly suggested that fluorescence quenching was mostly from the latter mechanism, the exciton energy transfer between the assembled QDs. As shown in Figure S3, for the water-soluble QDs, due to their large size distribution, a certain amount of spectral broadening is always unavoidable, and actually there is a measurable spectral overlap in the system. Therefore, as the QDs aggregate, an exciton energy transfer tends to occur between the QD aggregates and especially from the smaller QDs to larger ones. As the smaller QDs possess larger band gap, while larger QDs have a smaller band gap, the exciton energy transfer between the interparticles will lead to a red shift of the fluorescence spectra. On the other hand, due to the surface trap of the QDs, partially nonluminescent QDs exist in the QD ensemble. So the exciton will eventually be trapped by the surface defects, and this process will inevitably result in fluorescence self-quenching of the QDs.44,45 This was consistent with the results reported in our previous study for aptamer-capped QDs, where target adenosine induced aggregation of QDs, which was accompanied by a fluorescence quenching and bandshift.13 Furthermore, five types of nucleobases were investigated with NAC-QDs. It was observed that an aggregation of QDs occurred for all of the nucleobases; however, they displayed different aggregation behavior (Figure S4). This is because of the different acceptor ligands on the surface of QDs, which gives them distinct assembly dynamics. For example, nucleobase G triggered the strong aggregation of NAC-QDs. It was inferred that the electrostatic attraction between positively charged nucleobase G and negatively charged NAC-QDs resulted in the strong aggregation. In addition, hydrogen-bonding interactions may also be another important factor. As nucleobases and mercapto compounds are rich in H, O, and N, they are apt to form hydrogen bonds with each other (Figure S5). The aggregation of QDs arose from noncovalent interactions between interparticles; we believe that the aggregation of QDs was dominated by two processes: the collisions between interparticles and the binding of target with functional moieties on the surface of QDs. The self-assembly aggregation process relies on the probability of the collisions between particles.
differently charged ligands can be engineered to tune the electrostatic interactions between QDs and render them with distinct assembly behavior. The amino, dimethyl amine, formamide, and carboxyl groups are mainly functional organic groups in biological macromolecule scaffolds, and thus, they can be employed as potential surface acceptor candidates for the reorganization of biological macromolecules through hydrogen-bonding, van der Waals forces, and electrostatic forces. The bulk room-temperature linear absorption and fluorescence spectra of the different QDs are shown in Figure 2B. The peaks of the first exciton absorption transition ranged from 570 to 575 nm, and their fluorescence emission peak reached approximately 600 nm. The identical optical properties of the four kinds of fluorescent QDs make them convenient and suitable for pattern-sensing arrays. Self-Assembly Triggered Exciton Energy Transfer and Fluorescence Quenching. The addition of target analytes will lead to the aggregation or self-assembly of QDs and their fluorescence quenching. As a typical example, we first investigated the assembly and fluorescence response of NACQDs upon the addition of 4.5 mM nucleobase G. The introduction of G triggered the assembly of the NAC-QDs. Transmission electron microscope (TEM) images were taken to examine the morphology of QD assemblies. The introduction of nucleobase G led to an increase in the particle size of QD assemblies (Figure 3A). The morphology of the assemblies revealed the target-triggered self-assembly of the nanoparticles. Dynamic light scattering (DLS) was used to further validate the nucleobase G-mediated self-assembly. On addition of nucleobase G to the NAC-QD solution, the average Dh (hydrodynamic diameter) value increased from 11.4 ± 3.1 to 142.0 ± 5.9 nm (Figure 3A, inset), providing a strong indication of the conversion of single nanoparticles into aggregates. The nucleobase G-induced close packing of QDs significantly affected the luminescence properties of the resultant assemblies. Figure 3B shows the photoluminescence spectra of NAC-QDs before and after the addition of nucleobase G. The emission spectra of QD assemblies showed a loss in their fluorescence intensity of ∼44% and a 2.0 nm red shift of the fluorescence peak. The fluorescence quenching of QDs may result from the scattering of the QD aggregates or the exciton energy transfer between the aggregates. The former mechanism suggested that the strong scattering of the aggregates will decrease the fluorescent intensity of the QDs. In our system, the resonance rayleigh scattering of the QDs happened around 428 nm, and there were no spectral overlap with the excitation and fluorescence emission of QDs. Upon D
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 4. Evolution of fluorescence intensity of different QDs with the gradual addition of nucleobases. (A) MA-QDs, (B) DMAE-QDs, (C) NACQDs, and (D) TGA-QDs. The experiments were performed in phosphate buffer solution (pH = 8.4, 10 mM).
titration experiments were performed under the optimal reaction parameters (Figure 4). Four kinds of QDs were pipetted into each well and diluted in a phosphate buffer solution. The nucleobases were gradually added and then incubated for 60 min. The fluorescence intensity of QDs at 600 nm was recorded for quantitative analysis. We found that the four kinds of QDs exhibited different fluorescence responses to the nucleobases. For all kinds of QDs, their fluorescence was gradually quenched after the addition of nucleobases. In particular, MA-QDs were greatly quenched by a nucleobase, and their fluorescence was almost totally quenched even with a small amount of nucleobase (Figure 4A). DMAE-QDs were greatly quenched by nucleobase G. With the concentration of various nucleobases at 4.5 mM, the fluorescence of DMAEQDs fell to about 60% and reached a plateau. For NAC-QDs and TGA-QDs, with the gradual addition of nucleobases, their fluorescence intensity decreased in a gradient manner. Thus, according to titration fluorescence analysis, each of the five analytes generates a different response with different QDs, demonstrating the capacity of the system to detect small structural differences in a pattern-sensing array approach. The results provide the possibility that QDs can be employed as both recognition and signal elements for pattern sensing of biological nucleobases. Fluorescence QD-based Sensing Array. The structural information on the target nucleobases is listed in Table S2, with small differences in molecular weights and structure. Herein, once the fluorescence titration curve that provided the fluorescence quenching ability of various nucleobases was determined, we tested the ability of our sensor array to discriminate different nucleobases in a phosphate buffer using a 40−60 nM solution of QDs. We conducted the pattern sensing tests at 0.5 mM analyte nucleobases, the lowest concentration for which the nucleobases could be substantially differentiated
Thus, this is a concentration dependent process. A high concentration of QDs is in favor of their self-assembly and consequently highly efficient fluorescence self-quenching. In our previous work,13 it was found that the quenching efficiency reached a maximum with 50 nM QD. In addition, the aggregation process also relied on binding of the target with functional moieties on the surface of QDs, and thus, ligand coverage on the surface of QDs was a key factor for this fluorescent sensing. The ligand coverage was determined as 189 ligands per QD on average through ICP-AES (Table S1, Supporting Information). Four kinds of QDs were all prepared in a similar protocol, and excessive ligands were added in the reaction system, which guaranteed that there was no large deviation in the ligand coverage from batch to batch. This aggregation is a kinetic process and also greatly dependent on the reaction temperature and incubation time. In our previous work, it was investigated that high temperature was unfavorable to the aggregation of QDs.15 Therefore, in this system, the selfassembly of QDs was performed at room temperature, which provided a great convenience for the practical application. Furthermore, to provide a better understanding of the kinetic process, a real-time assay of the self-assembly was conducted. Usually, a self-assembly process is monitored by dynamic light scattering or transmission electron microscopy. Here, we monitored the kinetics of the self-assembly of QDs using fluorescence quenching efficiency. As shown in Figure S6, upon the addition of nucleobase G, the fluorescence of NAC-QDs was gradually quenched. It was determined that the reaction progressed to a substantial extent in 30 min and was almost completely finished in 60 min. An understanding of selfassembly kinetics in this system may lead to improvements in sensitivity and specificity of this novel detection technique. In order to determine the detailed fluorescence response of QDs to different concentrations of nucleobases, fluorescence E
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 5. Pattern-sensing array of nucleobases with the QD platforms. (A) Fluorescence response patterns of QDs in the 96-well microplate on the addition of nucleobase solutions (0.5 mM). The intensities were recorded at 600 nm. (B) Canonical score plot for the first two factors of simplified fluorescence-response patterns obtained with QD assembly arrays against nucleobases. The canonical scores were calculated by LDA for the identification.
using the given sensor array followed by LDA. The aggregation at a low concentration of target may require more reaction time. Here, the reaction time was further investigated, and the results demonstrated that the fluorescence intensity reached a platform after 1 h (Figure S7). Therefore, the reaction time was set at 1 h. The responses were compiled into a training matrix, and distinct fluorescence responses were observed for each analyte nucleobase (Table S3). In all, six replicates were obtained for each analyte nucleobase in each sensor, producing 120 data points (4 sensors × 5 nucleobases × 6 replicates) for each array (Figure 5A). LDA was used to quantitatively differentiate the fluorescence-response patterns of QDs with the nucleobases. LDA is a statistical technique that maximizes the ratio of between-class and within-class variances and so allows response patterns to be differentiated. For this purpose, we generated the fluorescence responses six times for each nucleobase against the four QDs. After the analysis, four canonical factors were generated (64.1, 31.6, 4.2, and 0.3% of the variation) that represented linear combinations of the response matrices obtained from the fluorescence-response patterns (4 sensors × 5 nucleobases × 6 replicates). The first two discrimination factors were plotted in 2D (Figure 5B). All 30 cases (5 bases × 6 replicates) were easily clustered into five distinct nucleobase groups. This clear discrimination meant that our technique can accurately detect and identify nucleobases and that the proposed strategy can give a different aggregation response and render distinct exciton energy transfer-based fluorescence response fingerprints for individual target nucleobases. To further analyze the raw data, hierarchical cluster analysis (HCA), a statistical classification method based on Euclidean distance, was performed by SPSS 11.46,47 As show in Figure S8A, the HCA dendrogram clearly showed four clusters (at a similarity level of approximately 1). According to the fluorescence pattern sensing of the four kinds of QDs with five nucleobases, a fingerprint-based barcode system for discrimination of the five nucleobases was plotted. The graphical barcodes for the five nucleobases are illustrated in Figure S8B, and the lines were scored based on the different fluorescence quenching of QDs. On the basis of this, different nucleobases could clearly be discriminated through the biobarcode. This biobarcode method does not require complicated instrumentation or experimental steps, providing
a convenient and accurate sensing model for nucleobases and showing great promise for point-of-care biomedical application. Identification of the Sample at Varying Concentration. After successful discrimination of nucleobases in a buffer, the next challenge was to detect nucleobases at an unknown concentration. Varying nucleobase concentrations would be expected to lead to the drastic alteration of fluorescence response patterns for the target, making identification of nucleobases with both an unknown identity and unknown concentration challenging. To enable the detection of unknown nucleobases, we have designed a protocol combining LDA and ultraviolet (UV) measurements. This was practiced in a modified procedure as described previously.22 The normalized UV−vis spectra of different nucleobases were determined (Figure S9), and it was observed that the nucleobases possessed maximum similarity of absorption capacity at 281.5 nm. Therefore, in this approach, a set of fluorescence response patterns was generated at analyte nucleobase concentrations after dilution several times to generate a standard UV absorption value at 281.5 nm (A281.5 = 2.6), the concentration for which the nucleobases could be substantially differentiated using the given sensor array followed by LDA. Therefore, this concentration could also be treated as the detection limit of this assay, with molar concentrations ranging from 0.34 mM for nucleobase G to 0.67 mM for nucleobase A. In our unknown identification protocol, the A281.5 value of the nucleobases was determined, and an aliquot subsequently diluted to A281.5 = 2.6 for recording the fluorescence response pattern against the QDbased pattern sensing array. Once the identity of the nucleobases was established by LDA, its initial concentration could be determined from the initial A281.5 value and corresponding molar extinction coefficient (ε281.5) according to the Beer−Lambert law (Figure S9). As shown in Figure S10A, the fluorescence response patterns where the nucleobase concentration is A281.5 = 2.6 are distinctly different from those generated from 0.5 mmol of nucleobases but retain a high degree of reproducibility (Table S4). As before, LDA accurately differentiates the nucleobases patterns. As shown in Figure S10B, the canonical fluorescence response patterns display excellent separation. The results clearly implied that the pattern sensors have practical applications of detecting and identifying nucleobases at unknown concentrations. Discrimination of Different Rare Bases. Almost all eukaryotic genomes contain a rare base in addition to the F
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 6. Pattern-sensing array of different rare bases with the QD platforms. (A) Chemical structure of the five representative rare bases. (B) Fluorescence response patterns of QDs in the 96-well microplate on the addition of rare base solutions (0.5 mM). (C) Canonical score plot for the first two factors of simplified fluorescence-response patterns obtained with QD assembly arrays against different rare bases. The canonical scores were calculated by LDA for the identification.
standard nucleobases. The most common of the rare bases include N4-acetylcytidine (ac4C), 5-hydroxymethyluracil (hm5U), 5,6-dihydrouracil (DHU), and so on. The rare bases deserve special attention because their presence is directly relevant to some questions concerning cellular functionalities. Apart from these nucleobases, a number of additional modified bases can be found by base composition analysis within genomic DNA. Some of these modifications could have arisen owing to oxidative stress and thus might serve as important analytical biomarkers to determine the degree of cell damage. Rare base and modified base analysis is a widely used analytical method in the fields of molecular biology and biochemistry.48 Having confirmed the conceptual validity of the sensing system for the nucleobases, we next turned to an analysis of different rare bases. Five different rare bases were chosen as representatives to demonstrate the validity of the discrimination, including N4-acetylcytidine (ac4C), 5-hydroxymethyluracil (hm5U), 5,6-dihydrouracil (DHU), xanthine (Xan), and hypoxanthine (HPX; Figure 6A). We conducted the pattern sensing tests at 0.5 mM analyte nucleobases, the same concentration as that of standard nucleobases. The responses were compiled into a training matrix, and distinct fluorescence responses were observed for each analyte nucleobase (Table S5). Six replicates were obtained for each analyte nucleobase in each sensor, producing a pattern array of 4 sensors × 5 nucleobases × 6 replicates (Figure 6B). After LDA analysis, the first two discrimination factors (69.0%, 23.6%) were plotted in 2D (Figure 6C). All 30 cases (5 bases × 6 replicates) were easily clustered into five distinct rare base groups. This clear discrimination meant that our technique can accurately detect and identify the five rare bases.
effects. Different nucleobases displayed distinct assembly behaviors and fluorescence responses to the four kinds of QDs. Through application of LDA, we are able to use these fluorescence changes to discriminate nucleobases in a simple, efficient, and general fashion. This strategy exploits the tunability of the QD nanoparticle surface to provide selective interactions with analytes, and the efficient fluorescence quenching of the assembled nanoparticle to impart efficient transduction of the binding event. The present study will broaden the application field of QD-based fluorescence sensors and give new direction to the development of sensitive sensingarray systems.
CONCLUSION In summary, a QD-based fluorescence pattern-sensing array constructed using target-triggered self-assembly was developed for the discrimination of nucleobases and rare bases, where the “chemical noses” signal was based on the QD assembly triggered fluorescence quenching due to exciton energy transfer
■
■
ASSOCIATED CONTENT
S Supporting Information *
FTIR, DLS, ICP-AES, Abs, training matrix, and HCA analysis. This material is available free of charge via the Internet at http://pubs.acs.org.
■
AUTHOR INFORMATION
Corresponding Author
*Tel./Fax: +86-731-88821566. E-mail:
[email protected]. Author Contributions †
Jianbo Liu and Gui Li contributed equally to this work.
Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (21205033, 21190040, J1210040), National Basic Research Program (2011CB911002).
■
REFERENCES
(1) Ma, W.; Qin, L. X.; Liu, F. T.; Gu, Z.; Wang, J.; Pan, Z. G.; James, T. D.; Long, Y. T. Sci. Rep. 2013, 3, 1537. (2) Wang, Q.; Xu, Y.; Zhao, X.; Chang, Y.; Liu, Y.; Jiang, L.; Sharma, J.; Seo, D.; Yan, H. J. Am. Chem. Soc. 2007, 129, 6380−6381.
G
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry (3) Li, F.; Gao, D.; Zhai, X.; Chen, Y.; Fu, F.; Wu, D.; Zhang, Z.; Zhang, X.; Wang, Q. Angew. Chem., Int. Ed. 2011, 50, 4202−4205. (4) Wang, Q. B.; Liu, Y.; Ke, Y. G.; Yan, H. Angew. Chem., Int. Ed. 2007, 47, 316−319. (5) Han, Z.; Qiu, F.; Eisenberg, R.; Holland, P. L.; Krauss, T. D. Science 2012, 338, 1321−1324. (6) Bear, J. C.; Hollingsworth, N.; McNaughter, P. D.; Mayes, A. G.; Ward, M. B.; Nann, T.; Hogarth, G.; Parkin, I. P. Angew. Chem., Int. Ed. 2014, 53, 1598−1601. (7) Giansante, C.; Carbone, L.; Giannini, C.; Altamura, D.; Ameer, Z.; Maruccio, G.; Loiudice, A.; Belviso, M. R.; Cozzoli, D.; Rizzo, A.; Gigli, G. J. Phys. Chem. C 2013, 117, 13305−13317. (8) Sharma, J.; Ke, Y. G.; Lin, C. X.; Chhabra, R.; Wang, Q. B.; Nangreave, J.; Liu, Y.; Yan, H. Angew. Chem., Int. Ed. 2008, 47, 5157− 5159. (9) Wang, Q. B.; Wang, H. N.; Lin, C. X.; Sharma, J.; Zou, S. L.; Liu, Y. Chem. Commun. 2010, 46, 240−242. (10) Koole, R.; Liljeroth, P.; de Mello Donegá, C.; Vanmaekelbergh, D. l.; Meijerink, A. J. Am. Chem. Soc. 2006, 128, 10436−10441. (11) Wheeler, D. A.; Zhang, J. Z. Adv. Mater. 2013, 25, 2878−2896. (12) Kim, T.; Noh, M.; Lee, H.; Joo, S. W.; Lee, S. Y.; Lee, K. J. Phys. Chem. B 2009, 113, 14487−14490. (13) Liu, J. B.; Liu, Y.; Yang, X. H.; Wang, K. M.; Wang, Q.; Shi, H.; Li, L. Anal. Chem. 2013, 85, 11121−11128. (14) Zhao, D.; Li, J. T.; Yang, T. M.; He, Z. K. Biosens. Bioelectron. 2014, 52, 29−35. (15) Liu, J.; Yang, X.; Wang, K.; Yang, R.; Ji, H.; Yang, L.; Wu, C. Chem. Commun. 2011, 47, 935−937. (16) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R. Chem. Rev. 2000, 100, 2595−2626. (17) Miranda, O. R.; You, C. C.; Phillips, R.; Kim, I. B.; Ghosh, P. S.; Bunz, U. H. F.; Rotello, V. M. J. Am. Chem. Soc. 2007, 129, 9856− 9857. (18) Wang, S.; Kong, H.; Gong, X. Y.; Zhang, S. C.; Zhang, X. R. Anal. Chem. 2014, 86, 8261−8266. (19) Wright, A. T.; Anslyn, E. V. Chem. Soc. Rev. 2006, 35, 14−28. (20) Xu, S.; Lu, X.; Yao, C.; Huang, F.; Jiang, H.; Hua, W.; Na, N.; Liu, H.; Ouyang, J. Anal. Chem. 2014, 86, 11634−11639. (21) Yang, X. F.; Li, J.; Pei, H.; Zhao, Y.; Zuo, X. L.; Fan, C. H.; Huang, Q. Anal. Chem. 2014, 86, 3227−3231. (22) You, C. C.; Miranda, O. R.; Gider, B.; Ghosh, P. S.; Kim, I. B.; Erdogan, B.; Krovi, S. A.; Bunz, U. H. F.; Rotello, V. M. Nat. Nanotechnol. 2007, 2, 318−323. (23) De, M.; Rana, S.; Akpinar, H.; Miranda, O. R.; Arvizo, R. R.; Bunz, U. H. F.; Rotello, V. M. Nat. Chem. 2009, 1, 461−465. (24) Rana, S.; Singla, A. K.; Bajaj, A.; Elci, S. G.; Miranda, O. R.; Mout, R.; Yan, B.; Jirik, F. R.; Rotello, V. M. ACS Nano 2012, 6, 8233−8240. (25) Bajaj, A.; Miranda, O. R.; Phillips, R. L.; Kim, I. B.; Jerry, D. J.; Bunz, U. H. F.; Rotello, V. M. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 10912−10916. (26) Elci, S. G.; Moyan, D. F.; Rana, S.; Tonga, G. Y.; Phillips, R. L.; Bunz, U. H. F.; Rotello, V. M. Chem. Sci. 2013, 4, 2076−2080. (27) Phillips, R. L.; Miranda, O. R.; You, C. C.; Rotello, V. M.; Bunz, U. H. F. Angew. Chem., Int. Ed. 2008, 47, 2590−2594. (28) Pei, H.; Li, J.; Lv, M.; Wang, J. Y.; Gao, J. M.; Lu, J. X.; Li, Y. P.; Huang, Q.; Hu, J.; Fan, C. H. J. Am. Chem. Soc. 2012, 134, 13843− 13849. (29) Chou, S. S.; De, M.; Luo, J. Y.; Rotello, V. M.; Huang, J. X.; Dravid, V. P. J. Am. Chem. Soc. 2012, 134, 16725−16733. (30) Wan, Y.; Sun, Y.; Qi, P.; Wang, P.; Zhang, D. Biosens. Bioelectron. 2014, 55, 289−293. (31) Lu, Y. X.; Liu, Y. Y.; Zhang, S.; Wang, S.; Zhang, S. C.; Zhang, X. R. Anal. Chem. 2013, 85, 6571−6574. (32) José, M.; Costa-Fernández; Pereiro, R.; Sanz-Medel, A. Trend Anal. Chem. 2006, 25, 207−218. (33) Sang, L. J.; Wang, H. F. Anal. Chem. 2014, 86, 5706−5712. (34) Moyano, D. F.; Rana, S.; Bunz, U. H. F.; Rotello, V. M. Faraday Discuss. 2011, 152, 33−42.
(35) El-Boubbou, K.; Zhu, D. C.; Vasileiou, C.; Borhan, B.; Prosperi, D.; Li, W.; Huang, X. F. J. Am. Chem. Soc. 2010, 132, 4490−4499. (36) Chee, M.; Yang, R.; Hubbell, E.; Berno, A.; Huang, X. C.; Stern, D.; Winkler, J.; Lockhart, D. J.; Morris, M. S.; Fodor, S. P. Science 1996, 274, 610−614. (37) Peter, E.; Nielsen. Sci. Med. 1998, 5, 48−55. (38) Tan, W.; Wang, K.; Drake, T. J. Curr. Opin. Chem. Biol. 2004, 8, 547−553. (39) Li, S. P.; Yang, F. Q.; Tsim, K. W. K. J. Pharm. Biomed. Anal. 2006, 41, 1571−1584. (40) Gaponik, N.; Talapin, D. V.; Rogach, A. L.; Hoppe, K.; Shevchen-ko, E. V.; Kornowski, A.; Eychmüller, A.; Weller, H. J. Phys. Chem. B 2002, 106, 7177−7185. (41) Kuang, R.; Kuang, X.; Pan, S. Y.; Zheng, X. D.; Duan, J. C.; Duan, Y. Q. Microchim. Acta 2010, 169, 109−115. (42) Zhao, D.; He, Z. K.; Chan, W. H.; Martin, M. F. J. Phys. Chem. C 2009, 113, 1293−1300. (43) Jiang, H.; Ju, H. X. Anal. Chem. 2007, 79, 6690−6696. (44) Crooker, S. A.; Hollingsworth, J. A.; Tretiak, S.; Klimov, V. I. Phys. Rev. Lett. 2002, 89, 186802. (45) Kumara, M. T.; Tripp, B. C.; Muralidharan, S. J. Phys. Chem. C 2007, 111, 5276−5280. (46) Kong, H.; Wang, H.; Zhang, S. C.; Zhang, X. R. Analyst. 2011, 136, 3643−3648. (47) Kong, H.; Lu, Y. X.; Wang, H.; Wen, F.; Zhang, S. C.; Zhang, X. R. Anal. Chem. 2012, 84, 4258−4261. (48) Bonilla, J. V.; Srivatsa, G. S. Handbook of Analysis of Oligonucleotides and Related Products; CRC Press: Boca Raton, FL, 2011.
H
DOI: 10.1021/ac503819e Anal. Chem. XXXX, XXX, XXX−XXX
