Nanodot Probes
Array-Based Sensing of Proteins and Bacteria By Using Multiple Luminescent Nanodots as Fluorescent Probes Yu Tao, Xiang Ran, Jinsong Ren,* and Xiaogang Qu* The development of sensitive and convenient methods for monitoring protein levels is of great value in medical diagnostics, pathogen detection, and proteomics.[1] Presence of certain biomarker proteins and/or irregular protein concentration levels provide essential information for the early diagnosis of hypoalbuminemia,[2] cancers,[3] Alzheimer’s disease,[4] prostatisis,[5] HIV,[6] and other diseases. So far, the most extensively used detection method for proteins is the enzyme-linked immunosorbent assay (ELISA),[7] in which the antibodies recognize the proteins through a “lock–key” approach. Despite its high sensitivity, the limited supply of high-specificity antibodies forms a major bottleneck for further application of this method.[8] In addition, the utilization of this strategy is also restricted because of the high production cost and instability of antibodies.[9] The “chemical nose–tongue” strategy,[10] which provides an alternative approach to protein sensing, has been widely explored in recent years.[1b,9,11] In this approach, differential interactions of analytes with a receptor array generate a pattern that is used for identification.[11j] Although promising, a key challenge for the ‘nose’ strategy of protein detection is to develop effective probes. To date, various probes, including fluorescent polymers,[9] green fluorescent protein,[11j] enzymes,[1b] fluorescence dyes,[11g] and fluorescently labeled DNAs,[11i] were introduced. While the wide quantification span and good selectivity of these probes are advantageous in “nose” sensing, simplification of the probe synthesis and optimization procedures are still great challenges.[12] Here, we use seven luminescent nanodots as novel fluorescent probes in the sensing array [graphene quantum dots (GQDs, Figure 1d), CdTe quantum dots (QDs, Figure 1g,k), carboxyl-carbon dots (CDs-COOH, Figure 1e,i), polyethylenimine functionalized carbon dots (PEI-CDs,
Dr. Y. Tao, Dr. X. Ran, Prof. J. Ren, Prof. X. Qu Laboratory of Chemical Biology Division of Biological Inorganic Chemistry State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Changchun 130022, China E-mail: [email protected]; [email protected] Dr. Y. Tao Graduate School of the Chinese Academy of Sciences Chinese Academy of Sciences Changchun 130022, China DOI: 10.1002/smll.201400661 small 2014, 10, No. 18, 3667–3671
Figure 1f,j), BSA-templated gold nanoclusters (BSA-AuNCs, Figure 1b), lysozyme-templated gold nanoclusters (LysAuNCs, Figure 1c), and DNA-templated silver nanoclusters (AgNCs, Figure 1a)]. As compared to conventional organic labels, these fluorescent probes simultaneously address the concerns of simple synthesis, low-cost, high brightness, excellent photostability, and aqueous solubility,[13] and hold great potential for use in protein analysis. Our strategy for the creation of protein sensors is to use graphene oxide (GO, Figure 1h,l) for protein recognition, with displacement of fluorescent probes to generate the output. As depicted in Scheme 1, GO, which is utilized as both a recognition element and a fluorescence quencher, can efficiently quench the fluorescence of the reporter probes by means of π−π stacking, hydrophobic, or electrostatic interactions. Subsequent binding of protein analytes displaces the fluorescent probes through binding competition, regenerating the fluorescence. Proteins are complex amphiphilic biopolymers that feature hydrophobic and hydrophilic patches on their surfaces, which makes them well-known for their adhesiveness to GO surfaces.[11o] Thus, after the addition of proteins to the fluorescent probe–GO conjugate, fluorescence is restored as a consequence of the presence of free fluorescent probes. In some cases, further fluorescent decrease can be observed, perhaps by analyte-protein-induced aggregation of the involved fluorescent probes.[11g,11i,11n] The differential fluorescence responses provide an efficient means of identification. This fluorescent indicator displacement assay does not require special instruments, and its high sensitivity and high speed effectively facilitate protein detection. Ten proteins with diverse structural features including molecular weight (MW) and isoelectric point (pI) were used as the target analytes (Table S1, Supporting Information). A fluorescence titration was first conducted to assess the complexation between GO and the fluorescent nanodots. The intrinsic fluorescence emissions of all the nanodots were significantly quenched on addition of GO (Figure S1, Supporting Information). The changes of fluorescence intensities with increasing GO concentrations were plotted (Figure S2, Supporting Information). The Stern−Volmer binding/ quenching constants (KS−V) were obtained by using nonlinear regression (Table S2, Supporting Information).[11g] The variation in complex stabilities and the binding stoichiometry demonstrated the crucial effect of the fluorescent probes in GO–probe affinity. As the different binding characteristics of GO with fluorescent probes were established, the GO–probe conjugates were used to sense proteins. The ten proteins
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Figure 1. Transmission electron microscopy (TEM) images of a) AgNCs, b) BSA-AuNCs, c) Lys-AuNCs, and d) GQDs. Typical atomic force probe (AFM) images and height profiles of e,i) CDs-COOH, f,j) PEI-CDs, g,k) QDs, and h,l) GO.
which we chose had different sizes and charges, with isoelectric points (pI) that varied from 4.2–11.2 and molecular weights (MW) ranging from 12.3–540 kDa. Among these proteins, there were some pairs of proteins that had comparable molecular weights and/or pI values, which provided a challenging testbed for protein discrimination. Initially, these proteins were tested on the GO−fluorescent probe sensing platform with various fluorescent probes. The presence of different proteins led to differential fluorescence responses due to their interactions with GO and the probes (Figure 2a). As can be seen in Figure 2a, fluorescence enhancement could be observed for most probes, which suggests that bound probes were released into the solution through the competitive displacement of proteins. Addition of some probes resulted in a decrease in fluorescence intensity, which might be a result of analyte protein–probe aggregation due to the higher affinity of proteins to probes than to GO.[11j] Significantly, the
fluorescence changes were reproducible and depended on the types of proteins, which indicates differentiation in the probe displacement. The resultant fluorescence response patterns were then analyzed by using linear discriminant analysis (LDA), which maximized the ratio of betweenclass variance to the within-class variance, enabling maximal separability.[9,14] Six replicates were tested for each protein sample in each probe, and the raw data were subjected to LDA to generate seven canonical factors (52.9%, 23.0%, 19.1%, 2.9%, 1.2%, 0.6%, and 0.3% of the variation), which represented linear combinations of the training matrix (seven fluorescent probes × ten proteins × six replicates). The first three most significant discrimination factors were employed to generate a 3D plot (Figure 2b). In this plot, each point represented the response pattern for a single protein sample against the sensor array. The ten different protein groups displayed excellent separation with no overlap, which explicitly demonstrates the ability of these fluorescent nanodots to discriminate proteins. To improve the resolving power of this sensing platform, we tested if the sensing platform could identify proteins at different concentrations. The data in Figure 3a indicate that this GO−fluorescent probe sensor array is sufficiently sensitive to detect proteins at nanomolar concentrations. Notably, since factor (1) was greater than 95%, it was possible simply to use factor (1) to identify the proteins. The linearity of the dose− response curve (Figure 3b) suggests that Scheme 1. Design and preparation of the sensor array. GO−protein interactions were investigated by using fluorescence displacement transduction. In this mechanism, fluorescent the GO−probe interactions are homoreporters were initially quenched (“off”) through GO binding. Displacement of quenched geneous and stable and that the sensor fluorophore by analyte proteins restores the fluorescence. array is highly reproducible.[11i] The
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Figure 2. Protein identification with the GO−fluorescent probe sensing platform. a) Fluorescence response patterns of the probes against various proteins (all at 200 nm): β-galactosidase (Gal), bovine serum albumin (BSA), cytochrome c (CytC), Proteinase K (ProK), horseradish peroxidase (HRP), lysozyme (Lys), Urease (Ure), glucose oxidase (Glu), hemoglobin (Hem), and esterase (Est). Error bars represent standard deviations of six parallel measurements. b) Canonical score plot for the GO−fluorescent probe sensor array. All ten proteins were well-separated and properly identified.
robustness of the fluorescent probe-based protein sensing arrays was also tested by using unknown protein samples that were randomly selected from the ten types of proteins. The unknown samples were ranked in terms of Mahalonobis distance to the groups generated through the training matrix (Table S3, Supporting Information) and the nearest samples were returned to the respective groups.[11e] For the 50 samples studied, 47 were correctly identified, which indicates a detection accuracy of 94%. Furthermore, the protein recognition ability of the fluorescent nanodots was not sacrificed in mixtures. We tested mixtures of CytC and Ure with different molar ratios (CytC/Ure = 80/20, 60/40, 40/60, and 80/20 with 200 nm total protein). As illustrated in Figure 3c, these mixtures, as well as pure CytC and Ure, were clearly distinguished from each other in the LDA plot and properly arranged with the order of ratios in the dimension of the first factor. For a further study, we also tested whether the GO−fluorescent probe sensing platform could be employed to identify proteins in human serum. Proteins (all at 1 µm) small 2014, 10, No. 18, 3667–3671
were spiked into the human serum sample, which was a complex matrix with high overall protein content. Because of the presence of the high optical density (OD) background of human serum, higher concentrations of the probes and GO (50 µg mL–1) were employed. Under these conditions, high sensing reproducibility was retained, and we obtained precise identification of ten proteins with 100% identification accuracy and essentially no overlap in the canonical score plot (Figure S3, Supporting Information). All these results demonstrate that this GO−fluorescent probe sensor array allows the discrimination of protein targets with high accuracy. Resistant bacterial infection represents a major burden to modern healthcare in both developed and developing nations.[15] Thus, the rapid and efficient identification of bacteria, especially resistant bacteria, is an important issue in food safety, environmental monitoring, and clinical diagnosis and treatment.[11e,16] One of the major differences between nonresistant and drug-resistant bacteria is that they express different proteins.[17] Inspired by the exceptional performance of the sensor array for protein detection, we tested if the array could be used to differentiate between nonresistant and drug-resistant bacteria. As can be seen in Figure 3d, the differentiation of methicillin-resistant staphylococcus aureus (MRSA) and staphylococcus aureus (SA) with different concentration ratios (MRSA/SA = 80/20, 60/40, 40/60 and 80/20), as well as pure MRSA and SA, suggests suitable identification of nonresistant and drug-resistant bacteria. Six different strains of bacteria were also tested, and the resultant fluorescence responses were analyzed by using LDA (Figure S4, Supporting Information). With the GO−fluorescent probe sensor array, four types of nonresistant bacteria [escherichia coli (EC), alcaligenes faecalis (AF), bacillus subtilis (BS), and staphylococcus aureus (SA)] and two types of drug-resistant bacteria [kanamycin-resistant escherichia coli (KREC) and methicillin-resistant staphylococcus aureus (MRSA)] were well-separated from each other. All these results unambiguously manifest that our sensor platform holds substantial promise for bacterial discrimination. In conclusion, the integration of multiple luminescent nanodots with GO provides an easily accessible yet potentially powerful biodiagnostic tool to detect protein targets. The fluorescence of probes is quenched by GO; the presence of proteins disrupts the probe–GO interaction, which produces distinct fluorescence response patterns. This assay technique has several unprecedented advantages. First of all, as compared with conventional organic labels, these luminescent nanodots offer some unique merits such as simple synthesis, low-cost, high brightness, excellent photostability, and aqueous solubility. Significantly, this approach allows the identification of proteins at nanomolar concentrations with high reproducibility. LDA was successfully used to identify 50 unknown protein samples (ten different proteins) with an accuracy of 94%. Furthermore, this biosensor can also be expanded to discriminate between nonresistant and drugresistant bacteria, which is important for diagnosis of bacterial infections. This work enables the construction of novel probe-based protein detector arrays with potential applications in medical diagnostics.
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Figure 3. a,b) Identification of proteins at various concentrations using the GO−fluorescent probe sensor array. a) Canonical score plot for fluorescence response patterns against different concentrations of CytC (10, 50, 100, 200, and 500 nm). Since factor (1) was greater than 95%, it was possible simply to use factor (1) to identify the protein. b) Plot of the first discriminant factor vs the CytC concentration. c) Canonical score plot against protein mixtures. Right to left: pure CytC; 80% CytC + 20% Ure; 60% CytC + 40% Ure; 40% CytC + 60% Ure; 20% CytC + 80% Ure; pure Ure. In each case, the total protein concentration was 200 nm. d) Canonical score plot against bacterial mixtures. Right to left: pure SA (OD = 0.05); 80% SA (OD = 0.04) + 20% MRSA (OD = 0.01); 60% SA (OD = 0.03) + 40% MRSA (OD = 0.02); 40% SA (OD = 0.02) + 60% MRSA (OD = 0.03); 20% SA (OD = 0.01) + 80% MRSA (OD = 0.04); pure MRSA (OD = 0.05).
Experimental Section Materials and Instrumentation: Lysozyme, horseradish peroxidase (HRP), bovine serum albumin (BSA), cytochrome c, Proteinase K, urease, glucose oxidase, and hemoglobin were purchased from Shanghai Sangon Biological Engineering Technology & Services (Shanghai, China). All other reagents were all of analytical reagent grade and used as received. Nanopure water (18.2 MΩ; Millpore Co., USA) was used throughout the experiment. Fluorescence measurements were carried out by using a JASCO FP-6500 spectrofluorometer (Jasco International Co., Japan). AFM measurements were performed using a Nanoscope V multimode atomic force microscope (Veeco Instruments, USA). Tapping mode was used to acquire the images under ambient conditions. TEM images were recorded using a FEI TECNAI G2 20 high-resolution transmission electron microscope operating at 200 kV. Fluorescence Titration: In the fluorescence titration study, fluorescence spectra were collected with a JASCO FP-6500 spectrofluorometer equipped with a xenon lamp excitation source. During the titration, the initial emission spectrum was recorded for the fluorescent probes. Aliquots of a solution of GO (1 mg mL–1) were subsequently added to the solution of the probes. After each addition, a fluorescence spectrum was recorded. The fluorescent titration curves were fitted by using the Stern−Volmer quenching equation and the Stern–Volmer quenching constants KS–V were obtained.[11g] Analyte Protein Response: For construction of the protein sensor array, the fluorescent probes and GO were diluted with
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phosphate-buffered saline (PBS). After 30 min of incubation, the solutions were loaded into a conventional quartz cuvette and the fluorescence intensities were recorded. Subsequently, protein solution (final concentration 200 nm) was added. After incubation for 30 min, the fluorescence intensities were recorded again. The differences between the readings before and after addition of protein were used as the fluorescence responses. The ten protein targets were tested against seven fluorescent probes six times. The raw data matrix was processed using classical LDA. Similar procedures were also performed to identify analytical samples (various concentrations of protein, and mixtures of protein and bacteria). In the case of serum studies, human serum was diluted with PBS buffer to produce a 10% serum sample. The concentrations of GO and the proteins were 50 µg mL–1 and 1 µm, respectively. Unknown Identification: To test unknown protein samples, 50 samples were tested with the same procedures as for the training samples, and the resulting fluorescence response patterns were subjected to LDA. Identifications were made by evaluating Mahalanobis distance-square proximities to known group centers from the training matrix. Bacterial Culture: The bacteria strains were grown in Luria broth (LB) medium in a shaker at 37 °C. The resultant bacterial cells were isolated by centrifugation (3500 rpm). Then the bacterial cells were rinsed with PBS, and bacterial concentrations were adjusted by measuring the optical density at 600 nm. Bacteria Identification: For bacteria identification, bacteria were separated from the growth medium and resuspended in PBS
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to achieve an optical density (OD) of 1.0 at 600 nm. After ultrasonic cell disintegration, the cell suspensions were centrifuged at 4000 rpm for 20 min. Then, the bacterial supernatants were collected. Afterwards, the fluorescent probes and GO were diluted with PBS. After 30 min of incubation, the solutions were loaded into a conventional quartz cuvette and the fluorescence intensities recorded. Subsequently, the bacterial supernatant was added into each fluorescent probe–GO conjugate up to a final concentration with an OD of 0.05. After incubation for 30 min, the fluorescence intensities were recorded again. The change in fluorescence intensity was used as the output response. The bacteria were detected against seven fluorescent probes six times. The raw data matrix was processed using classical LDA.
Supporting Information Supporting Information is available from the Wiley Online Library or from the author.
Acknowledgements Financial support was provided by the National Basic Research Program of China (2012CB720602 and 2011CB936004) and the National Natural Science Foundation of China (grants 21210002, 91213302).
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Received: March 12, 2014 Published online: May 19, 2014
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Array-Based Sensing of Proteins and Bacteria By Using Multiple Luminescent Nanodots as Fluorescent Probes Yu Tao, Xiang Ran, Jinsong Ren,* and Xiaogang Qu* The development of sensitive and convenient methods for monitoring protein levels is of great value in medical diagnostics, pathogen detection, and proteomics.[1] Presence of certain biomarker proteins and/or irregular protein concentration levels provide essential information for the early diagnosis of hypoalbuminemia,[2] cancers,[3] Alzheimer’s disease,[4] prostatisis,[5] HIV,[6] and other diseases. So far, the most extensively used detection method for proteins is the enzyme-linked immunosorbent assay (ELISA),[7] in which the antibodies recognize the proteins through a “lock–key” approach. Despite its high sensitivity, the limited supply of high-specificity antibodies forms a major bottleneck for further application of this method.[8] In addition, the utilization of this strategy is also restricted because of the high production cost and instability of antibodies.[9] The “chemical nose–tongue” strategy,[10] which provides an alternative approach to protein sensing, has been widely explored in recent years.[1b,9,11] In this approach, differential interactions of analytes with a receptor array generate a pattern that is used for identification.[11j] Although promising, a key challenge for the ‘nose’ strategy of protein detection is to develop effective probes. To date, various probes, including fluorescent polymers,[9] green fluorescent protein,[11j] enzymes,[1b] fluorescence dyes,[11g] and fluorescently labeled DNAs,[11i] were introduced. While the wide quantification span and good selectivity of these probes are advantageous in “nose” sensing, simplification of the probe synthesis and optimization procedures are still great challenges.[12] Here, we use seven luminescent nanodots as novel fluorescent probes in the sensing array [graphene quantum dots (GQDs, Figure 1d), CdTe quantum dots (QDs, Figure 1g,k), carboxyl-carbon dots (CDs-COOH, Figure 1e,i), polyethylenimine functionalized carbon dots (PEI-CDs,
Dr. Y. Tao, Dr. X. Ran, Prof. J. Ren, Prof. X. Qu Laboratory of Chemical Biology Division of Biological Inorganic Chemistry State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Changchun 130022, China E-mail: [email protected]; [email protected] Dr. Y. Tao Graduate School of the Chinese Academy of Sciences Chinese Academy of Sciences Changchun 130022, China DOI: 10.1002/smll.201400661 small 2014, 10, No. 18, 3667–3671
Figure 1f,j), BSA-templated gold nanoclusters (BSA-AuNCs, Figure 1b), lysozyme-templated gold nanoclusters (LysAuNCs, Figure 1c), and DNA-templated silver nanoclusters (AgNCs, Figure 1a)]. As compared to conventional organic labels, these fluorescent probes simultaneously address the concerns of simple synthesis, low-cost, high brightness, excellent photostability, and aqueous solubility,[13] and hold great potential for use in protein analysis. Our strategy for the creation of protein sensors is to use graphene oxide (GO, Figure 1h,l) for protein recognition, with displacement of fluorescent probes to generate the output. As depicted in Scheme 1, GO, which is utilized as both a recognition element and a fluorescence quencher, can efficiently quench the fluorescence of the reporter probes by means of π−π stacking, hydrophobic, or electrostatic interactions. Subsequent binding of protein analytes displaces the fluorescent probes through binding competition, regenerating the fluorescence. Proteins are complex amphiphilic biopolymers that feature hydrophobic and hydrophilic patches on their surfaces, which makes them well-known for their adhesiveness to GO surfaces.[11o] Thus, after the addition of proteins to the fluorescent probe–GO conjugate, fluorescence is restored as a consequence of the presence of free fluorescent probes. In some cases, further fluorescent decrease can be observed, perhaps by analyte-protein-induced aggregation of the involved fluorescent probes.[11g,11i,11n] The differential fluorescence responses provide an efficient means of identification. This fluorescent indicator displacement assay does not require special instruments, and its high sensitivity and high speed effectively facilitate protein detection. Ten proteins with diverse structural features including molecular weight (MW) and isoelectric point (pI) were used as the target analytes (Table S1, Supporting Information). A fluorescence titration was first conducted to assess the complexation between GO and the fluorescent nanodots. The intrinsic fluorescence emissions of all the nanodots were significantly quenched on addition of GO (Figure S1, Supporting Information). The changes of fluorescence intensities with increasing GO concentrations were plotted (Figure S2, Supporting Information). The Stern−Volmer binding/ quenching constants (KS−V) were obtained by using nonlinear regression (Table S2, Supporting Information).[11g] The variation in complex stabilities and the binding stoichiometry demonstrated the crucial effect of the fluorescent probes in GO–probe affinity. As the different binding characteristics of GO with fluorescent probes were established, the GO–probe conjugates were used to sense proteins. The ten proteins
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Figure 1. Transmission electron microscopy (TEM) images of a) AgNCs, b) BSA-AuNCs, c) Lys-AuNCs, and d) GQDs. Typical atomic force probe (AFM) images and height profiles of e,i) CDs-COOH, f,j) PEI-CDs, g,k) QDs, and h,l) GO.
which we chose had different sizes and charges, with isoelectric points (pI) that varied from 4.2–11.2 and molecular weights (MW) ranging from 12.3–540 kDa. Among these proteins, there were some pairs of proteins that had comparable molecular weights and/or pI values, which provided a challenging testbed for protein discrimination. Initially, these proteins were tested on the GO−fluorescent probe sensing platform with various fluorescent probes. The presence of different proteins led to differential fluorescence responses due to their interactions with GO and the probes (Figure 2a). As can be seen in Figure 2a, fluorescence enhancement could be observed for most probes, which suggests that bound probes were released into the solution through the competitive displacement of proteins. Addition of some probes resulted in a decrease in fluorescence intensity, which might be a result of analyte protein–probe aggregation due to the higher affinity of proteins to probes than to GO.[11j] Significantly, the
fluorescence changes were reproducible and depended on the types of proteins, which indicates differentiation in the probe displacement. The resultant fluorescence response patterns were then analyzed by using linear discriminant analysis (LDA), which maximized the ratio of betweenclass variance to the within-class variance, enabling maximal separability.[9,14] Six replicates were tested for each protein sample in each probe, and the raw data were subjected to LDA to generate seven canonical factors (52.9%, 23.0%, 19.1%, 2.9%, 1.2%, 0.6%, and 0.3% of the variation), which represented linear combinations of the training matrix (seven fluorescent probes × ten proteins × six replicates). The first three most significant discrimination factors were employed to generate a 3D plot (Figure 2b). In this plot, each point represented the response pattern for a single protein sample against the sensor array. The ten different protein groups displayed excellent separation with no overlap, which explicitly demonstrates the ability of these fluorescent nanodots to discriminate proteins. To improve the resolving power of this sensing platform, we tested if the sensing platform could identify proteins at different concentrations. The data in Figure 3a indicate that this GO−fluorescent probe sensor array is sufficiently sensitive to detect proteins at nanomolar concentrations. Notably, since factor (1) was greater than 95%, it was possible simply to use factor (1) to identify the proteins. The linearity of the dose− response curve (Figure 3b) suggests that Scheme 1. Design and preparation of the sensor array. GO−protein interactions were investigated by using fluorescence displacement transduction. In this mechanism, fluorescent the GO−probe interactions are homoreporters were initially quenched (“off”) through GO binding. Displacement of quenched geneous and stable and that the sensor fluorophore by analyte proteins restores the fluorescence. array is highly reproducible.[11i] The
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Figure 2. Protein identification with the GO−fluorescent probe sensing platform. a) Fluorescence response patterns of the probes against various proteins (all at 200 nm): β-galactosidase (Gal), bovine serum albumin (BSA), cytochrome c (CytC), Proteinase K (ProK), horseradish peroxidase (HRP), lysozyme (Lys), Urease (Ure), glucose oxidase (Glu), hemoglobin (Hem), and esterase (Est). Error bars represent standard deviations of six parallel measurements. b) Canonical score plot for the GO−fluorescent probe sensor array. All ten proteins were well-separated and properly identified.
robustness of the fluorescent probe-based protein sensing arrays was also tested by using unknown protein samples that were randomly selected from the ten types of proteins. The unknown samples were ranked in terms of Mahalonobis distance to the groups generated through the training matrix (Table S3, Supporting Information) and the nearest samples were returned to the respective groups.[11e] For the 50 samples studied, 47 were correctly identified, which indicates a detection accuracy of 94%. Furthermore, the protein recognition ability of the fluorescent nanodots was not sacrificed in mixtures. We tested mixtures of CytC and Ure with different molar ratios (CytC/Ure = 80/20, 60/40, 40/60, and 80/20 with 200 nm total protein). As illustrated in Figure 3c, these mixtures, as well as pure CytC and Ure, were clearly distinguished from each other in the LDA plot and properly arranged with the order of ratios in the dimension of the first factor. For a further study, we also tested whether the GO−fluorescent probe sensing platform could be employed to identify proteins in human serum. Proteins (all at 1 µm) small 2014, 10, No. 18, 3667–3671
were spiked into the human serum sample, which was a complex matrix with high overall protein content. Because of the presence of the high optical density (OD) background of human serum, higher concentrations of the probes and GO (50 µg mL–1) were employed. Under these conditions, high sensing reproducibility was retained, and we obtained precise identification of ten proteins with 100% identification accuracy and essentially no overlap in the canonical score plot (Figure S3, Supporting Information). All these results demonstrate that this GO−fluorescent probe sensor array allows the discrimination of protein targets with high accuracy. Resistant bacterial infection represents a major burden to modern healthcare in both developed and developing nations.[15] Thus, the rapid and efficient identification of bacteria, especially resistant bacteria, is an important issue in food safety, environmental monitoring, and clinical diagnosis and treatment.[11e,16] One of the major differences between nonresistant and drug-resistant bacteria is that they express different proteins.[17] Inspired by the exceptional performance of the sensor array for protein detection, we tested if the array could be used to differentiate between nonresistant and drug-resistant bacteria. As can be seen in Figure 3d, the differentiation of methicillin-resistant staphylococcus aureus (MRSA) and staphylococcus aureus (SA) with different concentration ratios (MRSA/SA = 80/20, 60/40, 40/60 and 80/20), as well as pure MRSA and SA, suggests suitable identification of nonresistant and drug-resistant bacteria. Six different strains of bacteria were also tested, and the resultant fluorescence responses were analyzed by using LDA (Figure S4, Supporting Information). With the GO−fluorescent probe sensor array, four types of nonresistant bacteria [escherichia coli (EC), alcaligenes faecalis (AF), bacillus subtilis (BS), and staphylococcus aureus (SA)] and two types of drug-resistant bacteria [kanamycin-resistant escherichia coli (KREC) and methicillin-resistant staphylococcus aureus (MRSA)] were well-separated from each other. All these results unambiguously manifest that our sensor platform holds substantial promise for bacterial discrimination. In conclusion, the integration of multiple luminescent nanodots with GO provides an easily accessible yet potentially powerful biodiagnostic tool to detect protein targets. The fluorescence of probes is quenched by GO; the presence of proteins disrupts the probe–GO interaction, which produces distinct fluorescence response patterns. This assay technique has several unprecedented advantages. First of all, as compared with conventional organic labels, these luminescent nanodots offer some unique merits such as simple synthesis, low-cost, high brightness, excellent photostability, and aqueous solubility. Significantly, this approach allows the identification of proteins at nanomolar concentrations with high reproducibility. LDA was successfully used to identify 50 unknown protein samples (ten different proteins) with an accuracy of 94%. Furthermore, this biosensor can also be expanded to discriminate between nonresistant and drugresistant bacteria, which is important for diagnosis of bacterial infections. This work enables the construction of novel probe-based protein detector arrays with potential applications in medical diagnostics.
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Figure 3. a,b) Identification of proteins at various concentrations using the GO−fluorescent probe sensor array. a) Canonical score plot for fluorescence response patterns against different concentrations of CytC (10, 50, 100, 200, and 500 nm). Since factor (1) was greater than 95%, it was possible simply to use factor (1) to identify the protein. b) Plot of the first discriminant factor vs the CytC concentration. c) Canonical score plot against protein mixtures. Right to left: pure CytC; 80% CytC + 20% Ure; 60% CytC + 40% Ure; 40% CytC + 60% Ure; 20% CytC + 80% Ure; pure Ure. In each case, the total protein concentration was 200 nm. d) Canonical score plot against bacterial mixtures. Right to left: pure SA (OD = 0.05); 80% SA (OD = 0.04) + 20% MRSA (OD = 0.01); 60% SA (OD = 0.03) + 40% MRSA (OD = 0.02); 40% SA (OD = 0.02) + 60% MRSA (OD = 0.03); 20% SA (OD = 0.01) + 80% MRSA (OD = 0.04); pure MRSA (OD = 0.05).
Experimental Section Materials and Instrumentation: Lysozyme, horseradish peroxidase (HRP), bovine serum albumin (BSA), cytochrome c, Proteinase K, urease, glucose oxidase, and hemoglobin were purchased from Shanghai Sangon Biological Engineering Technology & Services (Shanghai, China). All other reagents were all of analytical reagent grade and used as received. Nanopure water (18.2 MΩ; Millpore Co., USA) was used throughout the experiment. Fluorescence measurements were carried out by using a JASCO FP-6500 spectrofluorometer (Jasco International Co., Japan). AFM measurements were performed using a Nanoscope V multimode atomic force microscope (Veeco Instruments, USA). Tapping mode was used to acquire the images under ambient conditions. TEM images were recorded using a FEI TECNAI G2 20 high-resolution transmission electron microscope operating at 200 kV. Fluorescence Titration: In the fluorescence titration study, fluorescence spectra were collected with a JASCO FP-6500 spectrofluorometer equipped with a xenon lamp excitation source. During the titration, the initial emission spectrum was recorded for the fluorescent probes. Aliquots of a solution of GO (1 mg mL–1) were subsequently added to the solution of the probes. After each addition, a fluorescence spectrum was recorded. The fluorescent titration curves were fitted by using the Stern−Volmer quenching equation and the Stern–Volmer quenching constants KS–V were obtained.[11g] Analyte Protein Response: For construction of the protein sensor array, the fluorescent probes and GO were diluted with
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phosphate-buffered saline (PBS). After 30 min of incubation, the solutions were loaded into a conventional quartz cuvette and the fluorescence intensities were recorded. Subsequently, protein solution (final concentration 200 nm) was added. After incubation for 30 min, the fluorescence intensities were recorded again. The differences between the readings before and after addition of protein were used as the fluorescence responses. The ten protein targets were tested against seven fluorescent probes six times. The raw data matrix was processed using classical LDA. Similar procedures were also performed to identify analytical samples (various concentrations of protein, and mixtures of protein and bacteria). In the case of serum studies, human serum was diluted with PBS buffer to produce a 10% serum sample. The concentrations of GO and the proteins were 50 µg mL–1 and 1 µm, respectively. Unknown Identification: To test unknown protein samples, 50 samples were tested with the same procedures as for the training samples, and the resulting fluorescence response patterns were subjected to LDA. Identifications were made by evaluating Mahalanobis distance-square proximities to known group centers from the training matrix. Bacterial Culture: The bacteria strains were grown in Luria broth (LB) medium in a shaker at 37 °C. The resultant bacterial cells were isolated by centrifugation (3500 rpm). Then the bacterial cells were rinsed with PBS, and bacterial concentrations were adjusted by measuring the optical density at 600 nm. Bacteria Identification: For bacteria identification, bacteria were separated from the growth medium and resuspended in PBS
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to achieve an optical density (OD) of 1.0 at 600 nm. After ultrasonic cell disintegration, the cell suspensions were centrifuged at 4000 rpm for 20 min. Then, the bacterial supernatants were collected. Afterwards, the fluorescent probes and GO were diluted with PBS. After 30 min of incubation, the solutions were loaded into a conventional quartz cuvette and the fluorescence intensities recorded. Subsequently, the bacterial supernatant was added into each fluorescent probe–GO conjugate up to a final concentration with an OD of 0.05. After incubation for 30 min, the fluorescence intensities were recorded again. The change in fluorescence intensity was used as the output response. The bacteria were detected against seven fluorescent probes six times. The raw data matrix was processed using classical LDA.
Supporting Information Supporting Information is available from the Wiley Online Library or from the author.
Acknowledgements Financial support was provided by the National Basic Research Program of China (2012CB720602 and 2011CB936004) and the National Natural Science Foundation of China (grants 21210002, 91213302).
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Received: March 12, 2014 Published online: May 19, 2014
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