Bacteria-Instructed Click Chemistry between Functionalized Gold

Jun 11, 2019 - Ganesh, N. V.; Sadowska, J. M.; Sarkar, S.; Howells, L.; McGiven, J.; Bundle, D. R. Molecular Recognition of Brucella A and M Antigens ...
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Research Article Cite This: ACS Appl. Mater. Interfaces 2019, 11, 23093−23101

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Bacteria-Instructed Click Chemistry between Functionalized Gold Nanoparticles for Point-of-Care Microbial Detection Xiao-Zhou Mou,†,# Xiao-Yi Chen,†,# Jianhao Wang,⊥,# Zhaotian Zhang,‡ Yanmei Yang,*,§ Zhang-Xuan Shou,† Yue-Xing Tu,† Xuancheng Du,⊥ Chun Wu,‡ Yuan Zhao,⊥ Lin Qiu,⊥ Pengju Jiang,⊥ Chunying Chen,∥ Dong-Sheng Huang,*,† and Yong-Qiang Li*,‡

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†

Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People’s Hospital (People’s Hospital of Hangzhou Medical College), Hangzhou 310014, China ‡ State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China § College of Chemistry, Chemical Engineering and Materials Science, Shandong Normal University, Jinan 250014, China ∥ CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China ⊥ School of Pharmaceutical Engineering and Life Science, Changzhou University, Changzhou 213164, China S Supporting Information *

ABSTRACT: Bacterial infections pose mounting public health concerns and cause an enormous medical and financial burden today. Rapid and sensitive detection of pathogenic bacteria at the point of care (POC) remains a paramount challenge. Here, we report a novel concept of bacteriainstructed click chemistry and employ it for POC microbial sensing. In this concept of bacteria-instructed click chemistry, we demonstrate for the first time that pathogenic bacteria can capture and reduce exogenous Cu2+ to Cu+ by leveraging their unique metabolic processes. The produced Cu+ subsequently acts as a catalyst to trigger the click reaction between gold nanoparticles (AuNPs) modified with azide and alkyne functional molecules, resulting in the aggregation of nanoparticles with a color change of the solution from red to blue. In this process, signal amplification from click chemistry is complied with the aggregation of functionalized AuNPs, thus presenting a robust colorimetric strategy for sensitive POC sensing of pathogenic bacteria. Notably, this colorimetric strategy is easily integrated in a smartphone app as a portable platform to achieve one-click detection in a mobile way. Moreover, with the help of the magnetic preseparation process, this smartphone app-assisted platform enables rapid (within 1 h) detection of Escherichia coli with high sensitivity (40 colony-forming units/mL) in the complex artificial sepsis blood samples, showing great potential for clinical early diagnosis of bacterial infections. KEYWORDS: antimicrobial hydrogel, biofilm eradication, chronic wound healing, pH-switchable drug release, nanofibers

1. INTRODUCTION

it can rapidly and easily be monitored with the naked eye, without the aid of trained operators and advanced instruments.11 Several exquisite colorimetric assays that enable the POC detection of pathogenic bacteria have been developed based on colloidal gold nanoparticles (AuNPs).12−16 However, its sensitivity has not reached that of gold standard bacterial culture, making it difficult to distinguish bacteria with low concentrations. Moreover, the AuNP-based colorimetric assay is usually susceptible to interference from ambient conditions (i.e., pH value, ion concentration, and solvent), thus compromising

Infectious bacterial diseases pose a significant source of morbidity and mortality and cause enormous medical and financial burden.1,2 Early diagnosis is prerequisite and crucial to overcoming bacterial infectious threats.3 Standard diagnostic methods, bacterial culture, and polymerase chain reaction-based assays are laborious and usually take hours to obtain results, limiting their extensive applications in point-of-care (POC) detection, particularly in resource-constrained environments.4−6 Recently, several elegant techniques have been developed for bacterial detection such as fluorescence imaging, surfaceenhanced Raman spectroscopy, and immunological and colorimetric assays.7−10 In particular, colorimetric assay is convenient and attractive in POC and bedside diagnosis because © 2019 American Chemical Society

Received: May 28, 2019 Accepted: June 11, 2019 Published: June 11, 2019 23093

DOI: 10.1021/acsami.9b09279 ACS Appl. Mater. Interfaces 2019, 11, 23093−23101

Research Article

ACS Applied Materials & Interfaces the specificity and accuracy of bacterial detection.12 To address these challenges, colorimetric assay that enables POC detection of bacteria with high selectivity and sensitivity is highly desired. By harnessing the metabolic pathways of microbes to catalyze exogenous reactions and produce desired materials, research into bacterial metabolic engineering provides a unique perspective for colorimetric POC sensing of bacteria.17,18 In particular, recent investigation on microbial copper homeostasis mechanisms has found that bacteria can adapt to copper-rich surroundings through their metabolic pathway involving a variety of copper-binding systems and redox enzyme cascades for toxic Cu2+ binding and reduction.19,20 In this process, cupric reductase NDH-2 in bacteria is responsive for Cu2+ reduction, and the formed Cu+ can then be transmitted to the CopA (a Cu + -translocating P-type ATPase) for cytoplasmic Cu + detoxification.19 In virtue of this unique capability for Cu2+ binding and reduction, bacteria can instruct various specific chemical reactions catalyzed by Cu+.21 Inspired by this, it would be particularly advantageous for the colorimetric POC detection of bacteria if we can specifically correlate the bacteria-instructing chemical reactions with AuNP-based colorimetric assays. Cu+catalyzed click chemistry (alkyne−azide cycloaddition) would be an ideal candidate because it can be selectively catalyzed by the trace amount of Cu+, and the reaction processes are generally tolerant to ambient conditions.22 Meanwhile, based on the aggregation of AuNPs functionalized with azide and alkyne functional molecules, click chemistry-assisted colorimetric systems have been developed and widely used in biosensing and bioanalysis.23−26 Therefore, we hypothesize that by coopting bacterial metabolic processes to instruct the click chemistry between functionalized AuNPs, a robust strategy amenable for the colorimetric POC detection of pathogenic bacteria could be developed. Herein, we present the colorimetric strategy of bacteriainstructed click chemistry for POC microbial sensing based on AuNPs modified with azide and alkyne functional molecules, respectively (azide- and alkyne-AuNPs). In this strategy, exogenous Cu2+ is first captured and reduced to Cu+ by pathogenic bacteria of detected samples, where Cu+ subsequently acts as a catalyst to trigger the click reaction between the azide- and alkyne-AuNPs, resulting in the nanoparticles to aggregate with a color change of the solution from red to blue (as illustrated in Scheme 1). Based on this color change of AuNP

solution, POC detection of pathogenic bacteria can be rapidly and easily achieved just by naked eyes. By considering the signal amplification of fixed-point click reaction and its tolerance to ambient conditions, the colorimetric strategy of bacteriainstructed click chemistry would possess high detection sensitivity and selectivity. Furthermore, by combining this strategy with a magnetic preseparation process and home-made smartphone app, a field-portable platform for one-click sensing of bacteria with outstanding sensitivity and selectivity could be easily achieved without any professional training or complex instrumentation, showing great potential for POC detection of pathogenic bacteria in clinical settings.

2. RESULTS AND DISCUSSION 2.1. Synthesis and Characterization of Azide- and Alkyne-AuNPs. In typical experiments, the azide- and alkyneAuNPs were prepared by first modifying AuNPs with 11mercaptoundecanoic acid (11-MUA) based on the ligandexchange process,27 and then azide and alkynyl functional molecules were conjugated with the carboxyl group of 11-MUA, respectively, through a chemical covalent coupling method (Figures S1 and S2).28 Thiol-containing polyethylene glycol molecules were also used in the ligand-exchange process as a stabilizing agent to keep the functionalized AuNPs stably dispersed.29 The whole preparation process of azide- and alkyneAuNPs was easily monitored by the broader and red-shifted surface plasmon resonance absorption peaks (Figure 1a) as well as increased hydrodynamic sizes (Figure 1b) and dramatically changed zeta potentials of AuNPs (Figure 1c). The considerably small zeta potentials of azide- and alkyne-AuNPs (−9.6 and −8.8 mV) were essential to minimize the effect of electrostatic interaction between the functionalized AuNPs and interfering substances on the colorimetric result.30 From the transmission electron microscopy (TEM) images, it was found that the prepared azide- and alkyne-AuNPs both had the homogeneous spherical structures with a size around 14 nm (Figure S3). Moreover, dynamic light scattering measurement showed that the hydrodynamic sizes of azide- and alkyne-AuNPs remained stable during 6 days of storage in phosphate-buffered saline (PBS) buffer, showing high structural stability and excellent solubility (Figure 1d). 2.2. Bacteria-Instructed Click Chemistry between Azide- and Alkyne-AuNPs. To evaluate the feasibility of bacteria-instructed click chemistry between the azide- and alkyne-AuNPs, a model bacterium of Escherichia coli (E. coli) with a concentration of 106 colony-forming unit/mL (CFU/ mL) was added into the mixture of azide- and alkyne-AuNPs in the presence of Cu2+ at room temperature. As we expected, the color of the mixture changed from red to blue after incubation with E. coli for 1 h (Figure 2a, photograph 3 and 4), proving our hypothesis that bacteria can reduce Cu2+ to Cu+ in situ and then instruct the click reaction between the azide- and alkyne-AuNPs to result in the aggregation of AuNPs. This change in the aggregation state of the functionalized AuNP systems was also observed by UV−vis absorption spectroscopy analysis (Figure S4), TEM and dynamic light scattering measurement (Figure 2b), confirming the occurrence of Cu+-catalyzed click reaction between azide- and alkyne-AuNPs. In addition, compared to the solution of functionalized AuNPs (Figure 2a, photograph 1), there was no color change for the mixture of E. coli and functionalized AuNPs without Cu2+ (Figure 2a, photograph 2), indicating the necessity of copper source reduction in this bacteria-instructed click reaction. Moreover, this bacteria-

Scheme 1. Conceptual Illustration of the Colorimetric Strategy of Bacteria-Instructed Click Chemistry for POC Microbial Detection

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DOI: 10.1021/acsami.9b09279 ACS Appl. Mater. Interfaces 2019, 11, 23093−23101

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Figure 1. (a) UV−vis absorption spectra of AuNPs, azide-, and alkyne-AuNPs in DI water. (b) Hydrodynamic sizes of AuNPs, azide-, and alkyneAuNPs in DI water. (c) Zeta potentials of AuNPs, azide-, and alkyne-AuNPs in DI water. (d) Hydrodynamic sizes of AuNPs, azide-, and alkyne-AuNPs in PBS buffer during 6 day storage. (b−d) Representation of the values of hydrodynamic size and zeta potential of the mean of three independent experiments, and the error bars indicate the standard deviation (SD) from the mean.

NDH-2 in bacteria is responsive for Cu2+ reduction, and only Cu+ is formed in this process.19 Because the aggregation of functionalized AuNPs can be monitored visually, the bacteriainstructed click chemistry provides a convenient but robust colorimetric strategy to report the presence of pathogenic microbes. 2.3. Click Chemistry-Assisted Colorimetric Strategy for POC Detection of Pathogenic Bacteria. For highsensitivity detection of pathogenic bacteria, the essential components of this bacteria-instructed click chemistry system (concentrations of functionalized AuNPs and Cu2+, bacteria reaction time) were systematically optimized. First, E. coli bacteria with lower concentration (103 CFU/mL) were added into the mixture of Cu2+ and functionalized AuNPs with various concentrations to study the effect of functionalized AuNPs’ concentration (azide-AuNP/alkyne-AuNP = 1:1) on colorimetric bacterial detection. As shown in Figure 3a, with the decrease of functionalized AuNPs concentration, the color of the mixture of E. coli, Cu2+, and functionalized AuNPs changed from red to purple, indicating that the concentration of functionalized AuNPs indeed had obvious effects on bacterial detection sensitivity. The shapes of UV−vis absorption spectra were consistent with the solution color changes in which the absorption at 625 nm gradually increased with the decrease of functionalized AuNPs concentration, indicating greater degrees of AuNP aggregation in solutions (Figure 3a). From the plot of A625/A522 obtained from the above result of the UV−vis absorption spectrum versus concentration of functionalized AuNPs, 5.4 nM of functionalized AuNPs was chosen for optimal colorimetric detection of bacteria (Figure 3b). Similarly, the effect of Cu2+ concentration on colorimetric detection of bacteria was also investigated. As shown in Figures S7 and 3c, by comprehensively considering the influence of Cu2+ concentration on bacterial growth and bacteria-instructed click reaction, the optimal 100 nM of Cu2+ was determined. Finally, the effect of bacterial reaction time on colorimetric detection was studied. As shown in Figure 3d, after incubation with E. coli

Figure 2. (a) Photographs of 1−4 showed the essential components of the bacteria-instructed click reaction system listed in the table below. (b) TEM images and hydrodynamic sizes of the corresponding solutions of photographs 3 and 4 shown in (a).

instructed click chemistry was found to be tolerant to different ambient conditions (pH value, ion concentration, and solvent), thus exhibiting great potential for POC microbial detection in different clinical samples (Figure S5). Furthermore, to investigate the aggregation of functionalized AuNPs (azideand alkyne-AuNPs) in the complex sample and confirm the specific Cu2+ reduction-associated mechanism of bacteriainstructed click chemistry, the aggregation of the functionalized AuNPs−Cu2+ ensemble was studied after incubation with bare blood (for sepsis sensing purpose), blood with E. coli, or blood containing a reductant (sodium ascorbate), respectively. As shown in Figure S6, no aggregation was observed for the AuNPs−Cu2+ ensemble after incubation with the bare blood, indicating the good stability of our functionalized AuNPs in the complex sample. By contrast, significant aggregation was found for the AuNPs−Cu2+ ensemble after incubation with the blood containing E. coli as well as sodium ascorbate (Figure S6). This result combined with Figure 2 can confirm the specific bacteriainstructed Cu2+ reduction mechanism underlying the aggregation of azide- and alkyne-AuNPs. According to the previous paper on the copper homeostasis in bacteria, cupric reductase 23095

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Figure 3. (a) UV−vis absorption spectra of the mixtures of Cu2+ (100 nM) and functionalized AuNPs with various concentrations (azide-AuNP/ alkyne-AuNP = 1:1) after incubating 103 CFU/mL of E. coli for 1 h. The inset shows the corresponding photographs of the mixtures. (b) Effect of functionalized AuNPs’ concentration on the bacteria-instructed click reaction. (c) Effect of Cu2+ concentration on the bacteria-instructed click reaction. The concentration of functionalized AuNPs used was 5.4 nM, and the bacterial reaction time was 1 h. (d) Effect of bacterial reaction time on the bacteria-instructed click reaction. The concentrations of functionalized AuNPs and Cu2+ used were 5.4 and 100 nM, respectively. (b−d) Representation of A625/A522 values obtained from the corresponding UV−vis absorption spectra of the mean of three independent experiments, and the error bars indicate the SD from the mean.

Figure 4. (a) Typical photographs of the click chemistry system with different concentrations of E. coli from 102 to 107 CFU/mL. (b) “Color scan” function interface of our smartphone app under a condition of 107 CFU/mL of E. coli. (c) Color card obtained by the app corresponding to the photographs shown in a. (d) Linear calibration curve between the B/R values and E. coli concentrations ranging from 102 to 107 CFU/mL. (e) Linear calibration curves between the B/R values and B. subtilis, S. aureus, and P. aeruginosa concentrations, respectively, ranging from 102 to 107 CFU/mL. (d,e) Representation of B/R values of the mean of nine photographs from three independent experiments, and the error bars indicate the SD from the mean.

bacteria for 30 min, the A625/A522 values for the mixture of Cu2+ and functionalized AuNPs stopped increasing and became stable. Therefore, the reaction time for this bacteria-instructed click chemistry system was fixed at 0.5 h in our experiments.

To evaluate the detection limit of the bacteria-instructed click chemistry, E. coli with various concentrations was added into the optimal click chemistry system (2.7 nM of azide- and alkyneAuNPs, 100 nM of Cu2+, 0.5 h of bacterial reaction time), and 23096

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Figure 5. (a) Schematic illustration of the process of selective POC sensing of specific bacterial strain by combining bacterial magnetic separation and smartphone app-assisted colorimetric detection. (b) Capture efficiency of the MSE. coli system toward E. coli bacteria with different concentrations ranging from 10 to 107 CFU after 20 min incubation. (c) DNase-triggered release efficiencies of E. coli with different concentrations captured by the MSE. coli system. (b,c) Representation of the values of capture and release efficiency of the mean of three independent experiments, and the error bars indicate the SD from the mean.

S11). For the slope difference of linear calibration curves obtained for different pathogenic bacteria, we speculate that it may be closely related with the reduction ability of these bacteria for Cu2+.34 By importing the linear calibration curves obtained into the app, one-click determination of bacterial concentration in unknown samples could be easily achieved via the consecutive steps of “color scan” and “concentration analyze”, providing a portable platform for POC sensing of pathogenic bacteria (Figure S12). 2.4. Portable Platform for Selective POC Detection of Pathogenic Bacteria in Complex Sepsis Blood Samples. In addition to general bacterial sensing, selective detection of specific bacterial strain in the presence of other bacterial strains, the common condition in actual samples, has greater clinical significance.35,36 To this end, we further constructed the bacterial separation system based on our previous work and combined it with the smartphone app-assisted colorimetric platform. This bacterial separation system was based on iron oxide magnetic nanoparticles functionalized with bacterial species-identifiable aptamers to achieve selective separation and enrichment of specific bacterial strain (Figures S13 and S14).37 After magnetic separation, the enriched bacteria were released from the magnetic nanoparticles upon the treatment of DNase to instruct the click reaction between azide- and alkyneAuNPs and finally diagnosed by the smartphone app-assisted platform (Figure 5a). The magnetic preseparation process can improve bacterial detection sensitivity by enrichment on one hand and achieve bacterial detection selectivity on the other hand. Here, the functionalized magnetic system specifically targeting E. coli (MSE. coli) was prepared at the current stage, and its performance for E. coli capture and DNase-triggered release was evaluated. As shown in Figure 5b, the MSE. coli system exhibited outstanding capture efficiency (>85%) toward E. coli with different numbers ranging from 10 to 107 CFU within short times (about 20 min). Conversely, only a small fraction of S. aureus was captured (capture efficiency < 10%) by the MSE. coli system after 60 min incubation, indicating the robust capture selectivity of the MSE. coli system (Figure S15). Moreover, the

corresponding phenomena of color changes of the click chemistry system were investigated. As shown in Figure 4a, it was found that the distinct color change of the system was not observed when bacterial concentrations were below 103 CFU/ mL, indicating that the minimum concentration of E. coli detected by the naked eye for the bacteria-instructed click chemistry was approximately 103 CFU/mL. Although this detection limit of bacteria is acceptable for the naked eye, it is still not sensitive enough for bacterial detection in practical applications. It is well known that the change of RGB pixel values is more sensitive than that of colors, and the widespread adoption of smartphone provides a portable platform for photo imaging and data analysis.31−33 Therefore, we further developed a smartphone app (“bacteria analyzer”) with RGB pixel values recording and quantification ability to analyze the color difference of the bacteria-instructed click chemistry system. This smartphone app was developed in SWIFT language and compiled to run on the IOS 9.3 operating system (Figures S8 and S9, refer to the Methods and Experiments section for details). Figure 4b shows the “color scan” function interface of this app, by which the color card and RGB pixel values of photographs of the bacteria-instructed click chemistry system could be directly outputted. Figure 4c shows the obtained color cards corresponding to the systems of bacteria-instructed click chemistry shown in Figure 3a. With regard to the RGB pixel alone (Figure S10), the ratio of the blue to red pixel (B/R) was calculated in the app to reflect the color change of the bacteriainstructed click chemistry system from red to blue. As shown in Figure 4d, a linear calibration curve (y = −0.14605 + 0.16254x, R2 = 0.996) was obtained between the B/R values and E. coli bacterial concentrations ranged from 102 to 107 CFU/mL. More importantly, it is worth to note that linear calibration curves were also obtained for other pathogenic bacteria including Bacillus subtilis (B. subtilis) (y = −0.15219 + 0.15339x, R2 = 0.986), Staphylococcus aureus (S. aureus) (y = −0.14126 + 0.15591x, R2 = 0.992), and Pseudomonas aeruginosa (P. aeruginosa) (y = −0.13723 + 0.1683x, R2 = 0.983) between the B/R values and bacterial concentrations (Figures 4e and 23097

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3. CONCLUSIONS In summary, by hijacking the bacterial metabolic process for Cu2+ binding and reduction to instruct the click chemistry between azide- and alkyne-AuNPs, we report a general and robust colorimetric strategy for POC detection of pathogenic bacteria. In virtue of the signal amplification of fixed-point click reaction and its tolerance to ambient conditions, this colorimetric strategy enables rapid, selective, and sensitive POC bacterial detection by naked eyes. Notably, this strategy is easily integrated in a smartphone app to achieve one-click reads in mobile way. Moreover, by combining this field-portable smartphone platform with a magnetic preseparation process, selective detection of E. coli with high sensitivity down to trace concentration in the complex artificial sepsis blood sample is successfully realized within 1 h, showing great potential for clinical POC diagnosis of bacterial infections. We believe that the proposed concept of bacteria-instructed click chemistry could also be employed as a potential strategy to design microbeactivated theranostic materials against bacterial infections.

captured E. coli was found to be efficiently released from the MSE. coli system after DNase treatment (release efficiency > 90%), laying a solid foundation for subsequent smartphone appassisted selective POC sensing of E. coli (Figure 5c). By combining the MSE. coli system with the smartphone appassisted colorimetric platform, selective E. coli detection in complex artificial sepsis blood samples (5 mL containing around 200 CFU of E. coli and 104 CFU of B. subtilis, S. aureus, and P. aeruginosa) was carried out. Sepsis caused by the presence of pathogenic bacteria in the bloodstream is a serious infection syndrome with high mortality.38,39 As shown in Figure 6a, after

4. METHODS AND EXPERIMENTS 4.1. Materials. Tetrachloroauric acid tetrahydrate (HAuCl4· 3H2O), 11-mercaptoundecanoic acid, HS-poly(ethylene glycol) (PEG) (Mw = 350), and trisodium citrate were purchased from Sinopharm Chemical Reagent. N-(3-(Dimethylamino)propyl-N′ethylcarbodiimide)hydrochloride (EDC), N-hydroxysulfosuccinimide sodium salt (NHS), Iron(III) chloride hexahydrate (FeCl3·6H2O), 1octadecene, poly(allylamine hydrochloride) (PAH, Mw = 15 000 Da), 11-azido-3,6,9-trioxaundecan-1-amine, sodium oleate, and propargylamine were purchased from Sigma-Aldrich. The E. coli-identifiable aptamers (5′-HOOC-ACACCATAATATGCCGTAAGGAGAGGCCTGTTGGGAGCGCCGTAGAG-3′) were purchased from Sangon Biotech. All other chemicals were obtained from Adamas-β and used without further purification. Deionized (DI) water (Millipore Milli-Q grade, 18.2 MΩ) was used in all the experiments. 4.2. Preparation of Azide- and Alkyne-AuNPs. AuNPs were synthesized by the classic citrate reduction method.40,41 Briefly, 2 mL of 1% gold(III) chloride trihydrate solution was added into 200 mL of double-distilled water and heated under reflux until boiling. Then, 5 mL of 1% trisodium citrate solution was added under vigorous stirring. Boiling was continued for 15 min to obtain the AuNPs with a size around 14 nm. For azide and alkyne group functionalization, AuNPs were first modified by HS-PEG (Mw = 350) and 11-mercaptoundecanoic acid (11-MUA) (HS-PEG/11-MUA/AuNPs = 1500:1000:1) to obtain the PEG−AuNPs−COOH through ligand-exchange reactions,27,42 and then 11-azido-3,6,9-trioxaundecan-1-amine and propargylamine (11-azido-3,6,9-trioxaundecan-1-amine/propargylamine/ 11-MUA = 5:1) were conjugated with the carboxyl group of PEG− AuNPs−COOH through standard EDC/NHS chemical covalent coupling procedures,28,43 to obtain the azide- and alkyne-AuNPs, respectively. 4.3. Bacterial Culture. Bacteria of S. aureus (ATCC 6538), B. subtilis (ATCC 6051), E. coli (ATCC 8739), and P. aeruginosa (ATCC 9027) were used in our experiments. Prior to experiments, the bacteria were grown overnight in Luria-Bertani broth medium (LB) and harvested at the exponential growth phase via centrifugation. The supernatant was then discarded, and the cell pellet was resuspended in PBS. The bacterial concentration could be monitored photometrically by measuring the optical density (OD) at a wavelength of 600 nm. Before performing bacterial colorimetric detection experiments, the OD600 values of bacterial stock solutions were readjusted to 0.1, which corresponded to the concentrations of 2 × 108, 1 × 108, 2 × 108, and 1.5 × 108 CFU/mL for S. aureus, B. subtilis, E. coli, and P. aeruginosa, respectively, obtained based on the gold standard colony counting method. 4.4. Toxicity of Cu2+ to Bacterial Growth. Different volumes of CuCl2 solution (100 μM) were added into 1 mL of E. coli LB solution

Figure 6. (a) Experimental photographs for the selective POC sensing of E. coli from complex artificial sepsis blood samples. (b) “Concentration analyze” function interface of the smartphone app for concentration determination of enriched E. coli from the artificial sepsis blood samples. (c) Detected E. coli numbers of four parallel artificial sepsis blood samples by the smartphone app-based platform.

magnetic separation and DNase-triggered release, enriched E. coli from the complex sepsis blood samples was added into the optimal click reaction system (1 mL) and finally diagnosed by the smartphone app. Figure 6b shows the “concentration analyze” function interface of the smartphone app for E. coli concentration determination. By making use of the B/R values obtained (0.23) and the linear calibration curve (y = −0.14605 + 0.16254x, R2 = 0.996), E. coli concentration (205 CFU/mL) was directly outputted on the screen. In addition, more parallel trials were conducted to test the repeatability of our method. As shown in Figure 6c, for the four parallel trials, the smartphone app-based platform provided consistent results, which have a good match with the number of added E. coli in the artificial sepsis blood sample measured by the gold standard blood culture method. Compared to the long turnaround time (>24 h) of bacterial blood culture, the whole process of bacterial magnetic separation, DNase-triggered release and smartphone app sensing can be completed within 1 h, meeting the time requirements of clinical POC sensing. Furthermore, the magnetic bacterial separation and enrichment process improve the sensitivity of detection, achieving the early diagnosis of sepsis down to trace concentrations of blood bacteria (around 40 CFU/mL). The preparation of more magnetic systems targeting other pathogenic bacterial strains besides E. coli is now in progress (Figures S16 and S17), and it is foreseeable that early POC diagnosis of clinical bacterial infections with high sensitivity would become more convenient based on our portable platform in the near future. 23098

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Research Article

ACS Applied Materials & Interfaces with a concentration of 1 × 103 CFU/mL to make final Cu2+ concentrations ranging from 10 nM to 10 μM in the mixture. After 12 h incubation, the OD readings of bacterial solutions were monitored by measuring the OD600. The bacterial solution without Cu2+ was used as the control. By comparing the OD600 values of bacterial solution with Cu2+ to the control, the toxicity of Cu2+ to bacterial growth was evaluated. 4.5. Bacteria-Instructed Click Reaction between Azide- and Alkyne-AuNPs. To prepare the click reaction system, azide- and alkyne-functionalized AuNP solutions with same concentrations were first mixed, and then CuCl2 solution was added as the copper-ion source for subsequent bacterial reduction and click reaction. To conduct the bacteria-instructed click reaction, the mixtures of functionalized AuNPs and Cu2+ were incubated with different kinds of pathogenic bacterial PBS solutions, and the color changes of the mixtures after incubation were analyzed by UV−vis absorption spectroscopy. For the optimization of the click reaction system for bacterial detection, the effects of functionalized AuNP concentration (azide-AuNP/alkyneAuNP = 1:1), Cu2+ concentration, and bacterial reaction time were systematically investigated. To investigate the tolerance of the click reaction system to mediums, three different mediums including DI water, NaCl (0.1 M), and PBS (0.01 M, pH 7.4), were added into the mixture of functionalized AuNPs and Cu2+, and the color changes of the mixtures after incubation were investigated. 4.6. Development of “Bacteria Analyzer” Smartphone App. The homemade “Bacteria Analyzer” smartphone app is developed in Swift language (Eclipse code) and compiled to run on the IOS 9.3 operating system. This smartphone app can be downloaded from the Apple app store, and the whole code of this smartphone app is available upon request. The flow chart of this software is shown in the Supporting Information (Figure S8). The main menu and user interface of this smartphone app consists of two function buttons called “color scan” and “concentration analyze” and works as follows. Upon touching the button of “color scan”, the corresponding “color scan” function interface will be loaded by which RGB values of images of the system of bacteria-instructed click reaction can be recorded and quantified. In the “color scan” function interface, images of the system of bacteriainstructed click reaction can be either taken by the smartphone camera or loaded from the smartphone album, and RGB values of the selected zone from the center of images will be calculated and outputted by touching the “scan” function button on-screen. With regard to RGB values, the ratio of blue to red (B/R) is calculated to judge the color difference of the system of bacteria-instructed click reaction, and linear calibration curves between B/R values and bacterial concentrations ranged from 102 to 107 CFU/mL are obtained in our previous experiments. These linear calibration curves for different pathogenic bacteria are imported into the app in advance, and quantitative bacterial concentration in the detected sample can be determined and directly outputted in the “concentration analyze” function interface based on the B/R values obtained from the corresponding images of the bacteriainstructed click reaction system. In order to reduce the effects of lighting conditions on RGB value variations when the images were taken by the smartphone camera, a tight box with light-emitting diode light was used when samples were taken by the smartphone camera to protect the environmental light and control illumination inside the box. In addition, a focal length of approximate 5 cm between the sample and smartphone camera was fixed to improve the precision and accuracy of the sample image. 4.7. MSE. coli System-Based E. coli Separation and Enrichment. To prepare the MSE. coli system, magnetic Fe3O4 nanoparticles with a size of 17 nm were first synthesized by thermal decomposition of the iron-oleate complex and then coated with PAH and modified by the E. coli-identifiable aptamer according to our previous work.37 Subsequently, PBS solution was spiked with different concentrations of E. coli (10, 102, 103, 104, 105, and 106 CFU/mL) to produce E. colicontaminated samples. For E. coli identification and separation, the MSE. coli system (50 μg/mL of iron element) was incubated with 1 mL of E. coli-contaminated samples for 20 min at 37 °C. After incubation, an external magnet was applied to achieve magnetic separation of E. coli. The capture efficiency (C %) is defined as the proportion of E. coli

separated from the test samples, and it is calculated based on the equation: C % = 1 − (Na/Nb). In this equation, “Na” and “Nb” represent the number of E. coli in the test samples after and before magnetic separation, respectively, and they are determined using the standard colony counting method in our experiments. To trigger the release of magnetic enriched E. coli, DNase was added into the MSE. coli system with E. coli binding and incubated for 8 min to release the enriched E. coli. After incubation, an external magnet was applied to retrieve the magnetic nanoparticles. The release efficiency (R %) is defined as the proportion of E. coli released from the magnetic nanoparticles, and it is calculated based on the equation: R % = 1 − (Nc/ Nd). In this equation, “Nc” and “Nd” represent the number of E. coli binding on the magnetic nanoparticles after and before DNase treatment, respectively, and they are determined using the standard colony counting method in our experiments. 4.8. Selective Detection of E. coli in Artificial Sepsis Blood Samples. Five mL of fresh whole blood obtained from the eyeball of healthy mice was spiked with 200 CFU of E. coli and 104 CFU of B. subtilis, S. aureus, and P. aeruginosa to produce the complex artificial sepsis blood sample. After magnetic separation by the MSE. coli system (50 μg/mL of iron element), the enriched E. coli was released upon the treatment of DNase and then was added into the optimal click reaction system (1 mL, 2.7 nM of azide- and alkyne-AuNPs, 100 nM of Cu2+). After 30 min incubation, the solution of the mixture was imaged, and the RGB value of the image obtained was recorded and quantified based on the “color scan” function of our smartphone app. Based on the B/R values obtained above and the linear calibration curves stored in the smartphone app, the concentrations of bacteria could be determined and directly outputted by the “concentration analyze” function of our smartphone app.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.9b09279. Additional experimental data (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (D.-S.H.). *E-mail: [email protected] (Y.Y.). *E-mail: [email protected] (Y.-Q.L.). ORCID

Chunying Chen: 0000-0002-6027-0315 Yong-Qiang Li: 0000-0003-1551-3020 Author Contributions #

X.-Z.M., X.-Y.C., and J.W. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (31500802, 21628201), the Natural Science Foundation of Jiangsu Province (BK20150350), Funds of Science Technology Department of Zhejiang Province (LGF18H180008), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). This work was also supported by the 333 Project of Jiangsu Province (BRA2017437), and Jiangsu Key Research and Development Plan (Society Development) (BE2018639). 23099

DOI: 10.1021/acsami.9b09279 ACS Appl. Mater. Interfaces 2019, 11, 23093−23101

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ACS Applied Materials & Interfaces

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