Simultaneous Sensing of Seven Pathogenic Bacteria by Guanidine-Functionalized Upconversion Fluorescent Nanoparticles.

The method capable of simultaneously detecting multiple target bacterial pathogens is necessary and of great interest. In this research, we demonstrated our initial effort to simultaneously detect seven common foodborne bacteria by developing a straightforward upconversion fluorescence sensing approach. The fluorescent nanosensor was constructed from a designed guanidine-functionalized upconversion fluorescent nanoparticles (UCNPs@GDN), tannic acid, and hydrogen peroxide (HP) and could quantify pathogenic bacteria in a nonspecific manner because the luminescence of the upconversion fluorescent nanoparticle was effectively strengthened in the presence of bacteria. When the developed nanosensor was applied to quantify multiple bacteria including Escherichia coli, Salmonella, Cronobacter sakazakii, Shigella flexneri, Vibrio parahaemolyticus, Staphylococcus aureus, and Listeria monocytogenes, a linear range of 103 to 108 cfu mL-1 and a detection limit of 1.30 × 102 cfu mL-1 have been obtained for the seven model mixture bacteria. In addition, the similar linear range and detection limit were also obtained for the detection of single bacteria. The present approach also exhibited acceptable recovery values ranging from 70.0 to 118.2% for bacteria in real samples (water, milk, and beef). All these results suggested that the guanidine-functionalized upconversion fluorescent nanosensor could be considered as a promising candidate for the rapid detection and surveillance of microbial pollutants in food and water.

Introduction

Pathogenic bacteria plays a major role in a variety of human diseases, it has been shown that Escherichia coli, Salmonella, Cronobacter sakazakii, Shigella flexneri,Vibrio parahaemolyticus, Staphylococcus aureus, and Listeria monocytogenes are commonly encountered foodborne-disease-related bacterial pathogens, which are the closest hazard source associated with public health worldwide.[1−6] The extensive use of antibiotics leads to these drug-resistant pathogens presented in food and water, which causes serious infectious diseases, with high medical cost and mortality rate.[3,7] Therefore, real-time screening and surveillance for various foodborne pathogens enable the reduction of disease outbreaks and ensure food safety. At present, conventional colony culture and counting are widely applied as the current gold-standard method to estimate the number of viable bacteria in various samples.[8] In addition, nucleic acid-based methods and immunology-based methods are also utilized as sensitive and specific detection techniques.[8−10] With the development of affinity reagents such as antibody, antibiotic, phage, aptamer, and peptide, many molecular recognition based methods have also been constructed for the rapid screening of pathogenic bacteria.[11−15] Despite these merits, these techniques suffer from problems such as taxing, time-consuming, tedious sample pretreatment, expensive instrumentation, and professional operation, which make them impossible to meet the need of the rapid quantitative determination of multiple pathogens. Thus, a compelling and urgent need exists to improve the current methods for quickly sensing various pathogenic bacteria.

Nowadays, approaches capable of detecting pathogenic bacteria such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry, surface-enhanced Raman scattering, electrochemical techniques, and fluorescent techniques have received great attention.[16−19] Among them, approaches based on optical sensing materials have been attracting much attention for the past few years, especially, driven by the rapid development and application of novel nanomaterials such as gold nanoparticles, gold nanorods, quantum dots, silica nanoparticles, and magnetic nanoparticles. These approaches have advantages over conventional approaches that include simplicity, rapidness, low cost, excellent stability, high selectivity, and sensitivity.[20−26] It is worth noting that upconversion fluorescent nanoparticles (UCNPs), which have been widely applied in the fields of sensor, biology imaging, and photodynamic therapy, are attracting more and more attention because of emitting visible luminescence under near-infrared (NIR) excitation, multicolor tunable property, excellent photo-stability, negligible background fluorescence, narrow emission spectrum, and less toxic elements.[27−32] Sensors based on integration UCNPs with biomolecules such as antigen, antibody, and aptamer have been successfully constructed for sensing bacteria with high sensitivity and specificity.[33−38] However, these approaches are still greatly challenging, especially when applied to the simultaneous detection of multiple bacteria. Moreover, most of the biomolecules still suffered poor stability, fabricating cost, short shelf lifespan, and strict storage conditions.[10,39]

Here, we report a straightforward upconversion fluorescence sensing approach for the simultaneous detection of seven common foodborne bacteria, although a fiber probe for the simultaneous sensing labeled E. coli and S. aureus based amino-functionalized UCNPs have been previously reported.[40] In consideration of the fact that the natural materials with a spherical or discal shape such as yeast cells, human cell, cyanobacteria, and diatoms have the ability to focus light for fluorescence enhancement.[40] It is worth noting that bacterium as a widespread natural materials, usually carried the net negative surface charge,[39−41] thus the positively charged guanidine group, which has two parallel hydrogen donor sites and is vital for the enzyme to bind anionic substrates in nature,[42,43] is chosen as the recognition elements. The electrostatic interaction or hydrogen bond interactions could be potentially formed between bacteria and the functional groups of nanoparticle surfaces. In addition, tannic acid (TA) can be oxidized by hydrogen peroxide (HP), and the resulting product acts as a sensitizer and stabilizer in the sensing system. Our suggested upconversion fluorescent sensing approach provides a straightforward strategy for the simultaneous broad-spectrum bacterial quantitative determination and demonstrates its considerable capability of bacterial quantification for complex real samples such as water, milk, and beef.

Results and Discussion

Here, UCNPs@GDN integrated the advantages of UCNPs with guanidine groups, which gave them a significant fluorescence emission under the excitation of NIR light and a strong positive surface charge. The results of transmission electron microscopy (TEM) indicated that UCNPs were hexagonal with a particle size of around 20 nm (Figure S1a), a thin layer of the guanidine group based materials with a thickness of around 3 nm has been coated onto the surface of UCNPs by the modification procedure, moreover, the crystal morphology of UCNPs@GDN has no significant change (Figure S1b). The surface potential of UCNPs@GDN is +36.8 mV, which is far stronger than that of UCNPs (Figure S1c). From the spectrum of Fourier transform infrared (FT-IR), we can also observe that the characteristic absorption peak at 1632.16 cm–1 arose from the C=N bond stretching vibration of the guanidine group of UCNPs@GDN (Figure S2). All these results indicated that the guanidine group has been well grafted onto the surface of UCNPs materials. The reliability and stability of the signal output is quite a critical factor in the fluorescence sensing system. No significant fluorescence intensity change of UCNPs@GDN is found with the change of the concentration of 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), pH value, temperature, and storage time (Figure S3), indicating that the obtained UCNPs@GDN have good environment adaptability.

UCNPs@GDN capable of producing stronger fluorescence has been designed as a sensor for the detection of bacteria. Simultaneously, the strongly basic guanidine group was introduced onto the surface of UCNPs as the main functional group, which could interact with various anions via charge pairing interactions and/or hydrogen bonding. On the other hand, the exterior surface of bacterial cells exhibited negative surface charge and could potentially form electrostatic interactions with the guanidine group carrying a positive charge. Indeed, the bacteria could cause UCNPs@GDN to emit the stronger fluorescence, and the fluorescence intensity was enhanced with the increment of the concentration of bacteria from 105 to 109 cfu mL–1, which made it possible to quantify bacteria (Figure ). We speculated that UCNPs strongly interacted with bacteria, where the light could propagate and distribute along with the bacteria. The output light could be focused by bacteria, and cause the intensity gradient.[40,44] Thus, the fluorescence enhancement of UCNP materials was achieved in the presence of bacteria.

Figure 1

Fluorescence emission spectrum of UCNPs@GDN (1.0 mg mL–1) treated with different concentrations of E. coli.

In order to further improve the sensitivity of the sensing system, HP and TA were also added into the system. Here, TA possesses multiple phenolic hydroxyl groups that can be oxidized into multiple quinone groups by HP.[45−47] The quinone groups can form stronger interactions with the amino group of the surface protein of bacteria and the guanidine group of UCNPs@GDN than that of phenolic hydroxyl groups,[48−50] and thus promote the interaction between bacteria and UCNPs@GDN as the bridging groups and improve the fluorescence response of UCNPs@GDN to bacteria. Compared Figure a with 2b, we can observe that only TA was added into the system, the slight improvement of the fluorescence response of UCNPs@GDN to bacteria was obtained. The reason may be attributed to the facts that TA could interact with amino acids, proteins, or/and polysaccharides on the surface of bacteria,[51,52] On the other hand, phenolic hydroxyl of TA could connect with the guanidine group via the hydrogen-bond interaction, which could bridge bacteria and UCNPs@GDN to enhance the combination. However, only HP was introduced into the system, the small reduction of the fluorescence response was observed (Figures c and S4). The reason may be attributed to the reduced interaction between UCNPs@GDN and bacteria. Because HP can interact with the guanidine group of materials,[53] which hampers the electric transformation from UCNPs to guanidine groups. In addition, the groups on the surface of bacteria can interact with HP,[54] which weakened the interaction with UCNPs@GDN. Thus, TA and HP were simultaneously added into this system, a prominent fluorescence response of UCNPs@GDN to bacteria was obtained (Figure d), which confirmed our speculation. These results indicated that the present fluorescence nanosystem was enabled to sensing bacteria more sensitively, and the schematic illustration was showed in Figure .

Figure 2

Fluorescence responses of different systems to E. coli. (a) UCNPs@GDN; (b) UCNPs@GDN + TA; (c) UCNPs@GDN + HP; (d) UCNPs@GDN + TA + HP. UCNPs@GDN (1.0 mg mL–1), TA (4.0 mg mL–1), HP (5.0 M) and E. coli (108 cfu mL–1).

Figure 3

(A) Schematic representation of synthesis for UCNPs@GDN nanomaterials. (B) Schematic representation of bacteria sensor detection based on the fluorescence nanosystem.

In order to further optimize the sensing conditions, the effects of UCNPs@GDN, TA, HP, buffer system, and pH value on the fluorescent signal of UCNPs@GDN were investigated in the presence/absence of E. coli, respectively. According to the results (Figure S5), UCNPs@GDN (1.0 mg mL–1), TA (4.0 mg mL–1), HP (5.0 M), HEPES buffer system, (10 mM) and pH = 8.0 were chosen as the optimum detection conditions for the subsequent experiments.

Here, seven common foodborne pathogenic bacteria including E. coli, Salmonella, C. sakazakii, S. flexneri, V. parahaemolyticus, S. aureus, and L. monocytogenes were detected and quantified by the suggested protocol, respectively. As shown in Figure , the UCNPs@GDN fluorescence intensity increased with the increase of E. coli or Salmonella content. The plot of fluorescence intensity versus the logarithm of bacteria concentration showed a linear relationship in the concentration range of 103 to 108 cfu mL–1. The quantification of other pathogenic bacteria also showed a linear range of 103 to 108 cfu mL–1 (Figures S6–S10), and their regression equations and the limit of detection (LOD) were listed in Table . (The LOD was determined as 3 SK–1, in which S is the standard deviation of control measurements, and K is the slope of the linear equation). These results obtained demonstrated that the established strategy is superior to those of previous reports.[18,55] The signal intensity caused by the seven bacteria was very close at the same concentration, which might have resulted from the very close quantity levels in bacteria cells. Thus, the uniform mixed bacteria containing seven bacteria were assayed with the proposed protocol, a good linear relationship was obtained by the plotting fluorescence intensity versus the logarithm of bacteria concentration in the range of 103 to 108 cfu mL–1 and the regression equation could be described as Y = −300.98 + 146.57X with a correlation coefficient of 0.9902, the LOD for the uniform mixed bacteria was estimated to be 1.3 × 102 cfu mL–1, which showed the excellent detection ability and the application potential of the UCNPs@GDN in quantifying the total number of bacteria (Figure ).

Figure 4

(A) Fluorescence emission spectra of UCNPs@GDN (1.0 mg mL–1) with different concentrations of E. coli. (B) The plot of fluorescence intensity vs the logarithm of E. coli concentration. (C) Fluorescence emission spectra of UCNPs@GDN (1.0 mg mL–1) with different concentrations of Salmonella. (D) The plot of fluorescence intensity vs the logarithm of Salmonella concentration. The test was conducted under the optimizing conditions (n = 3).

Table 1

Curve Equation, Linear Range, Detection Limit, and Correlation Coefficient of Different Bacteria

bacteria	regression equations	linear range (cfu mL–1)	LOD (cfu mL–1)	R2
E. coli	Y = −152.45 + 103.90X	103 to 108	7.9 × 102	0.9938
S. aureus	Y = −198.06 + 126.14X	103 to 108	2.5 × 102	0.9962
Salmonella	Y = −284.04 + 132.82X	103 to 108	2.0 × 102	0.9977
L. monocytogenes	Y = −399.99 + 156.68X	103 to 108	1.0 × 102	0.9978
C. sakazakii	Y = −228.54 + 144.62X	103 to 108	1.3 × 102	0.9950
S. flexneri	Y = −178.88 + 125.32X	103 to 108	2.5 × 102	0.9922
V. parahaemolyticus	Y = −227.75 + 122.54X	103 to 108	3.2 × 102	0.9936
mixed bacteria	Y = −300.98 + 146.57X	103 to 108	1.3 × 102	0.9902

Figure 5

(A) Fluorescence emission spectra of UCNPs@GDN (1.0 mg mL–1) with different concentrations of uniform mixed bacteria. (B) The plot of fluorescence intensity vs the logarithm of uniform mixed bacteria concentration. The test was conducted under optimizing conditions (n = 3).

In order to further evaluate the application potential for sensing bacteria, the environmental sample (tap water), and food sample (milk and beef) were spiked with standard bacteria solutions and detected by the present protocol, respectively. The recovery values were in the range of 70.0–118.2% with a relative standard deviation (RSD) of 4.3–9.9% (see in Tables and S1–S7). Compared with previous reports, the present approach exhibited advantages in target bacteria and response time (Table S8). These results revealed the application potential of the proposed sensing strategy for bacteria in complex sample matrixes.

Table 2

Recovery Tests of Mixed Bacteria Spiked in Real Samples (n = 5)

sample	added (cfu mL–1)	found (cfu mL–1)	RSD (%)	recovery (%)
tap water	1.8 × 104	1.9 × 104	9.7	105.6
	1.8 × 106	1.6 × 106	4.6	88.9
	1.8 × 108	1.4 × 108	6.7	77.8
milk	3.5 × 103	3.9 × 103	9.8	111.4
	3.5 × 105	3.0 × 105	4.3	85.7
	3.5 × 107	2.5 × 107	7.8	71.4
beef	6.4 × 103	7.1 × 103	9.9	110.9
	6.4 × 105	5.6 × 105	8.4	87.5
	6.4 × 107	4.7 × 107	9.7	73.4

Conclusions

In conclusion, a facile and rapid upconversion fluorescent nanosystem based on UCNPs@GDN has been developed for the simultaneous broad-spectrum bacterial quantitative determination in food and water. Compared with the traditional approach, the proposed sensing approach was highly sensitive, low cost, and time-saving. The recovery study of bacteria in spiked environmental and food samples demonstrated the application potential of the present sensing approach for real sample detection. This upconversion fluorescent sensing strategy could be extended to detect other pathogenic bacteria, and more kinds of functionalized UCNPs should be exploited for sensing of pathogenic bacteria.

Materials and Methods

Materials

Yb (CH3COO)3·4H2O (99.9%) and Er (CH3COO)3·XH2O (99.9%) were purchased from Alfa Aesar Co. Ltd. Y (CH3COO)3·4H2O (99.9%) was obtained from Sigma-Aldrich. 3-Aminopropyltriethoxysilane (APS, 97%) was brought from J&K Chemical. S-ethylisothiourea hydrobromide (98%), tetraethyl orthosilicate (TEOS, 98%), oleic acid (OA, 90%), and 1-octadecene (ODE, 90%) were obtained from TCI Chemical. Triton X-100 was brought from GFCO Chemical. TA (87%) and HP (30%) were ordered from Sinopharm Chemical Reagent Co., Ltd. Luria-Bertani (LB) broth, agar powder, nutrient agar/broth, brain heart infusion (BHI) broth, 3-morpholinopropanesulfonic acid (99.5%), HEPES (99.5%), and phosphate buffer (PB) were ordered from Beijing Solarbio Science & Technology Co., Ltd. E. coli (ATCC25922), L. monocytogenes (ATCC7644), C. sakazakii (ATCCBAA894), Salmonella (CICC10867), S. aureus (ATCC25923), S. flexneri (ATCC12022), and V. parahaemolyticus (ATCC17802) were ordered from BeNa Culture Collection Co., Ltd. Double distilled water (18.2 MΩ cm–1) was prepared using a Water Pro water purification system. Milk and beef were obtained from the local supermarket. Tap water was obtained from the water pipe in Tianjin University of Science and Technology campus. Other reagents were of analytical grade without further purification.

Synthesis of UCNPs@GDN

UCNPs@GDN were prepared as per the previous procedure.[56] First, the oil-solvent UCNPs were synthesized. 1 mM RE (CH3COO)3 [Y/Yb/Er 78:20:2] was dissolved in a three-necked flask with OA (6 mL) and ODE (17 mL), and this system was kept at 160 °C for 30 min to form a transparent solution under an argon atmosphere and vigorous stirring. Next, the system was kept at room temperature for 1 h, and 10 mL methanol solution with NaOH (2.5 mM) and NH4F (4 mM) was dropwise added to keep for another 30 min. Then, methanol was removed by heated evaporation at 70 °C. Subsequently, the system was kept at 300 °C for 1 h with an argon atmosphere and vigorous stirring, then cooled down to collect UCNPs via centrifugation (12 186g, 10 min). The obtained materials were washed with ethanol and dried in air. Second, UCNPs@NH2 were obtained by the inverse microemulsion method following the procedure,[56] UCNPs (0.03 M) were dispersed into cyclohexane (18 mL) with Triton X-100 (0.3 mL). Subsequently, ammonia solution (0.24 mL) and Triton X-100 (1.2 mL) were added under ultrasonication for 20 min. Then, TEOS (40 μL) and APS (40 μL) were dropwise added with stirring for 48 h. UCNPs@NH2 were collected via centrifugation (12 186g, 10 min). Finally, standard hydrogen electrode (10 mg) and UCNPs@NH2 (10 mg) were dispersed in PB buffer (10 mM, 30 mL, pH = 8.0) under stirring, and the system was heated to 70 °C for 3 h under an argon atmosphere. And then the obtained UCNPs@GDN were washed with water and ethanol twice, and dried in air.

Apparatus

UCNPs material fluorescence spectra were measured on an F-2500 fluorescence spectrometer (Hitachi, Japan) under the excitation of a 980 nm diode laser (1 W, continuous wave with 1 m fiber). UCNP material morphologies were observer by a JEOL 2010F (JEOL, Japan) TEM. UCNP material FT-IR spectra (4000–400 cm–1) in KBr were measured in a Vector 22 FT-IR spectrophotometer (Bruker, Germany). Zeta potentials of UCNP material were recorded in neutral water solution at room temperature with a Zetasizer Nano ZS90 (Malvern). Ultraviolet–visible (UV–vis) absorption spectra were measured in a Shimadzu UV-2700 UV–vis spectrophotometer (Shimadzu, Japan).

Bacteria Culture

Liquid cultures of E. coli, Salmonella, S. aureus, C. sakazakii, and S. flexneri were grown and rocked in 20 mL of the Luria Bertani broth, respectively. L. monocytogenes and V. parahaemolyticus were obtained by liquid cultures in 20 mL of the BHI broth and 20 mL of the nutrient broth with 3% NaCl, respectively. After incubation at 37 °C for 24 h, the bacteria culture was centrifuged at 3000g for 5 min and washed with HEPES (10 mM, pH = 8.0) three times to collect bacteria, and the model bacterial mixture was prepared as the equal volume of seven bacteria, then the optical density value of the bacteria solution was detected at 600 nm and reached 0.5 (109 cfu mL–1). The strains and their mixture were diluted by ten-fold and applied as a standard stock for further experiments. The plate counting method (reference: Food safety detection methods of National Standards of the People’s Republic of China, GB4789.2-2016)[57] was applied to count as the standard bacteria.

Optimization and Detection

Five main factors have been optimized for the detection of bacteria including the concentration of UCNPs@GDN, TA, HP, buffer system, and pH value. E. coli (108 cfu mL–1) has been adopted as the example bacteria. The obtained optimum detection condition of the nanosensor system to quantify E. coli was also transferred to quantify S. aureus, Salmonella, L. monocytogenes, C. sakazakii, S. flexneri, V. parahaemolyticus, and the seven model mixture bacteria, respectively. The diluted standard bacteria were added to the nanosystem under the optimized conditions. And after shaking (200 rpm) for 30 min, the fluorescence intensity was recorded, and the fluorescence intensity at the peak of 550 nm was chosen for statistical analysis.

Real Sample Preparation and Measurement

Tap water, beef, and milk were chosen as the real samples, respectively. First, all the samples were sterilization under high temperature to ensure no bacteria in these samples. For tap water, the standard strains were directly added without any pretreatment. For beef, the standard bacteria were added after homogenizing to remove sediment. For milk, the samples were first diluted with pure water (1:100), and then the standard bacteria were added. Both the plate count method and the developed based assay were used to analyze the bacterial containing samples, respectively.