Home Journals Articles Updates About
CN
image
收藏切换
Rapid detection of pathogenic bacteria based on a universal dual-recognition FRET sensing system constructed with aptamer-quantum dots and lectin-gold nanoparticles
收藏切换
PDF
Yaqing Zhanga, Yan Liua, Yun Yanga, Linyao Lia, Xiaoqi Taob, Erqun Songa, *
Chinese Chemical Letters | 2023, 34(8) : 108102
Less
收藏切换
Chinese Chemical Letters | 2023, 34(8): 108102
• Communication •
Rapid detection of pathogenic bacteria based on a universal dual-recognition FRET sensing system constructed with aptamer-quantum dots and lectin-gold nanoparticles
Full
Yaqing Zhanga, Yan Liua, Yun Yanga, Linyao Lia, Xiaoqi Taob, Erqun Songa, *
Affiliations
  • a Key Laboratory of Luminescence Analysis and Molecular Sensing, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
  • b College of Food Science, Southwest University, Chongqing 400715, China
Published: 2023-08-15 doi: 10.1016/j.cclet.2022.108102
Outline
收藏切换

The threat to public health from bacterial infections has led to an urgent need to develop simpler, faster and more reliable bacterial detection methods. In this work, we developed a universal dual-recognition based sandwich fluorescence resonance energy transfer (FRET) sensor by using specific aptamer-modified quantum dots (Aptamer-QDs) as energy donor and lectin concanavalin A (Con A) modified gold nanoparticles (Con A-AuNPs) as energy acceptor to achieve rapid and sensitive detection of Escherichia coli (E. coli) within 0.5 h. In the presence of the target E. coli, the energy donor of Aptamer-QDs and acceptor of Con A-AuNPs were close to each other, causing changes of FRET signals. Based on the constructed FRET sensor, a linear detection range of from 102 cfu/mL to 2 × 108 cfu/mL with the detection limit of 45 cfu/mL for E. coli was achieved. Furthermore, the FRET sensor was applied to detect E. coli in the milk and orange juice with the detection limit of 300 cfu/mL and 200 cfu/mL, respectively and recovery rate from 83.1% to 112.5%. The strategy holds great promise in pathogenic bacteria detection due to its rapid and sensitivity.

Bacteria  /  Fluorescence resonance energy transfer  /  Dual recognition  /  Aptamer  /  Concanavalin A
Yaqing Zhang, Yan Liu, Yun Yang, Linyao Li, Xiaoqi Tao, Erqun Song. Rapid detection of pathogenic bacteria based on a universal dual-recognition FRET sensing system constructed with aptamer-quantum dots and lectin-gold nanoparticles[J]. Chinese Chemical Letters, 2023 , 34 (8) : 108102 - . DOI: 10.1016/j.cclet.2022.108102
Full Text
Less
Pathogen monitoring and control is a critical step in ensuring food safety and the health of the general public. Over the years, there have been many threats to human health from bacterial infections such as Escherichia coli (E. coli), leading to a surge of ingenious strategies for detecting microorganisms in connection with food safety, medical diagnostics, water quality, and bioterrorism [1–4]. Commonly, the conventional method for detecting pathogenic bacteria is primarily based on bacteria culture, which is sensitive and inexpensive but bears the limitation of time-consuming and labor-intensive procedures [5]. Immunological assay such as enzyme linked immunosorbent assay (ELISA) is a rapid method but suffers from relatively low sensitivity [6–9]. Nucleic acid-based assays such as polymerase chain reaction (PCR) have shown advantages of high sensitivity and high specificity for pathogenic bacteria detection but suffering expensive equipment, complicated procedures, and the need for skillful technicians [10,11]. Hence, it is urgent to develop a novel, simple, inexpensive and time saving approach to determine bacterial with high selectivity and sensitivity.
Fluorescence resonance energy transfer (FRET) is a distance-dependent energy transfer phenomenon (from a donor in excited energy state to a nearby acceptor) which has been exploring to design various sensors [12–14]. In recent years, nanomaterials derived energy donors and acceptors have been employed to construct FRET optical sensors for bacterial detection [15–23]. Wang et al. used antibody functionalized two-color quantum dots (QDs) as donor and acceptor to detect Salmonella enteritidis in eggshell [24]. Heli et al. developed a FRET immunosensor based on antibody functionalized QDs and gold nanoparticles (AuNPs) for sensitive identifying of E. coli [25]. However, the inherent defects of antibody, including low screening efficiency and low stability, make the antibody-based strategies limited for application [19,20,24–26].
As a group of proteins, lectins can strongly bind to specific carbohydrate moieties on the surface of bacteria [27,28] and are particularly interesting candidates for using as molecular recognition elements because of their ease of production and intrinsic stability [29,30]. Concanvalin A (Con A), a type of legume lectin, could specifically bind with terminal α-D-mannosyl and α-D-glucosyl groups on the surface of bacteria and has been employing as recognition molecule to develop sensors for detecting pathogen [29–34]. However, the sensitivity of current Con A based methods is not very satisfied, and moreover the methods involve complex operation and time-consuming. Aptamers are single-stranded DNA or RNA oligonucleotides that can recognize and bind to specific targets, including whole cells, proteins, peptides and small molecules [35], and have been attracting great attention in the fields of biosensor due to their unique characteristics such as ease of synthesis and modification, good reproducibility [36,37].
In this study, a new FRET sensor based on aptamer-modified quantum dots (Aptamer-QDs) as energy donor and Con A-modified gold nanoparticles (Con A-AuNPs) as energy receptor was constructed to detect bacteria sensitively. As a great threat to human health, E. coli needed to be strictly monitored and employed as the testing model in this study. The Con A and aptamer molecules exhibit broad-spectrum and high-specific binding on the surface of E. coli, respectively, providing an opportunity to construct the FRET sensor on the surface of bacteria. As shown in Scheme 1, in the presence of target of E. coli, the aptamer and Con A bound to E. coli simultaneously, making the energy donor (Aptamer-QDs) and acceptor (Con A-AuNPs) dramatically closed to each other and subsequently the FRET "on", thus achieving the detection of E. coli. Moreover, based on the proposed FRET sensor, E. coli in milk and orange juice could be detected sensitively and rapidly in one-step. The mode of the FRET sensor is also suitable for the detection of other bacteria, which is of great significance for the development of bacterial detection methods.
Con A-AuNPs were prepared through a one pot process via reduction of HAuCl4 by NaBH4 in the presence of Con A which served as stabilizer. UV-vis spectra revealed the maximum absorption peak of Con A-AuNPs at 518.5 nm (Fig. 1A). The diameter of spherical Con A-AuNPs was around 8.7 nm as indicated by TEM images and statistical analysis (Fig. 1B). The zeta potential of Con A-AuNPs was about −43.4 mV ± 0.6 mV (Fig. 1C), providing enough surface charge to stabilize the nanoprobes. FTIR spectra showed that Con A-AuNPs had the similar infrared characteristic spectrum as Con A. Specifically, the stretching vibration of the groups of N-H, O-H and C-O showed typical absorption peaks at 3467, 1639, 1399 cm−1 (Fig. 1D), which means Con A was involved in the particles of Con A-AuNPs. Then the binding affinity of the generated Con A-AuNPs toward bacteria was further examined by incubating Con A-AuNPs with E. coli (B), S. typhimurium (C), P. aeruginosa (D), V. parahemolyticus (E), S. enteritidis (F), respectively for 0.5 h, following subjected to centrifugation at 1100 g for 10 min. As shown in Fig. S1 (Supporting information), red precipitates were observed at the bottom of the tube except the tube containing the Con A-AuNPs alone after centrifugation (top row) while white precipitates were observed in the tube containing bacteria alone after centrifugation (bottom row), suggesting the binding of Con A-AuNPs with bacteria. The complex of Aptamer-QDs could be easily obtained according the published procedures [38], and the aptamer sequence information shown in Table S1 in Supporting Information. As shown in Fig. 1E, Aptamer-QDs in agarose gel shifted fast compared with streptavidin-QDs due to the couple of negative charged of DNA aptamer onto streptavidin-QDs. The absorbance spectrum of Con A-AuNPs showed spectral overlap with the emission spectrum of Aptamer-QDs (Fig. 1F), suggesting that the Aptamer-QDs and Con A-AuNPs could serve as energy donor-acceptor pair in FRET sensor.
The feasibility of Aptamer-QDs and Con A-AuNPs based FRET strategy for E. coli detection was first investigated by measuring the fluorescence response after they incubated with the target E. coli. As shown in Fig. 2A, the fluorescence of the QDs was reduced in the presence of the target bacteria E. coli with fluorescence quenching efficiency about 26.50% (the fluorescence quenching efficiency of η was calculated by the equation η = (F0 − F)/F0 × 100%, where F0 and F were the fluorescence intensity of the FRET sensor system in the absence and presence of E. coli or other bacteria, respectively). However, when there were only non-target bacteria such as S. aureus or S. typhimurium, or when the aptamer was replaced with a random DNA sequence (RanSeq) which could not recognize E. coli, only a slight fluorescence intensity decrease was observed. The η for all the bacteria samples were summarized with a bar graph for more clear understanding (the inset in Fig. 2A). Moreover, the fluorescence emission of Aptamer-QDs alone, Aptamer-QDs with E. coli, Aptamer-QDs with S. aureus, and Aptamer-QDs with S. typhimurium were measured, respectively. As shown in Fig. S2 (Supporting information), after incubating with E. coli, S. aureus, and S. typhimurium, the fluorescence intensity of Aptamer-QDs did not change compared with that of Aptamer-QDs alone, indicating that the fluorescence intensity quenching of Aptamer-QDs was not caused by bacteria but the FRET occurred between the Aptamer-QDs and Con A-AuNPs once they bound onto the surface of E. coli. Meanwhile, the fluorescence quenching phenomenon for the target E. coli was visually observed under a fluorescence microscope. As shown in Fig. 2B, bright green fluorescence was observed around E. coli (a, b) after E. coli were treated only with Aptamer-QDs while no obvious fluorescence around E. coli (c, d) was observed when treated only with RanSeq-QDs, indicating the specific recognition other than non-specific binding of Aptamer-QD to E. coli. Interestingly, the green fluorescence around E. coli (e, f) was dramatically subdued when E. coli treated with Aptamer-QDs/Con A-AuNPs together, suggesting the occurrence of FRET between Aptamer-QDs and Con A-AuNPs.
To obtain the best assay performance, the dosages of Aptamer-QDs and Con A-AuNPs and the incubation time were optimized through the orthogonal experiment method employing the L9(33) orthogonal layout, in which fluorescence quenching efficiency was used as the evaluating index. Each of three major factors (dosages of Aptamer-QDs and Con A-AuNPs and the incubation time) was studied at three levels (Table S2 in Supporting information). The orthogonal experiment program, results, and analysis were shown in detail in Tables S3 and S4 (Supporting information). As shown in Table S4, the value of η reached a maximum when the dosages of Aptamer-QDs and Con A-AuNPs were 4 nmol/L and 30 nmol/L, respectively, and the optimal reaction time was 0.5 h.
After optimizing the assay conditions, the linear range and detection limit of the proposed strategy for E. coli detection were investigated. Specifically, a series of E. coli solutions with different concentrations (0, 102, 103, 104, 105, 106, 107, 5 × 107, 108 and 2 × 108 cfu/mL, respectively) were incubated with Aptamer-QDs and Con A-AuNPs simultaneously for 0.5 h, followed by the determination of the fluorescence intensity of each sample. As shown in Fig. 3, the η of the FRET sensor was linearly dependent upon the logarithm of the concentration of E. coli from 102 cfu/mL to 2 × 108 cfu/mL, with limit of detection (LOD) 45 cfu/mL (η = 0.05498log10N − 0.09095, R = 0.9920, N stands for the quantity of E. coli in cfu/mL); LOD was determined by the equation LOD = 3 S/K, where S was the standard deviation of the blank samples (n = 10) and K was the slope of the calibration curve).
In nature, E. coli may be usually coexist with other bacteria, including both Gram-positive (G+) and Gram-negative (G−) bacteria; therefore, the selectivity of the FRET sensor for E. coli should be verified. The selectivity of the FRET sensor for E. coli was tested by comparing the η of FRET sensor for detection of the E. coli and other interfering bacteria. Each of the four interfering bacteria (S. typhimurium (G−), P. aeruginosa (G−), L. monocytogenes (G+) and S. aureus (G+)), a mixture 1 of the four interfering bacteria together, and another mixture 2 (containing E. coli and four interfering bacteria) were assayed with the proposed FRET sensor. As shown in Fig. 4, the fluorescence intensity changes for the groups of interfering bacteria (individual interfering bacterium or the mixture 1) were almost negligible. The fluorescence intensity change of mixture 2 was almost the same as that of the E. coli. The above results indicate that the proposed FRET sensor has good selectivity for the detection of E. coli, which could be attributed to the specific recognition of aptamer to E. coli.
In order to demonstrate the applicability of the proposed FERT strategy for E. coli detection in real samples, two test samples (milk and orange juice) spiked with different concentrations of E. coli were analyzed. The method was performed after the samples were properly diluted without other pretreatment, and the interference of the real samples (the background signals produced by the pure real sample without spiking any E. coli) to the FRET-based sensor was also explored. Ten-fold dilution of milk and orange juice produced comparable background signals with that of binding buffer (blank) (Fig. 5A). The 10-fold dilution real samples spiked with E. coli were subjected to analysis with the constructed FRET sensor. The recoveries varied from 83.1% to 112.5%, with relative standard derivations (RSDs) in the range of 0.05%–0.50% (Table 1), indicating that the proposed FRET strategy could be applied for detection of E. coli in real samples. The LODs of the FRET sensor for E. coli in milk and orange juice sample were 300 cfu/mL and 200 cfu/mL, respectively (Fig. 5B), suggesting that the sensor has a good application for the detection of E. coli in real samples. Meanwhile, the reported methods for E. coli assay plus with the proposed method in this work were summarized and made a simple comparison (Table S5 in Supporting information). Compared with these methods reported, the as-proposed strategy in this study is more sensitivity and time-saving for detection of E. coli.
In this study, a rapid and facile of FRET strategy was developed for the detection of E. coli. With this strategy, pathogen of E. coli could be specifically detected within 0.5 h over a range from 102 cfu/mL to 2 × 108 cfu/mL with a detection limit of 45 cfu/mL in one step. E. coli spiked in authentic samples was also quantified with good recoveries. Since Con A is a broad-spectrum recognition molecule for most bacteria, this work demonstrates a universal platform for pathogenic bacteria assay by simply changing the aptamer to detect different target bacteria.
Declaration of competing interest
Less
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Acknowledgments
Less
This work was supported by the National Natural Science Foundation of China (Nos. 22174116, 21974110), and Chongqing Science Funds for Distinguished Young Scientists (No. cstc2021jcyj-jqx0024), the Innovation Research Group at higher Education Institutions in Chongqing, Chongqing Education Committee (No. CXQT21006).
Supplementary materials
Less
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cclet.2022.108102.
References
Less
[1]
J. Zhou, L.J. Yin, Y.N. Dong, et al., Anal. Chim. Acta 1127 (2020) 225-233.
[2]
M.J. Hajipour, A.A. Saei, E.D. Walker, et al., Adv. Sci. 8 (2021) 2100556.
[3]
L. Varadi, J.L. Luo, D.E. Hibbs, et al., Chem. Soc. Rev. 46 (2017) 4818-4832.
[4]
A.L. Furst, M.B. Francis, Chem. Rev. 119 (2019) 700-726.
[5]
Y. Wang, J.K. Salazar, Compr. Rev. Food Sci. Food Saf. 15 (2016) 183-205.
[6]
B. Pang, C. Zhao, L. Li, et al., Anal. Biochem. 542 (2018) 58-62.
[7]
S. Umesha, H.M. Manukumar, Crit. Rev. Food Sci. Nutr. 58 (2018) 84-104.
[8]
T.X. Wei, D. Du, M.J. Zhu, et al., ACS Appl. Mater. Interfaces 8 (2016) 6329-6335.
[9]
I.H. Cho, J. Irudayaraj, Int. J. Food Microbiol. 164 (2013) 70-75.
[10]
A. Garrido, M.J. Chapela, M. Ferreira, et al., Food Control 24 (2012) 128-135.
[11]
J.D. Barak, K. Sananikone, M.J. Delwiche, Lett. Appl. Microbiol. 41 (2005) 112-118.
[12]
L.L. Wu, C.S. Huang, B.P. Emery, et al., Chem. Soc. Rev. 49 (2020) 5110-5139.
[13]
C.H. Zhang, P.J. Ni, B. Wang, Chin. Chem. Lett. 33 (2022) 757-761.
[14]
Y. Xian, M.X. Zhou, S.C. Han, Chin. Chem. Lett. 31 (2020) 468-472.
[15]
J.Y. Shi, C.Y. Chan, Y.T. Pang, et al., Biosens. Bioelectron. 67 (2015) 595-600.
[16]
B.R. Jin, S.R. Wang, M. Lin, et al., Biosens. Bioelectron. 90 (2017) 525-533.
[17]
N. Duan, S.J. Wu, S.L. Dai, et al., Microchim. Acta 182 (2015) 917-923.
[18]
M.Q. Yu, H. Wang, F. Fu, et al., Anal. Chem. 89 (2017) 4085-4090.
[19]
Z. Dehghani, J. Mohammadnejad, M. Hosseini, B. bakhshi, A.H. Rezayan, Food Chem. 309 (2020) 125690.
[20]
E.J. Kim, E.B. Kim, S.W. Lee, et al., Biosens. Bioelectron. 87 (2017) 150-156.
[21]
F. Fu, Y.Q. Zhang, L.Y. Li, et al., Anal. Chem. 92 (2020) 11462-11468.
[22]
X.Q. Tao, Z.Y. Liao, Y.Q. Zhang, Chin. Chem. Lett. 32 (2021) 791-795.
[23]
L.S. Liang, M. Chen, Y.L. Tong, W.G. Tan, Z.G. Chen, Anal. Chim. Acta 1186 (2021) 339090.
[24]
B.B. Wang, Q. Wang, Y.G. Jin, et al., J. Photochem. Photobiol. A 299 (2015) 131-137.
[25]
B. Heli, A. Ajji, Sci. Rep. 10 (2020) 14367.
[26]
E. Heyduk, T. Heyduk, Anal. Biochem. 396 (2010) 298-303.
[27]
J. Hirabayashi, J. Biochem. 144 (2008) 139-147.
[28]
J.Q. Gao, D.J. Liu, Z.X. Wang, Anal. Chem. 82 (2010) 9240-9247.
[29]
M. Gamella, S. Campuzano, C. Parrado, et al., Talanta 78 (2009) 1303-1309.
[30]
Z.M. Li, Y.C. Fu, W.H. Fang, Y.B. Li, Sensors 15 (2015) 19212-19224.
[31]
H. Kearns, R. Goodacre, L.E. Jamieson, et al., Anal. Chem. 89 (2017) 12666-12673.
[32]
X. Liu, Z. Lei, F.Y. Liu, et al., Biosens. Bioelectron. 58 (2014) 92-100.
[33]
L.B. Zheng, Y. Wan, P. Qi, et al., Talanta 167 (2017) 600-606.
[34]
H.Y. Yang, H.F. Zhou, H.Y. Ha, et al., Sens. Actuators B: Chem. 229 (2016) 297-304.
[35]
E.J. Cho, J.W. Lee, A.D. Ellington, Annu. Rev. Anal. Chem. 2 (2009) 241-264.
[36]
A.B. Iliuk, L.H. Hu, W.A. Tao, Anal. Chem. 83 (2011) 4440-4452.
[37]
Y. Zhang, X.Z. Hu, Q.J. Wang, Y. Zhang, Chin. Chem. Lett. 33 (2022) 2817-2831.
[38]
H.P. Huang, J.J. Zhu, Biosens. Bioelectron. 25 (2009) 927-930.
Appendix
Less
Year 2023 volume 34 Issue 8
PDF
44
24
Cite this Article
BibTeX
Article Info
doi: 10.1016/j.cclet.2022.108102
  • Receive Date：2022-06-29
  • Online Date：2025-11-21
  • Published：2023-08-15
Article Data
Affiliations
History
  • Received：2022-06-29
  • Revised：2022-12-01
  • Accepted：2022-12-23
Affiliations
    a Key Laboratory of Luminescence Analysis and Molecular Sensing, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
    b College of Food Science, Southwest University, Chongqing 400715, China
References
Share
https://castjournals.cast.org.cn/joweb/ccl/EN/10.1016/j.cclet.2022.108102
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House