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Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3 |
1. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
2. State Key Laboratory of Component-based Chinese Medicine, Tianjin 301617, China
3. Yangtze River Pharmaceutical Group Jiangsu Longfengtang Traditional Chinese Medicine Co., Ltd., Taizhou 225321, China
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Abstract Foodborne pathogens are widely present in water, air, food, dust, and excrement, the infectious diseases caused by these seriously endanger human health. Therefore, it is essential to develop rapid detection methods for pathogens. Since pathogens in actual samples often co-exist, the simultaneous and sensitive detection of multiple foodborne pathogens is problematic in microbial detection. Molecular biology and immunohistochemical analysis techniques have made some attempts in this detection field. Still, due to the limitations of primer design and antibodies, the effects of these two techniques in practical applications are not very satisfactory. Surface-enhanced Raman spectroscopy (SERS) technology has gained essential applications in the simultaneous detection of multiple pathogens due to its rapid, non-destructive, high-resolution, no interference from water, and in-situ detection. This review systematically summarizes the application strategies of SERS technology in the simultaneous detection of multiple pathogenic bacteria, including the application principle, application characteristics, and application effects. Firstly, a brief overview of the combination between the SERS substrate materials and the pathogens is introduced. Then the direct and indirect methods used in detecting multiple pathogens are presented separately. The direct method is simple and fast, and the spectral information of the pathogen itself is obtained directly through the signal amplification of the substrate material, which helps to study the information of the pathogen itself. It is widely used in multiple pathogenic bacteria discriminant, quantitative, and point-of-care testing (POCT). However, a large amount of spectral information, often needs to be used in conjunction with multivariate statistical analysis methods, imaging techniques, and microfluidic devices. Raman reporter molecules and recognition elements such as aptamers and antibodies are needed in the indirect method, converting the detection of pathogenic bacteria into the analysis of signal molecules. The significantly improves the sensitivity and specificity of the detection assay. Simultaneous analysis of multiple pathogenic bacteria can be achieved at the gene, protein, and cellular levels. Besides, a more comprehensive detection system that integrates bacteria’s separation, identification, and inactivation can be built by combining with other identification elements and functional molecules, which has essential prospects in analyzing multiple pathogens in actual samples such as clinical blood. Finally, the existing problems and following efforts of SERS technology are pointed out, which can be a reference for designing and applications of SERS technology in the rapid and sensitive detection of multiple pathogenic bacteria.
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Received: 2021-09-18
Accepted: 2022-06-09
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Corresponding Authors:
WANG Hai-xia
E-mail: whxtcm@tjutcm.edu.cn
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[1] Fung F, Wang H S, Menon S. Biomedical Journal, 2018, 41(2): 88.
[2] Keba A, Rolon M L, Tamene A, et al. International Dairy Journal, 2020, 109: 104762.
[3] Cui Z, Ojaghian M R, Tao Z, et al. Journal of Applied Microbiology, 2016, 120(5): 1357.
[4] Marjan M, Akhtar H, Louis M J. Trends in Analytical Chemistry, 2018, 107: 60.
[5] Zhou X, Hu Z W, Yang D T, et al. Advanced Science, 2020, 7(23): 2001739.
[6] KöKer T, Tang N, Tian C, et al. Nature Communications, 2018, 9(1): 607.
[7] Premasiri W R, Moir D T, Klempner M S, et al. The Journal of Physical Chemistry B, 2005, 109(1): 312.
[8] Akanny E, Bonhommé A, Commun C, et al. Journal of Raman Spectroscopy, 2020, 51(4): 619.
[9] Martinez L A, Spano J L, Bird T E, et al. Sensing and Bio-Sensing Research, 2019, 24: 100282.
[10] Huang D Q, Zhuang Z F, Wang Z, et al. Applied Surface Science, 2019, 497: 143825.
[11] Witkowska E, Korsak D, Kowalska A, et al. Analytical and Bioanalytical Chemistry, 2018, 410: 5019.
[12] Berus S, Witkowska E, Niciński K, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 233: 118088.
[13] Gao S Y, Pearson B, He L L, et al. Journal of Microbiological Methods, 2018, 147: 69.
[14] Zhou H B, Yang D T, Ivleva N P, et al. Analytical Chemistry, 2014, 86(3): 1525.
[15] Wang P X, Pang S, Chen J H, et al. Analyst, 2016, 141(4): 1356.
[16] Wang H Y, Zhou Y F, Jiang X X, et al. Angewandte Chemie, 2015, 127(17): 5221.
[17] Knauer M, Ivleva N P, Niessner R, et al. Analytical and Bioanalytical Chemistry, 2012, 402(8): 2663.
[18] Mungroo N A, Oliveira G, Neethirajan S. Microchimica Acta, 2016, 183(2): 697.
[19] Duan N, Shen M F, Qi S, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 230: 118103.
[20] Lei M L, Xu C X, Shan Y Q, et al. Analytical and Bioanalytical Chemistry, 2020, 412: 8117.
[21] Li Y Z, Lu C, Zhou S S, et al. Sensors and Actuators B: Chemical, 2020, 317: 128182.
[22] Zhang D, Huang L, Liu B, et al. Theranostics, 2019, 9(17): 4849.
[23] Bai X R, Shen A G, Hu J M. Analytical Methods, 2020, 12(40): 4885.
[24] Wu Z Z. Food Analytical Methods, 2019, 12: 1086.
[25] Liu H B, Du X J, Zang Y X, et al. Journal of Agricultural and Food Chemistry, 2017, 65(47): 10290.
[26] Zhang C Y, Wang C W, Xiao R, et al. Journal of Materials Chemistry B, 2018, 6(22): 3751.
[27] Yuan K S, Mei Q S, Guo X J, et al. Chemical Science, 2018, 9(47): 8781.
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