The Application of Hybridization Chain Reaction in the Detection of Foodborne Pathogens
Abstract
:1. Introduction
2. Overview of HCR Technology
2.1. Principles and Types of HCR Technology
2.2. HCR Technical Characteristics
3. HCR in Foodborne Pathogen Detection
3.1. Colorimetry
3.2. Fluorescence
3.3. Electrochemistry
3.4. SERS Surface-Enhanced Raman Scattering
3.5. HCR Combined with Portable Equipment for Point-of-Care Detection
3.6. Others
4. Summary, Challenges, and Perspectives
Directions for Development
- HCR is still more focused on bacterial DNA/RNA detection in foodborne pathogen detection due to its amplification properties based on nucleic acids. However, this often involves tedious operational steps such as cell lysis, target extraction, and preservation. With the continuous development of aptamer technology, various types of aptamers with particular recognition of foodborne pathogenic bacteria have emerged, making HCR more convenient, effective, and promising for the direct detection of whole bacterial cells, and further research should focus more on the detection of intact target bacteria to reduce complex operations and shorten detection time, and to extend the application to a broader range of pathogenic bacteria and actual samples.
- In food testing, the main focus is still on the rapid, accurate, and sensitive identification of foodborne pathogens. Sensitive “real-time monitoring” and “timely eradication” of pathogen loads during food production, processing, and distribution (e.g., pasteurization in milk) is essential to ensure food quality, shelf-life stability, economic cost savings, and protection of human life and health. The programmable and easily modifiable capabilities of HCR, combined with the evolving nanomaterials technology for sensitive identification, real-time monitoring, and timely eradication, are also areas of great potential for future exploration, requiring pioneers to continue to forge ahead.
- The simultaneous detection of multiple targets is critical in samples where more than one bacterium is often present. At present, HCR technology for pathogen detection is still dominated by a single strain. Although multi-target simultaneous detection methods have been proposed—for example, Lai’s group proposed a fluorescent HCR-ELISA method [103] based on the previous HCR-ELISA single-component colorimetric strategy [32] to achieve simultaneous detection of three pathogens—they are still relatively few and need to expand continually. In addition, although a single detection mode can achieve high sensitivity and specificity and often cannot avoid background interference and false positive problems, a multi-mode output approach with reasonable accuracy is a good solution and still has a broad research space.
- Finally, the amplification capacity of HCR is improving and even surpassing the amplification capacity of PCR technology. Due to its enzyme-free and mild conditions, it has a vast market and research potential in POC strategy and product development. It is important to note that the detection of pathogenic bacteria usually involves three steps: identification, signal amplification, and signal output. In contrast, HCR can act as an intermediate bridge, unifying the magnetic enrichment pre-processing technology with various signal transduction output platforms. Powered by microfluidic technology, it shows extraordinary potential for extracting and separating analytes from complex sample matrices for high-throughput one-plot or full-process detection. Sample-answer assays are bound to be a trend in the future and therefore require attention to research into miniaturized, integrated, and automated analytical systems. We expect more HCR-based POC strategies to emerge for foodborne pathogen detection for food safety.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Signal Transduction | Detected Pathogens and Target Type | Strategy | Linear Range | LOD | Detection Time | Food Samples | Ref. |
---|---|---|---|---|---|---|---|
Colorimetry | E. coli O157:H7; cell | ELISA; HRP enzyme | 5 × 102–1 × 107 CFU/mL | 1.08 × 102 CFU/mL in pure culture and 2.6 × 102 CFU/mL in milk | ~7.5 h | Milk | [32] |
Salmonella spp. and S. aureus; DNA | G-quadruplex DNAzyme | 1–1 × 105 CFU/mL | 10 CFU/mL | 130 min | Skim milk | [33] | |
Escherichia coli; cell | Two types of mechanisms based on AuNP | 1 × 102–1 × 104 CFU/mL | 10 CFU/mL | ~80 min | Tap water and milk | [34] | |
S. aureus; mecA gene | Multi-color; AuNBPs | 10–100 pM | 2.71 pM | 1 h | Milk | [38] | |
L. monocytogenes; genomic DNA | GOx/HRP enzyme cascade; Boolean logic function | 0.1–10 nM (colorimetry); 0−50 nM (electrochemistry) | 1.12 nM (colorimetry); 0.04 nM (electrochemistry) | 2 h | Vegetable, meat, and milk extracts | [39] | |
Fluorescence | S. enteritidis; invA gene | MBs; hairpin labeled FAM | 7.4 × 101–7.4 × 108 CFU/mL | 74 CFU/mL | ~2.5 h | Lettuce | [41] |
Mycobacterium tuberculosis; IS6110 gene | MBs; hairpin labeled TAMRA; turn-off | 0.01–100 nM | 10 pM | ~2 h | N/A | [42] | |
emetic Bacillus cereus; genomic DNA | MBs; hairpin labeled FAM; flow cytometry | 7.6–7.6 × 106 CFU/mL | 7.6 CFU/mL in buffer and 9.2 × 102 CFU/mL in milk | ~4.5 h | Milk | [43] | |
S. enteritidis; invA gene | “GO fluorescence” platform; hairpin labeled FAM; turn-on | 4.2 × 101–4.2 × 107 CFU/mL | 4.2 × 101 CFU/mL in buffer and 4.2 × 102 CFU/mL in milk | 3.5 h | Milk | [44] | |
E. coli O157:H7; fliCh7 gene | “PCR-HCR” dual-signal amplification; SYBR Green I embed dsDNA | 7.2 × 101–7.2 × 106 CFU/mL | 7.2 × 101 CFU/mL in buffer and 7.2 × 102 CFU/mL in milk | ~3.5 h | Milk | [46] | |
S. aureus; DNA | “HCR-3WJ-NEASA” circuit dual-signal amplification; CQ | 10 pM–10 nM | 6.7 pM | 0.5 h | Milk | [47] | |
S. aureus; 16S rRNA | “FAM-SYBR Green I” cooperative effect; “GO fluorescence” platform | 50 pM–100 nM | 50 pM | ~1.5 h | Milk | [48] | |
MRSA; agrA gene transcription (its mRNA) | “FAM-SYBR Green I” cooperative effect; “GO fluorescence” platform; strand-displacement polymerization reaction (SDPR) | 10 fM–100 pM | 10 fM | ~1 h | N/A | [49] | |
MRSA; agrC gene transcription (its mRNA) | “FAM-SYBR Green I” cooperative effect; “GO fluorescence” platform; Nb.BbvcI-assisted target recycling amplification (NATR) | 10 fM–100 pM | 7.5 fM | 50 min | N/A | [50] | |
Vibrio Parahaemolyticus; gyrB gene | FRET; four-way branch migration HCR circuits | 0.2–60 nM | 0.067 nM | 90 min | N/A | [51] | |
S. Typhimurium; cell | FRET; ratio fluorescence; CRISPR-Cas12a system and TDN-hHCR | 10–108 CFU/mL | 8 CFU/mL | ~2.5 h | Milk and egg white | [53] | |
E. coli O157:H7; cell | FRET; ratio fluorescence; CQ | 4.9 × 101–4.9 × 106 CFU/mL | 3.5 × 101 CFU/mL | ~3 h | Milk | [54] | |
S. aureus; cell | IFE; UCNPs; g-C3N4 NSs | 10–106 CFU/mL | 1 CFU/mL | 3 h | Tap water and milk | [57] | |
Electrochemistry | S. aureus; cell | pMB NPs; DHCR | 10–108 CFU/mL | 1 CFU/mL | ~3 h | Milk and pear juice | [69] |
E. coli O157:H7; cell | Fc-Dox; multiple amplification through the 3D DNA walker, RCA, and HCR | 10–104 CFU/mL | 7 CFU/mL | ~2 h | N/A | [70] | |
S. aureus; 16S rRNA | Silver wire across electrodes; hairpin labeled AuNPs | 50–107 CFU/mL | 50 CFU/mL | 100 min | Milk | [71] | |
S. Typhimurium;cell | CRISPR-Cas12a; electrode surface modification of electrical response reporting probes | 104–108 CFU/mL | 20 CFU/mL | ~2.5 h | Milk | [72] | |
Clostridium Perfringens; DNA | DNA-Walker; methylene blue; dual-mode output | 1–108 CFU/g | 1 CFU/g | N/A | Chicken, beef, duck, mutton, and pork | [78] | |
Surface-enhanced Raman scattering | S. aureus; cell | Au@Ag/4-ATP; PDMS-based SERS platform | 28–2.8 × 106 CFU/mL | 0.25 CFU/mL | 2.5 h | Milk | [80] |
S. Typhimurium; cell | AgNP/DAPI; competitive indirect strategies | 10–105 CFU/mL | 6 CFU/mL | 3.5 h | Milk | [81] | |
S. Typhimurium; cell | 4-NTP/AuNPs; G-quadruplex DNAzyme | 5–105 CFU/mL | 4 CFU/mL | 105 min | Milk and tap water | [82] | |
POC test | S. enteritidis; DNA | Lateral flow; Sandwich structure | 2.5 pM–500 nM | 1.76 pM | ~25 min | N/A | [83] |
S. enteritidis; 16S rRNA | Lateral flow; “initiators-on-a-string”complex | 102–104 CFU/mL | 53.65 CFU/mL | ~40 min | Milk | [84] | |
E. coli O157:H7; 16S rRNA | Lateral flow; ISD | 102–105 CFU/mL | 102 CFU/mL | ~1.5 h | Milk | [85] | |
Vibrio parahaemolyticus; cell | Lateral flow; MBs; sandwich structure | 103–108 CFU/mL | 2.6 × 103 CFU/mL | 67 min | Shrimp | [92] | |
E. coli O157:H7; cell | Microfluidic chip; PtNPs | 5 × 102–5 × 107 CFU/mL | 250 CFU/mL in buffer and 400 CFU/mL in milk | 75 min | Milk | [95] | |
S. aureus; S. enteritidis | Microfluidic chip; multi-mode analysis | 3.6 × 101–3.6 × 106 CFU/mL | 4 and 8 CFU/mL, respectively | 15 min | Nonfat milk powder and raw ground pork | [96] | |
E. coli O157:H7; cell | Pregnancy test strips and MOF; sandwich structure | 103–107 CFU/mL | 530 CFU/mL | 86 min | Milk | [93] | |
S. aureus; cell | Personal glucose meter; the invertase catalyzes sucrose into glucose | 3–3 × 103 CFU/mL | 2 CFU/mL | 4.5 h | Peach juice, milk, and water samples | [94] | |
Others | |||||||
Surface plasmon resonance (SPR) | S. aureus, Klebsiella pneumoniae and Escherichia coli; DNA | MNAzyme | 1 × 102–1 × 106 CFU/mL | 67 CFU/mL of S. aureus, 57 CFU/mL of K. pneumonia and 61 CFU/mL of E. coli | 4 h | N/A | [97] |
Chemiluminescence (CL) | L. monocytogenes; hlyA gene | Cloth-based microfluidics; G-quadruplex DNAzyme | 2 × 10−3–2 × 106 pM | 1.1 fM | 80 min | Milk | [98] |
Electrochemiluminescence (ECL) | E. coli O157:H7; genomic DNA | Cloth-based microfluidics; Ru(bpy)32+ | 102–107 CFU/mL | 38 CFU/mL | ~100 min | Milk | [99] |
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Zhao, J.; Guo, Y.; Ma, X.; Liu, S.; Sun, C.; Cai, M.; Chi, Y.; Xu, K. The Application of Hybridization Chain Reaction in the Detection of Foodborne Pathogens. Foods 2023, 12, 4067. https://doi.org/10.3390/foods12224067
Zhao J, Guo Y, Ma X, Liu S, Sun C, Cai M, Chi Y, Xu K. The Application of Hybridization Chain Reaction in the Detection of Foodborne Pathogens. Foods. 2023; 12(22):4067. https://doi.org/10.3390/foods12224067
Chicago/Turabian StyleZhao, Jinbin, Yulan Guo, Xueer Ma, Shitong Liu, Chunmeng Sun, Ming Cai, Yuyang Chi, and Kun Xu. 2023. "The Application of Hybridization Chain Reaction in the Detection of Foodborne Pathogens" Foods 12, no. 22: 4067. https://doi.org/10.3390/foods12224067
APA StyleZhao, J., Guo, Y., Ma, X., Liu, S., Sun, C., Cai, M., Chi, Y., & Xu, K. (2023). The Application of Hybridization Chain Reaction in the Detection of Foodborne Pathogens. Foods, 12(22), 4067. https://doi.org/10.3390/foods12224067