Europium Nanoparticle-Based Lateral Flow Strip Biosensors Combined with Recombinase Polymerase Amplification for Simultaneous Detection of Five Zoonotic Foodborne Pathogens.
Bei JinBiao MaQing MeiShujuan XuXin DengYi HongJiali LiHanyue XuMingzhou ZhangPublished in: Biosensors (2023)
The five recognized zoonotic foodborne pathogens, namely, Listeria monocytogenes , Staphylococcus aureus , Streptococcus suis , Salmonella enterica and Escherichia coli O157:H7 , pose a major threat to global health and social-economic development. These pathogenic bacteria can cause human and animal diseases through foodborne transmission and environmental contamination. Rapid and sensitive detection for pathogens is particularly important for the effective prevention of zoonotic infections. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) combined with recombinase polymerase amplification (RPA) were developed for the simultaneous quantitative detection of five foodborne pathogenic bacteria. Multiple T lines were designed in a single test strip for increasing the detection throughput. After optimizing the key parameters, the single-tube amplified reaction was completed within 15 min at 37 °C. The fluorescent strip reader recorded the intensity signals from the lateral flow strip and converted the data into a T/C value for quantification measurement. The sensitivity of the quintuple RPA-EuNP-LFSBs reached a level of 10 1 CFU/mL. It also exhibited good specificity and there was no cross-reaction with 20 non-target pathogens. In artificial contamination experiments, the recovery rate of the quintuple RPA-EuNP-LFSBs was 90.6-101.6%, and the results were consistent with those of the culture method. In summary, the ultrasensitive bacterial LFSBs described in this study have the potential for widespread application in resource-poor areas. The study also provides insights in respect to multiple detection in the field.
Keyphrases
- loop mediated isothermal amplification
- label free
- sensitive detection
- escherichia coli
- staphylococcus aureus
- quantum dots
- global health
- gram negative
- risk assessment
- healthcare
- endothelial cells
- biofilm formation
- gold nanoparticles
- public health
- machine learning
- candida albicans
- human health
- high intensity
- high resolution
- big data
- deep learning
- nucleic acid
- artificial intelligence
- solid phase extraction