Development of A Novel EvaGreen-Dye Based Recombinase Aided Amplification Assay Using Self-Avoiding Molecular Recognition System Primers
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Updated: Aug 8, 2026

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
Published on: March 30, 2015
Xinxin Shen1, Shaowei Hua1, Zijin Zhao1
1National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Introduction:
Fluorescent probe-based recombinase aided amplification (RAA) offers the advantages of rapidity and simplicity but is limited by the requirement for complex and lengthy probe design, restricting its widespread application.
Methods:
A novel EvaGreen dye-based RAA (EvaGreen-RAA) assay utilizing self-avoiding molecular recognition system (SAMRS) primers was developed for the detection of Pseudomonas fluorescens (PF) and Bacillus cereus (BC) in milk. Conventional RAA was used as a reference method. Sensitivity was evaluated using nucleic acids from recombinant plasmids and simulated milk specimens. Additionally, a dual EvaGreen-RAA assay was investigated for simultaneous detection of mixed BC and PF in simulated milk specimens.
Results:
The EvaGreen-RAA demonstrated superior sensitivity compared to conventional RAA, with detection limits of 1 copy/µL versus 10 copies/µL for both BC and PF plasmids, respectively. In simulated milk specimens, EvaGreen-RAA detected BC and PF at concentrations of 100 CFU/mL and 200 CFU/mL, respectively, compared to 400 CFU/mL and 600 CFU/mL for conventional RAA. The dual EvaGreen-RAA assay successfully detected mixed BC and PF in simulated milk specimens at concentrations of 200 CFU/mL for each pathogen.
Conclusion:
The EvaGreen-RAA assay demonstrated significant advantages in terms of simplicity and enhanced sensitivity compared to fluorescent probe-based RAA, offering a novel approach for developing multiplex pathogen detection systems using melting curve analysis.
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