Highly sensitive colorimetric aptasensor for 17β-estradiol detection in milk based on the repetitive-loop aptamer

Area of Science:

• Analytical Chemistry
• Biotechnology
• Food Science

Background:

• 17β-estradiol (E2) contamination in food poses health risks, necessitating reliable monitoring systems.
• Existing detection methods may lack the required sensitivity or specificity for trace contaminants.
• Aptasensors offer a promising alternative for rapid and sensitive analyte detection.

Purpose of the Study:

• To develop a novel, sensitive, and colorimetric aptasensor for the detection of 17β-estradiol (E2).
• To design and evaluate repetitive-loop aptamers for enhanced binding affinity to E2.
• To assess the aptasensor's performance in detecting E2 in complex matrices like milk.

Main Methods:

• Design and synthesis of repetitive-loop aptamers targeting E2.
• Conjugation of aptamers with gold nanoparticles (AuNPs) to create a colorimetric sensor.
• Characterization of aptamer binding capabilities and sensor sensitivity using spectrophotometry.
• Validation of the aptasensor's selectivity and accuracy in spiked milk samples.

Main Results:

• Designed aptamers (L2-L5) showed improved E2 binding compared to the original aptamer (L1).
• The L3-aptasensor achieved sensitive E2 detection in the range of 0.05-0.8 nM, with a limit of detection of 13.1 pM.
• The L3-aptasensor demonstrated 7.7-fold higher sensitivity than the L1-aptasensor and high selectivity against similar compounds.
• Accurate E2 detection in spiked milk samples with high recovery rates (100.1%-113.0%) and low relative standard deviations (5.24%-11.06%).

Conclusions:

• A novel aptasensor based on repetitive-loop aptamers and AuNPs provides enhanced sensitivity for E2 detection.
• The developed aptasensor is selective, accurate, and reliable for monitoring E2 contamination in food products like milk.
• This technology holds potential for practical application in food safety and human health monitoring.