A comparison between oestradiol aptamers as receptors in CNT FET biosensors

SENSORS & DIAGNOSTICS(2023)

Cited 0|Views3
No score
Abstract
Point of care tests for measuring the concentration of small molecules such as oestradiol are both highly desirable for healthcare and challenging to design. Field effect transistors functionalised with DNA aptamers, FET aptasensors, are a promising candidate for such tests, however, the performance of FET aptasensors does not consistently keep pace with the performance of particular aptamers in isolation. To better understand the cause of this discrepancy, we compare the performance of two oestradiol aptamers in carbon nanotube network FET aptasensors, and further characterise these aptamers using circular dichroism spectroscopy. We show that both aptamers work effectively as sensors at a much higher analyte concentration, 10-6 M, than would be predicted by published Kd values, approximately 10-8 M. We show qualitatively different behaviour between otherwise identical sensors based on the aptamer they are functionalised with, at analyte concentrations well below the limit of detection. Our results suggest that the discrepancy between predicted and realised performance of carbon nanotube network FET aptasensors has two contributing factors: the difference between the ionic environment used for sensing and the environment the aptamers have been characterised in, and the use of SELEX methods that produce aptamers with minimal structure shift on binding. To optimise the sensing response from FET aptasensors, aptamers should be selected for large structure shifts upon binding, and so that they exhibit strong binding in the ionic environment that will be used for sensing. Two oestradiol aptamers as receptors on carbon nanotube sensors are compared. We show differences in sensor behaviour between the aptamers that are not explained by published KD values, which aid in understanding and optimising these sensors.
More
Translated text
Key words
oestradiol aptamers,biosensors,receptors
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined