FOR IMMEDIATE RELEASE | April 20, 2020
Diagnostic biosensor quickly detects SARS-CoV-2 from nasopharyngeal swabs
“Rapid Detection of COVID-19 Causative Virus (SARS-CoV-2) in Human Nasopharyngeal Swab Specimens Using Field-Effect Transistor-Based Biosensor”
According to many experts, early diagnosis and management are critical for slowing the spread of SARS-CoV-2, the new coronavirus that causes COVID-19. Therefore, the race is on to develop diagnostic tests for the virus that are faster, easier and more accurate than existing ones. Now, researchers reporting in ACS Nano have developed a field-effect transistor-based biosensor that detects SARS-CoV-2 in nasopharyngeal swabs from patients with COVID-19, in less than one minute.
Currently, most diagnostic tests for COVID-19 rely on a technique called real-time reverse transcription-polymerase chain reaction (RT-PCR), which amplifies SARS-CoV-2 RNA from patient swabs so that tiny amounts of the virus can be detected. However, the method takes at least 3 hours, including a step to prepare the viral RNA for analysis. Edmond Changkyun Park, Seung Il Kim and colleagues wanted to develop a faster diagnostic test that could analyze patient samples directly from a tube of buffer containing the swabs, without any sample preparation steps.
The team based their test on a field-effect transistor –– a sheet of graphene with high electronic conductivity. The researchers attached antibodies against the SARS-CoV-2 spike protein to the graphene. When they added either purified spike protein or cultured SARS-CoV-2 virus to the sensor, binding to the antibody caused a change in the electrical current. Next, the team tested the technique on nasopharyngeal swabs collected from patients with COVID-19 or healthy controls. Without any sample preparation, the sensor could discriminate between samples from sick and healthy patients. The new test was about 2–4 times less sensitive than RT-PCR, but different materials could be explored to improve the signal-to-noise ratio, the researchers say.
The authors acknowledge funding from the National Research Council of Science and Technology funded by the Ministry of Science and ICT, Korea and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health & Welfare, Korea.
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