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Thursday, September 23, 2021

New research: Device that detects SARS-CoV-2 in saliva sample in one hour

The new diagnostic, which relies on CRISPR technology, can be assembled for about $15, but those costs could come down significantly if the devices were produced at large scale, the researchers say.

By: Express News Service | New Delhi |
Updated: August 11, 2021 10:58:33 am
A health worker collects samples for a test for Covid-19 (Photo by Karen Ducey/Getty Images)

Engineers at MIT and Harvard University have designed a small tabletop device that can detect SARS-CoV-2 from a saliva sample in about an hour. In a new study in Science Advances, they showed it is as accurate as PCR tests, MIT said in a press statement. It said the device can also be used to detect specific viral mutations linked to some of the variants now circulating.

The new diagnostic, which relies on CRISPR technology, can be assembled for about $15, but those costs could come down significantly if the devices were produced at large scale, the researchers say. It is based on SHERLOCK, a CRISPR-based tool.

First, a pre-processing step disables enzymes called salivary nucleases, which destroy nucleic acids such as RNA. Once the sample goes into the device, the nucleases are inactivated by heat and two chemical reagents. Then, viral RNA is extracted and concentrated by passing the saliva through a membrane.

This RNA sample is then exposed to freeze-dried CRISPR/Cas components. The reaction amplifies the RNA sample and then detects the target RNA sequence, if present.

The researchers designed the device, which they call minimally instrumented SHERLOCK (miSHERLOCK), so that it can have up to four modules that each look for a different target RNA sequence. The original module detects any strain of SARS-CoV-2. Other modules are specific to mutations including B.1.1.7, P.1, and B.1.351.

The Delta variant was not yet widespread when the researchers performed this study, but they say it should be straightforward to design a new module to detect that variant.

Source: MIT

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