New biobased tool quickly detects variants of the coronavirus

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Researchers at Cornell University have developed a bioelectric device that can detect and classify new variants of the coronavirus to identify the most harmful variants. It has the potential to do the same to other viruses.

The sensor instrument uses a cell membrane, also called a biomembrane, on a microchip that mimics the cellular environment for – and the biological steps of – infection. This allows researchers to quickly characterize variants of concern and analyze the mechanisms driving the spread of the disease, without getting bogged down in the complexity of living systems.

“We periodically see these worrying variants popping up in the news, like delta, omicron and so on, and it drives everyone a little crazy. The first thoughts are: ‘Does my vaccine cover this new variant? How concerned should I be? ‘” says Susan Daniel, professor of chemical engineering and senior author of the paper published in 2011 Nature communication. “It takes a while to determine whether a variant is really a cause for concern or whether it will simply disappear.”

While many biological elements have been placed on microchips, from cells to organelles and organ-like structures, the new platform differs from those devices because it essentially recapitulates the biological cues and processes that lead to initiating an infection at the cell’s cell membrane. a single cell. Essentially, it fools a variant into behaving as if it were in an actual cellular system of its potential host.

“There could potentially be a link between how well a variant can deliver its genome across the biomembrane layer and how concerning that variant might be in terms of its ability to infect humans,” Daniel said. “If it can release its genome very effectively, maybe that’s an indicator that a variant of concern should be something we need to keep a close eye on or formulate a new vaccine containing it. If it doesn’t release it very well, then maybe that variant. The key point is that we need to classify these variants quickly so that we can make informed decisions, and we can do this very quickly with our devices. These tests take several minutes to perform, and they are “label-free.” ‘ meaning you don’t actually have to tag the virus to track its progress.

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Because the researchers can faithfully mimic the biological conditions and signals that activate a virus, they can also change those signals and see how the virus responds.

“When it comes to understanding the basic science of how infection occurs and what signals can help or hinder it, this is a unique tool,” Daniel said. “Because you can uncouple many aspects of the response sequence and identify which factors promote or hinder infection.”

The platform can be adapted for other viruses, such as influenza and measles, as long as researchers know which cell type tends to be infected, and what biological quirks allow a specific infection to flourish. For example, influenza requires a pH drop to activate the hemagglutinin, and the coronavirus has an enzyme that activates the spike protein.

“Each virus has its own way of doing things. And you need to know what they are to replicate that infection process on a chip,” Daniel said. “But once you know them, you can build the platform to meet all those specific circumstances.”

Co-authors include PhD student Ambika Pachaury; and Konstantinos Kallitsis and Zixuan Lu from the University of Cambridge.

The research was supported by the Defense Advanced Research Projects Agency (DARPA), the Army Research Office, Cornell’s Smith Fellowship for Postdoctoral Innovation, the Schmidt Futures Program and the National Science Foundation.

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