Cornell University has introduced a bioelectric device that can quickly detect harmful variants of Corona, and possibly other viruses as well. This chip-based device uses a biomembrane to simulate the process of infection development in a cell, which helps assess viral load quickly and efficiently
Researchers from Cornell University have developed a bioelectrical device capable of identifying and classifying variants of Corona, including the most dangerous, by imitating the infection process on a chip.
This device, which uses a biomembrane to simulate the cellular environment, can quickly determine the threat level of each variant and adapt to other viruses such as influenza and measles. It is a fast and efficient tool for early viral reactivity characterization.
Advanced virus detection technology
The device uses a cellular membrane, a biomembrane, on a biological chip that simulates the cellular environment and the biological stages of the infection. This allows researchers to quickly characterize worrisome variants and analyze the mechanics that drive the spread of the disease, without getting entangled in the complexity of living systems.
"In the news we see the emergence of worrisome variants like delta, omicron, etc., and it scares everyone," said Susan Daniel, professor of chemical engineering and lead author of the paper published July 3 in Nature Communications. “The first thoughts are, 'Does my vaccine cover this new variant? How worried should I be?' It takes some time to determine if a variant is a real cause for concern or if it will simply disappear.”
Unique features of the biochip platform
While many biological components have already been placed on chips, from cells to organelles and organ-like structures, the new platform differs from those devices because it truly mimics the biological cues and processes that lead to the initiation of infection in the cellular membrane of a single cell. In effect, it makes the variant behave as if it were in a real cellular system of its potential host.
"There may be a correlation between how well a variant can transfer its genome through the biomembrane layer and how worrisome that variant can be in terms of its ability to infect humans," Daniel said. "If it can release its genome very efficiently, it may be an indicator that we need to monitor the variant more closely or develop a new vaccine that includes it. If he doesn't release it well, maybe this variant is less of a concern. The key point is that we need to classify the variants quickly so that we can make informed decisions, and we can do that very quickly with our new devices. They are quick and take only a few minutes, and are 'labelless', that is, there is no need to label the virus to track its progress."
Possible implications for viral research
Because researchers can faithfully recreate the biological conditions and triggers that activate a virus, they can also change the triggers and see how the virus responds.
"In terms of understanding the basic science of how infection occurs and what the triggers are that can help or hinder it, this is a unique tool," said Daniel. "Because you can separate many aspects of the reaction sequence, and identify which factors promote or inhibit infection."
Ability to adapt to different viruses
The platform can be adapted to other viruses, such as influenza and measles, as long as researchers know which cell type has a tendency to become infected, as well as which biological conditions allow a particular infection to flourish. For example, influenza requires a drop in pH to activate its hemagglutinin, and the coronavirus has an enzyme that activates its spike protein.
"Each virus has its own ways of causing damage. And you need to know what they are in order to reproduce the infection process on the chip," said Daniel. "But once you know them, you can build the platform to fit each of those specific conditions."
The research was supported by the Defense Advanced Research Projects Agency (DARPA), the US Army Research Office, Cornell's Smith Postdoctoral Innovation Fellowship, the Schmidt Futures Program, and the (US) National Science Foundation.
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