Recreating the biological steps of viral infection on a cell-free bioelectronic platform to profile viral variants of concern.
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Peer-reviewed
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Abstract
Viral mutations frequently outpace technologies used to detect harmful variants. Given the continual emergence of SARS-CoV-2 variants, platforms that can identify the presence of a virus and its propensity for infection are needed. Our electronic biomembrane sensing platform recreates distinct SARS-CoV-2 host cell entry pathways and reports the progression of entry as electrical signals. We focus on two necessary entry processes mediated by the viral Spike protein: virus binding and membrane fusion, which can be distinguished electrically. We find that closely related variants of concern exhibit distinct fusion signatures that correlate with trends in cell-based infectivity assays, allowing us to report quantitative differences in their fusion characteristics and hence their infectivity potentials. We use SARS-CoV-2 as our prototype, but we anticipate that this platform can extend to other enveloped viruses and cell lines to quantifiably assess virus entry.
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Acknowledgements: S.D. and R.O. acknowledge funding for this project, sponsored by the Defense Advanced Research Projects Agency (DARPA) Army Research Office and accomplished under Cooperative Agreement Number W911NF-18-2-0152. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein. The fabrication of microelectrodes in this work was performed at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (Grant NNCI-2025233). Z.C. and S.D. acknowledge the Smith Fellowship for Postdoctoral Innovation from Cornell University. Z. C. acknowledges the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures program. A.P. acknowledges support from the National Science Foundation Graduate Research Fellowship Program. We thank Juliana D. Carten and Jordan P. Fitzgerald for useful discussions and assistance with the editing of the final manuscript. Figures 1–3 and Supplementary Figs. S7 and S9 were partially created with BioRender.com with a license.
Funder: National Science Foundation Graduate Research Fellowship Program
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Journal ISSN
2041-1723

