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Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope.

ABSTRACT: The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to pose a substantial global health threat. Along with vaccines and targeted therapeutics, there is a critical need for rapid diagnostic solutions. In this work, we use computational protein modeling tools to suggest molecular beacon architectures that function as conformational switches for high-sensitivity detection of the SARS-CoV-2 spike protein receptor binding domain (S-RBD). Integrating these beacons on a miniaturized total internal reflection fluorescence (mini-TIRF) microscope, we detect the S-RBD and pseudotyped SARS-CoV-2 with limits of detection in the femtomolar range. We envision that our designed mini-TIRF platform will serve as a robust platform for point-of-care diagnostics for SARS-CoV-2 and future emergent viral threats.

SUBMITTER: Tripathy SP

PROVIDER: S-EPMC9401610 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope.

Tripathy Soumya P SP Ponnapati Manvitha M Bhat Suhaas S Jacobson Joseph J Chatterjee Pranam P

Science advances 20220824 34

The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to pose a substantial global health threat. Along with vaccines and targeted therapeutics, there is a critical need for rapid diagnostic solutions. In this work, we use computational protein modeling tools to suggest molecular beacon architectures that function as conformational switches for high-sensitivity detection of the SARS-CoV-2 spike protein receptor binding domain (S-RBD). Integrating these beac ...[more]

PMID: 36001655

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Project description:Effective and efficient management of human betacoronavirus severe acute respiratory syndrome (SARS)-CoV-2 virus infection i.e., COVID-19 pandemic, required sensitive and selective sensors with short sample-to-result durations for performing desired diagnostics. In this direction, one appropriate alternative approach to detect SARS-CoV-2 virus protein at low level i.e., femtomolar (fM) is exploring plasmonic metasensor technology for COVID-19 diagnostics, which offers exquisite opportunities in advanced healthcare programs, and modern clinical diagnostics. The intrinsic merits of plasmonic metasensors stem from their capability to squeeze electromagnetic fields, simultaneously in frequency, time, and space. However, the detection of low-molecular weight biomolecules at low densities is a typical drawback of conventional metasensors that has recently been addressed using toroidal metasurface technology. This research is focused on the fabrication of a miniaturized plasmonic immunosensor based on toroidal electrodynamics concept that can sustain robustly confined plasmonic modes with ultranarrow lineshapes in the terahertz (THz) frequencies. By exciting toroidal dipole mode using our quasi-infinite metasurface and a judiciously optimized protocol based on functionalized gold nanoparticles (AuNPs) conjugated with the specific monoclonal antibody specific to spike protein (S1) of SARS-CoV-2 virus onto the metasurface, the resonance shifts for diverse concentrations of the spike protein are monitored. Possessing molecular weight around ~76 kDa allowed to detect the presence of SARS-CoV-2 virus protein with significantly low as limit of detection (LoD) was achieved as ~4.2 fM. We envisage that outcomes of this research will pave the way toward the use of toroidal metasensors as practical technologies for rapid and precise screening of SARS-CoV-2 virus carriers, symptomatic or asymptomatic, and spike proteins in hospitals, clinics, laboratories, and site of infection.

Dataset Information

Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope.

Publications

Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope.

Similar Datasets

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