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Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip.

ABSTRACT: Nucleic acids and proteins are the two most important target types used in molecular diagnostics. In many instances, simultaneous sensitive and accurate detection of both biomarkers from the same sample would be desirable, but standard detection methods are highly optimized for one type and not cross-compatible. Here, we report the simultaneous multiplexed detection of SARS-CoV-2 RNAs and antigens with single molecule sensitivity. Both analytes are isolated and labeled using a single bead-based solid-phase extraction protocol, followed by fluorescence detection on a multi-channel optofluidic waveguide chip. Direct amplification-free detection of both biomarkers from nasopharyngeal swab samples is demonstrated with single molecule detection sensitivity, opening the door for ultrasensitive dual-target analysis in infectious disease diagnosis, oncology, and other applications.

SUBMITTER: Meena GG

PROVIDER: S-EPMC9186413 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip.

Meena G G GG Stambaugh A M AM Ganjalizadeh V V Stott M A MA Hawkins A R AR Schmidt H H

APL photonics 20210601 6

Nucleic acids and proteins are the two most important target types used in molecular diagnostics. In many instances, simultaneous sensitive and accurate detection of both biomarkers from the same sample would be desirable, but standard detection methods are highly optimized for one type and not cross-compatible. Here, we report the simultaneous multiplexed detection of SARS-CoV-2 RNAs and antigens with single molecule sensitivity. Both analytes are isolated and labeled using a single bead-based ...[more]

PMID: 35693725

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Project description:A large-scale diagnosis of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is essential to downregulate its spread within as well as across communities and mitigate the current outbreak of the pandemic novel coronavirus disease 2019 (COVID-19). Herein, we report the development of a rapid (less than 5 min), low-cost, easy-to-implement, and quantitative paper-based electrochemical sensor chip to enable the digital detection of SARS-CoV-2 genetic material. The biosensor uses gold nanoparticles (AuNPs), capped with highly specific antisense oligonucleotides (ssDNA) targeting viral nucleocapsid phosphoprotein (N-gene). The sensing probes are immobilized on a paper-based electrochemical platform to yield a nucleic-acid-testing device with a readout that can be recorded with a simple hand-held reader. The biosensor chip has been tested using samples collected from Vero cells infected with SARS-CoV-2 virus and clinical samples. The sensor provides a significant improvement in output signal only in the presence of its target-SARS-CoV-2 RNA-within less than 5 min of incubation time, with a sensitivity of 231 (copies ?L-1)-1 and limit of detection of 6.9 copies/?L without the need for any further amplification. The sensor chip performance has been tested using clinical samples from 22 COVID-19 positive patients and 26 healthy asymptomatic subjects confirmed using the FDA-approved RT-PCR COVID-19 diagnostic kit. The sensor successfully distinguishes the positive COVID-19 samples from the negative ones with almost 100% accuracy, sensitivity, and specificity and exhibits an insignificant change in output signal for the samples lacking a SARS-CoV-2 viral target segment (e.g., SARS-CoV, MERS-CoV, or negative COVID-19 samples collected from healthy subjects). The feasibility of the sensor even during the genomic mutation of the virus is also ensured from the design of the ssDNA-conjugated AuNPs that simultaneously target two separate regions of the same SARS-CoV-2 N-gene.

Dataset Information

Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip.

Publications

Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip.

Similar Datasets

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