Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Feb:143:107982.
doi: 10.1016/j.bioelechem.2021.107982. Epub 2021 Oct 15.

Carbon nanotube field-effect transistor (CNT-FET)-based biosensor for rapid detection of SARS-CoV-2 (COVID-19) surface spike protein S1

Affiliations

Carbon nanotube field-effect transistor (CNT-FET)-based biosensor for rapid detection of SARS-CoV-2 (COVID-19) surface spike protein S1

Mazin A Zamzami et al. Bioelectrochemistry. 2022 Feb.

Abstract

The large-scale diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important for traceability and treatment during pandemic outbreaks. We developed a fast (2-3 min), easy-to-use, low-cost, and quantitative electrochemical biosensor based on carbon nanotube field-effect transistor (CNT-FET) that allows digital detection of the SARS-CoV-2 S1 in fortifited saliva samples for quick and accurate detection of SARS-CoV-2 S1 antigens. The biosensor was developed on a Si/SiO2 surface by CNT printing with the immobilization of a anti-SARS-CoV-2 S1. SARS-CoV-2 S1 antibody was immobilized on the CNT surface between the S-D channel area using a linker 1-pyrenebutanoic acid succinimidyl ester (PBASE) through non-covalent interaction. A commercial SARS-CoV-2 S1 antigen was used to characterize the electrical output of the CNT-FET biosensor. The SARS-CoV-2 S1 antigen in the 10 mM AA buffer pH 6.0 was effectively detected by the CNT-FET biosensor at concentrations from 0.1 fg/mL to 5.0 pg/mL. The limit of detection (LOD) of the developed CNT-FET biosensor was 4.12 fg/mL. The selectivity test was performed by using target SARS-CoV-2 S1 and non-target SARS-CoV-1 S1 and MERS-CoV S1 antigens in the 10 mM AA buffer pH 6.0. The biosensor showed high selectivity (no response to SARS-CoV-1 S1 or MERS-CoV S1 antigen) with SARS-CoV-2 S1 antigen detection in the 10 mM AA buffer pH 6.0. The biosensor is highly sensitive, saves time, and could be a helpful platform for rapid detection of SARS-CoV-2 S1 antigen from the patients saliva.

Keywords: Biosensor; Carbon nanotubes; Electrical immunosensor; Field-effect transistor; Severe acute respiratory syndrome coronavirus-2.

PubMed Disclaimer

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Scheme 1
Scheme 1
Schematic diagram of CNT-FET biosensor and SARS-CoV-2 S1 testing steps. SWCNT as a sensing nanomaterial and anti-SARS-CoV-2 S1 immobilized on the CNT via PBASE (linker). The CNT-FET biosensor sense the SARS-CoV-2 spike protein based on the corresponding effects on the electrical signal properties.
Fig. 1
Fig. 1
(A) Schematic representation of CNT-FET device illustrating structural components and thickness of fabricated nanomaterial. The CNT network makes contact with metal electrodes (source-drain). A SU-8 photoresist on the source-drain electrodes is used to prevent leakage of gate current. (B) Optical image of single die CNT-FET device from wafer. (C) Optical microscope image of CNT-FET device showing source-drain electrodes. (D) Schematic structure of FET circuit diagram and CNT network situated between source-drain electrodes on conductive substrate. (E) SEM image of deposited CNT network on surface. (F) Surface functionalization of CNT-FET by 2 mM PBASE (linker). (G) XPS spectra of CNT-FET surface before (red line) and after PBASE treatment (blue line). Change in N1s peak at 400.2 eV assigned for PBASE was successfully modified the CNT surface.
Fig. 2
Fig. 2
Scanning electron photographs of channel area: (A) bare SiO2 surface, (B) Bare surface after CNT printing, and (C) PBASE modification, anti-SARS-CoV-2 S1 immobilization, and binding of AuNPs–conjugated SARS-CoV-2 S1 antigen. (D) Enlarged image at 200,000 × magnification to measure gold nanoparticle size in the nm range.
Fig. 3
Fig. 3
Western blot images showing the detection of SARS-CoV-2 S1 by anti-SARS-CoV-2 S1-mAb antibodies. Lanes 1, 2, 3 represent loaded recombinant SARS-CoV-2 S1, SARS-CoV-1 S1, and MERS-CoV S1 antigens. For each antigen concentration, 1 µg/mL sample was loaded in SDS-PAGE.
Fig. 4
Fig. 4
(A) I-V characteristics of CNT-FET device at each biochemical treatment steps. Source-drain current (IDS) versus gate voltage (Vg) characteristics of bare CNT-FET (black line). After 2 mM PBASE modification and I-V characterstics of the CNT-FET (red line). SARS-CoV-2 S1 antibody immobilized CNT-FET surface (light blue) and SARS-CoV-2 S1 antigen drop (green line). The 100 fg/mL SARS-CoV-2 S1 was prepared in10 mM AA buffer of pH 6.0. All the transfer characteristics are measured under ambient conditions. (B) Real time senror response characteristics after dropping increasing concentration (0.1, 1, 10, 100, and 5000 fg/mL) of SARS-CoV-2 S1 antigen. (C) Calibration curve after normalizing biosensor response versus applied concentration of target SARS-CoV-2 S1 antigen. Fixed drain-source voltage (Vds = 0.5 V) and Vg = -1.5 V. Error bars represent the standard deviation of three biosensor replicates. Each experiment was repeated a minimum of three times and typical results are shown.
Fig. 5
Fig. 5
(A) Real-time biosensor response for selectivity test of target SARS-CoV-2 S1 and non-target SARS-CoV-1 S1 and MERS-CoV S1 antigens. Antigen concentration was 100 fg/mL, prepared in 10 mM AA buffer at pH 6.0. (B) Normalized response (ΔI/Io) of target SARS-CoV-2 S1 and non-target SARS-CoV-2 S1 and MERS-CoV S1 antigens. Error bars represent the standard deviation of three biosensor replicates. Each experiment was repeated a minimum of three times and typical results are shown. (C) Normalized real-time biosensor response (ΔI/Io) versus time characteristics after SARS-CoV-2 S1 non-fortified and fortified saliva dropping. The used antigen concentration in fortified saliva was 100 fg/mL (D) Normalized real-time biosensor response (ΔI/Io) versus SARS-CoV-2 S1 antigen (0.1, 1, 10, 100, 1000, 2000, 3000, 4000 and 5000 fg/mL) fortified saliva. Error bars represent the standard deviation of three biosensor replicates. Each experiment was repeated a minimum of three times and typical results are shown.
Fig. 6
Fig. 6
(A) CNT-FET reproducibility test of 24 CNT-FET biosensors from 3 batches. 12 CNT-FET used for SARS-CoV-2 S1 fortified saliva and 12 CNT-FET used for only saliva test. Fixed drain-source voltage (Vds = 0.5 V) and Vg = -1.5 V were applied in the electrical signal measurements. (B) Normalized real-time biosensor response (ΔI/Io) versus time characteristics after 0.01 × PBST washing and drying (reused CNT-FET biosensor). Target SARS-CoV-2 S1 and non-target SARS-CoV-1 S1 and MERS-CoV S1 antigens were tested. Each antigens 100 fg/mL were prepared in10 mM AA buffer of pH 6.0.

References

    1. WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020, (2020).
    1. Cui J., Li F., Shi Z.-L. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019;17(3):181–192. - PMC - PubMed
    1. Stadler K., Masignani V., Eickmann M., Becker S., Abrignani S., Klenk H.-D., Rappuoli R. SARS-beginning to understand a new virus. Nat Rev Microbiol. 2003;1(3):209–218. - PMC - PubMed
    1. Memish Z.A., Perlman S., Van Kerkhove M.D., Zumla A. Middle East respiratory syndrome. Lancet. 2020;395(10229):1063–1077. - PMC - PubMed
    1. Zhu N.a., Zhang D., Wang W., Li X., Yang B.o., Song J., Zhao X., Huang B., Shi W., Lu R., Niu P., Zhan F., Ma X., Wang D., Xu W., Wu G., Gao G.F., Tan W. A novel coronavirus from patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–733. - PMC - PubMed

MeSH terms

LinkOut - more resources