A single-molecule RNA electrical biosensor for COVID-19

08 June 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The COVID-19 pandemic shows a critical need for rapid, inexpensive, and ultrasensitive early detection methods based on biomarker analysis to reduce mortality rates by containing the spread of epidemics. This can be achieved through electrical detection of nucleic acids at the single-molecule level. In particular, the scanning tunneling microscopic-assisted break junction (STM-BJ) method can be utilized to detect individual nucleic acid molecules with high specificity and sensitivity in liquid samples. Herein, we demonstrate single-molecule electrical detection of RNA coronavirus biomarkers, including those of SARS-CoV-2 as well as those of different variants and subvariants. Our target sequences include a conserved sequence in the human coronavirus family, a conserved target specific for the SARS-CoV-2 family, and specific targets at the variant and subvariant levels. Our results demonstrate that it is possible to distinguish between different variants of the COVID-19 virus using electrical conductance signals, as recently suggested by theoretical approaches. Furthermore, we propose a strategy to detect new variants by analyzing electrical fingerprints from multiple sequences. This could allow for a rapid response early in new outbreaks. These results pave the way for future miniaturized single-molecule electrical biosensors that could be game changers for the COVID-19 pandemic, other infectious diseases, and several other public health applications.


COVID-19 detection
single-molecule biosensors
biomolecular electronics
pathogen screening

Supplementary materials

Supplementary Materials: A single-molecule RNA electrical biosensor for COVID-19
To fully understand the study, it's recommended to read both the main text and the supplementary information. The supplementary information has extra details and results that support the findings in the main text. Any data not included in the main text can be found there. Reading both sources will provide a complete understanding of the research.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.