Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning
55 Pages Posted: 30 Sep 2020 Publication Status: Review CompleteMore...
Rapid, robust virus detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactants and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probes. More importantly, the sensitivity of the LC to the severe acute respiratory syndrome (SARS) ssRNA, with a 3 base pair-mismatch compared to the SARS-CoV-2 ssRNA, was measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we designed a LC-based diagnostic kit and a smartphone-based application (App) to enable automatic detection of SARS-CoV-2 ssRNA, which can be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.
Funding: J.P. and X.W. thank the funding support by the startup funds of The Ohio State University (OSU) and X.W. thanks OSU Institute for Materials Research Kickstart Facility Grant. X.B. thanks the funding support by the startup funds of Davidson School of Chemical Engineering at Purdue University. S.S. and R.Q. thanks the funding support by Office of Naval Research (ONR Grant N00014-17-1-2928).
Conflict of Interest: The Ohio State University has filed a patent application (Application Number 63066000) on the work described in this manuscript. The inventors listed on the patent application are X.W., X.B., Q.R., X.Y. and A.M.R. The authors declare no other competing interests.
Keywords: COVID-19, SARS-CoV-2, biosensor, liquid crystals, point-of-care detection kit, machine learning
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