Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning

Yang Xu; Adil M Rather; Shuang Song; Jen-Chun Fang; Robert L Dupont; Ufuoma I Kara; Yun Chang; Joel A Paulson; Rongjun Qin; Xiaoping Bao; Xiaoguang Wang

doi:10.1016/j.xcrp.2020.100276

Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning

Cell Rep Phys Sci. 2020 Dec 23;1(12):100276. doi: 10.1016/j.xcrp.2020.100276. Epub 2020 Nov 17.

Authors

Yang Xu¹, Adil M Rather¹, Shuang Song², Jen-Chun Fang¹, Robert L Dupont¹, Ufuoma I Kara¹, Yun Chang³, Joel A Paulson^{1

4}, Rongjun Qin^{2

5

6}, Xiaoping Bao³, Xiaoguang Wang^{1

4}

Affiliations

¹ William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH 43210, USA.
² Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA.
³ Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
⁴ Sustainability Institute, The Ohio State University, Columbus, OH 43210, USA.
⁵ Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
⁶ Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA.

Abstract

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 the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the SARS ssRNA, with a 3-bp mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we design a LC-based diagnostic kit and a smartphone-based application (app) to enable automatic detection of SARS-CoV-2 ssRNA, which could be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.

Keywords: COVID-19; RNA; SARS-CoV-2; biosensor; liquid crystals; machine learning; point-of-care detection kit; self-assembly.