Digital Microfluidic Multiplex RT-qPCR for SARS-CoV-2 Detection and Variants Discrimination

Author:

Ho Kuan-Lun1ORCID,Ding Jing1,Fan Jia-Shao2,Tsui Wai Ning Tiffany3,Bai Jianfa34,Fan Shih-Kang1

Affiliation:

1. Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA

2. Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA

3. Kansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS 66506, USA

4. Department of Diagnostic Medicine/Pathobiology, Kansas State University, Manhattan, KS 66506, USA

Abstract

Continuous mutations have occurred in the genome of the SARS-CoV-2 virus since the onset of the COVID-19 pandemic. The increased transmissibility of the mutated viruses has not only imposed medical burdens but also prolonged the duration of the pandemic. A point-of-care (POC) platform that provides multitarget detection will help to track and reduce disease transmissions. Here we detected and discriminated three genotypes of SARS-CoV-2, including the wildtype and two variants of concern (VOCs), the Delta variant and Omicron variant, through reverse transcription quantitative polymerase chain reaction (RT-qPCR) on a digital microfluidics (DMF)-based cartridge. Upon evaluating with the RNA samples of Omicron variant, the DMF RT-qPCR presented a sensitivity of 10 copies/μL and an amplification efficiency of 96.1%, capable for clinical diagnosis. When spiking with SARS-CoV-2 RNA (wildtype, Delta variant, or Omicron variant) and 18S rDNA, the clinical analog samples demonstrated accurate detection and discrimination of different SARS-CoV-2 strains in 49 min.

Funder

S.K.F.’s startup

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering

Reference50 articles.

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