Digital immunoassay for rapid detection of SARS-CoV-2 infection in a broad spectrum of animals

The ability of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) to infect a wide range of species raises significant concerns regarding both human-to-animal and animal-to-human transmission. There is an increasing demand for highly sensitive, rapid, and simple diagnostic assays that can detect viral infection across various species. In this study, we developed a biosensor assay that adapted a monoclonal-antibody (mAb)-based blocking ELISA format into an Activate Capture + Digital Counting (AC + DC)-based biosensor immunoassay. The assay employs a photonic crystal (PC) biosensor, gold-nanoparticle (AuNP), SARS-CoV-2 nucleocapsid (N) protein, and specific anti-N monoclonal antibody to detect antibody responses in animals exposed to SARS-CoV-2. Based on an evaluation of 176 cat serum samples with known antibody status, an optimal percentage of inhibition (PI) cut-off value of 0.588 resulted in a diagnostic sensitivity of 98.3% and a diagnostic specificity of 96.5%. The test is highly repeatable with low variation coefficients of 2.04%, 2.71%, and 4.87% across a single PC, between different PCs but within a single run, and between different runs, respectively. The test was further employed to detect antibody responses in multiple animal species as well as investigate the dynamics of antibody response in experimentally infected cats. This test platform provides an important tool for rapid field surveillance of SARS-CoV-2 infection across multiple species.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

VMS - Pathobiology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Li, Siyan

Contributors dc:contributor

• Fang, Ying
• Alam, Tauqeer
• Jarosinski, Keith
• Cunningham, Brian

• Sars-cov-2 Detection
• Photonic Crystal Biosensor
• Monoclonal Antibody
• Blocking Biosensor Assay

Statement dc:rights

• Copyright 2024 Siyan Li

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127294

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127294