Issue 17, 2022

An integrated magneto-opto-fluidic biosensor for rapid on-chip assay of respiratory viruses of livestock

Abstract

Respiratory disease is one of the most important causes of economic loss in swine production. In the USA, porcine reproductive and respiratory syndrome virus (PRRSV) and influenza A virus (IAV) are currently the top two primary viruses causing swine respiratory diseases. The commonly used PCR-based virus detection methods require virus extraction, nucleic acid purification, and detection, which are relatively time-consuming and expensive. This work reports an integrated magneto-opto-fluidic (iMOF) platform, in which antibody functionalized magnetic nanoparticles (MNPs) can enable efficient enrichment of multiple swine respiratory viruses and a photonic crystal (PC) biosensor can transduce the amount of captured MNP–virus nanoparticles to the change of their reflection signatures. Owing to the high refractive index of Fe2O3 MNPs, the use of MNPs can significantly enhance the PC sensor output. The proof-of-concept validation involves using antibody-functionalized MNPs to recognize IAV and PRRSV and transferring the formed MNP–virus conjugates onto the surface of the PC biosensors to quantify these viruses. The iMOF platform offers a high sensitivity of 3.5 TCID50 mL−1 and 5.9 TCID50 mL−1 for detecting IAV and PRRSV, respectively, and a rapid turnaround within one hour, including the MNP–virus conjugation, enrichment, and detection. The on-chip virus platform has a great potential for in-field surveillance of viral infections.

Graphical abstract: An integrated magneto-opto-fluidic biosensor for rapid on-chip assay of respiratory viruses of livestock

Article information

Article type
Paper
Submitted
02 May 2022
Accepted
05 Jul 2022
First published
25 Jul 2022
This article is Open Access
Creative Commons BY license

Lab Chip, 2022,22, 3236-3244

An integrated magneto-opto-fluidic biosensor for rapid on-chip assay of respiratory viruses of livestock

Q. Zhang, G. Rawal, J. Qian, H. Ibrahim, J. Zhang, L. Dong and M. Lu, Lab Chip, 2022, 22, 3236 DOI: 10.1039/D2LC00406B

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