Increased sensitivity for lateral flow immunoassays through signal amplification methods
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Abe, Koji
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Abstract
The simple and inexpensive sandwich-immunoassay-based lateral flow tests (LFTs, also known as lateral flow immunoassays, LFIAs, and rapid diagnostic tests, RDTs) are one of the general approaches for detection of disease targets (e.g. nucleoprotein of the influenza virus). However, the use of these LFTs is limited because of their well-recognized lack of sensitivity. To improve the sensitivity of LFTs for influenza diagnosis while maintaining a simple and rapid assay procedure, we proposed signal enhancement methods of antigen detection using three approaches: (1) increasing the absorbance of gold nanoparticles using gold enhancement and silver enhancement solutions, (2) enzymatic creation and precipitation of visible dyes, and (3) nucleic acid amplification test (NAAT)-based signal enhancement. Using the enzymatic amplification-based detection, we developed a prototype of a point-of-care (POC) device including a dry conjugate pad and the assay sensitivity was compared with a commercially available kit. Since reagent delivery format is also a key factor to improve the sensitivity, we investigated two formats: premixed and sequential delivery. An improved reagent delivery format achieved better assay sensitivity. To elucidate the underlying mechanism related to the reagent delivery format, a preliminary model using experimental data was developed to predict assay performance and potential minimum limit of detection (LOD). Finally, to further improve the sensitivity, we developed a NAAT-based protein detection method (i.e. immuno-NAAT) that can be performed on nitrocellulose. These methods were demonstrated using the assay stack for influenza nucleoprotein as a model analyte. The NAAT-based method showed ~10,000-fold better LOD compared to conventional gold nanoparticle-based LFT; this is the first study of immuno-NAAT performed on nitrocellulose, which is a feasible platform for POC diagnostics.
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Thesis (Ph.D.)--University of Washington, 2018
