Pomegranate-inspired Silica Nanotags Enable Sensitive Dual-Modal Detection of Rabies Virus Nucleoprotein
Analytical Chemistry • 2020
Publication Information
Authors
Jiaojiao Zhou, Meishen Ren, Wenjing Wang, Liang Huang, Zhicheng Lu, Zhiyong Song, Mohamed Frahat Foda, Ling Zhao, Heyou Han
Keywords
Not Available
Journal
Analytical Chemistry
Publisher
American Chemical Society
Volume
92
Issue
13
Pages
Not Available
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
The outbreak of rabies virus (RABV) in Asia and Africa has attracted widespread concern due to its 100% mortality rate, and RABV detection is crucial to its diagnosis and treatment. Herein, we report a sensitive and reliable strategy for the dual-modal RABV detection using pomegranate-shaped dendritic silica nanospheres fabricated with densely incorporated quantum dots (QDs) and horseradish peroxidase (HRP)-labeled antibody. The immunoassay involves the specific interaction between virus and nanospheres-conjugated antibody coupled with robust fluorescence signal originating from QDs and naked-eye discernible colorimetric signal on the oxTMB. The ultrahigh loading capacity of QDs enables the detection limit down to 8 pg/mL via fluorescence modality, a 348-fold improvement as compared with conventional enzyme-linked immunosorbent assay (ELISA). In addition, the detection range was from 1.20 × 102 to 2.34 × 104 pg/mL by plotting the absorbance at 652 nm with RABV concentrations with a detection limit of 91 pg/mL, which is nearly 2 order of magnitude lower than that of the conventional ELISA. Validated with 12 brain tissue samples, our immunoassay results are completely consistent with polymerase chain reaction (PCR) results. Compared with the PCR assay, our approach requires no complex sample pretreatments or expensive instruments. This is the first report on RABV diagnosis using nanomaterials for colorimetry-based prescreening and fluorescence-based quantitative detection, which may pave the way for virus-related disease diagnosis and clinical analysis.
Staff Members - Benha University