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Combining magnetic nanoparticle with biotinylated nanobodies for rapid and sensitive detection of influenza H3N2
Our objective is to develop a rapid and sensitive assay based on magnetic beads to detect the concentration of influenza H3N2. The possibility of using variable domain heavy-chain antibodies (nanobody) as diagnostic tools for influenza H3N2 was investigated. A healthy camel was immunized with inacti...
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Published in: | Nanoscale research letters 2014-09, Vol.9 (1), p.528-528, Article 528 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Our objective is to develop a rapid and sensitive assay based on magnetic beads to detect the concentration of influenza H3N2. The possibility of using variable domain heavy-chain antibodies (nanobody) as diagnostic tools for influenza H3N2 was investigated. A healthy camel was immunized with inactivated influenza H3N2. A nanobody library of 8 × 10
8
clones was constructed and phage displayed. After three successive biopanning steps, H3N2-specific nanobodies were successfully isolated, expressed in
Escherichia coli
, and purified. Sequence analysis of the nanobodies revealed that we possessed four classes of nanobodies against H3N2. Two nanobodies were further used to prepare our rapid diagnostic kit. Biotinylated nanobody was effectively immobilized onto the surface of streptavidin magnetic beads. The modified magnetic beads with nanobody capture specifically influenza H3N2 and can still be recognized by nanobodies conjugated to horseradish peroxidase (HRP) conjugates. Under optimized conditions, the present immunoassay exhibited a relatively high sensitive detection with a limit of 50 ng/mL. In conclusion, by combining magnetic beads with specific nanobodies, this assay provides a promising influenza detection assay to develop a potential rapid, sensitive, and low-cost diagnostic tool to screen for influenza infections. |
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ISSN: | 1931-7573 1556-276X 1556-276X |
DOI: | 10.1186/1556-276X-9-528 |