SERS-based nanobiosensing for ultrasensitive detection of the p53 tumor suppressor
Fabio Domenici, Anna Rita Bizzarri, Salvatore Cannistraro Biophysics and Nanoscience Centre, Faculty of Science, Università della Tuscia, Viterbo, Italy Background: One of the main challenges in biomedicine is improvement of detection sensitivity to achieve tumor marker recognition at...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Dove Medical Press
2011-09-01
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Series: | International Journal of Nanomedicine |
Online Access: | http://www.dovepress.com/sers-based-nanobiosensing-for-ultrasensitive-detection-of-the-p53-tumo-a8313 |
Summary: | Fabio Domenici, Anna Rita Bizzarri, Salvatore Cannistraro Biophysics and Nanoscience Centre, Faculty of Science, Università della Tuscia, Viterbo, Italy Background: One of the main challenges in biomedicine is improvement of detection sensitivity to achieve tumor marker recognition at a very low concentration when the disease is not significantly advanced. A pivotal role in cancer defense is played by the p53 tumor suppressor, therefore its detection with high sensitivity may contribute considerably to early diagnosis of cancer. In this work, we present a new analytical method based on surface-enhanced Raman spectroscopy which could significantly increase the sensitivity of traditional bioaffinity techniques. p53 molecules were anchored to gold nanoparticles by means of the bifunctional linker 4-aminothiophenol (4-ATP). The characteristic vibrational bands of the p53-4-ATP nanoparticle system were then used to identify the p53 molecules when they were captured by a recognition substrate comprising a monolayer of azurin in molecules possessing significant affinity for this tumor suppressor. The Raman signal enhancement achieved by 4-ATP-mediated crosslinking of p53 to 50 nm gold nanoparticles enabled detect of this protein at a concentration down to 5 × 10-13 M. Keywords: surface-enhanced Raman spectroscopy, p53, ultrasensitive detection, atomic force microscopy |
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ISSN: | 1176-9114 1178-2013 |