Rapid and label-free single-nucleotide discrimination via an integrative nanoparticle-nanopore approach.
ACS Nano
; 6(10): 8815-23, 2012 Oct 23.
Article
em En
| MEDLINE
| ID: mdl-22994459
Single-nucleotide polymorphism (SNP) is an important biomarker for disease diagnosis, treatment monitoring, and development of personalized medicine. Recent works focused primarily on ultrasensitive detection, while the need for rapid and label-free single-nucleotide discrimination techniques, which are crucial criteria for translation into clinical applications, remains relatively unexplored. In this work, we developed a novel SNP detection assay that integrates two complementary nanotechnology systems, namely, a highly selective nanoparticle-DNA detection system and a single-particle sensitive nanopore readout platform, for rapid detection of single-site mutations. Discrete nanoparticle-DNA structures formed in the presence of perfectly matched (PM) or single-mismatched (SM) targets exhibited distinct size differences, which were resolved on a size-tunable nanopore platform to generate corresponding "yes/no" readout signals. Leveraging the in situ reaction monitoring capability of the nanopore platform, we demonstrated that real-time single-nucleotide discrimination of a model G487A mutation, responsible for glucose-6-phosphate dehydrogenase deficiency, can be achieved within 30 min with no false positives. Semiquantification of DNA samples down to picomolar concentration was carried out using a simple parameter of particle count without the need for sample labeling or signal amplification. The unique combination of nanoparticle-based detection and nanopore readout presented in this work brings forth a rapid, specific, yet simple biosensing strategy that can potentially be developed for point-of-care application.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
DNA
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Análise Mutacional de DNA
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Técnicas Biossensoriais
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Polimorfismo de Nucleotídeo Único
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Nanotecnologia
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Nanopartículas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article