DNA-directed formation of plasmonic core-satellite nanostructures for quantification of hepatitis C viral RNA.


Journal

Chemical science
ISSN: 2041-6520
Titre abrégé: Chem Sci
Pays: England
ID NLM: 101545951

Informations de publication

Date de publication:
29 May 2024
Historique:
received: 05 02 2024
accepted: 19 04 2024
medline: 31 5 2024
pubmed: 31 5 2024
entrez: 31 5 2024
Statut: epublish

Résumé

Hepatitis C virus (HCV) continues to be a significant public health challenge, affecting an estimated 71 million people globally and posing risks of severe liver diseases. Despite advancements in treatments, diagnostic limitations hinder the global elimination efforts targeted by 2030. This study introduces an innovative diagnostic approach, integrating catalytic hairpin assembly (CHA) with plasmonic core-satellite gold nanoparticle (AuNP) assemblies, to enable sensitive and specific detection of HCV RNA. We optimized the stoichiometry of DNA hairpins to form highly stable three-way junctions (3WJs), minimizing non-specific reactions in an enzyme-free, isothermal amplification process. The resulting dual-transduction biosensor combines colorimetric and surface-enhanced Raman spectroscopy (SERS) techniques, utilizing the Raman reporter malachite green isothiocyanate (MGITC) for signal generation. Our system targets a conserved 23-nucleotide sequence within the HCV 5'-UTR, essential for RNA replication, facilitating pan-genotypic HCV detection that complements direct-acting antiviral strategies. We evaluated the biosensor's efficacy using fluorescence spectroscopy, native PAGE, AFM, and TEM. Findings indicate that the 60 nm core AuNPs surrounded by 20 nm satellite AuNPs achieved a ten-fold increase in sensitivity over the 10 nm satellites, detecting HCV RNA concentrations as low as 1.706 fM. This sensitivity is crucial, given the extremely low viral loads present during early infection stages. Our research demonstrates the promise of enzyme-free molecular biosensors for HCV, with the potential to provide cost-efficient, rapid, point-of-care testing, although further sensitivity enhancements are needed to address the challenges of early-stage detection.

Identifiants

pubmed: 38817589
doi: 10.1039/d4sc00891j
pii: d4sc00891j
pmc: PMC11134388
doi:

Types de publication

Journal Article

Langues

eng

Pagination

8112-8126

Informations de copyright

This journal is © The Royal Society of Chemistry.

Déclaration de conflit d'intérêts

There are no conflicts to declare.

Auteurs

Siddhant Jaitpal (S)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.
Center for Remote Health Technologies & Systems, Texas A&M Engineering Experiment Station 600 Discovery Drive College Station TX 77840-3006 USA.

Ka Wai Ng (KW)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.
Center for Remote Health Technologies & Systems, Texas A&M Engineering Experiment Station 600 Discovery Drive College Station TX 77840-3006 USA.

Angela Michelle San Juan (AM)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.
Center for Remote Health Technologies & Systems, Texas A&M Engineering Experiment Station 600 Discovery Drive College Station TX 77840-3006 USA.

Cecilia Martinez (C)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.

Christian Phillips (C)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.

Sayantan Tripathy (S)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.
Center for Remote Health Technologies & Systems, Texas A&M Engineering Experiment Station 600 Discovery Drive College Station TX 77840-3006 USA.

Samuel Mabbott (S)

Department of Biomedical Engineering, Texas A&M University 600 Discovery Drive College Station TX 77840-3006 USA smabbott@tamu.edu.
Center for Remote Health Technologies & Systems, Texas A&M Engineering Experiment Station 600 Discovery Drive College Station TX 77840-3006 USA.

Classifications MeSH