DNA sequence detection using surface-enhanced resonance Raman spectroscopy in a homogeneous multiplexed assay

Area of Science:

• Molecular Biology
• Nanotechnology
• Spectroscopy

Background:

• Specific DNA sequence detection is crucial for molecular biology and diagnostics.
• Fluorescence spectroscopy is a dominant detection technology, but novel methods are needed.
• Current methods require sensitive and specific DNA identification for disease diagnosis.

Purpose of the Study:

• To demonstrate surface-enhanced resonance Raman scattering (SERRS) for specific DNA sequence detection.
• To develop modified SERRS-active probes for enhanced DNA detection sensitivity and specificity.
• To validate the SERRS method for detecting polymerase chain reaction (PCR) products and multiplexed real-world samples.

Main Methods:

• Utilized surface-enhanced resonance Raman scattering (SERRS) with silver nanoparticles.
• Designed SERRS-active probes with modified affinities for single- and double-stranded DNA.
• Incorporated locked nucleic acid (LNA) residues into DNA probes for improved discrimination.
• Applied the method to detect polymerase chain reaction (PCR) products and multiplexed MRSA sequences.

Main Results:

• Demonstrated discernible differences in SERRS signals correlated to DNA hybridization events.
• Showcased reduced SERRS signal upon hybridization due to double-stranded DNA's low affinity for silver nanoparticles.
• Achieved greater discrimination between exact DNA matches and mismatches using LNA probes compared to unmodified probes.
• Successfully detected PCR products and multiplexed methicillin-resistant Staphylococcus aureus (MRSA) sequences.

Conclusions:

• SERRS coupled with modified probes offers a sensitive and specific method for DNA sequence detection.
• The assay principle relies on differential affinity of DNA structures to silver nanoparticles, altering SERRS signals.
• LNA modification enhances probe performance, improving discrimination accuracy.
• This versatile SERRS approach is applicable to real-world diagnostic scenarios, including pathogen detection.