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Hybrid lipid-AuNP clusters as highly efficient SERS substrates for biomedical applications
Jacopo Cardellini1,2, Caterina Dallari3,4,5, Ilaria De Santis1
1Department of Chemistry "Ugo Schiff" and CSGI, University of Florence, Florence, Italy.
Nature Communications
|September 12, 2024
Summary
We developed LipoGold Tags, a simple method for creating Surface-Enhanced Raman Scattering (SERS) probes. These tags enable ultrasensitive detection of diseases like GM1 gangliosidosis by enhancing Raman signals from embedded molecules.
Area of Science:
- Nanotechnology
- Biomedical Diagnostics
- Spectroscopy
Background:
- Surface-Enhanced Raman Scattering (SERS) offers ultrasensitive detection but is hindered by complex SERS tag preparation.
- Existing methods for metallic nanoparticles (NPs) functionalized with Raman-active molecules (RRs) suffer from poor stability and reproducibility.
Purpose of the Study:
- To introduce a simplified platform, LipoGold Tags, for the facile production of SERS tags.
- To demonstrate the enhanced SERS signal and diagnostic potential of LipoGold Tags.
Main Methods:
- Gold nanoparticles (AuNPs) clusters were formed via self-assembly on lipid vesicles.
- Raman-active molecules (RRs) were embedded within the lipid bilayer to leverage enhanced electromagnetic fields.
- Concentrations of RRs and lipid vesicles were modulated to optimize SERS enhancement, with structural characterization performed.
Main Results:
- LipoGold Tags demonstrated significantly increased Raman signals due to embedded RRs experiencing enhanced electromagnetic fields.
- The platform showed versatility by successful functionalization with biomolecular probes, including antibodies.
- Intracellular GM1 alterations were detected, distinguishing healthy donors from patients with infantile GM1 gangliosidosis.
Conclusions:
- LipoGold Tags represent a simplified and robust approach to SERS probe production.
- This platform offers a promising advancement for ultrasensitive diagnostics and imaging applications.
- The successful detection of GM1 alterations highlights the potential for clinical disease diagnosis.
