Akademik Veri Yönetim Sistemi
JOM, cilt.71, ss.1281-1290, 2019 (SCI-Expanded)
Combining multiple modalities is central to developing the new methods for sensing and imaging that are required for comprehensive understanding of events at the molecular level. Various imaging modalities have been developed using metallic nanoparticles owing to their exceptional physical and chemical properties. Due to their localized surface plasmon resonance characteristics, gold and silver nanoparticles exhibit unique optoelectronic properties commonly used in biomedical sciences and engineering. Self-assembled monolayers or physical adsorption have previously been adapted to functionalize the surfaces of nanoparticles with biomolecules for targeted imaging. However, depending on differences among the functional groups used on the nanoparticle surface, wide variation in the displayed biomolecular property to recognize its target may result. In the last decade, the properties of inorganic binding peptides have been proven advantageous for assembling selective functional nano-entities or proteins onto nanoparticle surfaces. Herein we explored the formation of self-assembled hybrid metallic nano-architectures composed of gold and silver nanoparticles with fluorescent proteins for use as bimodal imaging probes. We employed metal-binding peptide-based assembly to self-assemble green fluorescence protein onto metallic substrates of various geometries. Assembly of the green fluorescent proteins, genetically engineered to incorporate gold- or silver-binding peptides onto metallic nanoparticles, resulted in the generation of hybrid-, biomodal-imaging probes in a single step. Green fluorescent activity on gold and silver surfaces can be monitored using both plasmonic and fluorescent signatures. Our results demonstrate a novel bimodal imaging system that can be finely tuned with respect to nanoparticle size and protein concentration. Resulting hybrid probes may mitigate the limitation of depth penetration into biologic tissues and provide a high signal-to-noise ratio and sensitivity.