Protease-Activated Quantum Dot Probes
West, Jennifer L.
Master of Science
Protease activity has been demonstrated to be an important prognostic and predictive marker in diseases such as cancer and stroke. As such, much attention has been given to the development of diagnostic tools that would allow one to assay their activity in living tissues. Protease activity is regulated at many levels including transcription, translation, activation, and inhibition and in order to derive the maximum prognostic benefit, it is essential to study their activity within this complex environment. Initial attempts to accomplish this goal involved the use of organic fluorophores pairs that utilize Fluorescence Resonance Energy Transfer (FRET) but suffered many drawbacks. Quantum Dots (QD) have addressed many of the drawbacks of organic fluorophores in various optical imaging applications. These include a decreased sensitivity to photobleaching and chemical degradation, size-tunable narrow emission peaks, and broad absorbance allowing excitation of multiple peak emission QD by one excitation source. In this work, we propose to utilize Quantum Dots (QD) linked to gold nanoparticles (AuNPs) by protease cleavable peptide sequences to serve as probes for assaying protease activity both in vivo and in vitro. This work involved the synthesis and characterization of the various components necessary for the probe design as well as the optimization of probe characteristics to achieve highly biocompatible probes that exhibit both a high level of quenching and maximum fluorescence recovery in the presence of protease. Probe functionality was optimized and it was determined that probes with AuNP:QD ratio of 10.1 and peptide linker length of 6.5 nm resulted in highest and most linear fluorescent signal gain. The ability to multiplex probes was also validated by developing spectrally orthogonal probes sensitive to collagenase and cathepsin K. Our design is expected to have many applications in the research and understanding of the role of proteases in disease and as a predictive tool for the prognosis of diseases such as cancer.
Applied sciences; Biomedical engineering