Comparison and optimization of extracellular vesicle (EV) capturing on functional thin films for their molecular profiling

Detta är en Master-uppsats från KTH/Tillämpad fysik

Sammanfattning: Extracellular vesicles (EVs) are lipid bilayer encapsulated nanoparticles which have emerged as an excellent source of biomarkers for multiple diseases, including cancer. However, they are highly heterogeneous in their molecular compositions which remains a major challenge hindering the utilization of their biomarker potential. A single-EV analysis is essential to both discovery and detect EVs that carry disease-specific signature. In this work, we designed plasmonic nanohole array for capturing single EVs and perform fluorescence detection of their membrane proteins by exploiting plasmonic amplification of the fluorescence signal. The design of the array was optimized using COMSOL Multiphysics-based simulation. Nanohole arrays with three different periodicities were fabricated on aluminum thin film on glass substrate. The substrates were then functionalized with three different methods for investigation of antibody-free capturing techniques, which are electrostatic interaction, hydrophobic interaction, and size-selective capturing. After surface functionalization with each of the techniques, genetically engineered EVs expressing mNeonGreen (mNG) were incubated and their capture efficiency were compared. The presence of single-EVs within plasmonic nanoholes was verified through both fluorescence analysis and atomic force microscopy (AFM). Fluorescence intensities of mNG-EVs recorded with the plasmonic chip with different periodicities showed intensity variations in agreement with the simulation results. Furthermore, the EVs were immunostained with R-phycoerythrin (R-PE) conjugated CD-9 to demonstrate the possibility of general and multimarker fluorescence detection. In a separate experiment, DOPC liposomes were synthesized and their deformability was analyzed by using AFM. The nanohole array provides a basis for a future platform of EV analyses, promising to capture the signature arising from low expressing proteins.

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