Enable Placental Extracellular Vesicle Isolation Using Binary Temperature-responsive Reagent System
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Fu, Zirui
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Abstract
Extracellular vesicles (EVs) are cell-secreted lipid bilayer vesicles which play important roles in human cell signaling. In recent years, the researches regarding the placenta derived EVs which are secreted from the syncytiotrophoblast (STB), have discovered the potentials of placental EVs as a novel non-invasive marker for monitoring placental development and functions in real time. With specific STB markers, principally placental alkaline phosphatase (PLAP), we can distinguish STB-derived EV (STBEV) from those produced by other cell types. Due to the complexity of pregnancy plasma sample, we propose the binary temperature-responsive reagent system for fast and specific isolation of PLAP+ EV from pregnancy plasma. The proposed binary reagent system consists of temperature-responsive conjugate and magnetic nanoparticles. The synthesis of the conjugate included the polymerization of temperature-responsive poly(N-isopropylacrylamide) (pNIPAAm) via Reversible addition-fragmentation chain transfer (RAFT) and the conjugation of synthesized pNIPAAm with antibody. 1H NMR was used to confirm the composition of pNIPAAm and the GPC characterization showed the number average molecular weight (Mn) of the synthesized pNIPAAm was 29070 with a polydispersity index (PDI) at 1.186. The polymer-antibody conjugation was characterized by SDS-PAGE, which confirmed the optimal polymer/antibody molar ratio was 200:1. While tested on fluorescent proteins, the resulting conjugates stably achieved over 90% target protein capture while applying 10:1 conjugate/target molar ratio. To demonstrate the practical utilities of binary reagent system on placental EV separation, we used PLAP antibody to perform purified placental EV separation and pregnancy plasma separation. Digital Droplet PCR (ddPCR) and RT-PCR characterizations confirmed the specific placental EV isolation from pregnancy plasma by PLAP antibody via binary reagent system.
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Thesis (Master's)--University of Washington, 2020
