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Suh, Yung Doug
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Effectual Labeling of Natural Killer Cells with Upconverting Nanoparticles by Electroporation for In Vivo Tracking and Biodistribution Assessment

Author(s)
Park, Hye SunKim, JongwooCho, Mi YoungCho, Youn-JooSuh, Yung DougNam, Sang HwanHong, Kwan Soo
Issued Date
2020-11
DOI
10.1021/acsami.0c12849
URI
https://scholarworks.unist.ac.kr/handle/201301/58723
Fulltext
https://pubs.acs.org/doi/10.1021/acsami.0c12849
Citation
ACS APPLIED MATERIALS & INTERFACES, v.12, no.44, pp.49362 - 49370
Abstract
Natural killer (NK) cells, which are cytotoxic lymphocytes of the innate immune system and recognize cancer cells via various immune receptors, are promising agents in cell immunotherapy. To utilize NK cells as a therapeutic agent, their biodistribution and pharmacokinetics need to be evaluated following systemic administration. Therefore, in vivo imaging and tracking with efficient labeling and quantitative analysis of NK cells are required. However, the lack of the phagocytic capacity of NK cells makes it difficult to establish breakthroughs in cell labeling and subsequent in vivo studies. Herein, an effective labeling of upconverting nanoparticles (UCNPs) in NK cells is proposed using electroporation with high sensitivity and stability. The labeling performance of UCNPs functionalized with carboxy-polyethylene glycol (PEG) is better than with methoxy-PEG or with amine-PEG. The labeling efficiency becomes higher, but cell damage is greater as electric field increases; thus, there is an optimum electroporation condition for internalization of UCNPs into NK cells. The tracking and biodistribution imaging analyses of intravenously injected NK cells show that the labeled NK cells are initially distributed primarily in lungs and then spread to the liver and spleen. These advances will accelerate the application of NK cells as key components of immunotherapy against cancer.
Publisher
AMER CHEMICAL SOC
ISSN
1944-8244
Keyword (Author)
upconverting nanoparticlescell labelingelectroporationnatural killer cellimmunotherapyin vivo image tracking

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