期刊
ACS APPLIED MATERIALS & INTERFACES
卷 13, 期 48, 页码 57067-57074出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c20657
关键词
tetrahedral framework nucleic acids (TFNAs); different sizes; endocytosis; proliferation; migration
资金
- National Key R&D Program of China [2019YFA0110600]
- National Natural Science Foundation of China [81970916, 82101077]
- Open Research Program of National Facility for Translational Medicine (Shanghai) [TMSK-2021-204]
- Sichuan University Postdoctoral Interdisciplinary Innovation Fund
- Fundamental Research Funds for the Central Universities
- Postdoctoral Science Foundation of China [2021M692271, 2020T130443]
This study focuses on the interaction of differently sized TFNAs with ASCs, revealing that T21 has the best membrane-penetrating ability and enhances cell proliferation and migration. Importantly, T21 can access the brain and accumulate over time.
With the advent of nanotechnology, DNA nanostructures have been widely applied in various fields, particularly biology and biomedicine. Tetrahedral framework nucleic acids (TFNAs), a novel type of DNA nanomaterial, have attracted considerable attention due to their simple synthesis, high accessibility, structural stability, and versatility. However, to date, the interaction of differently sized TFNAs with living systems and their ability to be endocytosed and biodistributed in mouse is still not fully understood. To screen for the optimal TFNA size and structures, TFNA endocytosis, proliferation, and migration were tested in adipose stem cells (ASCs). We found that the internalization of differently sized TFNAs in ASCs was remarkably different. Although all TFNAs could enter ASCs, T21 had the best membrane-penetrating ability. After exposure of ASCs to TFNAs of different sizes, the proliferation and migration of cells were enhanced, especially with T21. Importantly, T21 could access the brain and accumulate over time. This study improves our understanding of the influence of TFNA size on the biological behavior of ASCs, which will help in choosing optimal TFNA size for biomedical applications.
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