The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Differential Roles of the Protein Corona in the Cellular Uptake of Nanoporous Polymer Particles by Monocyte and Macrophage Cell Lines
Open accessAbstract
Many biomolecules, mainly proteins, adsorb onto polymer particles to form a dynamic protein corona in biological environments. The protein corona can significantly influence particle-cell interactions, including internalization and pathway activation. In this work, we demonstrate the differential roles of a given protein corona formed in cell culture media in particle uptake by monocytes and macrophages. By exposing disulfide-stabilized poly(methacrylic acid) nanoporous polymer particles (PMASH NPPs) to complete cell growth media containing 10% fetal bovine serum, a protein corona, with the most abundant component being bovine serum albumin, was characterized. Upon adsorption onto the PMASH NPPs, native bovine serum albumin (BSA) was found to undergo conformational changes. The denatured BSA led to a significant decrease in internalization efficiency in human monocytic cells, THP-1, compared with the bare particles, due to reduced cell membrane adhesion. In contrast, the unfolded BSA on the NPPs triggered class A scavenger receptor-mediated phagocytosis in differentiated macrophage-like cells (dTHP-1) without a significant impact on the overall internalization efficiency. Taken together, this work demonstrates the disparate effects of a given protein corona on particle-cell interactions, highlighting the correlation between protein corona conformation in situ and relevant biological characteristics for biological functionalities.
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Cites
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Monopoli, M. P., Walczyk, D., Campbell, A., Elia, G., Lynch, I., Baldelli Bombelli, F., & Dawson, K. A. (2011) · Journal of the American Chemical Society · doi:10.1021/ja107583h · Cited by 1,835
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Dobrovolskaia, M. A., & McNeil, S. E. (2007) · Nature Nanotechnology · doi:10.1038/nnano.2007.223 · Cited by 1,823
Walkey, C. D., & Chan, W. C. W. (2012) · Chem. Soc. Rev. · doi:10.1039/c1cs15233e · Cited by 1,758
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Metadata
- Authors
- Yan, Y., Gause, K. T., Kamphuis, M. M. J., Ang, C.-S., O’Brien-Simpson, N. M., Lenzo, J. C., Reynolds, E. C., Nice, E. C., & Caruso, F.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-11-20
- Volume
- 7
- Issue
- 12
- Pages
- 10960-10970
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2071985438
- Cited by
- 322
- References
- 4
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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