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A Spatially Varying Charge Model for Regulating Site-selective Protein Adsorption and Cell Behaviors

Abstract

Implanted materials that enter the body first interact with proteins in body fluids, and cells then perceive and respond to the foreign implant through this layer of adsorbed proteins. Thus, spatially specific regulation of protein adsorption on an implant surface is pivotal for mediating subsequent cellular behaviors. Unlike the surface modulation strategy for traditional biomaterials, in this research, materials with a nonuniform spatial distribution of surface charges were designed to achieve site-selective protein adsorption and further influence cell behavior by charge regulation. Spatially varying microdomains with different levels of piezoelectricity were generated via a focus laser beam-induced phase transition. In addition, after polarization, the zones with different levels of piezoelectricity showed significant differences in surface charge density. The results of scanning Kelvin probe force microscopy (SKPM) showed that the surface charge on the material exhibits a nonuniform spatial distribution after laser irradiation and polarization. Site-specific charge-mediated selective protein adsorption was demonstrated through a protein adsorption experiment. Cell behavior analysis showed that the increase in charge density was conducive to promoting cell adhesion and the formation of filopodia while the nonuniform spatial distribution of charge promoted an oriented arrangement of cells; both features accelerated cell migration. This study provides a new method for spatially regulating protein adsorption through surface charges to further influence cell behaviors.

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Publication details

The article was received on 19 Sep 2018, accepted on 21 Nov 2018 and first published on 22 Nov 2018


Article type: Paper
DOI: 10.1039/C8BM01158C
Citation: Biomater. Sci., 2018, Accepted Manuscript
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    A Spatially Varying Charge Model for Regulating Site-selective Protein Adsorption and Cell Behaviors

    K. Zhang, J. Xing, J. Chen, Z. Wang, J. Zhai, T. Yao, G. Tan, S. Qi, D. Chen, P. Yu and C. Ning, Biomater. Sci., 2018, Accepted Manuscript , DOI: 10.1039/C8BM01158C

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