Taking Orders from Light: Photo-Switchable Working/Inactive Smart Surfaces for Protein and Cell Adhesion

• Published in: ACS applied materials & interfaces Vol. 9; no. 10; pp. 8498 - 8507
• Main Authors: Zhang, Junji, Ma, Wenjing, He, Xiao-Peng, Tian, He
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 15.03.2017
• Subjects: Carbohydrates; Cell Adhesion; Electrochemical Techniques; Electrochemistry; Light; Proteins; Ultraviolet Rays; azobenzene; monosaccharide protein recognition; photoswitching; cell adhesion; self-assembled monolayer
• ISSN: 1944-8244; 1944-8252
• DOI: 10.1021/acsami.6b15599

Photoresponsive smart surfaces are promising candidates for a variety of applications in optoelectronics and sensing devices. The use of light as an order signal provides advantages of remote and noninvasive control with high temporal and spatial resolutions. Modification of the photoswitches with target biomacromolecules, such as peptides, DNA, and small molecules including folic acid derivatives and sugars, has recently become a popular strategy to empower the smart surfaces with an improved detection efficiency and specificity. Herein, we report the construction of photoswitchable self-assembled monolayers (SAMs) based on sugar (galactose/mannose)-decorated azobenzene derivatives and determine their photoswitchable, selective protein/cell adhesion performances via electrochemistry. Under alternate UV/vis irradiation, interconvertible high/low recognition and binding affinity toward selective lectins (proteins that recognize sugars) and cells that highly express sugar receptors are achieved. Furthermore, the cis-SAMs with a low binding affinity toward selective proteins and cells also exhibit minimal response toward unselective protein and cell samples, which offers the possibility in avoiding unwanted contamination and consumption of probes prior to functioning for practical applications. Besides, the electrochemical technique used facilitates the development of portable devices based on the smart surfaces for on-demand disease diagnosis.