Using Cell Membranes as Recognition Layers to Construct Ultrasensitive and Selective Bioelectronic Affinity Sensors

Conventional sandwich immunosensors rely on antibody recognition layers to selectively capture and detect target antigen analytes. However, the fabrication of these traditional affinity sensors is typically associated with lengthy and multistep surface modifications of electrodes and faces the chall...

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Published inJournal of the American Chemical Society Vol. 144; no. 38; pp. 17700 - 17708
Main Authors Vargas, Eva, Zhang, Fangyu, Ben Hassine, Amira, Ruiz-Valdepeñas Montiel, Victor, Mundaca-Uribe, Rodolfo, Nandhakumar, Ponnusamy, He, Putian, Guo, Zhongyuan, Zhou, Zhidong, Fang, Ronnie H., Gao, Weiwei, Zhang, Liangfang, Wang, Joseph
Format Journal Article
LanguageEnglish
Published American Chemical Society 28.09.2022
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Summary:Conventional sandwich immunosensors rely on antibody recognition layers to selectively capture and detect target antigen analytes. However, the fabrication of these traditional affinity sensors is typically associated with lengthy and multistep surface modifications of electrodes and faces the challenge of nonspecific adsorption from complex sample matrices. Here, we report on a unique design of bioelectronic affinity sensors by using natural cell membranes as recognition layers for protein detection and prevention of biofouling. Specifically, we employ the human macrophage (MΦ) membrane together with the human red blood cell (RBC) membrane to coat electrochemical transducers through a one-step process. The natural protein receptors on the MΦ membrane are used to capture target antigens, while the RBC membrane effectively prevents nonspecific surface binding. In an attempt to detect tumor necrosis factor alpha (TNF-α) cytokine using the bioelectronic affinity sensor, it demonstrates a remarkable limit of detection of 150 pM. This new sensor design integrates natural cell membranes and electronic transduction, which offers synergistic functionalities toward a broad range of biosensing applications.
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ISSN:0002-7863
1520-5126
DOI:10.1021/jacs.2c07956