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Research Article

Highly efficient and multidimensional extraction of targets from complex matrices using aptamer-driven recognition

Jie Wang1Haijing Shen1Chi Huang1Qinqin Ma1Yaning Tan1Fenglei Jiang1Chao Ma2Quan Yuan1( )
Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education),College of Chemistry and Molecular Sciences, Wuhan University,Wuhan,430072,China;
Hefei National Laboratory for Physical Sciences at the Microscale,University of Science and Technology of China,Hefei,230026,China;
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Abstract

Adsorbents are widely employed in both fundamental and applied research areas such as separation technology, biotechnology, and environmental science. Selectivity and reusability are two most important requirements for adsorbents. Aptamers exhibit perfect selectivity and easy regeneration, which make them uniquely effective adsorption materials. Herein, we have rationally designed novel aptamer-based adsorbents and investigated their performance in extraction/separation of targets from an aqueous solution. These adsorbents can selectively extract targets from complicated sample matrices containing background com­pounds. Moreover, they can also be easily recycled without a significant loss of adsorption capacity. Notably, the adsorbents did not affect the activity of isolated biological samples, revealing their potential for the purification/separation of biomolecules. Composite adsorbents were constructed using aptamer-based adsorbents and a porous polymer, displaying highly efficient target separation from aqueous solution. Finally, separation columns were constructed, and targets in the aqueous solution were efficiently separated by these columns. The aptamer- based adsorbents described here exhibit great promise for potential applications in separation technology, biotechnology, and environment-related areas.

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Nano Research
Pages 145-156
Cite this article:
Wang J, Shen H, Huang C, et al. Highly efficient and multidimensional extraction of targets from complex matrices using aptamer-driven recognition. Nano Research, 2017, 10(1): 145-156. https://doi.org/10.1007/s12274-016-1273-9

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Received: 26 June 2016
Revised: 05 August 2016
Accepted: 31 August 2016
Published: 29 September 2016
© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016
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