American Chemical Society
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Low-Noise Solid-State Nanopore Enhancing Direct Label-Free Analysis for Small Dimensional Assemblies Induced by Specific Molecular Binding

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journal contribution
posted on 2021-01-21, 17:33 authored by Ruiping Wu, Yesheng Wang, Zhentong Zhu, Chunmiao Yu, Huan Li, Bingling Li, Shaojun Dong
Solid-state nanopores show special potential as a new single-molecular characterization for nucleic acid assemblies and molecular machines. However, direct recognition of small dimensional species is still quite difficult due the lower resolution compared with biological pores. We recently reported a very efficient noise-reduction and resolution-enhancement mechanism via introducing high-dielectric additives (e.g., formamide) into conical glass nanopore (CGN) test buffer. Based on this advance, here, for the first time, we apply a bare CGN to directly recognize small dimensional assemblies induced by small molecules. Cocaine and its split aptamer (Capt assembly) are chosen as the model set. By introducing 20% formamide into CGN test buffer, high cocaine-specific distinguishing of the 113 nt Capt assembly has been realized without any covalent label or additional signaling strategies. The signal-to-background discrimination is much enhanced compared with control characterizations such as gel electrophoresis and fluorescence resonance energy transfer (FRET). As a further innovation, we verify that low-noise CGN can also enhance the resolution of small conformational/size changes happening on the side chain of large dimensional substrates. Long duplex concatamers generated from the hybridization chain reaction (HCR) are selected as the model substrates. In the presence of cocaine, low-noise CGN has sensitively captured the current changes when the 26 nt aptamer segment is assembled on the side chain of HCR duplexes. This paper proves that the introduction of the low-noise mechanism has significantly improved the resolution of the solid-state nanopore at smaller and finer scales and thus may direct extensive and deeper research in the field of CGN-based analysis at both single-molecular and statistical levels, such as molecular recognition, assembly characterization, structure identification, information storage, and target index.