Label-Free Resonance Rayleigh Scattering Amplification for Lipopolysaccharide Detection and Logical Circuit by CRISPR/Cas12a-Driven Guanine Nanowire Assisted Non-Cross-Linking Hybridization Chain Reaction

化学 清脆的 生物分子 纳米技术 适体 计算生物学 生物系统 分子生物学 生物化学 基因 材料科学 生物
作者
Zhong Feng Gao,Lin Lin Zheng,Lu Ming Dong,Jin Ze Li,Yizhong Shen,Pu Chen,Fan Xia
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:94 (16): 6371-6379 被引量:32
标识
DOI:10.1021/acs.analchem.2c00848
摘要

Although the CRISPR/Cas system has pioneered a new generation of analytical techniques, there remain many challenges in developing a label-free, accurate, and reliable CRISPR/Cas-based assay for reporting the levels of low abundance biomolecules in complex biological samples. Here, we reported a novel CRISPR-derived resonance Rayleigh scattering (RRS) amplification strategy and logical circuit based on a guanine nanowire (G-wire) assisted non-cross-linking hybridization chain reaction (GWancHCR) for label-free detection of lipopolysaccharide (LPS). In the presence of a target, the protospacer-adjacent motif-inserted aptamer is rationally designed to specifically combine with LPS rather than Cas12a, suppressing the trans-cleavage activity of CRISPR/Cas12a and retaining the reporter probes to trigger non-cross-linking aggregation. Owing to the automatic hybridization chain reaction (HCR), in the presence of Mg2+, the released G-quadruplex sequence aggregated to assemble the G-wire superstructure through non-cross-linking. As a result, a dramatically amplified RRS intensity is observed, allowing for reporting LPS levels in a low detection limit of 0.17 pg/mL and a wide linear range among 1.0-100.0 ng/mL. Moreover, this reaction event is capable of programming to perform classical Boolean logic tree analysis, including basic logic computing and complex integrated logic circuits. This study comprehensively analyzed with respect to information flow, matter (molecular events), and energy (RRS), revealing the potential promise in designing of molecular-level "Internet of Things", intelligent computing, and sensing systems.
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