DNA-encoded plasmonic bubbles aggregating dual-microRNA SERS signals for cancer diagnosis

IF 13.9 Q1 CHEMISTRY, MULTIDISCIPLINARY Aggregate (Hoboken, N.J.) Pub Date : 2024-08-08 DOI:10.1002/agt2.636
Yu Yang, Hao Lu, Dan Fang, Yuyuan Zhang, Yuteng Tang, Songsong Zhao, Jun Yan, Xiaojie Qin, Jianlei Shen, Fan Yang
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Abstract

Solid bubbles have expanded the SERS assay toolbox, but their detection performance in biofluids is still hampered by the irrational design of the plasmonic sensing interface. A plasmonic bubble aggregate-driven DNA-encoded SERS assay is reported here that enables simultaneous, ultrasensitive, and specific detection of multiple miRNAs in blood samples for accurate cancer diagnosis. In this assay, the buoyancy of plasmonic bubbles allows them to self-aggregate at a droplet apex for SERS reconfiguration, form single-layer bubble aggregates with plasmonic nanogaps, and prevent the coffee ring effect during evaporation assembly. Furthermore, DNA-encoded plasmonic bubbles seamlessly couple with dual-color catalytic hybridization assembly to amplify the specific miRNA-responsive Raman signal, and function as both an analyte concentrator and a Raman signal aggregator without external forces. Using these merits, this magnet-free, portable assay achieves femtomolar dual-miRNA quantitation with single-base resolution, simultaneous miRNA detection across four cell lines, and accurate cancer diagnosis (AUC = 1) via analyzing 40 blood samples with machine learning, thus providing a promising tool for clinical diagnosis.

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用于癌症诊断的 DNA编码质子气泡聚集双微粒体 RNA SERS 信号
固体气泡扩展了 SERS 检测工具箱,但其在生物流体中的检测性能仍然受到等离子传感界面设计不合理的影响。本文报告了一种由质子气泡聚合体驱动的 DNA 编码 SERS 检测方法,它能同时、超灵敏、特异性地检测血液样本中的多种 miRNA,从而准确诊断癌症。在这种检测方法中,质子气泡的浮力使它们能够在液滴顶点自我聚集以进行 SERS 重构,形成具有质子纳米间隙的单层气泡聚集体,并防止蒸发组装过程中的咖啡环效应。此外,DNA编码的质子气泡与双色催化杂交组件无缝耦合,以放大特定的 miRNA 响应拉曼信号,并在无外力作用下同时充当分析物浓缩器和拉曼信号聚集器。利用这些优点,这种无磁铁、便携式检测方法实现了单碱基分辨率的飞摩尔双miRNA定量,同时检测四种细胞系的miRNA,并通过机器学习分析40份血液样本,准确诊断癌症(AUC = 1),从而为临床诊断提供了一种前景广阔的工具。
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magnesium chloride (MgCl2)
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4-mercaptobenzoic acid (4-MBA)
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Crystal violet (CV)
来源期刊
CiteScore
17.40
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0.00%
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审稿时长
7 weeks
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