基于扭曲混合微流控芯片和多修饰纳米探针的动态液体集成单细胞SERS平台用于癌细胞的无标记检测

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL Analytical Chemistry Pub Date : 2025-04-04 DOI:10.1021/acs.analchem.4c06051
Jiaqi Yang, Ziyun Ye, Qilu Xue, Dandan Li, Minghui Liang, Guoqian Li, Huanhuan Liu, Langlang Yi, Bo Hu, Pengju Yin, Guanqun Ge, Klyuyev Dmitriy, Alexandre Maciuk, Bruno Figadere
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引用次数: 0

摘要

表面增强拉曼散射(SERS)已成为检测单细胞的一种有效的光谱技术。然而,由于纳米探针在生物流体中的聚集以及纳米探针与细胞的低组合降低了SERS检测的灵敏度,因此很难在动态液体中实现单细胞水平的无标记检测。本文开发了一种动态液体集成单细胞SERS (DLISC-SERS)平台,用于单个癌细胞的无标记检测。DLISC-SERS由三部分组成,包括实现纳米探针和细胞高效结合的扭曲混合微流控芯片,通过环形鞘流实现三维动态液体聚焦的商用同轴针,以及提供低噪声SERS检测区域的石英毛细管。扭转混合微流控芯片的混合强度几乎是直接混合微流控芯片的3.67倍。该多功能修饰纳米探针Ag NSs@PEG@3COOH可以在生物流体中稳定分散至少30分钟。基于片段加权相似性的KNN模型对单细胞光谱进行分类,灵敏度、特异性和准确度分别高达100、99.4%和99.5%。该模型的三向分类准确率为95.2%。DLISC-SERS平台是单细胞水平检测癌细胞的有力工具。
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Dynamic Liquid Integrated Single-Cell SERS Platform Based on the Twisted Mixing Microfluidic Chip and Multi-Modified Nanoprobe for the Label-Free Detection of Cancer Cells
Surface-enhanced Raman scattering (SERS) has emerged as a potent spectroscopic technique for the detection of single cells. However, it is difficult to achieve label-free detection at the single-cell level in dynamic liquids because nanoprobe aggregation in biological fluids and the low combination of nanoprobes and cells reduce the sensitivity of SERS detection. Herein, a dynamic liquid integrated single-cell SERS (DLISC-SERS) platform is developed for the label-free detection of single cancer cells. DLISC-SERS consists of three components, including a twisted mixing microfluidic chip to achieve an efficient combination of nanoprobes and cells, a commercial coaxial needle to accomplish 3D dynamic liquid focusing by annular sheath flow, and a quartz capillary to offer a SERS detection area with low noise. The mixing intensity of the twisted mixing microfluidic chip is almost 3.67-fold higher than that of straight mixing. The multifunctionally modified nanoprobe, Ag NSs@PEG@3COOH, can be stably dispersed in biological fluids for at least 30 min. The segment weighting similarity-based KNN model can classify single-cell spectra with sensitivity, specificity, and accuracy up to 100, 99.4, and 99.5%, respectively. The accuracy of the model for three-way classification is 95.2%. The DLISC-SERS platform is a powerful tool for detecting cancer cells at the single-cell level.
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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