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利用微流控芯片结合多重 SERS 纳米载体和多元分析对循环肿瘤细胞表型进行原位分析。

Combining Multiplex SERS Nanovectors and Multivariate Analysis for In Situ Profiling of Circulating Tumor Cell Phenotype Using a Microfluidic Chip.

机构信息

Advanced Photonics Center, Southeast University, Nanjing, 210096, China.

出版信息

Small. 2018 May;14(20):e1704433. doi: 10.1002/smll.201704433. Epub 2018 Apr 17.

Abstract

Isolating and in situ profiling the heterogeneous molecular phenotype of circulating tumor cells are of great significance for clinical cancer diagnosis and personalized therapy. Herein, an on-chip strategy is proposed that combines size-based microfluidic cell isolation with multiple spectrally orthogonal surface-enhanced Raman spectroscopy (SERS) analysis for in situ profiling of cell membrane proteins and identification of cancer subpopulations. With the developed microfluidic chip, tumor cells are sieved from blood on the basis of size discrepancy. To enable multiplex phenotypic analysis, three kinds of spectrally orthogonal SERS aptamer nanovectors are designed, providing individual cells with composite spectral signatures in accordance with surface protein expression. Next, to statistically demultiplex the complex SERS signature and profile the cellular proteomic phenotype, a revised classic least square algorithm is employed to obtain the 3D phenotypic information at single-cell resolution. Combined with categorization algorithm partial least square discriminate analysis, cells from different human breast cancer subtypes can be reliably classified with high sensitivity and selectivity. The results demonstrate that this platform can identify cancer subtypes with the spectral information correlated to the clinically relevant surface receptors, which holds great potential for clinical cancer diagnosis and precision medicine.

摘要

分离和原位分析循环肿瘤细胞的异质分子表型对于临床癌症诊断和个性化治疗具有重要意义。本文提出了一种在芯片上的策略,将基于大小的微流控细胞分离与多种光谱正交的表面增强拉曼光谱(SERS)分析相结合,用于原位分析细胞膜蛋白和鉴定癌症亚群。通过开发的微流控芯片,根据大小差异从血液中筛选肿瘤细胞。为了实现多指标表型分析,设计了三种光谱正交的 SERS 适体纳米载体,根据表面蛋白表达为单个细胞提供复合光谱特征。接下来,为了统计地解复用复杂的 SERS 特征并分析细胞蛋白质组表型,采用修正的经典最小二乘法算法以单细胞分辨率获得 3D 表型信息。结合分类算法偏最小二乘判别分析,可以高灵敏度和选择性地可靠分类来自不同人类乳腺癌亚型的细胞。结果表明,该平台可以使用与临床相关表面受体相关的光谱信息来识别癌症亚型,这对于临床癌症诊断和精准医学具有很大的潜力。

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