循环肿瘤细胞非标记表面增强拉曼光谱鉴定方法研究

Circulating tumor cells (CTCs) are tumor cells coming from situ growth or metastatic lesions shedding into the peripheral blood. Clinical studies have shown that the CTCs can be used as a marker of cancer diagnosis, therapeutic assessments, prognosis judgment, and so on. Currently the identification of CTCs needs to disrupt cells, or to detect the specific molecules on the surface of cells by labeling, whose accuracy depends on the expression of targets molecules. This project intends to develop a label-free and non-invasive identification method for CTCs based on surface-enhanced Raman scattering (SERS). SERS is a technique of molecular vibration spectroscopy that can reveal the great differences of nucleus mass ratio between tumor cells and normal cells, as well as the differences of other molecular components. We have developed several multivariate statistical analysis methods which can sensitively analyte Raman spectroscopy data. On these bases, this identification method plans to employ metal film with periodic nanostructures as SERS substrates, by optimizing the parameters of nanostructures, achieving label-free SERS measurements of cells with high-sensitivities and high-stabilities. Then on the base of using this substrate to study the spectral characteristics of tumor cells and normal cells, a hybridization multivariate statistical analysis method jointing feature selection and pattern recognition is developed for non-invasively and quickly identifying tumor cells SERS spectra.

循环肿瘤细胞(Circulating tumor cells，CTCs)是由原发或转移病灶脱落进入外周血的肿瘤细胞，CTCs的检测可以有效的用于癌症的诊断、评估和预后等。当前CTCs的鉴定通过破碎细胞，或者通过标记检测细胞表面特异性分子，其结果依赖目标分子的表达。本课题拟发展一种基于表面增强拉曼散射（SERS）技术的CTCs非标记无损伤鉴定方法。SERS是一种分子振动谱技术，能揭示肿瘤细胞与正常细胞核质比的巨大差异以及其他分子成分的差异。本课题组前期发展的多种多变量统计分析方法可高灵敏分析拉曼光谱。在此基础上，本方法拟采用周期性结构金属膜为SERS基底，通过对SERS基底结构参数的优化，实现对细胞高灵敏高稳定性的非标记SERS测量。然后在利用该基底研究肿瘤细胞和正常细胞SERS光谱特性的基础上，发展一个联合特征选择技术和模式识别技术的复合多变量统计分析方法，实现对肿瘤细胞的快速无损鉴别。

循环肿瘤细胞的鉴定是循环肿瘤细胞检测的重要环节，快速准确的鉴定循环肿瘤细胞对循环肿瘤细胞的检测具有重要意义。拉曼光谱是一种分子振动谱，能够提供关于分子成分和结构的指纹信息。肿瘤细胞的核质比一般远大于正常细胞。本项目利用银膜SERS基底对多种肿瘤细胞、临床组织分离肿瘤细胞和血液细胞的SERS光谱进行了研究，结果表明大部分肿瘤细胞的核酸特征峰730cm-1高于白细胞，红细胞没有核酸特征峰。项目还发展了残差神经网络分类算法，实现了对肿瘤细胞和血液细胞的快速准确识别。本研究为循环肿瘤细胞的非标记检测奠定了坚实的基础。本项目还对相关领域进行了拓展研究，包括基于血清SERS光谱技术的膀胱癌分期鉴定方法研究；石墨烯-金属纳米粒子复合SERS免疫检测体系研究；基于光学弱测量技术的手性混合物纯度检测方法研究。