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Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging

机译:拉曼光谱成像技术鉴定和分化外周血单细胞

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摘要

Medical diagnosis can be improved significantly by fast, highly sensitive and quantitative cell identification from easily accessible body fluids. Prominent examples are disseminated tumor cells circulating in the peripheral blood of cancer patients. These cells are extremely rare and therefore difficult to detect. In this contribution we present the Raman spectroscopic characterization of different cells that can be found in peripheral blood such as leukocytes, leukemic cells and solid tumor cells. Leukocytes were isolated from the peripheral blood from healthy donors. Breast carcinoma derived tumor cells (MCF-7, BT-20) and myeloid leukaemia cells (OCI-AML3) were prepared from cell cultures. Raman images were collected from dried cells on calcium fluoride slides using 785 nm laser excitation. Unsupervised statistical methods (hierarchical cluster analysis and principal component analysis) were used to visualize spectral differences and cluster formation according to the cell type. With the help of supervised statistical methods (support vector machines) a classification model with 99.7% accu- racy rates for the differentiation of the cells was built. The model was successfully applied to identify single cells from an independent mixture of cells based on their vibrational spectra. The classification was confirmed by fluorescence staining of the cells after the Raman measurement.
机译:通过从易于获取的体液中进行快速,高度敏感和定量的细胞鉴定,可以大大改善医学诊断。突出的例子是在癌症患者外周血中循环的弥散性肿瘤细胞。这些细胞极为罕见,因此难以检测。在这项贡献中,我们介绍了在外周血中可以发现的不同细胞的拉曼光谱表征,例如白细胞,白血病细胞和实体瘤细胞。从健康供体的外周血中分离出白细胞。从细胞培养物中制备乳腺癌来源的肿瘤细胞(MCF-7,BT-20)和骨髓性白血病细胞(OCI-AML3)。使用785 nm激光激发从氟化钙玻片上的干燥细胞中收集拉曼图像。无监督统计方法(层次聚类分析和主成分分析)用于根据细胞类型可视化光谱差异和聚类形成。借助监督统计方法(支持向量机),建立了具有99.7%准确率的细胞分化分类模型。该模型已成功应用于根据振动光谱从独立的细胞混合物中识别单个细胞。通过拉曼测量后细胞的荧光染色来确认分类。

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