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首页> 外文会议>Biomedical vibrational spectroscopy VI: Advances in research and industry >LABEL-FREE HAEMOGRAM USING WAVELENGTH MODULATED RAMAN SPECTROSCOPY FOR IDENTIFYING IMMUNE-CELL SUBSET
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LABEL-FREE HAEMOGRAM USING WAVELENGTH MODULATED RAMAN SPECTROSCOPY FOR IDENTIFYING IMMUNE-CELL SUBSET

机译：使用波长调制拉曼光谱的无标签血流图识别免疫细胞亚群

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Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy ＞85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CDlc+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.
机译：哺乳动物血液中的白细胞形成了针对各种危险病原体的强大保护系统。有几种类型的免疫细胞在整个免疫系统中具有特定作用。免疫细胞的数量和类型在疾病状态中会发生变化，识别免疫细胞的类型可提供有关人的健康状况的信息。有几个免疫细胞亚群，它们在形态上基本相同，需要外部标记才能区分。在这里，我们展示了使用波长调制拉曼光谱（WMRS）和合适的机器学习算法作为无标记方法来区分不同的紧密免疫细胞亚群的可行性。对来自单细胞的WMRS数据进行主成分分析（PCA），使用共聚焦拉曼显微镜进行特征缩减，然后使用支持向量机（SVM）对各种细胞亚群进行二元辨别，得出的准确度> 85％。该方法成功地区分了未接触和未固定的CD4 + CD3 +和CD8 + CD3 + T淋巴细胞亚群的纯化种群，以及具有高度特异性的CD56 + CD3-自然杀伤细胞。还证明了它足够灵敏，可以识别独特的拉曼信号，从而可以清楚地区分包括CD303 + CD45 +浆细胞样细胞和CDlc + CD141 +髓样树突细胞在内的树突细胞亚群。这项研究的结果清楚地表明WMRS是高度敏感的，可以区分形态相同的细胞类型。

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