Broadband CARS Hyperspectral Classification of Single Immune Cells.

Ryan Muddiman, Sarah Harkin, Marion Butler, Bryan Hennelly
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Abstract

Broadband CARS is a coherent Raman scattering technique that provides access to the full biological vibrational spectrum within milliseconds, facilitating the recording of widefield hyperspectral Raman images. In this work, BCARS hyperspectral images of unstained cells from two different cell lines of immune lineage (T cell [Jurkat] and pDCs [CAL-1]) were recorded and analyzed using multivariate statistical algorithms in order to determine the spectral differences between the cells. A classifier was trained which could distinguish the known cells with a 97% out-of-bag accuracy. The classifier was then applied to unlabeled samples containing a mixture of the two cell types on the same coverslip. This work demonstrates single-cell analysis of pDCs (CAL-1) and T cells (Jurkat) using BCARS. This approach enables an initial validation of cellular classification. We further demonstrate the capability of BCARS cell classification using single spectra of 5 ms acquisition time.

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单个免疫细胞的宽带CARS高光谱分类。
宽带CARS是一种相干拉曼散射技术,可以在几毫秒内访问整个生物振动光谱,促进宽视场高光谱拉曼图像的记录。在这项工作中,记录了来自两种不同免疫谱系细胞系(T细胞[Jurkat]和pDCs [CAL-1])的未染色细胞的BCARS高光谱图像,并使用多元统计算法进行分析,以确定细胞之间的光谱差异。训练了一个分类器,该分类器可以以97%的袋外准确率区分已知细胞。然后将分类器应用于未标记的样品,该样品含有同一盖盖上的两种细胞类型的混合物。这项工作展示了使用BCARS对pDCs (CAL-1)和T细胞(Jurkat)进行单细胞分析。这种方法可以对细胞分类进行初步验证。我们进一步证明了BCARS细胞分类的能力,使用5 ms采集时间的单光谱。
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