Hedegaard Martin A B, Bergholt Mads S, Stevens Molly M
Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
J Biophotonics. 2016 May;9(5):542-50. doi: 10.1002/jbio.201500238. Epub 2016 Feb 2.
Imaging by Raman spectroscopy enables unparalleled label-free insights into cell and tissue composition at the molecular level. With established approaches limited to single image analysis, there are currently no general guidelines or consensus on how to quantify biochemical components across multiple Raman images. Here, we describe a broadly applicable methodology for the combination of multiple Raman images into a single image for analysis. This is achieved by removing image specific background interference, unfolding the series of Raman images into a single dataset, and normalisation of each Raman spectrum to render comparable Raman images. Multivariate image analysis is finally applied to derive the contributing 'pure' biochemical spectra for relative quantification. We present our methodology using four independently measured Raman images of control cells and four images of cells treated with strontium ions from substituted bioactive glass. We show that the relative biochemical distribution per area of the cells can be quantified. In addition, using k-means clustering, we are able to discriminate between the two cell types over multiple Raman images. This study shows a streamlined quantitative multi-image analysis tool for improving cell/tissue characterisation and opens new avenues in biomedical Raman spectroscopic imaging.
拉曼光谱成像能够在分子水平上对细胞和组织成分进行无与伦比的无标记洞察。由于现有方法仅限于单图像分析，目前对于如何在多个拉曼图像中量化生化成分尚无通用指南或共识。在此，我们描述了一种广泛适用的方法，可将多个拉曼图像组合成单个图像进行分析。这是通过去除图像特定的背景干扰、将一系列拉曼图像展开为单个数据集以及对每个拉曼光谱进行归一化以生成可比较的拉曼图像来实现的。最后应用多变量图像分析来推导用于相对定量的有贡献的“纯”生化光谱。我们使用对照细胞的四个独立测量的拉曼图像以及来自替代生物活性玻璃的经锶离子处理的细胞的四个图像来展示我们的方法。我们表明可以量化细胞每单位面积的相对生化分布。此外，使用k均值聚类，我们能够在多个拉曼图像上区分这两种细胞类型。这项研究展示了一种简化的定量多图像分析工具，用于改善细胞/组织表征，并为生物医学拉曼光谱成像开辟了新途径。
