Quantitative hyperspectral coherent Raman scattering microscopy merges imaging with spectroscopy and utilises quantitative data analysis algorithms to extract physically meaningful chemical components, spectrally and spatially-resolved, with sub-cellular resolution. This label-free non-invasive method has the potential to significantly advance our understanding of the complexity of living multicellular systems. Here, we have applied an in-house developed hyperspectral coherent anti-Stokes Raman scattering (CARS) microscope, combined with a quantitative data analysis pipeline, to imaging living mouse liver organoids as well as fixed mouse brain tissue sections xenografted with glioblastoma cells. We show that the method is capable of discriminating different cellular sub-populations, on the basis of their chemical content which is obtained from an unsupervised analysis, i.e. without prior knowledge. Specifically, in the organoids, we identify sub-populations of cells at different phases in the cell cycle, while in the brain tissue, we distinguish normal tissue from cancer cells, and, notably, tumours derived from transplanted cancer stem cells versus non-stem glioblastoma cells. The ability of the method to identify different sub-populations was validated by correlative fluorescence microscopy using fluorescent protein markers. These examples expand the application portfolio of quantitative chemical imaging by hyperspectral CARS microscopy to multicellular systems of significant biomedical relevance, pointing the way to new opportunities in non-invasive disease diagnostics.

中文翻译：

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使用无标记高光谱 CARS 显微镜通过定量化学成像识别多细胞系统中的亚群

定量高光谱相干拉曼散射显微镜将成像与光谱学相结合，并利用定量数据分析算法提取具有物理意义的化学成分，并具有亚细胞分辨率的光谱和空间分辨率。这种无标记的非侵入性方法有可能显着增进我们对活多细胞系统复杂性的理解。在这里，我们应用了内部开发的高光谱相干反斯托克斯拉曼散射（CARS）显微镜，结合定量数据分析流程，对活体小鼠肝脏类器官以及移植有胶质母细胞瘤细胞的固定小鼠脑组织切片进行成像。我们表明，该方法能够根据从无监督分析（即无需先验知识）获得的化学含量来区分不同的细胞亚群。具体来说，在类器官中，我们识别了细胞周期不同阶段的细胞亚群，而在脑组织中，我们将正常组织与癌细胞区分开来，特别是区分源自移植癌症干细胞的肿瘤与非干细胞的肿瘤。胶质母细胞瘤细胞。通过使用荧光蛋白标记的相关荧光显微镜验证了该方法识别不同亚群的能力。这些例子将高光谱 CARS 显微镜定量化学成像的应用范围扩展到具有重要生物医学相关性的多细胞系统，为非侵入性疾病诊断的新机遇指明了道路。