Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
Analyst. 2013 Jul 21;138(14):3957-66. doi: 10.1039/c3an00507k. Epub 2013 May 3.
FTIR micro-spectral images of Caki-2 cells cytospun onto calcium fluoride (CaF2) slides were used to build a computational model in order to discriminate between the biochemical events of the continuous cell cycle during proliferation. Multivariate analysis and machine learning techniques such as PCA, PLSR and SVMs were used to highlight the chemical differences among the cell cycle phases and also to point out the need for removing the distortion of the spectra due to the morphology of the cells. Results showed cell cycle dependant scattering profiles that enabled the training of a SVM in order to recognise, with a relative high accuracy, each cell cycle phase purely with the scattering curve removed from the FTIR data after being subject to the RMieS-EMSC algorithm.
将人肾透明细胞癌细胞系(Caki-2)离心涂覆到氟化钙(CaF2)载玻片上的 FTIR 微光谱图像被用于建立一个计算模型,以便在增殖过程中区分连续细胞周期的生化事件。多元分析和机器学习技术,如 PCA、PLSR 和 SVMs,用于突出细胞周期阶段之间的化学差异,并指出需要消除由于细胞形态引起的光谱失真。结果显示出细胞周期依赖的散射分布,这使得可以训练 SVM,以便使用从 FTIR 数据中去除散射曲线后,通过 RMieS-EMSC 算法对其进行处理,相对高精度地识别每个细胞周期阶段。