Department of Applied Chemistry and Institute of Molecular Science, National Chiao Tung University, Hsinchu 30010, Taiwan.
Anal Chem. 2012 Jul 3;84(13):5661-8. doi: 10.1021/ac300834f. Epub 2012 Jun 11.
Cellular processes are intrinsically complex and dynamic, in which a myriad of cellular components including nucleic acids, proteins, membranes, and organelles are involved and undergo spatiotemporal changes. Label-free Raman imaging has proven powerful for studying such dynamic behaviors in vivo and at the molecular level. To construct Raman images, univariate data analysis has been commonly employed, but it cannot be free from uncertainties due to severely overlapped spectral information. Here, we demonstrate multivariate curve resolution analysis for time-lapse Raman imaging of a single dividing yeast cell. A four-dimensional (spectral variable, spatial positions in the two-dimensional image plane, and time sequence) Raman data "hypercube" is unfolded to a two-way array and then analyzed globally using multivariate curve resolution. The multivariate Raman imaging thus accomplished successfully disentangles dynamic changes of both concentrations and distributions of major cellular components (lipids, proteins, and polysaccharides) during the cell cycle of the yeast cell. The results show a drastic decrease in the amount of lipids by ~50% after cell division and uncover a protein-associated component that has not been detected with previous univariate approaches.
细胞过程本质上是复杂和动态的，其中涉及到包括核酸、蛋白质、膜和细胞器在内的无数细胞成分，并经历时空变化。无标记拉曼成像已被证明可用于研究体内和分子水平的这种动态行为。为了构建拉曼图像，通常采用单变量数据分析，但由于光谱信息严重重叠，它不能免除不确定性。在这里，我们展示了用于单个分裂酵母细胞的时程拉曼成像的多变量曲线解析分析。将四维（光谱变量、二维图像平面中的空间位置和时间序列）拉曼数据“超立方体”展开成二维数组，然后使用多变量曲线解析进行全局分析。因此，成功完成的多变量拉曼成像分离了酵母细胞细胞周期中主要细胞成分（脂质、蛋白质和多糖）的浓度和分布的动态变化。结果表明，细胞分裂后脂质的量急剧减少了约 50%，并揭示了以前的单变量方法未检测到的与蛋白质相关的成分。
