Disentangling Dynamic Changes of Multiple Cellular Components during the Yeast Cell Cycle by in Vivo Multivariate Raman Imaging
Disentangling Dynamic Changes of Multiple Cellular Components during the Yeast Cell Cycle by in Vivo Multivariate Raman Imaging
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DOI：
10.1021/ac300834f
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发表时间：
2012-07-03
影响因子：
7.4
通讯作者：
Shigeto, Shinsuke
中科院分区：
文献类型：
--
作者：
Huang, Chuan-Keng;Ando, Masahiro;Shigeto, Shinsuke
细胞过程本质上是复杂和动态的，其中涉及无数的细胞成分，包括核酸、蛋白质、膜和细胞器，并经历时空变化。无标签拉曼成像已被证明在体内和分子水平上研究这种动态行为是强有力的。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，无法摆脱不确定性。在这里，我们展示了多元曲线分辨率分析的时间推移拉曼成像的单个分裂酵母细胞。一个四维（光谱变量、二维图像平面的空间位置和时间序列）拉曼数据“超立方体”被展开。到一个双向阵列，然后使用多元曲线分辨率进行全局分析。因此，多元拉曼成像成功地解开了酵母细胞周期中主要细胞成分（脂质、蛋白质和多糖）浓度和分布的动态变化。结果显示，在细胞分裂后，脂质数量急剧减少了近50%，并揭示了以前的单变量方法未检测到的蛋白质相关成分。
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 similar to 50% after cell division and uncover a protein-associated component that has not been detected with previous univariate approaches.