標題: 利用單變數與多變數拉曼成像法研究活體酵母菌之細胞週期動態學
In Vivo Univariate and Multivariate Raman Imaging Study of the Dynamics of Yeast Cell Cycle
作者: 黃傳耿
Huang, Chuan-Keng
重藤真介
Shinsuke, Shigeto
應用化學系碩博士班
關鍵字: 活體分裂酵母菌;細胞週期;單變數拉曼成像法;多變數拉曼成像法;in vivo fission yeast cell;cell cycle;univariate Raman imaging;multivariate Raman imaging
公開日期: 2011
摘要: 在生物體的複製過程中,細胞分裂週期扮演著相當重要的角色,主要分成G2、M、G1和S四個階段。過去數十年,分子生物學已經建立了細胞分裂週期的基本架構,但是對於細胞內的分子物質動態學,例如脂質與蛋白質等,仍留存許多尚未明瞭的部份。對於探討活體生物分子的動態學研究,拉曼成像法不需外加標靶物質的特性,已被證明具有相當優良的潛力。因此在此研究中,我們將呈現利用實驗室自製的高靈敏度拉曼顯微光譜儀,研究單一活體酵母菌細胞隨時間變化的拉曼影像。實驗中,細胞分裂期間的各階段 (G2 → M → G1/S → G2) 分子組成與分佈的動態變化,已清楚地被觀測到。我們利用單一變數與多變數的分析方法建構兩種不同的拉曼影像。單變數分析法提供非常簡單且方便的方法建構拉曼影像,但對於具有多樣性拉曼光譜的生物樣品來說,此方法所得的結果,很可能受到來自多種分子造成的重疊光譜資訊所干擾。因此,我們試圖利用多變數曲線解析法(Multivariate Curve Resolution,MCR) 解決這個問題。在酵母菌的細胞週期實驗中,分子隨時間變化的分佈與組成可以透過六個變數的多變數曲線解析法得到藉由三個主要組成(變數)的時間相關之拉曼影像與歸一化的細胞內拉曼訊號的強度總和,我們觀測到脂質與蛋白質的濃度從G2至M再至G1/S階段有逐漸增加的趨勢,並且在細胞分裂前達到最大值。並且研究結果顯示,在細胞分裂之後,脂質的拉曼訊號總強度劇烈地下降至約略一半。此外,我們利用多變數曲線解析法,發現了一種與蛋白質相關的組成,這是在先前使用單一變數分析法所沒有觀察到的。
The cell cycle, which typically includes G2, M, G1, and S phases, plays a pivotal role in the reproduction of all living cells and organisms. Molecular biology of the cell cycle has been established over the decades, but intracellular dynamics of molecular species involved in the cell cycle, including proteins and lipids, remain largely unexplored. Label-free Raman imaging has proven itself powerful for studying such dynamic behaviors in vivo and at the molecular level. Here, we present time-lapse Raman imaging of a single live fission yeast cell achieved with a laboratory-built high-sensitivity Raman microspectrometer. We visualize dynamic changes in molecular composition and distribution during the yeast cell cycle (G2 → M → G1/S → G2). We construct Raman images by two approaches, namely, univariate and multivariate data analyses. Univariate data analysis is a simple and convenient approach to obtain Raman images. However, it may suffer from overlapped spectral information, especially for biological samples. To overcome this difficulty, we also attempt multivariate curve resolution (MCR). Time-dependent variations in distributions and compositions during the cell cycle are well accounted for by assuming six components in the MCR analysis. Time-lapse Raman images and normalized integrated intensities for the three major components derived from MCR show that the concentrations of lipids and proteins increase from G2 to M and G1/S phases and reach their maxima right before cytoplasmic division takes place. Moreover, the results show a drastic decrease in amount of lipids by ~50 % after cell division. Interestingly, the MCR analysis uncovers a protein-associated component that has not been detected with the univariate approach.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079558509
http://hdl.handle.net/11536/41436
Appears in Collections:Thesis


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