Tsukada Yuki, Aoki Kazuhiro, Nakamura Takeshi, Sakumura Yuichi, Matsuda Michiyuki, Ishii Shin
Laboratory for Systems Biology, Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.
PLoS Comput Biol. 2008 Nov;4(11):e1000223. doi: 10.1371/journal.pcbi.1000223. Epub 2008 Nov 14.
Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6-8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function.
延时荧光显微镜技术的进步使我们能够直接观察动态细胞现象。尽管这些技术本身促进了对动态细胞功能的理解，但获取的大量图像催生了对自动处理工具的需求，以提取统计信息。分析延时细胞图像的一个潜在问题是缺乏严格的方法来提取形态动力学特性。在此，我们提出一种名为边缘演化跟踪（EET）的算法，用于使用延时显微镜图像量化细胞边缘周围局部形态变化与局部荧光强度之间的关系。该算法通过定义细分边缘及其在连续帧中的相应位置，使我们能够追踪局部边缘的延伸和收缩。因此，该算法通过考虑时间偏移，能够研究局部形态变化与荧光信号局部强度之间的互相关。通过将EET应用于Rho家族GTP酶Rac1、Cdc42和RhoA的荧光共振能量转移图像，我们研究了局部面积差异与GTP酶活性之间的互相关。计算得到的相关性随时间偏移如预期那样变化，但令人惊讶的是，相关系数的峰值出现在形态变化有6 - 8分钟的时间偏移时，且先于Rac1或Cdc42的活性。我们的方法能够通过考虑关系中的时间偏移来量化局部形态变化和局部蛋白质活性的动态，并对它们之间的关系进行统计研究。因此，该算法扩展了延时成像数据的价值，以更好地理解细胞功能的动态。
