Cheng Jie, Zhu Xiaoping, Cheng Hao, Zhao Hong, Wong Stephen T C
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:136-9. doi: 10.1109/EMBC.2013.6609456.
Actin is one of the most abundant proteins in eukaryote cells, playing a key role in cell dynamic morphological alterations and tumor metastatic spread. To investigate the relationship between the distribution patterns of actin and the aggressiveness of cancer cells, we developed an image analysis framework for quantifying cell F-actin distributions examined with fluorescence microscopy. The images are first segmented with multichannel information of both F-actin and nuclear staining. Using the watershed method and Voronoi tessellation, the cells can be well segmented based on both F-actin and nuclear information. Altogether, sixteen F-actin distribution features are calculated for each individual cell. A linear Support Vector Machine (SVM) is then applied in the feature space to separate cells with different modes of motility. Our results show that cells with different modes of motility can be distinguished by F-actin distributions. To our knowledge, this is the first study managing to distinguish cancer cells with different aggressiveness based on quantitative analysis of F-actin distribution patterns.
肌动蛋白是真核细胞中含量最丰富的蛋白质之一，在细胞动态形态改变和肿瘤转移扩散中起关键作用。为了研究肌动蛋白的分布模式与癌细胞侵袭性之间的关系，我们开发了一个图像分析框架，用于量化通过荧光显微镜检查的细胞F-肌动蛋白分布。首先利用F-肌动蛋白和细胞核染色的多通道信息对图像进行分割。使用分水岭法和Voronoi镶嵌，基于F-肌动蛋白和细胞核信息可以很好地分割细胞。总共为每个单独的细胞计算16个F-肌动蛋白分布特征。然后在特征空间中应用线性支持向量机（SVM）来分离具有不同运动模式的细胞。我们的结果表明，具有不同运动模式的细胞可以通过F-肌动蛋白分布来区分。据我们所知，这是第一项基于F-肌动蛋白分布模式的定量分析成功区分具有不同侵袭性癌细胞的研究。
