Alizadeh Elaheh, Lyons Samanthe Merrick, Castle Jordan Marie, Prasad Ashok
Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.
School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA.
Integr Biol (Camb). 2016 Nov 7;8(11):1183-1193. doi: 10.1039/c6ib00100a.
We study the shape characteristics of osteosarcoma cancer cell lines on surfaces of differing hydrophobicity using Zernike moments to represent cell shape. We compare the shape characteristics of four invasive cell lines with a corresponding less-invasive parental line on three substrates. Cell shapes of each pair of cell lines are quite close and display overlapping characteristics. To quantitatively study shape changes in high-dimensional parameter space we project down to principal component space and define a vector that summarizes average shape differences. Using this vector we find that three of the four pairs of cell lines show similar changes in shape, while the fourth pair shows a very different pattern of changes. We find that shape differences are sufficient to enable a neural network to classify cells accurately as belonging to the highly invasive or the less invasive phenotype. The patterns of shape changes were also reproducible for repetitions of the experiment. We also find that shape changes on different substrates show similarities between the eight cells studied, but the differences were typically not enough to permit classification. Our paper strongly suggests that shape may provide a means to read out the phenotypic state of some cell types, and shape analysis can be usefully performed using a Zernike moment representation.
我们使用泽尼克矩来表示细胞形状，研究骨肉瘤癌细胞系在不同疏水性表面上的形状特征。我们在三种基质上比较了四种侵袭性细胞系与相应侵袭性较低的亲本细胞系的形状特征。每对细胞系的细胞形状非常接近，并呈现出重叠特征。为了在高维参数空间中定量研究形状变化，我们投影到主成分空间并定义一个总结平均形状差异的向量。使用这个向量，我们发现四对细胞系中的三对显示出相似的形状变化，而第四对显示出非常不同的变化模式。我们发现形状差异足以使神经网络准确地将细胞分类为高度侵袭性或侵袭性较低的表型。实验重复时，形状变化模式也是可重复的。我们还发现，不同基质上的形状变化在所研究的八个细胞之间显示出相似性，但差异通常不足以进行分类。我们的论文强烈表明，形状可能提供一种读出某些细胞类型表型状态的方法，并且使用泽尼克矩表示可以有效地进行形状分析。
