变形虫细胞运动性是许多生物过程的基础,如胚胎发生,免疫反应,伤口愈合,和癌症转移。它的特征是特定的细胞形状变化:膜突起的延伸和缩回,被称为伪足。研究这种类型的细胞运动机制的常用方法是研究突变细胞系运动的表型差异。为了描述这种差异,需要方法来量化迁移细胞的轮廓动力学。AmoePy是一个基于Python的软件包,提供细胞分割工具,轮廓检测以及分析和模拟轮廓动力学。首先,从移动细胞的延时显微镜记录的每一帧中提取细胞轮廓作为节点链的数字表示。然后,当细胞轮廓随时间演变时,跟踪这些节点的动态(称为虚拟标记)。从这些数据来看,可以计算出表征轮廓动力学的各种量,例如虚拟标记的位移或标记链的局部拉伸率。它们的动力学通常在时空图上可视化,所谓的kypographs,其中显示沿细胞轮廓的不同位置的时间演变。使用AmoePy,您可以直接创建kymograph图和视频从实验亮场或荧光图像的活动细胞的堆栈。本章提供了有关如何安装和使用AmoePy的动手指南。
Amoeboid cell motility is fundamental for a multitude of biological processes such as embryogenesis, immune responses, wound healing, and cancer metastasis. It is characterized by specific cell shape changes: the extension and retraction of membrane protrusions, known as pseudopodia. A common approach to investigate the mechanisms underlying this type of cell motility is to study phenotypic differences in the locomotion of mutant cell lines. To characterize such differences, methods are required to quantify the contour dynamics of migrating cells. AmoePy is a Python-based software package that provides tools for cell segmentation, contour detection as well as analyzing and simulating contour dynamics. First, a digital representation of the cell contour as a chain of nodes is extracted from each frame of a time-lapse microscopy recording of a moving cell. Then, the dynamics of these nodes-referred to as virtual markers-are tracked as the cell contour evolves over time. From these data, various quantities can be calculated that characterize the contour dynamics, such as the displacement of the virtual markers or the local stretching rate of the marker chain. Their dynamics is typically visualized in space-time plots, the so-called kymographs, where the temporal evolution is displayed for the different locations along the cell contour. Using AmoePy, you can straightforwardly create kymograph plots and videos from stacks of experimental bright-field or fluorescent images of motile cells. A hands-on guide on how to install and use AmoePy is provided in this chapter.