Canver Adam C, Morss Clyne Alisa
1College of Medicine,Drexel University,245 North 15th Street,Philadelphia,PA 19102,USA.
2Mechanical Engineering and Mechanics,Drexel University,3141 Chestnut Street,Philadelphia,PA 19104,USA.
Microsc Microanal. 2017 Feb;23(1):22-33. doi: 10.1017/S1431927617000071.
Quantitative analysis of multicellular organization, cell-cell junction integrity, and substrate properties is essential to understand the mechanisms underlying collective cell migration. However, spatially and temporally defining these properties is difficult within collectively migrating cell groups due to challenges in accurate cell segmentation within the monolayer. In this paper, we present Matlab®-based algorithms to spatially quantify multicellular organization (migration distance, interface roughness, and cell alignment, area, and morphology), cell-cell junction integrity, and substrate features in confocal microscopy images of two-dimensional collectively migrating endothelial monolayers. We used novel techniques, including measuring the migrating front roughness using a parametric curve formulation, automatically binning cells to obtain data as a function of distance from the migrating front, using iterative morphological closings to fully define cell boundaries, quantifying β-catenin localization as a measure of cell-cell junction integrity, and skeletonizing fibronectin to determine fiber length and orientation. These algorithms are widely accessible, as they use common fluorescent markers and Matlab® functions, and provide high-throughput critical feature quantification within collectively migrating cell groups. These image analysis algorithms can help standardize feature quantification among different experimental techniques, cell types, and research groups studying collective cell migration.
对多细胞组织、细胞间连接完整性和底物特性进行定量分析,对于理解集体细胞迁移的潜在机制至关重要。然而,由于在单层内准确进行细胞分割存在挑战,在集体迁移的细胞群体中在空间和时间上定义这些特性很困难。在本文中,我们提出了基于Matlab®的算法,用于在二维集体迁移的内皮单层共聚焦显微镜图像中对多细胞组织(迁移距离、界面粗糙度以及细胞排列、面积和形态)、细胞间连接完整性和底物特征进行空间定量。我们使用了新颖的技术,包括使用参数曲线公式测量迁移前沿粗糙度、自动对细胞进行分箱以获取作为距迁移前沿距离函数的数据、使用迭代形态学闭运算来完全定义细胞边界、将β-连环蛋白定位量化作为细胞间连接完整性的指标,以及对纤连蛋白进行骨架化以确定纤维长度和方向。这些算法广泛适用,因为它们使用常见的荧光标记和Matlab®函数,并在集体迁移的细胞群体中提供高通量关键特征定量。这些图像分析算法有助于在研究集体细胞迁移的不同实验技术、细胞类型和研究组之间实现特征量化的标准化。