Henkel Andreas W, Al-Abdullah Lulwa A A D, Al-Qallaf Mohammed S, Redzic Zoran B
Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait.
Front Neuroinform. 2019 Mar 12;13:15. doi: 10.3389/fninf.2019.00015. eCollection 2019.
Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.
在密集培养物中对单个细胞及其细胞过程的迁移率进行量化是一项挑战,因为不可能对单个细胞进行追踪。我们开发了一种用于细胞结构分割的软件,并实施了两种算法来测量运动速度。对复杂算法进行了测试,以分离细胞和细胞成分,这是获取有意义的运动数据的重要前提。进行质膜分割以测量膜收缩动力学和细胞器运输。在不同细胞类型上测试了算法的判别性能和灵敏度,并在计算机模拟细胞上进行校准以获得细胞速度的绝对值。根据细胞运动的复杂性,两种运动算法在不同的实验设置中都有优势。相关算法(COPRAMove)在大多数测试条件下表现最佳,并且与分化算法(DiffMove)相比,对可变细胞密度、亮度和焦点变化的敏感度较低。总之,我们的软件可成功用于分析和量化密集细胞培养物中的细胞和亚细胞运动。