Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, NY 13244, USA Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA.
Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244, USA Department of Physics, Syracuse University, Syracuse, NY 13244, USA.
J R Soc Interface. 2014 Aug 6;11(97):20140386. doi: 10.1098/rsif.2014.0386.
Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer-based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.
理解模型环境中单细胞和群体细胞的运动性是当前生物学和生物工程许多研究工作的基础。为了阐明尽管细胞间存在可变性，但细胞行为的细微差异，我们引入了一种用于长时间跟踪大量细胞的算法，并提出了一组基于物理学的度量标准，用于量化细胞轨迹的差异。我们的算法称为用于体外环境的基于自动轮廓的跟踪（ACTIVE），是为受核染色或转染影响的贴壁细胞群体设计的。ACTIVE 与现有的跟踪软件不同，因为它可以适应图像强度的可变性和多细胞相互作用，如分裂和遮挡。当应用于活细胞实验的低对比度图像时，与基准跟踪程序相比，ACTIVE 将分析细胞遮挡事件的误差降低了多达 43%，同时还可以跟踪细胞分裂和由此产生的子细胞关系。ACTIVE 生成的大型数据集使我们能够开发出能够捕捉不同基底上细胞轨迹之间细微差异的度量标准。我们展示了在基于形状记忆聚合物的纳米形貌上以不同密度研究的数千个细胞的细胞迁移数据，并确定了几个定量差异，包括两个“对照”基底之间的意外差异。我们预计 ACTIVE 将立即对需要大量细胞进行准确长时间尺度迁移数据的研究人员有用。
