Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI), Jena, Germany.
Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany.
Sci Rep. 2019 Mar 1;9(1):3317. doi: 10.1038/s41598-019-39725-x.
Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.
免疫细胞的迁移和相互作用通常通过体外迁移和对抗测定的延时显微镜来研究。为了客观地量化细胞的动态行为，可以应用用于自动细胞跟踪的软件工具。然而，许多现有的跟踪算法只能识别整个细胞轨迹的相当短的片段，并依赖于细胞染色来增强细胞分割。虽然我们之前开发的分割方法能够跟踪无标记的细胞，但它仍然经常只能识别短的轨迹片段。在这项研究中，我们确定了轨迹片段化的来源，并提供了解决方案来获得更长的细胞轨迹。这是通过改进低对比度细胞的检测和优化间隙大小参数的值来实现的，该参数定义了在片段之间丢失的细胞位置的数量，这些片段被接受仍然将它们连接成一个轨迹。我们发现，增强的轨迹识别将细胞轨迹的平均长度提高了 2.2 倍。整体识别细胞轨迹将能够研究和量化更复杂的细胞行为模式，例如迁移模式的转变或吞噬效率对先前相互作用的数量和类型的依赖性。这种定量分析将提高我们对免疫细胞在健康和疾病中如何相互作用和发挥功能的理解。
