Mokhtari Zeinab, Mech Franziska, Zitzmann Carolin, Hasenberg Mike, Gunzer Matthias, Figge Marc Thilo
Applied Systems Biology, HKI-Center for Systems Biology of Infection, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena, Germany ; Friedrich Schiller University Jena, Germany.
PLoS One. 2013 Dec 6;8(12):e80808. doi: 10.1371/journal.pone.0080808. eCollection 2013.
Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.
细胞迁移是许多不同生物过程动态变化的驱动力。尽管如今显微镜实验已常规进行，通过这些实验可在空间和时间上观察细胞群体，但图像数据中包含的有价值信息常常被忽视，因为统计分析是在细胞群体层面而非单细胞层面进行的。基于图像的系统生物学是一种现代方法，旨在通过开发用于解释图像数据的新策略和工具，对生物过程进行定量分析和建模。在本研究中，我们朝着对细胞轨迹数据进行全自动特征描述和无参数分类迈出了第一步，这种分类可普遍应用于从图像数据中获取的被跟踪对象。实现这一目标的要求包括：（i）结合单细胞轨迹的不同测量方法，如轨迹体积的限制率和非球形度，以及（ii）以交错方式计算这些测量值，以便从轨迹片段的所有可能组合中检索局部信息。我们针对合成细胞轨迹群体以及从显微镜实验获得的体外中性粒细胞轨迹证明，通过这种方式可充分利用轨迹数据中包含的信息，且不需要任何先验知识，这使得分析保持无偏且通用。通过在交错测量的参数空间中对细胞轨迹进行凝聚层次聚类，可在所有细胞群体中识别出表现出相同类型迁移行为的细胞。对特征模式的识别对于推进我们对生物过程动态变化的认识非常有必要。
