Glauche I, Lorenz R, Hasenclever D, Roeder I
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
Cell Prolif. 2009 Apr;42(2):248-63. doi: 10.1111/j.1365-2184.2009.00586.x. Epub 2009 Feb 27.
The analysis of individual cell fates within a population of stem and progenitor cells is still a major experimental challenge in stem cell biology. However, new monitoring techniques, such as high-resolution time-lapse video microscopy, facilitate tracking and quantitative analysis of single cells and their progeny. Information on cellular development, divisional history and differentiation are naturally comprised into a pedigree-like structure, denoted as cellular genealogy. To extract reliable information concerning effecting variables and control mechanisms underlying cell fate decisions, it is necessary to analyse a large number of cellular genealogies.
Here, we propose a set of statistical measures that are specifically tailored for the analysis of cellular genealogies. These measures address the degree and symmetry of cellular expansion, as well as occurrence and correlation of characteristic events such as cell death. Furthermore, we discuss two different methods for reconstruction of lineage fate decisions and show their impact on the interpretation of asymmetric developments. In order to illustrate these techniques, and to circumvent the present shortage of available experimental data, we obtain cellular genealogies from a single-cell-based mathematical model of haematopoietic stem cell organization.
Based on statistical analysis of cellular genealogies, we conclude that effects of external variables, such as growth conditions, are imprinted in their topology. Moreover, we demonstrate that it is essential to analyse timing of cell fate-specific changes and of occurrence of cell death events in the divisional context in order to understand the mechanisms of lineage commitment.
在干细胞生物学中,分析干细胞和祖细胞群体中单个细胞的命运仍是一项重大的实验挑战。然而,诸如高分辨率延时视频显微镜等新的监测技术有助于对单个细胞及其子代进行追踪和定量分析。有关细胞发育、分裂历史和分化的信息自然地包含在一种类似谱系的结构中,称为细胞谱系。为了提取有关影响细胞命运决定的变量和控制机制的可靠信息,有必要分析大量的细胞谱系。
在此,我们提出了一组专门为分析细胞谱系量身定制的统计方法。这些方法涉及细胞扩增的程度和对称性,以及诸如细胞死亡等特征事件的发生和相关性。此外,我们讨论了两种重建谱系命运决定的不同方法,并展示了它们对不对称发育解释的影响。为了说明这些技术,并规避目前可用实验数据的不足,我们从基于单细胞的造血干细胞组织数学模型中获得细胞谱系。
基于对细胞谱系的统计分析,我们得出结论,外部变量(如生长条件)的影响在其拓扑结构中有所体现。此外,我们证明,为了理解谱系定向的机制,在分裂背景下分析细胞命运特异性变化的时间和细胞死亡事件的发生至关重要。