Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching
Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching
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DOI：
10.1111/j.1365-313x.2009.04117.x
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发表时间：
2010-04-01
期刊：
影响因子：
7.2
通讯作者：
Reddy, G. Venugopala
中科院分区：
文献类型：
--
作者：
Liu, Min;Yadav, Ram Kishor;Reddy, G. Venugopala
高等植物的茎尖分生组织（sam）具有干细胞生态位。干细胞生态位的细胞被组织成具有不同功能和细胞行为的空间域。细胞生长动力学和基因表达变化之间的协调相互作用对于确保干细胞稳态和器官分化至关重要。探索细胞生长模式和基因表达动态之间的因果关系需要定量方法来分析从延时图像的细胞行为。尽管实时成像方法的技术突破揭示了sam细胞生长模式的时空动态，但尚未开发出用于细胞分割和细胞自动跟踪的强大计算方法。本文提出了一种基于局部图匹配的拟南芥SAMs细胞和细胞分裂自动跟踪方法。地对空导弹的细胞在空间上紧密聚集，这对计算细胞的时空对应关系提出了独特的挑战。局部图匹配原理有效地利用了细胞相对位置的几何结构和拓扑结构来获取时空对应关系。跟踪器集成了多个切片上的信息，其中一个细胞可以正确成像，从而在嘈杂的实时成像数据集中提供细胞跟踪的鲁棒性。依靠局部几何和拓扑，该方法能够跟踪高曲率区域的细胞，如原始生长区域。细胞跟踪器不仅可以计算细胞在时空尺度上的对应关系，还可以检测细胞分裂事件，并在分裂时识别子细胞，从而允许从72小时内捕获的图像中自动估计细胞谱系。本文提出的方法应该能够定量分析细胞生长模式，从而促进SAM生长的硅模型的发展。
P>Shoot apical meristems (SAMs) of higher plants harbor stem-cell niches. The cells of the stem-cell niche are organized into spatial domains of distinct function and cell behaviors. A coordinated interplay between cell growth dynamics and changes in gene expression is critical to ensure stem-cell homeostasis and organ differentiation. Exploring the causal relationships between cell growth patterns and gene expression dynamics requires quantitative methods to analyze cell behaviors from time-lapse imagery. Although technical breakthroughs in live-imaging methods have revealed spatio-temporal dynamics of SAM-cell growth patterns, robust computational methods for cell segmentation and automated tracking of cells have not been developed. Here we present a local graph matching-based method for automated-tracking of cells and cell divisions of SAMs of Arabidopsis thaliana. The cells of the SAM are tightly clustered in space which poses a unique challenge in computing spatio-temporal correspondences of cells. The local graph-matching principle efficiently exploits the geometric structure and topology of the relative positions of cells in obtaining spatio-temporal correspondences. The tracker integrates information across multiple slices in which a cell may be properly imaged, thus providing robustness to cell tracking in noisy live-imaging datasets. By relying on the local geometry and topology, the method is able to track cells in areas of high curvature such as regions of primordial outgrowth. The cell tracker not only computes the correspondences of cells across spatio-temporal scale, but it also detects cell division events, and identifies daughter cells upon divisions, thus allowing automated estimation of cell lineages from images captured over a period of 72 h. The method presented here should enable quantitative analysis of cell growth patterns and thus facilitating the development of in silico models for SAM growth.