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题目:: Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching.
作者:: Liu(Min),Yadav(Ram Kishor),Roy-Chowdhury(Amit),Reddy(G Venugopala)
状态:: 发布时间2010-04-22 , 更新时间 2010-04-22
期刊:: Plant J
摘要:: 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.
语言:: eng
DOI:: 10.1111/j.1365-313X.2009.04117.x

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