CellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images

被引：59

作者：

Pound, Michael P. ^{[1
]}

French, Andrew P. ^{[1
]}

Wells, Darren M. ^{[1
]}

Bennett, Malcolm J. ^{[1
]}

Pridmore, Tony P. ^{[1
,2
]}

机构：

[1] Univ Nottingham, Ctr Plant Integrat Biol, Loughborough LE12 5RD, Leics, England

[2] Univ Nottingham, Sch Comp Sci, Nottingham NG12 5RD, England

来源：

PLANT CELL | 2012年 / 24卷 / 04期

基金：

英国工程与自然科学研究理事会; 英国生物技术与生命科学研究理事会;

关键词：

ARABIDOPSIS; ALGORITHM; GROWTH;

D O I：

10.1105/tpc.112.096289

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

It is increasingly important in life sciences that many cell-scale and tissue-scale measurements are quantified from confocal microscope images. However, extracting and analyzing large-scale confocal image data sets represents a major bottleneck for researchers. To aid this process, CellSeT software has been developed, which utilizes tissue-scale structure to help segment individual cells. We provide examples of how the CellSeT software can be used to quantify fluorescence of hormone-responsive nuclear reporters, determine membrane protein polarity, extract cell and tissue geometry for use in later modeling, and take many additional biologically relevant measures using an extensible plug-in toolset. Application of CellSeT promises to remove subjectivity from the resulting data sets and facilitate higher-throughput, quantitative approaches to plant cell research.

引用

页码：1353 / 1361

页数：9

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