title: CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images.
abstract: 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.
authors.affiliation: Centre for Plant Integrative Biology, University of Nottingham, Nottingham LE12 5RD, United Kingdom. michael.pound@nottingham.ac.uk
authors.forename: Michael P
authors.initials: MP
authors.lastname: Pound
authors.forename: Andrew P
authors.initials: AP
authors.lastname: French
authors.forename: Darren M
authors.initials: DM
authors.lastname: Wells
authors.forename: Malcolm J
authors.initials: MJ
authors.lastname: Bennett
authors.forename: Tony P
authors.initials: TP
authors.lastname: Pridmore
mesh_terms: D017360:Arabidopsis / Q000166:cytology* / Q000378:metabolism; D015415:Biomarkers / Q000378:metabolism; D002462:Cell Membrane / Q000378:metabolism; D002467:Cell Nucleus / Q000378:metabolism; D005453:Fluorescence; D017930:Genes, Reporter; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D059828:Plant Cells / Q000378:metabolism*; D012984:Software; D013223:Statistics as Topic
chemical_list: D015415:Biomarkers