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Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003029
Pubmed ID
Authors

Henrik Failmezger, Benjamin Jaegle, Andrea Schrader, Martin Hülskamp, Achim Tresch

Abstract

Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.

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The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Germany 1 2%
Belgium 1 2%
Unknown 53 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 11 20%
Student > Master 9 16%
Student > Bachelor 6 11%
Professor 2 4%
Other 7 13%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 45%
Biochemistry, Genetics and Molecular Biology 7 13%
Engineering 3 5%
Environmental Science 2 4%
Computer Science 2 4%
Other 8 14%
Unknown 9 16%