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PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics.

Birgit Möller1, Yvonne Poeschl2,3, Romina Plötner4

  • 1Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany katharina.buerstenbinder@ipb-halle.de birgit.moeller@informatik.uni-halle.de.

Plant Physiology
|September 22, 2017
PubMed
Summary

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A new ImageJ tool, PaCeQuant, automatically quantifies pavement cell (PC) shape complexity. This enables robust analysis of PC development, crucial for understanding leaf growth and improving crop yield.

Area of Science:

  • Plant biology
  • Cell biology
  • Image analysis

Background:

  • Pavement cells (PCs) are key epidermal cells influencing leaf growth, fitness, and crop yield.
  • The complex, lobed morphology of PCs presents challenges for quantitative developmental studies.
  • Existing methods lack robust automation for analyzing PC shape complexity.

Purpose of the Study:

  • To introduce PaCeQuant, an ImageJ-based tool for automated pavement cell shape quantification.
  • To provide a robust, efficient, and reproducible method for analyzing PC shape characteristics.
  • To facilitate large-scale studies of PC development and genotype-specific variations.

Main Methods:

  • Development of PaCeQuant, an ImageJ plugin for automatic cell boundary detection and segmentation.

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  • Extraction of 27 shape features, including global, contour-based, skeleton-based, and PC-specific descriptors.
  • Inclusion of lobe classification at two- and three-cell junctions, with R script for analysis and visualization.

    • Main Results:

    • PaCeQuant successfully automates the segmentation and quantification of PC shape parameters.
    • The tool extracts a comprehensive set of 27 shape features, capturing cellular complexity.
    • Validation confirmed PaCeQuant's accuracy compared to manual segmentation and existing tools.

    Conclusions:

    • PaCeQuant offers a powerful platform for quantitative analysis of pavement cell morphology.
    • The tool enables efficient study of PC development across different plant species and genotypes.
    • This advancement supports research into leaf growth mechanisms and crop improvement strategies.