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Publications - Molecular Signal Processing

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Publications

Schilling, S.; Stenzel, I.; von Bohlen, A.; Wermann, M.; Schulz, K.; Demuth, H.-U.; Wasternack, C. Isolation and characterization of the glutaminyl cyclases from Solanum tuberosum and Arabidopsis thaliana: implications for physiological functions Biol. Chem 388, 145-153, (2007) DOI: 10.1515/BC.2007.016

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Printed publications

Poeschl, Y.; Möller, B.; Müller, L.; Bürstenbinder, K. User-friendly assessment of pavement cell shape features with PaCeQuant: Novel functions and tools Methods Cell Biol (2020) DOI: 10.1016/bs.mcb.2020.04.010

Leaf epidermis pavement cells develop complex jigsaw puzzle-like shapes in many plant species, including the model plant Arabidopsis thaliana. Due to their complex morphology, pavement cells have become a popular model system to study shape formation and coordination of growth in the context of mechanically coupled cells at the tissue level. To facilitate robust assessment and analysis of pavement cell shape characteristics in a high-throughput fashion, we have developed PaCeQuant and a collection of supplemental tools. The ImageJ-based MiToBo plugin PaCeQuant supports fully automatic segmentation of cell contours from microscopy images and the extraction of 28 shape features for each detected cell. These features now also include the Largest Empty Circle criterion as a proxy for mechanical stress. In addition, PaCeQuant provides a set of eight features for individual lobes, including the categorization as type I and type II lobes at two- and three-cell junctions, respectively. The segmentation and feature extraction results of PaCeQuant depend on the quality of input images. To allow for corrections in case of local segmentation errors, the LabelImageEditor is provided for user-friendly manual postprocessing of segmentation results. For statistical analysis and visualization, PaCeQuant is supplemented with the R package PaCeQuantAna, which provides statistical analysis functions and supports the generation of publication-ready plots in ready-to-use R workflows. In addition, we recently released the FeatureColorMapper tool which overlays feature values over cell regions for user-friendly visual exploration of selected features in a set of analyzed cells.
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