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Open Access Highly Accessed Methodology

CalloseMeasurer: a novel software solution to measure callose deposition and recognise spreading callose patterns

Ji Zhou1, Thomas Spallek12, Christine Faulkner13 and Silke Robatzek1*

Author Affiliations

1 The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK

2 Present address: RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan

3 Present address: Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, OX3 0BP, UK

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Plant Methods 2012, 8:49  doi:10.1186/1746-4811-8-49

Published: 17 December 2012

Abstract

Background

Quantification of callose deposits is a useful measure for the activities of plant immunity and pathogen growth by fluorescence imaging. For robust scoring of differences, this normally requires many technical and biological replicates and manual or automated quantification of the callose deposits. However, previously available software tools for quantifying callose deposits from bioimages were limited, making batch processing of callose image data problematic. In particular, it is challenging to perform large-scale analysis on images with high background noise and fused callose deposition signals.

Results

We developed CalloseMeasurer, an easy-to-use application that quantifies callose deposition, a plant immune response triggered by potentially pathogenic microbes. Additionally, by tracking identified callose deposits between multiple images, the software can recognise patterns of how a given filamentous pathogen grows in plant leaves. The software has been evaluated with typical noisy experimental images and can be automatically executed without the need for user intervention. The automated analysis is achieved by using standard image analysis functions such as image enhancement, adaptive thresholding, and object segmentation, supplemented by several novel methods which filter background noise, split fused signals, perform edge-based detection, and construct networks and skeletons for extracting pathogen growth patterns. To efficiently batch process callose images, we implemented the algorithm in C/C++ within the Acapella™ framework. Using the tool we can robustly score significant differences between different plant genotypes when activating the immune response. We also provide examples for measuring the in planta hyphal growth of filamentous pathogens.

Conclusions

CalloseMeasurer is a new software solution for batch-processing large image data sets to quantify callose deposition in plants. We demonstrate its high accuracy and usefulness for two applications: 1) the quantification of callose deposition in different genotypes as a measure for the activity of plant immunity; and 2) the quantification and detection of spreading networks of callose deposition triggered by filamentous pathogens as a measure for growing pathogen hyphae. The software is an easy-to-use protocol which is executed within the Acapella software system without requiring any additional libraries. The source code of the software is freely available at https://sourceforge.net/projects/bioimage/files/Callose webcite.

Keywords:
Callose deposition; Quantification; Immunity; Flagellin; Flg22; Bacteria; Defence response; Oomycete; Hyaloperonospora arabidopsidis; Encasements; Pathogen; Image analysis