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 AcapellaTM 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.
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.
Ji Zhou, Thomas Spallek, Christine Faulkner and Silke Robatzek