Immunity against potentially infectious pathogens in plants involves a plethora of defence responses such as the deposition of callose, a 1–3 β-linked glucan polymer [1, 2]. Imaging callose deposition has emerged as a widely used method to quantify the activity of plant defences to a range of different pathogens and pathogen-derived molecules (e.g. flg22 derived from bacterial flagellin) in different plant genotypes and mutants [3, 4]. Measuring callose deposition is also a popular way to determine the activity of pathogen-derived virulence factors that interfere with the plant immune pathways to the benefit of the pathogen [5, 6]. While the principle method of callose staining with aniline blue, followed by clearance of the leaves and taking microscopy images under UV light is well established , this approach is hampered by the fact that callose deposits can differ between replicate samples due to biological variation. Moreover, the pattern of spreading callose deposits can vary in response to different pathogen species as well as modes of infection [8, 9]. To take these variations and differences into consideration, it is necessary to acquire a larger number of images, use more accurate solutions to quantify callose deposition and to measure pathogen growth patterns.
Improved quantification methods based on automated large-scale image processing will provide better measurements of defence responses, allowing the detection of subtle differences and thereby promoting our understanding of the mechanisms of plant immunity. The usefulness of quantitative bioimage analysis has been demonstrated for high-throughput microscopy in plant endomembrane trafficking [10, 11] and monitoring plasmodesmata development . Software solutions developed in these studies allowed comparative measurements of endosomal compartments and plasmodesmata revealing significant differences between different plant genotypes, chemical treatments, biotic and abiotic stresses and during plant development that in many cases were not possible to be observed by the human eye [10, 12].
To date, measurements of callose deposits mostly rely on ImageJ  and FIJI  and/or some related plugins to extract quantifiable data from images of aniline-blue stained leaves [15, 16]. Another emerging software package that contains similar functions for quantifying particle-like objects is ICY . Although these software tools enable the detection of callose signals from microscope images, they are limited in their ability to accurately measure callose deposits. For example, we utilised both FIJI and ICY to process a typical callose image (Additional file 1). We followed the image processing workflow previously published and applied “Auto Threshold” and “Particle Analyze” functions in FIJI (Additional file 1A) and the “Spots Detector” method in ICY (Additional file 1B). The results suggest that the two software tools lacked sufficient functions to filter false detected objects as well as to reliably conduct shape/size measurements on detected callose deposits. The results were even more erroneous whilst batch processing callose images (e.g. using macro scripting in FIJI and selecting the “batch input detection” mode in ICY). Because most plant microscopy images contain autofluorescing noise signals derived from chloroplasts, xylem vessels, trichomes, and/or out of focus particle-like signals (typical background signals for plant leaf images), the current available image analysis tools can lead to incorrect detection and imprecise size/shape measurements. Furthermore, in practice these software tools are still semi-automated – manual inputs are required to enhance image quality, choose thresholding algorithms, and/or adjust filtering methods, which makes the image processing of callose deposition time consuming, error-prone, and not applicable for batch processing.
To overcome the above limitations, we developed CalloseMeasurer (v1.0) – a robust software solution that can automate the detection of callose deposits with a very high degree of accuracy and also recognise growth patterns of filamentous pathogen species. This software is based on the Acapella software framework (V2.0, PerkinElmer), which is designed for performing high content and high-throughput bioimage analysis. The usefulness and applicability of CalloseMeasurer are demonstrated with two example experiments.