Abstract
Callose is a polysaccharide that can be fluorescently stained to study many developmental and immune functions in plants. High-throughput methods to accurately gather quantitative measurements of callose from confocal images are useful for many applications in plant biology. Previous callose quantification methods relied upon binary local thresholding, which had the disadvantage of not being able to differentiate callose in conditions with low contrast from background material. Here, a measurement approach that utilizes the Ilastik supervised machine learning imagery data collection software is described. The Ilastik software method provided superior efficiency for acquiring counts of callose deposits. We also determined the accuracy of these methods as compared to manual counts. We demonstrate that the automated software methods are both good predictors of manual counts, but that the Ilastik counts are significantly closer. Researchers can use this information to guide their choice of method to quantify callose in their work.
Highlights
Callose is a polysaccharide, composed of β-1,3-linked glucose, which is found throughout the plant body and performs a variety of structural, developmental, and immune functions [1–3]
Counts of callose were obtained from the same images manually and by using a Fiji local binary thresholding method which segmented the callose deposits from the background
There were significant differences between the number of callose deposits detected by each testing system in the phloem (Figure 1)
Summary
Callose is a polysaccharide, composed of β-1,3-linked glucose, which is found throughout the plant body and performs a variety of structural, developmental, and immune functions [1–3]. Aniline blue is a fluorescent staining compound that binds to callose in plant tissue [4]. Because this stain is safe to handle and easy to use, fluorescent imagery of callose is often employed to study the biological processes in which it plays a role [5–8]. Binary thresholding methods rely on the premise that the pixels of the object of interest contrast substantially with the pixels of the background of the image. Such methods are sensitive to noise, the presence of artifacts with the same brightness as the object to be measured, and user calibration of the various filter and threshold parameters [9]. Data can be lost when important objects are erroneously segmented to the background instead of the foreground
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