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Callose, a beta-(1,3)-D-glucan polymer, is essential for regulating intercellular trafficking via plasmodesmata (PD). Pathogens manipulate PD-localized proteins to enable intercellular trafficking by removing callose at PD, or conversely by increasing callose accumulation at PD to limit intercellular trafficking during infection. Plant defense hormones like salicylic acid regulate PD-localized proteins to control PD and intercellular trafficking during immune defense responses such as systemic acquired resistance. Measuring callose deposition at PD in plants has therefore emerged as a popular parameter for assessing likely intercellular trafficking activity during plant immunity. Despite the popularity of this metric there is no standard for how these measurements should be made. In this study, we compared three commonly used methods for identifying and quantifying PD callose by aniline blue staining were evaluated to determine the most effective in the Nicotiana benthamiana leaf model. The results reveal that the most reliable method used aniline blue staining and fluorescent microscopy to measure callose deposition in fixed tissue. Manual or semi-automated workflows for image analysis were also compared and found to produce similar results although the semi-automated workflow produced a wider distribution of data points.
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Staining and automated image quantification of callose in Arabidopsis cotyledons and leaves.
Callose is a β-1,3-glucan polysaccharide that is deposited at discrete sites in the plant cell wall in response to microbial pathogens, likely contributing to protection against pathogen infection. Increased callose deposition also occurs in response to the 22-amino acid peptide flg22, a pathogen-associated molecular pattern (PAMP) derived from bacterial flagellin protein. Here, we provide protocols for callose staining using aniline blue in cotyledon and leaf tissue of the model plant Arabidopsis thaliana. Aniline blue stain utilizes a fluorochrome that complexes with callose for its visualization by microscopy using an ultraviolet (UV) filter. For robust quantification of callose deposits, we outline an automated image analysis workflow utilizing the freely available Fiji (Fiji Is Just ImageJ; NIH) software and a Trainable Weka Segmentation (TWS) plugin. Our methodology for automated analysis of large batches of images can be easily adapted to quantify callose in other tissues and plant species, as well as to quantify fluorescent structures other than callose.
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- Award ID(s):
- 1758843
- PAR ID:
- 10296975
- Editor(s):
- Anderson, CT; Elizabeth S. Haswell, ES; null
- Date Published:
- Journal Name:
- Methods in cell biology
- Volume:
- 160
- ISSN:
- 0091-679X
- Page Range / eLocation ID:
- 181 - 199
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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