3.8 Article

Sandwich Enzyme-Linked Immunosorbent Assay for Quantification of Callose

Journal

METHODS AND PROTOCOLS
Volume 5, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/mps5040054

Keywords

callose; enzyme-linked immunosorbent assay (ELISA); Xanthomonas campestris pv; musacearum; banana; biotic stress

Funding

  1. Bill & Melinda Gates Foundation

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Various methods exist for callose quantification, but many have limitations and technical difficulties. This study introduces a Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA) method for callose quantification, which successfully quantified callose levels in different tissues of banana plants inoculated with Xcm bacteria. The method demonstrates advantages in terms of specificity, reproducibility, and high throughput capabilities.
The existing methods of callose quantification include epifluorescence microscopy and fluorescence spectrophotometry of aniline blue-stained callose particles, immuno-fluorescence microscopy and indirect assessment of both callose synthase and beta-(1,3)-glucanase enzyme activities. Some of these methods are laborious, time consuming, not callose-specific, biased and require high technical skills. Here, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA). Tissue culture-derived banana plantlets were inoculated with Xanthomonas campestris pv. musacearum (Xcm) bacteria as a biotic stress factor inducing callose production. Banana leaf, pseudostem and corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification. Callose levels were significantly different in banana tissues of Xcm-inoculated and control groups except in the pseudostems of both banana genotypes. The method described here could be applied for the quantification of callose in different plant species with satisfactory level of specificity to callose, and reproducibility. Additionally, the use of 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases. We provide step-by-step detailed descriptions of the method.

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