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Quantification of Callose in Plant Tissues by Enzyme-Linked Immunosorbent Assay/ Immunofluorescence Spectrophotometry.

Mustafa AS, Ssenku JE, Muhumuza E, Dinesh-Kumar SP, Ssali RT, Athman SY, Tugume AK.

Journal of visualized experiments : JoVE · 22 May 2026 · 10.3791/65833

Abstract

The existing methods of callose quantification from plant tissues include epifluorescence microscopy, fluorescence spectrophotometry, immunofluorescence microscopy, and indirect assessment of both callose synthase and β-(1,3)-glucanase activities. However, some of these methods have significant limitations, which include being time-consuming, non-specific to callose, labor-intensive, subjective, high autofluorescence, low sensitivity, being more qualitative rather than quantitative, and requiring the acquisition of software resources and technical skills. Therefore, there is a pressing need to explore alternative methods for callose quantification in plant tissues. It was hypothesized that immunofluorescence spectrophotometry or enzyme-linked immunosorbent assay (ELISA) that uses callose-specific antibodies could overcome some of the limitations of the current callose quantification methods. Biotic stress was administered by inoculating tissue culture-derived banana plantlets with Xanthomonas vasicola pv. musacearum (Xvm) bacteria which induced callose production. Banana corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification using the new immunofluorescence spectrophotometry method. Callose production in the corms of Xvm-inoculated and control groups varied significantly in both the banana genotypes (independent sample t-test, p < 0.05). The immunofluorescence spectrophotometry method described here could be applied for the quantification of callose in different plant tissues with high specificity to callose, sensitivity, reliability, and reproducibility. Additionally, the use of a 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases.

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