Quantification of Callose Deposition in the Phloem of Woody Stems Using Supervised Machine Learning-Driven Automated Image Analysis with the IlastiKlean R Package.

Abstract

Callose deposition in the phloem is an innate part of plant development and a response to biotic and abiotic stress, aiding in stress mitigation but potentially also compromising phloem functionality. Measuring callose using aniline blue staining is widely employed, but accurate quantification is hindered by image qualities such as texture and fluorescent artifacts. Here, we describe a method to quantify callose levels in the phloem of woody plants using aniline blue staining, confocal microscopy, and automated supervised machine learning-driven image analysis supported by the IlastiKlean R package. Bark peel samples from woody plants are collected from shoots, stained, and imaged to assess callose deposition. The microscopy images are preprocessed and analyzed using Fiji, Ilastik, and the IlastiKlean R package, which allows accurate quantification of the number, size, and distribution of callose deposits. This quantitative measure can be used to study, screen, and engineer plants that are better adapted to biotic or abiotic stresses, and it serves as an important tool for basic and foundational studies of callose deposition in the phloem.

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