RRQuant: High-throughput quantification of Arabidopsis hypocotyl epidermal integrity

Abstract

Maintaining epidermal integrity is essential for plant growth and survival, yet quantitative methods to assess defects in cell adhesion, cell integrity and cuticle function remain limited. Here, we present RRQuant, a standardised, high-throughput workflow for quantifying epidermal integrity defects in seedlings using Ruthenium Red staining. RRQuant integrates deep learning–based segmentation with automated image analysis, staining and morphological quantification, and statistical evaluation. The workflow is accompanied by a detailed user guide, and an interactive R Shiny application for data visualisation and quality control. This pipeline converts qualitative staining patterns into reproducible quantitative measurements, enabling large-scale phenotyping and comparative studies across genotypes and treatments. We demonstrate the usefulness of RRQuant by quantitatively comparing adhesion defective phenotypes and discuss its potential for adaptation to other organs, staining methods, and pigment-based assays. By providing an open and extensible platform, RRQuant bridges the gap between traditional qualitative assessments and modern quantitative phenotyping in plant biology.