Pavement cells in the Arabidopsis thaliana epidermis span a wide range of sizes and ploidy levels, but rules that generate this heterogeneity across an organ remain unclear. Clark et al. identify a shared genetic pathway that promotes large, polyploid pavement cells in both sepals and leaves, then ask whether the familiar “scattered” distribution of giant cells is truly random. By combining whole-tissue imaging with two independent computational randomization approaches that regenerate tissues from segmented images while preserving cell size distributions and key boundary constraints, together with a stochastic cell-autonomous model, the authors show how an initially random pattern can later appear clustered relative to a changing random baseline as tissues grow and subdivide. The study provides a quantitative framework for testing spatial organization in cellular mosaics where point-based methods fail, and it shows how proliferation history can convert early stochastic fate decisions into a statistically non-random mature pattern.

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.