RRQuant: High-throughput quantification of Arabidopsis hypocotyl epidermal integrity. Bogdziewiez, L., Lisica, L., Baba, A. I., Erguvan, Ö., Heymans, A., Atakhani, A., Sjölander, J., Demes-Causse, E., Rigaud, A., & Verger, S. Quantitative Plant Biology, 7:e19, January, 2026. ![RRQuant: High-throughput quantification of Arabidopsis hypocotyl epidermal integrity [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{bogdziewiez_rrquant_2026,
title = {{RRQuant}: {High}-throughput quantification of {Arabidopsis} hypocotyl epidermal integrity},
volume = {7},
issn = {2632-8828},
shorttitle = {{RRQuant}},
url = {https://www.cambridge.org/core/journals/quantitative-plant-biology/article/rrquant-highthroughput-quantification-of-arabidopsis-hypocotyl-epidermal-integrity/D4B4A188B27AD729A5BD4DBFDE9661DF},
doi = {10.1017/qpb.2026.10053},
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.},
language = {en},
urldate = {2026-11-05},
journal = {Quantitative Plant Biology},
author = {Bogdziewiez, Léa and Lisica, Lucija and Baba, Abu Imran and Erguvan, Özer and Heymans, Adrien and Atakhani, Asal and Sjölander, Johan and Demes-Causse, Elsa and Rigaud, Audrey and Verger, Stéphane},
month = jan,
year = {2026},
keywords = {Arabidopsis, adhesion, cuticle, deep learning, epidermis, hypocotyl, image analysis, plant},
pages = {e19},
}
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