RootScan: Software for high-throughput analysis of root anatomical traits

• Published in: Plant and soil Vol. 357; no. 1-2; pp. 189 - 203
• Main Authors: Burton, Amy L., Williams, Michael, Lynch, Jonathan P., Brown, Kathleen M.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.08.2012; Springer Netherlands; Springer Nature B.V
• Subjects: Agronomy; Agronomy. Soil science and plant productions; Animal, plant and microbial ecology; Biological and medical sciences; Biomedical and Life Sciences; computer software; Corn; Correlation analysis; Cross-sections; Data collection; Ecology; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Genotype & phenotype; Genotypes; Image analysis; Image contrast; Life Sciences; Phenotypic traits; Pixels; Plant Physiology; Plant roots; Plant Sciences; Plants; Regular Article; Root stele; roots; Software; Soil Science & Conservation; Soil-plant relationships. Soil fertility; Soil-plant relationships. Soil fertility. Fertilization. Amendments; user interface; Xylem vessels; Maize; Aerenchyma; Trait; Root; Anatomy; Phenotyping; Plant tissue; Monocotyledones; Vegetals; Zea mays; Parenchyma; C4-Type; Cereal crop; Phenotype; Computer program; Gramineae; Analysis; Angiospermae; Vegetative apparatus; Spermatophyta; Software; Soil plant relation
• DOI: 10.1007/s11104-012-1138-2
• ISSN: 0032-079X

Background and aims RootScan is a program for semi-automated image analysis of anatomical traits in root cross-sections. Methods RootScan uses pixel thresholds to separate the cross-section from its background and to divide it into tissue regions. Area measurements and object counts are performed within various regions of interest. A graphical user interface permits the user to see which regions are selected, to edit those selections, and to rate and comment on the data. The structure of the program allows for organized workflow and increased data collection efficiency. Results The program collects data on more than 20 variables per image including areas of the cross-section, stele, cortex, aerenchyma lacunae, xylem vessels, and counts of cortical cells and cell files. An increased rate of data collection allows collection of four times more variables in less time than is possible with current methods. Correlation analysis shows that RootScan data is equal or greater in accuracy than data collected with Photoshop. Conclusions Compared with currently available tools, this software offers considerable improvements in the amount and quality of data, ease of use, and time needed for data collection. RootScan permits phenotypic scoring of physiologically and agronomically important traits on a large number of genotypes.