Uncovering the secrets of maize roots: High-throughput phenotyping reveals genetic drivers of growth

By combining image-based phenotyping with gene expression analysis, the research reveals significant variability in root morphology and transcriptional profiles across genotypes.

Maize plays a critical role in ensuring global food security, but understanding the root systems that support its growth remains a challenge due to their underground nature. Traditional methods such as rhizoboxes and towel assays are often expensive and labor-intensive, limiting scalability. Quantitative root trait phenotyping has emerged as a critical tool for understanding how plants absorb water and nutrients, a globally important crop with highly plastic and diverse root architectures. With environmental stresses mounting due to climate change, breeders need faster, more scalable ways to associate root traits with genetic and molecular profiles. Due to these challenges, innovative, efficient, and automated root phenotyping methods are urgently needed to unlock the genetic potential of crops like maize.

A study (DOI: 10.1016/j.plaphe.2025.100008) published in Plant Phenomics on 28 February 2025 by Dior R. Kelley ’s team, Iowa State University, not only improve our understanding of maize root architecture but also offer a valuable foundation for future breeding strategies aimed at improving drought resistance, nutrient use efficiency, and crop resilience.

To investigate maize root development across genotypes, researchers developed a high-throughput phenotyping pipeline integrating open-source tools—RootPainter for image segmentation, ImageJ/Fiji for image cleanup, and RhizoVision for trait extraction. A total of 271 images from 22 field-grown maize inbreds were analyzed, yielding 56 distinct root traits. To remove non-root stem tissue, diameter thresholding was applied, improving the accuracy of root-specific trait quantification. The pipeline revealed extensive phenotypic variation among genotypes, with W153R exhibiting the most distinctive traits compared to the reference genotype B73. To assess whether early-stage root traits predict mature architecture, primary root lengths of 10-day-old seedlings were compared with adult root traits across 15 genotypes; only five showed weak to moderate correlations, suggesting genotype-dependent developmental plasticity. Transcriptomic analysis of 11 inbreds identified thousands of differentially expressed genes (DEGs), most of which were unique to specific genotypes. Shared gene ontology (GO) terms across DEGs pointed to roles in hormone signaling, stress response, and cell wall organization—particularly auxin metabolism and β-1,3-glucan pathways. Weighted Gene Co-expression Network Analysis (WGCNA) revealed modules strongly associated with specific genotypes and traits, including the ‘grey60’ module enriched in genes linked to glucan synthase complexes and the ‘cyan’ module involved in metabolic and stress pathways. These findings provide a foundation for understanding the genetic regulation of root morphology and improving root traits in maize breeding programs.

The automated phenotyping pipeline opens the door to scalable, reproducible root analysis in field settings, enabling plant breeders and molecular biologists to more efficiently select for favorable root traits. The findings also suggest that focusing on hormone-regulated genes such as those related to auxin signaling and cell wall remodeling could offer novel breeding targets. The approach provides a framework for integrating omics data, improving drought resilience, and optimizing nutrient uptake in maize and other crops. As climate variability intensifies, tools like this will be essential for engineering robust, high-yielding crop systems tailored to local environmental conditions.

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References

DOI

10.1016/j.plaphe.2025.100008

Original Source URL

https://doi.org/10.1016/j.plaphe.2025.100008

Funding information

This work was supported by the United States Department of Agriculture (USDA), the National Institute of Food and Agriculture (NIFA), the Agriculture and Food Research Initiative (AFRI) award number GRANT12907916 to DRK and JWW; the Hatch Act State of Iowa funds IOW03649 and IOW05745 to DRK; the Hatch Act and State of Iowa funds IOW04108 to JWW; the Iowa State University Plant Science Institute (JWW); the ISU Crop Bioengineering Center (DRK and JWW); and a Department of Defense (DOD) Science, Mathematics, and Research for Transformation (SMART) scholarship to JBC. HS was supported by NSF BIORETS award number 2147083.

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.