Early Evaluation of Root System Architecture in Maize Using X-ray Microscopy of Root Tips

There is a demand for an increase in crop production because of the growing population, but water shortage hinders the expansion of wheat cultivation, one of the most important crops worldwide. Polyethylene glycol (PEG) was used to mimic drought stress due to its high osmotic potentials generated in plants subjected to it. This study aimed to determine the root system architecture (RSA) plasticity of eight bread wheat genotypes under osmotic stress in relation to the oxidative status and mitochondrial membrane potential of their root tips. Osmotic stress application resulted in differences in the RSA between the eight genotypes, where genotypes were divided into adapted genotypes that have non-significant decreased values in lateral roots number (LRN) and total root length (TRL), while non-adapted genotypes have a significant decrease in LRN, TRL, root volume (RV), and root surface area (SA). Accumulation of intracellular ROS formation in root tips and elongation zone was observed in the non-adapted genotypes due to PEG-induced oxidative stress. Mitochondrial membrane potential (∆Ψm) was measured for both stress and non-stress treatments in the eight genotypes as a biomarker for programmed cell death as a result of induced osmotic stress, in correlation with RSA traits. PEG treatment increased scavenging capacity of the genotypes from 1.4-fold in the sensitive genotype Gemmiza 7 to 14.3-fold in the adapted genotype Sakha 94. The adapted genotypes showed greater root trait values, ∆Ψm plasticity correlated with high scavenging capacity, and less ROS accumulation in the root tissue, while the non-adapted genotypes showed little scavenging capacity in both treatments, accompanied by mitochondrial membrane permeability, suggesting mitochondrial dysfunction as a result of oxidative stress.

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Quantifying the impact of soil compaction on root system architecture in tomato (Solanum lycopersicum) by X-ray micro-computed tomography

Annals of Botany ◽

10.1093/aob/mcs031 ◽

2012 ◽

Vol 110 (2) ◽

pp. 511-519 ◽

Cited By ~ 86

Author(s):

Saoirse R. Tracy ◽

Colin R. Black ◽

Jeremy A. Roberts ◽

Craig Sturrock ◽

Stefan Mairhofer ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

Solanum Lycopersicum ◽

System Architecture ◽

Soil Compaction ◽

Root System Architecture ◽

Micro Computed Tomography ◽

X Ray ◽

The Impact

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Quantifying the effect of soil moisture content on segmenting root system architecture in X-ray computed tomography images

Plant and Soil ◽

10.1007/s11104-013-1596-1 ◽

2013 ◽

Vol 370 (1-2) ◽

pp. 35-45 ◽

Cited By ~ 31

Author(s):

Susan Zappala ◽

Stefan Mairhofer ◽

Saoirse Tracy ◽

Craig J. Sturrock ◽

Malcolm Bennett ◽

...

Keyword(s):

Computed Tomography ◽

Soil Moisture ◽

Moisture Content ◽

Root System ◽

System Architecture ◽

Soil Moisture Content ◽

Root System Architecture ◽

X Ray ◽

Computed Tomography Images ◽

X Ray Computed

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X-Ray Computed Tomography Reveals the Response of Root System Architecture to Soil Texture

PLANT PHYSIOLOGY ◽

10.1104/pp.16.00397 ◽

2016 ◽

Vol 171 (3) ◽

pp. 2028-2040 ◽

Cited By ~ 31

Author(s):

Eric D. Rogers ◽

Daria Monaenkova ◽

Medhavinee Mijar ◽

Apoorva Nori ◽

Daniel I. Goldman ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

Soil Texture ◽

System Architecture ◽

Root System Architecture ◽

X Ray ◽

X Ray Computed

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Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging

Plant Phenomics ◽

10.34133/2021/9859254 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

M. R. Shao ◽

N. Jiang ◽

M. Li ◽

A. Howard ◽

K. Lehner ◽

...

Keyword(s):

Root System ◽

System Architecture ◽

Abiotic Stress Tolerance ◽

3D Models ◽

Developmental Time ◽

Root System Architecture ◽

Maize Root ◽

Crop Species ◽

X Ray ◽

Pulling Force

The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.

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RSAtrace3D: robust vectorization software for measuring monocot root system architecture

BMC Plant Biology ◽

10.1186/s12870-021-03161-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shota Teramoto ◽

Takanari Tanabata ◽

Yusaku Uga

Keyword(s):

Root System ◽

System Architecture ◽

Three Dimensional ◽

Root System Architecture ◽

Ct Images ◽

X Ray ◽

Single Root ◽

Base Points ◽

Using Data ◽

Crown Roots

Abstract Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

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High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

Plant Methods ◽

10.1186/s13007-020-00612-6 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Shota Teramoto ◽

Satoko Takayasu ◽

Yuka Kitomi ◽

Yumiko Arai-Sanoh ◽

Takanari Tanabata ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

High Throughput ◽

System Architecture ◽

Three Dimensional ◽

Root System Architecture ◽

X Ray ◽

X Ray Computed

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PB-11 3D-Modeling of Arabidopsis Root System Architecture by X-ray Micro-CT at SPring-8: Observation at Different Experimental Hutches

Microscopy ◽

10.1093/jmicro/dfz076 ◽

2019 ◽

Vol 68 (Supplement_1) ◽

pp. i51-i51

Author(s):

Tomofumi Kurogane ◽

Daisuke Tamaoki ◽

Sachiko Yano ◽

Fumiaki Tanigaki ◽

Toru Shimazu ◽

...

Keyword(s):

Root System ◽

3D Modeling ◽

System Architecture ◽

Root System Architecture ◽

Micro Ct ◽

Arabidopsis Root ◽

X Ray

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In Situ Visualization and Quantification of Three-Dimensional Root System Architecture and Growth Using X-Ray Computed Tomography

Vadose Zone Journal ◽

10.2136/vzj2014.03.0024 ◽

2014 ◽

Vol 13 (8) ◽

pp. vzj2014.03.0024 ◽

Cited By ~ 27

Author(s):

Nicolai Koebernick ◽

Ulrich Weller ◽

Katrin Huber ◽

Steffen Schlüter ◽

Hans-Jörg Vogel ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

System Architecture ◽

Three Dimensional ◽

Root System Architecture ◽

X Ray ◽

In Situ Visualization ◽

X Ray Computed

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Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multi-trait index and Bayesian networks

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab178 ◽

2021 ◽

Author(s):

Santosh Sharma ◽

Shannon R M Pinson ◽

David R Gealy ◽

Jeremy D Edwards

Keyword(s):

Oryza Sativa ◽

Root System ◽

System Architecture ◽

Genomic Prediction ◽

Agronomic Traits ◽

Oryza Sativa L ◽

Root System Architecture ◽

Root Tips ◽

Phenotypic Data ◽

Genomic Predictions

Abstract Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 x Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction methods. We found GPAs were improved using multi-trait (BN) compared to single trait genomic prediction in traits with low to moderate heritability. Two groups of individuals were selected based on genomic predictions and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on genomic predictions did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multi-trait genomic prediction model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

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