High-throughput Root Phenotyping Research Articles

High-throughput root phenotyping and association analysis identified potential genomic regions for phosphorus use efficiency in wheat (Triticum aestivum L.)

High-throughput root phenotyping and association analysis identified potential genomic regions for phosphorus use efficiency in wheat (Triticum aestivum L.)

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

BackgroundRoot systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions.ResultsOur results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R2=0.82\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2 = 0.82$$\\end{document}) and root surface area (R2=0.8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2 = 0.8$$\\end{document}). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits.ConclusionOur findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars.

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils.

High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils.

Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits.

Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.

High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings

BackgroundHigh-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge.ResultsWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy (R^2=0.996 and 0.923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations (R^2=0.938 for lateral root growth).ConclusionsWe designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.

Recent advances in methods for in situ root phenotyping.

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

BackgroundBreakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA).ResultsRoots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001.ConclusionsThe experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Fully-automated root image analysis (faRIA)

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

ROOT PHENOTYPING FROM X-RAY COMPUTED TOMOGRAPHY: SKELETON EXTRACTION

Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.

Determination of root system architecture variation of drought adapted sorghum genotypes using high throughput root phenotyping

Sorghum bicolor (L.) is an important cereal crop in Ethiopia primarily growing as food grain crop. In spite of the huge genetic potential, productivity of sorghum is low due to frequency of drought occurrence. Root system architecture influences the amount of water important for maintaining crop yield under moisture stress. This study was conducted with the objective of estimating of genetic variability of 214 sorghum genotypes for root and shoot traits during seedling stage. The experiment was conducted at greenhouse of Horticulture and Plant science Department of Jimma University using randomized complete block design. Analysis of variance showed that highly significant difference among the genotypes (P < 0.01) in all studied traits. Root angle ranged between 14.5° (Acc#23650) and 27.37° (Acc#6723). Root number ranged between 6 and 15, root length between 40 and 72 cm and root to shoot ratio ranged between 0.17 and 0.78. Genotypes from Ethiopian Biodiversity (IBC) and ICRISAT were showed more root angle genetic variations. Similarly for other root system architecture (RSA) traits such as root length, root number and root dry weight, Ethiopian Biodiversity (IBC) and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) genotypes showed more genetic diversity than the released varieties. Phenotypic coefficient of variation (PCV) was between 29.19% for leaf area and 13.46% for root length, and genotypic coefficient variation (GCV) was 28.19% for leaf area and 11.94% for root angle. Estimates of broad sense heritability were between 40% for root dry weight and 94.18% for leaf area. Generally the information generated from this study could help to identified promising genotypes with suitable root traits for potential breeding under specific environmental conditions.

An improved method for the segmentation of roots from X-ray computed tomography 3D images: Rootine v.2

BackgroundX-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality.ResultsWe demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability.ConclusionThe proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.

Characterization and association mapping for drought adaptation in Ethiopian sorghum (Sorghum bicolor (L.) Moench) germplasm

Knowledge of drought related traits and their mechanisms of drought adaptation are the key component in selecting genotypes that withstand the effects of drought in sorghum. The objectives of this study were to assess genetic variability among sorghum germplasm, to map QTL associated with root and shoot traits, and to identify tightly linked SSR markers. The experiment was carried out at Jimma University under greenhouse condition. One hundred thirty-six sorghum genotypes were characterized for twelve traits using a high throughput root phenotyping platform in a randomized complete block design with three replications. Among these genotypes, 108 of them were used for studying population structure and trait-marker association analysis using 39 SSR markers. The analysis of variance indicated that highly significant difference (P < 0.01) were observed among the genotypes for all the studied traits. The coefficients of correlation showed that there was a significant positive and negative association among different drought related traits. The first three principal components (PCs) with eigenvalues greater than one accounted for 56.4% of the total genotype variation, the remaining 43.60% accounted for the last nine principal components. All 136 genotype were grouped into four clusters whereby a different member within a cluster being assumed to be more closely related in terms of the trait under consideration with each other than that member in different clusters is. Moderate genotypic variation was exhibited for leaf area, shoot fresh weight, shoot dry weight, root to shoot ratio and root angle. Highest phenotypic variation was exhibited for leaf area, shoot fresh weight, shoot dry weight, root dry weight and root to shoot ratio. Broad sense heritability ranged from 19.35% for root length to 71.08% for shoot fresh weight and high heritability was recorded for shoot fresh weight (71.08%), leaf area (70.22%) and root angle (66.22%). High heritability combined with high genetic advance was observed for shoot fresh weight, root angle, and leaf area. The 108 genotypes were grouped into three distinct subgroups. The plots of LD (r2) showed a clear trend on linkage disequilibrium decay and based on trend line it is around 15–20 cM. A total of 25 significant marker-trait associations/QTLs (P ≤ 0.05) were detected with 14 SSR markers.

Effect of phosphorus supply on root traits of two Brassica oleracea L. genotypes

BackgroundPhosphorus (P) deficiency limits crop production worldwide. Crops differ in their ability to acquire and utilise the P available. The aim of this study was to determine root traits (root exudates, root system architecture (RSA), tissue-specific allocation of P, and gene expression in roots) that (a) play a role in P-use efficiency and (b) contribute to large shoot zinc (Zn) concentration in Brassica oleracea.ResultsTwo B. oleracea accessions (var. sabellica C6, a kale, and var. italica F103, a broccoli) were grown in a hydroponic system or in a high-throughput-root phenotyping (HTRP) system where they received Low P (0.025 mM) or High P (0.25 mM) supply for 2 weeks. In hydroponics, root and shoot P and Zn concentrations were measured, root exudates were profiled using both Fourier-Transform-Infrared spectroscopy and gas-chromatography-mass spectrometry and previously published RNAseq data from roots was re-examined. In HTRP experiments, RSA (main and lateral root number and lateral root length) was assessed and the tissue-specific distribution of P was determined using micro-particle-induced-X-ray emission. The C6 accession had greater root and shoot biomass than the F103 accession, but the latter had a larger shoot P concentration than the C6 accession, regardless of the P supply in the hydroponic system. The F103 accession had a larger shoot Zn concentration than the C6 accession in the High P treatment. Although the F103 accession had a larger number of lateral roots, which were also longer than in the C6 accession, the C6 accession released a larger quantity and number of polar compounds than the F103 accession. A larger number of P-responsive genes were found in the Low P treatment in roots of the F103 accession than in roots of the C6 accession. Expression of genes linked with “phosphate starvation” was up-regulated, while those linked with iron homeostasis were down-regulated in the Low P treatment.ConclusionsThe results illustrate large within-species variability in root acclimatory responses to P supply in the composition of root exudates, RSA and gene expression, but not in P distribution in root cross sections, enabling P sufficiency in the two B. oleracea accessions studied.

From lab to field: Open tools facilitating the translation of maize root traits

Abstract Variation of root traits is associated with soil resource acquisition under abiotic stresses. However, plant breeding programs targeting beneficial root traits are hampered by lacking appropriate phenotyping tools. The availability of high-throughput root phenotyping technologies for seedlings as well as for mature plants grown in the field enabled us to investigate trait translation from the lab to the field. Here we used the established phenotyping techniques Rhizoslides, Shovelomics, and the image-based root phenotyping software, Digital Imaging of Root Traits (DIRT) to phenotype root traits of thirty seedlings and mature maize donor lines from the Drought Tolerance Maize for Africa (DTMA) project. The experiments were carried out in a controlled-environment and a field trial under well-watered and drought conditions. We found significant correlation between seedlings seminal and crown root number in the field (r = 0.68). Primary root branching of seedlings was negatively correlated with crown root branching of field-grown plants (r=-0.54). Our study showed indirect translation of traits between primary root length (r = 0.29) and primary root branching (r = 0.20) with plant performance in the field under drought. Our results suggest that root traits of maize seedlings are predictive for mature maize root traits and plant performance in the field. Our findings suggest that using open tools for high-throughput root phenotyping to screen in maize seedling could be beneficial for breeding programs and paves a cost-efficient way for plant improvement and breeding programs in developing countries.

MyROOT 2.0: An automatic tool for high throughput and accurate primary root length measurement

The automatic measurement of external physical traits (i.e. phenotyping) of plant organs, such as root length –which is highly correlated with plant viability– is one of the current bottlenecks in academic and agricultural research. Although many root length measurement software tools are available to the community, plant scientists often find their usability is limited, the measurements they provide are not accurate enough or they are too limited to specific image characteristics. In response to that, this work describes MyROOT 2.0, an automatic software tool jointly developed by plant scientists and computer vision engineers to create a high throughput root length measurement tool that reduces user intervention to a minimum. Using Arabidopsis thaliana seedlings grown on agar plates as a case study, MyROOT 2.0 is capable of detecting the root regions of interest in a fully automatic manner with an accuracy of 98%. Furthermore, this work also presents previously unreported experiments to evaluate several constituting modules of MyROOT 2.0, such as the ability to determine image scale automatically with subpixel accuracy, or the influence of training the hypocotyl detector using wildtype or mutant samples. Finally, when compared to state-of-the-art root length measurement software tools, MyROOT 2.0 achieves the highest root detection rate, obtaining measurements which are four times more accurate than its competitors. This makes MyROOT 2.0 an attractive tool for high throughput root phenotyping.

Towards a deeper integrated multi-omics approach in the root system to develop climate-resilient rice

Roots are the only organ system to uptake water and nutrients from the soil. The root system is crucial for plants to survive and adapt to environmental stresses. Therefore, the root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development have been cloned and characterized. In addition, with the advances in technologies related to omics analysis, such as high-throughput sequencing, transcriptome analysis of roots has also progressed. In contrast, high-throughput root phenotyping has not been established not only in rice but also in whole plants because roots are hidden underground. This deficiency represents a bottleneck for utilizing an integrated multi-omics approach for molecular breeding of RSA. We first summarized previous transcriptome analyses for root development under various abiotic stresses such as drought, salinity, and heat, and assessed the current status of root phenotyping technology and modeling in rice. This knowledge allowed us to contemplate the possibility of applying an integrated multi-omics dataset from RSA to molecular breeding of climate-resilient rice.

Root phenotypic detection of different vigorous maize seeds based on Progressive Corrosion Joining algorithm of image

BackgroundThe root phenotypes of different vigorous maize seeds vary a lot. Imaging roots of growing maize is a non-invasive, affordable and high throughput approach. However, it’s difficult to get integral root images because of the block of the soil. The paper proposed an algorithm to repair incomplete root images for maize root fast non-invasive phenotyping detection.ResultsA two-layer transparent stress growth device with two concentric cylinders was developed as mesocosms and the maize seeds were planted in the annulus of it. The maize roots grow in soil against two acrylic plastic surfaces due to the press of the small growing area to acquire more root details during roots visualization and imaging. Even though, parts of the roots are occluded which means that it’s tough to extract the information of root general physical construction. For recovering gaps from disconnected root segments, Progressive Corrosion Joining (PCJ) algorithm was proposed based on the physiological characteristics of hydrotropism, geostrophic and continuity with three steps which are root image thinning, progressive corrosion and joining processing respectively. The experiments indicate that maize phenotyping parameters are negative correlation with seed aging days. And specifically, Root Number (RTN), Root Length (RTL), Root Width (RTW) and Root Extension Length (REL) of unaged and 14-day-aged maize seeds are decreased from 15.40, 82.40 mm, 1.53 mm and 82.20 mm to 4.58, 38.6 mm, 1.35 mm and 55.20 mm, and the growing speed of them are changed from 1.68 per day, 8.80 mm/d, 0.06 mm/d, 9.0 mm/d to 0.70 per day, 4.3 mm/d, 0.05 mm/d and 5.70 mm/d respectively. Whereas Root Extension Angle (REA) is basically irrelevant with the level of maize seed aging.ConclusionThe developed double-layer Annular Root Phenotyping Container (ARPC) can satisfy the general physical construction of maize as well as push each root growing along the inner wall of the container which help to acquire more root information. The presented novel PCJ algorithm can recover the missing parts, even for big gaps, of maize roots effectively according to root morphological properties. The experiments show that the proposed method can be applied to evaluate the vigor of maize seeds which has vast application prospect in high throughput root phenotyping area.

Genetic Screening for Mutants with Altered Seminal Root Numbers in Hexaploid Wheat Using a High-Throughput Root Phenotyping Platform.

Roots are the main channel for water and nutrient uptake in plants. Optimization of root architecture provides a viable strategy to improve nutrient and water uptake efficiency and maintain crop productivity under water-limiting and nutrient-poor conditions. We know little, however, about the genetic control of root development in wheat, a crop supplying 20% of global calorie and protein intake. To improve our understanding of the genetic control of seminal root development in wheat, we conducted a high-throughput screen for variation in seminal root number using an exome-sequenced mutant population derived from the hexaploid wheat cultivar Cadenza. The screen identified seven independent mutants with homozygous and stably altered seminal root number phenotypes. One mutant, Cadenza0900, displays a recessive extra seminal root number phenotype, while six mutants (Cadenza0062, Cadenza0369, Cadenza0393, Cadenza0465, Cadenza0818 and Cadenza1273) show lower seminal root number phenotypes most likely originating from defects in the formation and activation of seminal root primordia. Segregation analysis in F2 populations suggest that the phenotype of Cadenza0900 is controlled by multiple loci whereas the Cadenza0062 phenotype fits a 3:1 mutant:wild-type segregation ratio characteristic of dominant single gene action. This work highlights the potential to use the sequenced wheat mutant population as a forward genetic resource to uncover novel variation in agronomic traits, such as seminal root architecture.

A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine

X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning (R2 = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling.