Dataset Information

Quantifying the influence of water deficit on root and shoot growth in wheat using X-ray Computed Tomography.

ABSTRACT: The potential increased frequency and severity of drought associated with environmental change represents a significant obstacle to efforts aimed at enhancing food security due to its impact on crop development, and ultimately, yield. Our understanding of the impact of drought on crop growth in terms of plant aerial tissues is much more advanced than knowledge of the below-ground impacts. We undertook an experiment using X-ray Computed Tomography that aimed to support measurements of infrared gas exchange from plant shoots with quantification of 3D root architecture traits and the associated soil structural characteristics. Winter wheat (cv. Zebedee) was assessed at two early growth stages (14 and 21 days) under four water treatments (100, 75, 50 and 25 % of a notional field capacity (FC) and across two soil types (sandy loam and clay loam)). Plants generally grew better (to a larger size) in sandy loam soil as opposed to clay loam soil, most likely due to the soil structure and the associated pore network. All plants grew poorly under extreme water stress and displayed optimal growth at 75 % of FC, as opposed to 100 %, as the latter was most likely too wet. The optimal matric potential for root and shoot growth, inferred from the water release curve for each soil type, was higher than that for photosynthesis, stomatal conductance and transpiration suggesting root and shoot growth was more affected by soil water content than photosynthesis-related characteristics under water deficit conditions. With incidences of drought likely to increase, identification of wheat cultivars that are more tolerant of these conditions is important. Studies that consider the impact of water stress on both plant shoots and roots, and the role of the soil pore system such as this offer considerable potential in supporting these efforts.

SUBMITTER: Khalil AM

PROVIDER: S-EPMC7469715 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Quantifying the influence of water deficit on root and shoot growth in wheat using X-ray Computed Tomography.

Khalil A M AM Murchie E H EH Mooney S J SJ

AoB PLANTS 20200726 5

The potential increased frequency and severity of drought associated with environmental change represents a significant obstacle to efforts aimed at enhancing food security due to its impact on crop development, and ultimately, yield. Our understanding of the impact of drought on crop growth in terms of plant aerial tissues is much more advanced than knowledge of the below-ground impacts. We undertook an experiment using X-ray Computed Tomography that aimed to support measurements of infrared ga ...[more]

PMID: 32905427

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Project description: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.

Project description:Water deficit may occur at any stage of plant growth, with any intensity and duration. Phenotypic acclimation and the mechanism of adaptation vary with the evolutionary background of germplasm accessions and their stage of growth. Faba bean is considered sensitive to various kinds of drought. Hence, we conducted a greenhouse experiment in rhizotrons under contrasting watering regimes to explore shoot and root traits and drought avoidance mechanisms in young faba bean plants. Eight accessions were investigated for shoot and root morphological and physiological responses in two watering conditions with four replications. Pre-germinated seedlings were transplanted into rhizotron boxes filled with either air-dried or moist peat. The water-limited plants received 50-ml water at transplanting and another 50-ml water 4 days later, then no water was given until the end of the experimental period, 24 days after transplanting. The well-watered plants received 100 ml of water every 12 h throughout the experimental period. Root, stem, and leaf dry mass, their mass fractions, their dry matter contents, apparent specific root length and density, stomatal conductance, SPAD value, and Fv/Fm were recorded. Water deficit resulted in 3-4-fold reductions in shoot biomass, root biomass, and stomatal conductance along with 1.2-1.4-fold increases in leaf and stem dry matter content and SPAD values. Total dry mass and apparent root length density showed accession by treatment interactions. Accessions DS70622, DS11320, and ILB938/2 shared relatively high values of total dry mass and low values of stomatal conductance under water deficit but differed in root distribution parameters. In both treatments, DS70622 was characterized by finer roots that were distributed in both depth and width, whereas DS11320 and ILB938/2 produced less densely growing, thicker roots. French accession Mélodie/2 was susceptible to drought in the vegetative phase, in contrast to previous results from the flowering phase, showing the importance of timing of drought stress on the measured response. Syrian accession DS70622 explored the maximum root volume and maintained its dry matter production, with the difference from the other accessions being particularly large in the water-limited treatment, so it is a valuable source of traits for avoiding transient drought.

Project description:PurposeComputed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.MethodsCT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed.ResultsThe median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001.ConclusionCT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.

Project description:Background:X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required. Results:We have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at?<?0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over 3 weeks. Conclusions:We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method 'RSAvis3D' and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

Dataset Information

Quantifying the influence of water deficit on root and shoot growth in wheat using X-ray Computed Tomography.

Publications

Quantifying the influence of water deficit on root and shoot growth in wheat using X-ray Computed Tomography.

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