RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues

Polystyrene nanoplastics induce cell type-dependent secondary wall reinforcement in rice (Oryza sativa) roots and reduce root hydraulic conductivity

Nanoplastics (NPs) have been demonstrated the ability to penetrate plant roots and cause stress. However, the extent of NPs penetration into various root tissues and the corresponding plant defense mechanisms remain unclear. This study examined the penetration and accumulation patterns of polystyrene nanoplastics (PS-NPs) in different cell types within rice roots, and explored how the roots quickly modify their cell wall structure in response. The findings showed that fully developed sclerenchyma cells in rice roots effectively prevented the invasion of PS-NPs. Meanwhile, PS-NPs triggered the accumulation of lignin and suberin in specific cells such as the exodermis, sclerenchyma, and xylem vessels. PS-NPs at a concentration of 50 mg L-1 increased cell wall thickness by 18.6 %, 21.1 %, and 22.4 % in epidermis, exodermis, and sclerenchyma cells, respectively, and decreased root hydraulic conductivity by 14.8 %. qPCR analysis revealed that PS-NPs influenced the cell wall synthesis pathway, promoting the deposition of lignin and suberin monomers on the secondary wall through the up-regulation of genes such as OsLAC and OsABCG. These results demonstrate that PS-NPs can induce cell type-specific strengthening of secondary walls and barrier formation in rice roots, suggesting the potential role of plant secondary wall development in mitigating NPs contamination risks in crops.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Utilization of Spectral Indices for High-Throughput Phenotyping

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.

Microstructure investigation of plant architecture with X-ray microscopy

In recent years, the plant morphology has been well studied by multiple approaches at cellular and subcellular levels. Two-dimensional (2D) microscopy techniques offer imaging of plant structures on a wide range of magnifications for researchers. However, subcellular imaging is still challenging in plant tissues like roots and seeds. Here we use a three-dimensional (3D) imaging technology based on the X-ray microscope (XRM) and analyze several plant tissues from different plant species. The XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast. We also utilized a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen. Multiple plant samples including rice, tobacco, Arabidopsis and maize were used to display the differences of phenotypes. Our work indicates that the XRM is a powerful tool to investigate plant microstructure in high-resolution scale. Our work also provides evidence that evaluate and quantify tissue specific differences for a range of plant species. We also characterize novel plant tissue phenotypes by the XRM, such as seeds in Arabidopsis, and utilize them for novel observation measurement. Our work represents an evaluated spatial and temporal resolution solution on seed observation and screening.

Climate-smart crops: key root anatomical traits that confer flooding tolerance

Plants require water, but a deficit or excess of water can negatively impact their growth and functioning. Soil flooding, in which root-zone is filled with excess water, restricts oxygen diffusion into the soil. Global climate change is increasing the risk of crop yield loss caused by flooding, and the development of flooding tolerant crops is urgently needed. Root anatomical traits are essential for plants to adapt to drought and flooding, as they determine the balance between the rates of water and oxygen transport. The stele contains xylem and the cortex contains aerenchyma (gas spaces), which respectively contribute to water uptake from the soil and oxygen supply to the roots; this implies that there is a trade-off between the ratio of cortex and stele sizes with respect to adaptation to drought or flooding. In this review, we analyze recent advances in the understanding of root anatomical traits that confer drought and/or flooding tolerance to plants and illustrate the trade-off between cortex and stele sizes. Moreover, we introduce the progress that has been made in modelling and fully automated analyses of root anatomical traits and discuss how key root anatomical traits can be used to improve crop tolerance to soil flooding.

GRANAR, a Computational Tool to Better Understand the Functional Importance of Monocotyledon Root Anatomy

Root hydraulic conductivity is a limiting factor along the water pathways between the soil and the leaf, and root radial conductivity is itself defined by cell-scale hydraulic properties and anatomical features. However, quantifying the influence of anatomical features on the radial conductivity remains challenging due to complex time-consuming experimental procedures. We present an open-source computational tool, the Generator of Root Anatomy in R (GRANAR; http://granar.github.io), that can be used to rapidly generate digital versions of contrasted monocotyledon root anatomical networks. GRANAR uses a limited set of root anatomical parameters, easily acquired with existing image analysis tools. The generated anatomical network can then be used in combination with hydraulic models to estimate the corresponding hydraulic properties. We used GRANAR to reanalyze large maize (Zea mays) anatomical datasets from the literature. Our model was successful at creating virtual anatomies for each experimental observation. We also used GRANAR to generate anatomies not observed experimentally over wider ranges of anatomical parameters. The generated anatomies were then used to estimate the corresponding radial conductivities with the hydraulic model MECHA (model of explicit cross-section hydraulic architecture). Our simulations highlight the large importance of the width of the stele and the cortex. GRANAR is a computational tool that generates root anatomical networks from experimental data. It enables the quantification of the effect of individual anatomical features on the root radial conductivity.

Versatile method for quantifying and analyzing morphological differences in experimentally obtained images

Analyzing high-resolution images to gain insight into anatomical properties is an essential tool for investigation in many scientific fields. In plant biology, studying plant phenotypes from micrographs is often used to build hypotheses on gene function. In this report, we discuss a bespoke method for inspecting the significance in the differences between the morphologies of several plant mutants at cellular level. By examining a specific example in the literature, we will detail the approach previously used to quantify the effects of two gene families on the vascular development of hypocotyls in Arabidopsis thaliana. The method incorporates a MATLAB algorithm and statistical tools which can be modified and enhanced to tailor to different research questions in future studies.

Crop Phenomics: Current Status and Perspectives

Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

Connecting the dots between computational tools to analyse soil-root water relations

In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.

High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.)

BACKGROUND

The biomechanical properties of maize stalks largely determine their lodging resistance, which affects crop yield per unit area. However, the quantitative and qualitative relationship between micro-phenotypes and the biomechanics of maize stalks is still under examined. In particular, the roles of the number, geometry, and distribution of vascular bundles of stalks in maize lodging resistance remain unclear. Research on these biomechanical properties will benefit from high-resolution micro-phenotypic image acquisition capabilities, which have been improved by modern X-ray imaging devices such as micro-CT and the development of micro-phenotyping analysis software. Hence, high-throughput image analysis and accurate quantification of anatomical phenotypes of stalks are necessary.

RESULTS

We have updated VesselParser version 1.0 to version 2.0 and have improved its performance, accuracy, and computation strategies. Anatomical characteristics of the second and third stalk internodes of the cultivars 'Jingke968' and 'Jingdan38' were analyzed using VesselParser 2.0. The relationships between lodging resistance and anatomical phenotypes of stalks between the two different maize varieties were investigated. The total area of vascular bundles in the peripheral layer, auxiliary axis diameter, and total area of vascular bundles were revealed to have the highest correlation with mechanical properties, and anatomical phenotypes of maize stalk were better predictors of mechanical properties than macro features observed optically from direct measurement, such as diameter and perimeter.

CONCLUSIONS

This study demonstrates the utility of VesselParser 2.0 in assessing stalk mechanical properties. The combination of anatomical phenotypes and mechanical behavior research provides unique insights into the problem of stalk lodging, showing that micro phenotypes of vascular bundles are good predictors of maize stalk mechanical properties that may be important indices for the evaluation and identification of the biomechanical properties to improve lodging resistance of future maize varieties.

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a 'sample preparation protocol' for maize materials (i.e., stem, leaf, and root) suitable for ordinary microcomputed tomography (micro-CT) scanning. Based on high-resolution CT images of maize stem, leaf, and root, we describe two protocols for the phenotypic analysis of vascular bundles: (1) based on the CT image of maize stem and leaf, we developed a specific image analysis pipeline to automatically extract 31 and 33 phenotypic traits of vascular bundles; (2) based on the CT image series of maize root, we set up an image processing scheme for the three-dimensional (3-D) segmentation of metaxylem vessels, and extracted two-dimensional (2-D) and 3-D phenotypic traits, such as volume, surface area of metaxylem vessels, etc. Compared with traditional manual measurement of vascular bundles of maize materials, the proposed protocols significantly improve the efficiency and accuracy of micron-scale phenotypic quantification.

Phenotyping of plants in competitive but controlled environments: a study of drought response in transgenic wheat

In recent years, the interest in new technologies for wheat improvement has increased greatly. To screen genetically modified germplasm in conditions more realistic for a field situation we developed a phenotyping platform where transgenic wheat and barley are grown in competition. In this study, we used the platform to (1) test selected promoter and gene combinations for their capacity to increase drought tolerance, (2) test the function and power of our platform to screen the performance of transgenic plants growing in competition, and (3) develop and test an imaging and analysis process as a means of obtaining additional, non-destructive data on plant growth throughout the whole growth cycle instead of relying solely on destructive sampling at the end of the season. The results showed that several transgenic lines under well watered conditions had higher biomass and/or grain weight than the wild-type control but the advantage was significant in one case only. None of the transgenics seemed to show any grain weight advantage under drought stress and only two lines had a substantially but not significantly higher biomass weight than the wild type. However, their evaluation under drought stress was disadvantaged by their delayed flowering date, which increased the drought stress they experienced in comparison to the wild type. Continuous imaging during the season provided additional and non-destructive phenotyping information on the canopy development of mini-plots in our phenotyping platform. A correlation analysis of daily canopy coverage data with harvest metrics showed that the best predictive value from canopy coverage data for harvest metrics was achieved with observations from around heading/flowering to early ripening whereas early season observations had only a limited diagnostic value. The result that the biomass/leaf development in the early growth phase has little correlation with biomass or grain yield data questions imaging approaches concentrating only on the early development stage.

Segmentation of 3D images of plant tissues at multiple scales using the level set method

BACKGROUND

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

RESULTS

We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue.

CONCLUSION

The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.

Novel tools for quantifying secondary growth

Secondary growth occurs in dicotyledons and gymnosperms, and results in an increased girth of plant organs. It is driven primarily by the vascular cambium, which produces thousands of cells throughout the life of several plant species. For instance, even in the small herbaceous model plant Arabidopsis, manual quantification of this massive process is impractical. Here, we provide a comprehensive overview of current methods used to measure radial growth. We discuss the issues and problematics related to its quantification. We highlight recent advances and tools developed for automated cellular phenotyping and its future applications.