Affordable and robust phenotyping framework to analyse root system architecture of soil-grown plants

Roles of Histone Acetylation and Deacetylation in Root Development

Roots are usually underground plant organs, responsible for anchoring to the soil, absorbing water and nutrients, and interacting with the rhizosphere. During root development, roots respond to a variety of environmental signals, contributing to plant survival. Histone post-translational modifications play essential roles in gene expression regulation, contributing to plant responses to environmental cues. Histone acetylation is one of the most studied post-translational modifications, regulating numerous genes involved in various biological processes, including development and stress responses. Although the effect of histone acetylation on plant responses to biotic and abiotic stimuli has been extensively reviewed, no recent reviews exist focusing on root development regulation by histone acetylation. Therefore, this review brings together all the knowledge about the impact of histone acetylation on root development in several plant species, mainly focusing on Arabidopsis thaliana. Here, we summarize the role of histone acetylation and deacetylation in numerous aspects of root development, such as stem cell niche maintenance, cell division, expansion and differentiation, and developmental zone determination. We also emphasize the gaps in current knowledge and propose new perspectives for research toward deeply understanding the role of histone acetylation in root development.

Field phenotyping for African crops: overview and perspectives

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction

BACKGROUND

Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images.

RESULTS

We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%.

CONCLUSIONS

We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.

PiSpy: An affordable, accessible, and flexible imaging platform for the automated observation of organismal biology and behavior

A great deal of understanding can be gleaned from direct observation of organismal growth, development, and behavior. However, direct observation can be time consuming and influence the organism through unintentional stimuli. Additionally, video capturing equipment can often be prohibitively expensive, difficult to modify to one's specific needs, and may come with unnecessary features. Here, we describe PiSpy, a low-cost, automated video acquisition platform that uses a Raspberry Pi computer and camera to record video or images at specified time intervals or when externally triggered. All settings and controls, such as programmable light cycling, are accessible to users with no programming experience through an easy-to-use graphical user interface. Importantly, the entire PiSpy system can be assembled for less than $100 using laser-cut and 3D-printed components. We demonstrate the broad applications and flexibility of PiSpy across a range of model and non-model organisms. Designs, instructions, and code can be accessed through an online repository, where a global community of PiSpy users can also contribute their own unique customizations and help grow the community of open-source research solutions.

Propagation Methods Decide Root Architecture of Chinese Fir: Evidence from Tissue Culturing, Rooted Cutting and Seed Germination

The root is the main organ of a plant for absorbing resources and whose spatial distribution characteristics play an important role in the survival of seedlings after afforestation. Chinese fir (Cunninghamia lanceolata) is one of China’s most important plantation species. To clarify the effects of propagation methods on root growth and spatial distribution characteristics of Chinese fir trees, sampled trees cultivated by seed germination, tissue culture, and asexual cutting of Chinese fir were taken as the research objects. The root morphology, geometric configuration, and spatial distribution characteristics of different trees were analyzed. The influence of geometric root morphology on its spatial distribution pattern was explored by correlation analysis, and the resource acquisition characteristics reflected by the roots of Chinese fir trees with different propagation methods are discussed. The main results showed that the root mean diameter (1.56 mm, 0.95 mm, and 0.97 mm), root volume (2.98 m3, 10.25 m3, and 4.07 m3), root tip count (397, 522, and 440), main root branch angle (61°, 50° and 32°) and other geometric configurations of Chinese fir under seed germination, tissue culture and rooted cutting respectively, were significantly different, which resulted in different distribution characteristics of roots in space. Chinese fir seed germination had noticeable axial roots, and the growth advantage was obvious in the vertical direction. A fishtail branch structure (TI = 0.87) was constructed. The shallow root distribution of tissue culture and rooted cutting was obvious, and belonged to the fork branch structure (TI = 0.71 and 0.74, respectively). There was a tradeoff in the spatial growth of the root system of Chinese fir trees with different propagation methods to absorb nutrients from heterogeneous soil patches. A negative correlation was present between the root system and root amplitude. There was an opposite spatial growth trend of Chinese fir trees with different propagation methods in the vertical or horizontal direction. In conclusion, selecting suitable propagation methods to cultivate Chinese fir trees is beneficial to root development and the “ideal” configuration formation of resource acquisition to improve the survival rate of Chinese fir afforestation.

Current Techniques to Study Beneficial Plant-Microbe Interactions

Many different experimental approaches have been applied to elaborate and study the beneficial interactions between soil bacteria and plants. Some of these methods focus on changes to the plant and others are directed towards assessing the physiology and biochemistry of the beneficial plant growth-promoting bacteria (PGPB). Here, we provide an overview of some of the current techniques that have been employed to study the interaction of plants with PGPB. These techniques include the study of plant microbiomes; the use of DNA genome sequencing to understand the genes encoded by PGPB; the use of transcriptomics, proteomics, and metabolomics to study PGPB and plant gene expression; genome editing of PGPB; encapsulation of PGPB inoculants prior to their use to treat plants; imaging of plants and PGPB; PGPB nitrogenase assays; and the use of specialized growth chambers for growing and monitoring bacterially treated plants.

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.

May the dark be with roots: a perspective on how root illumination may bias in vitro research on plant-environment interactions

Roots anchor plants to the soil, providing them with nutrients and water while creating a defence network and facilitating beneficial interactions with a multitude of living organisms and climatological conditions. To facilitate morphological and molecular studies, root research has been conducted using in vitro systems. However, under natural conditions, roots grow in the dark, mainly in the absence of illumination, except for the relatively low illumination of the upper soil surface, and this has been largely ignored. Here, we discuss the results found over the last decade on how experimental exposure of roots to light may bias root development and responses through the alteration of hormonal signalling, cytoskeleton organization, reactive oxygen species or the accumulation of flavonoids, among other factors. Illumination alters the uptake of nutrients or water, and also affects the response of the roots to abiotic stresses and root interactions with the microbiota. Furthermore, we review in vitro systems created to maintain roots in darkness, and provide a comparative analysis of root transcriptomes obtained with these devices. Finally, we identify other experimental variables that should be considered to better mimic soil conditions, whose improvement would benefit studies using in vitro cultivation or enclosed ecosystems.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

Breeding custom-designed crops for improved drought adaptation

The current pace of crop improvement is inadequate to feed the burgeoning human population by 2050. Higher, more stable, and sustainable crop production is required against a backdrop of drought stress, which causes significant losses in crop yields. Tailoring crops for drought adaptation may hold the key to address these challenges and provide resilient production systems for future harvests. Understanding the genetic and molecular landscape of the functionality of alleles associated with adaptive traits will make designer crop breeding the prospective approach for crop improvement. Here, we highlight the potential of genomics technologies combined with crop physiology for high-throughput identification of the genetic architecture of key drought-adaptive traits and explore innovative genomic breeding strategies for designing future crops.

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.

Specialized Plant Growth Chamber Designs to Study Complex Rhizosphere Interactions

The rhizosphere is a dynamic ecosystem shaped by complex interactions between plant roots, soil, microbial communities and other micro- and macro-fauna. Although studied for decades, critical gaps exist in the study of plant roots, the rhizosphere microbiome and the soil system surrounding roots, partly due to the challenges associated with measuring and parsing these spatiotemporal interactions in complex heterogeneous systems such as soil. To overcome the challenges associated with in situ study of rhizosphere interactions, specialized plant growth chamber systems have been developed that mimic the natural growth environment. This review discusses the currently available lab-based systems ranging from widely known rhizotrons to other emerging devices designed to allow continuous monitoring and non-destructive sampling of the rhizosphere ecosystems in real-time throughout the developmental stages of a plant. We categorize them based on the major rhizosphere processes it addresses and identify their unique challenges as well as advantages. We find that while some design elements are shared among different systems (e.g., size exclusion membranes), most of the systems are bespoke and speaks to the intricacies and specialization involved in unraveling the details of rhizosphere processes. We also discuss what we describe as the next generation of growth chamber employing the latest technology as well as the current barriers they face. We conclude with a perspective on the current knowledge gaps in the rhizosphere which can be filled by innovative chamber designs.