A novel tracking tool for the analysis of plant-root tip movements

A system for the study of roots 3D kinematics in hydroponic culture: a study on the oscillatory features of root tip

BACKGROUND

The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.

RESULTS

The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length.

CONCLUSIONS

The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.

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.

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.

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales

Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.

RTIP: A FULLY AUTOMATED ROOT TIP TRACKER FOR MEASURING PLANT GROWTH WITH INTERMITTENT PERTURBATIONS

RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequences at sub-pixel accuracy. The fully automated RTip tracker is designed for high-throughput analysis of plant phenotyping experiments with episodic perturbations. RTip is able to auto-skip past these manual intervention perturbation activity, i.e. when the root tip is not under the microscope, image is distorted or blurred. RTip provides the most accurate root growth velocity results with the lowest variance (i.e. localization jitter) compared to six tracking algorithms including the top performing unsupervised Discriminative Correlation Filter Tracker and the Deeper and Wider Siamese Network. RTip is the only tracker that is able to automatically detect and recover from (occlusion-like) varying duration perturbation events.

The Bio-Engineering Approach for Plant Investigations and Growing Robots. A Mini-Review

It has been 10 years since the publication of the first article looking at plants as a biomechatronic system and as model for robotics. Now, roboticists have started to look at plants differently and consider them as a model in the field of bioinspired robotics. Despite plants have been seen traditionally as passive entities, in reality they are able to grow, move, sense, and communicate. These features make plants an exceptional example of morphological computation - with probably the highest level of adaptability among all living beings. They are a unique model to design robots that can act in- and adapt to- unstructured, extreme, and dynamically changing environments exposed to sudden or long-term events. Although plant-inspired robotics is still a relatively new field, it has triggered the concept of growing robotics: an emerging area in which systems are designed to create their own body, adapt their morphology, and explore different environments. There is a reciprocal interest between biology and robotics: plants represent an excellent source of inspiration for achieving new robotic abilities, and engineering tools can be used to reveal new biological information. This way, a bidirectional biology-robotics strategy provides mutual benefits for both disciplines. This mini-review offers a brief overview of the fundamental aspects related to a bioengineering approach in plant-inspired robotics. It analyses the works in which both biological and engineering aspects have been investigated, and highlights the key elements of plants that have been milestones in the pioneering field of growing robots.

Taking inspiration from climbing plants: methodologies and benchmarks-a review

One of the major challenges in robotics and engineering is to develop efficient technological solutions that are able to cope with complex environments and unpredictable constraints. Taking inspiration from natural organisms is a well-known approach to tackling these issues. Climbing plants are an important, yet innovative, source of inspiration due to their ability to adapt to diverse habitats, and can be used as a model for developing robots and smart devices for exploration and monitoring, as well as for search and rescue operations. This review reports the main methodologies and approaches used by scientists to investigate and extract the features of climbing plants that are relevant to the artificial world in terms of adaptation, movement, and behaviour, and it summarizes the current available climbing plant-inspired engineering solutions.

Impairment in karrikin but not strigolactone sensing enhances root skewing in Arabidopsis thaliana

Roots form highly complex systems varying in growth direction and branching pattern to forage for nutrients efficiently. Here mutations in the KAI2 (KARRIKIN INSENSITIVE) α/β-fold hydrolase and the MAX2 (MORE AXILLARY GROWTH 2) F-box leucine-rich protein, which together perceive karrikins (smoke-derived butenolides), caused alteration in root skewing in Arabidopsis thaliana. This phenotype was independent of endogenous strigolactones perception by the D14 α/β-fold hydrolase and MAX2. Thus, KAI2/MAX2 effect on root growth may be through the perception of endogenous KAI2-ligands (KLs), which have yet to be identified. Upon perception of a ligand, a KAI2/MAX2 complex is formed together with additional target proteins before ubiquitination and degradation through the 26S proteasome. Using a genetic approach, we show that SMAX1 (SUPPRESSOR OF MAX2-1)/SMXL2 and SMXL6,7,8 (SUPPRESSOR OF MAX2-1-LIKE) are also likely degradation targets for the KAI2/MAX2 complex in the context of root skewing. In A. thaliana therefore, KAI2 and MAX2 act to limit root skewing, while kai2's gravitropic and mechano-sensing responses remained largely unaffected. Many proteins are involved in root skewing, and we investigated the link between MAX2 and two members of the SKS/SKU family. Though KLs are yet to be identified in plants, our data support the hypothesis that they are present and can affect root skewing.

Growth Rate Normalization Method to Assess Gravitropic Root Growth

Time-lapse imaging of roots is highly suitable for depicting gravitropic growth behaviors. However, roots may show faster or slower bending kinetics when compared to control as a result of differences in overall root growth. Accordingly, conditions that cause differential organ growth require growth rate normalization to compare gravitropic curvature. Here, we describe a simple normalization method for gravitropic root growth evaluation. We exemplify this method by exposing seedlings to distinct environmental conditions or disturbing the cellular auxin contents. This data shows that the method is suitable to discriminate between gravitropic and overall organ growth defects.

KymoRod: a method for automated kinematic analysis of rod-shaped plant organs

A major challenge in plant systems biology is the development of robust, predictive multiscale models for organ growth. In this context it is important to bridge the gap between the, rather well-documented molecular scale and the organ scale by providing quantitative methods to study within-organ growth patterns. Here, we describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs that does not require adding markers at the organ surface. The method allows for the simultaneous analysis of root and hypocotyl growth, provides spatio-temporal information on curvature, growth anisotropy and relative elemental growth rate and can cope with complex organ movements. We demonstrate the performance of the method by documenting previously unsuspected complex growth patterns within the growing hypocotyl of the model species Arabidopsis thaliana during normal growth, after treatment with a growth-inhibiting drug or in a mechano-sensing mutant. The method is freely available as an intuitive and user-friendly Matlab application called KymoRod.

Assessing Gravitropic Responses in Arabidopsis

Arabidopsis thaliana was the first higher organism to have its genome sequenced and is now widely regarded as the model dicot. Like all plants, Arabidopsis develops distinct growth patterns in response to different environmental stimuli. This can be seen in the gravitropic response of roots. Methods to investigate this particular tropism are presented here. First, we describe a high-throughput time-lapse photographic analysis of root growth and curvature response to gravistimulation allowing the quantification of gravitropic kinetics and growth rate at high temporal resolution. Second, we present a protocol that allows a quantitative evaluation of gravitropic sensitivity using a homemade 2D clinostat. Together, these approaches allow an initial comparative analysis of the key phenomena associated with root gravitropism between different genotypes and/or accessions.

Root phenotyping: from component trait in the lab to breeding

In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential.