Plant Methods

BackgroundStomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato and Arabidopsis. ResultsThe method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process which only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 76% to 99%, depending on the species, with a high correlation between measures of number, size and aperture between measurements using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. ConclusionsWe developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

Root systems of crops play a significant role in agro-ecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes and a good soil structure. Minirhizotrons, consisting of transparent tubes that create windows into the soil, have shown to be effective to non-invasively investigate the root system. Root traits, like root length observed around the tubes of minirhizotron, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, are time consuming and labor intensive. Therefore, an objective method for high throughput image analysis that provides data for field root-phenotyping is necessary. In this study we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample is performed. Training and segmentation are done using "Root-Painter". Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer". To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 58,000 images. Mainly the results show a high correlation (R=0.81) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1 - 99.6 %. Our pipeline,combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.

Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost system for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this system, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput method to support such research.

In the past few years, there has been an explosion in single-cell transcriptomics datasets, yet in vivo confirmation of these datasets is hampered in plants due to lack of robust validation methods. Likewise, modeling of plant development is hampered by paucity of spatial gene expression data. RNA fluorescence in situ hybridization (FISH) enables investigation of gene expression in the context of tissue type. Despite development of FISH methods for plants, easy and reliable whole mount FISH protocols have not yet been reported. We adapt a 3-day whole mount RNA-FISH method for plant species based on a combination of prior protocols that employs hybridization chain reaction (HCR), which amplifies the probe signal in an antibody-free manner. Our whole mount HCR RNA-FISH method shows expected spatial signals with low background for gene transcripts with known spatial expression patterns in Arabidopsis inflorescences and monocot roots. It allows simultaneous detection of three transcripts in 3D. We also show that HCR RNA-FISH can be combined with endogenous fluorescent protein detection and with our improved immunohistochemistry (IHC) protocol. The whole mount HCR RNA-FISH and IHC methods allow easy investigation of 3D spatial gene expression patterns in entire plant tissues.

The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of orchid root growth of multiple species over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time-lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.

Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R2=0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R2=0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R2=0.83) with actual canopy measurements. The iPhones surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R2=0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This TemplateThe template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the "Styles" menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com.

BackgroundIt has been proposed that engineering the C4 photosynthetic pathway into C3 crops could significantly increase yield. This goal requires an increase in the chloroplast compartment of bundle sheath cells in C3 species. To facilitate large-scale testing of candidate regulators of chloroplast development in the rice bundle sheath, a simple and robust method to phenotype this tissue in C3 species is required. ResultsWe established a leaf ablation method to accelerate phenotyping of rice bundle sheath cells. The approach allowed bundle sheath cell dimensions, chloroplast area and chloroplast number per cell to be measured. Using this method, bundle sheath cell dimensions of maize were also measured and compared with rice. Our data show that bundle sheath width but not length significantly differed between C3 rice and C4 maize. Comparison of paradermal versus transverse bundle sheath cell width indicated that bundle sheath cells were intact after leaf ablation. Moreover, comparisons of planar chloroplast areas and chloroplast numbers per bundle sheath cell between wild-type and transgenic rice lines expressing the maize GOLDEN-2 (ZmG2) showed that the leaf ablation method allowed differences in chloroplast parameters to be detected. ConclusionsLeaf ablation is a simple approach to accessing bundle sheath cell files in C3 species. We show that this method is suitable for obtaining parameters associated with bundle sheath cell size, chloroplast area and chloroplast number per cell.

Current developments in light-emitting diodes (LEDs) technologies have opened new perspectives for sustainable and highly efficient indoor cultivation. The introduction of LEDs not only allows a reduction in the production costs on a quantitative level, it also offers opportunities to manipulate and optimise qualitative traits. Indeed, while plants respond strongest to red and blue lights for photosynthesis, the whole light spectrum has an effect on plant shape, development, and chemical composition. In order to evaluate LEDs as an alternative to traditional lighting sources, the species-specific plant responses to distinct wavelengths need to be evaluated under controlled conditions. Here, we tested the possibility to use light composition gradients in combination with semi-automated phenotyping to rapidly explore the phenotypic responses of different species to variations in the light spectrum provided by LED sources. Plants of seven different species (Arabidopsis thaliana, Ocimum basilicum, Solanum lycopersicum, Brachypodium distachyon, Oryza sativa, Euphorbia peplus, Setaria viridis) were grown under standard white fluorescent light for 30 days, then transferred to a Red:Blue gradient for another 30 days and finally returned to white light. In all species, differences in terms of dimension, shape, and color were rapidly observed across the gradient and the overall response was widely species-dependent. The experiment yielded large amounts of imaging-based phenotypic data and we suggest simple data analysis methods to aggregate the results and facilitate comparisons between species. Similar experimental setups will help achieve rapid environmental optimization, screen new crop species and genotypes, or develop new gene discovery strategies.

BackgroundCell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana. However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. ResultsWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. ConclusionOur framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.

Live imaging is one of the most powerful methods to reveal the morphogenesis of plant organs. However, the highly three-dimensional structure of plant organs always poses technical challenges. For example, the basal region of leaf primordia is rarely observed because of the shape of leaf primordia and the sudden shift in geometry at the point where the leaf primordium connects to the hypocotyl. In this work, we developed a new live-imaging system that is suitable for observing the developmental process of the basal region of Arabidopsis leaf primordia at early stages. Using this system, we achieved continuous observation of the basal region of early Arabidopsis leaf primordia for more than 50 hours.

Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.

Sample preparation in genomics is a critical step that is often overlooked in molecular workflows and impacts the success of downstream genetic applications. This study explores the use of a recently developed focused ultrasound extraction (FUSE) technique to enable the rapid release of DNA from plant tissues for genetic analysis. FUSE generates a dense acoustic cavitation bubble cloud that pulverizes targeted tissue into acellular debris. This technique was applied to leaf samples of American chestnut (Castanea dentata), tulip poplar (Liriodendron tulipifera), red maple (Acer rubrum), and chestnut oak (Quercus montana). We observed that FUSE can extract high quantities of DNA in 9-15 minutes, compared to the 30 minutes required for conventional DNA extraction. FUSE extracted DNA quantities of 24.33 {+/-} 6.51 ng/mg and 35.32 {+/-} 9.21 ng/mg from American chestnut and red maple, respectively, while conventional methods yielded 6.22 {+/-} 0.87 ng/mg and 11.51 {+/-} 1.95 ng/mg, respectively. The quality of the DNA released by FUSE allowed for successful amplification and next-generation sequencing. These results indicate that FUSE can improve DNA extraction efficiency for leaf tissues. Continued development of this technology aims to adapt to field-deployable systems to increase the cataloging of genetic biodiversity, particularly in low-resource biodiversity hotspots.

Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracy remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding.

BackgroundRecent developments in hybridization chain reaction (HCR) have enabled robust simultaneous localization of multiple mRNA transcripts using fluorescence in situ hybridization (FISH). Once multiple split initiator oligonucleotide probes bind their target mRNA, HCR uses DNA base-pairing of fluorophore-labeled hairpin sets to self-assemble into large polymers, amplifying the fluorescence signal and reducing non-specific background. Few studies have applied HCR in plants, despite its demonstrated utility in whole mount animal tissues and cell culture. Our aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy. ResultsApplication of HCR to 10 {micro}m paraffin sections of 17-day-old Setaria viridis (green millet) inflorescences using confocal microscopy revealed that the transcripts of the transcription factor KNOTTED 1 (KN1) were localized to developing floret meristem and vascular tissue while SHATTERING 1 (SH1) and MYB26 transcripts were co-localized to the breakpoint below the floral structures (the abscission zone). We also used methacrylate de-embedment with 1.5 {micro}m and 0.5 {micro}m sections of 3-day-old Arabidopsis thaliana seedlings to show tissue specific CHLOROPHYLL BINDING FACTOR a/b (CAB1) mRNA highly expressed in photosynthetic tissues and ELONGATION FACTOR 1 ALPHA (EF1) highly expressed in meristematic tissues of the shoot apex. The housekeeping gene ACTIN7 (ACT7) mRNA was more uniformly distributed with reduced signals using lattice structured-illumination microscopy. HCR using 1.5 {micro}m methacrylate sections was followed by backscattered imaging and scanning electron microscopy thus demonstrating the feasibility of correlating fluorescent localization with ultrastructure. ConclusionHCR was successfully adapted for use with both paraffin and methacrylate de-embedment on diverse plant tissues in two model organisms, allowing for concurrent cellular and subcellular localization of multiple mRNAs, antibodies and other affinity probe classes. The mild hybridization conditions used in HCR made it highly amenable to observe immunofluorescence in the same section. De-embedded semi-thin methacrylate sections with HCR were compatible with correlative electron microscopy approaches. Our protocol provides numerous practical tips for successful HCR and affinity probe labeling in electron microscopy-compatible, sectioned plant material.

Root phenotyping is a challenging task that would require monitoring root growth in soil under dark conditions to mimic natural conditions, while allowing the shoot to grow in light. Most existing methods involve exposing the roots to light, which substantially alters their growth and function. In this paper, we present an improved imaging system that can overcome this limitation of experiments performed in laboratories. The Dynamic Dark Root imaging Chamber (DDrC) enables continuous monitoring and image acquisition to track the dynamic development of root architecture under controlled growth conditions. Our imaging system is based on a Raspberry Pi camera module and infrared LEDs, which do not induce any stress responses in the roots. The DDrC setup is simple, affordable, and suitable for dynamic phenotyping experiments. We provide a detailed tutorial for the assembly and adjustment of the imaging chamber. We conclude that our system is a valuable tool for studying the genetic and environmental factors that affect the root system architecture and development, and for identifying the root traits that are related to plant adaptation and performance.

Plant cells produce reactive oxygen species (ROS) as by-products of oxygen metabolism and for signal transduction. Depending on their concentration and their site of production, ROS can cause oxidative damage within the cell, and must be effectively scavenged. Detoxification of the most stable ROS, hydrogen peroxide (H2O2), via the glutathione-ascorbate pathway may transiently alter the glutathione redox potential (EGSH). Changes in EGSH can thus be considered as a proxy of the oxidative load in the cell. Genetically encoded probes based on roGFP2 enable extended opportunities for in vivo monitoring of H2O2 and EGSH dynamics. Here, we report detailed protocols for live monitoring of both parameters in the cytosol with the probes Grx1-roGFP2 for EGSH and roGFP2-Orp1 for H2O2, respectively. The protocols have been adapted for live cell imaging with high lateral resolution on a confocal microscope and for multiparallel measurements in whole organs or intact seedlings in a fluorescence microplate reader. Elicitor-induced ROS generation is used as an example for illustration of the opportunities for dynamic ROS measurements that can easily be transferred to other scientific questions and model systems.

Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy. The method allows coupled measurements to be collected in <15 s and consists of: (1) stomatal conductance measurements using a handheld porometer; (2) in situ collection of epidermal images with a digital microscope, 3D-printed leaf clip and Python-based app; and (3) automated deep learning analysis of stomatal features. The YOLOv8-M model trained on images collected in the field achieved strong performance metrics with an mAP@0.5 of 97.1% for stomatal detection. Validation in large field trials of 200 wheat genotypes with two irrigation treatments captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress. HighlightChaplin et al., have developed FieldDino which enables rapid, high-throughput phenotyping of stomatal traits, advancing plant breeding research by integrating streamlined in-field measurements with automated deep learning analysis.

High-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage - an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage. HighlightPhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.

Irrigation and fertilization stress in plants are limitations for securing global food production. Sustainable agriculture is at the heart of global goals because threats of a rapidly growing population and climate changes are affecting agricultural productivity. Plant phenotyping is defined as evaluating plant traits. Traditionally, this measurement is performed manually but with advanced technology and analysis, these traits can be observed automatically and nondestructively. A high correlation between plant traits, growth, biomass, and final yield has been found. From the early stages of plant development, lack of irrigation and fertilization directly influence developing stages, thus the final crop yield is significantly reduced. In order to evaluate drought and fertilization stress, plant height, as a morphological trait, is the most common one used in precision-agriculture research. The present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants for fine-scale phenotyping. This research demonstrates two different group conditions: 1) Normal conditions; and 2) zero irrigation and zero fertilization. The statistical analysis results show a successfully distinguished early stress with the volumetric traits providing new insights on identifying the key phenotypes and growth stages influenced by drought stress.

BackgroundAutomating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. ResultsWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCANs ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. ConclusionOverall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.