Quantitative analysis of venation patterns of Arabidopsis leaves by supervised image analysis

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Machine learning for image-based multi-omics analysis of leaf veins

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

A rapid method to quantify vein density in C4 plants using starch staining

C4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C4 plants compared with the ancestral C3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C4 species demonstrating natural variation for this trait.

INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1-dependent inositol polyphosphates regulate auxin responses in Arabidopsis

The combinatorial phosphorylation of myo-inositol results in the generation of different inositol phosphates (InsPs), of which phytic acid (InsP6) is the most abundant species in eukaryotes. InsP6 is also an important precursor of the higher phosphorylated inositol pyrophosphates (PP-InsPs), such as InsP7 and InsP8, which are characterized by a diphosphate moiety and are also ubiquitously found in eukaryotic cells. While PP-InsPs regulate various cellular processes in animals and yeast, their biosynthesis and functions in plants has remained largely elusive because plant genomes do not encode canonical InsP6 kinases. Recent work has shown that Arabidopsis (Arabidopsis thaliana) INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1 (ITPK1) and ITPK2 display in vitro InsP6 kinase activity and that, in planta, ITPK1 stimulates 5-InsP7 and InsP8 synthesis and regulates phosphate starvation responses. Here we report a critical role of ITPK1 in auxin-related processes that is independent of the ITPK1-controlled regulation of phosphate starvation responses. Those processes include primary root elongation, root hair development, leaf venation, thermomorphogenic and gravitropic responses, and sensitivity to exogenously applied auxin. We found that the recombinant auxin receptor complex, consisting of the F-Box protein TRANSPORT INHIBITOR RESPONSE1 (TIR1), ARABIDOPSIS SKP1 HOMOLOG 1 (ASK1), and the transcriptional repressor INDOLE-3-ACETIC ACID INDUCIBLE 7 (IAA7), binds to anionic inositol polyphosphates with high affinity. We further identified a physical interaction between ITPK1 and TIR1, suggesting a localized production of 5-InsP7, or another ITPK1-dependent InsP/PP-InsP isomer, to activate the auxin receptor complex. Finally, we demonstrate that ITPK1 and ITPK2 function redundantly to control auxin responses, as deduced from the auxin-insensitive phenotypes of itpk1 itpk2 double mutant plants. Our findings expand the mechanistic understanding of auxin perception and suggest that distinct inositol polyphosphates generated near auxin receptors help to fine-tune auxin sensitivity in plants.

Using quantitative methods to understand leaf epidermal development

As the interface between plants and the environment, the leaf epidermis provides the first layer of protection against drought, ultraviolet light, and pathogen attack. This cell layer comprises highly coordinated and specialised cells such as stomata, pavement cells and trichomes. While much has been learned from the genetic dissection of stomatal, trichome and pavement cell formation, emerging methods in quantitative measurements that monitor cellular or tissue dynamics will allow us to further investigate cell state transitions and fate determination in leaf epidermal development. In this review, we introduce the formation of epidermal cell types in Arabidopsis and provide examples of quantitative tools to describe phenotypes in leaf research. We further focus on cellular factors involved in triggering cell fates and their quantitative measurements in mechanistic studies and biological patterning. A comprehensive understanding of how a functional leaf epidermis develops will advance the breeding of crops with improved stress tolerance.

A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.

Quantification of spatial metal accumulation patterns in Noccaea caerulescens by X-ray fluorescence image processing for genetic studies

BACKGROUND

Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation.

RESULTS

Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H²) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach.

CONCLUSIONS

A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H² estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.

grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves

Beyond facilitating transport and providing mechanical support to the leaf, veins have important roles in the performance and productivity of plants and the ecosystem. In recent decades, computational image analysis has accelerated the extraction and quantification of vein traits, benefiting fields of research from agriculture to climatology. However, most of the existing leaf vein image analysis programs have been developed for the reticulate venation found in dicots. Despite the agroeconomic importance of cereal grass crops, like Oryza sativa (rice) and Zea mays (maize), a dedicated image analysis program for the parallel venation found in monocots has yet to be developed. To address the need for an image-based vein phenotyping tool for model and agronomic grass species, we developed the grass vein image quantification (grasviq) framework. Designed specifically for parallel venation, this framework automatically segments and quantifies vein patterns from images of cleared leaf pieces using classical computer vision techniques. Using image data sets from maize inbred lines and auxin biosynthesis and transport mutants in maize, we demonstrate the utility of grasviq for quantifying important vein traits, including vein density, vein width and interveinal distance. Furthermore, we show that the framework can resolve quantitative differences and identify vein patterning defects, which is advantageous for genetic experiments and mutant screens. We report that grasviq can perform high-throughput vein quantification, with precision on a par with that of manual quantification. Therefore, we envision that grasviq will be adopted for vein phenomics in maize and other grass species.

Automated and accurate segmentation of leaf venation networks via deep learning

Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm² ). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.

HyLength: a semi-automated digital image analysis tool for measuring the length of roots and fungal hyphae of dense mycelia

In plant-fungus phenotyping, determining fungal hyphal and plant root lengths by digital image analysis can reduce labour and increase data reproducibility. However, the degree of software sophistication is often prohibitive and manual measuring is still used, despite being very time-consuming. We developed the HyLength tool for measuring the lengths of hyphae and roots in in vivo and in vitro systems. The HyLength was successfully validated against manual measures of roots and fungal hyphae obtained from all systems. Compared with manual methods, the HyLength underestimated Medicago sativa roots in the in vivo system and Rhizophagus irregularis hyphae in the in vitro system by about 12 cm per m and allowed to save about 1 h for a single experimental unit. As regards hyphae of R. irregularis in the in vivo system, the HyLength overestimated the length by about 21 cm per m compared with manual measures, but time saving was up to 20.5 h per single experimental unit. Finally, with hyphae of Aspergillus oryzae, the underestimation was about 8 cm per m with a time saving of about 10 min for a single germinating spore. By benchmarking the HyLength against the AnaMorf plugin of the ImageJ/Fiji, we found that the HyLength performed better for dense fungal hyphae, also strongly reducing the measuring time. The HyLength can allow measuring the length over a whole experimental unit, eliminating the error due to sub-area selection by the user and allowing processing a high number of samples. Therefore, we propose the HyLength as a useful freeware tool for measuring fungal hyphae of dense mycelia.

LeafScope: A Portable High-Resolution Multispectral Imager for In Vivo Imaging Soybean Leaf

Portable devices for measuring plant physiological features with their isolated measuring chamber are playing an increasingly important role in plant phenotyping. However, currently available commercial devices of this type, such as soil plant analysis development (SPAD) meter and spectrometer, are dot meters that only measure a small region of the leaf, which does not perfectly represent the highly varied leaf surface. This study developed a portable and high-resolution multispectral imager (named LeafScope) to in-vivo image a whole leaf of dicotyledon plants while blocking the ambient light. The hardware system is comprised of a monochrome camera, an imaging chamber, a lightbox with different bands of light-emitting diodes (LEDs) array, and a microcontroller. During measuring, the device presses the leaf to lay it flat in the imaging chamber and acquires multiple images while alternating the LED bands within seconds in a certain order. The results of an experiment with soybean plants clearly showed the effect of nitrogen and water treatments as well as the genotype differences by the color and morphological features from image processing. We conclude that the low cost and easy to use LeafScope can provide promising imaging quality for dicotyledon plants, so it has great potential to be used in plant phenotyping.

Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.

AutoTube: a novel software for the automated morphometric analysis of vascular networks in tissues

Due to their involvement in many physiologic and pathologic processes, there is a great interest in identifying new molecular pathways that mediate the formation and function of blood and lymphatic vessels. Vascular research increasingly involves the image-based analysis and quantification of vessel networks in tissue whole-mounts or of tube-like structures formed by cultured endothelial cells in vitro. While both types of experiments deliver important mechanistic insights into (lymph)angiogenic processes, the manual analysis and quantification of such experiments are typically labour-intensive and affected by inter-experimenter variability. To bypass these problems, we developed AutoTube, a new software that quantifies parameters like the area covered by vessels, vessel width, skeleton length and branching or crossing points of vascular networks in tissues and in in vitro assays. AutoTube is freely downloadable, comprises an intuitive graphical user interface and helps to perform otherwise highly time-consuming image analyses in a rapid, automated and reproducible manner. By analysing lymphatic and blood vascular networks in whole-mounts prepared from different tissues or from gene-targeted mice with known vascular abnormalities, we demonstrate the ability of AutoTube to determine vascular parameters in close agreement to the manual analyses and to identify statistically significant differences in vascular morphology in tissues and in vascular networks formed in in vitro assays.

Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat

BACKGROUND

Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.

RESULTS

Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way.

CONCLUSIONS

Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.

Mechanical Stress Induces Remodeling of Vascular Networks in Growing Leaves

Differentiation into well-defined patterns and tissue growth are recognized as key processes in organismal development. However, it is unclear whether patterns are passively, homogeneously dilated by growth or whether they remodel during tissue expansion. Leaf vascular networks are well-fitted to investigate this issue, since leaves are approximately two-dimensional and grow manyfold in size. Here we study experimentally and computationally how vein patterns affect growth. We first model the growing vasculature as a network of viscoelastic rods and consider its response to external mechanical stress. We use the so-called texture tensor to quantify the local network geometry and reveal that growth is heterogeneous, resembling non-affine deformations in composite materials. We then apply mechanical forces to growing leaves after veins have differentiated, which respond by anisotropic growth and reorientation of the network in the direction of external stress. External mechanical stress appears to make growth more homogeneous, in contrast with the model with viscoelastic rods. However, we reconcile the model with experimental data by incorporating randomness in rod thickness and a threshold in the rod growth law, making the rods viscoelastoplastic. Altogether, we show that the higher stiffness of veins leads to their reorientation along external forces, along with a reduction in growth heterogeneity. This process may lead to the reinforcement of leaves against mechanical stress. More generally, our work contributes to a framework whereby growth and patterns are coordinated through the differences in mechanical properties between cell types.

A Journey Through a Leaf: Phenomics Analysis of Leaf Growth in Arabidopsis thaliana

In Arabidopsis, leaves contribute to the largest part of the aboveground biomass. In these organs, light is captured and converted into chemical energy, which plants use to grow and complete their life cycle. Leaves emerge as a small pool of cells at the vegetative shoot apical meristem and develop into planar, complex organs through different interconnected cellular events. Over the last decade, numerous phenotyping techniques have been developed to visualize and quantify leaf size and growth, leading to the identification of numerous genes that contribute to the final size of leaves. In this review, we will start at the Arabidopsis rosette level and gradually zoom in from a macroscopic view on leaf growth to a microscopic and molecular view. Along this journey, we describe different techniques that have been key to identify important events during leaf development and discuss approaches that will further help unraveling the complex cellular and molecular mechanisms that underlie leaf growth.

Blobs and curves: object-based colocalisation for plant cells

Blobs and curves occur everywhere in plant bioimaging: from signals of fluorescence-labelled proteins, through cytoskeletal structures, nuclei staining and cell extensions such as root hairs. Here we look at the problem of colocalisation of blobs with blobs (protein-protein colocalisation) and blobs with curves (organelle-cytoskeleton colocalisation). This article demonstrates a clear quantitative alternative to pixel-based colocalisation methods and, using object-based methods, can quantify not only the level of colocalisation but also the distance between objects. Included in this report are computational algorithms, biological experiments and guidance for those looking to increase their use of computationally-based and quantified analysis of bioimages.

Molecular systems governing leaf growth: from genes to networks

Arabidopsis leaf growth consists of a complex sequence of interconnected events involving cell division and cell expansion, and requiring multiple levels of genetic regulation. With classical genetics, numerous leaf growth regulators have been identified, but the picture is far from complete. With the recent advances made in quantitative phenotyping, the study of the quantitative, dynamic, and multifactorial features of leaf growth is now facilitated. The use of high-throughput phenotyping technologies to study large numbers of natural accessions or mutants, or to screen for the effects of large sets of chemicals will allow for further identification of the additional players that constitute the leaf growth regulatory networks. Only a tight co-ordination between these numerous molecular players can support the formation of a functional organ. The connections between the components of the network and their dynamics can be further disentangled through gene-stacking approaches and ultimately through mathematical modelling. In this review, we describe these different approaches that should help to obtain a holistic image of the molecular regulation of organ growth which is of high interest in view of the increasing needs for plant-derived products.

Quantitative neutron imaging of water distribution, venation network and sap flow in leaves

Quantitative neutron imaging is a promising technique to investigate leaf water flow and transpiration in real time and has perspectives towards studies of plant response to environmental conditions and plant water stress. The leaf hydraulic architecture is a key determinant of plant sap transport and plant-atmosphere exchange processes. Non-destructive imaging with neutrons shows large potential for unveiling the complex internal features of the venation network and the transport therein. However, it was only used for two-dimensional imaging without addressing flow dynamics and was still unsuccessful in accurate quantification of the amount of water. Quantitative neutron imaging was used to investigate, for the first time, the water distribution in veins and lamina, the three-dimensional venation architecture and sap flow dynamics in leaves. The latter was visualised using D2O as a contrast liquid. A high dynamic resolution was obtained by using cold neutrons and imaging relied on radiography (2D) as well as tomography (3D). The principle of the technique was shown for detached leaves, but can be applied to in vivo leaves as well. The venation network architecture and the water distribution in the veins and lamina unveiled clear differences between plant species. The leaf water content could be successfully quantified, though still included the contribution of the leaf dry matter. The flow measurements exposed the hierarchical structure of the water transport pathways, and an accurate quantification of the absolute amount of water uptake in the leaf was possible. Particular advantages of neutron imaging, as compared to X-ray imaging, were identified. Quantitative neutron imaging is a promising technique to investigate leaf water flow and transpiration in real time and has perspectives towards studies of plant response to environmental conditions and plant water stress.

Deep evolutionary comparison of gene expression identifies parallel recruitment of trans-factors in two independent origins of C4 photosynthesis

With at least 60 independent origins spanning monocotyledons and dicotyledons, the C4 photosynthetic pathway represents one of the most remarkable examples of convergent evolution. The recurrent evolution of this highly complex trait involving alterations to leaf anatomy, cell biology and biochemistry allows an increase in productivity by ∼ 50% in tropical and subtropical areas. The extent to which separate lineages of C4 plants use the same genetic networks to maintain C4 photosynthesis is unknown. We developed a new informatics framework to enable deep evolutionary comparison of gene expression in species lacking reference genomes. We exploited this to compare gene expression in species representing two independent C4 lineages (Cleome gynandra and Zea mays) whose last common ancestor diverged ∼ 140 million years ago. We define a cohort of 3,335 genes that represent conserved components of leaf and photosynthetic development in these species. Furthermore, we show that genes encoding proteins of the C4 cycle are recruited into networks defined by photosynthesis-related genes. Despite the wide evolutionary separation and independent origins of the C4 phenotype, we report that these species use homologous transcription factors to both induce C4 photosynthesis and to maintain the cell specific gene expression required for the pathway to operate. We define a core molecular signature associated with leaf and photosynthetic maturation that is likely shared by angiosperm species derived from the last common ancestor of the monocotyledons and dicotyledons. We show that deep evolutionary comparisons of gene expression can reveal novel insight into the molecular convergence of highly complex phenotypes and that parallel evolution of trans-factors underpins the repeated appearance of C4 photosynthesis. Thus, exploitation of extant natural variation associated with complex traits can be used to identify regulators. Moreover, the transcription factors that are shared by independent C4 lineages are key targets for engineering the C4 pathway into C3 crops such as rice.

ClearedLeavesDB: an online database of cleared plant leaf images

BACKGROUND

Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers.

DESCRIPTION

The Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database.

CONCLUSIONS

We developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface allows storage of large collections and integrates with leaf image analysis applications via an open application programming interface (API). The open API allows uploading of processed images and other trait data to the database, further enabling distribution and documentation of analyzed data within the community. The initial database is seeded with nearly 19,000 cleared leaf images representing over 40 GB of image data. Extensible storage and growth of the database is ensured by using the data storage resources of the iPlant Discovery Environment. ClearedLeavesDB can be accessed at http://clearedleavesdb.org.

Cell to whole-plant phenotyping: the best is yet to come

Imaging and image processing have revolutionized plant phenotyping and are now a major tool for phenotypic trait measurement. Here we review plant phenotyping systems by examining three important characteristics: throughput, dimensionality, and resolution. First, whole-plant phenotyping systems are highlighted together with advances in automation that enable significant throughput increases. Organ and cellular level phenotyping and its tools, often operating at a lower throughput, are then discussed as a means to obtain high-dimensional phenotypic data at elevated spatial and temporal resolution. The significance of recent developments in sensor technologies that give access to plant morphology and physiology-related traits is shown. Overall, attention is focused on spatial and temporal resolution because these are crucial aspects of imaging procedures in plant phenotyping systems.

Understanding of Leaf Development-the Science of Complexity

The leaf is the major organ involved in light perception and conversion of solar energy into organic carbon. In order to adapt to different natural habitats, plants have developed a variety of leaf forms, ranging from simple to compound, with various forms of dissection. Due to the enormous cellular complexity of leaves, understanding the mechanisms regulating development of these organs is difficult. In recent years there has been a dramatic increase in the use of technically advanced imaging techniques and computational modeling in studies of leaf development. Additionally, molecular tools for manipulation of morphogenesis were successfully used for in planta verification of developmental models. Results of these interdisciplinary studies show that global growth patterns influencing final leaf form are generated by cooperative action of genetic, biochemical, and biomechanical inputs. This review summarizes recent progress in integrative studies on leaf development and illustrates how intrinsic features of leaves (including their cellular complexity) influence the choice of experimental approach.

Leaf venation: structure, function, development, evolution, ecology and applications in the past, present and future

The design and function of leaf venation are important to plant performance, with key implications for the distribution and productivity of ecosystems, and applications in paleobiology, agriculture and technology. We synthesize classical concepts and the recent literature on a wide range of aspects of leaf venation. We describe 10 major structural features that contribute to multiple key functions, and scale up to leaf and plant performance. We describe the development and plasticity of leaf venation and its adaptation across environments globally, and a new global data compilation indicating trends relating vein length per unit area to climate, growth form and habitat worldwide. We synthesize the evolution of vein traits in the major plant lineages throughout paleohistory, highlighting the multiple origins of individual traits. We summarize the strikingly diverse current applications of leaf vein research in multiple fields of science and industry. A unified core understanding will enable an increasing range of plant biologists to incorporate leaf venation into their research.

Brassinosteroid production and signaling differentially control cell division and expansion in the leaf

Brassinosteroid (BR) hormones control plant growth through acting on both cell expansion and division. Here, we examined the role of BRs in leaf growth using the Arabidopsis BR-deficient mutant constitutive photomorphogenesis and dwarfism (cpd). We show that the reduced size of cpd leaf blades is a result of a decrease in cell size and number, as well as in venation length and complexity. Kinematic growth analysis and tissue-specific marker gene expression revealed that the leaf phenotype of cpd is associated with a prolonged cell division phase and delayed differentiation. cpd-leaf-rescue experiments and leaf growth analysis of BR biosynthesis and signaling gain-of-function mutants showed that BR production and BR receptor-dependent signaling differentially control the balance between cell division and expansion in the leaf. Investigation of cell cycle markers in leaves of cpd revealed the accumulation of mitotic proteins independent of transcription. This correlated with an increase in cyclin-dependent kinase activity, suggesting a role for BRs in control of mitosis.

X-ray imaging of leaf venation networks

Leaf venation networks mediate many plant resource fluxes and are therefore of broad interest to research questions in plant physiology, systematics, paleoecology, and physics. However, the study of these networks is limited by slow and destructive imaging methods. X-ray imaging of leaf veins is potentially rapid, of high resolution, and nondestructive. Here, we have developed theory for absorption- and phase-contrast X-ray imaging. We then experimentally test these approaches using a synchrotron light source and two commercially available X-ray instruments. Using synchrotron light, we found that major veins could be consistently visualized using absorption-contrast imaging with X-ray energies < 10 keV, while both major and minor veins could be consistently visualized with the use of an iodine contrast agent at an X-ray energy of 33.269 keV. Phase-contrast imaging at a range of energies provided high resolution but highlighted individual cell walls more than veins. Both approaches allowed several hundred samples to be processed per d. Commercial X-ray instruments were able to resolve major veins and some minor veins using absorption contrast. These results show that both commercial and synchrotron X-ray imaging can be successfully applied to leaf venation networks, facilitating research in multiple fields.

Rosette tracker: an open source image analysis tool for automatic quantification of genotype effects

Image analysis of Arabidopsis (Arabidopsis thaliana) rosettes is an important nondestructive method for studying plant growth. Some work on automatic rosette measurement using image analysis has been proposed in the past but is generally restricted to be used only in combination with specific high-throughput monitoring systems. We introduce Rosette Tracker, a new open source image analysis tool for evaluation of plant-shoot phenotypes. This tool is not constrained by one specific monitoring system, can be adapted to different low-budget imaging setups, and requires minimal user input. In contrast with previously described monitoring tools, Rosette Tracker allows us to simultaneously quantify plant growth, photosynthesis, and leaf temperature-related parameters through the analysis of visual, chlorophyll fluorescence, and/or thermal infrared time-lapse sequences. Freely available, Rosette Tracker facilitates the rapid understanding of Arabidopsis genotype effects.