Quantifying leaf venation patterns: two-dimensional maps

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.

How dense can you be? New automatic measures of vein density in angiosperm leaves

Premise

Because of the trade-off between water loss and carbon dioxide assimilation, the conductivity of the transpiration path in a leaf is an important limit on photosynthesis. Closely packed veins correspond to short paths and high assimilation rates while widely spaced veins are associated with higher resistance to flow and lower maximum photosynthetic rates. Vein length per area (VLA) has become the standard metric for comparing leaves with different vein densities; its measurement typically utilizes digital image processing with varying amounts of human input.

Methods and Results

Here, we propose three new ways of measuring vein density using image analysis that improve on currently available procedures: (1) areole area distributions, (2) a sizing transform, and (3) a distance map. Each alternative has distinct practical, statistical, and biological limitations and advantages. In particular, we advocate the log-transformed modal distance map of a vein mask as an estimator to replace VLA as a standard metric for vein density.

Conclusions

These methods, for which open-source code appropriate for high-throughput automation is provided, improve on VLA by producing determinate measures of vein density as distributions rather than point estimates. Combined with advances in image quality and computational efficiency, these methods should help clarify the physiological and evolutionary significance of vein density.

Network feature-based phenotyping of leaf venation robustly reconstructs the latent space

Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands (e.g., hydraulic conductivity, transpiration efficiency, and tolerance to damage and blockage) constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. To better understand the morphological diversities and constraints on leaf vein networks, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images. Because the one-dimensional distribution patterns align with the Pareto front that optimizes transport efficiency, construction cost, and robustness to damage, as predicted by the earlier theoretical study, our findings suggested that venation patterns are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins.

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components

BACKGROUND

Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically.

RESULTS

In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the "size-free" space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences.

CONCLUSIONS

The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.

Biometric Identification of Taxodium spp. and Their Hybrid Progenies by Electrochemical Fingerprints

The use of electrochemical fingerprints for plant identification is an emerging application in biosensors. In this work, Taxodium ascendens, T. distichum, T. mucronatum, and 18 of their hybrid progenies were collected for this purpose. This is the first attempt to use electrochemical fingerprinting for the identification of plant hybrid progeny. Electrochemical fingerprinting in the leaves of Taxodium spp. was recorded under two conditions. The results showed that the electrochemical fingerprints of each species and progeny possessed very suitable reproducibility. These electrochemical fingerprints represent the electrochemical behavior of electrochemically active substances in leaf tissues under specific conditions. Since these species and progenies are very closely related to each other, it is challenging to identify them directly using a particular electrochemical fingerprinting. Therefore, electrochemical fingerprints measured under different conditions were used to perform pattern recognition. We can identify different species and progenies by locating the features in different pattern maps. We also performed a phylogenetic study with data from electrochemical fingerprinting. The results proved that the electrochemical classification results and the relationship between them are closely related.

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.

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%.

The arrangement of lateral veins along the midvein of leaves is not related to leaf phyllotaxis

Positions of leaves along a stem usually adhere to a genetically determined, species-specific pattern known as a leaf phyllotaxis. We investigated whether the arrangement of lateral secondary veins along primary midveins adhered to a species-specific pattern that resembled an alternate or opposite phyllotaxis. We analyzed the venation of temperate dicotyledonous species from different taxonomic groups and chose 18 woody and 12 herbaceous species that have reticulated leaf venation. The arrangement of the lateral veins was neither alternate nor opposite for any of the species. Lateral vein arrangements were instead mixtures of symmetric and asymmetric patterns. Our results show that lateral vein arrangements are related neither to stem-level leaf phyllotaxis (alternate vs. opposite) nor to life form (woody vs. herbaceous). Our results are therefore generally consistent with the canalization hypothesis that the locations of lateral veins are not completely specified genetically prior to leaf formation.

Linking leaf hydraulic properties, photosynthetic rates, and leaf lifespan in xerophytic species: a test of global hypotheses

PREMISE OF THE STUDY

Leaf venation and its hierarchal traits are crucial to the hydraulic and mechanical properties of leaves, reflecting plant life-history strategies. However, there is an extremely limited understanding of how variation in leaf hydraulics affects the leaf economic spectrum (LES) or whether venation correlates more strongly with hydraulic conductance or biomechanical support among hierarchal orders.

METHODS

We examined correlations of leaf hydraulics, indicated by vein density, conduit diameter, and stomatal density with light-saturated photosynthetic rates, leaf lifespan (LLS), and leaf morpho-anatomical traits of 39 xerophytic species grown in a common garden.

KEY RESULTS

We found positive relationships between light-saturated, area-based photosynthetic rates, and vein densities, regardless of vein orders. Densities of leaf veins had positive correlations with stomatal density. We also found positive relationships between LLS and vein densities. Leaf area was negatively correlated with the density of major veins but not with minor veins. Most anatomical traits were not related to vein densities.

CONCLUSIONS

We developed a network diagram of the correlations among leaf hydraulics and leaf economics, which suggests functional trade-offs between hydraulic costs and lifetime carbon gain. Leaf hydraulics efficiency and carbon assimilation were coupled across species. Vein construction costs directly coordinated with the LLS. Our findings indicate that hierarchal orders of leaf veins did not differ in the strength of their correlations between hydraulic conductance and biomechanical support. These findings clarify how leaf hydraulics contributes to the LES and provide new insight into life-history strategies of these xerophytic species.

Synchrotron-based phase-sensitive imaging of leaves grown from magneto-primed seeds of soybean

Experiments were conducted to study the effects of static magnetic fields (SMFs) on the venation network of soybean leaves using the synchrotron-based X-ray micro-imaging technique. The seeds of soybean (Glycine max, variety JS-335) were pretreated with different SMFs from 50 to 300 mT in steps of 50 mT for 1 h. The phase-contrast images obtained showed that, as the strength of the SMF increased, the area, width of the midrib, area of the midrib and minor vein of the middle leaflets of third trifoliate leaves also increased up to the SMF strength of 200 mT (1 h) and decreased thereafter. Quantification of the major conducting vein also showed the differences in the major and minor vein structures of the soybean leaves as compared with control leaves. Further, the phase-retrieval technique has been applied to make the segmentation process easy and to quantify the major and minor veins in the venation network. The width and area of midrib enhancement by pre-treatment with SMF implies an enhancement in the uptake of water, which in turn causes an increased rate of photosynthesis and stomatal conductance.

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.

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.

Reading the leaves: A comparison of leaf rank and automated areole measurement for quantifying aspects of leaf venation

The reticulate venation that is characteristic of a dicot leaf has excited interest from systematists for more than a century, and from physiological and developmental botanists for decades. The tools of digital image acquisition and computer image analysis, however, are only now approaching the sophistication needed to quantify aspects of the venation network found in real leaves quickly, easily, accurately, and reliably enough to produce biologically meaningful data. In this paper, we examine 120 leaves distributed across vascular plants (representing 118 genera and 80 families) using two approaches: a semiquantitative scoring system called "leaf ranking," devised by the late Leo Hickey, and an automated image-analysis protocol. In the process of comparing these approaches, we review some methodological issues that arise in trying to quantify a vein network, and discuss the strengths and weaknesses of automatic data collection and human pattern recognition. We conclude that subjective leaf rank provides a relatively consistent, semiquantitative measure of areole size among other variables; that modal areole size is generally consistent across large sections of a leaf lamina; and that both approaches-semiquantitative, subjective scoring; and fully quantitative, automated measurement-have appropriate places in the study of leaf venation.

Standardized mapping of nodulation patterns in legume roots

Optimizing nodulation in legumes is a target for crop improvement, and the spatial control of nodulation is just beginning to be unravelled. However, there is currently no method for standard phenotyping of nodulation patterns. Here we present a method and software for the quantitative analysis of nodulation phenotypes. Roots of nodulated peas (Pisum sativum), wild-type and two mutants, were photographed. Data from the photographs were extracted using custom image and data analysis software. The software makes it possible to extract each nodule's position along primary and lateral roots, and to represent the nodulated root system in a standardized way independent of the way roots are arranged in the soil. A wide variety of nodulation and root variables are calculated, and average spatial nodulation patterns can be computed from multiple samples. Standardized spatial analysis of nodulation patterns opens the way for comparative analyses among genotypes of a single legume species, as here in pea. This approach could also be used to compare nodulation patterns among crops, among plants grown under different environmental conditions, or among plants exposed to different pharmacological treatments. The proposed method should therefore prove useful for studies on nodule organogenesis and nodule physiology and for optimizing nodulation in crops.

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.

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.

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.

Quantifying loopy network architectures

Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

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

The study of transgenic Arabidopsis lines with altered vascular patterns has revealed key players in the venation process, but details of the vascularization process are still unclear, partly because most lines have only been assessed qualitatively. Therefore, quantitative analyses are required to identify subtle perturbations in the pattern and to test dynamic modeling hypotheses using biological measurements. We developed an online framework, designated Leaf Image Analysis Interface (LIMANI), in which venation patterns are automatically segmented and measured on dark-field images. Image segmentation may be manually corrected through use of an interactive interface, allowing supervision and rectification steps in the automated image analysis pipeline and ensuring high-fidelity analysis. This online approach is advantageous for the user in terms of installation, software updates, computer load and data storage. The framework was used to study vascular differentiation during leaf development and to analyze the venation pattern in transgenic lines with contrasting cellular and leaf size traits. The results show the evolution of vascular traits during leaf development, suggest a self-organizing mechanism for leaf venation patterning, and reveal a tight balance between the number of end-points and branching points within the leaf vascular network that does not depend on the leaf developmental stage and cellular content, but on the leaf position on the rosette. These findings indicate that development of LIMANI improves understanding of the interaction between vascular patterning and leaf growth.

Functional specialization of the plant miR396 regulatory network through distinct microRNA-target interactions

MicroRNAs (miRNAs) are ∼21 nt small RNAs that regulate gene expression in animals and plants. They can be grouped into families comprising different genes encoding similar or identical mature miRNAs. Several miRNA families are deeply conserved in plant lineages and regulate key aspects of plant development, hormone signaling, and stress response. The ancient miRNA miR396 regulates conserved targets belonging to the GROWTH-REGULATING FACTOR (GRF) family of transcription factors, which are known to control cell proliferation in Arabidopsis leaves. In this work, we characterized the regulation of an additional target for miR396, the transcription factor bHLH74, that is necessary for Arabidopsis normal development. bHLH74 homologs with a miR396 target site could only be detected in the sister families Brassicaceae and Cleomaceae. Still, bHLH74 repression by miR396 is required for margin and vein pattern formation of Arabidopsis leaves. MiR396 contributes to the spatio-temporal regulation of GRF and bHLH74 expression during leaf development. Furthermore, a survey of miR396 sequences in different species showed variations in the 5′ portion of the miRNA, a region known to be important for miRNA activity. Analysis of different miR396 variants in Arabidopsis thaliana revealed that they have an enhanced activity toward GRF transcription factors. The interaction between the GRF target site and miR396 has a bulge between positions 7 and 8 of the miRNA. Our data indicate that such bulge modulates the strength of the miR396-mediated repression and that this modulation is essential to shape the precise spatio-temporal pattern of GRF2 expression. The results show that ancient miRNAs can regulate conserved targets with varied efficiency in different species, and we further propose that they could acquire new targets whose control might also be biologically relevant.

Plants and other multicellular organisms need precise spatio-temporal control of gene expression, and this regulatory capacity depends, in part, on small RNAs. MicroRNAs (miRNAs) are one class of ∼21 nt small RNAs that originate from endogenous fold-back precursors found in plants and animals. They recognize complementary target sites in target mRNAs and guide them to cleavage or translational arrest. Studies of conserved miRNA networks in Arabidopsis and other plants have revealed that they fulfill essential regulatory roles. Most of the ancient miRNAs regulate transcription factors involved in plant development and hormone signaling. Here, we characterize the miR396 regulatory network. While miR396 regulates GRF transcription factors, at least in angiosperms and gymnosperms, this miRNA additionally regulates another transcription factor of the bHLH class but only in Arabidopsis thaliana and closely related species. Most conspicuously, the regulation of both conserved and new targets is important for leaf development in Arabidopsis. We also show that miRNA variants can exist in certain species and that they can display an enhanced activity towards their targets. In summary, we propose that conserved miRNA regulatory networks might expand their functions by the recruitment of additional targets as well as by slight variations in the small RNA sequences.

Whole organ, venation and epidermal cell morphological variations are correlated in the leaves of Arabidopsis mutants

Despite the large number of genes known to affect leaf shape or size, we still have a relatively poor understanding of how leaf morphology is established. For example, little is known about how cell division and cell expansion are controlled and coordinated within a growing leaf to eventually develop into a laminar organ of a definite size. To obtain a global perspective of the cellular basis of variations in leaf morphology at the organ, tissue and cell levels, we studied a collection of 111 non-allelic mutants with abnormally shaped and/or sized leaves, which broadly represent the mutational variations in Arabidopsis thaliana leaf morphology not associated with lethality. We used image-processing techniques on these mutants to quantify morphological parameters running the gamut from the palisade mesophyll and epidermal cells to the venation, whole leaf and rosette levels. We found positive correlations between epidermal cell size and leaf area, which is consistent with long-standing Avery's hypothesis that the epidermis drives leaf growth. In addition, venation parameters were positively correlated with leaf area, suggesting that leaf growth and vein patterning share some genetic controls. Positional cloning of the genes affected by the studied mutations will eventually establish functional links between genotypes, molecular functions, cellular parameters and leaf phenotypes.

Control of leaf expansion: a developmental switch from metabolics to hydraulics

Leaf expansion is the central process by which plants colonize space, allowing energy capture and carbon acquisition. Water and carbon emerge as main limiting factors of leaf expansion, but the literature remains controversial about their respective contributions. Here, we tested the hypothesis that the importance of hydraulics and metabolics is organized according to both dark/light fluctuations and leaf ontogeny. For this purpose, we established the developmental pattern of individual leaf expansion during days and nights in the model plant Arabidopsis (Arabidopsis thaliana). Under control conditions, decreases in leaf expansion were observed at night immediately after emergence, when starch reserves were lowest. These nocturnal decreases were strongly exaggerated in a set of starch mutants, consistent with an early carbon limitation. However, low-light treatment of wild-type plants had no influence on these early decreases, implying that expansion can be uncoupled from changes in carbon availability. From 4 d after leaf emergence onward, decreases of leaf expansion were observed in the daytime. Using mutants impaired in stomatal control of transpiration as well as plants grown under soil water deficit or high air humidity, we gathered evidence that these diurnal decreases were the signature of a hydraulic limitation that gradually set up as the leaf developed. Changes in leaf turgor were consistent with this pattern. It is concluded that during the course of leaf ontogeny, the predominant control of leaf expansion switches from metabolics to hydraulics. We suggest that the leaf is better armed to buffer variations in the former than in the latter.

Leaf extraction and analysis framework graphical user interface: segmenting and analyzing the structure of leaf veins and areoles

Interest in the structure and function of physical biological networks has spurred the development of a number of theoretical models that predict optimal network structures across a broad array of taxonomic groups, from mammals to plants. In many cases, direct tests of predicted network structure are impossible given the lack of suitable empirical methods to quantify physical network geometry with sufficient scope and resolution. There is a long history of empirical methods to quantify the network structure of plants, from roots, to xylem networks in shoots and within leaves. However, with few exceptions, current methods emphasize the analysis of portions of, rather than entire networks. Here, we introduce the Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI), a user-assisted software tool that facilitates improved empirical understanding of leaf network structure. LEAF GUI takes images of leaves where veins have been enhanced relative to the background, and following a series of interactive thresholding and cleaning steps, returns a suite of statistics and information on the structure of leaf venation networks and areoles. Metrics include the dimensions, position, and connectivity of all network veins, and the dimensions, shape, and position of the areoles they surround. Available for free download, the LEAF GUI software promises to facilitate improved understanding of the adaptive and ecological significance of leaf vein network structure.

Decoding leaf hydraulics with a spatially explicit model: principles of venation architecture and implications for its evolution

Leaf venation architecture is tremendously diverse across plant species. Understanding the hydraulic functions of given venation traits can clarify the organization of the vascular system and its adaptation to environment. Using a spatially explicit model (the program K_leaf), we subjected realistic simulated leaves to modifications and calculated the impacts on xylem and leaf hydraulic conductance (K(x) and K(leaf), respectively), important traits in determining photosynthesis and growth. We tested the sensitivity of leaves to altered vein order conductivities (1) in the absence or (2) presence of hierarchical vein architecture, (3) to major vein tapering, and (4) to modification of vein densities (length/leaf area). The K(x) and K(leaf) increased with individual vein order conductivities and densities; for hierarchical venation systems, the greatest impact was from increases in vein conductivity for lower vein orders and increases in density for higher vein orders. Individual vein order conductivities were colimiting of K(x) and K(leaf), as were their densities, but the effects of vein conductivities and densities were orthogonal. Both vein hierarchy and vein tapering increased K(x) relative to xylem construction cost. These results highlight the important consequences of venation traits for the economics, ecology, and evolution of plant transport capacity.

Shifts in leaf vein density through accelerated vein formation in C4 Flaveria (Asteraceae)

BACKGROUND AND AIMS

Leaf venation in many C(4) species is characterized by high vein density, essential in facilitating rapid intercellular diffusion of C(4) photosynthetic metabolites between different tissues (mesophyll, bundle sheath). Greater vein density has been hypothesized to be an early step in C(4) photosynthesis evolution. Development of C(4) vein patterning is thought to occur from either accelerated or prolonged procambium formation, relative to ground tissue development.

METHODS

Cleared and sectioned tissues of phylogenetically basal C(3) Flaveria robusta and more derived C(4) Flaveria bidentis were compared for vein pattern in mature leaves and vein pattern formation in developing leaves.

KEY RESULTS

In mature leaves, major vein density did not differ between C(3) and C(4) Flaveria species, whereas minor veins were denser in C(4) species than in C(3) species. The developmental study showed that both major and minor vein patterning in leaves of C(3) and C(4) species were initiated at comparable stages (based on leaf length). An additional vein order in the C(4) species was observed during initiation of the higher order minor veins compared with the C(3) species. In the two species, expansion of bundle sheath and mesophyll cells occurred after vein pattern was complete and xylem differentiation was continuous in minor veins. In addition, mesophyll cells ceased dividing sooner and enlarged less in C(4) species than in C(3) species.

CONCLUSIONS

Leaf vein pattern characteristic to C(4) Flaveria was achieved primarily through accelerated and earlier offset of higher order vein formation, rather than other modifications in the timing of vein pattern formation, as compared with C(3) species. Earlier cessation of mesophyll cell division and reduced expansion also contributed to greater vein density in the C(4) species. The relatively late expansion of bundle sheath and mesophyll cells shows that vein patterning precedes ground tissue development in C(4) species.

Describing long-range patterns in leaf vasculature by metaphoric fields

Some patterns in dicotyledonous leaf vasculature depict rather precise, long-range structural features. This work identifies and quantifies these previously unrecognized features in terms of an empirically derived mathematical formalism that generates wave-like spatial patterns referred to as metaphoric fields. These patterns were used to specify regularities in the long-range structure of dicot leaf vasculature, and were found to account significantly for the predominant features of all 27 dicot species studied. The conserved features of these metaphoric fields are discussed in terms of existing models for leaf pattern formation based on efflux-protein mediated auxin transport in a developing cellular field. This work highlights the complex, regular, long-range structures existing in leaf vascular patterns, and provides a means for specifying and identifying the inherent global features of vascular patterns which must be accounted for in functional developmental models.

In silico leaf venation networks: growth and reorganization driven by mechanical forces

Development commonly involves an interplay between signaling, genetic expression and biophysical forces. However, the relative importance of these mechanisms during the different stages of development is unclear. Leaf venation networks provide a fitting context for the examination of these questions. In mature leaves, venation patterns are extremely diverse, yet their local structure satisfies a universal property: at junctions between veins, angles and diameters are related by a vectorial equation analogous to a force balance. Using a cell proliferation model, we reproduce in silico the salient features of venation patterns. Provided that vein cells are given different mechanical properties, tensile forces develop along the veins during growth, causing the network to deform progressively. Our results suggest that the local structure of venation networks results from a reorganization driven by mechanical forces, independently of how veins form. This conclusion is supported by recent observations of vein development in young leaves and by the good quantitative agreement between our simulations and data from mature leaves.