Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies

PREMISE

The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.

METHODS AND RESULTS

MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold-based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.

CONCLUSIONS

MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.

Hyb-Seq provides new insights into the phylogeny and evolution of the Chrysanthemum zawadskii species complex in China

A species complex is an assemblage of closely related species with blurred boundaries, and from which species could arise from different speciation processes and/or a speciation continuum. Such a complex can provide an opportunity to investigate evolutionary mechanisms acting on speciation. The Chrysanthemum zawadskii species complex in China, a monophyletic group of Chrysanthemum, consists of seven species with considerable morphological variation, diverse habitats and different distribution patterns. Here, we used Hyb-Seq data to construct a well-resolved phylogeny of the C. zawadskii complex. Then, we performed comparative analyses of variation patterns in morphology, ecology and distribution to investigate the roles of geography and ecology in this complex's diversification. Lastly, we implemented divergence time estimation, species distribution modelling and ancestral area reconstruction to trace the evolutionary history of this complex. We concluded that the C. zawadskii complex originated in the Qinling-Daba mountains during the early Pliocene and then spread west and northward along the mountain ranges to northern China. During this process, geographical and ecological factors imposing different influences resulted in the current diversification and distribution patterns of this species complex, which is composed of both well-diverged species and diverging lineages on the path of speciation.

A cell wall-associated gene network shapes leaf boundary domains

Boundary domains delimit and organize organ growth throughout plant development almost relentlessly, building plant architecture and morphogenesis. Boundary domains display reduced growth and orchestrate development of adjacent tissues in a non-cell-autonomous manner. How these two functions are achieved remains elusive despite the identification of several boundary-specific genes. Here, we show using morphometrics at the organ and cellular levels that leaf boundary domain development requires SPINDLY (SPY), an O-fucosyltransferase, to act as cell growth repressor. Furthermore, we show that SPY acts redundantly with the CUP-SHAPED COTYLEDON transcription factors (CUC2 and CUC3), which are major determinants of boundaries development. Accordingly, at the molecular level CUC2 and SPY repress a common set of genes involved in cell wall loosening, providing a molecular framework for the growth repression associated with boundary domains. Atomic force microscopy confirmed that young leaf boundary domain cells have stiffer cell walls than marginal outgrowth. This differential cell wall stiffness was reduced in spy mutant plants. Taken together, our data reveal a concealed CUC2 cell wall-associated gene network linking tissue patterning with cell growth and mechanics.

Digital morphometrics: Application of MorphoLeaf in shape visualization and species delimitation, using Cucurbitaceae leaves as a model

PREMISE

Plant leaves are one of the most important organs for plant identification due to their variability across different taxonomic groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology-based species delimitation, image-analysis digital solutions have made it easy to detect more characters to complement existing leaf data sets.

METHODS

Here, we apply MorphoLeaf to generate a morphometric data set from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, the reparameterization of leaf contours, and data quantification and normalization. A statistical analysis was performed on the resulting data set.

RESULTS

A principal component analysis revealed that leaf blade area, blade perimeter, tooth area, tooth perimeter, the measure of the distance from tooth position to the tip, and the measure of the distance from tooth position to the base are important and informative landmarks that contribute to the variation within the species studied.

DISCUSSION

MorphoLeaf can be applied to quantitatively track leaf diversity, thereby functionally integrating morphometrics and shape visualization into the digital identification of plants. The success of digital morphometrics in leaf outline analyses presents researchers with opportunities to carry out more accurate image-based research in areas such as plant development, evolution, and phenotyping.

A network-based framework for shape analysis enables accurate characterization of leaf epidermal cells

Cell shape is crucial for the function and development of organisms. Yet, versatile frameworks for cell shape quantification, comparison, and classification remain underdeveloped. Here, we introduce a visibility graph representation of shapes that facilitates network-driven characterization and analyses across shapes encountered in different domains. Using the example of complex shape of leaf pavement cells, we show that our framework accurately quantifies cell protrusions and invaginations and provides additional functionality in comparison to the contending approaches. We further show that structural properties of the visibility graphs can be used to quantify pavement cell shape complexity and allow for classification of plants into their respective phylogenetic clades. Therefore, the visibility graphs provide a robust and unique framework to accurately quantify and classify the shape of different objects.

Linking Genes to Shape in Plants Using Morphometrics

A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.

Integrating Multiple Lines of Evidence to Explore Intraspecific Variability in a Rare Endemic Alpine Plant and Implications for Its Conservation

We studied the ecology, distribution, and phylogeography of Tephroseris balbisiana, a rare plant whose range is centered to the South-Western Alps. Our aim was to assess the extent of intraspecific variability within the nominal species and the conservation status of isolated populations. We studied genetic diversity across the whole species range. We analyzed leaf traits, which are distinctive morphological characters within the Tephroseris genus. A clear pattern of genetic variation was found among populations of T. balbisiana, which clustered according to their geographic position. On the contrary, there was a strong overlap in the morphological space of individuals across the species' range, with few peripheral populations diverging in their leaf morphology. Studying habitat suitability by means of species distribution models, we observed that T. balbisiana range is primarily explained by solar radiation and precipitation seasonality. Environmental requirements could explain the genetic and morphological uniformity of T. balbisiana in its core distribution area and justify genetic, morphological, and ecological divergences found among the isolated populations of the Apennines. Our findings emphasize the need to account for the whole diversity of a species, comprising peripheral populations, in order to better estimate its status and to prioritize areas for its conservation.

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

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

Spatiotemporal control of cell growth by CUC3 shapes leaf margins

How a shape arises from the coordinated behavior of cells is one of the most fascinating questions in developmental biology. In plants, fine spatial and temporal controls of cell proliferation and cell expansion sustain differential growth that defines organ shape and size. At the leaf margin of Arabidopsis thaliana, interplay between auxin transport and transcription factors named CUP SHAPED COTYLEDON (CUCs), which are involved in the establishment of boundary domain identity, were reported to trigger differential growth, leading to serration. Cellular behaviors behind these differential growths remain scarcely described. Here, we used 3D and time lapse imaging on young leaves at different stages of development to determine the sequence of cellular events resulting in leaf serrations. In addition, we showed that the transcription factor CUC3 is a negative regulator of cell growth and that its expression dynamics in a small number of cells at the leaf margin is tightly associated with the control of differential growth.

Ectopic expression of the transcription factor CUC2 restricts growth by cell cycle inhibition in Arabidopsis leaves

Plant leaf margins produce small outgrowths or teeth causing serration in a regular arrangement, which is specified by auxin maxima. In Arabidopsis, the spatiotemporal pattern of auxin dependents on both, the transcription factor CUC2 and the signal peptide EPFL2, a ligand of the growth-promoting receptor kinase ERECTA (ER). Ectopic expression of CUC2 can have contrary effects on leaf growth. Ubiquitous expressed CUC2 suppresses growth in the whole leaf, whereas cuc2-1D mutants have enlarged leaves, through ER-dependent cell proliferation in the teeth. Here we investigated the growth dynamics of cuc2-1D leaves and the growth restricting the function of CUC2 using the ubiquitous inducible CUC2-GR transgene. In time courses, we dissected the serration promoting the function of CUC2 in the leaf margin and ectopic growth inhibition by CUC2 in the leaf plate. We found that CUC2 limits growth rather by cell cycle inhibition than by cell size control. Furthermore, endogenous CUC2 was rapidly induced by CUC2-GR indicating a possible auto-inducible feedback. In contrast, EPFL2 was quickly decreased by transient CUC2 induction but increased in cuc2-3 mutant leaves suggesting that CUC2 can also counteract the EPFL2-ER pathway. Therefore, tooth growth promotion and growth inhibition by CUC2 involve partially the same mechanism but in contrary ways.

Biomimetic Groundwork for Thermal Exchange Structures Inspired by Plant Leaf Design

Geometry is a determining factor for thermal performance in both biological and technical systems. While biology has inspired thermal design before, biomimetic translation of leaf morphology into structural aspects of heat exchangers remains largely unaddressed. One determinant of plant thermal endurance against environmental exposure is leaf shape, which modulates the leaf boundary layer, transpiration, evaporative cooling, and convective exchange. Here, we lay the research groundwork for the extraction of design principles from leaf shape relations to heat and mass transfer. Leaf role models were identified from an extensive literature review on environmentally sensitive morphology patterns and shape-dependent exchange. Addressing canopy sun-shade dimorphism, sun leaves collected from multiple oak species exceeded significantly in margin extension and shape dissection. Abstracted geometries (i.e., elongated; with finely toothed edges; with few large-scale teeth) were explored with paper models of the same surface area in a controlled environment of minimal airflow, which is more likely to induce leaf thermal stress. For two model characteristic dimensions, evaporation rates were significantly faster for the dissected geometries. Shape-driven transfer enhancements were higher for the smaller models, and finely toothed edges reached local cooling up to 10 °C below air temperature. This investigation breaks new ground for solution-based biomimetics to inform the design of evaporation-assisted and passively enhanced thermal systems.

Mangroves in the Leaves: Anatomy, Physiology, and Immunity of Epithemal Hydathodes

Hydathodes are organs found on aerial parts of a wide range of plant species that provide almost direct access for several pathogenic microbes to the plant vascular system. Hydathodes are better known as the site of guttation, which is the release of droplets of plant apoplastic fluid to the outer leaf surface. Because these organs are only described through sporadic allusions in the literature, this review aims to provide a comprehensive view of hydathode development, physiology, and immunity by compiling a historic and contemporary bibliography. In particular, we refine the definition of hydathodes.We illustrate their important roles in the maintenance of plant osmotic balance, nutrient retrieval, and exclusion of deleterious chemicals from the xylem sap. Finally, we present our current understanding of the infection of hydathodes by adapted vascular pathogens and the associated plant immune responses.

Mechanics, geometry and genetics of epidermal cell shape regulation: different pieces of the same puzzle

Pavement cells in the leaf epidermis of many plant species have intricate shapes that fit together much like the pieces of a jigsaw puzzle. They provide an accessible system to understand the development of complex cell shape. Since a protrusion in one cell must fit into the indentation in its neighbor, puzzle cells are also a good system to study how cell shape is coordinated across a plant tissue. Although molecular mechanisms have been proposed for both the patterning and coordination of puzzle cells, evidence is accumulating that mechanical and/or geometric cues may play a more significant role than previously thought.

Dissecting the pathways coordinating patterning and growth by plant boundary domains

Boundary domains play important roles during morphogenesis in plants and animals, but how they contribute to patterning and growth coordination in plants is not understood. The CUC genes determine the boundary domains in the aerial part of the plants and, in particular, they have a conserved role in regulating leaf complexity across Angiosperms. Here, we used tooth formation at the Arabidopsis leaf margin controlled by the CUC2 transcription factor to untangle intertwined events during boundary-controlled morphogenesis in plants. Combining conditional restoration of CUC2 function with morphometrics as well as quantification of gene expression and hormone signaling, we first established that tooth morphogenesis involves a patterning phase and a growth phase. These phases can be separated, as patterning requires CUC2 while growth can occur independently of CUC2. Next, we show that CUC2 acts as a trigger to promote growth through the activation of three functional relays. In particular, we show that KLUH acts downstream of CUC2 to modulate auxin response and that expressing KLUH can compensate for deficient CUC2 expression during tooth growth. Together, we reveal a genetic and molecular network that allows coordination of patterning and growth by CUC2-defined boundaries during morphogenesis at the leaf margin.

During organogenesis, patterning, the definition of functional subdomains, has to be strictly coordinated with growth. How this is achieved is still an open question. In plants, boundary domains are established between neighboring outgrowing structures and play a role not only in the separation of these structures but also in their formation. To further understand how these boundary domains control morphogenesis, we used as a model system the formation of small teeth along the leaf margin of Arabidopsis, which is controlled by the CUP-SHAPED COTYLEDON2 (CUC2) boundary gene. The CUC genes determine the boundary domains in the aerial part of the plants and in particular they have been shown to have a conserved role in regulating serration and leaflet formation across Angiosperms and thus are at the root of patterning in diverse leaf types. We manipulated the expression of this gene using an inducible gene expression that allowed restoration of CUC2 expression in its own domain at different developmental stages and for different durations, and followed the effects on patterning and growth. Thus, we showed that while CUC2 is required for patterning it is dispensable for sustained growth of the teeth, acting as a trigger for growth by the activation of several functional relays. We further showed that these findings are not specific to the inducible restoration of CUC2 function by analyzing multiple mutants.

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world's growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future.

Contour analysis for interpretable leaf shape category discovery

BACKGROUND

The categorical description of leaf shapes is of paramount importance in ecology, taxonomy and paleobotanical studies. Classification systems proposed by domain experts support these descriptions. Despite the importance of these visual descriptive systems, classifications based on this expert's knowledge may be ambiguous or limited when representing shapes in unknown scenarios, as expected for biological exploratory domains. This work proposes a novel strategy to automatically discover the shape categories in a set of unlabeled leaves by only using the leaf-shape information. In particular, we overcome the task of discovering shape categories from different plant species for three different biological settings.

RESULTS

The proposed method may successfully infer the unknown underlying shape categories with an F-score greater than 92%.

CONCLUSIONS

The approach also provided high levels of visual interpretability, an essential requirement in the description of biological objects. This method may support morphological analysis of biological objects in exploratory domains.

Leaf development and evolution

Plant leaves are differentiated organs that arise sequentially from a population of pluripotent stem cells at the shoot apical meristem (SAM). There is substantial diversity in leaf shape, much of which depends on the size and arrangement of outgrowths at the leaf margin. These outgrowths are generated by a patterning mechanism similar to the phyllotactic processes producing organs at the SAM, which involves the transcription factors CUP-SHAPED COTYLEDON and the phytohormone auxin. In the leaf, this patterning mechanism creates sequential protrusions and indentations along the margin. The size, shape, and distribution of these protrusions also depend on the overall growth of the leaf lamina. Globally, growth is regulated by a complex genetic network controlling the distribution of cell proliferation and the timing of differentiation. Evolutionary changes in margin form arise from changes in two different classes of homeobox genes that modify the outcome of marginal patterning in diverse ways, and are under intense investigation.

Getting leaves into shape: a molecular, cellular, environmental and evolutionary view

Leaves arise from groups of undifferentiated cells as small primordia that go through overlapping phases of morphogenesis, growth and differentiation. These phases are genetically controlled and modulated by environmental cues to generate a stereotyped, yet plastic, mature organ. Over the past couple of decades, studies have revealed that hormonal signals, transcription factors and miRNAs play major roles during leaf development, and more recent findings have highlighted the contribution of mechanical signals to leaf growth. In this Review, we discuss how modulating the activity of some of these regulators can generate diverse leaf shapes during development, in response to a varying environment, or between species during evolution.

Arabidopsis phenotyping through geometric morphometrics

Background

Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units.

Results

Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed.

Conclusions

Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

Heterogeneity and Robustness in Plant Morphogenesis: From Cells to Organs

Development is remarkably reproducible, producing organs with the same size, shape, and function repeatedly from individual to individual. For example, every flower on the Antirrhinum stalk has the same snapping dragon mouth. This reproducibility has allowed taxonomists to classify plants and animals according to their morphology. Yet these reproducible organs are composed of highly variable cells. For example, neighboring cells grow at different rates in Arabidopsis leaves, sepals, and shoot apical meristems. This cellular variability occurs in normal, wild-type organisms, indicating that cellular heterogeneity (or diversity in a characteristic such as growth rate) is either actively maintained or, at a minimum, not entirely suppressed. In fact, cellular heterogeneity can contribute to producing invariant organs. Here, we focus on how plant organs are reproducibly created during development from these highly variable cells.

Quantification of complex modular architecture in plants

Morphometrics, the assignment of quantities to biological shapes, is a powerful tool to address taxonomic, evolutionary, functional and developmental questions. We propose a novel method for shape quantification of complex modular architecture in thalloid plants, whose extremely reduced morphologies, combined with the lack of a formal framework for thallus description, have long rendered taxonomic and evolutionary studies extremely challenging. Using graph theory, thalli are described as hierarchical series of nodes and edges, allowing for accurate, homologous and repeatable measurements of widths, lengths and angles. The computer program MorphoSnake was developed to extract the skeleton and contours of a thallus and automatically acquire, at each level of organization, width, length, angle and sinuosity measurements. Through the quantification of leaf architecture in Hymenophyllum ferns (Polypodiopsida) and a fully worked example of integrative taxonomy in the taxonomically challenging thalloid liverwort genus Riccardia, we show that MorphoSnake is applicable to all ramified plants. This new possibility of acquiring large numbers of quantitative traits in plants with complex modular architectures opens new perspectives of applications, from the development of rapid species identification tools to evolutionary analyses of adaptive plasticity.

MowJoe: a method for automated-high throughput dissected leaf phenotyping

BACKGROUND

Accurate and automated phenotyping of leaf images is necessary for high throughput studies of leaf form like genome-wide association analysis and other forms of quantitative trait locus mapping. Dissected leaves (also referred to as compound) that are subdivided into individual units are an attractive system to study diversification of form. However, there are only few software tools for their automated analysis. Thus, high-throughput image processing algorithms are needed that can partition these leaves in their phenotypically relevant units and calculate morphological features based on these units.

RESULTS

We have developed MowJoe, an image processing algorithm that dissects a dissected leaf into leaflets, petiolule, rachis and petioles. It employs image skeletonization to convert leaves into graphs, and thereafter applies algorithms operating on graph structures. This partitioning of a leaf allows the derivation of morphological features such as leaf size, or eccentricity of leaflets. Furthermore, MowJoe automatically places landmarks onto the terminal leaflet that can be used for further leaf shape analysis. It generates specific output files that can directly be imported into downstream shape analysis tools. We applied the algorithm to two accessions of Cardamine hirsuta and show that our features are able to robustly discriminate between these accessions.

CONCLUSION

MowJoe is a tool for the semi-automated, quantitative high throughput shape analysis of dissected leaf images. It provides the statistical power for the detection of the genetic basis of quantitative morphological variations.

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.

GDP-L-fucose is required for boundary definition in plants

The CUP-SHAPED COTYLEDON (CUC) transcription factors control plant boundary formation, thus allowing the emergence of novel growth axes. While the developmental roles of the CUC genes in different organs and across species are well characterized, upstream and downstream events that contribute to their function are still poorly understood. To identify new players in this network, we performed a suppressor screen of CUC2g-m4, a line overexpressing CUC2 that has highly serrated leaves. We identified a mutation that simplifies leaf shape and affects MURUS1 (MUR1), which is responsible for GDP-L-fucose production. Using detailed morphometric analysis, we show that GDP-L-fucose has an essential role in leaf shape acquisition by sustaining differential growth at the leaf margins. Accordingly, reduced CUC2 expression levels are observed in mur1 leaves. Furthermore, genetic analyses reveal a conserved role for GDP-L-fucose in different developmental contexts where it contributes to organ separation in the same pathway as CUC2. Taken together, our results reveal that GDP-L-fucose is necessary for proper establishment of boundary domains in various developmental contexts.

A common developmental program can produce diverse leaf shapes

Eudicot leaves have astoundingly diverse shapes. The central problem addressed in this paper is the developmental origin of this diversity. To investigate this problem, we propose a computational model of leaf development that generalizes the largely conserved molecular program for the reference plants Arabidopsis thaliana, Cardamine hirsuta and Solanum lycopersicum. The model characterizes leaf development as a product of three interwoven processes: the patterning of serrations, lobes and/or leaflets on the leaf margin; the patterning of the vascular system; and the growth of the leaf blade spanning the main veins. The veins play a significant morphogenetic role as a local determinant of growth directions. We show that small variations of this model can produce diverse leaf shapes, from simple to lobed to compound. It is thus plausible that diverse shapes of eudicot leaves result from small variations of a common developmental program.

Image Analysis in Plant Sciences: Publish Then Perish

Image analysis has become a powerful technique for most plant scientists. In recent years dozens of image analysis tools have been published in plant science journals. These tools cover the full spectrum of plant scales, from single cells to organs and canopies. However, the field of plant image analysis remains in its infancy. It still has to overcome important challenges, such as the lack of robust validation practices or the absence of long-term support. In this Opinion article, I: (i) present the current state of the field, based on data from the plant-image-analysis.org database; (ii) identify the challenges faced by its community; and (iii) propose workable ways of improvement.

Segmentation of 3D images of plant tissues at multiple scales using the level set method

BACKGROUND

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

RESULTS

We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue.

CONCLUSION

The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.