Anne-Gaëlle Rolland-Lagan

Modeling and visualization of leaf venation patterns

We introduce a class of biologically-motivated algorithms for generating leaf venation patterns. These algorithms simulate the interplay between three processes: (1) development of veins towards hormone (auxin) sources embedded in the leaf blade; (2) modification of the hormone source distribution by the proximity of veins; and (3) modification of both the vein pattern and source distribution by leaf growth. These processes are formulated in terms of iterative geometric operations on sets of points that represent vein nodes and auxin sources. In addition, a vein connection graph is maintained to determine vein widths. The effective implementation of the algorithms relies on the use of space subdivision (Voronoi diagrams) and time coherence between iteration steps. Depending on the specification details and parameters used, the algorithms can simulate many types of venation patterns, both open (tree-like) and closed (with loops). Applications of the presented algorithms include texture and detailed structure generation for image synthesis purposes, and modeling of morphogenetic processes in support of biological research.

Growth dynamics underlying petal shape and asymmetry.

Development commonly involves the generation of complex shapes from simpler ones. One way of following this process is to use landmarks to track the fate of particular points in a developing organ, but this is limited by the time over which it can be monitored. Here we use an alternative method, clonal analysis, whereby dividing cells are genetically marked and their descendants identified visually, to observe the development of Antirrhinum (snapdragon) petals. Clonal analysis has previously been used to estimate growth parameters of leaves and Drosophila wings but these results were not integrated within a dynamic growth model. Here we develop such a model and use it to show that a key aspect of shape—petal asymmetry—in the petal lobe of Antirrhinum depends on the direction of growth rather than regional differences in growth rate. The direction of growth is maintained parallel to the proximodistal axis of the flower, irrespective of changes in shape, implying that long-range signals orient growth along the petal as a whole. Such signals may provide a general mechanism for orienting growth in other growing structures.

Quantifying leaf venation patterns: two‐dimensional maps

The leaf vasculature plays crucial roles in transport and mechanical support. Understanding how vein patterns develop and what underlies pattern variation between species has many implications from both physiological and evolutionary perspectives. We developed a method for extracting spatial vein pattern data from leaf images, such as vein densities and also the sizes and shapes of the vein reticulations. We used this method to quantify leaf venation patterns of the first rosette leaf of Arabidopsis thaliana throughout a series of developmental stages. In particular, we characterized the size and shape of vein network areoles (loops), which enlarge and are split by new veins as a leaf develops. Pattern parameters varied in time and space. In particular, we observed a distal to proximal gradient in loop shape (length/width ratio) which varied over time, and a margin-to-center gradient in loop sizes. Quantitative analyses of vein patterns at the tissue level provide a two-way link between theoretical models of patterning and molecular experimental work to further explore patterning mechanisms during development. Such analyses could also be used to investigate the effect of environmental factors on vein patterns, or to compare venation patterns from different species for evolutionary studies. The method also provides a framework for gathering and overlaying two-dimensional maps of point, line and surface morphological data.

Vein patterning in growing leaves: axes and polarities

Network and branching structures play essential roles in transport and/or mechanical support in multicellular organisms. In plant leaves, vasculature contributes to both processes. Recent descriptions of network leaf vein patterning in the model plant Arabidopsis thaliana indicate that veins initially extend from local maxima of the plant hormone auxin in the leaf margin, and network patterns then form within the blade. Auxin influences patterning through a feedback between auxin-mediated gene activation and auxin transport. Patterning of the adaxial-abaxial domains of the leaf and patterning of the leaf margin are proposed to affect vein patterning through interactions with this feedback mechanism, which may in the future provide a united view of leaf development.

Computational Method for Quantifying Growth Patterns at the Adaxial Leaf Surface in Three Dimensions

Growth patterns vary in space and time as an organ develops, leading to shape and size changes. Quantifying spatiotemporal variations in organ growth throughout development is therefore crucial to understand how organ shape is controlled. We present a novel method and computational tools to quantify spatial patterns of growth from three-dimensional data at the adaxial surface of leaves. Growth patterns are first calculated by semiautomatically tracking microscopic fluorescent particles applied to the leaf surface. Results from multiple leaf samples are then combined to generate mean maps of various growth descriptors, including relative growth, directionality, and anisotropy. The method was applied to the first rosette leaf of Arabidopsis (Arabidopsis thaliana) and revealed clear spatiotemporal patterns, which can be interpreted in terms of gradients in concentrations of growth-regulating substances. As surface growth is tracked in three dimensions, the method is applicable to young leaves as they first emerge and to nonflat leaves. The semiautomated software tools developed allow for a high throughput of data, and the algorithms for generating mean maps of growth open the way for standardized comparative analyses of growth patterns.

Quantifying Shape Changes and Tissue Deformation in Leaf Development

A computational method for quantifying three-dimensional surfaces and local surface deformations of leaves during development provides a new tool to investigate the control of leaf morphogenesis. The analysis of biological shapes has applications in many areas of biology, and tools exist to quantify organ shape and detect shape differences between species or among variants. However, such measurements do not provide any information about the mechanisms of shape generation. Quantitative data on growth patterns may provide insights into morphogenetic processes, but since growth is a complex process occurring in four dimensions, growth patterns alone cannot intuitively be linked to shape outcomes. Here, we present computational tools to quantify tissue deformation and surface shape changes over the course of leaf development, applied to the first leaf of Arabidopsis (Arabidopsis thaliana). The results show that the overall leaf shape does not change notably during the developmental stages analyzed, yet there is a clear upward radial deformation of the leaf tissue in early time points. This deformation pattern may provide an explanation for how the Arabidopsis leaf maintains a relatively constant shape despite spatial heterogeneities in growth. These findings highlight the importance of quantifying tissue deformation when investigating the control of leaf shape. More generally, experimental mapping of deformation patterns may help us to better understand the link between growth and shape in organ development.

Morphogen-based simulation model of ray growth and joint patterning during fin development and regeneration.

The fact that some organisms are able to regenerate organs of the correct shape and size following amputation is particularly fascinating, but the mechanism by which this occurs remains poorly understood. The zebrafish (Danio rerio) caudal fin has emerged as a model system for the study of bone development and regeneration. The fin comprises 16 to 18 bony rays, each containing multiple joints along its proximodistal axis that give rise to segments. Experimental observations on fin ray growth, regeneration and joint formation have been described, but no unified theory has yet been put forward to explain how growth and joint patterns are controlled. We present a model for the control of fin ray growth during development and regeneration, integrated with a model for joint pattern formation, which is in agreement with published, as well as new, experimental data. We propose that fin ray growth and joint patterning are coordinated through the interaction of three morphogens. When the model is extended to incorporate multiple rays across the fin, it also accounts for how the caudal fin acquires its shape during development, and regains its correct size and shape following amputation.

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 nodules 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.

A quantitative framework for flower phenotyping in cultivated carnation (Dianthus caryophyllus L.).

Most important breeding goals in ornamental crops are plant appearance and flower characteristics where selection is visually performed on direct offspring of crossings. We developed an image analysis toolbox for the acquisition of flower and petal images from cultivated carnation (Dianthus caryophyllus L.) that was validated by a detailed analysis of flower and petal size and shape in 78 commercial cultivars of D. caryophyllus, including 55 standard, 22 spray and 1 pot carnation cultivars. Correlation analyses allowed us to reduce the number of parameters accounting for the observed variation in flower and petal morphology. Convexity was used as a descriptor for the level of serration in flowers and petals. We used a landmark-based approach that allowed us to identify eight main principal components (PCs) accounting for most of the variance observed in petal shape. The effect and the strength of these PCs in standard and spray carnation cultivars are consistent with shared underlying mechanisms involved in the morphological diversification of petals in both subpopulations. Our results also indicate that neighbor-joining trees built with morphological data might infer certain phylogenetic relationships among carnation cultivars. Based on estimated broad-sense heritability values for some flower and petal features, different genetic determinants shall modulate the responses of flower and petal morphology to environmental cues in this species. We believe our image analysis toolbox could allow capturing flower variation in other species of high ornamental value.

Fingerprinting Biogenic Aldehydes through Pattern Recognition Analyses of Excitation-Emission Matrices

Biogenic carbonyls, especially aldehydes, have previously demonstrated their potential to serve as early diagnostic biomarkers for disease and injury that have not been fully realized owing, in part, to the lack of a rapid and simple point‐of‐care method for aldehyde identification. The ability to determine which carbonyl compound is elevated and not just the total aldehydic load may provide more disease‐ or injury‐specific diagnostic information. Toward this end, a novel fluorophore is presented that is able to form a complex with biogenic carbonyls under catalyst‐free conditions so as to give a fluorescent fingerprint of the resulting hydrazone. The successful identification of bound carbonyls was accomplished with a newly described algorithm that applied principal curvature analysis of excitation–emission matrices to reduce surface features to ellipse representations, followed by a pattern‐matching routine. With this algorithm, carbonyls were identified over a range of concentrations, and mixture components were successfully parsed. Overall, the results presented lay the groundwork for novel implementations of chemometrics to low‐cost, rapid, and simple‐to‐implement point‐of‐care diagnostics.