Nathalie Wuyts

Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit

Leaves have a central role in plant energy capture and carbon conversion and therefore must continuously adapt their development to prevailing environmental conditions. To reveal the dynamic systems behaviour of leaf development, we profiled Arabidopsis leaf number six in depth at four different growth stages, at both the end‐of‐day and end‐of‐night, in plants growing in two controlled experimental conditions: short‐day conditions with optimal soil water content and constant reduced soil water conditions. We found that the lower soil water potential led to reduced, but prolonged, growth and an adaptation at the molecular level without a drought stress response. Clustering of the protein and transcript data using a decision tree revealed different patterns in abundance changes across the growth stages and between end‐of‐day and end‐of‐night that are linked to specific biological functions. Correlations between protein and transcript levels depend on the time‐of‐day and also on protein localisation and function. Surprisingly, only very few of >1700 quantified proteins showed diurnal abundance fluctuations, despite strong fluctuations at the transcript level.

PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context.

BackgroundRenewed interest in plant × environment interactions has risen in the post-genomic era. In this context, high-throughput phenotyping platforms have been developed to create reproducible environmental scenarios in which the phenotypic responses of multiple genotypes can be analysed in a reproducible way. These platforms benefit hugely from the development of suitable databases for storage, sharing and analysis of the large amount of data collected. In the model plant Arabidopsis thaliana, most databases available to the scientific community contain data related to genetic and molecular biology and are characterised by an inadequacy in the description of plant developmental stages and experimental metadata such as environmental conditions. Our goal was to develop a comprehensive information system for sharing of the data collected in PHENOPSIS, an automated platform for Arabidopsis thaliana phenotyping, with the scientific community.DescriptionPHENOPSIS DB is a publicly available (URL: http://bioweb.supagro.inra.fr/phenopsis/) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata. It provides modules coupled to a Web interface for (i) the visualisation of environmental data of an experiment, (ii) the visualisation and statistical analysis of phenotypic data, and (iii) the analysis of Arabidopsis thaliana plant images.ConclusionsFirstly, data stored in the PHENOPSIS DB are of interest to the Arabidopsis thaliana community, particularly in allowing phenotypic meta-analyses directly linked to environmental conditions on which publications are still scarce. Secondly, data or image analysis modules can be downloaded from the Web interface for direct usage or as the basis for modifications according to new requirements. Finally, the structure of PHENOPSIS DB provides a useful template for the development of other similar databases related to genotype × environment interactions.

Structural assessment of the impact of environmental constraints on Arabidopsis thaliana leaf growth: a 3D approach

Light and soil water content affect leaf surface area expansion through modifications in epidermal cell numbers and area, while effects on leaf thickness and mesophyll cell volumes are far less documented. Here, three-dimensional imaging was applied in a study of Arabidopsis thaliana leaf growth to determine leaf thickness and the cellular organization of mesophyll tissues under moderate soil water deficit and two cumulative light conditions. In contrast to surface area, thickness was highly conserved in response to water deficit under both low and high cumulative light regimes. Unlike epidermal and palisade mesophyll tissues, no reductions in cell number were observed in the spongy mesophyll; cells had rather changed in volume and shape. Furthermore, leaf features of a selection of genotypes affected in leaf functioning were analysed. The low-starch mutant pgm had very thick leaves because of unusually large palisade mesophyll cells, together with high levels of photosynthesis and stomatal conductance. By means of an open stomata mutant and a 9-cis-epoxycarotenoid dioxygenase overexpressor, it was shown that stomatal conductance does not necessarily have a major impact on leaf dimensions and cellular organization, pointing to additional mechanisms for the control of CO(2) diffusion under high and low stomatal conductance, respectively.

Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images

Images of cellular structures in growing plant roots acquired using confocal laser scanning microscopy have some unusual properties that make motion estimation challenging. These include multiple motions, non-Gaussian noise and large regions with little spatial structure. In this paper, a method for motion estimation is described that uses a robust multi-frame likelihood model and a technique for estimating uncertainty. An efficient region-based matching approach was used followed by a forward projection method. Over small timescales the dynamics are simple (approximately locally constant) and the change in appearance small. Therefore, a constant local velocity model is used and the MAP estimate of the joint probability over a set of frames is recovered. Occurrences of multiple modes in the posterior are detected, and in the case of a single dominant mode, motion is inferred using Laplace’e method. The method was applied to several Arabidopsis thaliana root growth sequences with varying levels of success. In addition, comparative results are given for three alternative motion estimation approaches, the Kanade–Lucas–Tomasi tracker, Black and Anandan’s robust smoothing method, and Markov random field based methods.

Performance of Low-Level Motion Estimation Methods for Confocal Microscopy of Plant Cells in vivo

The performance of various low-level motion estimation methods applied to fluorescence labelled growing cellular structures imaged using confocal laser scanning microscopy is investigated. This is a challenging and unusual domain for motion estimation methods. A selection of methods are discussed that can be contrasted in terms of how much spatial or temporal contextual information is used. The Lucas Kanade feature tracker, a spatially and temporally localised method, was, as one would expect, accurate around resolvable structure. It was not able to track the smaller, repetitive cell structure in the root tip and was somewhat prone to identifying spurious features. This approach is improved by developing a full multi-frame, robust, Bayesian method, and it is demonstrated that by using extra frames with motion constraints reduces such errors. Next, spatially global methods are discussed, including robust variational smoothing and Markov Random Field (MRF) modelling. A key conclusion that is drawn from investigation of these methods is that generic low-level (robust) smoothing functions do not provide good results in this application and that this is probably due to the large regions with little stable structure. Furthermore, contrary to recently reported successes, graph cuts and loopy belief propagation for MAP estimation of the MRF labels provided often poor and inconsistent estimates. The results suggest the need for greater emphasis on temporal smoothing for generic low-level motion estimation tools and more task specific, spatial constraints, perhaps in the form of high level models in order to accurately recover motion from such data. Finally, the form of the estimated growth is briefly discussed and related to contemporary biological models. We hope that this paper will assist non-specialists in applying state-of-the-art methods to this form of data.

The volumetric component of individual leaf expansion: Taking into account sub-epidermal tissues in the description of leaf expansion over time

Most leaf development studies at the cell and organ levels have been limited to the leaf surface, with data referring to the leaf surface area and to the number and surface area of epidermal cells. However, leaf sub-epidermal tissues, the palisade and spongy mesophyll, contain the main actors in photosynthesis. The number and thickness of palisade cell layers and the volume occupied by spongy mesophyll (cells and intercellular spaces) affect the accumulation of photosynthates and, as such, whole plant growth. Studies into the leaf phenotype of growth-affected Arabidopsis thaliana mutants have revealed a higher variability in leaf thickness than in leaf surface area. In general, there is no correlation between these two variables, which means that to describe a leaf phenotype, leaf volume has to be taken into account. A method has been developed for high-resolution imaging of leaves in three dimensions usingmultiphoton laser scanning microscopy, and for the analysis of images, providing data on volumes and volumetric proportions of cells and tissues and cell density. The method has been used in the study of A. thaliana leaf expansion from emergence to the onset of senescence for leaves located at different nodal positions in the rosette, completing our knowledge of individual leaf development processes with their volumetric component. The method will further be applied in the study of leaf plasticity in response to the environment for both A. thaliana and apple tree, a model and an agronomic species, respectively. (Texte integral)