Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions

Imaging as a tool to study leaf development in Arabidopsis thaliana

In contrast to humans and animals, the body plan of a plant is not completely defined within the embryonic stages. Organ formation continues throughout plant development and this iterative and modular process is continuously controlled by environmental cues such as light, gravity, temperature, humidity and chemicals. In most plant species, the above-ground plant body is dominated by leaves, the organs specialized in photosynthesis. This process converts carbon dioxide into organic components utilizing energy from sunlight; making leaves the energy production site and the growth engine of plants. In addition, in many cases the majority of a plant’s biomass consists of leaves, also making them important organs for the production of food, feed and bio-energy. The final leaf size is determined by the total number of cells and the average cell size that result from cell division and cell expansion, respectively. During leaf development of dicotyledonous species, a cell proliferation phase, characterized by actively dividing cells, is followed by a cell expansion phase, characterized by cell growth and differentiation. After expansion, cells mature and the final leaf size is reached. At the proliferation-to-expansion phase transition, cell division ceases along a longitudinal gradient from leaf tip to base. In this thesis, we set out to gain further insight in these developmental processes affecting leaf size, assisted by the use of imaging technology and automated image analysis. For these studies we used the model species Arabidopsis thaliana, focusing primarily on the epidermis of the developing leaves as divisions there are strictly anticlinal. Moreover this layer is thought to be the main tissue layer controlling leaf growth. As a first step, we developed different image analysis tools to allow for a better and more efficient analysis of the leaf developmental process. In the first place 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. We subsequently used this framework to study vascular differentiation during leaf development and to analyze the venation pattern in transgenic lines with contrasting cellular and leaf size traits. A major conclusion from this work was that, as vascular differentiation occurs relatively late in development, the influence of a fully functional and differentiated venation pattern on final leaf size is rather limited. Furthermore, we describe a proof-of-concept to automate the kinematic analysis of leaf growth based on DIC pictures, by a sophisticated image processing chain and a data analysis pipeline. Next, we also developed imaging scripts to extract complete seedlings grown on soil and on Petri dishes and integrated those into three phenotyping platforms which monitor plant growth. Finally, we investigated the potential of emerging imaging technologies, particularly X-ray computed tomography, for future applications in plant growth analysis. The newly developed kinematic analysis tools allowed us to show that the transcription factors, SHORT-ROOT (SHR) and SCARECROW (SCR), next to their specific roles in cortex/endodermis differentiation and stem cell maintenance in the root, primarily function as general regulators of cell proliferation in leaves. The analysis of leaf growth revealed how these proteins affect the cellular growth dynamics and formed the basis to unravel the molecular mechanism controlling this. It turned out that they promote leaf growth mainly by the down-regulation of cell cycle inhibitors, known to restrain the activity of the transcription factor, E2Fa, stimulating S-phase progression. Although the dynamics of cell division and cell expansion processes can be analyzed rigorously by the leaf growth kinematics, knowledge of cell cycle duration, cell expansion, and their interaction at the individual cell level is still poorly understood, not only because of technical obstacles to study these phenomena, but also because the processes are intimately intertwined, shown by the fact that a reduced cell proliferation is often compensated by an increase in cell size and vice versa. A mathematical model fitted to detailed cellular measurements retrieved by automated image analysis of microscopic drawings of the leaf epidermis, revealed that average cell cycle duration remains constant throughout leaf development. Surprisingly, no evidence for a maximum cell size threshold for cell division of pavement cells was found in this analysis. We could estimate the division and expansion parameters of pavement and guard cell populations within the growing leaf separately and the model predicted that neighboring cells of different sizes within the epidermis expand at distinctly different relative rates. We could finally verify this by direct observations using live imaging. The mathematical model helped us to gain a better and more detailed insight into the processes that define leaf growth. But the transition from cell proliferation to cell expansion was a developmental time point that was still not characterized in detail. Differences in the timing of this transition strongly affects the number of cells formed and therefore potentially also serves as a control point determining mature leaf size. Several genes have been identified that alter leaf size by affecting the transition from primary to secondary morphogenesis. We characterized the progression of the transition on the morphological and molecular level using transcriptome analysis and imaging algorithms to visualize and quantify the size and shape of pavement cells along the proximal-distal axis of the leaf during transition. Both analyses showed that the transition from cell proliferation to expansion was established and abolished abruptly. Furthermore, the establishment of the cell cycle arrest front occurs simultaneously with the onset of photomorphogenesis. We provide evidence that retrograde signaling from chloroplasts can affect the onset of transition, revealing a previously unknown level of regulatory complexity during the transition from primary to secondary morphogenesis

Phenotyping on microscopic scale using DIC microscopy

Image analysis of Arabidopsis (Arabidopsis thaliana) plants is an important method for studying plant growth. Most work on automated analysis focuses on full rosette analysis, often in a high-throughput monitoring system. In this talk we propose a new workflow that analysis plant growth on a microscopic scale. This approach results in more detail than the common growth measurements, i.e. analysis of the number of cells, the average cell size, etc. The proposed workflow uses differential interference contrast (DIC) microscopy to visualise cells. DIC microscopy is preferred over fluorescence techniques because it provides a very fast methodology (i.e. image analysis is already possible after 1 day) and it also results in clear contrast in the samples. Although these images are easy to interpret by a human operator, they pose several challenges for automated computer vision methods. In our proposed talk we circumvent most of these challenges by combining multiple images, acquired with different microscopy settings. This approach allows us to automatically segment and analyse cells in the images. The proposed workflow enables a new form of automated phenotyping on microscopic scale

3D reconstruction of maize plants in the phenoVision system

In order to efficiently study the impact of environmental changes, or the differences between various genotypes, large numbers of plants need to be measured. At the VIB, a system named \emph{PhenoVision} was built to automatically image plants during their growth. This system is used to evaluate the impact of drought on different maize genotypes. To this end, we require 3D reconstructions of the maize plants, which we obtain from voxel carving

GPU-based maize plant analysis: accelerating CNN segmentation and voxel carving

PHENOVISION is a high-throughput plant phenotyping system for crop plants in greenhouse conditions. A conveyor belt transports plants between automated irrigation stations and imaging cabins. The aim is to phenotype maize varieties grown under different conditions. To this end we model the plants in 3D and automate the measuring of the plants

Machine learning for maize plant segmentation

High-throughput plant phenotyping platforms produce immense volumes of image data. Here, a binary segmentation of maize colour images is required for 3D reconstruction of plant structure and measurement of growth traits. To this end, we employ a convolutional neural network (CNN) to perform this segmentation successfully

Robust plane-based calibration for linear cameras

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Chloroplasts are central players in sugar-induced leaf growth

Leaves are the plant's powerhouses, providing energy for all organs through sugar production during photosynthesis. However, sugars serve not only as a metabolic energy source for sink tissues but also as signaling molecules, affecting gene expression through conserved signaling pathways to regulate plant growth and development. Here, we describe an in vitro experimental assay, allowing one to alter the sucrose (Suc) availability during early Arabidopsis (Arabidopsis thaliana) leaf development, with the aim to identify the affected cellular and molecular processes. The transfer of seedlings to Suc-containing medium showed a profound effect on leaf growth by stimulating cell proliferation and postponing the transition to cell expansion. Furthermore, rapidly after transfer to Suc, mesophyll cells contained fewer and smaller plastids, which are irregular in shape and contain fewer starch granules compared with control mesophyll cells. Short-term transcriptional responses after transfer to Suc revealed the repression of well-known sugar-responsive genes and multiple genes encoded by the plastid, on the one hand, and up-regulation of a GLUCOSE-6-PHOSPHATE TRANSPORTER (GPT2), on the other hand. Mutant gpt2 seedlings showed no stimulation of cell proliferation and no repression of chloroplast-encoded transcripts when transferred to Suc, suggesting that GPT2 plays a critical role in the Suc-mediated effects on early leaf growth. Our findings, therefore, suggest that induction of GPT2 expression by Suc increases the import of glucose-6-phosphate into the plastids that would repress chloroplast-encoded transcripts, restricting chloroplast differentiation. Retrograde signaling from the plastids would then delay the transition to cell expansion and stimulate cell proliferation

Predictors of response and adherence to outpatient multimodal rehabilitation in patients with chronic low back pain

BACKGROUND AND AIMS: There is a growing need to identify patient pre-treatment characteristics that could predict the responsiveness to specific interventions. Therefore this study aimed to identify predictors of response to outpatient multimodal rehabilitation (favourable versus non-favourable outcome) and predictors of therapeutic adherence (drop-out versus adherence to therapy) in patients with chronic low back pain. METHODS: A total of 273 chronic low back patients participated in an exercise-based rehabilitation program of an outpatient rehabilitation centre in Belgium between September 2013 and February 2015. A univariate and a multivariate logistic regression analysis were performed to analyse predictors. A linear mixed model was used for analysis of repeated measures. RESULTS: A higher age (OR=0.962) and a higher Tampa Scale of Kinesiophobia score (OR=0.870) increased the odds to complete the treatment program, whereas higher levels of back pain intensity (OR=1.247) increased the odds for non-adherence. A higher Oswestry Disability Index score (OR=0.963) decreased the odds for a favorable outcome. The treatment program had significant group, time, and time-by-group interaction effects on back pain intensity, catastrophizing, kinesiophobia and depressive symptoms, with the favorable outcome group showing a significant improvement after treatment. CONCLUSIONS: Assessment of chronic low back pain patient pre-treatment characteristics such as age, degree of kinesiophobia, back pain intensity and disability levels is of great importance as they may allow therapists to identify patients with a good prognosis or patients at risk for non-responding to multimodal treatment program. Directing the treatment according to those characteristics could function as a tool to optimize treatment benefits. At the same time, patients at risk for non-responding could be identified and referred to a different treatment approach