HYPOTrace: Image Analysis Software for Measuring Hypocotyl Growth and Shape Demonstrated on Arabidopsis Seedlings Undergoing Photomorphogenesis (original) (raw)
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Genetics, 2013
Automated image acquisition, a custom analysis algorithm, and a distributed computing resource were used to add time as a third dimension to a quantitative trait locus (QTL) map for plant root gravitropism, a model growth response to an environmental cue. Digital images of Arabidopsis thaliana seedling roots from two independently reared sets of 162 recombinant inbred lines (RILs) and one set of 92 near isogenic lines (NILs) derived from a Cape Verde Islands (Cvi) × Landsberg erecta (Ler) cross were collected automatically every 2 min for 8 hr following induction of gravitropism by 90° reorientation of the sample. High-throughput computing (HTC) was used to measure root tip angle in each of the 1.1 million images acquired and perform statistical regression of tip angle against the genotype at each of the 234 RIL or 102 NIL DNA markers independently at each time point using a standard stepwise procedure. Time-dependent QTL were detected on chromosomes 1, 3, and 4 by this mapping meth...
New Phytologist, 2007
Using a novel setup, we assessed how fast growth of Nicotiana tabacum seedlings responds to alterations in the light regime and investigated whether starch-free mutants of Arabidopsis thaliana show decreased growth potential at an early developmental stage. • Leaf area and relative growth rate were measured based on pictures from a camera automatically placed above an array of 120 seedlings. Detection of total seedling leaf area was performed via global segmentation of colour images for preset thresholds of the parameters hue, saturation and value. • Dynamic acclimation of relative growth rate towards altered light conditions occurred within 1 d in N. tabacum exposed to high nutrient availability, but not in plants exposed to low nutrient availability. Increased leaf area was correlated with an increase in shoot fresh and dry weight as well as root growth in N. tabacum . Relative growth rate was shown to be a more appropriate parameter than leaf area for detection of dynamic growth acclimation. Clear differences in leaf growth activity were also observed for A. thaliana . • As growth responses are generally most flexible in early developmental stages, the procedure described here is an important step towards standardized protocols for rapid detection of the effects of changes in internal (genetic) and external (environmental) parameters regulating plant growth.
Computer-Assisted Image Analysis of Plant Growth, Thigmomorphogenesis, and Gravitropism
PLANT PHYSIOLOGY, 1985
A nonintrusive auxonometric system, based on the DARWIN image processor (Telewski et al. 1983 Plant Physiol 72: 177-181), is described and demonstrated in the analysis of gravitropism and thigmomorphogenesis in corn seedlings (Zea mays). Using this system, growth and bending of regularly shaped plants or organs can be quickly and accurately measured without, in any way, interfering with the plant. Furthermore, the growth and bending curves are automatically plotted. Thigmomorphogenesis in the aerial part of corn seedlings involves growth promotion at a low force load-and growth retardation at higher force loads. The time courses of the two kinds of response are somewhat different, with retardation occurring immeditely after mechanical perturbation and growth promotion taking somewhat longer to begin. Gravitropic experiments show that when dark-grown corn seedlings are placed on their side in the light, the resulting curvature is due to two consecutive morphological mechanisms. In the first instance, lasting for about 15 minutes, the elongation of the bottom edge of the plant accelerates, while the elongation of the top edge remains constant. After that, for the next 1.75 hours, the elongation of the top edge decelerates and stops while that of the bottom edge remains constant at the increased rate for most of the period. The measurements taken from both experiments at relatively high resolution (0.08-0.1 millimeter) show that the growth curves are not smooth but show many small irregularities which may or may not involve micronutations.
Arxiv preprint arXiv: …, 2010
Post-genomic research deals with challenging problems in screening genomes of organisms for particular functions or potential for being the targets of genetic engineering for desirable biological features. 'Phenotyping' of wild type and mutants is a time-consuming and costly effort by many individuals. This article is a preliminary progress report in research on large-scale automation of phenotyping steps (imaging, informatics and data analysis) needed to study plant gene-proteins networks that influence growth and development of plants. Our results undermine the significance of phenotypic traits that are implicit in patterns of dynamics in plant root response to sudden changes of its environmental conditions, such as sudden re-orientation of the root tip against the gravity vector. Including dynamic features besides the common morphological ones has paid off in design of robust and accurate machine learning methods to automate a typical phenotyping scenario, i.e. to distinguish the wild type from the mutants.
Image-Based Phenotyping of the Mature Arabidopsis Shoot System
Computer Vision - ECCV 2014 Workshops, 2015
The image-based phenotyping of mature plants faces several challenges from the image acquisition to the determination of quantitative characteristics describing their appearance. In this work a framework to extract geometrical and topological traits of 2D images of mature Arabidopsis thaliana is proposed. The phenotyping pipeline recovers the realistic branching architecture of dried and flattened plants in two steps. In the first step, a tracing approach is used for the extraction of centerline segments of the plant. In the second step, a hierarchical reconstruction is done to group the segments according to continuity principles. This paper covers an overview of the relevant processing steps along the proposed pipeline and provides an insight into the image acquisition as well as into the most relevant results from the evaluation process.
Quantitative analysis of venation patterns of Arabidopsis leaves by supervised image analysis
The Plant Journal, 2012
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.
Advanced imaging techniques for the study of plant growth and development
Trends in Plant Science, 2014
A variety of imaging methodologies are being used to collect data for quantitative studies of plant growth and development from living plants. Multi-level data, from macroscopic to molecular, and from weeks to seconds, can be acquired. Furthermore, advances in parallelized and automated image acquisition enable the throughput to capture images from large populations of plants under specific growth conditions. Image-processing capabilities allow for 3D or 4D reconstruction of image data and automated quantification of biological features. These advances facilitate the integration of imaging data with genome-wide molecular data to enable systems-level modeling. Combining imaging and modeling Even the simplest organisms are highly complex systems in which countless dynamic biochemical processes occur simultaneously. To reach a comprehensive and quantitative understanding of such a complex molecular machine, the ability to accurately characterize dynamic processes at different scales is essential. Traditional molecular, genetic and biochemical studies have successfully identified regulators of plant growth and development; however these approaches often fail to address the timing of molecular events. To capture the dynamic behavior of biological systems, molecular activities need to be analyzed with regard to their spatial and temporal properties. To generate a comprehensive model of developmental processes, gene expression patterns have to be recorded with high
Plant Physiology, 2009
Plant development is genetically determined but it is also plastic, a fundamental duality that can be investigated provided large number of measurements can be made in various conditions. Plasticity of gravitropism in wild-type Arabidopsis (Arabidopsis thaliana) seedling roots was investigated using automated image acquisition and analysis. A bank of computer-controlled charge-coupled device cameras acquired images with high spatiotemporal resolution. Custom image analysis algorithms extracted time course measurements of tip angle and growth rate. Twenty-two discrete conditions defined by seedling age (2, 3, or 4 d), seed size (extra small, small, medium, or large), and growth medium composition (simple or rich) formed the condition space sampled with 1,216 trials. Computational analyses including dimension reduction by principal components analysis, classification by k-means clustering, and differentiation by wavelet convolution showed distinct response patterns within the conditio...
2016
With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of...
Computers and Electronics in Agriculture, 2017
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R 2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.