Article

Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery.

Department of Biochemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand. .
Plant Methods (Impact Factor: 2.59). 02/2011; 7(1):3. DOI: 10.1186/1746-4811-7-3
Source: PubMed

ABSTRACT Arabidopsis thaliana is a useful model organism for deciphering the genetic determinants of seed size; however the small size of its seeds makes measurements difficult. Bulk seed weights are often used as an indicator of average seed size, but details of individual seed is obscured. Analysis of seed images is possible but issues arise from variations in seed pigmentation and shadowing making analysis laborious. We therefore investigated the use of a consumer level scanner to facilitate seed size measurements in conjunction with open source image-processing software.
By using the transmitted light from the slide scanning function of a flatbed scanner and particle analysis of the resulting images, we have developed a method for the rapid and high throughput analysis of seed size and seed size distribution. The technical variation due to the approach was negligible enabling us to identify aspects of maternal plant growth that contribute to biological variation in seed size. By controlling for these factors, differences in seed size caused by altered parental genome dosage and mutation were easily detected. The method has high reproducibility and sensitivity, such that a mutant with a 10% reduction in seed size was identified in a screen of endosperm-expressed genes. Our study also generated average seed size data for 91 Arabidopsis accessions and identified a number of quantitative trait loci from two recombinant inbred line populations, generated from Cape Verde Islands and Burren accessions crossed with Columbia.
This study describes a sensitive, high-throughput approach for measuring seed size and seed size distribution. The method provides a low cost and robust solution that can be easily implemented into the workflow of studies relating to various aspects of seed development.

0 Bookmarks
 · 
102 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The MINICHROMOSOME MAINTENANCE 2-7 (MCM2-7) complex, a ring-shaped heterohexamer, unwinds the DNA double helix ahead of the other replication machinery. Although there is evidence that individual components might have other roles, the essential nature of the MCM2-7 complex in DNA replication has made it difficult to uncover these. Here, we present a detailed analysis of Arabidopsis thaliana mcm2-7 mutants and reveal phenotypic differences. The MCM2-7 genes are coordinately expressed during development, although MCM7 is expressed at a higher level in the egg cell. Consistent with a role in the egg cell, heterozygous mcm7 mutants resulted in frequent ovule abortion, a phenotype that does not occur in other mcm mutants. All mutants showed a maternal effect, whereby seeds inheriting a maternal mutant allele occasionally aborted later in seed development with defects in embryo patterning, endosperm nuclear size, and cellularization, a phenotype that is variable between subunit mutants. We provide evidence that this maternal effect is due to the necessity of a maternal store of MCM protein in the central cell that is sufficient for maintaining seed viability and size in the absence of de novo MCM transcription. Reducing MCM levels using endosperm-specific RNAi constructs resulted in the up-regulation of DNA repair transcripts, consistent with the current hypothesis that excess MCM2-7 complexes are loaded during G1 phase, and are required during S phase to overcome replicative stress or DNA damage. Overall, this study demonstrates the importance of the MCM2-7 subunits during seed development and suggests that there are functional differences between the subunits.
    Plant Molecular Biology 06/2014; · 3.52 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.
    PLoS ONE 01/2014; 9(5):e96889. · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Measuring grain characteristics is an integral component of cereal breeding and research into genetic control of seed development. Measures such as thousand grain weight are fast, but do not give an indication of variation within a sample. Other methods exist for detailed analysis of grain size, but are generally costly and very low throughput. Grain colour analysis is generally difficult to perform with accuracy, and existing methods are expensive and involved.
    Plant Methods 01/2014; 10:23. · 2.59 Impact Factor

Full-text (2 Sources)

Download
41 Downloads
Available from
Jun 1, 2014