Background: 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.
Results: 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.
Conclusions: 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.