RESUMO
BACKGROUND: Accurate characterization of complex plant phenotypes is critical to assigning biological functions to genes through forward or reverse genetics. It can also be vital in determining the effect of a treatment, genotype, or environmental condition on plant growth or susceptibility to insects or pathogens. Although techniques for characterizing complex phenotypes have been developed, most are not cost effective or are too imprecise or subjective to reliably differentiate subtler differences in complex traits like growth, color change, or disease resistance. RESULTS: We designed an inexpensive imaging protocol that facilitates automatic quantification of two-dimensional visual phenotypes using computer vision and image processing algorithms applied to standard digital images. The protocol allows for non-destructive imaging of plants in the laboratory and field and can be used in suboptimal imaging conditions due to automated color and scale normalization. We designed the web-based tool PhenoPhyte for processing images adhering to this protocol and demonstrate its ability to measure a variety of two-dimensional traits (such as growth, leaf area, and herbivory) using images from several species (Arabidopsis thaliana and Brassica rapa). We then provide a more complicated example for measuring disease resistance of Zea mays to Southern Leaf Blight. CONCLUSIONS: PhenoPhyte is a new cost-effective web-application for semi-automated quantification of two-dimensional traits from digital imagery using an easy imaging protocol. This tool's usefulness is demonstrated for a variety of traits in multiple species. We show that digital phenotyping can reduce human subjectivity in trait quantification, thereby increasing accuracy and improving precision, which are crucial for differentiating and quantifying subtle phenotypic variation and understanding gene function and/or treatment effects.
RESUMO
Plants make drastic changes to their transcriptome to appropriately respond to environmental change, and the regulation of genes that are specific to abiotic and biotic stresses is a key to plant survival. The coordination of defence gene transcription is often coupled with significant adjustments in the levels of expression of primary metabolic and structural genes to relocate resources, repair damage and/or induce senescence. This complicates the process of finding suitable 'housekeeping' or reference genes to use in measurements of gene expression by real-time reverse transcription (RT-PCR) in response to herbivore attack. Several software programs have been developed to identify candidate reference genes, but measurement of their expression may still not yield an appropriate gene or suite of genes for normalization. This is especially true in plant-herbivore interactions where tissue damage is immediate and continuous. Here, we show that 12 traditional reference genes customarily used in RT-PCR analysis are not stably expressed after insect attack. We describe the pitfalls of using traditional reference genes and why insect attack may be affecting whole cell metabolism. We propose a method using RNA quantification in combination with an external spike of commercially available mRNA as normalization factors in studies involving herbivory, multiple stress treatments or species where stable reference genes are unknown.