title: An unbiased method for the quantitation of disease phenotypes using a custom-built macro plugin for the program ImageJ.
abstract: Accurate evaluation of disease phenotypes is considered a key step to study plant-microbe interactions, as the rate of host colonization by the pathogenic microbe directly reflects whether the defense response of the plant is compromised. Although several techniques were developed to quantitate the amount of infection, only a few of them are inherently suitable for large disease screens. Here, I describe an unbiased method to quantitate disease phenotypes which manifest themselves by visible symptoms contrasting with the remaining unaffected parts of the host tissue. The method utilizes a macro plugin written for the image processing program "ImageJ" to calculate two values which determine the disease index for a specific treatment. In case the disease symptoms are not clear, a transgenic pathogenic fungus expressing the GUS gene is suitable for high-throughput disease screens, since staining for GUS activity facilitates an easy detection of the blue-stained pathogen. I illustrate the versatility of this method by analyzing a data set from a functional silencing screening experiment in resistant tomato that was inoculated with a GUS-expressing strain of the fungus Cladosporium fulvum. The method calculates a disease index for each silenced plant and thereby provides a basis for the unbiased identification of candidate host genes required for full resistance to this fungus.
authors.affiliation: Laboratory of Phytopathology, Wageningen University, Wageningen, The Netherlands. ahmed.abd@wur.nl
authors.forename: Ahmed
authors.initials: A
authors.lastname: Abd-El-Haliem
mesh_terms: D002963:Cladosporium / Q000235:genetics* / Q000302:isolation & purification; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018551:Solanum lycopersicum / Q000235:genetics / Q000382:microbiology*; D010935:Plant Diseases / Q000235:genetics / Q000382:microbiology*; D018515:Plant Leaves / Q000235:genetics / Q000382:microbiology; D012984:Software