[en] Microorganisms often form multicellular structures,such as biofilms and structured colonies, which can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated
imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. We have developeda platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. The strategy
enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colonymorphology can be expressed achanges in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive web application that integrates the raw and processed
images across all time points, allowing exploration of the image-based features and principal components associated with morphological development. The web application YIMAA
is available at http://yimaa.cs.tut.fi.