Quantitative Analysis of Colony Morphology in Yeast

Abstract

Microorganisms often form multicellular structures such as biofilms and structured colonies that 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. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our 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 colony morphology can be expressed as changes 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 Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) 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.

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

“…A number of labs have analyzed phenotypic variation in response to a range of environmental conditions [4,64,65] and delved into the genetic basis of variation in specific traits such as heat tolerance [66], gene/protein expression [67,68], sporulation efficiency [69-71], colony morphology [72-75], sulfur uptake [76], and carbon regulation [36,55]. The vast majority of these studies used growth or expression level [67] as readout.…”

Quantifying the dominant growth mechanisms of dimorphic yeast using a lattice-based model

“…We find that 81% of these matches come from the DLG region, 7% are from the intermediate region and 12% are from the uniform region. For the biased example, the best match is given by (s, c 0 ) ¼ (29,3), which lies in the intermediate region, and differs from the experimental value by only 8.6 Â 10 25 . In contrast with the unbiased example, the top 100 matches are more diverse, comprising 18% DLG growth, 46% intermediate growth and 36% uniform growth.…”

“…Bacterial colonies have been described by fractal dimension [11,14] and related scaling laws [12], while fingers growing from bacterial colonies have been quantified by the dimensionless width and amplitude [13]. Yeast invasiveness has been classified using the total colony volume and invasive colony volume [27,28], while a systematic analysis of 427 features identified six as important for classifying colony morphology, with the fractal dimension, average entropy texture within the colony and the area of the colony structure the most important features [29]. The size of the colony perimeter relative to that of its area and the coefficient of variation of the colony boundary as a function of angle have also been used to describe yeast colonies [25].…”

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

“…A number of labs have analyzed phenotypic variation in response to a range of environmental conditions [4,65,66] and delved into the genetic basis of variation in specific traits such as heat tolerance [67], gene/protein expression [68,69], sporulation efficiency [70–72], colony morphology [73–76], sulfur uptake [77], and carbon regulation [36,55]. The vast majority of these studies used growth or expression level [68] as readout.…”