Quantitative analysis of colony morphology in yeast.
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.
Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA.
Pekka
P
Ruusuvuori
Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg.
Jake
J
Lin
Pacific Northwest Diabetes Research Institute, Seattle, WA.
Adrian C
AC
Scott
Pacific Northwest Diabetes Research Institute, Seattle, WA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA.
Zhihao
Z
Tan
Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
Saija
S
Sorsa
Institute of Biomedical Technology, University of Tampere, Tampere, Finland.
Aleksi
A
Kallio
Institute of Biomedical Technology, University of Tampere, Tampere, Finland.
Matti
M
Nykter
Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA.
Olli
O
Yli-Harja
Department of Signal Processing, Tampere University of Technology, Tampere, Finland; Institute for Systems Biology, Seattle, WA.
Ilya
I
Shmulevich
Pacific Northwest Diabetes Research Institute, Seattle, WA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA.
Aimée M
AM
Dudley
D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D020407:Internet; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000254:growth & development; D012984:Software
colony morphology; image analysis; phenotype; software; time-lapse; yeast