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Presentation Information     2007-01-15 (15:15)   •  The seminar room at Vi2

Speaker Antti Niemistö
Comment Tampere University of Technology
Type External presentation
Title Quantitative image analysis for understanding cellular signalling dynamics and peroxisome assembly in yeast
Abstract The presentation consists of two parts, both focusing on image analysis in the case of the yeast Saccharomyces cerevisiae. In the first part, a microfluidic platform is used to study the molecular dynamics of the pheromone response system of yeast. This is done by screening gene knock-out strains against dynamically changing alpha-factor environments. To enable measurement of pheromone stimulation, each yeast strain has a pheromone response regulated green fluorescent protein (GFP) gene inserted into its genome. Microscopy imaging is used to measure the expression of the GFP reporter. Two channels are acquired: a differential interference contrast (DIC) bright field channel that shows the yeast cells and a fluorescent channel that shows the GFP response. Automated image analysis is employed to obtain both population statistics as well as time-course data of tracked single cells over time.

In the second part of the presentation, image analysis is applied for studying peroxisome biogenesis in yeast. Peroxisomes are inducible, single membrane organelles, involved in a variety of processes that include beta-oxidation of fatty acids, cholesterol biosynthesis, and amino acid metabolism. A gene knock-out library of 4000 yeast strains is assessed in the study, emphasizing the need for automated high-throughput analysis. Each strain is imaged with a multimodal confocal microscope: a bright field channel visualizes the yeast cells and a fluorescent channel visualizes the peroxisomes using a green fluorescent protein (GFP). Automated image analysis is used to obtain quantitative information on peroxisomes, such as number of peroxisomes for each cell, peroxisome sizes, and average intensity of the GFP response of each peroxisome. The aim is to integrate the obtained quantitative data with other data such as gene expression data. For example, one of the goals is to find good gene predictors of the parameters obtained by image analysis.