References of "Ruusuvuori, Pekka"
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See detailUnidirectional P-body transport during the yeast cell cycle.
Garmendia-Torres, Cecilia; Skupin, Alexander UL; Michael, Sean A. et al

in PloS one (2014), 9(6), 99428

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell ... [more ▼]

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained "hovering" near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions. [less ▲]

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See detailQuantitative analysis of colony morphology in yeast
Ruusuvuori, Pekka; Lin, Jake UL; Shmulevich, Ilya et al

in BioTechniques (2013)

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 ... [more ▼]

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. [less ▲]

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