Speaker
Description
Cell cycle regulation and cell size control in yeast is governed by complex protein interactions and can vary greatly depending on genetic disposition. A powerful tool to study cell cycle progression is high-throughput live-cell imaging where dynamic cellular processes can be observed for single cells over time. However, studies based on this technique are limited in scope due to the lack of fully automated analysis pipelines and the need for time-consuming manual annotation work. Hence, many cell-cycle related studies observe only bulk-level phenotypes and thereby lose out on valuable information.
We developed a Deep Learning based pipeline for yeast cell segmentation, tracking and classification of cell cycle stages, which makes use of time-contextual information, outperforming comparable state-of-the-art methods, which are working on 2D input data. Using this pipeline, we could lift the bottleneck posed by data analysis and managed to generate a unique single-cell phenotypic dataset consisting of more than 23 thousand complete cell cycles, which are described by features like phase durations and mother-bud volume ratios at divisions. This was achieved fully automatically allowing us to further perform experiments to aim at a dataset consisting of more than 100 thousand cell cycles of the S. cerevisiae strains from a collection of genetically diverse strains.
By analysing phenotypes of cell-cycle progression in the context of genomic features and environmental variables, we hope to 1) gain general insights into patterns of cell-cycle regulation across S. cerevisiae, for instance size sensing at the G1-S transition, 2) analyse domestication traces and their impact on cell-cycle related phenotypes and 3) discover candidate genes which might play still unknown parts in the regulation of cell growth and division.
| Authors | Benedikt Mairhörmann*, Pascal Falter-Braun, Kurt M. Schmoller |
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| Keywords | Image Analysis, Deep Learning, Genetics, Cell Cycle |
