Phenotyping single cells of Saccharomyces cerevisiae using an end-to-end analysis of high-content time-lapse microscopy

Abstract

The field of systems biology has developed under the acknowledgement that quantitative approaches are necessary to fully understand the complexities that shape cells' structure and behaviour. The development of automated microscopy protocols has led to the accumulation of high-throughput data sets but the high resolution of modern imaging hardware compounded with time series generates an analysis bottleneck.

I develop machine learning tools to track Saccharomyces cerevisiae cells in time lapses that are resilient experimental and technical noise. Then I coordinate cell segmentation and tracking software to develop software that automates analyses and makes them reproducible. I then study the protein aggregation dynamics of metabolic enzymes that form reversible aggregates via phase separation. Using single-cell information provides a deeper understanding of the impact of these proteins on yeast cells' growth and division. Heterogeneous responses, distinguishable by using mutant strains with varying aggregation phenotypes, provide several novel insights.

This work shows that the development of computational tools can make the acquisition of biological insight faster and increases reproducibility while providing a toolset to tackle the challenge of dealing with high-content microscopy data - one without existing solutions able to scale computationally. it also furthers biological understanding of a conserved phenomenon such as reversible protein aggregation, in a way only possible by studying behaviour and physiology of thousands of cells at the same time. my thesis aims to pave the way for more comprehensive analyses using aggregated high-quality data.