Tracking Single Cell Lineages in Saccharomyces Cerevisiae
Date of Award
Doctor of Philosophy (PhD)
Molecular, Cellular, and Developmental Biology
The ability to measure the number of gene-specific mRNA molecules in individual mammalian cells has transformed the transcriptomics field. Among the key technologies enabling single-cell mRNA sequencing has been Droplet Sequencing (Drop-Seq). While this method works efficiently for mammalian cells, its direct application to yeast cells has been problematic due to cell-type specific differences such as size, doublet formation rate, and cell wall. Here we introduce yeast DropSeq, a single-cell RNA sequencing method for the study of transcriptomics in yeast. I modified and optimized the original Drop-Seq method to address the issues that emerged from smaller cell sizes and the presence of the cell wall. I also quantified the rate of doublet formation through a species-mixing experiment. As proof-of-principle application of yeast DropSeq, I investigated the transcriptomic effects of mycophenolic acid (MPA), a lifespan-extending compound that decreases de novo GMP synthesis. I compared transcript levels between cells treated with MPA and cells treated with DMSO and/or guanine, MPA’s epistatic agent. I discovered that isogenic populations of yeast cells contain transcriptionally distinct subpopulations in all treatment conditions excluding MPA-treated cells. I found that cells treated with MPA experience an upregulation of genes coding for proteins involved in antioxidation, pre-RNA processing, translation initiation, tRNA synthetase and tRNA methyltransferase, histone and nucleosome assembly, and ribosome component transport. Conversely, a downregulation of mRNA expression was observed for genes encoding the 40S and 60S ribosomal subunits, and for genes involved in mitochondrial function. Yeast DropSeq will accelerate biological discovery by facilitating droplet-based transcriptomics of yeast cells.Additionally, I present the development of a lineage tracking cell barcoding mechanism. This tracking mechanism, termed the dynamic barcode, relies on a CRISPR-based system that targets itself to introduce changes. The components of the system include a Cas9 protein tagged with an estrogen-binding domain to control localization and a self-excising and self-targeting gRNA cassette that can be placed under an mRNA promoter. The self-excising (RGR) system allows the gRNA to be expressed under an mRNA, RNA polII promoter, and the self-targeting (stg) system allows the gRNA to target itself and produce progressive changes in its sequence. The goal of this system is to make progressive cuts in its target sequence (itself) over time to mark changes in cells that would allow for the tracking of their ages. I induce cutting in the system and show all the possible changes, both insertions and deletions, that are made using traditional sequencing of single colonies. With further modifications, such as extending the length of the self-targeting region, this system will allow for the tracking of multiple generations over a period of time. Ultimately, this system will be combined with a daughter-specific promoter, such that the self-targeting and self-excising gRNA will target and cut itself only in daughter cells. This will allow only daughter cells to accumulate changes in the dynamic barcode, with the youngest cells accruing the most changes. This dynamic barcode could therefore be used to determine ages of cells within a culture.
Urbonaite, Guste, "Tracking Single Cell Lineages in Saccharomyces Cerevisiae" (2021). Yale Graduate School of Arts and Sciences Dissertations. 212.