The rise of high throughput single-cell RNA sequencing increased our understanding of cellular population dynamics and the heterogeneity and stochasticity between individual cells. These gains have thus far been lost on microbial cells due to complicating factors that rendered microbes incompatible with technologies developed on mammalian cells. However, not all these drawbacks are present within Eukaryotic yeast cells, making them an ideal target microbe for technological development. In this dissertation, the development of mDrop-seq, a high throughput scRNA-seq for yeast species, is displayed through the processing of thousands of cells of two yeast species. In the first chapter, we use the model organism S. cerevisiae for initial development, testing, and profiling of 35,109 total yeast cells. In doing so, we test appropriate lysis conditions and time to allow for droplet microfluidic compatible cell lysis. S. cerevisiae cells are subjected to a 42°C heat shock in order to determine mDrop-seq capability to detect a large scale stress response at single cell resolution. Analytical pipelines for single-cell analysis that were developed for mammalian data are shown to work with yeast libraries, allowing for differential gene expression (DGE), clustering analysis, cell cycle assignment, and pseudo-time trajectory analysis. In the second chapter, we described further modifying and testing mDrop-seq on the clinically relevant species Candida albicans. Despite challenges such as thicker cell walls, we display mDrop-seq’s ability to process C. albicans cells using exposure to the antifungal drug Fluconazole. The final chapter of this dissertation uses mDrop-seq to search for sources of variation and batch effects within our data. We show that the activation of stress response pathways causes a reduction in transcriptomic variation between C. albicans cells. In total, the chapters of this dissertation show mDrop-seq’s value as a low cost, scalable scRNA-seq technology for yeast species.