@article{THESIS,
      recid = {6429},
      author = {Dohn, Ryan Patrick},
      title = {Investigating Cellular Variability in Fungal Pathogens by  Developing mDrop-seq, a High Throughput, Single Cell RNAseq  Technology for Yeast Species},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2023-06},
      number = {THESIS},
      abstract = {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.},
      url = {http://knowledge.uchicago.edu/record/6429},
      doi = {https://doi.org/10.6082/uchicago.6429},
}