Single-cell DNA sequencing (scDNA-seq) can identify genetic differencesbetween individual cells and has broad applications in studying biology. For example, because scDNA-seq preserves haplotypes, it enables the addition of information about the fitness of different combinations of mutations into studies that quantify the fitness of individual mutations. However, it requires separating cells manually or using machinery, which is time-consuming and costly as every cell requires a separate reaction. Thus, most studies are limited to a few hundred cells, and scaling up is expensive and challenging. This problem also makes it difficult to multiplex samples or to study multiple sample types in the same experiment. To solve these problems, I introduce a novel method for sequencing DNA in heterogeneous cell populations by using the cell itself as a container for sequencing reactions, eliminating the need to isolate individual cells. The method involves diffusing DNA polymerase and barcoded primers into intact cells and amplifying its DNA Intracellularly. To ensure that DNA from each cell can be uniquely identified, I use combinatorial barcoding, which assigns a specific barcode to each cell using a unique combination of non-unique nucleotide block sequences. This allows for the pooling of cells, making the method multiplexable and enabling the analysis of dozens of samples containing thousands of cells. The method is flexible and allows for targeted sequencing of a region of interest and whole genome sequencing. I optimize the method for various organisms and applications so it can be made accessible to a wide range of research groups.

Protein misfolding is a problem faced by all organisms, but the reasons behind misfolded protein toxicity are largely unknown. It is difficult to pinpoint one exact mechanism as the effects of misfolded proteins can be widespread and variable between cells. To better understand their impacts, here I explore the consequences of misfolded proteins and if they affect all cells equally or affect some cells more than others. To investigate cell subpopulations, I built and optimized a cutting-edge single-cell RNA sequencing platform (scRNAseq) for yeast. By using scRNAseq, I can study the expression variability of many genes (i.e. how the transcriptomes of single cells differ from one another). To induce misfolding and study how single cells deal with this stress, I use engineered strains with varying degrees of an orthogonal misfolded protein. When I computationally cluster the cells expressing misfolded proteins by their sequenced transcriptomes, I see more cells with the severely misfolded protein in subpopulations undergoing canonical stress responses. For example, I see these cells tend to overexpress chaperones, and upregulate mitochondrial biogenesis and transmembrane transport. Both of these are hallmarks of the “Generalized” or “Environmental Stress Response” (ESR) in yeast. Interestingly, I do not see all components of the ESR upregulated in all cells, which may suggest that the massive transcriptional changes characteristic of the ESR are an artifact of having defined the ESR in bulk studies. Instead, I see some cells activate chaperones, while others activate respiration in response to stress. Another intriguing finding is that growth supporting proteins, such as ribosomes, have particularly heterogeneous expression levels in cells expressing misfolded proteins. This suggests that these cells potentially reallocate their metabolic functions at the expense of growth but not all cells respond the same. In sum, by using my novel single-cell approach, I have gleaned new insights about how cells respond to stress. which can help me better understand diseased cells. These results also teach how cells contend with mutation, which commonly causes protein misfolding and is the raw material of evolution. My results are the first to explore single-cell transcriptional responses to protein misfolding and suggest that the toxicity from misfolded proteins may affect some cells’ transcriptomes differently than others.