Population Scale Transcriptome Profiling from Mixed Samples using Single-Cell Analysis
One powerfull tool in understanding how genetic variations can lead to changes in gene expression across human population involves measuring mRNA expression levels in large scale, with high-throughput sequencing. Still, large scale analysis is often limited by the amount of sample handling and batch effects. To circumvent these problems in transcriptome profiling, I tested the strategy of pooling multiple samples and using single cell RNA sequencing technology. I used the Drop-seq technique to sequence the RNA from I 0 lymphoblastoid cell lines in individuals involved in the International HapMap project. Multiplexing RNA-sequencing led to the creation of a standardized pipeline that maps SNPs from Drop-seq reads to accurately identify each of the I 0 individuals, and to quantify genes containing high level of expression. Approximately 82.9% of Drop-seq reads were aligned and re-assigned across the ten individuals in this experiment, while 74.9% of the single cells were matched via SNPs. The results illustrate that single-cell analysis of multiple individuals can be conducted in a sole experiment, thus effectively reducing multi-sample handling and batch effects that would occur in sequencing individuals one at a time.
Biological sciences honors program; Drop-seq; lymphoblastoid cell lines; expression vanation; batch effect; single cell
B.S. of Biological Sciences
Bachelor of Science
dissertation or thesis