Population Scale Transcriptome Profiling from Mixed Samples using Single-Cell Analysis

Abstract

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.