- Kang, Hyun Min;
- Subramaniam, Meena;
- Targ, Sasha;
- Nguyen, Michelle;
- Maliskova, Lenka;
- McCarthy, Elizabeth;
- Wan, Eunice;
- Wong, Simon;
- Byrnes, Lauren;
- Lanata, Cristina M;
- Gate, Rachel E;
- Mostafavi, Sara;
- Marson, Alexander;
- Zaitlen, Noah;
- Criswell, Lindsey A;
- Ye, Chun Jimmie
Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-β and perform eQTL analysis on 23 pooled samples.