Summary

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.

Overall design

HiSeq 2500 data for sequencing of PBMCs from SLE patients and 2 controls. We collected 1M cells each from frozen PBMC samples that were Ficoll isolated and prepared using the 10x Single Cell instrument according to standard protocol. Samples A, B, and C were prepared on the instrument directly following thaw, while samples 2.1 and 2.2 were cultured for 6 hours with (B) or without (A) IFN-beta stimulation prior to loading on the 10x Single Cell instrument.

Contributors

Hyun Min Kang†✉️, Meena Subramaniam†, Sasha Targ†, Chun Jimmie Ye✉️

Contact

sashatarg@gmail.com(Sasha Targ)