UCSF

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 cell and detect droplets containing two cells. 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 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 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We also apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN- and perform eQTL analysis on 23 pooled samples.

Systemic lupus erythematosus (SLE) is an autoimmune disease defined by a broad range of symptoms that disproportionately affects women. Our knowledge of which immune cells mediate the etiology and pathogenesis of the disease remains incomplete. Identifying pathogenic cells using bulk gene expression analysis is confounded by the functional overlap and frequency variation of immune cell types. Here, we used multiplexed single-cell RNA-seq (scRNA-seq) to profile ~1 million peripheral blood mononuclear cells from 134 SLE cases and 58 healthy controls. Cases were marked by a reduction of naive CD4+ T cells, clonal restriction of effector memory CD8+ T cells, and elevated expression of interferon-stimulated genes in classical monocytes. An additional 15 cases experiencing active disease flares displayed increased expansion of effector memory CD8+ T cells and the presence of macrophages not seen in managed disease. Although cell-type-specific expression contributed most to inter-individual expression variability across all cells, cell composition accounted for more variability in genes differentially expressed in cases. We integrated dense genotyping data to map thousands of genetic variants, including SLE-associations, whose effects on expression are modified by cell type or interferon activation. Population-scale scRNA-seq analysis reveals changes in cell composition and state associated with SLE, and when integrated with genetic data, ascribes function to disease-associated and disease-modified variants.