Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains experimentally challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These capabilities enable simple experimental designs where cells from genetically diverse samples are multiplexed and captured at higher throughput than standard workflows. To demonstrate the performance of our method, we sequenced 3 multiplexed pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets enriched for multiple cell types and at rates consistent with previous estimates. We further demonstrate the utility of sample multiplexing by characterizing cell type-specific responses and interindividual variability in 2 pools of PBMCs from 8 additional lupus patients before and after cytokine stimulation. Demuxlet enables droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.