Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes

Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring 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 two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of 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. 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.