Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes. Homo sapiens

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. 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.