Supplementary MaterialsSupplementary Information Supplementary Figures 1-10, Supplementary Tables 1-6 and Supplementary References ncomms14049-s1. place in 6?min, with 50% cell capture efficiency. To demonstrate the system’s technical performance, we collected transcriptome data from 250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to Necrostatin 2 racemate demonstrate the system’s ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients. Understanding of biological systems requires the knowledge of their individual components. Single-cell RNA-sequencing (scRNA-seq) can be used to dissect transcriptomic heterogeneity that is masked in population-averaged measurements1,2. scRNA-seq studies have led to the discovery of novel cell types and provided insights into regulatory networks during development3. However, previously described scRNA-seq methods face practical challenges when scaling to tens of thousands of cells or when it is necessary to capture as many cells as possible from a limited sample4,5,6,7,8,9. Commercially available, microfluidic-based approaches have limited throughput5,6. Plate-based methods often require time-consuming fluorescence-activated cell sorting (FACS) into many plates that must be processed separately4,9. Droplet-based techniques have enabled processing of tens of thousands of cells in a single experiment7,8, but current approaches require Necrostatin 2 racemate generation of custom microfluidic devices and reagents. To overcome these challenges, we developed a droplet-based system that enables 3 messenger RNA (mRNA) digital counting of thousands of single cells. Approximately 50% of cells loaded into the system can be Mouse monoclonal to OTX2 captured, and up to eight samples can be processed in parallel per run. Reverse transcription takes place inside each droplet, and barcoded complementary DNAs (cDNAs) are amplified in bulk. The resulting libraries then undergo Illumina short-read sequencing. An analysis pipeline, Cell Ranger, processes the sequencing data and enables automated cell clustering. Here we first demonstrated comparable sensitivity of the system to existing droplet-based methods by performing scRNA-seq on cell lines and synthetic RNAs. Next, we profiled 68k fresh peripheral blood mononuclear cells (PBMCs) and demonstrated the scRNA-seq platform’s ability to dissect large immune populations. Last, we developed a computational method to distinguish donor from host cells in bone marrow transplant samples by genotype. We combined this method with clustering analysis to compare subpopulation changes in acute myeloid leukemia (AML) patients. This analysis enables transplant monitoring of the complex interplay between donor and host cells. Results Droplet-based platform enables barcoding of cells The scRNA-seq microfluidics platform builds on the GemCode technology, which has been used for genome haplotyping, structural variant analysis and assembly of a human genome10,11,12. The core of the technology is a Gel bead in EMulsion (GEM). GEM generation takes place in an 8-channel microfluidic chip that encapsulates single gel beads at 80% fill rate (Fig. 1aCc). Each gel bead is functionalized with barcoded oligonucleotides that consists of: (i) sequencing adapters and primers, (ii) a 14?bp barcode drawn from 750,000 designed sequences to index GEMs, (iii) a 10?bp randomer to index molecules (unique molecular identifier, UMI) and (iv) an anchored 30?bp oligo-dT to prime polyadenylated RNA transcripts (Fig. 1d). Within each microfluidic channel, 100,000 GEMs are Necrostatin 2 racemate formed per 6-min run, encapsulating thousands of cells in GEMs. Cells are loaded at a limiting dilution to minimize co-occurrence of multiple cells in.