Quantitative Profiling of Large and Complex Single Cell Populations

Discrimination of discrete cell types in complex tissues is critical to fully elucidating biological systems. Current methods for single cell RNA-sequencing (scRNA-seq) face practical challenges such as throughput limitations, custom set-up requirements, and complicated experimental protocols. We describe a fully-integrated, droplet-based system that enables 3’ mRNA digital counting of up to tens of thousands of single cells per sample with >65% cell capture efficiency and a low doublet rate (<1% per 1000 cells). We have used this system to profile over a million cells from the cortex and hippocampus regions of E18 murine brain. In addition to identifying expected major neuronal and glial cell types, we also detected rare interneurons without complex enrichment procedures. We have also demonstrated the ability of the system to characterize large immune populations from peripheral blood and bone marrow. Coupling the droplet-based system to a novel computational method to distinguish cells from multiple individuals via de novo variant calling, we study host and donor cell chimerism in an allogeneic hematopoietic stem cell transplant setting. In a proof of concept experiment, we discover new insights into the disease state of an AML patient before and after transplant that were not readily achievable with traditional PCR, FACS-based analysis. We envision that our technology will enable widespread adoption of high throughput single cell mRNA analysis, and accelerate the characterization of diverse developmental systems as well as tumor samples in basic and clinical research.