Massively parallel digital transcriptional profiling of single cells

Created on 26th July 2016

Grace X.Y. Zheng; Jessica M Terry; Phillip Belgrader; Paul Ryvkin; Zachary W. Bent; Ryan Wilson; Solongo B. Ziraldo; Tobias D. Wheeler; Geoff P. McDermott; Junjie Zhu; Mark T. Gregory; Joe Shuga; Luz Montesclaros; Donald A Masquelier; Stefanie Y. Nishimura; Michael Schnall-Levin; Paul W Wyatt; Christopher M. Hindson; Rajiv Bharadwaj; Alexander Wong; Kevin D. Ness; Lan W. Beppu; Joachim Deeg; Christopher McFarland; Keith R. Loeb; William J. Valente; Nolan G. Ericson; Emily A. Stevens; Jerald P. Radich; Tarjei S. Mikkelsen; Benjamin J. Hindson; Jason H Bielas;

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of up to tens of thousands of single cells per sample. Cell encapsulation in droplets takes place in ~6 minutes, with ~50% cell capture efficiency, up to 8 samples at a time. The speed and efficiency allow the processing of precious samples while minimizing stress to cells. To demonstrate the system′s technical performance and its applications, we collected transcriptome data from ~¼ million single cells across 29 samples. First, we validate the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. Then, we profile 68k peripheral blood mononuclear cells (PBMCs) to demonstrate the system′s ability to characterize large immune populations. Finally, we use sequence variation in the transcriptome data to determine host and donor chimerism at single cell resolution in bone marrow mononuclear cells (BMMCs) of transplant patients. This analysis enables characterization of the complex interplay between donor and host cells and monitoring of treatment response. This high-throughput system is robust and enables characterization of diverse biological systems with single cell mRNA analysis.

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