Massively Parallel Digital Transcriptional Profiling of Single Cells

Overview

Journal Nat Commun

Specialty Biology

Date 2017 Jan 17

PMID 28091601

Citations 3006

Authors

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

Jason G Underwood

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

H 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

Affiliations

Soon will be listed here.

Abstract

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 tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes 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 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.

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