Details

Autor(en) / Beteiligte

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

Titel

Massively parallel digital transcriptional profiling of single cells

Ist Teil von

• Nature communications, 2017-01, Vol.8 (1), p.14049-14049, Article 14049

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2017

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.