Massively parallel digital transcriptional profiling of single cells

Массивно-параллельное цифровое транскриптомное профилирование отдельных клеток
Tarjei S. Mikkelsen, Joe Shuga, Alexander Wong, Jason G. Underwood, Jerald P. Radich, Michael Schnall-Levin, Grace Zheng, Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary Bent, Ryan J. Wilson, Solongo B. Ziraldo, Tobias D. Wheeler, Geoff P. McDermott, Junjie Zhu, Mark Gregory, Luz Montesclaros, Donald A Masquelier, Stefanie Y. Nishimura, Paul W. Wyatt, Christopher M. Hindson, Rajiv Bharadwaj, Kevin D. Ness, Lan Beppu, H. Joachim Deeg, Christopher McFarland, Keith R. Loeb, William J. Valente, Nolan G. Ericson, Emily A. Stevens, Benjamin J. Hindson, Jason H. Bielas, Christopher McFarland
2017-01-16

3' mRNA countingdroplet-based systemhost-donor chimerismperipheral blood mononuclear cellssingle-cell transcriptomics
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.
1
A droplet-based system enables massively parallel 3′ mRNA counting of tens of thousands of single cells per sample.
2
Profiling 68,000 peripheral blood mononuclear cells showed that the system can characterize large immune-cell populations.
3
The system generated transcriptome profiles for approximately 250,000 single cells across 29 samples and demonstrated sensitivity for detecting rare populations.
4
Transcript sequence variation enabled single-cell resolution of host–donor chimerism in bone marrow cells from transplant patients.
5
Up to eight samples can be encapsulated simultaneously in approximately 6 minutes, with approximately 50% cell-capture efficiency.

Droplet-based single-cell 3' mRNA counting system for massively parallel transcriptional profiling

massively parallel 3′ mRNA transcriptome profiling and characterization of cell populations, including rare populations and host–donor chimerism, at single-cell resolution

Publication Details
Publication Date
2017-01-16
Journal
Publisher
ISSN
Access Type
Author Information
Authors
Tarjei S. Mikkelsen
Joe Shuga
Alexander Wong
Jason G. Underwood
Jerald P. Radich
Michael Schnall-Levin
Grace Zheng
Jessica M. Terry
Phillip Belgrader
Paul Ryvkin
Zachary Bent
Ryan J. Wilson
Solongo B. Ziraldo
Tobias D. Wheeler
Geoff P. McDermott
Junjie Zhu
Mark Gregory
Luz Montesclaros
Donald A Masquelier
Stefanie Y. Nishimura
Paul W. Wyatt
Christopher M. Hindson
Rajiv Bharadwaj
Kevin D. Ness
Lan Beppu
H. Joachim Deeg
Christopher McFarland
Keith R. Loeb
William J. Valente
Nolan G. Ericson
Emily A. Stevens
Benjamin J. Hindson
Jason H. Bielas
Christopher McFarland
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%