Massively parallel digital transcriptional profiling of single cells.
Massively parallel digital transcriptional profiling of single cells.
复制标题
DOI:
10.1038/ncomms14049
复制
发表时间:
2017-01-16
影响因子:
16.6
通讯作者:
Bielas JH
中科院分区:
文献类型:
--
作者:
Zheng GX;Terry JM;Belgrader P;Ryvkin P;Bent ZW;Wilson R;Ziraldo SB;Wheeler TD;McDermott GP;Zhu J;Gregory MT;Shuga J;Montesclaros L;Underwood JG;Masquelier DA;Nishimura SY;Schnall-Levin M;Wyatt PW;Hindson CM;Bharadwaj R;Wong A;Ness KD;Beppu LW;Deeg HJ;McFarland C;Loeb KR;Valente WJ;Ericson NG;Stevens EA;Radich JP;Mikkelsen TS;Hindson BJ;Bielas JH
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. Single-cell gene expression analysis is challenging. This work describes a new droplet-based single cell RNA-seq platform capable of processing tens of thousands of cells across 8 independent samples in minutes, and demonstrates cellular subtypes and host–donor chimerism in transplant patients.