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大规模平行数字化单细胞转录组分析。

Massively parallel digital transcriptional profiling of single cells.

机构信息

10x Genomics Inc., Pleasanton, California, 94566, USA.

Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.

出版信息

Nat Commun. 2017 Jan 16;8:14049. doi: 10.1038/ncomms14049.

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.

摘要

对单个细胞的转录组进行特征分析是理解复杂生物系统的基础。我们描述了一种基于液滴的系统,能够对每个样本的数万单个细胞进行 3' mRNA 计数。细胞包封,每次最多可容纳 8 个样本,耗时约 6 分钟,细胞捕获效率约为 50%。为了展示系统的技术性能,我们从 29 个样本中的约 25 万个单个细胞中收集了转录组数据。我们使用细胞系和合成 RNA 验证了该系统的灵敏度及其检测稀有群体的能力。我们对 68k 个外周血单核细胞进行了分析,以证明该系统能够对大型免疫群体进行特征分析。最后,我们使用转录组数据中的序列变异,从移植患者骨髓单核细胞中以单细胞分辨率确定宿主和供体嵌合体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef71/5241818/1032adf137b4/ncomms14049-f1.jpg

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