生物
胚胎干细胞
干细胞
细胞生物学
单细胞测序
核糖核酸
计算生物学
RNA序列
转录组
单细胞分析
人口
基因
基因表达
诱导多能干细胞
遗传学
再生医学
电池类型
细胞分化
细胞
表型
重编程
成体干细胞
外显子组测序
社会学
人口学
作者
Allon M. Klein,Linas Mažutis,Ilke Akartuna,Naren Tallapragada,Adrian Veres,Victor C. Li,Leonid Peshkin,David A. Weitz,Marc W. Kirschner
出处
期刊:Cell
[Elsevier]
日期:2015-05-01
卷期号:161 (5): 1187-1201
被引量:2793
标识
DOI:10.1016/j.cell.2015.04.044
摘要
It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.
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