Stem cell transcriptome profiling via massive-scale mRNA sequencing
Stem cell transcriptome profiling via massive-scale mRNA sequencing
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DOI：
10.1038/nmeth.1223
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发表时间：
2008-07-01
期刊：
影响因子：
48
通讯作者：
Grimmond, Sean M.
中科院分区：
文献类型：
--
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
我们开发了一个庞大的RNA测序方案，简短的定量随机RNA库或SQRL，以几乎完整的方式调查转录组的复杂性，动力学和序列含量。该方法生成了定向，随机提出的线性cDNA库，可针对下一代短标签测序进行优化。我们使用Applied Biosystems Solid Technologh，我们调查了未分化的小鼠胚胎干细胞（ESC）和胚胎体（EBS）的poly（a）（+）转录组。这些文库捕获了表达，状态特异性表达，单核苷酸多态性（SNP），重复元素的转录活性以及已知和新的替代剪接事件的基因组景观。我们调查了转录复杂性对控制ESC多能和分化的关键信号通路模型的影响完全的。
We developed a massive-scale RNA sequencing protocol, short quantitative random RNA libraries or SQRL, to survey the complexity, dynamics and sequence content of transcriptomes in a near-complete fashion. This method generates directional, random-primed, linear cDNA libraries that are optimized for next-generation short-tag sequencing. We surveyed the poly(A)(+) transcriptomes of undifferentiated mouse embryonic stem cells (ESCs) and embryoid bodies (EBs) at an unprecedented depth (10 Gb), using the Applied Biosystems SOLiD technology. These libraries capture the genomic landscape of expression, state-specific expression, single-nucleotide polymorphisms (SNPs), the transcriptional activity of repeat elements, and both known and new alternative splicing events. We investigated the impact of transcriptional complexity on current models of key signaling pathways controlling ESC pluripotency and differentiation, highlighting how SQRL can be used to characterize transcriptome content and dynamics in a quantitative and reproducible manner, and suggesting that our understanding of transcriptional complexity is far from complete.