Stem cell transcriptome profiling via massive-scale mRNA sequencing

被引:730
作者
Cloonan, Nicole [1 ]
Forrest, Alistair R. R. [1 ]
Kolle, Gabriel [1 ]
Gardiner, Brooke B. A. [1 ]
Faulkner, Geoffrey J. [1 ]
Brown, Mellissa K. [1 ]
Taylor, Darrin F. [1 ]
Steptoe, Anita L. [1 ]
Wani, Shivangi [1 ]
Bethel, Graeme [1 ]
Robertson, Alan J. [1 ]
Perkins, Andrew C. [1 ]
Bruce, Stephen J. [1 ]
Lee, Clarence C. [2 ]
Ranade, Swati S. [2 ]
Peckham, Heather E. [2 ]
Manning, Jonathan M. [2 ]
McKernan, Kevin J. [2 ]
Grimmond, Sean M. [1 ]
机构
[1] Univ Queensland, Inst Mol Biosci, Express Genom Lab, St Lucia, Qld 4072, Australia
[2] Appl Biosyst Inc, Beverly, MA 01915 USA
基金
澳大利亚研究理事会;
关键词
D O I
10.1038/nmeth.1223
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:613 / 619
页数:7
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