Nature Methods
- 5, 613 - 619 (2008)
Published online: 30 May 2008; | doi:10.1038/nmeth.1223
Stem cell transcriptome profiling via massive-scale mRNA sequencingNicole Cloonan1, 4, Alistair R R Forrest1, 3, 4, Gabriel Kolle1, 4, Brooke B A Gardiner1, Geoffrey J Faulkner1, Mellissa K Brown1, Darrin F Taylor1, Anita L Steptoe1, Shivangi Wani1, Graeme Bethel1, Alan J Robertson1, Andrew C Perkins1, Stephen J Bruce1, Clarence C Lee2, Swati S Ranade2, Heather E Peckham2, Jonathan M Manning2, Kevin J McKernan2 & Sean M Grimmond11
Expression Genomics Laboratory, Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St. Lucia, Queensland, 4072, Australia. 2
Applied Biosystems Inc., 500 Cummings Center, Beverly, Massachusetts 01915, USA. 3
Present address: The Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, 4111, Australia. 4
These authors contributed equally to this work.
Correspondence should be addressed to Sean M Grimmond s.grimmond@imb.uq.edu.au 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.
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