Tweet
Just caught this last night. The good folks at The University of Queensland in collaboration with ABI have used SOLiD to take a close look at the transcriptome of mouse embryonic stem cells and embryoid bodies.
Stem cell transcriptome profiling via massive-scale mRNA sequencing
Nicole 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 Grimmond1
1 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.
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.
Nicole 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 Grimmond1
1 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.
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.