Details

Autor(en) / Beteiligte

• Perkins, Andrew C
• Taylor, Darrin F
• Manning, Jonathan M
• Lee, Clarence C
• Wani, Shivangi
• McKernan, Kevin J
• Bethel, Graeme
• Gardiner, Brooke B A
• Peckham, Heather E
• Robertson, Alan J
• Bruce, Stephen J
• Ranade, Swati S
• Faulkner, Geoffrey J
• Brown, Mellissa K
• Forrest, Alistair R R
• Grimmond, Sean M
• Cloonan, Nicole
• Steptoe, Anita L
• Kolle, Gabriel

Titel

Stem cell transcriptome profiling via massive-scale mRNA sequencing

Ist Teil von

• Nature methods, 2008-07, Vol.5 (7), p.613-619

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2008

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.