Stem cell transcriptome profiling via massive-scale mRNA sequencing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-07

AUTHORS

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

ABSTRACT

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. More... »

PAGES

613-619

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.1223

DOI

http://dx.doi.org/10.1038/nmeth.1223

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048586936

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18516046


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