Deep-transcriptome and ribonome sequencing redefines the molecular networks of pluripotency and the extracellular space in human embryonic stem cells
- Gabriel Kolle1,
- Jill L. Shepherd1,
- Brooke Gardiner1,
- Karin S. Kassahn1,
- Nicole Cloonan1,
- David L.A. Wood1,
- Ehsan Nourbakhsh1,
- Darrin F. Taylor1,
- Shivangi Wani1,
- Hun S. Chy2,
- Qi Zhou2,
- Kevin McKernan3,
- Scott Kuersten3,
- Andrew L. Laslett2,4 and
- Sean M. Grimmond1,5
- 1Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, Queensland 4072, Australia;
- 2CSIRO Materials Science and Engineering, Clayton, Victoria 3168, Australia;
- 3Life Technologies, Beverly, Massachusetts 01915, USA;
- 4Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria 3800, Australia
Abstract
Recent RNA-sequencing studies have shown remarkable complexity in the mammalian transcriptome. The ultimate impact of this complexity on the predicted proteomic output is less well defined. We have undertaken strand-specific RNA sequencing of multiple cellular RNA fractions (>20 Gb) to uncover the transcriptional complexity of human embryonic stem cells (hESCs). We have shown that human embryonic stem (ES) cells display a high degree of transcriptional diversity, with more than half of active genes generating RNAs that differ from conventional gene models. We found evidence that more than 1000 genes express long 5′ and/or extended 3′UTRs, which was confirmed by “virtual Northern” analysis. Exhaustive sequencing of the membrane-polysome and cytosolic/untranslated fractions of hESCs was used to identify RNAs encoding peptides destined for secretion and the extracellular space and to demonstrate preferential selection of transcription complexity for translation in vitro. The impact of this newly defined complexity on known gene-centric network models such as the Plurinet and the cell surface signaling machinery in human ES cells revealed a significant expansion of known transcript isoforms at play, many predicting possible alternative functions based on sequence alterations within key functional domains.
Footnotes
-
↵5 Corresponding author.
E-mail s.grimmond{at}imb.uq.edu.au.
-
[Supplemental material is available for this article.]
-
Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.119321.110.
- Received December 21, 2010.
- Accepted August 23, 2011.
- Copyright © 2011 by Cold Spring Harbor Laboratory Press
Freely available online through the Genome Research Open Access option.











