Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Stem cell transcriptome profiling via massive-scale mRNA sequencing

Nature Methods volume 5, pages 613–619 (2008)Cite this article

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic of SQRL method.
Figure 2: Visualization of SQRL data on the UCSC genome browser.
Figure 3: Analysis of the quantitative nature and sensitivity levels of SQRL.
Figure 4: Potential transcript variants for genes encoding components of the TGFB pathway.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Boguski, M.S. & Schuler, G.D. Establishing a human transcript map. Nat. Genet. 10, 369–371 (1995).

    Article  CAS  Google Scholar 

  2. Lee, Y. et al. The TIGR gene indices: clustering and assembling EST and known genes and integration with eukaryotic genomes. Nucleic Acids Res. 33, D71–D74 (2005).

    Article  CAS  Google Scholar 

  3. Carninci, P. et al. The transcriptional landscape of the mammalian genome. Science 309, 1559–1563 (2005).

    Article  CAS  Google Scholar 

  4. Kawai, J. et al. Functional annotation of a full-length mouse cDNA collection. Nature 409, 685–690 (2001).

    Article  Google Scholar 

  5. Okazaki, Y. et al. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420, 563–573 (2002).

    Article  Google Scholar 

  6. Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

    Article  CAS  Google Scholar 

  7. Cawley, S. et al. Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116, 499–509 (2004).

    Article  CAS  Google Scholar 

  8. Engstrom, P.G. et al. Complex loci in human and mouse genomes. PLoS Genet. 2, 564–577 (2006).

    Article  CAS  Google Scholar 

  9. Kapranov, P. et al. RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488 (2007).

    Article  CAS  Google Scholar 

  10. Gardina, P.J. et al. Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics 7, 325 (2006).

    Article  Google Scholar 

  11. Clark, T.A., Sugnet, C.W. & Ares, M. Genome-wide analysis of mRNA processing in yeast using splicing-specific microarrays. Science 296, 907–910 (2002).

    Article  CAS  Google Scholar 

  12. Reinartz, J. et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Brief. Funct. Genomic. Proteomic. 1, 95–104 (2002).

    Article  CAS  Google Scholar 

  13. Velculescu, V.E. et al. Serial analysis of gene expression. Science 270, 484–487 (1995).

    Article  CAS  Google Scholar 

  14. Shiraki, T. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl. Acad. Sci. USA 100, 15776–15781 (2003).

    Article  CAS  Google Scholar 

  15. Kim, J.B. et al. Polony multiplex analysis of gene expression (PMAGE) in mouse hypertrophic cardiomyopathy. Science 316, 1481–1484 (2007).

    Article  CAS  Google Scholar 

  16. Bruce, S.J. et al. Dynamic transcription programs during ES cell differentiation towards mesoderm in serum versus serum-free (BMP4) culture. BMC Genomics 8, 365 (2007).

    Article  Google Scholar 

  17. Hirst, C.E. et al. Transcriptional profiling of mouse and human ES cells identifies SLAIN1, a novel stem cell gene. Dev. Biol. 293, 90–103 (2006).

    Article  CAS  Google Scholar 

  18. Faulkner, G.J. et al. A rescue strategy for multimapping short sequence tags refines surveys of transcriptional activity by CAGE. Genomics 91, 281–288 (2008).

    Article  CAS  Google Scholar 

  19. Karolchik, D. et al. The UCSC genome browser database: 2008 update. Nucleic Acids Res. 36 (Suppl. 1), D773–779 (2008).

    CAS  PubMed  Google Scholar 

  20. Thierry-Mieg, D. & Thierry-Mieg, J. AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol. 7 (Suppl. 1), S12 (2006).

    Article  Google Scholar 

  21. Kiyosawa, H. et al. Disclosing hidden transcripts: mouse natural sense-antisense transcripts tend to be poly(A) negative and nuclear localized. Genome Res. 15, 463–474 (2005).

    Article  CAS  Google Scholar 

  22. Jeck, W.R. et al. Extending assembly of short DNA sequences to handle error. Bioinformatics 23, 2942–2944 (2007).

    Article  CAS  Google Scholar 

  23. Kent, W.J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    Article  CAS  Google Scholar 

  24. Zhou, Q. et al. A gene regulatory network in mouse embryonic stem cells. Proc. Natl. Acad. Sci. USA 104, 16438–16443 (2007).

    Article  CAS  Google Scholar 

  25. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

    Article  CAS  Google Scholar 

  26. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science published online, doi:10.1126/science.1158441 (1 May 2008).

  27. Bruce, S.J. et al. In vitro differentiation of murine embryonic stem cells toward a renal lineage. Differentiation 75, 337–349 (2007).

    Article  CAS  Google Scholar 

  28. Matz, M. et al. Amplification of cDNA ends based on template-switching effect and step-out PCR. Nucleic Acids Res. 27, 1558–1560 (1999).

    Article  CAS  Google Scholar 

  29. Ohtake, H. et al. Determination of the capped site sequence of mRNA based on the detection of Cap-dependent nucleotide addition using an anchor ligation method. DNA Res. 11, 305–309 (2004).

    Article  CAS  Google Scholar 

  30. Schmidt, W.M. & Mueller, M.W. CapSelect: a highly sensitive method for 5′ CAP–dependent enrichment of full-length cDNA in PCR-mediated analysis of mRNAs. Nucleic Acids Res. 27, e31 (1999).

    Article  CAS  Google Scholar 

  31. Shendure, J. et al. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 1728–1732 (2005).

    Article  CAS  Google Scholar 

  32. Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

A.R.R.F. and S.M.G. are funded by the Australian National Health and Medical Research Council. A.R.R.F. is a C.J. Martin fellow (428261), and S.M.G. is a senior research fellow. N.C. is a University of Queensland postdoctoral fellow. G.J.F. and M.K.B. are supported by Australian Postgraduate awards. We acknowledge the Australian Research Council Centre for Functional and Applied Genomics Array Facility for expression profiling, and R. Nutter, G. Weightman and L. Stubberfield for support of this initiative.

Author information

Author notes

  1. Alistair R R Forrest

    Present address: Present address: The Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, 4111, Australia.,

  2. Nicole Cloonan, Alistair R R Forrest and Gabriel Kolle: These authors contributed equally to this work.

Authors and Affiliations

  1. Expression Genomics Laboratory, Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St. Lucia, 4072, Queensland, Australia

    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 & Sean M Grimmond

  2. Applied Biosystems Inc., 500 Cummings Center, Beverly, 01915, Massachusetts, USA

    Clarence C Lee, Swati S Ranade, Heather E Peckham, Jonathan M Manning & Kevin J McKernan

Authors
  1. Nicole Cloonan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alistair R R Forrest
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel Kolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brooke B A Gardiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Geoffrey J Faulkner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mellissa K Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Darrin F Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anita L Steptoe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shivangi Wani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Graeme Bethel
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alan J Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Andrew C Perkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Stephen J Bruce
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Clarence C Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Swati S Ranade
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Heather E Peckham
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jonathan M Manning
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Kevin J McKernan
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Sean M Grimmond
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.C. created and integrated the sequence mapping and visualization pipeline, performed SOLiD sequencing bioinformatics, SNP analysis and splicing studies. A.R.R.F. conceived and pioneered the SQRL library strategy, performed preliminary genomic analysis, and developed the initial visualization methods. G.K. led the array-SQRL analyses, RT-PCR, pathway analysis and contributed to SNP analysis. B.B.A.G., M.K.B., G.K. and N.C. contributed to method design. A.L.S., G.K., S.J.B. and A.C.P. contributed to sample generation. G.K., A.R.R.F. and B.B.A.G. constructed libraries. C.C.L., S.S.R., B.B.A.G., G.B. and K.J.M. contributed to library sequencing. N.C., G.K., G.J.F., A.R.R.F., S.M.G., D.F.T., H.E.P. and J.M.M. contributed to data analysis. G.K., A.J.R., S.W., N.C. and A.L.S. contributed to experimental validation. S.M.G. supervised the work and prepared the manuscript with N.C., G.K. and A.R.R.F.

Corresponding author

Correspondence to Sean M Grimmond.

Ethics declarations

Competing interests

C.C.L., S.S.R., H.E.P., J.M.M. and K.J.M. are employed by Applied Biosystems, a manufacturer of DNA sequencing instrumentation and supplies.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–16, Supplementary Methods (PDF 3878 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cloonan, N., Forrest, A., Kolle, G. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5, 613–619 (2008). https://doi.org/10.1038/nmeth.1223

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1223

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing