Stem cell transcriptome profiling via massive-scale mRNA sequencing

N. Cloonan; A.R. Forrest; G. Kolle; B.B. Gardiner; G.J. Faulkner; M.K. Brown; D.F. Taylor; A.L. Steptoe; S. Wani; G. Bethel; A.J. Robertson; A.C. Perkins; S.J. Bruce; C.C. Lee; S.S. Ranade; H.E. Peckham; J.M. Manning; K.J. McKernan; S.M. Grimmond

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

Stem cell transcriptome profiling via massive-scale mRNA sequencing

N. Cloonan, A.R. Forrest, G. Kolle, B.B. Gardiner, G.J. Faulkner, M.K. Brown, D.F. Taylor, A.L. Steptoe, S. Wani, G. Bethel, A.J. Robertson, A.C. Perkins, S.J. Bruce, C.C. Lee, S.S. Ranade, H.E. Peckham, J.M. Manning, K.J. McKernan, S.M. Grimmond

Royal (Dick) School of Veterinary Studies

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

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.

Original language	Undefined/Unknown
Pages (from-to)	613-619
Number of pages	7
Journal	Nature Methods
Volume	5
Issue number	7
DOIs	https://doi.org/10.1038/nmeth.1223
Publication status	Published - 2008

Keywords / Materials (for Non-textual outputs)

Animals Cell Differentiation Embryonic Stem Cells/cytology/ metabolism Expressed Sequence Tags Gene Expression Profiling/ methods/statistics & numerical data Gene Library Mice Pluripotent Stem Cells/cytology/metabolism Polymorphism, Single Nucleotide RNA, Messenger/ genetics Sensitivity and Specificity Sequence Analysis, RNA/ methods Signal Transduction

Access to Document

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

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

Cite this

Cloonan, N., Forrest, A. R., Kolle, G., Gardiner, B. B., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., Robertson, A. J., Perkins, A. C., Bruce, S. J., Lee, C. C., Ranade, S. S., Peckham, H. E., Manning, J. M., McKernan, K. J., & Grimmond, S. M. (2008). Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods, 5(7), 613-619. https://doi.org/10.1038/nmeth.1223

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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.",

keywords = "Animals Cell Differentiation Embryonic Stem Cells/cytology/ metabolism Expressed Sequence Tags Gene Expression Profiling/ methods/statistics & numerical data Gene Library Mice Pluripotent Stem Cells/cytology/metabolism Polymorphism, Single Nucleotide RNA, Messenger/ genetics Sensitivity and Specificity Sequence Analysis, RNA/ methods Signal Transduction",

author = "N. Cloonan and A.R. Forrest and G. Kolle and B.B. Gardiner and G.J. Faulkner and M.K. Brown and D.F. Taylor and A.L. Steptoe and S. Wani and G. Bethel and A.J. Robertson and A.C. Perkins and S.J. Bruce and C.C. Lee and S.S. Ranade and H.E. Peckham and J.M. Manning and K.J. McKernan and S.M. Grimmond",

note = "18516046 Cloonan, Nicole Forrest, Alistair R R Kolle, Gabriel Gardiner, Brooke B A Faulkner, Geoffrey J Brown, Mellissa K Taylor, Darrin F Steptoe, Anita L Wani, Shivangi Bethel, Graeme Robertson, Alan J Perkins, Andrew C Bruce, Stephen J Lee, Clarence C Ranade, Swati S Peckham, Heather E Manning, Jonathan M McKernan, Kevin J Grimmond, Sean M Research Support, Non-U.S. Gov't United States Nature methods Nat Methods. 2008 Jul;5(7):613-9. Epub 2008 May 30.",

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Cloonan, N, Forrest, AR, Kolle, G, Gardiner, BB, Faulkner, GJ, Brown, MK, Taylor, DF, Steptoe, AL, Wani, S, Bethel, G, Robertson, AJ, Perkins, AC, Bruce, SJ, Lee, CC, Ranade, SS, Peckham, HE, Manning, JM, McKernan, KJ & Grimmond, SM 2008, 'Stem cell transcriptome profiling via massive-scale mRNA sequencing', Nature Methods, vol. 5, no. 7, pp. 613-619. https://doi.org/10.1038/nmeth.1223

TY - JOUR

T1 - Stem cell transcriptome profiling via massive-scale mRNA sequencing

AU - Cloonan, N.

AU - Forrest, A.R.

AU - Kolle, G.

AU - Gardiner, B.B.

AU - Faulkner, G.J.

AU - Brown, M.K.

AU - Taylor, D.F.

AU - Steptoe, A.L.

AU - Wani, S.

AU - Bethel, G.

AU - Robertson, A.J.

AU - Perkins, A.C.

AU - Bruce, S.J.

AU - Lee, C.C.

AU - Ranade, S.S.

AU - Peckham, H.E.

AU - Manning, J.M.

AU - McKernan, K.J.

AU - Grimmond, S.M.

N1 - 18516046 Cloonan, Nicole Forrest, Alistair R R Kolle, Gabriel Gardiner, Brooke B A Faulkner, Geoffrey J Brown, Mellissa K Taylor, Darrin F Steptoe, Anita L Wani, Shivangi Bethel, Graeme Robertson, Alan J Perkins, Andrew C Bruce, Stephen J Lee, Clarence C Ranade, Swati S Peckham, Heather E Manning, Jonathan M McKernan, Kevin J Grimmond, Sean M Research Support, Non-U.S. Gov't United States Nature methods Nat Methods. 2008 Jul;5(7):613-9. Epub 2008 May 30.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Animals Cell Differentiation Embryonic Stem Cells/cytology/ metabolism Expressed Sequence Tags Gene Expression Profiling/ methods/statistics & numerical data Gene Library Mice Pluripotent Stem Cells/cytology/metabolism Polymorphism, Single Nucleotide RNA,

U2 - http://dx.doi.org/10.1038/nmeth.1223

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

M3 - Article

SN - 1548-7105

VL - 5

SP - 613

EP - 619

JO - Nature Methods

JF - Nature Methods

IS - 7

ER -