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Stem cell transcriptome profiling via massive-scale mRNA sequencing

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.

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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.

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Gene Expression Omnibus

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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.

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Authors and Affiliations

Authors

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.

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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.

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

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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

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