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mRNA-Seq whole-transcriptome analysis of a single cell

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Abstract

Next-generation sequencing technology is a powerful tool for transcriptome analysis. However, under certain conditions, only a small amount of material is available, which requires more sensitive techniques that can preferably be used at the single-cell level. Here we describe a single-cell digital gene expression profiling assay. Using our mRNA-Seq assay with only a single mouse blastomere, we detected the expression of 75% (5,270) more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads. Moreover, 8–19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte, which unambiguously demonstrated the complexity of the transcript variants at whole-genome scale in individual cells. Finally, for Dicer1−/− and Ago2−/− (Eif2c2−/−) oocytes, we found that 1,696 and 1,553 genes, respectively, were abnormally upregulated compared to wild-type controls, with 619 genes in common.

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Figure 1: Schematic of the single-cell whole-transcriptome analysis.
Figure 2: mRNA-Seq of single blastomeres and oocytes.
Figure 3: Comparison of mRNA-Seq and microarray assays.
Figure 4: Correlation plots of the quantile-normalized mRNA-Seq reads for oocytes.
Figure 5: Coverage plots.

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Gene Expression Omnibus

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  • 19 April 2009

    In the version of this article initially published online, Figure 2d was a duplicate of Figure 2c. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. Mardis, E.R. The impact of next-generation sequencing technology on genetics. Trends Genet. 24, 133–141 (2008).

    Article  CAS  Google Scholar 

  2. Wold, B. & Myers, R.M. Sequence census methods for functional genomics. Nat. Methods 5, 19–21 (2008).

    Article  CAS  Google Scholar 

  3. Schuster, S.C. Next-generation sequencing transforms today's biology. Nat. Methods 5, 16–18 (2008).

    Article  CAS  Google Scholar 

  4. Cloonan, N. & Grimmond, S.M. Transcriptome content and dynamics at single-nucleotide resolution. Genome Biol. 9, 234 (2008).

    Article  Google Scholar 

  5. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  Google Scholar 

  6. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  Google Scholar 

  7. Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).

    Article  CAS  Google Scholar 

  8. Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

    Article  CAS  Google Scholar 

  9. Wang, E.T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

    Article  CAS  Google Scholar 

  10. Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    Article  CAS  Google Scholar 

  11. Pan, Q., Shai, O., Lee, L.J., Frey, B.J. & Blencowe, B.J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).

    Article  CAS  Google Scholar 

  12. Li, H. et al. Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model. Proc. Natl. Acad. Sci. USA 105, 20179–20184 (2008).

    Article  CAS  Google Scholar 

  13. Saitou, M., Barton, S.C. & Surani, M.A. A molecular programme for the specification of germ cell fate in mice. Nature 418, 293–300 (2002).

    Article  CAS  Google Scholar 

  14. Chambers, I. et al. Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230–1234 (2007).

    Article  CAS  Google Scholar 

  15. Toyooka, Y., Shimosato, D., Murakami, K., Takahashi, K. & Niwa, H. Identification and characterization of subpopulations in undifferentiated ES cell culture. Development 135, 909–918 (2008).

    Article  CAS  Google Scholar 

  16. Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).

    Article  Google Scholar 

  17. Kurimoto, K., Yabuta, Y., Ohinata, Y. & Saitou, M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protoc. 2, 739–752 (2007).

    Article  CAS  Google Scholar 

  18. Maekawa, M., Yamamoto, T., Kohno, M., Takeichi, M. & Nishida, E. Requirement for ERK MAP kinase in mouse preimplantation development. Development 134, 2751–2759 (2007).

    Article  CAS  Google Scholar 

  19. Blake, W.J., Kærn, M., Cantor, C.R. & Collins, J.J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003).

    Article  CAS  Google Scholar 

  20. Raser, J.M. & O'Shea, E.K. Noise in gene expression: origins, consequences, and control. Science 309, 2010–2013 (2005).

    Article  CAS  Google Scholar 

  21. Hamatani, T., Carter, M.G., Sharov, A.A. & Ko, M.S. Dynamics of global gene expression changes during mouse preimplantation development. Dev. Cell 6, 117–131 (2004).

    Article  CAS  Google Scholar 

  22. Tang, F. et al. Maternal microRNAs are essential for mouse zygotic development. Genes Dev. 21, 644–648 (2007).

    Article  CAS  Google Scholar 

  23. Murchison, E.P. et al. Critical roles for Dicer in the female germline. Genes Dev. 21, 682–693 (2007).

    Article  CAS  Google Scholar 

  24. O'Carroll, D. et al. A Slicer-independent role for Argonaute 2 in hematopoiesis and the microRNA pathway. Genes Dev. 21, 1999–2004 (2007).

    Article  CAS  Google Scholar 

  25. de Vries, W.N. et al. Expression of Cre recombinase in mouse oocytes: A means to study maternal effect genes. Genesis 26, 110–112 (2000).

    Article  CAS  Google Scholar 

  26. Tam, O.H. et al. Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 453, 534–538 (2008).

    Article  CAS  Google Scholar 

  27. Rambhatla, L., Patel, B., Dhanasekaran, N. & Latham, K.E. Analysis of G protein alpha subunit mRNA abundance in preimplantation mouse embryos using a rapid, quantitative RT-PCR approach. Mol. Reprod. Dev. 41, 314–324 (1995).

    Article  CAS  Google Scholar 

  28. Marzluff, W.F., Wagner, E.J. & Duronio, R.J. Metabolism and regulation of canonical histone mRNAs: life without a poly(A) tail. Nat. Rev. Genet. 9, 843–854 (2008).

    Article  CAS  Google Scholar 

  29. Nagy, A., Gertsenstein, M., Vintersten, K. & Behringer, R. Recovery and in vitro culture of preimplantation stage embryos. in Manipulating the Mouse Embryo 3rd edn. 194–200 (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, 2003).

  30. Gordon, D.M., Patashnik, O. & Kuperberg, G. New constructions for covering designs. J. Comb. Designs 3, 269–284 (1995).

    Article  Google Scholar 

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Acknowledgements

We thank C. Lee for excellent technical help. The work was supported by grants from the Wellcome Trust to M.A.S.

Author information

Authors and Affiliations

Authors

Contributions

K.L. designed the project. C.B., B.B.T., A.S., X.W. and K.L. contributed to data analysis, F.T. and M.A.S. contributed to the cDNA sample preparation, E.N., N.X. and Y.W. constructed libraries, C.L. and J.B. contributed to the library sequencing, F.T., E.N. and K.L. contributed to experimental validation, F.T., K.L. and M.A.S. wrote manuscript.

Corresponding authors

Correspondence to Kaiqin Lao or M Azim Surani.

Ethics declarations

Competing interests

C.B., Y.W., E.N., C.L., N.X., X.W., J.B., B.B.T., A.S., and K.L. are currently employees of Applied Biosystems.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 (PDF 1416 kb)

Supplementary Table 1

The number of reads for RefSeq transcripts of single wild-type, Dicer1−/−, Ago2−/− mature oocytes, and blastomeres of four-cell stage embryos. (TXT 2076 kb)

Supplementary Table 2

The Ct values of 11 genes which were detected by microarrays but not detected by our mRNA-Seq. (TXT 0 kb)

Supplementary Table 3

The Ct values of 380 early embryo marker genes of single wild-type, Dicer1−/−, Ago2−/− mature oocytes, and a single blastomere at the four-cell embryo stage. The corresponding number of reads for these genes by mRNA-Seq is also listed. (TXT 60 kb)

Supplementary Table 4

The number of reads for potential novel exon-exon junctions of RefSeq transcripts of single wild-type, Dicer1−/−, Ago2−/− mature oocytes, and blastomeres of four-cell stage embryos. (TXT 3315 kb)

Supplementary Table 5

The Ct values of 8 potential novel junctions for blastomeres of four-cell stage embryos. (TXT 1 kb)

Supplementary Table 6

The number of reads for RefSeq transcripts with multiple known transcript isoforms of single wild-type, Dicer1−/−, Ago2−/− mature oocytes, and a blastomere at the four-cell embryo stage. (TXT 141 kb)

Supplementary Table 7

The Ct values of exon-22, exon-23, and exon-24 specific real time PCR assays for the Dicer1 gene to confirm the deletion of exon 23 in the Dicer1−/− oocyte. (TXT 0 kb)

Supplementary Table 8

The fold changes, p-values, and FDR values of expressed genes in Dicer1−/− and Ago2−/− mature oocytes compared with wild-type controls based on global quanta normalized reads of single cell mRNA-Seq. We first quantile normalized the mRNA-Seq reads, then, used the Poisson model for the counts of reads for each transcript10 and a goodness-of-fit test to identify differentially expressed genes between samples, controlling false discovery rate (FDR) at a 5% level. (TXT 5136 kb)

Supplementary Table 9

Sequences of the primers used for single cell mRNA-Seq. P1 and P2 Adaptors are same as the SOLiD P1 and P2 Adaptors for library preparation; Library PCR Primer 1 and 2 – SOLiD library amplification primer sets. Amine modification at the 5' end prevents the ligation of the 5' end fragments of the double-stranded cDNA (after the shearing) to the SOLiD library adaptors, thereby eliminating end bias during sequencing. (XLS 17 kb)

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Tang, F., Barbacioru, C., Wang, Y. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6, 377–382 (2009). https://doi.org/10.1038/nmeth.1315

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