Article | Published:

mRNA-Seq whole-transcriptome analysis of a single cell

Nature Methods volume 6, pages 377382 (2009) | Download Citation

Subjects

This article has been updated

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Change history

  • 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.

Accessions

Gene Expression Omnibus

References

  1. 1.

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

  2. 2.

    & Sequence census methods for functional genomics. Nat. Methods 5, 19–21 (2008).

  3. 3.

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

  4. 4.

    & Transcriptome content and dynamics at single-nucleotide resolution. Genome Biol. 9, 234 (2008).

  5. 5.

    , & RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

  6. 6.

    , , , & Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

    , , , & RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

  11. 11.

    , , , & Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).

  12. 12.

    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).

  13. 13.

    , & A molecular programme for the specification of germ cell fate in mice. Nature 418, 293–300 (2002).

  14. 14.

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

  15. 15.

    , , , & Identification and characterization of subpopulations in undifferentiated ES cell culture. Development 135, 909–918 (2008).

  16. 16.

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

  17. 17.

    , , & Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protoc. 2, 739–752 (2007).

  18. 18.

    , , , & Requirement for ERK MAP kinase in mouse preimplantation development. Development 134, 2751–2759 (2007).

  19. 19.

    , , & Noise in eukaryotic gene expression. Nature 422, 633–637 (2003).

  20. 20.

    & Noise in gene expression: origins, consequences, and control. Science 309, 2010–2013 (2005).

  21. 21.

    , , & Dynamics of global gene expression changes during mouse preimplantation development. Dev. Cell 6, 117–131 (2004).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

    , , & 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).

  28. 28.

    , & Metabolism and regulation of canonical histone mRNAs: life without a poly(A) tail. Nat. Rev. Genet. 9, 843–854 (2008).

  29. 29.

    , , & 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. 30.

    , & New constructions for covering designs. J. Comb. Designs 3, 269–284 (1995).

Download references

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

Author notes

    • Fuchou Tang
    •  & Catalin Barbacioru

    These authors contributed equally to this work.

Affiliations

  1. Wellcome Trust–Cancer Research UK Gurdon Institute of Cancer and Developmental Biology, University of Cambridge, Cambridge, UK.

    • Fuchou Tang
    •  & M Azim Surani
  2. Molecular Cell Biology Division, Applied Biosystems, Foster City, California, USA.

    • Catalin Barbacioru
    • , Yangzhou Wang
    • , Ellen Nordman
    • , Clarence Lee
    • , Nanlan Xu
    • , Xiaohui Wang
    • , John Bodeau
    • , Brian B Tuch
    • , Asim Siddiqui
    •  & Kaiqin Lao

Authors

  1. Search for Fuchou Tang in:

  2. Search for Catalin Barbacioru in:

  3. Search for Yangzhou Wang in:

  4. Search for Ellen Nordman in:

  5. Search for Clarence Lee in:

  6. Search for Nanlan Xu in:

  7. Search for Xiaohui Wang in:

  8. Search for John Bodeau in:

  9. Search for Brian B Tuch in:

  10. Search for Asim Siddiqui in:

  11. Search for Kaiqin Lao in:

  12. Search for M Azim Surani in:

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.

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.

Corresponding authors

Correspondence to Kaiqin Lao or M Azim Surani.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12

Text files

  1. 1.

    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.

  2. 2.

    Supplementary Table 2

    The Ct values of 11 genes which were detected by microarrays but not detected by our mRNA-Seq.

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    Supplementary Table 5

    The Ct values of 8 potential novel junctions for blastomeres of four-cell stage embryos.

  6. 6.

    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.

  7. 7.

    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.

  8. 8.

    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.

Excel files

  1. 1.

    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.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.1315

Further reading