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Transcriptome Profiling of Single Mouse Oocytes

  • Maud BorenszteinEmail author
  • Laurène Syx
  • Nicolas ServantEmail author
  • Edith Heard
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1818)

Abstract

Single-cell RNA-sequencing (scRNAseq) enables the detection and quantification of mature RNAs in an individual cell. Assessing single cell transcriptomes can circumvent the limited amount of starting material obtained in oocytes or embryos, in particular when working with mutant mice. Here we outline our scRNAseq protocol to study mouse oocyte transcriptomes, derived from Tang et al., Nat Methods 6(5):377–382, 2009 . The method describes the different steps from single cell isolation and cDNA amplification to high-throughput sequencing. The bioinformatics pipeline used to analyze and compare genome-wide gene expression between individual oocytes is then described.

Key words

Single-cell RNA-sequencing Maternal pool Gene expression Single-cell bioinformatics pipeline 

References

  1. 1.
    Tang F, Barbacioru C, Wang Y et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382. https://doi.org/10.1038/NMETH.1315 CrossRefPubMedGoogle Scholar
  2. 2.
    Tang F, Barbacioru C, Nordman E et al (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 5(3):516–535. https://doi.org/10.1038/nprot.2009.236 CrossRefPubMedGoogle Scholar
  3. 3.
    Tang F, Lao K, Surani MA (2011) Development and applications of single-cell transcriptome analysis. Nat Methods 8(4):S6–S11. https://doi.org/10.1038/NMETH.1557 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Ancelin K, Syx L, Borensztein M et al (2016) Maternal LSD1/KDM1A is an essential regulator of chromatin and transcription landscapes during zygotic genome activation. elife 5:e08851. https://doi.org/10.7554/eLife.08851 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Borensztein M, Syx L, Ancelin K et al (2017) Xist-dependent imprinted X inactivation and the early developmental consequences of its failure. Nat Struct Mol Biol 24(3):226–233. https://doi.org/10.1038/nsmb.3365 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Borensztein M, Okamoto I, Syx L et al (2017) Contribution of epigenetic landscapes and transcription factors to X-chromosome reactivation in the inner cell mass. Nat Commun 8:1–14. https://doi.org/10.1038/s41467-017-01415-5 CrossRefGoogle Scholar
  7. 7.
    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4):R36. https://doi.org/10.1186/gb-2013-14-4-r36 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Nagy A, Gertsenstein M, Vintersten K, Behringer R (2003) Manipulating the mouse embryo: a laboratory manual. Cold Spring Harbor Laboratory Press, New YorkGoogle Scholar
  9. 9.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111. https://doi.org/10.1093/bioinformatics/btp120 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Liao Y, Smyth GK, Shi W (2014) Sequence analysis featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930. https://doi.org/10.1093/bioinformatics/btt656 CrossRefPubMedGoogle Scholar
  11. 11.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(550):1–21. https://doi.org/10.1186/s13059-014-0550-8 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, UPMC Paris-SorbonneParisFrance
  2. 2.Institut Curie, PSL Research University, Mines Paris Tech, INSERM U900ParisFrance

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