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Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data

  • Yuanhua Huang
  • Guido SanguinettiEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1935)

Abstract

Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. In this chapter, we review the challenges in splicing isoform quantification in scRNA-seq data and discuss BRIE (Bayesian regression for isoform estimation), a recently proposed Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from sequence features. We illustrate the usage of BRIE with a case study on 130 mouse cells during gastrulation.

Key words

Alternative splicing Isoform quantification Single-cell RNA-seq Bayesian model 

References

  1. 1.
    Grün D, van Oudenaarden A (2015) Design and analysis of single-cell sequencing experiments. Cell 163:799–810CrossRefGoogle Scholar
  2. 2.
    Grün D, Lyubimova A, Kester L et al (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251–255CrossRefGoogle Scholar
  3. 3.
    Gaublomme JT, Yosef N, Lee Y et al (2015) Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163:1400–1412CrossRefGoogle Scholar
  4. 4.
    Papalexi E, Satija R (2018) Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 18:35CrossRefGoogle Scholar
  5. 5.
    Scialdone A, Tanaka Y, Jawaid W et al (2016) Resolving early mesoderm diversification through single-cell expression profiling. Nature 535:289–293.  https://doi.org/10.1038/nature18633CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Wagner DE, Weinreb C, Collins ZM et al (2018) Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 80:eaar4362Google Scholar
  7. 7.
    Stubbington MJT, Lönnberg T, Proserpio V et al (2016) T cell fate and clonality inference from single-cell transcriptomes. Nat Methods 13:329CrossRefGoogle Scholar
  8. 8.
    Lönnberg T, Svensson V, James KR et al (2017) Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci Immunol 2(9):eaal2192CrossRefGoogle Scholar
  9. 9.
    Patel AP, Tirosh I, Trombetta JJ et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401CrossRefGoogle Scholar
  10. 10.
    Tirosh I, Izar B, Prakadan SM et al (2016) Dissecting the multicellular exosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–196.  https://doi.org/10.1126/science.aad0501.DissectingCrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Wang ET, Sandberg R, Luo S et al (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476CrossRefGoogle Scholar
  12. 12.
    Baralle FE, Giudice J (2017) Alternative splicing as a regulator of development and tissue identity. Nat Rev Mol Cell Biol 18:437CrossRefGoogle Scholar
  13. 13.
    Dillman AA, Hauser DN, Gibbs JR et al (2013) mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat Neurosci 16:499CrossRefGoogle Scholar
  14. 14.
    Scotti MM, Swanson MS (2016) RNA mis-splicing in disease. Nat Rev Genet 17:19CrossRefGoogle Scholar
  15. 15.
    Ziegenhain C, Vieth B, Parekh S et al (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65:631–643CrossRefGoogle Scholar
  16. 16.
    Faigenbloom L, Rubinstein ND, Kloog Y et al (2015) Regulation of alternative splicing at the single-cell level. Mol Syst Biol 11:845CrossRefGoogle Scholar
  17. 17.
    Song Y, Botvinnik OB, Lovci MT et al (2017) Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol Cell 67:148–161CrossRefGoogle Scholar
  18. 18.
    La Manno G, Soldatov R, Hochgerner H et al (2018) RNA velocity of single cells. Nature 560.7719:494CrossRefGoogle Scholar
  19. 19.
    Linker SM, Urban L, Clark S et al (2018) Combined single cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity. bioRxiv:328138Google Scholar
  20. 20.
    Huang Y, Sanguinetti G (2017) BRIE: transcriptome-wide splicing quantification in single cells. Genome Biol 18:123.  https://doi.org/10.1101/098517CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Huang Y, Sanguinetti G (2016) Statistical modeling of isoform splicing dynamics from RNA-seq time series data. Bioinformatics 32:2965–2972CrossRefGoogle Scholar
  22. 22.
    Liu P, Sanalkumar R, Bresnick EH et al (2016) Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Genome Res 26:1124–1133CrossRefGoogle Scholar
  23. 23.
    Xiong HY, Alipanahi B, Lee LJ et al (2015) The human splicing code reveals new insights into the genetic determinants of disease. Science 1254806:347Google Scholar
  24. 24.
    Katz Y, Wang ET, Airoldi EM, Burge CB (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods 7:1009–1015CrossRefGoogle Scholar
  25. 25.
    Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515CrossRefGoogle Scholar
  26. 26.
    Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525CrossRefGoogle Scholar
  27. 27.
    Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357CrossRefGoogle Scholar
  28. 28.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21CrossRefGoogle Scholar
  29. 29.
    Katz Y, Wang ET, Silterra J et al (2015) Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics 31:2400–2402CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.EMBL-European Bioinformatics InstituteCambridgeshireUK
  2. 2.School of InformaticsUniversity of EdinburghEdinburghUK

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