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Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq

Nature Protocols volume 11, pages 2081–2103 (2016)Cite this article

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Abstract

Parallel sequencing of a single cell's genome and transcriptome provides a powerful tool for dissecting genetic variation and its relationship with gene expression. Here we present a detailed protocol for G&T-seq, a method for separation and parallel sequencing of genomic DNA and full-length polyA(+) mRNA from single cells. We provide step-by-step instructions for the isolation and lysis of single cells; the physical separation of polyA(+) mRNA from genomic DNA using a modified oligo-dT bead capture and the respective whole-transcriptome and whole-genome amplifications; and library preparation and sequence analyses of these amplification products. The method allows the detection of thousands of transcripts in parallel with the genetic variants captured by the DNA-seq data from the same single cell. G&T-seq differs from other currently available methods for parallel DNA and RNA sequencing from single cells, as it involves physical separation of the DNA and RNA and does not require bespoke microfluidics platforms. The process can be implemented manually or through automation. When performed manually, paired genome and transcriptome sequencing libraries from eight single cells can be produced in ∼3 d by researchers experienced in molecular laboratory work. For users with experience in the programming and operation of liquid-handling robots, paired DNA and RNA libraries from 96 single cells can be produced in the same time frame. Sequence analysis and integration of single-cell G&T-seq DNA and RNA data requires a high level of bioinformatics expertise and familiarity with a wide range of informatics tools.

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Figure 1: Stepwise overview of the G&T-seq method.
Figure 2: Representative Agilent Bioanalyzer electropherogram plots from various stages of single-cell RNA and DNA amplification and library preparation.
Figure 3: Single-cell DNA-copy-number profiles.
Figure 4: Transcript detection in G&T-seq data from HCC38 and HCC38-BL cell lines.

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References

  1. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C. & Teichmann, S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Wang, Y. & Navin, N.E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Voet, T. et al. Single-cell paired-end genome sequencing reveals structural variation per cell cycle. Nucleic Acids Res. 41, 6119–6138 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Evrony, G.D. et al. Single-neuron sequencing analysis of l1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Evrony, G.D. et al. Cell lineage analysis in human brain using endogenous retroelements. Neuron 85, 49–59 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Leung, M.L., Wang, Y., Waters, J. & Navin, N.E. SNES: single nucleus exome sequencing. Genome Biol. http://dx.doi.org/10.1186/s13059-015-0616-2 (2015).

  12. de Bourcy, C.F. et al. A quantitative comparison of single-cell whole genome amplification methods. PLoS One 9, e105585 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hou, Y. et al. Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing. Gigascience 4, 37 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Huang, L., Ma, F., Chapman, A., Lu, S. & Xie, X.S. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Gundry, M., Li, W., Maqbool, S.B. & Vijg, J. Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res. 40, 2032–2040 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, J., Fan, H.C., Behr, B. & Quake, S.R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402–412 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Macaulay, I.C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Zong, C., Lu, S., Chapman, A.R. & Xie, X.S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Macaulay, I.C. & Voet, T. Single cell genomics: advances and future perspectives. PLoS Genet. 10, e1004126 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  21. Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA-Seq reveals non-genetic gene expression heterogeneity. Genome Biol. 14, R31 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pan, X. et al. Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc. Natl. Acad. Sci. USA 110, 594–599 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Vanneste, E. et al. Chromosome instability is common in human cleavage-stage embryos. Nat. Med. 15, 577–583 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Dey, S.S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li, W., Calder, R.B., Mar, J.C. & Vijg, J. Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles. Mutat. Res. 772, 55–62 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Han, L. et al. Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4, 6485 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Smallwood, S.A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Quinlan, A.R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.11–11.12.34 (2014).

    Article  Google Scholar 

  38. Marcel, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 3 (2011).

    Google Scholar 

  39. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Garvin, T. et al. Interactive analysis and assessment of single-cell copy-number variations. Nat. Methods 12, 1058–1060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Anders, S., Pyl, P.T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Baslan, T. et al. Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Res. 25, 714–724 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Nilsen, G. et al. Copynumber: efficient algorithms for single- and multi-track copy number segmentation. BMC Genomics 13, 591 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 8, 1280–1289 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. International HapMap, C. The International HapMap Project. Nature 426, 789–796 (2003).

  51. Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

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Acknowledgements

This work was supported by the Wellcome Trust (grants to T.V. & C.P.P.) and funding from the Research Foundation Flanders (FWO) and the KU Leuven to T.V. (FWO–G.0687.12; KU Leuven SymBioSys, PFV/10/016). W.H. and C.P.P. were also funded by the Medical Research Council. M.J.T. is supported by a Wellcome Trust Sanger Institute Clinical PhD fellowship (UK).

Author information

Authors and Affiliations

  1. Earlham Institute, Norwich Research Park, Norwich, UK

    Iain C Macaulay & Wilfried Haerty

  2. Sanger Institute–EBI Single–Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK

    Mabel J Teng, Parveen Kumar, Chris P Ponting & Thierry Voet

  3. Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridge, UK

    Mabel J Teng

  4. Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium

    Parveen Kumar & Thierry Voet

  5. MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK

    Chris P Ponting

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Contributions

I.C.M., C.P.P. and T.V. devised and developed the method and wrote the manuscript. M.J.T. assisted with method development. W.H. and P.K. performed bioinformatics analysis of data.

Corresponding authors

Correspondence to Iain C Macaulay, Chris P Ponting or Thierry Voet.

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Competing interests

T.V. is co-inventor on patent applications: WO/2011/157846, ‘Methods for haplotyping single cells’; WO/2014/053664, ‘High-throughput genotyping by sequencing’; and WO/2015/028576, ‘Haplotyping and copy number typing using polymorphic variant allelic frequencies’.

Integrated supplementary information

Supplementary Figure 1 Example robot deck layout for separation of DNA and RNA (steps 17-28)

Panel A shows the layout of the deck on the Beckman FxP robot for this process. Panel B shows a pictorial view of the deck layout with plates and tip types indicated.

Supplementary Figure 2 Example robot deck layout for separation of DNA and RNA (steps 47-55)

Panel A shows the layout of the deck on the Beckman FxP robot for this process. Panel B shows a pictorial view of the deck layout with plates and tip types indicated.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2 (PDF 314 kb)

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Macaulay, I., Teng, M., Haerty, W. et al. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat Protoc 11, 2081–2103 (2016). https://doi.org/10.1038/nprot.2016.138

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