Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-tube linear DNA amplification for genome-wide studies using a few thousand cells

Nature Protocols volume 7, pages 328–339 (2012)Cite this article

Subjects

Abstract

Linear amplification of DNA (LinDA) by T7 polymerase is a versatile and robust method for generating sufficient amounts of DNA for genome-wide studies with minute amounts of cells. LinDA can be coupled to a great number of global profiling technologies. Indeed, chromatin immunoprecipitation coupled to massive parallel sequencing (ChIP-seq) has been achieved for transcription factors and epigenetic modification of chromatin histones with 1,000 to 5,000 cells. LinDA largely simplifies reChIP-seq experiments to monitor co-binding at chromatin target sites. The single-tube design of LinDA is ideal for handling ultrasmall amounts of DNA (<30 pg) and is compatible with automation. The actual hands-on working time is less than 6 h with one overnight reaction. The present protocol describes all materials and critical steps, and provides examples and controls for LinDA. Applications of LinDA for genome-wide analyses of biobank samples and for the study of chromatin conformation and nuclear architecture are in progress.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic illustration of the LinDA protocol and its key features.
Figure 2: Comparative statistics illustrating peak detection relative to peak size for LinDA–ChIP-seq with different amounts of cells relative to the gold standard.
Figure 3: Comparison of GC content across unamplified versus LinDA-amplified ER-α ChIP profiles.

References

  1. Hurtado, A., Holmes, K.A., Ross-Innes, C.S., Schmidt, D. & Carroll, J.S. FOXA1 is a key determinant of estrogen receptor function and endocrine response. Nat. Genet. 43, 27–33 (2011).

    Article  CAS  Google Scholar 

  2. Ceschin, D.G. et al. Methylation specifies distinct estrogen-induced binding site repertoires of CBP to chromatin. Genes Dev. 25, 1132–1146 (2011).

    Article  CAS  Google Scholar 

  3. Bau, D. et al. The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules. Nat. Struct. Mol. Biol. 18, 107–114 (2011).

    Article  CAS  Google Scholar 

  4. Park, P.J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).

    Article  CAS  Google Scholar 

  5. Laird, P.W. Principles and challenges of genomewide DNA methylation analysis. Nat. Rev. Genet. 11, 191–203 (2010).

    Article  CAS  Google Scholar 

  6. Hah, N. et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622–634 (2011).

    Article  CAS  Google Scholar 

  7. Ozsolak, F. & Milos, P.M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12, 87–98 (2011).

    Article  CAS  Google Scholar 

  8. Sims, R.J. III et al. The C-terminal domain of RNA polymerase II is modified by site-specific methylation. Science 332, 99–103 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Mamanova, L. et al. FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nat. Methods 7, 130–132 (2010).

    Article  CAS  Google Scholar 

  11. Ingolia, N.T. Genome-wide translational profiling by ribosome footprinting. Methods Enzymol. 470, 119–142 (2010).

    Article  CAS  Google Scholar 

  12. Guo, H., Ingolia, N.T., Weissman, J.S. & Bartel, D.P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835–840 (2010).

    Article  CAS  Google Scholar 

  13. Adli, M., Zhu, J. & Bernstein, B.E. Genome-wide chromatin maps derived from limited numbers of hematopoietic progenitors. Nat. Methods 7, 615–618 (2010).

    Article  CAS  Google Scholar 

  14. Shankaranarayanan, P. et al. Single-tube linear DNA amplification (LinDA) for robust ChIP-seq. Nat. Methods 8, 565–567 (2011).

    Article  CAS  Google Scholar 

  15. Hoeijmakers, W.A., Bartfai, R., Francoijs, K.J. & Stunnenberg, H.G. Linear amplification for deep sequencing. Nat. Protoc. 6, 1026–1036 (2011).

    Article  CAS  Google Scholar 

  16. Liu, C.L., Bernstein, B.E. & Schreiber, S.L. Whole genome amplification by T7-based linear amplification of DNA (TLAD): overview. Cold Spring Harb. Protoc. doi:10.1101/pdb.top42 (2008).

  17. Liu, C.L., Schreiber, S.L. & Bernstein, B.E. Development and validation of a T7-based linear amplification for genomic DNA. BMC Genomics 4, 19 (2003).

    Article  CAS  Google Scholar 

  18. Zhao, H., Hastie, T., Whitfield, M.L., Borresen-Dale, A.L. & Jeffrey, S.S. Optimization and evaluation of T7-based RNA linear amplification protocols for cDNA microarray analysis. BMC Genomics 3, 31 (2002).

    Article  CAS  Google Scholar 

  19. van Bakel, H. et al. Improved genome-wide localization by ChIP-chip using double-round T7 RNA polymerase-based amplification. Nucleic Acids Res. 36, e21 (2008).

    Article  Google Scholar 

  20. Xu, Y., Grindley, N.D. & Joyce, C.M. Coordination between the polymerase and 5′-nuclease components of DNA polymerase I of Escherichia coli. J. Biol. Chem. 275, 20949–20955 (2000).

    Article  CAS  Google Scholar 

  21. Walker, G.T. Empirical aspects of strand displacement amplification. PCR Methods Appl. 3, 1–6 (1993).

    Article  CAS  Google Scholar 

  22. Rong, M., Durbin, R.K. & McAllister, W.T. Template strand switching by T7 RNA polymerase. J. Biol. Chem. 273, 10253–10260 (1998).

    Article  CAS  Google Scholar 

  23. Hurtado, A. et al. Regulation of ERBB2 by oestrogen receptor-PAX2 determines response to tamoxifen. Nature 456, 663–666 (2008).

    Article  CAS  Google Scholar 

  24. Mendoza-Parra, M.A., Shankaranarayanan, P. & Gronemeyer, H. Sequential chromatin immunoprecipitation protocol for global analysis through massive parallel sequencing (reChIP-seq). Protocol Exchange, doi:10.1038/protex.2011.1256 (2011).

  25. Fullwood, M.J. et al. An oestrogen receptor-α–bound human chromatin interactome. Nature 462, 58–64 (2009).

    Article  CAS  Google Scholar 

  26. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  27. Fanelli, M. et al. Pathology tissue-chromatin immunoprecipitation, coupled with high-throughput sequencing, allows the epigenetic profiling of patient samples. Proc. Natl. Acad. Sci. USA 107, 21535–21540 (2010).

    Article  CAS  Google Scholar 

  28. Adli, M. & Bernstein, B.E. Whole-genome chromatin profiling from limited numbers of cells using nano-ChIP-seq. Nat. Protoc. 6, 1656–1668 (2011).

    Article  CAS  Google Scholar 

  29. Spits, C. et al. Whole-genome multiple displacement amplification from single cells. Nat. Protoc. 1, 1965–1970 (2006).

    Article  CAS  Google Scholar 

  30. Rodrigue, S. et al. Whole genome amplification and de novo assembly of single bacterial cells. PLoS ONE 4, e6864 (2009).

    Article  Google Scholar 

  31. Kozarewa, I. et al. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nat. Methods 6, 291–295 (2009).

    Article  CAS  Google Scholar 

  32. Bath, A.J., Milsom, S.E., Gormley, N.A. & Halford, S.E. Many type IIs restriction endonucleases interact with two recognition sites before cleaving DNA. J. Biol. Chem. 277, 4024–4033 (2002).

    Article  CAS  Google Scholar 

  33. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  34. Cheung, M.S., Down, T.A., Latorre, I. & Ahringer, J. Systematic bias in high-throughput sequencing data and its correction by BEADS. Nucleic Acids Res. 39, e103 (2011).

    Article  CAS  Google Scholar 

  35. Dohm, J.C., Lottaz, C., Borodina, T. & Himmelbauer, H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36, e105 (2008).

    Article  Google Scholar 

  36. Nelson, J.R. et al. TempliPhi, phi29 DNA polymerase based rolling circle amplification of templates for DNA sequencing. Biotechniques (suppl.) 44–47 (2002).

  37. Hosono, S. et al. Unbiased whole-genome amplification directly from clinical samples. Genome Res. 13, 954–964 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

W.v.G. is supported by the Fondation pour la Recherche Médicale (aide aux projets innovants). This work was supported by funds from the Ligue National Contre le Cancer (laboratoire labelisé), the Institut National du Cancer (INCa) and the European Community contracts LSHC-CT-2005-518417 'EPITRON', LSHG-CT2005-018882 'X-TRA-NET', LSHM-CT2005-018652 'CRESCENDO' and HEALTH-F4-2009-221952 'ATLAS'.

Author information

Author notes

  1. Pattabhiraman Shankaranarayanan and Marco-Antonio Mendoza-Parra: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Cancer Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg-Illkirch, France

    Pattabhiraman Shankaranarayanan, Marco-Antonio Mendoza-Parra, Wouter van Gool & Hinrich Gronemeyer

  2. Wageningen University & Research Centre—Plant Breeding, Wageningen, The Netherlands

    Luisa M Trindade

Authors
  1. Pattabhiraman Shankaranarayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco-Antonio Mendoza-Parra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wouter van Gool
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luisa M Trindade
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hinrich Gronemeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.S., L.M.T. and H.G. designed and optimized LinDA. M.-A.M.-P. performed the F9 experiments and did the bioinformatics analysis together with P.S. and W.v.G.; H.G., P.S., M.-A.M.-P. and L.M.T. wrote the manuscript.

Corresponding authors

Correspondence to Pattabhiraman Shankaranarayanan or Hinrich Gronemeyer.

Ethics declarations

Competing interests

H.G., P.S. and L.M.T. have filed a patent application describing the methods presented in this study (EP11305531.3).

Supplementary information

Supplementary Fig. 1

Multidimensional ChIP-seq comparison including ChIPs done with very small numbers of cells. Four representative screenshots of ERα (purple) and RNA polymerase II (blue) profiles established from H3396 breast cancer cells2, compared with the corresponding H3K4me3 profiles (red; cumulative profiles of two biological replicates). These profiles were established by standard ChIP-seq procedure. Below the corresponding profiles derived LinDA-ChIP-seq assays done with 10,000 and 1,000 cells are displayed (black). Note the coherence between the profiles derived from 10,000 cells with the standard procedure, which is only partially maintained when 1,000 cells were used. (PDF 159 kb)

Supplementary Table 1

Quantification of yields of RNA and double-stranded DNA obtained from representative ChIPs performed with different numbers of cells and two different antibodies. LinDA was performed on ChIPs and total RNA was quantified after the in vitro transcription step; the total DNA was quantified after the final step just before Illumina sequencing. No LinDA was performed on the 2 million cell sample for ERα and the 1 million cells sample for the H3K4me3. The RNA and DNA amounts shown here for the ERα experiments correspond to the same ChIPs for which the sequencing data have been detailed in the Suppl. Table 1 of ref. 14. Note that these are independent ChIP experiments and that numbers cannot be extrapolated linearly. However, the LinDA data are representative and indicate an "apparent experimental amplification factor" between about 2,000-fold (for 5,000 cells, ERα) and 400-fold (for 100,000 cells, ERα (PDF 137 kb)

Supplementary Table 2

The antibody to cell ratios for ChIPs with small number of cells. ChIPs were performed with antibodies directed to ERa (sc-543, Santa Cruz Biotechnology) or H3K4me3 (AB8580, Abcam) using estrogen-treated H3396 breast cancer cells and the standard ChIP protocol. (PDF 124 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Shankaranarayanan, P., Mendoza-Parra, MA., van Gool, W. et al. Single-tube linear DNA amplification for genome-wide studies using a few thousand cells. Nat Protoc 7, 328–339 (2012). https://doi.org/10.1038/nprot.2011.447

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2011.447

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing