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.

Tagmentation-based whole-genome bisulfite sequencing

Abstract

Epigenetic modifications such as carbon 5 methylation of the cytosine base in a CpG dinucleotide context are involved in the onset and progression of human diseases. A comprehensive understanding of the role of genome-wide DNA methylation patterns, the methylome, requires quantitative determination of the methylation states of all CpG sites in a genome. So far, analyses of the complete methylome by whole-genome bisulfite sequencing (WGBS) are rare because of the required large DNA quantities, substantial bioinformatic resources and high sequencing costs. Here we describe a detailed protocol for tagmentation-based WGBS (T-WGBS) and demonstrate its reliability in comparison with conventional WGBS. In T-WGBS, a hyperactive Tn5 transposase fragments the DNA and appends sequencing adapters in a single step. T-WGBS requires not more than 20 ng of input DNA; hence, the protocol allows the comprehensive methylome analysis of limited amounts of DNA isolated from precious biological specimens. The T-WGBS library preparation takes 2 d.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Overview and components of T-WGBS library preparation.
Figure 2: PCR amplification curves of T-WGBS libraries.
Figure 3: Size distribution of T-WGBS libraries.
Figure 4: Reliability and reproducibility of T-WGBS.

References

  1. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  Google Scholar 

  2. Kobayashi, H. et al. Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 8, e1002440 (2012).

    Article  CAS  Google Scholar 

  3. Popp, C. et al. Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency. Nature 463, 1101–1105 (2010).

    Article  CAS  Google Scholar 

  4. Seisenberger, S. et al. The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells. Mol. Cell 48, 849–862 (2012).

    Article  CAS  Google Scholar 

  5. Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).

    Article  CAS  Google Scholar 

  6. Khulan, B. et al. Comparative isoschizomer profiling of cytosine methylation: the HELP assay. Genome Res. 16, 1046–1055 (2006).

    Article  CAS  Google Scholar 

  7. Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).

    Article  CAS  Google Scholar 

  8. Oda, M. et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 37, 3829–3839 (2009).

    Article  CAS  Google Scholar 

  9. Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    Article  CAS  Google Scholar 

  10. Adey, A. & Shendure, J. Ultra-low-input, tagmentation-based whole-genome bisulfite sequencing. Genome Res. 22, 1139–1143 (2012).

    Article  CAS  Google Scholar 

  11. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).

    Article  CAS  Google Scholar 

  12. Quail, M.A. et al. A large genome center's improvements to the Illumina sequencing system. Nat. Methods 5, 1005–1010 (2008).

    Article  CAS  Google Scholar 

  13. Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37, 853–862 (2005).

    Article  CAS  Google Scholar 

  14. Taiwo, O. et al. Methylome analysis using MeDIP-seq with low DNA concentrations. Nat. Protoc. 7, 617–636 (2012).

    Article  CAS  Google Scholar 

  15. Brinkman, A.B. et al. Whole-genome DNA methylation profiling using MethylCap-seq. Methods 52, 232–236 (2010).

    Article  CAS  Google Scholar 

  16. Gebhard, C. et al. Genome-wide profiling of CpG methylation identifies novel targets of aberrant hypermethylation in myeloid leukemia. Cancer Res. 66, 6118–6128 (2006).

    Article  CAS  Google Scholar 

  17. Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28, 1106–1114 (2010).

    Article  CAS  Google Scholar 

  18. Harris, R.A. et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28, 1097–1105 (2010).

    Article  CAS  Google Scholar 

  19. Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013).

    Article  Google Scholar 

  20. Sturm, D. et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012).

    Article  CAS  Google Scholar 

  21. Grunenwald, H.L, Caruccio, N., Jendrisak, J. & Dahl, G. Transposon end compositions and methods for modifying nucleic acids. US patent 20100120098A1 (2010).

  22. Hansen, K.D., Langmead, B. & Irizarry, R.A. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge excellent technical support by M. Helf and helpful discussions with D. Lipka. We also acknowledge the excellent support by the sequencing core facility at DKFZ. Work in the Plass laboratory was supported by the Helmholtz Foundation and the German Federal Ministry of Education and Science in the program for medical genome research (FKZ; no. 01KU1001A). Q.W. obtained support by the Humboldt Research Fellowship for Postdoctoral Researchers. A.A. is funded by a National Science Foundation Graduate Research Fellowship.

Author information

Authors and Affiliations

Authors

Contributions

A.A., J.S. and D.W. conceived the study. B.R., W.W. and V.H. contributed data. B.R., R.E. and C.P. contributed materials. W.W., M.B., S.W. and D.W. performed the experiments and analyzed data. Q.W., L.G. and V.H. performed the bioinformatic analysis. Q.W., L.G. and D.W. wrote the manuscript.

Corresponding author

Correspondence to Dieter Weichenhan.

Ethics declarations

Competing interests

A provisional US patent application has been deposited for aspects of these methods (A.A., J.S.).

Integrated supplementary information

Supplementary Figure 1 Control of size distribution of three DNA samples after tagmentation.

(a) Prior to oligo replacement/gap repair, the DNA has a size range of about 700–10,000 bp. The non-tagmented DNA (not shown) was ≥ 20 kb in size. (b) Three sequencing libraries prepared from the DNA shown in a without bisulfite treatment. FU, fluorescence units.

Supplementary Figure 2 PCR amplification curves and size distributions of T-WGBS libraries prepared from six different human blood samples.

Input DNA amount was 20 ng each. (a) PCR amplification curves indicate that all 6 samples are close to the plateau phase at cycle 12. (b) Size distributions of the six T-WGBS libraries ranging from about 200 bp to 600 bp with size peaks around 300 bp. The high spikes flanking the curves represent size markers. FU, fluorescence units.

Supplementary Figure 3 Base composition.

(a,b) Consistency in base composition of sequencing reads between T-WGBS (a) and conventional WGBS (b). The base composition bias in the first bases of the T-WGBS reads is caused by the Tn5 transposase which has a slight base preference at certain positions of the integration target sequence (please see the paragraph entitled ‘Limitations’ in the INTRODUCTION for further information).

Supplementary Figure 4 Scatter plot versions of the comparative methylation levels shown in Figure 4a,b.

(a) High consistency of methylation level estimates between T-WGBS and conventional WGBS at single CpGs covered at least 30-fold (r = 0.95). (b) High reproducibility of T-WGBS indicated by the similarity of the methylation levels (r = 0.92) in windows of 5 CpGs (read numbers too low for single CpG analysis) in libraries from two independent tagmentations analyzed on a single HiSeq2000 lane each.

Supplementary Figure 5 High consistency, 97.8% and 98.3%, between T-WGBS and conventional WGBS methylation data from two human blood samples, M and K, respectively.

For each sample, two lanes of T-WGBS data and three lanes of conventional WGBS were compared. (a,b) Methylation levels were calculated based on scanning windows of 5 CpGs with at least 30-fold coverage and displayed on density plots (a) and scatter plots (b). The dotted lines mark the 0.2 difference between the x and y axes. Consistency values were calculated by subtracting from 100% the respective numbers in the corners of the density plots displayed in (a); these numbers indicate the percentage of data points that fall above/below the envelope marked with dotted lines. The r values in (b) indicate Pearson correlation.

Supplementary Figure 6 High consistency of methylation levels between T-WGBS and conventional WGBS.

(a) 98.5% consistency between T-WGBS and conventional WGBS. (b) 98.1% consistency is in an analogous analysis as in (a) between the two sequencing strands, designated Watson and Crick, from the conventional WGBS. The lower figures show scatter plot versions of the figures at the top. Consistency defined as less than 0.2 (20%) difference in methylation level in windows of 5 CpGs. Consistency values were calculated by subtracting from 100% the respective numbers in the corners of the upper two density plots; these numbers indicate the percentage of data points that fall above/below the envelope marked with dotted lines (marking the 0.2 difference between the x and y axes). The r-values indicate Pearson correlation.

Supplementary Figure 7 Comparison of sequencing coverage of cytosines in CpG, CHG and CHH context (H can be A, C or T) between T-WGBS and conventional WGBS.

Coverage versus cytosine density in 1-kb windows for WGBS (red) and T-WGBS (blue). The coverage appears stable in genomic regions with 100–400 cytosines, likely because the majority of 1-kb regions has a cytosine density between 10% and 40%. Variation becomes larger in the two extremes of the cytosine density for both T-WGBS and conventional WGBS due to low counts in each category.

Supplementary Figure 8 Comparison of sequencing coverage in relation to the GC content in 200-bp windows between T-WGBS and conventional WGBS.

Coverage versus local GC content in 200-bp windows, roughly the median length of genomic DNA in the library fragments, for WGBS (red) and T-WGBS (blue). Overall, T-WGBS has a higher coverage along a wide range of GC content.

Supplementary information

Supplementary Methods

Sequencing data alignment and methylation calling methods (PDF 84 kb)

Supplementary Table 1

Comparison of read numbers, duplications, coverage, methylation level and conversion between T-WGBS and conventional WGBS. (XLS 29 kb)

Supplementary Table 2

Comparison of CpG coverage between conventional WGBS and T-WGBS. (XLS 22 kb)

Supplementary Figure 1

Control of size distribution of three DNA samples after tagmentation. (PDF 176 kb)

Supplementary Figure 2

PCR amplification curves and size distributions of T-WGBS libraries prepared from six different human blood samples. (PDF 74 kb)

Supplementary Figure 3

Base composition. (PDF 117 kb)

Supplementary Figure 4

Scatter plot versions of the comparative methylation levels shown in Figure 4a,b. (PDF 321 kb)

Supplementary Figure 5

High consistency, 97.8% and 98.3%, between T-WGBS and conventional WGBS methylation data from two human blood samples, M and K, respectively. (PDF 255 kb)

Supplementary Figure 6

High consistency of methylation levels between T-WGBS and conventional WGBS. (PDF 456 kb)

Supplementary Figure 7

Comparison of sequencing coverage of cytosines in CpG, CHG and CHH context (H can be A, C or T) between T-WGBS and conventional WGBS. (PDF 79 kb)

Supplementary Figure 8

Comparison of sequencing coverage in relation to the GC content in 200-bp windows between T-WGBS and conventional WGBS. (PDF 47 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wang, Q., Gu, L., Adey, A. et al. Tagmentation-based whole-genome bisulfite sequencing. Nat Protoc 8, 2022–2032 (2013). https://doi.org/10.1038/nprot.2013.118

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2013.118

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

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