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.

  • Protocol Extension
  • Published:

Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA

Nature Protocols volume 14, pages 2749–2780 (2019)Cite this article

Subjects

Abstract

Circulating cell-free DNA (cfDNA) comprises small DNA fragments derived from normal and tumor tissue that are released into the bloodstream. Recently, methylation profiling of cfDNA as a liquid biopsy tool has been gaining prominence due to the presence of tissue-specific markers in cfDNA. We have previously reported cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) as a sensitive, low-input, cost-efficient and bisulfite-free approach to profiling DNA methylomes of plasma cfDNA. cfMeDIP-seq is an extension of a previously published MeDIP-seq protocol and is adapted to allow for methylome profiling of samples with low input (ranging from 1 to 10 ng) of DNA, which is enabled by the addition of ‘filler DNA’ before immunoprecipitation. This protocol is not limited to plasma cfDNA; it can also be applied to other samples that are naturally sheared and at low availability (e.g., urinary cfDNA and cerebrospinal fluid cfDNA), and is potentially applicable to other applications beyond cancer detection, including prenatal diagnostics, cardiology and monitoring of immune response. The protocol presented here should enable any standard molecular laboratory to generate cfMeDIP-seq libraries from plasma cfDNA in ~3–4 d.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Comparison of cfMeDIP-seq with RRBS and WGBS.
Fig. 2: cfMeDIP-seq workflow created with BioRender.com.
Fig. 3: Profile of a representative cfDNA sample and resultant cfMeDIP-seq libraries from plasma collected from a healthy donor.
Fig. 4: Bioinformatics analysis of IP cfMeDIP-seq libraries generated from a collection of 21 healthy donors.
Fig. 5: Generation of λ filler DNA.

Similar content being viewed by others

Data availability

Data from cfMeDIP-seq carried out on a collection of 21 healthy donors were used to generate Fig. 4a,b. Data availability is restricted to processed tables generated from BAM/WIG files due to privacy concerns and to comply with institutional ethics regulations. Processed tables are available at https://github.com/bratmanlab/cfMeDIP_Protocol.

Code availability

The datasets and code are available in the cfMeDIP-seq_Protocol repository at https://github.com/bratmanlab/cfMeDIP_Protocol.

References

  1. Meissner, A. Epigenetic modifications in pluripotent and differentiated cells. Nat. Biotechnol. 28, 1079–1088 (2010).

    Article  CAS  Google Scholar 

  2. Varley, K. E. et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res. 23, 555–567 (2013).

    Article  CAS  Google Scholar 

  3. Jones, P. A. & Baylin, S. B. The epigenomics of cancer. Cell 128, 683–692 (2007).

    Article  CAS  Google Scholar 

  4. Kelly, T. K., De Carvalho, D. D. & Jones, P. A. Epigenetic modifications as therapeutic targets. Nat. Biotechnol. 28, 1069–1078 (2010).

    Article  CAS  Google Scholar 

  5. Schwarzenbach, H., Hoon, D. S. B. & Pantel, K. Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 11, 426–437 (2011).

    Article  CAS  Google Scholar 

  6. Corcoran, R. B. & Chabner, B. A. Application of cell-free DNA analysis to cancer treatment. N. Engl. J. Med. 379, 1754–1765 (2018).

    Article  CAS  Google Scholar 

  7. Diaz, L. A. & Bardelli, A. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32, 579–586 (2014).

    Article  Google Scholar 

  8. Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumor DNA. Nat. Rev. Cancer 17, 223–238 (2017).

    Article  CAS  Google Scholar 

  9. Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016).

    Article  CAS  Google Scholar 

  10. Jahr, S. et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 61, 1659–1665 (2001).

    CAS  PubMed  Google Scholar 

  11. Giacona, M. B. et al. Cell-free DNA in human blood plasma: length measurements in patients with pancreatic cancer and healthy controls. Pancreas 17, 89–97 (1998).

    Article  CAS  Google Scholar 

  12. Jung, K., Fleischhacker, M. & Rabien, A. Cell-free DNA in the blood as a solid tumor biomarker—a critical appraisal of the literature. Clin. Chim. Acta 411, 1611–1624 (2010).

    Article  CAS  Google Scholar 

  13. Thierry, A. R., El Messaoudi, S., Gahan, P. B., Anker, P. & Stroun, M. Origins, structures, and functions of circulating DNA in oncology. Cancer Metastasis Rev. 35, 347–376 (2016).

    Article  CAS  Google Scholar 

  14. 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 

  15. Grunau, C., Clark, S. J. & Rosenthal, A. Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29, E65–E65 (2001).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. Shen, S. Y. et al. Sensitive tumor detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).

    Article  CAS  Google Scholar 

  18. 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 

  19. Yamamoto, N. et al. Detection of aberrant promoter methylation of GSTP1, RASSF1A, and RARβ2 in serum DNA of patients with breast cancer by a newly established one-step methylation-specific PCR assay. Breast Cancer Res. Treat. 132, 165–173 (2012).

    Article  CAS  Google Scholar 

  20. Visvanathan, K. et al. Monitoring of serum DNA methylation as an early independent marker of response and survival in metastatic breast cancer: TBCRC 005 prospective biomarker study. J. Clin. Oncol. 35, 751–758 (2016).

    Article  Google Scholar 

  21. Wen, L. et al. Genome-scale detection of hypermethylated CpG islands in circulating cell-free DNA of hepatocellular carcinoma patients. Nat. Publ. Group 25, 1250–1264 (2015).

    CAS  Google Scholar 

  22. Irizarry, R. A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009).

    Article  CAS  Google Scholar 

  23. Lentini, A. et al. A reassessment of DNA-immunoprecipitation-based genomic profiling. Nat. Methods 15, 499–504 (2018).

    Article  CAS  Google Scholar 

  24. Riebler, A. et al. BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach. Genome Biol. 15, R35 (2014).

    Article  Google Scholar 

  25. Xu, J., Liu, S., Yin, P., Bulun, S. & Dai, Y. MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data. BMC Bioinformatics 19, 540 (2018).

    Article  CAS  Google Scholar 

  26. Lienhard, M. et al. QSEA—modelling of genome-wide DNA methylation from sequencing enrichment experiments. Nucleic Acids Res. 45, e44 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Fan, H. C., Blumenfeld, Y. J., Chitkara, U., Hudgins, L. & Quake, S. R. Analysis of the size distributions of fetal and maternal cell-free DNA by paired-end sequencing. Clin. Chem. 56, 1279–1286 (2010).

    Article  CAS  Google Scholar 

  29. Jiang, P. & Lo, Y. M. D. The long and short of circulating cell-free DNA and the Ins and outs of molecular diagnostics. Trends Genet. 32, 360–371 (2016).

    Article  CAS  Google Scholar 

  30. Zhou, Y., Chen, Y., Chen, S. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  Google Scholar 

  31. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  32. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  34. Lienhard, M., Grimm, C., Morkel, M., Herwig, R. & Chavez, L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics 30, 284–286 (2013).

    Article  Google Scholar 

  35. Kennedy, S. R. et al. Detecting ultralow-frequency mutations by duplex sequencing. Nat. Protoc. 9, 2586–2606 (2014).

    Article  CAS  Google Scholar 

  36. Morgan, M. AnnotationHub: client to access AnnotationHub resources: https://www.bioconductor.org/packages/release/bioc/html/AnnotationHub.html (accessed 1 March 2019).

Download references

Acknowledgements

D.D.D.C. and S.V.B. were supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at the Princess Margaret Cancer Centre. J.M.B. was supported by a fellowship from the Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21) training program. D.D.D.C. was supported by the University of Toronto McLaughlin Centre (MC-2015-02); the Canadian Institutes of Health Research (CIHR FDN 148430 and CIHR New Investigator Salary award 201512MSH-360794-228629); the Ontario Institute for Cancer Research (OICR), with funds from the province of Ontario; the Canada Research Chair (950-231346) and the Helen M Cooke Professorship from the Princess Margaret Cancer Foundation. S.V.B. was supported by a Career Development Award from the Conquer Cancer Foundation of ASCO. Any opinions, findings, and conclusions expressed in this article are those of the author(s) and do not necessarily reflect those of the American Society of Clinical Oncology or the Conquer Cancer Foundation. We acknowledge the Princess Margaret Cancer Centre Head & Neck Translational Program, supported by philanthropic funds from the Wharton Family, Joe’s Team, Gordon Tozer and the Reed Fund, as well as the labs of F.-F. Liu and G. Liu (University of Toronto), for the provision of plasma samples. We also thank the Princess Margaret Genomics Centre for carrying out the NGS sequencing and the Bioinformatics and HPC Core of the Princess Margaret Cancer Centre for expertise in generating the NGS data.

Author information

Author notes

  1. These authors contributed equally: Shu Yi Shen, Justin M. Burgener.

Authors and Affiliations

  1. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Shu Yi Shen, Justin M. Burgener, Scott V. Bratman & Daniel D. De Carvalho

  2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Justin M. Burgener, Scott V. Bratman & Daniel D. De Carvalho

  3. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada

    Scott V. Bratman

Authors
  1. Shu Yi Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin M. Burgener
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Scott V. Bratman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel D. De Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.Y.S. and D.D.D.C. designed and developed the cfMeDIP-seq protocol. S.Y.S. and J.M.B. performed and optimized the protocol with input from S.V.B. and D.D.D.C. S.Y.S. wrote the manuscript with assistance from J.M.B., S.V.B. and D.D.D.C. J.M.B performed the bioinformatic analysis of all representative cfMeDIP-seq libraries described in the article. All authors contributed to, reviewed and approved the final manuscript.

Corresponding author

Correspondence to Daniel D. De Carvalho.

Ethics declarations

Competing interests

D.D.D.C., S.Y.S., S.V.B. and J.M.B are listed as inventors/contributors on patents filed that are related to this work. S.V.B. is a co-inventor on a patent related to mutation-based ctDNA detection technology that has been licensed to Roche Molecular Diagnostics. D.D.D.C. and S.V.B. have received research funding from Nektar Therapeutics.

Additional information

Peer review information: Nature Protocols thanks Peter W. Laird, Eleni Tomazou and other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Protocol to which this paper is an extension

Taiwo, O. et al. Nat. Protoc. 7, 617–636 (2012) https://doi.org/10.1038/nprot.2012.012

Key reference using this protocol

Shen, S. Y. et al. Nature 563, 579–583 (2018): https://doi.org/10.1038/s41586-018-0703-0

This protocol is an extension to: Nat. Protoc. 7, 617–636 (2012), doi:10.1038/nprot.201.012

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, S.Y., Burgener, J.M., Bratman, S.V. et al. Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA. Nat Protoc 14, 2749–2780 (2019). https://doi.org/10.1038/s41596-019-0202-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-019-0202-2

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: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer