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

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.

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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.

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.

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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

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.

Correspondence to Daniel D. De Carvalho.

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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.

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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

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