Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jan 23:9:6.
doi: 10.3389/fgene.2018.00006. eCollection 2018.

Development of a Method to Implement Whole-Genome Bisulfite Sequencing of cfDNA from Cancer Patients and a Mouse Tumor Model

Elaine C Maggi  1 , Silvia Gravina  1 , Haiying Cheng  2 , Bilal Piperdi  2 , Ziqiang Yuan  3 , Xiao Dong  1 , Steven K Libutti  3 , Jan Vijg  1   4   5 , Cristina Montagna  1   6
Affiliations

Development of a Method to Implement Whole-Genome Bisulfite Sequencing of cfDNA from Cancer Patients and a Mouse Tumor Model

Elaine C Maggi et al. Front Genet. .

Abstract

The goal of this study was to develop a method for whole genome cell-free DNA (cfDNA) methylation analysis in humans and mice with the ultimate goal to facilitate the identification of tumor derived DNA methylation changes in the blood. Plasma or serum from patients with pancreatic neuroendocrine tumors or lung cancer, and plasma from a murine model of pancreatic adenocarcinoma was used to develop a protocol for cfDNA isolation, library preparation and whole-genome bisulfite sequencing of ultra low quantities of cfDNA, including tumor-specific DNA. The protocol developed produced high quality libraries consistently generating a conversion rate >98% that will be applicable for the analysis of human and mouse plasma or serum to detect tumor-derived changes in DNA methylation.

Keywords: DNA methylation; biomarker; cell-free DNA; cfDNA; circulating DNA; mouse cfDNA; non-invasive blood based screening; pancreatic cancer.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Representative bioanalyzer profiles of cfDNA. (A) The concentration of cfDNA per ml of plasma (human control and lung cancer and mice) or serum (human PNET) is shown for each of the samples analyzed. The horizontal bar indicates the average values for each sample set. (B) Healthy human control #6, (C) PNET patient #4, (D) lung cancer patient #9, (E) WT mouse #1, and (F) PDAC mouse #3. A peak with the highest DNA amount was detected around 160 bp (green arrow and dotted line). Secondary peaks at ∼320 bp (blue arrow and dotted line) were visible in all samples except the WT mouse control. Contaminating high molecular weight DNA was present in some samples (black arrow in B).
FIGURE 2
FIGURE 2
Representative bioanalyzer images of freshly isolated and bead-purified cfDNA. (A–C) Healthy human control, (D–F) WT mouse, and (G–I) PDAC mouse. The left columns depict the freshly isolated starting cfDNA (green arrow and dotted line) and the contaminating high molecular weight DNA (black arrow) evident by a wide peak. The middle column depicts the high molecular weight DNA removed from the same samples shown in (A–G) by the SPRI AMPure bead purification step after the 0.5X dilution step. The right column shows the purified cfDNA as recovered form the SPRI AMPure bead purification step after the 1.6X dilution step from the same samples shown in (A–G). The desired cfDNA peak is visible ∼150–200 bp (green arrow and dotted line).
FIGURE 3
FIGURE 3
Representative bioanalyzer images of cfDNA libraries. (A) Human and (B) mouse samples.
FIGURE 4
FIGURE 4
Sequencing results for each sample group. Averages are depicted for: (A) the number of reads, (B) the percent conversion calculated from percent methylated non-CpGs, (C) the number of sequences that uniquely aligned to the reference genome, (D) the total number of cytosines evaluated, (E) the percent of the total genome with at least 1X coverage, and (F) the mapping efficiency of the sequences. Error bars represent standard error of the mean for the individual samples in the group.
FIGURE 5
FIGURE 5
Integrative Genomics Viewer (IGV) visualization depicting representative human and mouse alignments. (A) Sequencing reads mapping to human chromosome 2 are visualized at low magnification covering the entire autosome. (B) Sequencing reads mapping to the entire mouse chromosome 1. (C,D) Depicts a zoomed in area spanning ∼40 kb mapping to the chromosomal region indicated by the red mark on the corresponding autosome ideogram on the top of the panel.
FIGURE 6
FIGURE 6
DNA methylation profile of human cfDNA. The averages of the DNA methylation levels within each group detected in all CpG sites that were common amongst the human samples is plotted for controls, lung cancer patients and PNET patients. The rows indicate individual CpG sites with each row showing the average methylation value of that site in each sample type. Red represents high methylation levels and green low methylation levels. Color scale with methylation levels is depicted above the heatmap. Region 1 in brackets indicates cfDNA hypermethylation in controls relative to tumors; region 2 indicates hypermethylated cfDNA regions in tumors relative to controls; region 3 indicates cfDNA methylation differences between the lung and PNETs samples.
FIGURE 7
FIGURE 7
DNA methylation profile of murine cfDNA. The averages of the DNA methylation levels detected in all CpG sites common amongst all the murine sample analyzed is plotted for WT animals and the PDAC murine model. Red represents high methylation levels and green low methylation levels. Color scale with methylation levels is depicted above the heatmap. Region 1 in brackets indicates hypermethylation in PDAC cfDNA; region 2 indicates hypermethylation in the WT cfDNA relative to PDAC samples.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Andrews S. R. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
    1. Bird A. (2002). DNA methylation patterns and epigenetic memory. Genes Dev. 16 6–21. 10.1101/gad.947102 - DOI - PubMed
    1. Board R. E., Knight L., Greystoke A., Blackhall F. H., Hughes A., Dive C., et al. (2008). DNA methylation in circulating tumour DNA as a biomarker for cancer. Biomark. Insights 2 307–319. - PMC - PubMed
    1. Brocks D., Assenov Y., Minner S., Bogatyrova O., Simon R., Koop C., et al. (2014). Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8 798–806. 10.1016/j.celrep.2014.06.053 - DOI - PubMed
    1. Bronkhorst A. J., Aucamp J., Pretorius P. J. (2015). Cell-free DNA: preanalytical variables. Clin. Chim. Acta 450 243–253. 10.1016/j.cca.2015.08.028 - DOI - PubMed
  NODES
chat 1
Project 1
twitter 2