NEW YORK (GenomeWeb) – An international team led by investigators in Israel have developed a DNA methylation-based method to identify tissues or cell types of origin for cell-free DNA (cfDNA) in the blood in an unbiased way.
"We propose a procedure which can be easily adapted to study the cellular contributions of cfDNA in many settings, opening a broad window into healthy and pathologic human tissue dynamics," co-corresponding authors Yuval Dor, Tommy Kaplan, and Ruth Shemer, and their colleagues at the Institute for Medical Research Israel-Canada, the Hebrew University of Jerusalem, and elsewhere wrote.
The researchers tapped into new and available DNA methylation profiles for more than two-dozen tissue or cell types, using this "reference methylome atlas" and their deconvolution algorithm to match cfDNA circulating in the blood back to its original tissue source in simulated and real datasets. They described the strategy in a paper published online today in Nature Communications, suggesting it could be used to study everything from tissue dynamics in healthy individuals to how cfDNA is released in the blood of individuals with conditions such as sepsis or cancer.
"We propose principles for effective plasma methylome deconvolution, including the key importance of a reference atlas consisting of cell type, rather than whole-tissue methylomes, and discuss the potential of global cfDNA methylation analysis as a diagnostic modality for early detecting and monitoring of disease," the authors explained.
Although researchers, clinicians, and commercial firms rely on the bits of cfDNA swimming in the bloodstream to do non-invasive prenatal testing, cancer profiling, and more, sequence data alone may not be enough for investigators focused on inflammatory and other conditions that involve altered cfDNA levels without clear DNA mutations, the team explained. Likewise, tissue-specific cfDNA features are expected to help in developing cell-free, circulating tumor DNA-based cancer approaches for detecting cancers with unknown primary sites.
Several teams have been pursuing a wide range of epigenetic or combined genetic and epigenetic strategies to identify the tissues of origin for cfDNA. In a 2016 study in Cell, for example, University of Washington researchers presented a nucleosome positioning-based approach for predicting cfDNA sources that is being pursued by the liquid biopsy startup Bellwether Bio.
For the new study, the researchers compiled methylation profiles from samples from healthy individuals profiled for the Cancer Genome Atlas project or studies with data submitted to NCBI's Gene Expression Omnibus, selecting datasets for specific cell types, when possible, as well as samples with relatively low levels of blood DNA. They also used Illumina Infinium methylation or EPIC BeadChip arrays to assess genome-wide methylation profiles in nine flow cytometry- or magnetic bead-sorted cell types, producing a reference methylation atlas that spanned 25 tissue or cell types.
In combination with the deconvolution algorithm, the team used the tissue-specific methylation reference set to analyze simulated, in silico combinations of data from 18 healthy individuals, as well as DNA samples from liver, lung, neuron, and colon samples mixed in vitro, pooled blood samples from 105 healthy individuals or individuals who had received pancreatic islet cell transplants.
In blood samples from healthy individuals, the researchers reported, about 55 percent of the circulating cfDNA appeared to originate from white blood cells, while 30 percent was traced back to red blood cell progenitors based on the DNA methylation patterns detected. Another 10 percent of the cfDNA seemed to coincide with vascular endothelial cell sources and 1 percent stemmed from hepatocytes.
When the team used its strategy to assess cfDNA in blood samples from 14 individuals with sepsis, it saw signs that a documented rise in cfDNA levels during sepsis largely stemmed from a rise in cfDNA from granulocyte cells and other leukocyte white blood cells. That analysis also uncovered increasing levels of cfDNA linked to hepatocyte cells in patients with other markers of liver cell damage.
From there, researchers tested the approach to cfDNA samples or datasets from individuals with cancer. In samples from 11 individuals with metastatic colon cancer, lung cancer, or breast cancer, the cfDNA methylation data led them most strongly back to the known tissue of origin in three of four colon cancer cases, all three breast cancer cases, and two of the four lung cancer cases considered.
From these and other analyses on cancer samples or datasets, the authors suggested that "deconvolution of the plasma methylome is a powerful tool for studying healthy human tissue dynamics and for identifying and monitoring a wide range of pathologies."