KU Medical Center part of study published in Nature introducing new immune-profiling method using only DNA from blood
The method offers a powerful alternative to conventional flow cytometry based on blood DNA rather than intact living cells
Flow cytometry is a powerful and complex technology used to count, sort or measure characteristics of cells and to detect biomarkers. It’s also widely used in research, as well as in clinical studies and diagnosis of disorders such as blood cancers. However, flow cytometry requires intact cells (usually fresh), that must be processed promptly to preserve cell integrity and surface markers. Those surface (and a few nuclear) markers are used to identify immune cell types.
Now, researchers at the University of Kansas Medical Center, Dartmouth’s and Dartmouth-Hitchcock’s Norris Cotton Cancer Center, Brown University School of Public Health and the University of California San Francisco (UCSF), have introduced a novel immune-profiling method capable of reporting specific immune cell types using only DNA from blood rather than from fresh cell samples. Their method, “Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling,” is published in Nature Communications.
“Our technology requires minimal input to use blood DNA samples stored under different conditions,” said lead author Lucas A. Salas, M.D., MPH, Ph.D., assistant professor of epidemiology at the Geisel School of Medicine at Dartmouth. “This is ideal in population epidemiological research and potentially for clinical settings where samples cannot be processed immediately.”
“Our paper offers a powerful alternative to conventional flow cytometry based on blood DNA rather than intact living cells,” adds co-author John Wiencke, Ph.D., of the UCSF Institute for Human Genetics.
The new approach offers the opportunity to ask and answer questions about the immune system in health and disease using the millions of stored blood samples from biobanks in the U.S. and worldwide—samples that already exist for other reasons. In the clinical setting, the complete cell blood count (CBC) differential is used routinely to diagnose patient conditions and is limited to five general immune cell types. In the new method, immune cell identification is extended to include 12 immune cell types, several that are not determined with CBC, such as naïve and memory T and B cells.
The research utilized a novel algorithm, IDOL (Identifying Optimal Libraries) developed in 2016 by Devin Koestler, Ph.D., associate professor in the Department of Biostatistics & Data Science at KU Medical Center.
“IDOL leverages a key feature of DNA methylation, namely, that it is cell-type specific, and computationally identifies the most informative DNA methylation markers to ensure accurate predictions of the various cell types,” said Koestler. “It’s a kind of computational framework that attempts to efficiently identify the most informative DNA methylation markers out of hundreds of thousands of possible candidates.”
Large-scale human population studies and clinical trials can now access detailed information about individual immune status in a standardized, cost-effective manner, without some of the limitations of existing methods. The advancement paves the way for new research of systemic immune factors in disease and aging.
“Not only does the approach return more than double the number of cell types compared with standard clinical methods, but because it doesn’t depend on surface markers or intact cells, it can be used with either fresh or archival blood,” said Salas.
When the method was applied to cancer patients, immune profile responses to chemotherapy and radiation therapy were observed. Corresponding author Brock C. Christensen, Ph.D., professor of epidemiology, of Community and Family Medicine, and of Molecular and Systems Biology at the Geisel School of Medicine at Dartmouth, is investigating how this new method may help predict response to immunotherapy.
“Detailed immune profiling with our new method is expected to uncover biomarkers of response to existing and emerging cancer immunotherapies as well as to other immunomodulatory drugs,” said Christensen. “This technology also has great potential in advancing cancer immunoprevention efforts.”
The team’s next steps are to evaluate the many potential uses for this new tool to understand how it will best and most immediately benefit clinicians and patients. Such technology could elicit a paradigm shift in the way clinicians, patients and researchers harness and understand information about the immune system in health and disease.