Document Type
Article
Publication Date
12-1-2022
Publication Title
Nature Communications
Department
Geisel School of Medicine
Abstract
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.
DOI
10.1038/s41467-021-27864-7
Original Citation
Salas, L.A., Zhang, Z., Koestler, D.C. et al. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat Commun 13, 761 (2022). https://doi.org/10.1038/s41467-021-27864-7
Dartmouth Digital Commons Citation
Salas, Lucas A.; Zhang, Ze; Koestler, Devin C.; Butler, Rondi A.; Hansen, Helen M.; Molinaro, Annette M.; Wiencke, John K.; Kelsey, Karl T.; and Christensen, Brock C., "Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling" (2022). Dartmouth Scholarship. 4281.
https://digitalcommons.dartmouth.edu/facoa/4281
