Journal
NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-27864-7
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Funding
- CDMRP/Department of Defense [W81XWH-20-1-0778]
- NIGMS [P20 GM104416-09/8299]
- NIH [P30 CA168524, P20 GM130423, P20 GM103428, R01 CA207360, P50 CA097257, R01CA253976, R01 CA216265 R01 CA253976, P20 GM130454, P30 DK117469]
- 2018 AACR-Johnson & Johnson Lung Cancer Innovation Science [18-90-52-MICH]
- Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health [P20 GM104416]
- NCI Cancer Center Support Grant [P30 CA023108]
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This study presents a deconvolution method for predicting DNA methylation levels in 12 leukocyte subtypes, expanding to include 56 immune profile variables. The accuracy of the method was validated in various applications, showing its potential for standardized investigation of immune profiles in human health and disease.
Deconvolution algorithms facilitate studying cell type-specific changes using bulk data from complex tissues. Here, the authors present a deconvolution method that predicts DNA methylation levels in 12 leukocyte subtypes using human microarray data and apply it to various examples. 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, naive and memory B cells, naive 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.
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