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.
Highlights
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues
The mean purity obtained from the flow cytometry confirmation step was 93%, with the lowest purity observed for the CD8mem samples (85%)
As only 459 (38%) of the 1200 probes in the EPIC IDOL-Ext library are common to both the EPIC and 450k array platforms, we developed a library for the legacy IlluminaHumanMethylation450k array platform, repeating the above-described optimization process after constraining the selection pool for the candidate list of CpGs to those only present on the 450k array
Summary
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Constrained projection/ quadratic programming (CP/QP) employs purified cell types as reference samples to generate a “reference library,” a matrix of differentially methylated sites among cell types, and yields highly accurate estimates of the underlying cell composition in mixed cell populations (e.g., peripheral blood)[9]. When the Illumina-HumanMethylation450k technology was released, Jaffe et al applied the Houseman method with the reference data developed by Reinius et al.[11,17]. They accurately discriminated CD8 and CD4T cells, but NK and granulocytes (neutrophils and eosinophils) discrimination performance showed room for improvement[11,17]. A distinct advantage of this library is the inclusion of more ethnically diverse male and female subjects
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