DNA methylation in blood-Potential to provide new insights into cell biology

Abstract

Epigenetics plays a fundamental role in cellular development and differentiation; epigenetic mechanisms, such as DNA methylation, are involved in gene regulation and the exquisite nuance of expression changes seen in the journey from pluripotency to final differentiation. Thus, DNA methylation as a marker of cell identify has the potential to reveal new insights into cell biology. We mined publicly available DNA methylation data with a machine-learning approach to identify differentially methylated loci between different white blood cell types. We then interrogated the DNA methylation and mRNA expression of candidate loci in CD4+, CD8+, CD14+, CD19+ and CD56+ fractions from 12 additional, independent healthy individuals (6 male, 6 female). 'Classic' immune cell markers such as CD8 and CD19 showed expected methylation/expression associations fitting with established dogma that hypermethylation is associated with the repression of gene expression. We also observed large differential methylation at loci which are not established immune cell markers; some of these loci showed inverse correlations between methylation and mRNA expression (such as PARK2, DCP2). Furthermore, we validated these observations further in publicly available DNA methylation and RNA sequencing datasets. Our results highlight the value of mining publicly available data, the utility of DNA methylation as a discriminatory marker and the potential value of DNA methylation to provide additional insights into cell biology and developmental processes.

Fig 1. Demonstration of immune cell population discrimination using sets of identified epigenetic markers (CpGs).

A) Hierarchical clustering of all 1173 identified probes demonstrates perfect separation of cellular populations. B) Plot of t-sne dimensions derived from all methylation sites for all 60 samples. Points on the plot represent individual samples. C) 2D t-sne plot of selected 1173 methylation markers identified via glmnet method. Points on the plot represent individual CpG sites. An interactive version of this panel is available (https://sirselim.github.io/tSNE_plotting/).

Fig 2. Heatmap representation of DNA methylation and gene expression data for all 11 genes investigated.

Expression and methylation measures were split into quartiles and their levels coloured accordingly.

Fig 3. Boxplots illustrating DNA methylation and gene expression levels for all 11 gene investigated.

Methylation and gene expression data for a given gene are in adjacent boxplots.

Fig 4. tSNE plot of sorted-cells from 211 samples based on 1025 CpG sites overlapping between the three publicly available datasets.

Points represent individual samples.

Fig 5. tSNE plot of RNA expression in publicly available data for sorted-cells for the 11 genes highlighted in this study (RNAseq data from GSE107011).

Points represent individual samples. CD8+ central memory/naive purple squares, CD8+ effector/memory/terminal effector (purple circles), CD4 Th17 cells (yellow diamonds), other CD4 T-helper cells (yellow circles), CD14 monocytes—classical (blue circles), intermediate (blue triangles), non-classical (blue crosses). Interactive versions of all figures are available online (https://sirselim.github.io/tSNE_plotting/).