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. 2014 Mar 5;15(3):R50.
doi: 10.1186/gb-2014-15-3-r50.

Quantitative reconstruction of leukocyte subsets using DNA methylation

William P Accomando, John K Wiencke, E Andres Houseman, Heather H Nelson, Karl T Kelsey

Quantitative reconstruction of leukocyte subsets using DNA methylation

William P Accomando et al. Genome Biol. .

Abstract

Background: Cell lineage-specific DNA methylation patterns distinguish normal human leukocyte subsets and can be used to detect and quantify these subsets in peripheral blood. We have developed an approach that uses DNA methylation to simultaneously quantify multiple leukocyte subsets, enabling investigation of immune modulations in virtually any blood sample including archived samples previously precluded from such analysis. Here we assess the performance characteristics and validity of this approach.

Results: Using Illumina Infinium HumanMethylation27 and VeraCode GoldenGate Methylation Assay microarrays, we measure DNA methylation in leukocyte subsets purified from human whole blood and identify cell lineage-specific DNA methylation signatures that distinguish human T cells, B cells, NK cells, monocytes, eosinophils, basophils and neutrophils. We employ a bioinformatics-based approach to quantify these cell types in complex mixtures, including whole blood, using DNA methylation at as few as 20 CpG loci. A reconstruction experiment confirms that the approach could accurately measure the composition of mixtures of human blood leukocyte subsets. Applying the DNA methylation-based approach to quantify the cellular components of human whole blood, we verify its accuracy by direct comparison to gold standard immune quantification methods that utilize physical, optical and proteomic characteristics of the cells. We also demonstrate that the approach is not affected by storage of blood samples, even under conditions prohibiting the use of gold standard methods.

Conclusions: Cell mixture distributions within peripheral blood can be assessed accurately and reliably using DNA methylation. Thus, precise immune cell differential estimates can be reconstructed using only DNA rather than whole cells.

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Figures

Figure 1
Figure 1
DNA methylation signatures distinguishing normal human leukocyte subtypes on a high-density DNA methylation microarray. Purified WBC subset samples are displayed in columns with cell type indicated at the bottom on the x-axis. Individual CpG loci are displayed in rows with the gene containing each locus indicated to the right on the y-axis. Methylation values range from completely unmethylated (yellow) to completely methylated (blue) as indicated in the key at the bottom left. Samples and loci are organized according to unsupervised, hierarchical clustering.
Figure 2
Figure 2
DNA methylation signatures distinguishing normal human leukocyte subtypes on a custom, low-density DNA methylation microarray. Purified WBC subset samples are displayed in columns with cell type indicated at the bottom on the x-axis. Individual CpG loci are displayed in rows with the gene containing each locus indicated to the right on the y-axis. Methylation values range from completely unmethylated (yellow) to completely methylated (blue) as indicated in the key at the bottom left. Samples and loci are organized according to unsupervised, hierarchical clustering.
Figure 3
Figure 3
Quantitative reconstruction of leukocyte subsets using a custom, low density DNA methylation microarray. In all panels, the x-axis indicates the quantities of specific WBC subsets determined using DNA methylation. Cell type is indicated by color and sample type is indicated by shape of the point, as described in the inset legends. Lines are drawn from the origin with a slope of one indicating ideal correspondence between the displayed values in each panel. (A) DNA from purified WBC subsets was combined in quantities mimicking human blood under clinical conditions. The expected quantity of each cell type is indicated by the y-axis. (B-D) Whole blood samples from disease-free human donors subjected to WBC subset quantification by established methods. Results of the established methods are shown on the y-axis and include manual 5-part differential (B), automated 5-part differential (C) and FACS (D).
Figure 4
Figure 4
Comparisons of DNA methylation-based immune cell quantification (using the LDMA) for different blood anticoagulants and storage conditions. All blood samples were from disease-free human donors. Lines are drawn from the origin with a slope of one indicating ideal correspondence between the displayed values in each panel. In all panels, cell type is indicated by color and shape of the point indicates (A) blood anticoagulant or (B-D) storage condition in the same blood anticoagulant, including heparin (B), EDTA (C), and citrate (D).
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