Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility

By Lovisa E. Reinius, N. Acevedo, M. Joerink, G. Pershagen, S. Dahlén, Dario Greco, C. Söderhäll, A. Scheynius, J. Kere. PLoS ONE, 2012

999 citations recorded by the source corpus.

Methylation of cytosines at CpG sites is a common epigenetic DNA modification that can be measured by a large number of methods, now even in a genome-wide manner for hundreds of thousands of sites. The application of DNA methylation analysis is becoming widely popular in complex disorders, for example, to understand part of the “missing heritability”. The DNA samples most readily available for methylation studies are derived from whole blood. However, blood consists of many functionally and developmentally distinct cell populations in varying proportions. We studied whether such variation might affect the interpretation of methylation studies based on whole blood DNA. We found in healthy male blood donors there is important variation in the methylation profiles of whole blood, mononuclear cells, granulocytes, and cells from seven selected purified lineages. CpG methylation between mononuclear cells and granulocytes differed for 22% of the 8252 probes covering the selected 343 genes implicated in immune-related disorders by genome-wide association studies, and at least one probe was differentially methylated for 85% of the genes, indicating that whole blood methylation results might be unintelligible. For individual genes, even if the overall methylation patterns might appear similar, a few CpG sites in the regulatory regions may have opposite methylation patterns (i.e., hypo/hyper) in the main blood cell types. We conclude that interpretation of whole blood methylation profiles should be performed with great caution and for any differences implicated in a disorder, the differences resulting from varying proportions of white blood cell types should be considered.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b5c944e5d11a89b811d1b282c33ff558d1261e73

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Citations in the corpus

Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “…cell type proportions using existing algorithms (Houseman et al., 2012) making use of ﬂow-sorted reference datasets currently available from whole blood, cord blood and prefrontal cortex assayed on the 450k array (Reinius et al., 2012; Bakulski et al., 2016; Guintivano, Aryee, and Kaminsky, 2013).”
Aging of blood can be tracked by DNA methylation changes at just three CpG sites (2014, methodology): “(a) To this end, DNAm profiles of various blood cell types from a dataset of Reinius and coworkers (GSE35069) [14] were analyzed for the three AR-GpG sites of the EpigeneticAging-Signature (n=6; age 38±13.6 years).”
DNA methylation age of human tissues and cell types (2013, methodology): “Data set 82 consists of blood cell type data from [82].”
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “We collected DHS and Illumina 450k DNA methylation data from the NIH Epigenomics Roadmap [17] and ENCODE [22, 23], as well as from other Illumina 450k studies profiling individual cell-types [24, 29] and normal tissues [26, 35] (Table 1).”
DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. (2016, positive): “dataset for reference.(17) Cell type correction was applied by inclusion of the six estimated cell type proportions (CD8T, CD4T, NK cells, B cells, monocytes, granulocytes) as covariates in cohort-specific statistical models.”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, background): “[81] from the DNA methylation profile of peripheral blood mononuclear cells (PBMCs) in six healthy donors.”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “As a source of leukocyte DNA methylation levels, we used data for peripheral blood mononuclear cells (PBMC) from six healthy donors (Reinius et al., 2012) (GEO: GSE35069).”
Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, methodology): “For each reported age-associated DMR, we identified the F-statistic (and resulting marginal P-value) for that probe for the effect of composition in the publicly available sorted Illumina 450k data [17].”
Reference-free cell mixture adjustments in analysis of DNA methylation data (2014, methodology): “A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies.”