DNA methylation arrays as surrogate measures of cell mixture distribution

By Eugene Andres Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit, Heather H. Nelson, John K. Wiencke, Karl T. Kelsey. BMC Bioinformatics, 2012

3,005 citations recorded by the source corpus.

There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/98e885542a1ed435f42eac80d67f5e335955161c

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Citations in the corpus

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.”
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease (2016, methodology): “Houseman’s estimation method [84], which is based on DNA methylation signatures from purified leukocyte samples, was used to estimate the proportions of cytotoxic (CD8+) T cells, helper (CD4+) T, natural killer, B cells, and granulocytes.”
Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity (2016, positive): “To estimate proportions of white blood cell types, we used the method by Houseman et al.22 They provide 500 CpG sites showing the most pronounced cell type specific methylation levels in an experiment based on purified cells.”
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, neutral): “So far however, only one reference-based algorithm (the Houseman algorithm) has been proposed for DNAm data [5] (see also [18, 19]).”
Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, methodology): “Relatedly, if the measurement of biological interest is the methylation level in a T cell, then measurement error of this value can be introduced by using methylation levels from whole blood samples because the measured value will be an assay of many cell types [11].”
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data (2016, methodology): “When heterogeneous tissues are analyzed in EWAS, a proportion of the observed differential DNAm signal can be due to cell-composition effects (Houseman et al., 2012, 2016; Paul and Beck, 2014). eFORGE can identify these Cell Reports 17, 2137–2150, November 15, 2016 2143 Top panel shows a blood…”
DNA methylation age of blood predicts all-cause mortality in later life (2015, positive): “Given the known influence of blood cell count on methylation [21], we adjusted for five types of white blood cell count (basophils, monocytes, lymphocytes, eosinophils, and neutrophils) that were measured at on the same blood that was analyzed for methylation.”
Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, positive): “…cell composition using DNA methylation (DNAm) data from our brain samples projected onto publicly-available DNAm data derived from cell lines (Table S6), including ES-derived NPCs 35 , and adult cortex tissue flow-sorted into neuronal and non-neuronal components using the NeuN antibody 34,36 .”
ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip (2015, methodology): “We used a robust linear regression model to test for association between maternal smoking and infant DNA methylation level adjusting for the following variables: cell type proportion (CD8T, CD4T, NK, Bcell, Mono and Gran) estimated using the Houseman method (13) from minfi R package, gestational age”
Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, background): “Since DNAmethylation levels are informative of the white blood cell composition [48], we build a linear predictor to infer the white blood cell composition of those samples lacking WBC measurements (Additional file 2).”
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors (2017, positive): “We and others have shown that the estimated blood cell counts have moderately high correlations with corresponding flow cytometric measures [91, 92].”