Cell-type deconvolution from DNA methylation: a review of recent applications

Area of Science:

• Epigenetics
• Bioinformatics
• Immunology

Background:

• DNA methylation (DNAm) serves as a biomarker for cell types.
• Cell-type deconvolution methods infer cellular composition from DNAm data.
• Understanding cell type proportions is crucial for disease biology.

Purpose of the Study:

• To review advances in cell-type deconvolution using DNA methylation.
• To highlight the utility of DNAm for inferring cell proportions in various biological contexts.
• To introduce Immunomethylomics as a novel field for epigenome-wide association studies (EWAS).

Main Methods:

• Cell-type deconvolution algorithms are categorized into reference-based and reference-free methods.
• Reference-based methods utilize cell-type specific differentially methylated regions (DMRs) from purified cells.
• Reference-free methods estimate cell proportions without prior cell-type specific DMRs, useful when such data is unavailable.

Main Results:

• Reference-based deconvolution, particularly in blood samples, enhances understanding of immune cell profiles and disease associations.
• These methods allow for the estimation of immune cell proportions from archival DNA samples.
• Bioinformatic analysis of DNAm data enables inference of immune cell proportions.

Conclusions:

• Cell-type deconvolution using DNAm is a powerful tool for dissecting disease mechanisms.
• Immunomethylomics, integrating DNAm and cell-type deconvolution, offers new avenues for EWAS.
• These approaches advance our ability to study the epigenetics of disease by resolving cellular heterogeneity.