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Updated: Jun 7, 2026

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Published on: February 19, 2020

DNA methylation-based immune cell profiling in mouse blood using MouseRS-CMD.

Samuel R Reynolds^1,2, Hannah G Stolrow^1,3, Min Kyung Lee¹

¹Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.

|June 5, 2026

Summary

This summary is machine-generated.

We developed MouseRS-CMD, a DNA methylation tool for mouse immune cell deconvolution. This method aids in analyzing immune cell composition in various samples, improving molecular study accuracy.

Keywords:

B cells CD4 + T cells CD8 + T cells DNA methylation Epigenetics Immune profiling Monocytes Mus musculus Natural killer cells Neutrophils

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Last Updated: Jun 7, 2026

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Published on: June 16, 2017

Area of Science:

Immunology
Genomics
Bioinformatics

Background:

Immune cell heterogeneity complicates molecular studies in mice.
Accurate immune cell profiling is crucial for understanding disease mechanisms.

Purpose of the Study:

To develop a DNA methylation-based computational tool for deconvolution of murine immune cell types.
To enable accurate immune cell profiling in archival and challenging sample types.

Main Methods:

Purification of major murine immune cell types (neutrophils, monocytes, NK cells, B cells, CD4+ T cells, CD8+ T cells).
DNA methylation measurement using Illumina Mouse Methylation BeadChip (>285,000 CpG loci).
Development of a reference library using the IDOL algorithm for cell deconvolution.

Main Results:

MouseRS-CMD identified an optimal reference library of 300 CpGs.
Achieved low Root Mean Square Error (RMSE) values (<2.7 for four cell types, <3.5 for all cell types).
Validated the tool using flow cytometry measurements in whole blood and eye samples.

Conclusions:

MouseRS-CMD provides a robust method for immune cell deconvolution in mice using DNA methylation data.
The tool facilitates immune profiling in archival samples, reducing confounding factors.
Enhances the reliability of molecular studies by accounting for immune cell heterogeneity.