Homo sapiens
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PROVIDER: PRJNA348735 | ENA |
REPOSITORIES: ENA
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Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.
2017-06-30 | GSE88824 | GEO
Project description:Differential DNA methylation in Multiple Sclerosis
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Project description:Cell-type-specific patterns of DNA methylation have been leveraged to develop methods for accurate and reproducible DNA-based cell typing in human blood and other biospecimens. Recently developed and standardized genome -scale DNA methylation arrays for mus musculus offer an instrument for quantifying cellular composition with epigenetic signatures specific to each cell type. We present a novel murine immune cell deconvolution tool that leverages DNA methylation profiles FlowSorted.Blood.IlluminaMouseMethylation. Separately from BALB/c and C57BL/6 mice, we used flow cytometry to purify polymorphonuclear neutrophil (PMN), monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cells. Genome-scale DNA methylation was measured with the Illumina MouseMethylation BeadChip array at >285,000 CpG loci in purified cells for reference profile development. For testing and validation, DNA methylation was measured in both eye bleed and terminal blood whole blood samples from four mouse strains. To benchmark and determine accuracy of reference libraries for DNA methylation cell type deconvolution, flow cytometry of independent whole blood samples from four mouse strains was used. The Identifying Optimal Libraries (IDOL) algorithm identified an optimal reference library of 300 CpGs for deconvolution with RMSE values across testing and validation samples of <1.7 for all cell types except B lymphocytes (RMSE<2.75). This methylation cytometry tool for mice offers the ability to conduct DNA-based immune profiling in archival samples and reduce confounding from cell type heterogeneity in molecular studies of whole blood samples.
2026-05-14 | GSE284254 | GEO
Project description:MethylPurify: tumor purity deconvolution and differential methylation detection from tumor DNA methylomes
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Project description:Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. This dataset is the validation dataset used to test the csSAM gene expression deconvolution algorithm as reported in the accompanying paper. Whole blood gene expression measurements for 24 pediatric renal transplant patients were analyzed on human specific HGU133V2.0 (+) whole genome expression arrays. Blood drawn using PaxGene Blood RNA Tubes (PreAnalytiX, Qiagen).
2010-04-06 | E-GEOD-20300 | biostudies-arrayexpress
Project description:Cell-type-specific differential methylation in rheumatoid arthritis peripheral blood samples
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Project description:DNA-based immune profiling in murine blood with methylation cytometry deconvolution
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Project description:Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogeneous biospecimens such as whole blood, offer a promising solution. However, their performance depends entirely on the library of DNA methylation markers being used as the basis for deconvolution. The objective of this study was to train and validate an algorithm for the identification of optimal DNA methylation libraries for the deconvolution of adult human whole blood. Purified granulocytes, monocytes, CD4T, CD8T, natural killer cells, and B cells from normal human subjects were purchased from AllCells LLC (Emeryville, CA). DNA extracted from purified leukocyte subtypes were mixed in predetermined proportions to reconstruct two distinct sets of white blood cell (WBC) mixtures, each consisting of six samples. An additional six whole blood (WB) samples from disease-free adult donors with available immune cell profiling data from flow cytometry were purchased from All-Cells LLC and were included in this investigation. All DNA samples were bisulfite modified using the Zymo EZ DNA Methylation kit (Irvine, CA) and profiled for DNA methylation using the Illumina HumanMethylation450 array platform.
2016-02-12 | E-GEOD-77797 | biostudies-arrayexpress
Project description:Genome-wide DNA methylation level was studied to determine whether multiple sclerosis patients (cases) has methylation differences comparing to normal controls in PBLs. We used Illumina HumanMethylation450 BeadChip array to determine the genome-wide DNA methylation difference in peripheral blood from multiple sclerosis patients (cases) and normal controls
2018-04-20 | GSE106648 | GEO
Project description:DNA methylation signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimer’s disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained accurate across a wide range of differentially methylated positions, underscoring its flexibility. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAM’s biological relevance. The workflow is publicly available as an interactive Shiny app (https://um-dementia-systems-biology.shinyapps.io/CEAM/) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.
2025-08-23 | GSE306227 | GEO