ArticleFrontiers in neuroscience2023
Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A cell-type-resolved meta-analysis reveals glial DNA methylation changes associated with aging and Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Pooled it
- Peripheral blood DNA methylation partially reports on immune cell type composition in the frontal brain.Brain, behavior, & immunity - health · 2026Article
- DNA methylation-based immune cell profiling in mouse blood using MouseRS-CMD.Genome biology · 2026Article
- Deconvolution of DNA methylation data reveals cell type-specific epigenomic signatures in endometriosis and eutopic endometrium.Molecular human reproduction · 2026Article
- Circulating neuron-derived cfDNA for blood-based detection of Alzheimer's and other neurodegenerative conditions.Frontiers in neurology · 2026Article
- Article
- Brain region and cell type-specific DNA methylation profiles in association with ADHD.Scientific reports · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data.Journal of data science : JDS · 2025Article
- Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution.Aging · 2024Article
- Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.Acta neuropathologica communications · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- Article
- Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles.BMC biology · 2024Article
- scMD facilitates cell type deconvolution using single-cell DNA methylation references.Communications biology · 2024Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Introduction: The human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved. Methods: Using DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells. Results: We demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia. Discussion: We expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.
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