ArticleBMC biology2020
Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites.
Article in BMC biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
- Brain-derived neurotrophic factor (BDNF) epigenomic modifications and brain-related phenotypes in humans: A systematic review.Neuroscience and biobehavioral reviews · 2023Pooled it
- Comparison of nanopore sequencing, MethylationEPIC array, and EM-Seq for DNA methylation detection.Computational biology and chemistry · 2026Article
- Cardiac epigenome in heart development and disease.Nature reviews. Cardiology · 2026Review
- Decades of omics in lung cancer research: a bibliometric analysis and visualization from 2004 to 2024.Journal of thoracic disease · 2026Article
- Epigenetic networks coordinate DNA methylation across the genome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- Study Protocol for the Health Outcomes in Pregnancy and Early Childhood (HOPE) Study: A Mother-Infant Study in American Samoa.medRxiv : the preprint server for health sciences · 2025Article
- Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age.Genome biology · 2025Article
- Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex.Genome biology · 2025Article
- Study protocol for the Health Outcomes in Pregnancy and Early Childhood (HOPE) Study: A mother-infant study in American Samoa.PloS one · 2025Article
- Cancer-associated fibroblasts reveal aberrant DNA methylation across different types of cancer.Clinical epigenetics · 2024Article
- Inflammatory breast cancer microenvironment repertoire based on DNA methylation data deconvolution reveals actionable targets to enhance the treatment efficacy.Journal of translational medicine · 2024Article
- Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies.Scientific reports · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma.Acta neuropathologica · 2024Article
- Epigenetic profiling reveals a strong association between lack of 5-ALA fluorescence andNeuro-oncology practice · 2023Article
- Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner.Advanced healthcare materials · 2023Article
- CimpleG: finding simple CpG methylation signatures.Genome biology · 2023Article
- Quantification of hematopoietic stem and progenitor cells by targeted DNA methylation analysis.Clinical epigenetics · 2023Article
- DNA Methylation Dynamics During Esophageal Epithelial Regeneration Following Repair with Acellular Silk Fibroin Grafts in Rat.Advanced biology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand-methylated or non-methylated-and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing.
resultsWe compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a "FibroScore" correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.
conclusionThis proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.