Evidence map›Paper›PMID 33234153›Full record

ArticleBMC biology2020

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites.

Marco Schmidt, Tiago Maié, Edgar Dahl, Ivan G Costa, Wolfgang Wagner

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Cardiac epigenome in heart development and disease.Nature reviews. Cardiology · 2026
    Review
  4. Article
  5. Epigenetic networks coordinate DNA methylation across the genome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Marco SchmidtHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Tiago MaiéInstitute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Edgar DahlRWTH centralized Biomaterial Bank (RWTH cBMB), Medical Faculty, RWTH Aachen University, Aachen, Germany.
Ivan G CostaInstitute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany.
Wolfgang WagnerHelmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany. wwagner@ukaachen.de.ORCID 0000-0002-1971-3217
RWTH Aachen University · DE

Funding

Bundesministerium für Bildung und Forschung 03VP06120Bundesministerium für Bildung und Forschung Fibromap Consortia e:MedCenter for Clinical Research RWTH Aachen University O3-3Deutsche Forschungsgemeinschaft KFO 344/1Deutsche Forschungsgemeinschaft WA1706/12-1 as part of the Clinical Research Unit CRU344Deutsche Forschungsgemeinschaft WA1706/8-1
6 · The paper itself

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.

Indexed as

CpG IslandsDNA MethylationEpigenesis, GeneticCell Physiological PhenomenaHumansCancerCell typesCpGDeconvolutionDNA methylationEpigeneticFibrosisHumanNNLSPyrosequencing

Identifiers

PMID33234153
PMCPMC7687708
OpenAlexW3110076201

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.