Evidence map›Paper›PMID 21266484›Full record

ArticleNucleic acids research2011

A high-throughput DNA methylation analysis of a single cell.

Martin Kantlehner, Roland Kirchner, Petra Hartmann, Joachim W Ellwart, Marianna Alunni-Fabbroni, Axel Schumacher

Abstract read
In one paragraph

Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
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

33 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Single molecule and single cell epigenomics.Methods (San Diego, Calif.) · 2015
    Review
  20. Review
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

6 authors.

Martin KantlehnerBeckman Coulter Biomedical GmbH, Advalytix Products, Munich, Germany.
Roland Kirchner
Petra Hartmann
Joachim W Ellwart
Marianna Alunni-Fabbroni
Axel Schumacher

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the field of epigenetics has grown dramatically and has become one of the most dynamic and fast-growing branches of molecular biology. The amount of diseases suspected of being influenced by DNA methylation is rising steadily and includes common diseases such as schizophrenia, bipolar disorder, Alzheimer's disease, diabetes, atherosclerosis, cancer, major psychosis, lupus and Parkinson's disease. Due to cellular heterogeneity of methylation patterns, epigenetic analyses of single cells become a necessity. One rationale is that DNA methylation profiles are highly variable across individual cells, even in the same organ, dependent on the function of the gene, disease state, exposure to environmental factors (e.g. radiation, drugs or nutrition), stochastic fluctuations and various other causes. Using a polymerase chain reaction (PCR)-slide microreaction system, we present here a methylation-sensitive PCR analysis, the restriction enzyme-based single-cell methylation assay (RSMA), in the analysis of DNA methylation patterns in single cells. This method addresses the problems of cell heterogeneity in epigenetics research; it is comparably affordable, avoids complicated microfluidic systems and offers the opportunity for high-throughput screening, as many single cells can be screened in parallel. In addition to this study, critical principles and caveats of single cell methylation analyses are discussed.

Indexed as

DNA MethylationSingle-Cell AnalysisCell LineCell Line, TumorCpG IslandsDNA Restriction EnzymesHigh-Throughput Screening AssaysHumansLymphocytesPolymerase Chain ReactionDNA Restriction Enzymes

Identifiers

PMID21266484
PMCPMC3074158

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.