Evidence map›Paper›PMID 32523982›Full record

ArticleScience advances2020

A transcriptional roadmap for 2C-like-to-pluripotent state transition.

Xudong Fu, Mohamed Nadhir Djekidel, Yi Zhang

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

49 citing papers in PubMed, 76 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Exit from totipotency in mammals: an epigenetic perspective.Frontiers in cell and developmental biology · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Life medicine · 2025
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
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  16. Review
  17. Article
  18. Article
  19. RNA mCell proliferation · 2024
    Article
  20. Article
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

3 authors at 1 institution in 1 country.

Xudong FuHoward Hughes Medical Institute, Boston, MA, USA.ORCID 0000-0001-7024-0200
Mohamed Nadhir DjekidelHoward Hughes Medical Institute, Boston, MA, USA.ORCID 0000-0001-5361-1858
Yi ZhangHoward Hughes Medical Institute, Boston, MA, USA.ORCID 0000-0002-2789-0811
Boston Children's Hospital · US

Funding

Understanding mechanism and improving efficiency of somatic cell nuclear transferR01HD092465 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI ZHANG, YI · 2017 to 2021
$2.4M
NICHD NIH HHS R01 HD092465
6 · The paper itself

Abstract

In mouse embryonic stem cell (ESC), a small cell population displays totipotent features by expressing a set of genes that are transiently active in 2-cell-stage embryos. These 2-cell-like (2C-like) cells spontaneously transit back into the pluripotent state. We previously dissected the transcriptional dynamics of the transition from pluripotency to the totipotent 2C-like state and identified factors that modulate the process. However, how 2C-like cells transit back into the pluripotent state remains largely unknown. In this study, we analyzed the transcriptional dynamics from the 2C-like state to pluripotent ESCs and identified an intermediate state. The intermediate state characterized by two-wave step up-regulation of pluripotent genes is different from the one observed during the 2C-like entry transition. Nonsense-mediated Dux mRNA decay plays an important role in the 2C-like state exit. Thus, our study not only provides a transcriptional roadmap for 2C-like-to-pluripotent state transition but also reveals a key molecular event driving the transition.

Identifiers

PMID32523982
PMCPMC7259939
OpenAlexW3031488517

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.