Evidence map›Paper›PMID 26405695›Full record

ArticleCell systems2015

Transcription factor competition allows embryonic stem cells to distinguish authentic signals from noise.

Cameron Sokolik, Yanxia Liu, David Bauer, Jade McPherson, Michael Broeker, Graham Heimberg, Lei S Qi, David A Sivak, Matt Thomson

Abstract read
In one paragraph

Article in Cell systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

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  14. Soft Statistical Mechanics for Biology.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  15. Article
  16. Article
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  18. Review
  19. Review
  20. Noise-driven cellular heterogeneity in circadian periodicity.Proceedings of the National Academy of Sciences of the United States of America · 2020
    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

9 authors.

Cameron SokolikCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco; San Francisco, California, 94158. USA.
Yanxia LiuCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA.
David BauerCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco; San Francisco, California, 94158. USA.
Jade McPhersonCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco; San Francisco, California, 94158. USA.
Michael BroekerCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco; San Francisco, California, 94158. USA.
Graham HeimbergCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA.
Lei S QiCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA.
David A SivakCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA.
Matt ThomsonCenter for Systems and Synthetic Biology, University of California, San Francisco; San Francisco, California, 94158. USA ; Department of Cellular and Molecular Pharmacology, University of California, San Francisco; San Francisco, California, 94158. USA.

Funding

Rewiring Therapeutic Cells: User-Controlled T Cells and Stem CellsP50GM081879 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARSHALL, WALLACE · 2010 to 2017
$19.0M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
A novel RNA-guided platform for sequence-specific cell reprogrammingDP5OD017887 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI QI, LEI STANLEY · 2013 to 2017
$2.0M
Quantitative models for controlling collective cell fate selection in stem cellsDP5OD012194 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMSON, MATTHEW W. · 2012 to 2016
$1.8M
NIGMS NIH HHS P50 GM081879NIGMS NIH HHS T32 GM067547NIH HHS DP5 OD012194NIH HHS DP5 OD017887
6 · The paper itself

Abstract

Stem cells occupy variable environments where they must distinguish stochastic fluctuations from developmental cues. Here, we use optogenetics to investigate how the pluripotency network in embryonic stem (ES) cells achieves a robust response to differentiation cues but not to gene expression fluctuations. We engineered ES cells in which we could quantitatively ontrol the endogenous mechanism of neural differentiation through a light-inducible Brn2 transgene and monitor differentiation status through a genome-integrated Nanog-GFP reporter. By exposing cells to pulses of Brn2, we find that the pluripotency network rejects Brn2 inputs that are below specific magnitude or duration thresholds, but allows rapid differentiation when both thresholds are satisfied. The filtering properties of the network arise through its positive feedback architecture and the intrinsic half-life of Nanog, which determines the duration threshold in the network. Together our results suggest that the dynamic properties of positive-feedback networks might determine how inputs are classified as signal or noise by stem cells.

Identifiers

PMID26405695
PMCPMC4576702

What OpenQuestion holds

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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.