Evidence map›Paper›PMID 30390414›Full record

ReviewThe FEBS journal2019

From single cells to tissue self-organization.

Aline Xavier da Silveira Dos Santos, Prisca Liberali

Abstract readReview
In one paragraph

Review in The FEBS journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  16. Considerations toward an epigenetic and common pathways theory of mental disorder.Journal of psychopathology and clinical science · 2023
    Article
  17. Review
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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

2 authors.

Aline Xavier da Silveira Dos SantosFriedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland.
Prisca LiberaliFriedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-organization is a process by which interacting cells organize and arrange themselves in higher order structures and patterns. To achieve this, cells must have molecular mechanisms to sense their complex local environment and interpret it to respond accordingly. A combination of cell-intrinsic and cell-extrinsic cues are decoded by the single cells dictating their behaviour, their differentiation and symmetry-breaking potential driving development, tissue remodeling and regenerative processes. A unifying property of these self-organized pattern-forming systems is the importance of fluctuations, cell-to-cell variability, or noise. Cell-to-cell variability is an inherent and emergent property of populations of cells that maximize the population performance instead of the individual cell, providing tissues the flexibility to develop and maintain homeostasis in diverse environments. In this review, we will explore the role of self-organization and cell-to-cell variability as fundamental properties of multicellularity-and the requisite of single-cell resolution for its understanding. Moreover, we will analyze how single cells generate emergent multicellular dynamics observed at the tissue level 'travelling' across different scales: spatial, temporal and functional.

Indexed as

Cell DifferentiationAnimalsCell PolarityEmbryonic Stem CellsEukaryotic CellsHomeostasisHumansMorphogenesisOrgan Culture TechniquesRegenerationSignal TransductionSingle-Cell AnalysisSpatio-Temporal Analysiscell-to-cell variabilitycrossing-scales technologiesdevelopmentemergent propertiesmulticellularityorganoidspattern formationregenerationself-organizationsymmetry-breaking

Identifiers

PMID30390414
PMCPMC6519261

What OpenQuestion holds

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LicenceCC BY-NC
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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.