Evidence map›Paper›PMID 41255712›Full record

ArticleNAR molecular medicine2025

Decoding transcriptional identity during neuron-astroglia cell fate driven by RAR-specific agonists.

Ariel Galindo-Albarrán, Aysis Koshy, Maria Grazia Mendoza-Ferri, Marco Antonio Mendoza-Parra

Abstract read
In one paragraph

Article in NAR molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Ariel Galindo-AlbarránUMR 8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry-val-d'Essonne, University Paris-Saclay, 91057 Évry, France.
Aysis KoshyUMR 8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry-val-d'Essonne, University Paris-Saclay, 91057 Évry, France.
Maria Grazia Mendoza-FerriUMR 8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry-val-d'Essonne, University Paris-Saclay, 91057 Évry, France.
Marco Antonio Mendoza-ParraUMR 8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, University of Evry-val-d'Essonne, University Paris-Saclay, 91057 Évry, France.ORCID https://orcid.org/0000-0002-1809-1727

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How cells respond to different signals leading to defined lineages is an open question to understand physiological differentiation leading to the formation of organs and tissues. Among the various morphogens, retinoic acid signaling, via the RXR/RAR nuclear receptors activation, is a key morphogen of nervous system development and brain homeostasis. Here, we analyze gene expression in ∼80 000 cells covering 16 days of monolayer mouse stem cell differentiation driven by the pan-RAR agonist all-trans retinoic acid, the RARα agonist BMS753 or the activation of both RARβ and RARγ receptors (BMS641 + BMS961). Furthermore, we have elucidated the role of these retinoids for driving nervous tissue formation within 90 days of brain organoid cultures, by analyzing >8000 distinct spatial regions over 28 brain organoids. Despite a delayed progression in BMS641 + BMS961, RAR-specific agonists led to a variety of neuronal subtypes, astrocytes, and oligodendrocyte precursors. Spatially resolved transcriptomics performed in organoids revealed spatially distinct RAR isotype expression leading to specialization signatures associated with matured tissues, including a variety of neuronal subtypes, retina-like tissue structure signatures and even the presence of microglia.

Identifiers

PMID41255712
PMCPMC12430019

What Socratic holds

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Registered trials

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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 Socratic graph.