Evidence map›Paper›PMID 40663586›Full record

ArticlePLoS biology2025

A toolkit for mapping cell identities in relation to neighbors reveals conserved patterning of neuromesodermal progenitor populations.

Matthew French, Rosa P Migueles, Alexandra Neaverson, Aishani Chakraborty, Tom Pettini, Benjamin Steventon, Erik Clark, J Kim Dale, Guillaume Blin, Valerie Wilson and 1 more

Abstract read
In one paragraph

Article in PLoS biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. N2B27 media formulations influence gastruloid development.Development (Cambridge, England) · 2025
    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

11 authors.

Matthew FrenchInstitute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
Rosa P MiguelesInstitute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
Alexandra NeaversonDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
Aishani ChakrabortyDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
Tom PettiniDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
Benjamin SteventonDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
Erik ClarkDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
J Kim DaleSchool of Life Sciences, University of Dundee, Dundee, United Kingdom.ORCID 0000-0002-9294-947X
Guillaume BlinInstitute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-9295-237X
Valerie WilsonInstitute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0003-4182-5159
Sally LowellInstitute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-4018-9480

Funding

Wellcome Trust
6 · The paper itself

Abstract

Patterning of cell fates is central to embryonic development, tissue homeostasis, and disease. Quantitative analysis of patterning reveals the logic by which cell-cell interactions orchestrate changes in cell fate. However, it is challenging to quantify patterning when graded changes in identity occur over complex 4D trajectories, or where different cell states are intermingled. Furthermore, comparing patterns across multiple individual embryos, tissues, or organoids is difficult because these often vary in shape and size. This problem is further exacerbated when comparing patterning between species. Here we present a toolkit of computational approaches to tackle these problems. These strategies are based on measuring properties of each cell in relation to the properties of its neighbors to quantify patterning, and on using embryonic landmarks in order to compare these patterns between embryos. We perform detailed neighbor-analysis of the caudal lateral epiblast of E8.5 mouse embryos, revealing local patterning in emergence of early mesoderm cells that is sensitive to inhibition of Notch activity. We extend this toolkit to compare mouse and chick embryos, revealing conserved 3D patterning of the caudal-lateral epiblast that scales across an order of magnitude difference in size between these two species. We also examine 3D patterning of gene expression boundaries across the length of Drosophila embryos. We present a flexible approach to examine the reproducibility of patterning between individuals, to measure phenotypic changes in patterning after experimental manipulation, and to compare of patterning across different scales and tissue architectures.

Indexed as

Body PatterningMesodermAnimalsCell DifferentiationChick EmbryoEmbryonic DevelopmentGene Expression Regulation, DevelopmentalGerm LayersMiceReceptors, NotchReceptors, Notch

Identifiers

PMID40663586
PMCPMC12303391

What OpenQuestion 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 OpenQuestion graph.