Evidence map›Paper›PMID 41662466›Full record

ArticlePLoS computational biology2026

Cell atlases and the developmental foundations of the phenotype.

Alicia Lou, Mónica Chagoyen, Juan F Poyatos

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

3 authors.

Alicia LouLogic of Genomic Systems Lab (CNB-CSIC), Madrid, Spain.ORCID https://orcid.org/0009-0000-0264-828X
Mónica ChagoyenComputational Systems Biology group (CNB-CSIC), Madrid, Spain.ORCID https://orcid.org/0000-0001-6911-1591
Juan F PoyatosLogic of Genomic Systems Lab (CNB-CSIC), Madrid, Spain.ORCID https://orcid.org/0000-0003-4575-8723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is widely acknowledged that development shapes phenotypes, yet the extent to which genes with similar expression patterns during development lead to equivalent organismal phenotypes when mutated remains unclear. Here, we propose addressing this issue, which we term the [Formula: see text]evelopment-to-[Formula: see text]henotype, or [Formula: see text]-[Formula: see text], rule, by leveraging single-cell gene expression atlases and phenotypic ontologies, using Caenorhabditis elegans as a model system. This framework quantifies the proportionality between developmental expression and phenotypic similarities, demonstrating that the relationship holds on average. Genes that strongly fulfill the rule exhibit broad "housekeeping" expression and are associated with systemic phenotypes, whereas weak similarities correspond to specific expression patterns and specialized phenotypes. Deviations from the [Formula: see text]-[Formula: see text] rule provide insights into developmental divergence and phenotypic degeneracy, highlighting genes with narrow functional roles but systemic phenotypic impact. Furthermore, genes that closely adhere to the rule exhibit the highest pleiotropic impact on organismal traits. Our analysis also identifies cell types, such as ASK neurons, as key mediators of phenotype-specific gene contributions, exemplified by their association with chemosensory behavior and chemotaxis. These findings validate the [Formula: see text]-[Formula: see text] rule and underscore the role of cells as critical mediators of the genotype-phenotype map, offering a unified framework to understand the developmental origins of phenotypic complexity.

Indexed as

Caenorhabditis elegansPhenotypeAnimalsComputational BiologyGene Expression Regulation, DevelopmentalSingle-Cell Gene Expression Analysis

Identifiers

PMID41662466
PMCPMC12904592

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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.