Evidence map›Paper›PMID 42086862›Full record

ArticleMolecular systems biology2026

Neighborhood-informed positional information for precise cell identity specification.

Michal Erez, Roy Friedman, Mor Nitzan

Abstract read
In one paragraph

Article in Molecular systems 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.

Michal Erez *School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.
Roy Friedman *School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel.ORCID http://orcid.org/0000-0001-5061-4084
Mor NitzanSchool of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel. mor.nitzan@mail.huji.ac.il.ORCID http://orcid.org/0000-0003-0074-9196

Funding

EC | Horizon Europe | Excellent Science | HORIZON EUROPE European Research Council (ERC) DecodeSC,101040660
6 · The paper itself

Abstract

During development, cells reliably establish their identities, a process that is enabled in part by positional information encoded in gene expression patterns. Previous works showed that cells in Drosophila embryos can utilize this information to decode their position along the anterior-posterior axis with a 1% embryo-length positional precision. However, this precision is insufficient to uniquely determine position, leading to a positional information gap. Here, we propose a neighborhood-informed information-theoretic framework which allows to quantitatively estimate the amount of information regarding position which exists in the microenvironment of each cell. We formulate how much additional information exists in neighboring cells as a function of spatial variation in gene expression. We show that the additional information encoded by local neighborhoods is sufficient to uniquely specify cell identities, closing the information gap on average across major patterning axes in Drosophila embryos, gastruloids, and the developing neural tube. Furthermore, neighborhood-informed decoders predict cell positions and downstream gene expression patterns more accurately than cell-independent decoders, resulting in lower decoding variability, which is maintained in mutant Drosophila embryos. Our results provide a basis for the analysis of cellular decision-making in the context of their microenvironments.

Indexed as

Body PatterningDrosophila melanogasterAnimalsDrosophilaDrosophila ProteinsEmbryo, NonmammalianGene Expression Regulation, DevelopmentalDrosophila Proteins

Identifiers

PMID42086862
PMCPMC13328375

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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.