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Record W4413048367 · doi:10.1242/dev.204663

Adhesion strength, cell packing density and cell surface buckling in pericellular matrix-mediated tissue cohesion

2025· article· en· W4413048367 on OpenAlexafffund
Rudolf Winklbauer, Olivia Luu, Debanjan Barua, Martina Nagel, Yunyun Huang

Bibliographic record

VenueDevelopment · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsMaterials scienceAdhesionCell adhesionBucklingAdhesiveMatrix (chemical analysis)StiffeningFibronectinComposite materialBiophysicsExtracellular matrixCell biologyBiology

Abstract

fetched live from OpenAlex

Pericellular matrix-mediated cell-cell adhesion in Xenopus gastrula tissues is characterized by a spectrum of narrow and wide cell contacts that alternate with the non-adhesive surfaces of the interstitial space. Here we show, first, that knockdown of a pericellular matrix adhesion molecule, fibronectin, diminishes contact abundance, and hence cell-packing density, without reducing adhesion strength. Second, we find that cell surfaces in gastrula tissues exhibit solid-like behavior in the form of buckling and crumpling, shape modifications that are typically seen in thin elastic films. We propose that both phenomena are explained by generic properties of the pericellular matrix: its compression and consequent stiffening by the interpenetration of matrix layers during adhesive contact formation. We argue that this renders part of the cell surface non-adhesive to form interstitial gaps, and both gap surfaces and contacts prone to buckling and crumpling in line with cell contractility fluctuations. In this elasto-capillary model of tissue cohesion, the size of the interstitial space is determined by the abundance of the pericellular matrix, not by adhesion strength.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.234
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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