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Record W3007253077 · doi:10.1073/pnas.1912656117

Spatial mapping of tissue properties in vivo reveals a 3D stiffness gradient in the mouse limb bud

2020· article· en· W3007253077 on OpenAlexafffund
Min Zhu, Hirotaka Tao, Mohammad Samani, Mengxi Luo, Xian Wang, Sevan Hopyan, Yu Sun

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsMesenchymeStiffnessMorphogenesisFibronectinBiologyViscoelasticityAnatomyBiological systemCell biologyComputer scienceEmbryoPhysicsExtracellular matrix

Abstract

fetched live from OpenAlex

Numerous hypotheses invoke tissue stiffness as a key parameter that regulates morphogenesis and disease progression. However, current methods are insufficient to test hypotheses that concern physical properties deep in living tissues. Here we introduce, validate, and apply a magnetic device that generates a uniform magnetic field gradient within a space that is sufficient to accommodate an organ-stage mouse embryo under live conditions. The method allows rapid, nontoxic measurement of the three-dimensional (3D) spatial distribution of viscoelastic properties within mesenchyme and epithelia. Using the device, we identify an anteriorly biased mesodermal stiffness gradient along which cells move to shape the early limb bud. The stiffness gradient corresponds to a Wnt5a -dependent domain of fibronectin expression, raising the possibility that durotaxis underlies cell movements. Three-dimensional stiffness mapping enables the generation of hypotheses and potentially the rigorous testing of mechanisms of development and disease.

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)0.001
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 score0.004
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.278
Teacher spread0.231 · 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

Citations89
Published2020
Admission routes2
Has abstractyes

Explore more

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