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Record W2972542730 · doi:10.1371/journal.pone.0221753

Nuclei deformation reveals pressure distributions in 3D cell clusters

2019· article· en· W2972542730 on OpenAlexafffund
Adele Khavari, Allen J. Ehrlicher

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchChalmers Tekniska HögskolaCanadian Cancer Society
KeywordsMechanobiologyBiophysicsNucleusCluster (spacecraft)Biological systemOsmotic pressureMulticellular organismConfocal microscopyDeformation (meteorology)Cell nucleusChemistryCellPhysicsBiologyCell biologyComputer science

Abstract

fetched live from OpenAlex

Measuring pressures within complex multi-cellular environments is critical for studying mechanobiology as these forces trigger diverse biological responses, however, these studies are difficult as a deeply embedded yet well-calibrated probe is required. In this manuscript, we use endogenous cell nuclei as pressure sensors by introducing a fluorescent protein localized to the nucleus and confocal microscopy to measure the individual nuclear volumes in 3D multi-cellular aggregates. We calibrate this measurement of nuclear volume to pressure by quantifying the nuclear volume change as a function of osmotic pressure in isolated 2D culture. Using this technique, we find that in multicellular structures, the nuclear compressive mechanical stresses are on the order of MPa, increase with cell number in the cluster, and that the distribution of stresses is homogenous in spherical cell clusters, but highly asymmetric in oblong clusters. This approach may facilitate quantitative mechanical measurements in complex and extended biological structures both in vitro and in vivo.

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 score0.026
Threshold uncertainty score0.296

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 head0.011
GPT teacher head0.204
Teacher spread0.192 · 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

Citations23
Published2019
Admission routes2
Has abstractyes

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