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Record W2530704424 · doi:10.1038/ncomms13127

Actomyosin-dependent dynamic spatial patterns of cytoskeletal components drive mesoscale podosome organization

2016· article· en· W2530704424 on OpenAlexaff
Marjolein B.M. Meddens, Elvis Pandžić, Johan A. Slotman, Dominique Guillet, Ben Joosten, Svenja F. B. Mennens, Laurent M. Paardekooper, Adriaan B. Houtsmuller, Koen van den Dries, Paul W. Wiseman, Alessandra Cambi

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University
FundersNational Institute of General Medical Sciences
KeywordsPodosomeCytoskeletonActinCell biologyMyosinBiophysicsBiologyCellBiochemistry

Abstract

fetched live from OpenAlex

Podosomes are cytoskeletal structures crucial for cell protrusion and matrix remodelling in osteoclasts, activated endothelial cells, macrophages and dendritic cells. In these cells, hundreds of podosomes are spatially organized in diversely shaped clusters. Although we and others established individual podosomes as micron-sized mechanosensing protrusive units, the exact scope and spatiotemporal organization of podosome clustering remain elusive. By integrating a newly developed extension of Spatiotemporal Image Correlation Spectroscopy with novel image analysis, we demonstrate that F-actin, vinculin and talin exhibit directional and correlated flow patterns throughout podosome clusters. Pattern formation and magnitude depend on the cluster actomyosin machinery. Indeed, nanoscopy reveals myosin IIA-decorated actin filaments interconnecting multiple proximal podosomes. Extending well-beyond podosome nearest neighbours, the actomyosin-dependent dynamic spatial patterns reveal a previously unappreciated mesoscale connectivity throughout the podosome clusters. This directional transport and continuous redistribution of podosome components provides a mechanistic explanation of how podosome clusters function as coordinated mechanosensory area.

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 score0.091
Threshold uncertainty score0.378

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 science0.0010.000
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 head0.007
GPT teacher head0.254
Teacher spread0.248 · 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

Citations71
Published2016
Admission routes1
Has abstractyes

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