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Record W2898826874 · doi:10.1371/journal.pgen.1007775

Stereotypic generation of axial tenocytes from bipartite sclerotome domains in zebrafish

2018· article· en· W2898826874 on OpenAlexafffund
C. Roger, Craig T. Jacobs, Priyanka Sharma, Katrinka M. Kocha, Peng Huang

Bibliographic record

VenuePLoS Genetics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital limb and hand anomalies
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersInstitute of Musculoskeletal Health and ArthritisAlberta Children's Hospital Research InstituteCanada Foundation for InnovationUniversity of Calgary
KeywordsSomiteZebrafishBiologyMyotomeAnatomyCell biologyEmbryoEmbryogenesisGenetics

Abstract

fetched live from OpenAlex

Development of a functional musculoskeletal system requires coordinated generation of muscles, bones, and tendons. However, how axial tendon cells (tenocytes) are generated during embryo development is still poorly understood. Here, we show that axial tenocytes arise from the sclerotome in zebrafish. In contrast to mouse and chick, the zebrafish sclerotome consists of two separate domains: a ventral domain and a previously undescribed dorsal domain. While dispensable for sclerotome induction, Hedgehog (Hh) signaling is required for the migration and maintenance of sclerotome derived cells. Axial tenocytes are located along the myotendinous junction (MTJ), extending long cellular processes into the intersomitic space. Using time-lapse imaging, we show that both sclerotome domains contribute to tenocytes in a dynamic and stereotypic manner. Tenocytes along a given MTJ always arise from the sclerotome of the adjacent anterior somite. Inhibition of Hh signaling results in loss of tenocytes and enhanced sensitivity to muscle detachment. Together, our work shows that axial tenocytes in zebrafish originate from the sclerotome and are essential for maintaining muscle integrity.

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 score0.004
Threshold uncertainty score0.453

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 head0.033
GPT teacher head0.246
Teacher spread0.213 · 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

Citations49
Published2018
Admission routes2
Has abstractyes

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