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Record W2956018098 · doi:10.1242/dev.178400

Single cell dynamics of embryonic muscle progenitor cells in zebrafish

2019· article· en· W2956018098 on OpenAlexafffund
Priyanka Sharma, Tyler D. Ruel, Katrinka M. Kocha, Shan Liao, Peng Huang

Bibliographic record

VenueDevelopment · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Children's Hospital Research InstituteCanada Foundation for InnovationUniversity of Calgary
KeywordsBiologyZebrafishEmbryonic stem cellCell biologyProgenitor cellProgenitorCellMyocyteStem cellGeneticsGene

Abstract

fetched live from OpenAlex

ABSTRACT Muscle stem cells hold a great therapeutic potential in regenerating damaged muscles. However, the in vivo behavior of muscle stem cells during muscle growth and regeneration is still poorly understood. Using zebrafish as a model, we describe the in vivo dynamics and function of embryonic muscle progenitor cells (MPCs) in the dermomyotome. These cells are located in a superficial layer external to muscle fibers and express many extracellular matrix (ECM) genes, including collagen type 1 α2 (col1a2). Utilizing a new col1a2 transgenic line, we show that col1a2+ MPCs display a ramified morphology with dynamic cellular processes. Cell lineage tracing demonstrates that col1a2+ MPCs contribute to new myofibers in normal muscle growth and also during muscle regeneration. A combination of live imaging and single cell clonal analysis reveals a highly choreographed process of muscle regeneration. Activated col1a2+ MPCs change from the quiescent ramified morphology to a polarized and elongated morphology, generating daughter cells that fuse with existing myofibers. Partial depletion of col1a2+ MPCs severely compromises muscle regeneration. Our work provides a dynamic view of embryonic muscle progenitor cells during zebrafish muscle growth and regeneration.

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 score0.388

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 head0.196
Teacher spread0.191 · 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

Citations27
Published2019
Admission routes2
Has abstractyes

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