Cellular Differentiation in Primary Cell Cultures from Single Zebrafish Embryos as a Model for the Study of Myogenesis
Bibliographic record
Abstract
Culturing cells in vitro can produce a uniform population for the study of cellular differentiation, which is especially useful for the quantification of gene expression or the observation of subcellular structures. In zebrafish, a handful of immortalized cell lines have been used for these purposes, despite being heavily selected by passaging. Methods for primary cell culture of zebrafish embryonic blastomeres have been previously reported, but require combining a large number of genetically heterogeneous embryos, meaning that subsequent cell cultures are not clonal. Without genetically uniform cultures, this model system cannot exploit the wealth of available embryonic lethal mutants in zebrafish. We therefore describe methods for the generation of zebrafish embryonic blastomere cell cultures from single genetically characterized embryos. We examined myogenic differentiation and gene expression in single-embryo cultures from early wild-type embryos, as well as embryos containing an embryonic lethal mutation of unc45b, a myosin chaperone known to be required for sarcomere organization during myogenesis. We also demonstrated the practical usefulness of this technique by experimentally manipulating expression of specific genes in individual embryos before cell culture using standard tools of zebrafish biology such as morpholino-oligonucleotide gene knockdown and transgene-mediated gene expression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".