Developmental stage determines efficiency of gene transfer to muscle satellite cells by in utero delivery of adeno-associated virus vector serotype 2/9
Bibliographic record
Abstract
Efficient gene transfer to muscle stem cells (satellite cells) has not been achieved despite broad transduction of skeletal muscle by systemically administered adeno-associated virus serotype 2/9 (AAV-9) in mice. We hypothesized that cellular migration during fetal development would make satellite cells accessible for gene transfer following in utero intravascular injection. We injected AAV-9 encoding green fluorescent protein (GFP) marker gene into the vascular space of mice ranging in ages from post-coital day 12 (E12) to postnatal day 1 (P1). Satellite cell transduction was examined using: immunohistochemistry and confocal microscopy, satellite cell migration assay, myofiber isolation and FACS analysis. GFP positive myofibers were detected in all mature skeletal muscle groups and up to 100% of the myofibers were transduced. We saw gestational variation in cardiac and skeletal muscle expression. E16 injection resulted in 27.7 ± 10.0% expression in satellite cells, which coincides with the timing of satellite cell migration, and poor satellite cell expression before and after satellite cell migration (E12 and P1). Our results demonstrate that efficient gene expression is achieved in differentiated myofibers and satellite cells after injection of AAV-9 in utero. These findings support the potential of prenatal gene transfer for muscle based treatment strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".