Efficient Gene Transfer into Chicken Gonads by Combining Transposons with Polyethylenimine
Bibliographic record
Abstract
Transposon mediated transfection is a promising, safe, and convenient way to generate transgenic chicken compared with virus-mediated technology and the in vitro modification of primordial germ cells (PGCs). To establish a simple method for in vivo transfection of chicken PGCs, we applied four different transposon systems (PB, SB, Tol2, and ZB) to investigate the gene transfer efficiency of chicken gonads via direct injection of a mixture of transposon and transposase plasmids and transfection reagent (polyethylenimine, PEI) into the subgerminal cavity of Hamburger and Hamilton stage 2-3 chick embryos. We also compared the effect of the amount of plasmids injected on the gene transfer efficiency of chicken gonads. We found that over 70% of the gonads were green fluorescent protein (GFP)-positive across all four transposon groups, and that the proportion of GFP-positive gonads was not significantly different between different transposons. Some GFP positive cells in gonads were confirmed as germ cells by co-labeling with the germ cell specific antibody. We also found that the proportions of GFP-positive gonads decreased significantly with a decrease of plasmid dose from 100 ng to 20 or 50 ng. Here we revealed that a combination of transposons with PEI is a simple and efficient method for gene transfer into chicken gonads and able to transfect PGCs in vivo that could be used for the production of transgenic chickens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".