Efficient Gamma‐Retroviral Transduction of Primary Human Skin Cells Using the EF‐c Peptide as a Transduction Enhancer
Bibliographic record
Abstract
Efficient gene transfer into cultured fibroblasts and keratinocytes during retroviral transduction is a critical step toward the treatment of genodermatoses such as epidermolysis bullosa. However, achieving high transduction rates is still a difficult task, particularly for the insertion of large coding sequences for which high viral titers cannot always be obtained. Multiple polycationic molecules, such as polybrene, which has been used in several clinical trials, have the ability to boost ex vivo retroviral gene transfer. However, the use of polybrene has been associated with a reduction of the proliferation and growth potential of human keratinocytes in culture. We developed a method for the efficient retroviral transduction of primary fibroblasts and keratinocytes using EF-c, a polycationic nanofibril-forming peptide. In comparison with polybrene, we found that the retroviral transduction efficiency with EF-c was increased 2.5- to 3.2-fold for fibroblasts, but not for keratinocytes. Moreover, the use of EF-c did not affect fibroblast proliferation and keratinocyte stem cell content, whereas polybrene induced a decrease in both. This method could have a positive impact on the development of ex vivo gene correction of genodermatoses, allowing for more efficient gene transfer into primary skin cells with little to no effect on proliferation and stem cell content. © 2022 Wiley Periodicals LLC. Basic Protocol: Fibroblast and keratinocyte transduction Support Protocol: Assessment of transduction efficiency through flow cytometry analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".