Optimization of chemical transfection in airway epithelial cell lines
Bibliographic record
Abstract
BACKGROUND: Chemical transfection is a widely employed technique in airway epithelium research, enabling the study of gene expression changes and effects. Additionally, it has been explored for its potential application in delivering gene therapies. Here, we characterize the transfection efficiency of EX-EGFP-Lv105, an EGFP-expressing plasmid into three cell lines commonly used to model the airway epithelium (1HAEo-, 16HBE14o-, and NCI-H292). RESULTS: We used six common and/or commercially available reagents with varying chemical compositions: Lipofectamine 3000 (L3000), FuGENE HD, ViaFect, jetOPTIMUS, EndoFectin, and calcium phosphate. Using L3000, 1HAEo- exhibited the highest transfection efficiency compared to 16HBE14o- and NCI-H292 (1HAEo-: 76.1 ± 3.2%, 16HBE14o-: 35.5 ± 1.2%, NCI-H292: 28.9 ± 2.23%). L3000 yielded the greatest transfection efficiency with the lowest impact on cellular viability, normalized to control, with a 11.3 ± 0.16% reduction in 1HAEo-, 16.3 ± 0.08% reduction in 16HBE14o-, and 17.5 ± 0.09% reduction in NCI-H292 at 48-hour post-transfection. However, jetOPTIMUS had a similar transfection efficiency in 1HAEo- (90.7 ± 4.2%, p = 0.94), but had significantly reduced cellular viability of 37.4 ± 0.11% (p < 0.0001) compared to L3000. In 16HBE14o-, jetOPTIMUS yielded a significantly higher transfection efficiency compared to L3000 (64.6 ± 3.2%, p < 0.0001) but significantly reduced viability of 33.4 ± 0.09% (p < 0.0001) compared to L3000. In NCI-H292, jetOPTIMUS yielded a lower transfection efficiency (22.6 ± 1.2%) with a significant reduction in viability (28.3 ± 0.9%, p < 0.0001). Other reagents varied significantly in their efficiency and impact on cellular viability in other cell lines. Changing the transfection mixture-containing medium at 6-hour post-transfection did not improve transfection efficiency or viability. However, pre-treatment of cell cultures with two rinses of 0.25% trypsin-EDTA improved transfection efficiency in 1HAEo- (85.2 ± 1.1% vs. 71.3 ± 1.0%, p = 0.004) and 16HBE14o- (62.6 ± 4.3 vs. 35.5 ± 1.2, p = 0.003). CONCLUSIONS: Transfection efficiencies can differ based on airway epithelial cell line, reagents, and optimization techniques used. Consideration and optimization of cell line and transfection conditions may be useful for improving nonviral genetic techniques in vitro.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".