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Record W4406746033 · doi:10.1186/s12896-025-00945-x

Optimization of chemical transfection in airway epithelial cell lines

2025· article· en· W4406746033 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Biotechnology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health ResearchMichael Smith Health Research BCCanadian Allergy, Asthma and Immunology Foundation
KeywordsTransfectionViability assayLipofectamineBiologyMolecular biologyCell cultureRespiratory epitheliumEpitheliumGeneRecombinant DNABiochemistryGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.228
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it