Lipofection of plasmid DNA into human mast cell lines using lipid nanoparticles generated by microfluidic mixing
Bibliographic record
Abstract
Mast cells are important immune cells that have significant roles in mediating allergy and asthma. Therefore, studying the molecular mechanisms regulating these and other processes in mast cells is important to elucidate. Methods such as lipofection, transduction, and electroporation are often employed to dissect these mechanisms by disrupting gene expression in mast cell lines. However, as with other leukocytes, human mast cells (HMCs) are often refractory to the delivery of plasmids by lipofection. In this study, we investigated the utility of lipid nanoparticles (LNPs) containing the ionizable cationic lipids 1,2-dioleoyloxy-3-dimethylaminopropane, 1,2-dioleyloxy-3-dimethylaminopropane, or 2,2-dilinoleyl-4-(2-dimethylaminoethyl)-[1,3]-dioxolane for the delivery of plasmid DNA into HMC lines. Herein, we demonstrate for the first time the use of LNPs to achieve significant and reproducible levels of plasmid DNA transfection in HMC-1.2 and laboratory of allergic diseases 2 (LAD2) cells. These levels reached 53.2% and 16.0% in HMC-1.2 and LAD2 cells, respectively; and outperformed Lipofectamine 3000 in both cases. Moreover, cell viability in the transfected cells remained above 65% for all LNP conditions tested. Together, these observations illustrate the efficacy of this technique for mast cell researchers and further support the use of LNPs for nucleic acid delivery into leukocytes.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".