Optimization of a lipid nanoparticle–based protocol for RNA transfection into primary mononuclear phagocytes
Bibliographic record
Abstract
The effective delivery of synthetic RNA into mononuclear phagocytes is a prerequisite for experimental research and therapeutic development. However, traditional methods are highly ineffective and toxic for these cells. Here, we aimed to optimize a transfection protocol for primary bone marrow-derived phagocytes, specifically dendritic cells and macrophages, using lipid nanoparticles generated by microfluidics. Our results show that a lipid mixture similar to that used in Moderna's COVID-19 messenger RNA vaccine outperforms the others tested. Improved messenger RNA transfection can be achieved by replacing uridine with methylpseudouridine but not methoxyuridine, which interferes with transfection. The addition of diphenyleneiodonium or apocynin can enhance transfection in a cell type-dependent manner without adverse effects, while apolipoprotein E provides no added value. These optimized transfection conditions can also be used for microRNA agonists and antagonists. In sum, this study offers a straightforward, highly efficient, reproducible, and nontoxic protocol to deliver RNA into different primary mononuclear phagocytes in culture.
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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 | 0.000 | 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".