Induction of Bleb Structures in Lipid Nanoparticle Formulations of mRNA Leads to Improved Transfection Potency
Bibliographic record
Abstract
The transfection potency of lipid nanoparticle (LNP) mRNA systems is critically dependent on the ionizable cationic lipid component. LNP mRNA systems composed of optimized ionizable lipids often display distinctive mRNA-rich "bleb" structures. Here, it is shown that such structures can also be induced for LNPs containing nominally less active ionizable lipids by formulating them in the presence of high concentrations of pH 4 buffers such as sodium citrate, leading to improved transfection potencies both in vitro and in vivo. Induction of bleb structure and improved potency is dependent on the type of pH 4 buffer employed, with LNP mRNA systems prepared using 300 mm sodium citrate buffer displaying maximum transfection. The improved transfection potencies of LNP mRNA systems displaying bleb structure can be attributed, at least in part, to enhanced integrity of the encapsulated mRNA. It is concluded that enhanced transfection can be achieved by optimizing formulation parameters to improve mRNA stability and that optimization of ionizable lipids to achieve enhanced potency may well lead to improvements in mRNA integrity through formation of the bleb structure rather than enhanced intracellular delivery.
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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 | 0.001 |
| 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 | 0.001 |
| 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".