Spontaneous, solvent-free entrapment of siRNA within lipid nanoparticles
Bibliographic record
Abstract
Lipid nanoparticle (LNP) formulations of nucleic acid are leading vaccine candidates for COVID-19, and enabled the first approved RNAi therapeutic, Onpattro. LNPs are composed of ionizable cationic lipids, phosphatidylcholine, cholesterol, and polyethylene glycol (PEG)-lipids, and are produced using rapid-mixing techniques. These procedures involve dissolution of the lipid components in an organic phase and the nucleic acid in an acidic aqueous buffer (pH 4). These solutions are then combined using a continuous mixing device such as a T-mixer or microfluidic device. In this mixing step, particle formation and nucleic acid entrapment occur. Previous work from our group has shown that, in the absence of nucleic acid, the particles formed at pH 4 are vesicular in structure, a portion of these particles are converted to electron-dense structures in the presence of nucleic acid, and the proportion of electron-dense structures increases with nucleic acid content. What remained unclear from previous work was the mechanism by which vesicles form electron-dense structures. In this study, we use cryogenic transmission electron microscopy and dynamic light scattering to show that efficient siRNA entrapment occurs in the absence of ethanol (contrary to the established paradigm), and suggest that nucleic acid entrapment occurs through inversion of preformed vesicles. We also leverage this phenomenon to show that specialized mixers are not required for siRNA entrapment, and that preformed particles at pH 4 can be used for in vitro transfection.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".