Lipid-siRNA Organization Modulates the Intracellular Dynamics of Lipid Nanoparticles
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are widely used for delivering therapeutic nucleic acids, yet the relationship between their internal structure and intracellular behavior, particularly before RNA release, remains unclear. Here, we elucidate how lipid-siRNA organization within LNPs can modulate their intracellular delivery dynamics. We use cryo-electron microscopy and photochemical assays to reveal that increased siRNA loading can reduce helper lipids' distribution to the LNP surface, while siRNA consistently localizes near the surface. These alterations in lipid-siRNA organization affect LNP membrane fluidity, enhancing LNP fusion with cellular membranes and promoting cytosolic siRNA delivery, primarily via macropinocytosis. Using photosensitive lipids and live cell imaging, we demonstrate that lipid-siRNA organization regulates LNP responsiveness to external stimuli, significantly affecting siRNA endosomal escape efficiency upon light activation. We further confirm this observation using convex lens-induced confinement microscopy and single-particle imaging. Overall, our findings provide critical insights into how lipid-siRNA organization shapes LNP intracellular dynamics, offering rational design principles for optimizing LNP-based RNA therapeutics.
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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) | 0.000 | 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 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".