Bypassing Endocytic Barriers: Visualizing Membrane Fusion and Endosomal Escape of Cubic Phase Lipid Nanoparticles (Cubosomes)
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are versatile platforms for drug delivery, offering solutions for targeted therapeutics, gene therapy, and vaccines. Cubosomes - cubic phase lipid nanoparticles - exhibit unique structural properties that may allow direct cytosolic delivery, bypassing endocytic pathways that often limit intracellular drug delivery. Despite their potential, the mechanisms of cubosome interactions with mammalian cells, particularly their membrane fusion behavior, remain unclear. This study employs a multimodal microscopy-based approach to investigate cubosome interactions with cells, focusing on membrane fusion, lipid exchange, and endosomal escape. Advanced imaging techniques, including transmission, scanning, and cryogenic scanning electron microscopy, and live-cell fluorescence imaging, are used alongside tailored cubosome variants to examine interactions at micro- and nanoscale dimensions. Cubosome behaviors are compared to other nanoparticleslike liposomes and gold nanoparticles. For the first time, membrane fusion with mammalian plasma and endosomal membranes is visualized at the nanoscale, revealing how cubosomes bypass conventional endocytic pathways to deliver cargo directly into the cytosol. This work provides critical insights into LNP-cell interactions, establishing cubosomes as promising candidates for overcoming endosomal escape limitations in drug delivery. These findings will aid in developing next-generation lipid-based nanocarriers, particularly for RNA therapeutics, where efficient cytosolic delivery is essential for therapeutic efficacy.
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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".