Light-Activated siRNA Endosomal Release (LASER) by Porphyrin Lipid Nanoparticles
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) have achieved clinical success in delivering small interfering RNAs (siRNAs) for targeted gene therapy. However, endosomal escape of siRNA into the cytosol remains a fundamental challenge for LNPs. Herein, we report a strategy termed light-activated siRNA endosomal release (LASER) to address this challenge. We established a porphyrin-LNP by incorporating porphyrin-lipids into the clinically approved Onpattro formulation. The porphyrin-LNP maintained the physical properties of an LNP and generated reactive oxygen species (ROS) when irradiated with near-infrared (NIR) light. Using confocal microscopy, we revealed that porphyrin-lipids within the LNP translocate to endosomal membranes during endocytosis. The translocated porphyrin-lipids generated ROS under light irradiation and enabled LASER through endosomal membranes disruption as observed through GAL-9 recruitment and transmission electron microscopy (TEM). By establishing a quantitative confocal imaging method, we confirmed that porphyrin-LNPs can increase siRNA endosomal escape efficiency by up to 2-fold via LASER and further enhance luciferase target knockdown by 4-fold more in luciferase-transfected prostate cancer cells. Finally, we formulated porphyrin-LNPs encapsulated with gold nanoparticles (GNP) and visualized the LASER effect within prostate tumors via TEM, confirming the light-activated endosomal membrane disruption and subsequent GNP release into cytosols in vivo . Overall, porphyrin-LNPs and the LASER approach enhanced siRNA endosomal escape and significantly improved knockdown efficacy. We believe the versatility of this technology could be applied to various 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) | 0.001 | 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".