Incorporation of cellular membrane protein extracts into lipid nanoparticles enhances their cellular uptake and mRNA delivery efficiency
Bibliographic record
Abstract
mRNA therapeutics enable transient expression of desired proteins within cells, holding great potential for advancements in vaccines, protein replacement therapies and gene editing approaches. Lipid nanoparticles (LNPs) are arguably the leading nanoplatform for mRNA delivery due to their scalability and transfection efficiency. However, their limited ability to target specific cell types, inefficient cellular uptake by many cell types, and endosomal entrapment represent challenges for improving targeted mRNA delivery. To address this, we evaluated a novel class of LNPs functionalized with cell-derived membrane proteins, that we refer to as hybrisomes. Membrane protein extracts (MPEs) were isolated from cultured cells using a mild detergent-based extraction protocol. Cy5-labeled mRNA encoding for eGFP was used to form LNPs and hybrisomes to investigate their internalization efficiency and mRNA delivery via flow cytometry and microscopy, with MPE content incorporated into hybrisomes during microfluidic mixing. MPEs were successfully incorporated into the lipid membrane of hybrisomes. Remarkably, the cellular uptake of hybrisomes was up to 15-fold higher than LNPs, while the mRNA delivery efficiency improved up to 8-fold depending on the MPE content incorporated into the hybrisomes. Further studies confirmed that the enhanced cellular uptake of hybrisomes and mRNA is partially explained by the presence of membrane proteins and hybrisomes' unique morphology including bleb-like structures. Moreover, the versatility of hybrisomes was demonstrated by producing formulations using MPEs isolated from different cell types, which led to variations in cellular uptake and mRNA delivery, suggesting that the cell type from which MPEs are derived influences their biological function. These findings pave the way for the development of more targeted and effective nanotherapeutic strategies.
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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".