Lyotropic Liquid Crystalline Phase Nanostructure and Cholesterol Enhance Lipid Nanoparticle Mediated mRNA Transfection in Macrophages
Bibliographic record
Abstract
Abstract Macrophages are unique immune cells attracting growing attention as a potential candidate for cell‐based therapy for infectious diseases and cancer. Strategies that can reprogramme or gene‐edit macrophages hold potential across a spectrum of acute and chronic conditions. Herein, lipid nanoparticles (LNPs) are developed containing the ionizable lipid SM‐102, helper lipid monoolein which is known for self‐assembly in aqueous solutions into the inverse cubic lyotropic liquid crystalline mesophase, and cholesterol as an mRNA nanocarrier. The immortalized alveolar macrophage cell line (MH‐S cells) is utilized to investigate how cholesterol concentration impacts on mRNA delivery which is further validated using primary mouse alveolar macrophages isolated from the bronchoalveolar compartment and human monocyte derived macrophages. By using high‐throughput synchrotron small angle X‐ray scattering (SAXS), an acidification‐induced non‐ordered to ordered internal nanostructure transition of the formulated LNPs is observed, following the transition sequence of inverse micellar to hexagonal to cubic mesophase in the pH range from 7 to 4. Cholesterol is identified as another crucial component for superior mRNA transfection in macrophages, contributing to nanostructure transition and protein corona variation. Successful ex vivo mRNA transfection is also achieved in primary macrophages, highlighting the prospectivity of reprogramming macrophages as a cell therapy for lung diseases.
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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".