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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".