A magnetic separation method for isolating and characterizing the biomolecular corona of lipid nanoparticles
Bibliographic record
Abstract
Lipid nanoparticle (LNP) formulations are a proven method for the delivery of nucleic acids for gene therapy as exemplified by the worldwide rollout of LNP-based RNAi therapeutics and mRNA vaccines. However, targeting specific tissues or cells is still a major challenge. After LNP administration, LNPs interact with biological fluids (i.e., blood), components of which adsorb onto the LNP surface forming a layer of biomolecules termed the "biomolecular corona (BMC)" which affects LNP stability, biodistribution, and tissue tropism. The mechanisms by which the BMC influences tissue- and cell-specific targeting remains largely unknown, due to the technical challenges in isolating LNPs and their corona from complex biological media. In this study, we present a new technique that utilizes magnetic LNPs to isolate LNP-corona complexes from unbound proteins present in human serum. First, we developed a magnetic LNP formulation, containing >40 superparamagnetic iron oxide nanoparticles (IONPs)/LNP, the resulting LNPs containing iron oxide nanoparticles (IOLNPs) displayed a similar particle size and morphology as LNPs loaded with nucleic acids. We further demonstrated the isolation of the IOLNPs and their corresponding BMC from unbound proteins using a magnetic separation (MS) system. The BMC profile of LNP from the MS system was compared to size exclusion column chromatography and further analyzed via mass spectrometry, revealing differences in protein abundances. This new approach enabled a mild and versatile isolation of LNPs and its corona, while maintaining its structural integrity. The identification of the BMC associated with an intact LNP provides further insight into LNP interactions with biological fluids.
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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 | 0.001 | 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".