Single-Particle Multiparametric Microscopy Reveals Structural, Size, and Payload Heterogeneity in mRNA-Loaded Lipid Nanoparticles
Bibliographic record
Abstract
Deciphering the heterogeneity of mRNA-containing lipid nanoparticles (LNPs) is essential for understanding the relationship between their microscopic properties and therapeutic function. Here, by combining alternating laser excitation (ALEX) with convex lens-induced confinement (CLiC) microscopy, we simultaneously measure size, multicolor fluorescence, mRNA payload, and Förster resonance energy transfer (FRET) of individual suspended LNPs containing labeled lipid and mRNA molecules. By varying formulation parameters, including ionizable lipids, formulation buffers, and molecular ratios, we investigated and correlated key microscopic properties for relevant vaccine formulations. While per-particle lipid fluorescence was lower for empty versus mRNA-loaded particles for all formulations, the relative size of empty versus mRNA-loaded particles depended upon the formulation and intraparticle structure. When comparing CLiC-ALEX to cryogenic transmission electron microscopy measurements (Cryo-TEM), for the LNP formulations with a major subpopulation of bleb-LNPs, the subpopulation of bleb-LNPs appears to overlap with the subpopulation of mRNA-containing LNPs. CLiC-ALEX also enabled quantification of per-particle mRNA fluorescence and FRET signals, thereby revealing heterogeneity in the mRNA copy number and mRNA-LNP structural arrangements; where the results were compared with biophysical estimates based on the LNP formulations. These rigorous biophysical insights are critical to inform our understanding of structure-activity relationships and inform the rational design of nanomedicines.
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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) | 0.000 | 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 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".