Orthogonal investigation at single-particle and ensemble levels uncovers lipoprotein-extracellular vesicle binding
Bibliographic record
Abstract
Mesoscale interactions—such as biomolecular coronas, transient associations, aggregation, and fusion—are increasingly recognized for their biological significance and, under certain conditions, are essential for extracellular nanoparticles to fulfill their functions. Among these interactions, the binding between extracellular vesicles and lipoproteins has recently gained attention for its potential impact on extracellular vesicle function and fate in vivo. These interactions must be understood to clarify and possibly engineer and exploit the biological modes of action of EVs. A bottom-up simplified system consisting of red blood cell-derived extracellular vesicles and purified human lipoproteins was used to investigate extracellular vesicles-lipoprotein binding in saline buffer and in human plasma. A customized toolbox of orthogonal analytical techniques was developed to characterize these interactions at multiple scales, also using label-free materials, while preserving their natural binding states. This toolbox includes fluorescence cross-correlation spectroscopy, super-resolution microscopy, flow cytometry, and Single Molecule Array assay. Our findings reveal that lipoproteins bind to red blood cell EVs with affinities ranging from 10 nM to 1 µM. The percentage of individual extracellular vesicles interacting with lipoproteins is dependent on the specific lipoprotein class and the incubation conditions, and is always considerable, with up to 100% EV interacting with High Density Lipoproteins in the presence of plasma proteins. Such binding is stable, proving resistant also to several washing steps. Our data depict the EV – lipoprotein interaction as a generalizable phenomenon, which is shared among all the lipoprotein classes to different degrees. This implies that in physiological conditions, EVs may be constantly associated with a certain number of lipoproteins in the bloodstream, with possible impacts on EV surface identity and, therefore, function. This finding advances our understanding of extracellular nanoparticle interactome, and provides a step forward in deciphering the physicochemical foundations of biodistribution and clearance mechanisms of natural, synthetic, and hybrid nanoparticles.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".