Selection of Fluorescent, Bioluminescent, and Radioactive Tracers to Accurately Reflect Extracellular Vesicle Biodistribution <i>in Vivo</i>
Bibliographic record
Abstract
Abstract The ability to track extracellular vesicles (EVs) in vivo without influencing their biodistribution is a key requirement for their successful development as drug delivery vehicles and therapeutic agents. Here, we evaluated the effect of five different optical and nuclear tracers on the in vivo biodistribution of EVs. Expi293F EVs were labeled using either a noncovalent fluorescent dye DiR, or covalent modification with 111indium-DTPA, or bioengineered with fluorescent (mCherry) or bioluminescent (Firefly and NanoLuc luciferase) proteins fused to the EV marker, CD63. To focus specifically on the effect of the tracer, we compared EVs derived from the same cell source and administered systemically by the same route and at equal dose into tumor-bearing BALB/c mice. 111Indium and DiR were the most sensitive tracers for in vivo imaging of EVs, providing the most accurate quantification of vesicle biodistribution by ex vivo imaging of organs and analysis of tissue lysates. Specifically, NanoLuc fused to CD63 altered EV distribution, resulting in high accumulation in the lungs, demonstrating that genetic modification of EVs for tracking purposes may compromise their physiological biodistribution. Blood kinetic analysis revealed that EVs are rapidly cleared from the circulation with a half-life below 10 min. Our study demonstrates that radioactivity is the most accurate EV tracking approach for a complete quantitative biodistribution study including pharmacokinetic profiling. In conclusion, we provide a comprehensive comparison of fluorescent, bioluminescent, and radioactivity approaches, including dual labeling of EVs, to enable accurate spatiotemporal resolution of EV trafficking in mice, an essential step in developing EV therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".