Dynamic Biodistribution of Extracellular Vesicles <i>in Vivo</i> Using a Multimodal Imaging Reporter
Bibliographic record
Abstract
Extracellular vesicles (EVs) are nanosized vesicles released by normal and diseased cells as a novel form of intercellular communication and can serve as an effective therapeutic vehicle for genes and drugs. Yet, much remains unknown about the in vivo properties of EVs such as tissue distribution, blood levels, and urine clearance, important parameters that will define their therapeutic effectiveness and potential toxicity. Here we combined Gaussia luciferase and metabolic biotinylation to create a sensitive EV reporter (EV-GlucB) for multimodal imaging in vivo, as well as monitoring of EV levels in the organs and biofluids ex vivo after administration of EVs. Bioluminescence and fluorescence-mediated tomography imaging on mice displayed a predominant localization of intravenously administered EVs in the spleen followed by the liver. Monitoring EV signal in the organs, blood, and urine further revealed that the EVs first undergo a rapid distribution phase followed by a longer elimination phase via hepatic and renal routes within six hours, which are both faster than previously reported using dye-labeled EVs. Moreover, we demonstrate systemically injected EVs can be delivered to tumor sites within an hour following injection. Altogether, we show the EVs are dynamically processed in vivo with accurate spatiotemporal resolution and target a number of normal organs as well as tumors with implications for disease pathology and therapeutic design.
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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".