Visualization and tracking of tumour extracellular vesicle delivery and RNA translation using multiplexed reporters
Bibliographic record
Abstract
Accurate spatiotemporal assessment of extracellular vesicle (EV) delivery and cargo RNA translation requires specific and robust live-cell imaging technologies. Here we engineer optical reporters to label multiple EV populations for visualization and tracking of tumour EV release, uptake and exchange between cell populations both in culture and in vivo. Enhanced green fluorescence protein (EGFP) and tandem dimer Tomato (tdTomato) were fused at NH2-termini with a palmitoylation signal (PalmGFP, PalmtdTomato) for EV membrane labelling. To monitor EV-RNA cargo, transcripts encoding PalmtdTomato were tagged with MS2 RNA binding sequences and detected by co-expression of bacteriophage MS2 coat protein fused with EGFP. By multiplexing fluorescent and bioluminescent EV membrane reporters, we reveal the rapid dynamics of both EV uptake and translation of EV-delivered cargo mRNAs in cancer cells that occurred within 1-hour post-horizontal transfer between cells. These studies confirm that EV-mediated communication is dynamic and multidirectional between cells with delivery of functional mRNA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".