ARMMs as a versatile platform for intracellular delivery of macromolecules
Bibliographic record
Abstract
Majority of disease-modifying therapeutic targets are restricted to the intracellular space and are therefore not druggable using existing biologic modalities. The ability to efficiently deliver macromolecules inside target cells or tissues would greatly expand the current landscape of therapeutic targets for future generations of biologic drugs, but remains challenging. Here we report the use of extracellular vesicles, known as arrestin domain containing protein 1 [ARRDC1]-mediated microvesicles (ARMMs), for packaging and intracellular delivery of a myriad of macromolecules, including the tumor suppressor p53 protein, RNAs, and the genome-editing CRISPR-Cas9/guide RNA complex. We demonstrate selective recruitment of these macromolecules into ARMMs. When delivered intracellularly via ARMMs, these macromolecules are biologically active in recipient cells. P53 delivered via ARMMs induces DNA damage-dependent apoptosis in multiple tissues in mice. Together, our results provide proof-of-principle demonstration that ARMMs represent a highly versatile platform for packaging and intracellular delivery of therapeutic macromolecules.
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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".