A Reverse Transcription Nucleic-Acid-Based Barcoding System for <i>In Vivo</i> Measurement of Lipid Nanoparticle mRNA Delivery
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are the most extensively validated clinical delivery vehicles for mRNA therapeutics, exemplified by their widespread use in the mRNA COVID-19 vaccines. The pace of lipid nanoparticle (LNP) development for mRNA therapeutics is restricted by the limitations of existing methods for large-scale LNP screening. To address this challenge, we developed Quantitative Analysis of Reverse Transcribed Barcodes (QuART), a novel nucleic-acid-based system for measuring LNP functional delivery in vivo. QuART uses a bacterial retron reverse transcription system to couple functional mRNA delivery into the cytoplasm with a cDNA barcode readout. Our results demonstrate that QuART can be used to identify functional mRNA delivery both in vitro in cell culture and in vivo in mice. Multiplexing of QuART could enable high-throughput screening of LNP formulations, facilitating the rapid discovery of promising LNP candidates for mRNA 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".