A duplex droplet digital PCR assay for absolute quantification and characterization of long self-amplifying RNA
Bibliographic record
Abstract
Self-amplifying messenger ribonucleic acid (saRNA) provides extended expression of genes of interest by encoding an alphavirus-derived RNA replicase and thus is 2-3 times larger than conventional messenger RNA. However, quality assessment of long RNA transcripts is challenging using standard techniques. Here, we utilized a multiplex droplet digital polymerase chain reaction (ddPCR) assay to assess the quality of saRNA produced from an in vitro transcription reaction and the replication kinetics in human cell lines. Using the one-step reverse transcription ddPCR, we show that an in vitro transcription generates 50-60% full-length saRNA transcripts. However, we note that the two-step reverse transcription ddPCR assay results in a 20% decrease from results obtained using the one-step and confirmed using capillary gel electrophoresis. Additionally, we provided three formulas that differ in the level of stringency and assumptions made to calculate the fraction of intact saRNA. Using ddPCR, we also showed that subgenomic transcripts of saRNA were 19-to-108-fold higher than genomic transcripts at different hours post-transfection of mammalian cells in copies. Therefore, we demonstrate that multiplex ddPCR is well suited for quality assessment of long RNA and replication kinetics of saRNA based on absolute quantification.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".