T7 RNA polymerase as a self-replicating label for antigen quantification
Bibliographic record
Abstract
Enzymes are used widely as labels in binding assays for protein analytes, because they provide signal amplification. Efforts at improving the assay sensitivity have been focused mainly on the synthesis of novel substrates, e.g. fluorogenic and chemiluminogenic ones. We report the investigation of T7 RNA polymerase (T7RP) as a label with unique characteristics for antigen quantification. In an in vitro, coupled (one-step) transcription/translation reaction, T7RP catalyzes the expression of an enzyme-coding DNA template to produce free enzyme (luciferase) in solution. We demonstrate that the generated luciferase is linearly related to the input T7RP in a range covering over four orders of magnitude. It is also shown that T7RP exhibits a significant level of self-replication (100-fold) in vitro by acting on a DNA template comprising the T7RP cDNA downstream of a T7 promoter. By combining the self-replication reaction with the expression of luciferase DNA, as low as 1400 T7RP molecules are detectable. Furthermore, the T7RP is biotinylated, complexed with streptavidin and used for antigen quantification in a microtiter well-based assay with high sensitivity and reproducibility.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".