Integrating Reverse Transcription Recombinase Polymerase Amplification with CRISPR Technology for the One-Tube Assay of RNA
Bibliographic record
Abstract
CRISPR-Cas systems integrated with nucleic acid amplification techniques improve both analytical specificity and sensitivity. We describe here issues and solutions for the successful integration of reverse transcription (RT), recombinase polymerase amplification (RPA), and CRISPR-Cas12a nuclease reactions into a single tube under an isothermal condition (40 °C). Specific detection of a few copies of a viral DNA sequence was achieved in less than 20 min. However, the sensitivity was orders of magnitude lower for the detection of viral RNA due to the slow initiation of RPA when the complementary DNA (cDNA) template remained hybridized to RNA. During the delay of RPA, the crRNA-Cas12a ribonucleoprotein (RNP) gradually lost its activity in the RPA solution, and nonspecific amplification reactions consumed the RPA reagents. We overcame these problems by taking advantage of the endoribonuclease function of RNase H to remove RNA from the RNA-cDNA hybrids and free the cDNA as template for the RPA reaction. As a consequence, we significantly enhanced the overall reaction rate of an integrated assay using RT-RPA and CRISPR-Cas12a for the detection of RNA. We showed successful detection of 200 or more copies of the S gene sequence of SARS-CoV-2 RNA within 5-30 min. We applied our one-tube assay to 46 upper respiratory swab samples for COVID-19 diagnosis, and the results from both fluorescence intensity measurements and end-point visualization were consistent with those of RT-qPCR analysis. The strategy and technique improve the sensitivity and speed of RT-RPA and CRISPR-Cas12a assays, potentially useful for both semi-quantitative and point-of-care analyses of RNA molecules.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".