Strategies to Improve Multi-enzyme Compatibility and Coordination in One-Pot SHERLOCK
Bibliographic record
Abstract
While molecular diagnostics generally require heating elements that supply high temperatures such as 95 °C in polymerase chain reaction and 60-69 °C in loop-mediated isothermal amplification, the recently developed CRISPR-based SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) platform can operate at 37 °C or a similar ambient temperature. This unique advantage may be translated into highly energy-efficient or equipment-free molecular diagnostic systems with unrestricted deployability. SHERLOCK is characterized by ultra-high sensitivity when performed in a traditional two-step format. For RNA sensing, the first step combines reverse transcription with recombinase polymerase amplification, while the second step consists of T7 transcription and CRISPR-Cas13a detection. The sensitivity drops dramatically, however, when all these components are combined into a single reaction mixture, and it largely remains an unmet need in the field to establish a high-performance one-pot SHERLOCK assay. An underlying challenge, conceivably, is the extremely complex nature of a one-pot formulation, crowding a large number of reaction types using at least eight enzymes/proteins. Although previous work has made substantial improvements by serving individual enzymes/reactions with accommodating conditions, we reason that the interactions among different enzymatic reactions could be another layer of complicating factors. In this study, we seek optimization strategies by which inter-enzymatic interference may be eliminated or reduced and cooperation created or enhanced. Several such strategies are identified for SARS-CoV-2 detection, each leading to a significantly improved reaction profile with faster and stronger signal amplification. Designed based on common molecular biology principles, these strategies are expected to be customizable and generalizable with various buffer conditions or pathogen types, thus holding broad applicability for integration into future development of one-pot diagnostics in the form of a highly coordinated multi-enzyme reaction system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".