Aptamer-antibody chimera sensors for sensitive, rapid and reversible molecular detection in complex samples
Bibliographic record
Abstract
Abstract The development of receptors suitable for the continuous detection of analytes in complex, interferent-rich samples remains challenging. Antibodies are highly sensitive but difficult to engineer in order to introduce signaling functionality, while aptamer switches are easy to construct but often yield only modest target sensitivity. We present here the programmable antibody and DNA aptamer switch (PANDAS), which combines the best features of both systems by using a nucleic acid tether to link an analyte-specific antibody to an internal strand-displacement (ISD)-based aptamer switch that recognizes the same target. The monoclonal antibody mediates initial analyte binding due to its higher affinity; the resulting increase in local analyte concentration then leads to cooperative binding and signaling by the ISD switch. We developed a PANDAS sensor for the clotting protein thrombin and show that this design achieves 100-fold enhanced sensitivity compared to using an aptamer alone. This design also exhibits reversible binding, enabling repeated measurements with temporal resolution of ∼10 minutes, and retains excellent sensitivity even in interferent-rich samples. With future development, this PANDAS approach could enable the adaptation of existing protein-binding aptamers with modest affinity into sensors that deliver excellent sensitivity and minute-scale resolution in minimally prepared biological specimens.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".