Simultaneous Detection of SARS-CoV-2 Nucleocapsid Protein and RNA by Aptamer-Based Proximity Ligation and Quantitative PCR
Bibliographic record
Abstract
The COVID-19 pandemic underscored the global need for rapid, sensitive, and multiplexed diagnostic assays for viral detection. Real-time quantitative polymerase chain reaction (RT-qPCR) remains the standard for SARS-CoV-2 RNA detection, while antigen-based protein assays provide faster, though less sensitive, alternatives. Here, we present a novel diagnostic platform that combines proximity ligation of aptamers (PLA) with RT-qPCR to enable simultaneous detection of both SARS-CoV-2 RNA and nucleocapsid (N) protein in a single vial. Six high-affinity aptamers against the N protein were identified via capillary electrophoresis-based systematic evolution of ligands by exponential enrichment (CE-SELEX). From these, ECK1 and ECK4 were selected based on binding affinity and spatial compatibility for PLA. The aptamer pair enabled target-induced ligation followed by detection using Cy5-labeled TaqMan probes. Concurrently, SARS-CoV-2 RNA was detected with FAM-labeled probes in the same RT-qPCR reaction. This dual-analyte assay was evaluated in buffer and complex biological matrices such as saliva. Sensitivity was further enhanced by integrating droplet digital PCR (ddPCR). Aptamer binding sites at the N protein were identified via diethylpyrocarbonate (DEPC) labeling and bottom-up proteomics. Our method introduces a scalable and adaptable strategy for multiplexed pathogen diagnostics with minimal sample processing.
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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 | 0.001 |
| 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".