Yoctomole detection of proteins using solid phase binding-induced DNA assembly
Bibliographic record
Abstract
A binding-induced DNA assembly (BINDA) strategy is able to covert target-binding to amplifiable DNA assembly, thereby extending advances in DNA amplification to the detection of other non-amplifiable molecules. We describe here the concept of solid phase BINDA and demonstrate an approach of detecting proteins using affinity capture of the specific proteins on magnetic beads followed by BINDA. The antibody-coated magnetic beads are first introduced to capture target proteins from sample solutions. The unbound components are removed by washing. Two affinity probes, each conjugated to a DNA motif, are utilized to bind to the captured protein molecules. The binding of the two probes to the same target molecule induces assembly of the two DNA motifs that are conjugated to affinity ligands. Enzymatic ligation of the two DNA motifs generates a new DNA sequence that is then measured with real-time PCR. The highly sensitive real-time PCR detection of the DNA assembly provides an indirect detection of the target proteins that is responsible for the binding-induced DNA assembly. The incorporation of solid phase affinity capture effectively reduces the potential interferences from complex matrix and further improves the sensitivity of protein detection by pre-concentration. An application of the technique to the determination of prostate specific antigen in dilute serum shows a detection limit of 200 yoctomole (2×10(-22) mol) from a 20-μL sample. The concentration detection limit (10 attomolar) is 10-fold lower than using the homogeneous BINDA. Substantial reduction of background is a main reason for the improvement in detection limits observed with the BINDA assays. Solid phase BINDA complements to homogeneous BINDA for accomplishing ultrasensitive protein detection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".