Quenching of Fluorophore-Labeled DNA Oligonucleotides by Divalent Metal Ions: Implications for Selection, Design, and Applications of Signaling Aptamers and Signaling Deoxyribozymes
Bibliographic record
Abstract
Recent years have seen a dramatic increase in the use of fluorescence-signaling DNA aptamers and deoxyribozymes as novel biosensing moieties. Many of these functional single-stranded DNA molecules are either engineered to function in the presence of divalent metal ion cofactors or designed as sensors for specific divalent metal ions. However, many divalent metal ions are potent fluorescence quenchers. In this study, we first set out to examine the factors that contribute to quenching of DNA-bound fluorophores by commonly used divalent metal ions, with the goal of establishing general principles that can guide future exploitation of fluorescence-signaling DNA aptamers and deoxyribozymes as biosensing probes. We then extended these studies to examine the effect of specific metals on the signaling performance of both a structure-switching signaling DNA aptamer and an RNA-cleaving and fluorescence-signaling deoxyribozyme. These studies showed extensive quenching was obtained when using divalent transition metal ions owing to direct DNA-metal ion interactions, leading to combined static and dynamic quenching. The extent of quenching was dependent on the type of metal ion and the concentration of supporting monovalent cations in the buffer, with quenching increasing with the number of unpaired electrons in the metal ion and decreasing with the concentration of monovalent ions. The extent of quenching was independent of the fluorophore, indicating that quenching cannot be alleviated simply by changing the nature of the fluorescent probe. Our results also show that the DNA sequence and the local secondary structure in the region of the fluorescent tag can dramatically influence the degree of quenching by divalent transition metal ions. In particular, the extent of quenching is predominantly determined by the fluorophore location with respect to guanine-rich and duplex regions within the strand sequence. Examination of the effect of both the type and concentration of metal ions on the performance of a fluorescence-signaling aptamer and a signaling deoxyribozyme confirms that judicious choice of divalent transition metal ions is important in maximizing signals obtained from such systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".