(Invited) Towards a FRET Based DNA SAM Biosensor for Detection of Nucleic Acids
Bibliographic record
Abstract
Rapid and sensitive detection of nucleic acids is an essential tool in many medical and clinical settings, that allows for fast diagnosis of bacterial and viral infections. Here, we present a DNA FRET-based biosensor to detect a hybridization event on a DNA SAM on gold surface. The monolayer was prepared by a potential assisted thiol exchange deposition, which provides better control over the surface coverage and its uniformity1. Our approach relies on a change in FRET (Förster Resonance Energy Transfer) signal caused by variations in distance between fluorophores. FRET imaging is a well-known, very sensitive technique, used commonly in many areas of chemistry and microbiology. FRET signal depends strongly on the distance between an acceptor and a donor fluorophore. It is an excellent tool to analyze small changes in DNA distance between adjacent strands and the surface2. Metal quenching also plays a major role, since fluorescence is strongly dependent on the distance from the metal surface3, which is an efficient quencher. Fluorophores, both acceptor and donor, are quenched significantly by the gold surface, this however, decreases substantially as the distance increases between the surface and fluorophore. As DNA is binding its complementary strands, the distance between the surface and the fluorophore labelled end increases. Using a self-assembly property of thiols on gold surface, we show that mixed ssDNA SAM composed of two strands of thiolated ssDNA, each labelled with a different FRET fluorophore, gives an 8-to-10-fold signal enhancement, upon DNA hybridization (see figure 1). Binding of a complementary strand rigidifies the adsorbed DNA structure, and as a result we observe the increase in signal due to weaker gold quenching and shorter distance between FRET participating fluorophores. Moreover, by utilizing a single crystal electrode, we can study different surface concentration regimes and fluorescence signal increases, based on surface crystallography and the electrode potential. (1) Leung, K. K.; Gaxiola, A. D.; Yu, H.-Z.; Bizzotto, D. Tailoring the DNA SAM Surface Density on Different Surface Crystallographic Features Using Potential Assisted Thiol Exchange. Electrochim. Acta 2018, 261, 188–197. https://doi.org/https://doi.org/10.1016/j.electacta.2017.12.114. (2) Verhaven, A.; Doneux, T.; Bizzotto, D. Application of FRET Microscopy to the Study of the Local Environment and Dynamics of DNA SAMs on Au Electrodes. Langmuir 2018, 34 (49), 14802–14810. https://doi.org/10.1021/acs.langmuir.8b02131. (3) Casanova-Moreno, J.; Yu, Z. L.; Massey-Allard, J.; Ditchburn, B.; Young, J. F.; Bizzotto, D. Luminescence in Electrochemistry; Springer International Publishing: Cham, 2017; pp 21– 77. Figure 1
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.058 |
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".