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Record W4412513382 · doi:10.1149/ma2025-01602915mtgabs

Investigation of Redox Probe Location on Single-Stranded DNA Using Streptavidin-Based Electrochemical Biosensing Platform

2025· article· en· W4412513382 on OpenAlexaff
Survanshu Saxena, Sandy Zakaria, Yingfu Li, Todd Hoare, Leyla Soleymani

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStreptavidinBiosensorRedoxElectrochemistryDNANanotechnologyChemistryMaterials scienceBiochemistryBiotinElectrodeInorganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Most electrochemical platforms utilize a thiolated capture DNA and 6-mercapto-1-hexanol (MCH) as a monolayer for performing electrochemical DNA biosensing (1–5). These platforms employ redox-labeled DNA, mostly using methylene blue (MB), to generate electrochemical signals following hybridization with capture DNA, resulting in the formation of double-stranded DNA (dsDNA) with MB positioned towards the electrode surface (1–5). While previous studies predominantly focus on using MB-labeled DNA to form dsDNA with thiolated capture DNA, this study explores the effect of MB positions at three different locations on biotinylated single-stranded DNA (DNA barcodes) within a non-thiolated electrochemical system that uses streptavidin-coated gold electrodes. We investigated biotinylated barcodes with MB labels positioned at three different locations: one near the 5’ end of the DNA, proximal to the electrode surface (MB1); one in the middle (MB2); and one near the 3’ distal end of the DNA (MB3). To analyze the performance of these three MB barcodes, we prepared streptavidin-coated nanostructured gold electrodes and measured current signals using square wave voltammetry (SWV) after the attachment of biotinylated MB barcodes with the streptavidin on the electrodes. We examined the time (ranging from 5 to 120 minutes) and temperature (room temperature (RT) and 37 ºC) kinetics for the barcodes, and we also determined the limit of detection (LOD) in buffer, covering concentrations from 0 to 1000 nM. Our barcode system was compared to the conventional thiol-DNA and MCH-backfilling-based electrochemical platform. Moreover, we explored the application of this platform as a signal-OFF system using complementary capture DNA. Our findings indicated that MBs located further from the electrode surface (MB2 and MB3) facilitated faster electron transfer than MB1, which was positioned closest to the electrode. This difference in electrochemical signal among the MB barcodes can be attributed to the flexibility of ssDNA, which makes methylene blue more accessible for electron transfer at the electrode surface in the cases of MB2 and MB3. During our examination of time and temperature kinetics, we observed that the current density signal from MB1 increased much more slowly than that from MB2 and MB3 at both temperatures. Notably, MB2 exhibited the fastest signal increase, showing significant response within 5 minutes at 37 ºC and 15 minutes at RT. The current density signal for MB2 and MB3 saturated after 30 minutes at 37 ºC, while the signal for MB1 continued to rise slowly. The reproducibility of the prepared system was confirmed when we tested another barcode sequence with similar MB positions. From the LOD analysis, we found that the lowest concentration was detectable for MB2 compared to the other barcodes. The streptavidin system (MB2 and MB3) demonstrated higher signaling and less variation than the capture DNA-based system, which required 4 µM of capture DNA to achieve the highest current density signal. When evaluating the signal-OFF assay, we observed a decrease in current density signals following the addition of complementary capture DNA to the MB-barcode immobilized streptavidin electrodes. MB1 exhibited the highest signal suppression (~80% suppression) among the other two barcodes (~60% suppression), likely due to its reduced flexibility after hybridization with capture DNA and the slower electron transfer through the streptavidin layer to the electrode surface. Overall, aside from the signal suppression study, the MB2 barcode showed better performance in terms of better LOD, faster electron transfer, higher current density signals than MB1 and MB3, and higher signal than a capture DNA-based system. Moreover, the results of this study will help as a guide for deciding the position of MB on the ssDNA in a signal-ON assay or on dsDNA in a signal-OFF assay and the use of a streptavidin-based electrochemical platform for biosensing applications. References: A. A. Lubin, B. Vander Stoep Hunt, R. J. White, K. W. Plaxco, Anal. Chem. 81, 2150–2158 (2009). R. Pandey et al., ACS Sensors. 7, 985–994 (2022). S. M. Traynor, G. A. Wang, R. Pandey, F. Li, L. Soleymani, Angew. Chemie. 132, 22806–22811 (2020). A. Victorious et al., Angew. Chemie Int. Ed. 61, e202204252 (2022). Z. Zhang, B. R. Adhikari, P. Sen, L. Soleymani, Y. Li, Adv. Agrochem. 2, 246–257 (2023).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.021
GPT teacher head0.268
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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