Integration of Photoelectrochemical Signal Transduction with RNA-Cleaving Dnazymes for Culture-Free Detection of Bacteria
Bibliographic record
Abstract
There is a growing interest in developing ultrasensitive biological sensors that can be used for rapid testing at the point-of-need.1 A major hurdle in developing such sensors for detecting pathogens such as bacteria is that they require target enrichment or amplification to deliver the required limit-of-detection.2 Among signal transduction strategies, photoelectrochemical (PEC) signal readout, built on the use of light for enhancing electrochemical reactions, is emerging as an ultrasensitive signal transduction mechanism.3 However, the existing PEC platforms fail to deliver single step testing due to the existence of multiple manual steps, including the addition of biological materials labelled with inorganic photoactive nanoparticles, for signal transduction.3 RNA-cleaving DNAzymes (RCDs), a class of synthetic nucleic acids, have been selected for precisely identifying specific bacterial species without the need for sample processing.4 RCDs are molecular switches that cleave a segment of themselves in response to a particular bacterial target, combining biological recognition with signal transduction.4 We developed photoactive RCDs by tagging them with TiO2 nanomaterials for combining these molecular switches with PEC signal readout. We designed these molecular switches to make and then break semiconductive heterostructures in response to bacterial targets. These photoactive RCDs were the foundational basis for the design novel and highly sensitive PEC bacterial sensor. We developed two photoactive materials for use in the PEC bacterial assay: TiO2 nanorod clusters (rutile) that form high surface area photoelectrodes and sub-nanometer sized TiO2-nanoparticles (anatase) that link to RCDs to create photoactive reporter probes. Combining TiO2-assemblies and TiO2-nanoparticles gives rise to a semiconductor heterostructure that massively improves the photoexcitation efficiency of the combined material system and improves photocurrent generation. Our PEC bacterial sensor makes use of this phenomenon for bacterial detection by utilizing photoactive RCDs to modulate photocurrent by breaking and then rebuilding the TiO2 heterostructures, as a signaling mechanism. The assay consists of a release electrode – modified with photoactive RCDs and a capture electrode – modified with single-stranded DNA probes. Upon target interaction, RCDs release photoactive reporters which are captured by the probes, decreasing the release electrode signal while raising the capture electrode signal. The resulting biosensor can detect E. coli bacterial contamination with high specificity and has achieved a very low limit of detection of 21 CFU/mL in buffer and 18 CFU/mL in lake water samples. These results have set a new record for amplification-free detection of bacteria, that does not rely on target enrichment, reagent addition, or sample processing. This presents a new tool for rapid and in-field water testing. References Nat. Microbiol., 1, 16089 (2016). L. Castillo-Henríquez et al., Sensors, 20, 6926 (2020). A. Victorious, S. Saha, R. Pandey, T. F. Didar, and L. Soleymani, Front. Chem., 7, 617 (2019). I. Cozma, E. M. McConnell, J. D. Brennan, and Y. Li, Biosens. Bioelectron., 177, 112972 (2021).
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".