Bioorthogonal DNA Adsorption on Polydopamine Nanoparticles Mediated by Metal Coordination for Highly Robust Sensing in Serum and Living Cells
Bibliographic record
Abstract
DNA-functionalized nanomaterials, such as various 2D materials, metal oxides, and gold nanoparticles, have been extensively explored as biosensors. However, their practical applications for selective sensing and imaging in biological samples remain challenging due to interference from the sample matrix. Bioorthogonal chemistry has allowed specific reactions in cells, and we want to employ this concept to design nanomaterials that can selectively adsorb DNA but not proteins or other abundant biomolecules. In this work, DNA oligonucleotides were found to be adsorbed on polydopamine nanoparticles (PDANs) via polyvalent metal-mediated coordination, and such adsorption bioorthogonally resisted DNA displacement by various biological ligands, showing better performance compared to graphene oxide and metal oxide nanoparticles for DNA detection. Using DNA/PDANs as biosensors, a detection limit of <1 nM target DNA was achieved in serum and other biological samples, and imaging of cancer-related microRNA in cells was demonstrated. The DNA binding mechanism on PDAN was further studied by ligand displacement experiments and X-ray photoelectron spectroscopy characterization, which demonstrated the critical role of polyvalent metal ions to bridge DNA with PDANs. This work provides fundamental insights into the biointerface science of PDANs with DNA, which can benefit applications in biosensor design, directed assembly of nanomaterials, bioimaging, and drug delivery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".