UV-Cross-Linked DNA Nanomaterials Enable Robust Nanopatterning of Folate Ligands for Enhanced Cellular Uptake
Bibliographic record
Abstract
The arrangement of ligands on a nanomaterial scaffold is a powerful approach to enhance targeted cellular delivery. However, nanomaterial-mediated delivery often employs imprecise ligand conjugation, limiting the exploration of optimal ligand density and spatial organization. To address these challenges, we developed DNA nanomaterials with precisely spaced folate ligands and rigidified them via a postassembly UV-based thymine cross-linking. These materials exhibit exceptional nuclease stability and maintain structural integrity both under biologically relevant conditions and during internalization into live HeLa cells. We used these nanomaterials as scaffolds for folate patterning and identified optimal modes of folate presentation for cellular uptake. Each step of the uptake process was probed, revealing the synergistic effects of structural stabilization and precise ligand patterning on the uptake mechanism, intracellular retention, and export dynamics. We then used our nanopatterned nanomaterials as delivery vectors for a gene-silencing nucleic acid payload. By integrating optimized ligand presentation and structural immobilization, we successfully achieved targeted gene silencing in folate receptor alpha-expressing cancer cells. This work showcases the effect that DNA nanostructure fidelity and rigidity have on the presentation of ligand moieties. It introduces UV cross-linking as a critical tool for structural stabilization of DNA nanomaterials, enabling applications in therapeutic delivery, diagnostics, and nanoscale cell surface engineering. In addition, this study reveals spatial principles of folate nanopatterning to enhance future targeted delivery systems with precision, stability, and biocompatibility.
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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) | 0.001 | 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".