Addressing the Instability of DNA Nanostructures in Tissue Culture
Bibliographic record
Abstract
DNA nanotechnology is an advanced technique that could contribute diagnostic, therapeutic, and biomedical research devices to nanomedicine. Although such devices are often developed and demonstrated using in vitro tissue culture models, these conditions may not be compatible with DNA nanostructure integrity and function. The purpose of this study was to characterize the sensitivity of 3D DNA nanostructures produced via the origami method to the in vitro tissue culture environment and identify solutions to prevent loss of nanostructure integrity. We examined whether the physiological cation concentrations of cell culture medium and the nucleases present in fetal bovine serum (FBS) used as a medium supplement result in denaturation and digestion, respectively. DNA nanostructure denaturation due to cation depletion was design- and time-dependent, with one of four tested designs remaining intact after 24 h at 37 °C. Adjustment of medium by addition of MgSO4 prevented denaturation. Digestion of nanostructures by FBS nucleases in Mg(2+)-adjusted medium did not appear design-dependent and became significant within 24 h and when medium was supplemented with greater than 5% FBS. We estimated that medium supplemented with 10% FBS contains greater than 256 U/L equivalent of DNase I activity in digestion of DNA nanostructures. Heat inactivation at 75 °C and inclusion of actin protein in medium inactivated and inhibited nuclease activity, respectively. We examined the impact of medium adjustments on cell growth, viability, and phenotype. Adjustment of Mg(2+) to 6 mM did not appear to have a detrimental impact on cells. Heat inactivation was found to be incompatible with in vitro tissue culture, whereas inclusion of actin had no observable effect on growth and viability. In two in vitro assays, immune cell activation and nanoparticle endocytosis, we show that using conditions compatible with cell phenotype and nanostructure integrity is critical for obtaining reliable experimental data. Our study thus describes considerations that are vital for researchers undertaking in vitro tissue culture studies with DNA nanostructures and some potential solutions for ensuring that nanostructure integrity and functions are maintained during experiments.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".