Designing Nanoparticle Surfaces with DNA Barcodes for Accurate In Vivo Quantification
Bibliographic record
Abstract
DNA barcoding is a common method for identifying the biodistribution of nanoparticles. DNA barcodes are typically encapsulated within nanoparticles to ensure accurate measurements by next-generation sequencing. This method limits the types of nanoparticles that can be screened. DNA can also be coated on nanoparticle surfaces. However, it is unclear whether surface-coated DNA can be used as barcodes because they can degrade, making the identification and quantification of nanoparticle designs challenging. Here, we developed strategies to reduce DNA degradation on nanoparticle surfaces, allowing surface-based DNA barcodes for biodistribution applications. We demonstrate that nanoparticle size, DNA density, and polymer length and density are essential design parameters for accurately identifying and quantifying nanoparticles in vivo. We found that chemical modification of DNA and shielding using neutral polymers reduce DNA degradation. We validated that surface barcoding can determine the in vivo distribution of nanoparticles. Our findings pave the way for the use of surface-based DNA barcodes for in vivo screening of nanoparticle formulations for targeted applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".