origamiFISH allows universal, label-free, single molecule visualization of DNA origami nanodevices across biological samples
Bibliographic record
Abstract
Structural DNA nanotechnology enables user-prescribed design of DNA nanostructures (DNs) for biological applications, but how DN design determines their bio-distribution and cellular interactions remain poorly understood. One challenge is that current methods for tracking DN fates in situ , including fluorescent-dye labeling, suffer from low sensitivity and dye-induced artifacts. Here we present origamiFISH, a label-free and universal method for single-molecule fluorescence detection of DNA origami nanostructures in cells and tissues. origamiFISH targets pan-DN scaffold sequences with hybridization chain reaction (HCR) probes to achieve thousand-fold signal amplification. We identify cell-type and shape-specific spatiotemporal uptake patterns within 1 minute of uptake and at picomolar DN concentrations, 10,000x lower than field standards. We additionally optimized compatibility with immunofluorescence and tissue clearing to visualize DN distribution within tissue cryo/vibratome-sections, slice cultures, and whole-mount organoids. Together, origamiFISH enables faithful mapping of DN interactions across subcellular and tissue barriers for guiding the development of DN-based therapeutics.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".