High-throughput, label-free detection of DNA origami in single-cell suspensions using origamiFISH-Flow
Bibliographic record
Abstract
Abstract Structural DNA nanotechnology enables custom fabrication of nanoscale devices and promises diverse biological applications. However, the effects of design on DNA nanostructure (DN)-cell interactions in vitro and in vivo are not yet well-characterized. origamiFISH is a recently developed technique for imaging DNs in cells and tissues. Compared to the use of fluorescent tags, origamiFISH offers label-free and structure-agnostic detection of DNs with significantly improved sensitivity. Here, we extend the origamiFISH technique to quantifying DNs in single-cell suspensions, including nonadherent cells such as subsets of immune cells, via readout by flow cytometry. This method, referred to as origamiFISH-Flow, is high-throughput (e.g., 10,000 cells per second) and compatible with immunostaining for concurrent cell-type and -state characterization. We demonstrate that origamiFISH-Flow enhances signal-to-noise ratio by up to 20-fold compared to dye labeling approaches, leading to the capture of >25-fold more DN + cells at low, single-picomolar DN uptake concentrations. We additionally show the use of origamiFISH-Flow to profile cell-type and shape-specific DN uptake patterns across cell lines and splenocytes and quantify in vivo DN accumulation in lymphoid organs. Together, origamiFISH-Flow offers a new tool to interrogate DN interactions with cells and tissues, while providing insights for tailoring their designs in bio-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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".