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, the origamiFISH technique is extended to quantify DNs in single‐cell suspensions, including in 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 cell‐state characterization. It is shown that origamiFISH‐Flow provides 20‐fold higher signal‐to‐noise ratio for DN detection compared to dye labeling approaches, leading to the capture of >25‐fold more DN + cells under single‐picomolar DN uptake concentrations. Additionally, the use of origamiFISH‐Flow is validated to profile the uptake of various DN shapes across multiple cell lines and splenocytes, as well as to 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".