Uptake and Fate of Fluorescently Labeled DNA Nanostructures in Cellular Environments: A Cautionary Tale
Bibliographic record
Abstract
Fluorescent dye labeling of DNA oligonucleotides and nanostructures is one of the most used techniques to track their fate and cellular localization inside cells.Here, we report that intracellular fluorescence, and even FRET signals, cannot be correlated with the cellular uptake of intact DNA structures.Live cell imaging revealed high colocalization of cyanine-labeled DNA oligos and nanostructures with phosphorylated small-molecule cyanine dyes, one of the degradation products from these DNA compounds.Nuclease degradation of the strands outside and inside the cell results in a misleading intracellular fluorescent signal.The signal is saturated by the fluorescence of the degradation product (phosphorylated dye).To test our hypothesis, we synthesized a range of DNA structures, including Cy3-and Cy5-labeled DNA cubes and DNA tetrahedra, and oligonucleotides with different stabilities toward nucleases.All give fluorescence signals within the mitochondria after cellular uptake and strongly colocalize with a free phosphorylated dye control.Kinetics experiments revealed that uptake of stable DNA structures is delayed.We also studied several parameters influencing fluorescent data: stability of the DNA strand, fixation methods that can wash away the signal, position of the dye on the DNA strand, and design of FRET experiments.DNA nanostructures hold tremendous potential for biomedical applications and biotechnology because of their biocompatibility, programmability, and easy synthesis.However, few examples of successful DNA machines in vivo have been reported.We believe this contribution can be used as a guide to design better cellular uptake experiments when using fluorescent dyes, in order to further propel the biological development, and application of DNA nanostructures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".