Correlation of precisely fabricated geometric characteristics of DNA-origami nanostructures with their cellular entry in human lens epithelial cells
Bibliographic record
Abstract
Human lens epithelial cells (hLECs) are critical for lens transparency, and their aberrant metabolic activity and gene expression can lead to cataract. Intracellular delivery to hLECs, especially to sub-cellular organelles (e.g., mitochondrion and nucleus), is a key step in engineering cells for cell- and gene- based therapies. Despite a broad variety of nano- and microparticles can enter cells, their spatial characteristics relevant to cellular uptake and localization remains elusive. To investigate cellular internalization of nanostructures in hLECs, herein, DNA nanotechnology was exploited to precisely fabricate four distinct, mass-controlled DNA-origami nanostructures (DONs) through computer-aided design. Ensembled DONs included the rods, ring, triangle, and octahedron with defined geometric parameters of accessible surface area, effective volume, compactness, aspect ratio, size and vertex number. Atomic force microscopy and agarose gel electrophoresis showed that four DONs self-assembled within 3.5h with up to 59% yield and exhibited structural intactness in cell culture medium for 4 h. Flow cytometry analysis of four Cy5-labelled DONs in hLECs HLE-B3 found time-dependent cellular uptake over 2 h, among which the octahedron and triangle had higher cellular accumulation than the rod and ring. More importantly, the vertex number among other geometric parameters was positively correlated with cellular entry. Confocal images further revealed that four DONs had preferential localization at mitochondria to nucleus at 2 h in HLE-B3 cells, and the degree of their biodistribution varied among DONs as evidenced by Manders' correlation coefficient. This study demonstrates the DONs dependent cellular uptake and intracellular compartment localization in hLECs, heralding the future design of structure-modulating delivery of nanomedicine for ocular therapy.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".