A Universal Platform for Macromolecular Deliveryinto Cells Using Gold Nanoparticle Layers via the Photoporation Effect
Bibliographic record
Abstract
Although promising, it is challenging to develop a simple and universal method for the high‐efficiency delivery of biomacromolecules into diverse cells. Here, a universal delivery platform based on gold nanoparticle layer (GNPL) surfaces is proposed. Upon laser irradiation, GNPL surfaces show such good photothermal properties that absorption of the laser energy causes a rapid increase in surface temperature, leading to enhanced membrane permeability of the cultured cells and the diffusion of macromolecules into the cytosol from the surrounding medium. The high‐efficiency delivery of different macromolecules such as dextran and plasmid DNA into different cell types is achieved, including hard‐to‐transfect mouse embryonic fibroblasts (mEFs) and human umbilical vein endothelial cells (HUVECs), while cell viability is well maintained under optimized irradiation conditions. The platform vastly outperforms the leading commercial reagent, Lipofectamine 2000, especially in transfecting hard‐to‐transfect cell lines (plasmid transfection efficiency: ≈53% vs ≈19% for mEFs and ≈44% vs ≈8% for HUVECs). Importantly, as the gold nanoparticles (GNPs) constituting the GNPL are firmly immobilized together, the potential cytotoxicity caused by endocytosis of GNPs is effectively avoided. This platform is reliable, efficient, and cost‐effective with high‐throughput and broad applicability across different cell types, opening up an innovative avenue for high‐efficiency intracellular delivery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".