Enhanced non-viral gene delivery by coordinated endosomal release and inhibition of β-tubulin deactylase
Bibliographic record
Abstract
Efficient non-viral gene delivery is highly desirable but often unattainable with some cell-types. We report here that non-viral DNA polyplexes can efficiently transfect differentiated neuronal and stem cells. Polyplex transfection centrifugation protocols was enhanced by including a simultaneous treatment with a DOPE/CHEMS lipid suspension and a microtubule inhibitor, Tubastatin A. Lipoplex transfection protocols were not improved by this treatment. This mechanism of action was unravelled by systematically identifying and rationally mitigating barriers limiting high transfection efficiency, allowing unexpected improvements in the transfection of mesenchymal stem cells (MSC), primary neuron and several hard-to-transfect cell types beyond what are currently achievable using cationic polymers. The optimized formulation and method achieved high transfection efficiency with no adverse effects on cell viability, cell proliferation or differentiation. High efficiency modification of MSC for cytokine overexpression, efficient generation of dopaminergic neuron using neural stem cells and enhanced genome editing with CRISPR-Cas9 were demonstrated. In summary, this study described a cost-effective method for efficient, rapid and scalable workflow for ex vivo gene delivery using a myriad of nucleic acids including plasmid DNA, mRNA, siRNA and shRNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".