Bio-Orthogonal Mediated Nucleic Acid Transfection of Cells via Cell Surface Engineering
Bibliographic record
Abstract
The efficient delivery of foreign nucleic acids (transfection) into cells is a critical tool for fundamental biomedical research and a pillar of several biotechnology industries. There are currently three main strategies for transfection including reagent, instrument, and viral based methods. Each technology has significantly advanced cell transfection; however, reagent based methods have captured the majority of the transfection market due to their relatively low cost and ease of use. This general method relies on the efficient packaging of a reagent with nucleic acids to form a stable complex that is subsequently associated and delivered to cells via nonspecific electrostatic targeting. Reagent transfection methods generally use various polyamine cationic type molecules to condense with negatively charged nucleic acids into a highly positively charged complex, which is subsequently delivered to negatively charged cells in culture for association, internalization, release, and expression. Although this appears to be a straightforward procedure, there are several major issues including toxicity, low efficiency, sorting of viable transfected from nontransfected cells, and limited scope of transfectable cell types. Herein, we report a new strategy (SnapFect) for nucleic acid transfection to cells that does not rely on electrostatic interactions but instead uses an integrated approach combining bio-orthogonal liposome fusion, click chemistry, and cell surface engineering. We show that a target cell population is rapidly and efficiently engineered to present a bio-orthogonal functional group on its cell surface through nanoparticle liposome delivery and fusion. A complementary bio-orthogonal nucleic acid complex is then formed and delivered to which chemoselective click chemistry induced transfection occurs to the primed cell. This new strategy requires minimal time, steps, and reagents and leads to superior transfection results for a broad range of cell types. Moreover the transfection is efficient with high cell viability and does not require a postsorting step to separate transfected from nontransfected cells in the cell population. We also show for the first time a precision transfection strategy where a single cell type in a coculture is target transfected via bio-orthogonal click chemistry.
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 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 | 0.001 |
| 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".