An Optimized Mouse Embryonic Stem Cell Based Reverse Poly-Transfection Technique for Rapid Exploration of Nucleic Acid Ratios
Bibliographic record
Abstract
Due to its relative simplicity and ease of use, transient transfection of mammalian cell lines with nucleic acids has become a mainstay in biomedical research. While most widely used cell lines have robust protocols for transfection in adherent two-dimensional culture, these protocols often do not translate well to less-studied lines or those with atypical, hard-to-transfect morphologies. Using mouse pluripotent stem cells grown in 2i/LIF media, a widely used culture model for regenerative medicine, this method outlines an optimized, rapid reverse transfection protocol capable of achieving higher transfection efficiency. Leveraging this protocol, a three-plasmid poly-transfection is performed, taking advantage of the higher-than-normal efficiency in plasmid delivery to study an expanded range of plasmid stoichiometry. This reverse poly-transfection protocol allows for a one-pot experimental method, enabling users to optimize plasmid ratios in a single well, rather than across several co-transfections. By facilitating the rapid exploration of the effect of DNA stoichiometry on the overall function of delivered genetic circuits, this protocol minimizes the time and cost of embryonic stem cell transfection.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".