An efficient low cost means of biophysical gene transfection in primary cells
Bibliographic record
Abstract
Efficient, facile gene modification of cells has become an indispensable part of modern molecular biology. For the majority of cell lines and several primary populations, such modifications can be readily performed through a variety of methods. However, many primary cell lines such as stem cells frequently suffer from poor transfection efficiency. Though several physical approaches have been introduced to circumvent these issues, they often require expensive/specialized equipment and/or consumables, utilize substantial cell numbers and often still suffer from poor efficiency. Viral methods are capable of transducing difficult cellular populations, however such methods can be time consuming for large arrays of gene targets, present biohazard concerns, and result in expression of viral proteins; issues of concern for certain experimental approaches. We report here a widely applicable, low-cost (< $100 CAD) method of electroporation, applicable to small (1-10 μl) cell volumes and composed of equipment readily available to the average investigator. Using this system we observe a sixfold increase in transfection efficiency in embryonic stem cell lines compared to commercial devices. Due to efficiency gains and reductions in volume and applied voltage, this process improves the survival of sensitive stem cell populations while reducing reagent requirements for protocols such as Cas9/gRNAs transfections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".