Efficient, Low-Cost Nucleofection of Passaged Chondrocytes
Bibliographic record
Abstract
Nucleofection of chondrocytes has been shown to be an adequate method of transfection. Using Amaxa's nucleofection system, transfection efficiencies up to 89% were achievable for vector (pmaxGFP) and 98% for siRNA (siGLO) into passaged chondrocytes. However, such methods rely on costly commercial kits with proprietary reagents limiting its use in basic science labs and in clinical translation. Bovine-passaged chondrocytes were plated in serum reduced media conditionsand then nucleofected using various in laboratory-produced buffers. Cell attachment, confluency, viability, and transfection efficiency was assessed following nucleofection. For each parameter the buffers were scored and a final rank for each buffer was determined. Buffer denoted as 1M resulted in no significant difference for cell attachment, confluency, and viability as compared to non-nucleofected controls. Nucleofection in 1M buffer, in the absence of DNA vectors, resulted in increased col2, ki67, ccnd1 mRNA levels, and decreased col1 mRNA levels at 4 days of culture. Flow cytometry revealed that the transfection efficiency of 1M buffer was comparable to that obtained using the Amaxa commercial kit. siRNA designed against lamin A/C resulted in an average reduction of lamin A and C proteins to 19% and 8% of control levels, respectively. This study identifies a cost-effective, efficient method of nonviral nucleofection of bovine-passaged chondrocytes using known buffer formulations. Human-passaged chondrocytes could also be successfully nucleofected in 1M buffer. Thus this method should facilitate cost-efficient gene targeting of cells used for articular cartilage repair in a research setting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".