High-level and high-throughput recombinant protein production by transient transfection of suspension-growing human 293-EBNA1 cells
Bibliographic record
Abstract
A scalable transfection procedure using polyethylenimine (PEI) is described for the human embryonic kidney 293 cell line grown in suspension. Green fluorescent protein (GFP) and human placental secreted alkaline phosphatase (SEAP) were used as reporter genes to monitor transfection efficiency and productivity. Up to 75% of GFP-positive cells were obtained using linear or branched 25 kDa PEI. The 293 cell line and two genetic variants, either expressing the SV40 large T-antigen (293T) or the Epstein-Barr virus (EBV) EBNA1 protein (293E), were tested for protein expression. The highest expression level was obtained with 293E cells using the EBV oriP-containing plasmid pCEP4. We designed the pTT vector, an oriP-based vector having an improved cytomegalovirus expression cassette. Using this vector, 10- and 3-fold increases in SEAP expression was obtained in 293E cells compared with pcDNA3.1 and pCEP4 vectors, respectively. The presence of serum had a positive effect on gene transfer and expression. Transfection of suspension-growing cells was more efficient with linear PEI and was not affected by the presence of medium conditioned for 24 h. Using the pTT vector, >20 mg/l of purified His-tagged SEAP was recovered from a 3.5 l bioreactor. Intracellular proteins were also produced at levels as high as 50 mg/l, representing up to 20% of total cell proteins.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".