Large‐scale transient transfection of serum‐free suspension‐growing HEK293 EBNA1 cells: Peptone additives improve cell growth and transfection efficiency
Bibliographic record
Abstract
Large-scale transient transfection of mammalian cells is a recent and powerful technology for the fast production of milligram amounts of recombinant proteins (r-proteins). As many r-proteins used for therapeutic and structural studies are naturally secreted or engineered to be secreted, a cost-effective serum-free culture medium that allows their efficient expression and purification is required. In an attempt to design such a serum-free medium, the effect of nine protein hydrolysates on cell proliferation, transfection efficiency, and volumetric productivity was evaluated using green fluorescent protein (GFP) and human placental secreted alkaline phosphate (SEAP) as reporter genes. The suspension growing, serum-free adapted HEK293SF-3F6 cell line was stably transfected with an EBNA1-expression vector to increase protein expression when using EBV oriP bearing plasmids. Compared to our standard serum-free medium, concomitant addition of the gelatin peptone N3 and removal of BSA slightly enhanced transfection efficiency and significantly increased volumetric productivity fourfold. Using the optimized medium formulation, transfection efficiencies between 40-60% were routinely obtained and SEAP production reached 18 mg/L(-1). To date, we have successfully produced and purified over fifteen r-proteins from 1-14-L bioreactors using this serum-free system. As examples, we describe the scale-up of two secreted his-tagged r-proteins Tie-2 and Neuropilin-1 extracellular domains (ED) in bioreactors. Each protein was successfully purified to >95% purity following a single immobilized metal affinity chromatography (IMAC) step. In contrast, purification of Tie-2 and Neuropilin-1 produced in serum-containing medium was much less efficient. Thus, the use of our new serum-free EBNA1 cell line with peptone-enriched serum-free medium significantly improves protein expression compared to peptone-less medium, and significantly increases their purification efficiency compared to serum-containing medium. This eliminates labor-intensive and expensive chromatographic steps, and allows for the simple, reliable, and extremely fast production of milligram amounts of r-proteins within 5 days posttransfection.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".