Multiplexed expression and screening for recombinant protein production in mammalian cells
Bibliographic record
Abstract
BACKGROUND: A variety of approaches to understanding protein structure and function require production of recombinant protein. Mammalian based expression systems have advantages over bacterial systems for certain classes of protein but can be slower and more laborious. Thus the availability of a simple system for production and rapid screening of constructs or conditions for mammalian expression would be of great benefit. To this end we have coupled an efficient recombinant protein production system based on transient transfection in HEK-293 EBNA1 (HEK-293E) suspension cells with a dot blot method allowing pre-screening of proteins expressed in cells in a high throughput manner. RESULTS: A nested PCR approach was used to clone 21 extracellular domains of mouse receptors as CD4 fusions within a mammalian GATEWAY expression vector system. Following transient transfection, HEK-293E cells grown in 2 ml cultures in 24-deep well blocks showed similar growth kinetics, viability and recombinant protein expression profiles, to those grown in 50 ml shake flask cultures as judged by western blotting. Following optimisation, fluorescent dot blot analysis of transfection supernatants was shown to be a rapid method for analysing protein expression yielding similar results as western blot analysis. Addition of urea enhanced the binding of glycoproteins to a nitrocellulose membrane. A good correlation was observed between the results of a plate based small scale transient transfection dot blot pre-screen and successful purification of proteins expressed at the 50 ml scale. CONCLUSION: The combination of small scale multi-well plate culture and dot blotting described here will allow the multiplex analysis of different mammalian expression experiments enabling a faster identification of high yield expression constructs or conditions prior to large scale protein production. The methods for parallel GATEWAY cloning and expression of multiple constructs in cell culture will also be useful for applications such as the generation of receptor protein microarrays.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".