Fluorescent labeling in semi-solid medium for selection of mammalian cells secreting high-levels of recombinant proteins
Bibliographic record
Abstract
BACKGROUND: Despite the powerful impact in recent years of gene expression markers like the green fluorescent protein (GFP) to link the expression of recombinant protein for selection of high producers, there is a strong incentive to develop rapid and efficient methods for isolating mammalian cell clones secreting high levels of marker-free recombinant proteins. Recently, a method combining cell colony growth in methylcellulose-based medium with detection by a fluorescently labeled secondary antibody or antigen has shown promise for the selection of Chinese Hamster Ovary (CHO) cell lines secreting recombinant antibodies. Here we report an extension of this method referred to as fluorescent labeling in semi-solid medium (FLSSM) to detect recombinant proteins significantly smaller than antibodies, such as IGF-E5, a 25 kDa insulin-like growth factor derivative. RESULTS: CHO cell clones, expressing 300 microg/ml IGF-E5 in batch culture, were isolated more easily and quickly compared to the classic limiting dilution method. The intensity of the detected fluorescent signal was found to be proportional to the amount of IGF-E5 secreted, thus allowing the highest producers in the population to be identified and picked. CHO clones producing up to 9.5 microg/ml of Tissue-Plasminogen Activator (tPA, 67 kDa) were also generated using FLSSM. In addition, IGF-E5 high-producers were isolated from 293SF transfectants, showing that cell selection in semi-solid medium is not limited to CHO and lymphoid cells. The best positive clones were collected with a micromanipulator as well as with an automated colony picker, thus demonstrating the method's high throughput potential. CONCLUSION: FLSSM allows rapid visualization of the high secretors from transfected pools prior to picking, thus eliminating the tedious task of screening a high number of cell isolates. Because of its rapidity and its simplicity, FLSSM is a versatile method for the screening of high producers for research and industry.
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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 | 0.001 | 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 | 0.004 |
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".