Endothelial intercellular cell adhesion molecule 1 contributes to cell aggregate formation in CHO cells cultured in serum‐free media
Bibliographic record
Abstract
Chinese hamster ovary (CHO) cells have been adapted to grow in serum-free media and in suspension culture to facilitate manufacturing needs. Some CHO cell lines, however, tend to form cell aggregates while being cultured in suspension. This can result in reduced viability and capacity for single cell cloning (SCC) via limiting dilution, and process steps to mitigate cell aggregate formation, for example, addition of anti-cell-aggregation agents. In this study, we have identified endothelial intercellular cell adhesion molecule 1 (ICAM-1) as a key protein promoting cell aggregate formation in a production competent CHO cell line, which is prone to cell aggregate formation. Knocking out (KO) the ICAM-1 gene significantly decreased cell aggregate formation in the culture media without anti-cell-aggregation reagent. This trait can simplify the process of transfection, selection, automated clone isolation, and so on. Evaluation in standard cell line development of ICAM-1 KO and wild-type CHO hosts did not reveal any noticeable impacts on titer or product quality. Furthermore, analysis of a derived nonaggregating cell line showed significant reductions in expression of cell adhesion proteins. Overall, our data suggest that deletion of ICAM-1 and perhaps other cell adhesion proteins can reduce cell aggregate formation and improve clonality assurance during SCC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".