Application of xCELLigence real-time cell analysis to the microplate assay for pertussis toxin induced clustering in CHO cells
Bibliographic record
Abstract
The microplate assay with Chinese Hamster Ovary (CHO) cells is currently used as a safety test to monitor the residual pertussis toxin (PT) amount in acellular pertussis antigens prior to vaccine formulation. The assay is based on the findings that the exposure of CHO cells to PT results in a concentration-dependent clustering response which can be used to estimate the amount of PT in a sample preparation. A major challenge with the current CHO cell assay methodology is that scoring of PT-induced clustering is dependent on subjective operator visual assessment using light microscopy. In this work, we have explored the feasibility of replacing the microscopy readout for the CHO cell assay with the xCELLigence Real-Time Cell Analysis system (ACEA BioSciences, a part of Agilent). The xCELLigence equipment is designed to monitor cell adhesion and growth. The electrical impedance generated from cell attachment and proliferation is quantified via gold electrodes at the bottom of the cell culture plate wells, which is then translated into a unitless readout called cell index. Results showed significant decrease in the cell index readouts of CHO cells exposed to PT compared to the cell index of unexposed CHO cells. Similar endpoint concentrations were obtained when the PT reference standard was titrated with either xCELLigence or microscopy. Testing genetically detoxified pertussis samples unspiked or spiked with PT further supported the sensitivity and reproducibility of the xCELLigence assay in comparison with the conventional microscopy assay. In conclusion, the xCELLigence RTCA system offers an alternative automated and higher throughput method for evaluating PT-induced clustering in CHO cells.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".