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A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics
Published on: September 22, 2011
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Development of Responsive Promoters and their Utilization for Stable CHO Sensor Cell Lines
1Boehringer Ingelheim Pharma GmbH & Co KG, Bioprocess Development Biologicals, Cell Line Development, Biberach, Germany. Nikolas.Zeh@boehringer-ingelheim.com.
Methods in Molecular Biology (Clifton, N.J.)
|June 26, 2024
Summary
Researchers developed sensor Chinese hamster ovary (CHO) cell lines that signal bioprocess limitations like low oxygen or high osmolality. This enables real-time monitoring for improved recombinant protein production.
Area of Science:
- Biotechnology
- Cell Biology
- Bioprocess Engineering
Background:
- Chinese hamster ovary (CHO) cells are crucial for recombinant protein production.
- Monitoring bioprocess parameters (e.g., oxygen, osmolality) is vital but labor-intensive.
- A cell line with a measurable response to external influences is needed for bioprocess modeling.
Purpose of the Study:
- To develop a protocol for generating responsive promoter elements in CHO cells.
- To create stable sensor cell lines that communicate process conditions.
- To enable real-time monitoring of bioprocess parameters.
Main Methods:
- Utilized hypoxia and osmolality sensing elements in CHO cells.
- Triggered expression via a minimal CMV promoter.
- Employed unstable variants of GFP and BFP for real-time assessment via flow cytometry.
- Integrated an automated sampling system with fluorescence microscopy.
Main Results:
- Successfully generated responsive promoters and stable sensor cell lines.
- Demonstrated real-time detection of limiting conditions through fluorescent protein expression.
- Established a continuous observation system for CHO cell cultures.
Conclusions:
- The developed sensor cell lines provide a valuable tool for monitoring and managing bioprocesses.
- This approach facilitates improved recombinant protein production by enabling timely adjustments.
- The system supports advanced bioprocess modeling and control strategies.
