Maximizing Efficiency in Cell Line Development: Techniques and Tips
Cell line development is essential for producing many modern therapies, including complex molecules and cell-based therapies1. New automation and genetic engineering technologies have made cell line development increasingly competitive. This acceleration in capabilities allows companies to bring novel therapeutics to market more quickly, giving companies that embrace the latest advances an edge over the competition2. Furthermore, faster, more efficient cell development can lead to incredible savings as companies traverse the expensive drug discovery pipeline within shorter timeframes3. Thus, companies must prioritize efficiency to achieve their goals. In recent years, we have witnessed improvements across all techniques that make up cell line development pipelines, including cell culture and screening. This article will explore how companies can maximize efficiency and continuously shorten their cell line development timelines.
Automation
Automation is revolutionizing the biomedical industry by streamlining processes across basic research and industrial applications. Automation uses robotics, software, artificial intelligence, and modern fluid-handling technologies to make cell line development faster and less risky. Automation is a fundamental aspect of modern biomedicine, enhancing the efficiency of all techniques that incorporate it. Automated instruments like the UP.SIGHT from CYTENA relieves scientists of repetitive, time-consuming tasks, allowing them to allocate more time to improving processes and analyzing data.
Reduced Risk
Cell line development processes require single-cell dispensing, which is time-consuming and prone to error and contamination when performed manually5. Loss of clones can be devastating for cell line development, often requiring scientists to start from scratch. Clonal lines can fail due to instability and contamination, which can come from microorganisms or other clones. Automated dispensing reduces contamination by eliminating the need for manual input. Dispensing using the UP.SIGHT is more gentle on cells than other dispensing methods, which can improve the stability and viability of susceptible cell lines like induced pluripotent stem cells.
Higher Efficiency
Automation facilitates high throughput in cell line development by streamlining dispensing and screening processes. Manual pipetting, screening, and data management are incredibly time-consuming. Automation allows scientists to seed more clones and more efficiently select the highest-performing ones to proceed with. The UP.SIGHT’s imaging technology, paired with the C.STUDIO software, makes it easy for scientists to monitor clones over time, allowing them to pinpoint the best ones early and save resources when scaling up production (Fig. 1).
Figure 1. The C.STUDIO software allows for accurate quantification of cell proliferation following single-cell seeding.
Scaling
Choosing when and how to scale up clones makes a huge difference in the overall efficiency of cell line development processes6. Ideally, researchers should use the least amount of resources possible without sacrificing efficiency and allocate resources toward selecting and propagating optimal clones. In practical terms, this means using a multiwell plate format to seed and grow clones and selecting high-performing clones as quickly as possible.
Microbioreactors offer small-scale culture and monitoring of cell lines during the early stages of development which are ideal for workflow efficiency. The S.NEST from CYTENA is a microbioreactor that monitors pH and dissolved oxygen (DO) in real time allowing for precise control and user-defined optimization of culture conditions.
Scaling up too early with poor-performing clones leads to a significant loss of resources and time, which increases exponentially the more clones are brought forward. The UP.SIGHT from CYTENA allows scientists to seed single cells in 96- or 384-well formats in 2 and 8 minutes, respectively, facilitating high throughput cell line development. The C.STUDIO allows the best clones to be selected early, meaning scientists optimize their scaling for speed and resource efficiency.
Culture Conditions
Optimal cell culture conditions are essential for streamlining cell line development processes. Optimized conditions mean cells achieve high levels of proliferation and target protein production. If cells become overconfluent or are handled incorrectly, they can become stressed, which reduces proliferation and may impact productivity7. Furthermore, inconsistent culture conditions may lead to clonal instability and the accumulation of genetic changes that reduce productivity8.
Induced pluripotent stem cells (iPSCs), for example, are incredibly valuable in several research and therapeutic applications. However, they are very sensitive to culture conditions, including excessive handling. Research from CYTENA has found that adding supplements to growth media eliminated the need for media changes, which increased the clonal recovery rate and growth rate of iPSCs after seeding. Read our application note to learn more.
Automated cell culture platforms remove much of the variation in cell culture that leads to these issues. The C.STATION from CYTENA offers an all-in-one solution for cell line development. It incorporates the UP.SIGHT for single-cell dispensing and imaging with automated liquid handling and cell culture to ensure clones are grown in consistent user-defined conditions. The C.STATION monitors colony growth and performs media swaps as necessary to ensure optimal growth with full automation.
Screening
Screening is essential to the cell line development process. It ensures that cells are fit for purpose and discards poorly performing clones. The aim is to select high-performing cells that produce large quantities of the target protein. The best way to monitor this is to directly measure the amount of the protein that the cells produce.
The F.QUANT is a dedicated assay from CYTENA that accurately measures the titer of target proteins produced by a cell. With the F.QUANT, scientists can ensure they select only the best cells to scale up and use for therapy production.
Quality Control
There are lots of potential pitfalls at every stage of cell line development. Therefore, keeping tabs on potential issues is essential for maximizing efficiency throughout the workflow. Robust quality control (QC) systems help ensure the reliability and consistency of cell lines, prevent contamination, verify genetic identity and stability, and ultimately maintain the productive capacity of cells.
Monoclonality
Verifying single-cell seeding is an early and essential QC step in cell line development. Demonstrable monoclonality is required for regulatory compliance and, therefore, for achieving success in therapy development. However, monoclonality can be challenging to prove, particularly with manual cell dispensing methods.
The UP.SIGHT from CYTENA images cells as they are being dispensed and after they have settled in the well, giving scientists robust evidence of monoclonality and allowing them to progress to the next stages of cell line development with confidence (Fig. 2). The UP.SIGHT ensures a >99.99% probability of clonal derivation and up to 80% clonal recovery rate, making it ideal for ambitious companies looking for a simple way to streamline their workflows.
Figure 2. The UP.SIGHT uses microfluidics to gently dispense single cells and provides images to help demonstrate monoclonality to regulator bodies.
Growth Issues
Cell viability and proliferation rate are essential factors to monitor throughout the cell line development process. However, researchers face many challenges when optimizing these parameters. Each cell line has specific requirements for optimal growth, so researchers must tailor their workflows accordingly. For instance, iPSCs are sensitive to multiple media swaps and seeding methods like FACS.
Regardless of type, all cells are susceptible to contamination and require a continuous supply of nutrients for optimal growth. Manual handling methods increase the chances of contamination and can be incredibly time-consuming, especially when multiple clones are being cultured simultaneously. The UP.SIGHT and C.STUDIO allow scientists to track cell growth over time, making it easy to spot contamination and other issues that disrupt cell proliferation and viability.
Conclusions and Future Trends
Maximizing efficiency in cell line development is crucial for gaining and maintaining an advantage over the competition. Automation, monoclonality assurance, risk reduction, and optimized scaling significantly enhance cell line development processes. Instruments like the UP.SIGHT and integrated platforms like the C.STATION ensure consistency and high performance in cell culture, while tools like F.QUANT facilitate optimized screening processes. Future trends in cell line development will likely focus on further integration of artificial intelligence and machine learning to optimize processes, predictive analytics for better clone selection, and continuous advancements in automation. These innovations will drive faster, more cost-effective development, ultimately accelerating the delivery of novel therapeutics to market. Companies that remain aligned with the latest developments can expect rapid and sustained improvement in their cell line development processes.
Ready to future-proof your workflows while shrinking your cell line development timeline? CYTENA is ready to provide ambitious researchers with the tools they need to pioneer the next generation of therapies. Contact one of our experts to learn more about the UP.SIGHT, or book a demo today.
References
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- Clarke H, Mayer‐Bartschmid A, Zheng C, et al. When will we have a clone? An industry perspective on the typical CLD timeline. Biotechnology Progress. Published online March 13, 2024:e3449. doi:10.1002/btpr.3449
- Schlander M, Hernandez-Villafuerte K, Cheng CY, Mestre-Ferrandiz J, Baumann M. How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment. PharmacoEconomics. 2021;39(11):1243-1269. doi:10.1007/s40273-021-01065-y
- Holland I, Davies JA. Automation in the Life Science Research Laboratory. Front Bioeng Biotechnol. 2020;8(571777). doi:10.3389/fbioe.2020.571777
- Guan XL, Chang DPS, Mok ZX, Lee B. Assessing variations in manual pipetting: An under-investigated requirement of good laboratory practice. J Mass Spectrom Adv Clin Lab. 2023;30:25-29. doi:10.1016/j.jmsacl.2023.09.001
- Karimi Alavijeh M, Lee YY, Gras SL. A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Eng Life Sci. 2024;24(7):e2400023. doi:10.1002/elsc.202400023
- Samoudi M, Masson HO, Kuo CC, Robinson CM, Lewis NE. From omics to Cellular mechanisms in mammalian cell factory development. Curr Opin Chem Eng. 2021;32:100688. doi:10.1016/j.coche.2021.100688
- Wurm F, Wurm M. Cloning of CHO Cells, Productivity and Genetic Stability—A Discussion. Processes. 2017;5(2):20. doi:10.3390/pr5020020
